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Assessing the psychological correlates of belief strength: contributing factors and role in behavior

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ASSESSING THE PSYCHOLOGICAL CORRELATES OF BELIEF STRENGTH:
CONTRIBUTING FACTORS AND ROLE IN BEHAVIOR

by

Jared Edward Reser
________________________________________________________________________

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PSYCHOLOGY)

May 2012

Copyright 2012 Jared Edward Reser

ii
Acknowledgements
I would like to thank Dr. David A. Walsh who guided me throughout the development of
this manuscript. His theoretical, computational and editorial advice has directed my
progress from the beginning. I would also like to extend warm acknowledgements to Jian
Li, Edward Lin and Andrew Larsen for their methodological and statistical contributions.
I would like to thank my dissertation committee members, William O. McClure, Stephen
J. Read, and Justin N. Wood. I would also like to thank my supportive family members,
William W. Reser, Cheryl L. Scott, Daniel M. Reser, and Paula J.G. Freund, for their
insights and recommendations.

iii
Table of Contents
Acknowledgements ii
List of Tables v
List of Figures vii
Abstract viii
Chapter 1: How to Think About Belief 1

Chapter 2: Belief as a Construct in Psychology and Philosophy 9

Chapter 3: Personal Epistemology 13

Chapter 4: Empirical Evidence and Logical Reasoning 18

Chapter 5: How Believing Goes Wrong 26

Chapter 6: False and Delusional Beliefs 37

Chapter 7: The Neuroscience of Belief 44

Chapter 8: Ontology of Belief 55

Chapter 9: The Neuroscience of Thought 58

Chapter 10: The Unconscious Mind and Belief 107

Chapter 11: Natural Selection and Belief 129

Chapter 12: Déjà vu, Emotion and False Feelings of Certainty 132

Chapter 13: Other People’s Beliefs 136

Chapter 14: Importance to Self-identity 146

Chapter 15: Attitude Change, Persuasion and Belief 150

Chapter 16: Psychological Correlates of Belief 192

Chapter 17: Health Beliefs and Their Relationship to Behavior and BMI 238

iv
Chapter 18: Final Conclusions 263

Bibliography: 274
Appendices:
Appendix A: Informed Consent for Study 1 292
Appendix B: Questionnaire for Study 1 293
Appendix C: Informed Consent for Study 2 312
Appendix D: Questionnaire for Study 2 313

v
List of Tables
Table A: Summary of Stepwise Regression Equations 206

Table 1: List of Belief Statements 211

Table 2: Averages of the Certainty Strength 213

Table 3: Percentage of Participants Judging the Belief Statements 214

Table 4: Averages of Self-rated Orientations for Parents and Children 215

Table 5: Mean Ratings of Permanence, Likeability, and Relevance 216

Table 6: Mean Ratings of Significance of Source by Belief for Children 217

Table 7: Mean Ratings of Significance of Source by Belief for Parents 217

Table 8: Summary Table for Correlations Between the Beliefs 220

Table 9: Correlations Between the Beliefs for Belief in Bigfoot 221

Table 10: Correlations Between the Beliefs for Belief in God 221

Table 11: Correlations Between the Beliefs for Belief in Women 222

Table 12: Correlations Between the Beliefs for Belief in Home 222

Table 13: Correlations Between the Beliefs for Belief in Exercise 223

Table 14: Stepwise Regression Analyses Predicting Child’s Belief 226

Table 15: Stepwise Regression Analyses Predicting Parents’ Belief 227

Table 16: Stepwise Regression Analyses Predicting Child’s Belief 229

Table 17: Stepwise Regression Analyses Predicting Child’s Belief 230

Table 18: Lists of Questions Responded to with Numerical Estimates 245
Table 19: Correlations among All Study Variables 248

Table 20: Stepwise Regression Analyses Health Behaviors and BMI 249

Table 21: Stepwise Regression of BMI on Behavioral IVs 252

vi

Table 22: Stepwise Regression of BMI on All IVs. 253

Table 23: Stepwise Regression Analyses of Health Behaviors and BMI 254

Table 24: Stepwise Regression of BMI on the Behavioral IVs 255

Table 25: Stepwise Regression of BMI on All IVs 256

vii
List of Figures
Figure 1: Polyassociativity Explained 64

Figure 2: A Depiction of Polyarticulated Neurocognition 75

Figure 3: Selection, Inclusion and Displacement in Working Memory 96
Figure 4: Frontal Association Areas Selectively Preserve Sensory Features 97
Figure 5: Features Cycle Between Association and Sensory Cortex 98

viii
Abstract
This dissertation examines the psychological foundations of personal belief by
conducting a review of classical and contemporary thought about belief, by hypothesizing
about ways to conceptualize belief and by presenting new evidence about belief from
empirical studies. Two studies measured the contributions of various constructs to belief
strength in an effort to examine the determinants and functions of personal belief. Study 1
collected data from over 250 child-parent pairs regarding how beliefs are formulated.
Participants rated their strength of belief in statements relative to the following
determinants: the importance of substantiating evidence, the perceived logic inherent in a
belief, the importance to self-identity, the influence of parents, the social community and
authority figures. Study 1 found that strength of certainty can be best predicted by one’s
estimate of their family member’s belief, the quality of empirical evidence that the person
can offer to support the belief, and the perceived importance of the belief to their sense of
self-identity. Study 2 investigated whether people's weight management beliefs predicted
diet and exercise behaviors and whether these behaviors in turn predicted BMI. These
expected results were strongly supported by the data gathered from 996 participants, who
responded to a questionnaire, reporting their height, weight, beliefs about various aspects
of weight management, and personal weight-management behaviors, including exercise
activities and eating habits. Overall, 40% of the variance in BMI within our sample,
including 49% of the variance in BMI in individuals older than 25, could be predicted by
a combination of health beliefs and their associated eating and exercise behavior.

1
Chapter 1: How to Think About Belief

“It is not disbelief that is dangerous to our society; it is belief”
-George Bernard Shaw

Preface
The longest running and most rewarding adventure of my life has revolved around
the discovery, testing and adoption of new beliefs. Scientific curiosity about “how things
work” caused me to expose myself to philosophical positions that uprooted some of my
earliest, most cherished beliefs. Religious agnosticism, psychological materialism,
scientific reductionism, moral relativism and physical determinism have replaced beliefs
that I have guarded since early childhood. The act of exposing a belief to rational
criticism, and being compelled to overturn and replace it, felt incredibly exciting and
constructive. At some point though, I realized that I had been constructing beliefs about
scientific concepts without actually knowing any of the science behind belief. This
dissertation is an attempt to better understand my own beliefs, how I came to acquire
them and how to guard against mistaken beliefs in the future.
The literature review includes a brief description of how other researchers have
thought about how beliefs are formulated and how and when they are subject to change.
Many different contributors to certainty strength have been recognized by previous
theoreticians and researchers over the years and the present study has attempted to
examine the most meaningful and compelling of these constructs. Several factors that
affect belief are identified such as: empirical evidence, logical thinking, competing
evidence, opinion of parents and friends, opinions of authorities, the influence of

2
experience, the bearing on self-identity, general likeability, expected permanence and
perceived relevance. Previous research on belief formulation has explored how existing
beliefs affect decision making and cognition but researchers have not investigated how
individuals appraise different components of belief certainty relative to each other. The
results of the experiments conducted by the author, found in the final chapters of the
dissertation, suggest that that strength of certainty can be predicted by the quality of
empirical evidence that people can offer to support the belief, by their estimates of their
parent’s certainty in the belief and by the perceived importance of the belief to their sense
of self-identity. Overall, the dissertation examines the fundamental question “why people
hold the beliefs they do” by delving into the physical, social and religious beliefs of
individual people and determining how they support, rationalize, think and feel about the
beliefs they hold.
Introduction
What determines belief? How do beliefs form and what circumstances influence
people to change them? How can beliefs be accurately described in terms of other,
pertinent psychological and neuroscientific phenomena? Although beliefs play an
instrumental role in all human activities and are highly consequential for individual and
societal behavior, there are no accepted mainstream or even academic answers to these
straightforward questions. In the search for answers, where does one begin?
The pertinent scholarly literature is scarce and inconsistent. Actually, belief may
be unique among common, psychological constructs because of the absence of any broad
synthesis of its terms. To advance toward such an understanding it is necessary to borrow

3
from a number of different approaches and disciplines, and to integrate and reconcile.
Areas, such as social psychology, cognitive psychology, philosophy of mind,
epistemology, linguistics and cognitive neuroscience, all make contributions; yet unlike
other fundamental psychological constructs, there seems to be little explicit reconciliation
between these contributions for beliefs.
This review will attempt to draw from a number of different sources in the
sciences and humanities to offer a description of previous treatments given to belief, to
reconceptualize the matter given the new perspectives, and to identify unknowns for
future study. Research on the dynamics of belief will be emphasized over semantics and
statics. The best way to study something as elusive as beliefs is probably to observe them
in action - during formation and change. To this end, the focus of this research is to
analyze both theoretical and empirical work on the foundation and plasticity of beliefs in
the hope of furthering our understanding of this highly consequential, natural
phenomenon.
Psychology and related disciplines have treated the construct of belief
inconsistently. Due to its ubiquitous use as a psychological construct, in folk psychology
and everyday life, it has been resistant to scientific operationalization. Belief is often
discussed but left undefined, and several well-received definitions are not only
inconsistent but also mutually contradictory (Furinghetti & Pehkonen, 2002). There is no
consensus concerning what criteria a thought must meet to qualify as a belief
(Eichenbaum & Bodkin, 2000). However, certain conventions are generally adhered to in

4
the literature. Generally, a belief is treated as a fundamental mental representation, and
therefore, a basic unit of cognition.
Belief is usually held to be the psychological state in which a person holds a
proposition, perception, inference, judgment or premise to be true (Green, 1971). Beliefs
can be created at the time they are needed during an activity or constructed presumptively
to account for a past event or to prepare for a future state of affairs. Talk of a belief
necessarily supposes an entity (the believer) and a proposition (the object of belief). It
also presupposes that the belief may not be well supported enough to constitute true
knowledge - that it is a conjecture or hypothesis (Abelson, 1979). Belief involves
conviction, possibly even devout conviction, but does not necessarily involve certainty.
Moreover, beliefs can be held about common workaday concepts but are usually invoked
in matters of importance or where there is a divide in credence. Thus beliefs often involve
stances on consequential topics, such as morality, faith, politics, science, personal
identity, history, religion, distribution of wealth, economics, or culture.
How beliefs are formulated and what protocol people use when formulating them
is an issue of contention, but also one of curious speculation and wonder. There is no
accepted “unified theory” of belief formation, and most researchers that endeavor to
grapple with the concept write about it as if it is mostly uncharted territory. Aside from
Plato’s early work on belief justification, there is only patchy common ground. In his
often cited dialogue, Theaetetus, Plato asserts that reason (personal logic), evidence
(empirical support), and guidance (social influence) provide the best justification for a
new belief (Cornford, 2003).

5
Psychologists have consistently identified these three contributors - reasoning,
evidence and the beliefs of others - to be among the most important determinants in belief
justification (Abelson, 1986b). Information relevant to how people search for these, how
they think about them and how they use them are essential to grasping belief formation
and change. We will focus on the constructive contributions of Plato’s logic, evidence
and social influence here but also consider what happens when these factors fail to guide
belief properly.
Most people assume that their beliefs, especially when they involve matters of
importance, are chosen conscientiously with a good amount of deliberation and
reasoning. This paper will consider several sources of evidence suggesting that often,
even consequential beliefs are chosen with very little declarative rationale and poorly
supported by critical thinking. Several fundamental mistakes of belief formation will be
examined, including the use of biases, fallacies, faulty heuristics and irrational tendencies
(such as the inclination to give preferential treatment to concepts that come to mind more
readily). One reason beliefs often evade systematic justification may be that much of the
cognitive process of analyzing and accepting a belief is below the level of conscious
awareness. Individuals often only have insight into mental processes that are guided by
consciousness; unfortunately, adults have been automating aspects of believing since
early childhood and becoming aware of these aspects can be very difficult after they are
made implicit. Even into adulthood, many false beliefs probably come about, unjustified,
due to a questionable association between two stimuli in the environment that should be
interrogated further, but is not.

6
As associations that take place in the brain, beliefs are not exclusively the
province of psychology. The neurobiology of how these associations occur, in terms of
neurons, synapses, binding and neuroanatomic space, will be discussed. The cortical and
conscious correlates of belief will be investigated and the existence of belief in non-
human animals will be considered. Derangements in the proper neurobiology of belief,
often result in deranged belief. Psychologically and/or biologically impaired belief
development can lead not only to false beliefs but also to the debilitating and socially
damning delusions of psychosis and dementia.
Hasty or incomplete belief formulation can lead people to espouse beliefs that are
unfounded. It seems that the way that humans are hard-wired, it is easy for them to
become convinced of something and even to act on this conviction, without going
through pertinent intervening operations, such as citing evidentiary facts or articulating
logical arguments. People routinely fail to search for and evaluate evidence, even when
forming their most cherished beliefs (Tavris & Aronson, 2007). Sometimes such
evaluations are clouded by snap judgments or emotions, which have the capacity to cause
us to become inordinately invested in a restricted subset of concerns without considering
contradictory information. “Feelings of certainty” are often responsible for premature
conviction and their derivation, emotional concomitants and validity are evaluated here.
Interestingly though, new research has shown that emotionally driven judgments can be
constructive and even advantageous in certain situations (Gladwell, 2005). Many beliefs
that are not analyzed conscientiously, or even consciously, may still be good beliefs. A
large body of recent literature has shown that, when made within one’s area of expertise,

7
snap judgments, hunches and intuitions can often lead to better answers than analytical
investigations (Gigerenzer, 2007). Such beliefs are also much faster. Choosing what to
believe, especially when one feels obligated to deliberate carefully, can be time intensive
and exhausting. Clearly, there are tradeoffs involved in belief formation involving
accuracy, efficiency and expediency.
When someone realizes that their beliefs lack logical and evidential support, they
often choose to appropriate beliefs from others. This is usually done when a belief is too
complex or unfamiliar; when someone does not have the skill or knowledge base to think
about it on their own. It seems that parents are a substantial source of belief for their
children, especially early in life. It is unclear though, how mothers and fathers transfer
their beliefs and whether children can accurately gauge their parents’ true sentiments.
Surely the social process of believing varies by belief. For beliefs of low or moderate
complexity, people often side with their parents or close friends. For beliefs of high
complexity though, many people feel compelled to side with a specialist in the pertinent
field, such as a scientist or philosopher. As we will see, people use a variety of heuristics
when deciding from whom to borrow ideas. Sometimes this borrowing is unintentional.
People can be unaware of the influence that others have on their beliefs. In fact, it is
possible to be oblivious to the impact of persuasion, even when being persuaded
coercively. Overall, it seems that many beliefs are the outcome of social pressures, a need
to fit in with others and tacit and unacknowledged expropriation.
In the pages that follow, we will elaborate further on the subject of belief
formation and change from a wide range of perspectives. We will consider how beliefs

8
are affected and constrained by attitudes, fallacies, heuristics, delusional thinking,
intuition, neuroscience, personality, persuasion, unconscious factors and self-identity.
These concerns will be traced back to the processes of belief formation and change,
focusing on the cognitive aspects of belief inception, endorsement and assimilation.
Where possible, we will draw inferences about belief from experimental studies and data
collection efforts. At this point in the evolution of belief research, however, we are highly
reliant on speculation, anecdote, personal observation and convergent validation. It is not
clear how much of this information can be neatly coordinated into a unified theory of
belief, but considering the existing knowledge about belief in this way should constitute a
good starting point. Overall, it is clear that the study of belief change is truly
multifaceted, should be intensely scrutinized and deserves much wider study.

9
Chapter 2: Belief as a Construct in Psychology and Philosophy

“I would never die for my beliefs because I might be wrong
-Bertrand Russel

The early psychological literature on attitudes and the age-old literature on
philosophy of knowledge, have substantially contributed to the demarcation and
exposition of what it means to believe. Systematic examination of beliefs began in the
early 20
th
century by psychologists, mainly in the arena of social psychology (Thompson,
1992). Much of this research was actually conducted with the intent to study the volatility
of attitudes and the power of persuasion, but the research was cut short. Behaviorism,
with its emphasis on observable behavior and ridicule of the study of cognitive processes,
ended most of the early research on beliefs and belief systems. As new developments in
cognitive psychology began to arise in the 1970s, interest in beliefs reemerged as
behaviorist ideology dwindled (Abelson, 1979). Around this time, beliefs began to be
viewed as conclusions about phenomena and their nature that both affective and logical
factors impacted (Green, 1971). The study of attitudes was resurrected and for quite a
while the best place to look for research on beliefs was in the literature on attitude
formation. Thereafter, the link between belief and attitude was made explicit (Underhill,
1988). Despite the fact that the relationship between these two concepts has not been
entirely clarified, we will consider some of the research efforts and theoretical work
within this area in the section on attitudes.

10
Philosophical thinking on belief is much older than the psychological research.
This research, historically, has also been more insular and more exploratory though.
Some philosophers believe that ‘belief’ cannot be defined, is not equivalent to the content
of any definite description and is difficult to describe in terms of its essential and
accidental properties (Hay, 2008). Philosophy has tended to be relatively abstract and
inconsistent in its treatment of beliefs whereas in psychology, a data driven pursuit, less
is written on the definition of belief but there is more agreement as to what constitutes a
belief (Green, 1971). The two disciplines contribute differently, but substantially, when
taken together.
Both the philosophical and psychological literature emphasize that most people
distinguish what they know from what they believe even though they consider both kinds
of statements to be true (Schwitzgebel, 2006). This distinction between belief and
knowledge originates from the philosophy of mind where it is a seminal concept. Both
psychologists and philosophers concur that belief systems often include a substantial
amount of episodic material from personal experience, folklore, cultural doctrine or
propaganda and contain strong references to the self-concept of the believer, a feature
usually left out of knowledge systems completely (Abelson, 1979). In addition, beliefs
can be held with varying degrees of certitude; one can be passionate or restrained about a
belief, whereas with knowledge you know something to be a fact or not. This difference,
where only beliefs can vary in certainty, leads many beliefs to become subjects of
powerful emotional or subjective feelings. The interrelationship between beliefs and
personal concerns is a potentially rich but mostly unexplored topic that will be elaborated

11
on in the section on self-identity. Empirical research has made it clear that a person’s
past, occupation, habitual activities, pride and ego all play a role in what they choose to
believe (Furinghetti & Pehkonen, 2002). In fact, the involvement of concerns related to
selfhood and individuality are a major factor that differentiates things that are believed
from things that are known.
A knowledge system is a set of proven facts that are accepted to be true; whereas,
a belief system is a set of nomologically related propositions that one holds to be true but
may not have been scientifically proven or sociologically accepted. There are caveats to
this though. Cognitive psychologist Robert Abelson (1979) has asserted that if every
normal person of a particular culture believes in an unproven supernatural construct, even
though this might constitute a false belief system to an observing anthropologist, it would
constitute a knowledge system for the members of this culture because of the unanimity
of belief. This brings an interesting concept into play, mainly that belief may be
distinguished from knowledge on the basis of either scientific grounds or by cultural
consensus. Most philosophers though, agree that a scientifically false belief should not be
considered knowledge even if it is totally sincere (Abelson, 1986a). Conversely, a truth
that is not believed by anyone does not constitute knowledge because for it to be
knowledge, a person must believe or know it. Equivalently, a person must believe a belief
for it to exist, even though according to some theorists, a person may hold a specific
belief, but not know it until they are forced by experience to formulate the belief
consciously (Hay, 2008).

12
There are other important facets to the relationship between knowledge and belief.
Knowledge requires belief, so it is epistemically impossible to know something but not
believe it. On the other hand, belief does not require knowledge nor does knowledge
about a particular belief necessarily constitute an endorsement of it (Abelson, 1979).
Often statements about belief entail faith such as a person believing in his or her favorite
sports team. This has been called “belief in” which indicates faith in something and is
usually commendatory or exhortatory (I believe in the power of love). Such beliefs refer
more to inner states of opinion than they do to an outer reality. Epistemology and
psychology have historically been less concerned with this type of belief and more
concerned with beliefs that can be formulated into subjective, personal statements on
topics involving knowledge more so than faith (Hay, 2008).
Plato and Socrates made what is regarded as an important distinction between
knowledge and belief, saying that knowledge is a direct perception of information about
the world and that belief is the qualification we put on the accuracy of that perception.
Plato in Theaetetus defined knowledge as “justified true belief (Cornford, 2003).” Since
this time, philosophers have seemed to relish the distinction between knowledge and
belief. This topic is interesting because it details how we piece our worlds together from
phenomenal experiences. The topic, known as epistemology- the philosophical study of
how humans use knowledge to justify beliefs - is a highly influential discipline that
appears particularly germane in our discussion of beliefs.

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Chapter 3: Personal Epistemology

“The outside world is something independent from man, something absolute, and the
quest for the laws which apply to this absolute appeared to me as the most sublime
scientific pursuit in life.”
-Max Plank

Epistemology is the branch of philosophy concerned with the nature and scope of
knowledge. Since epistemology is concerned primarily with determining what criteria
must be met by conjectures for them to constitute true knowledge, understanding it
should help us to better understand beliefs. A comprehensive account of the important
constructs in epistemology would be pedantic yet a review of its foundations should help
to elucidate the problems encountered by people who are trying to decide what to believe
and bring us closer to an understanding of the cognitive basis of belief formation and
change.
Opposing epistemological camps have helped to delineate ground rules for how to
think about beliefs. These camps have taken strong, opposing positions but in doing so
have generated and expounded upon fundamental viewpoints, most of which are not
necessarily incompatible with one another. Foundationalism represents the notion that
basic statements that cannot be falsified are self-evident and self-justifying, do exist and
give justificatory support to other derivative statements, creating a foundation for a
structure of knowledge. The doctrine of Fallibilism contradicts this assertion, arguing
that absolute certainty about knowledge is impossible and that all claims of knowledge, in
principle, could be incorrect. This nihilistic stance, where there is thought to be no

14
objective basis for truth, is not widely embraced but has never been satisfactorily
dismissed either (BonJour, 2002). Empiricists counter that it is possible to lay a
foundation for knowledge, and they insist that reports of sensation are the source and
criterion for knowledge. This empirical stance holds that sensory knowledge is
indubitable and can constitute epistemologically basic propositions (this will be discussed
further in the section on evidence). This tradition, along with rationalism, has formed the
foundation for modern science. Rationalists argue that true knowledge does exist and is
gained by reason but not by experiences. Rationalism is concerned with the logical paths
to knowledge and much of this literature involves the identification of fallacies that
interfere with or obfuscate logic. Here, to be reasonable, it is necessary that one’s
rationale has not committed to a fatal falsity.
In the study of logic, a fallacy is defined as a misconception resulting from
incorrect reasoning in rhetoric or argumentation (Hay, 2008). Fallacies include mistakes
in argument such as false dichotomy; appeal to common opinion; confusion of cause and
effect; drawing the wrong conclusion; appeal to emotion; misuse of a vague expression;
begging the question; false alternative; faulty analogy; omission of key evidence and use
of a red herring. Importantly, fallacious arguments are thought to be used often to support
belief (BonJour, 2002). Although some fallacies are specific to arguments between two
people and could probably not be generalized toward an “argument” someone is having
with themselves, personal beliefs are highly susceptible to common fallacious logic
(Dancy, 1991). Rationalism has produced these tools of logic which can be used to assay
the justification for individual beliefs.

15
Rationalism, Empiricism, Foundationalism and Fallibilism are each extreme
stances that allow important insight into how beliefs are generated and supported.
Commingling the messages from these schools of epistemological thought allows us to
see that good logic and trustworthy evidence can combine to erect a sincere and credible
belief system despite the fact that a degree of uncertainty will remain. Popular and
recently derived models of epistemic decision making map out how these stances affect
individuals when they are deciding what to believe. One particularly successful model,
the Reflective Judgment Model, illustrates how personal epistemic reasoning can attempt
to avoid fallacy and falsity.
The Reflective Judgment Model (RJM) is a theory of human decision making
designed to describe the development of reasoning by detailing how epistemic
assumptions change and how critical and reflective thinking skills inform belief. The
model has been supported by extensive longitudinal and cross-sectional research and
routinely informs the work of developmental and educational psychologists (King &
Kitchener, 1994). Reflective Judgment emphasizes that many problems cannot be solved
with certainty, that people know this and that they create strategies for dealing with
uncertainty. As they do this, they move up through a hierarchy of many stages of
proficiency that are divided into three main categories. The categories correspond to
modes of reasoning which are thought to develop in an invariant sequence: prereflective
reasoning, quasi-reflective reasoning and reflective reasoning.
Prereflective reasoning mediates the acquisition of beliefs through the word of an
authority figure or through firsthand observation. People who use this type of reasoning

16
do not question their beliefs and assume that they know things with complete certainty. A
person who uses quasi-reflective reasoning appreciates that knowledge claims contain
elements of uncertainty and uses evidence to support their beliefs but they are
inconsistent, idiosyncratic and subjective in their epistemic reasoning. Reflective
reasoning, on the other hand, is much more objective, is open to continuous reevaluation,
is conscious of the pitfalls of fallacious reasoning and is never certain but operates on the
basis of the “most reasonable” evaluations of available data. RJM provides a fine model
for different degrees of experience and acumen in belief formation and change. By
emphasizing the importance of comfort in the absence of certainty and openness to
constant reevaluation of the same beliefs, RJM sets a high standard and gives most
believers a lofty goal to aspire to.
Another popular paradigm discussed in the literature on belief formulation, the
Data-oriented Belief Revision (DBR) model, is consistent with this interpretation
(Paglieri, 2005). DBR operates on the assumption that data and beliefs are two separate
entities. Under this model, data are snippets of information collected and arranged by an
individual and beliefs are interpretations of the arrangements of this data that have been
accepted as true. According to this paradigm, and consonant with a good deal of other
research perspectives, a large number of logical, emotional and cognitive-developmental
determinants are thought to play roles in whether data is accepted or rejected (Paglieri,
2005). This is similar to RJM, and other models of belief because DBR’s
conceptualization of data is practically equivalent to the former’s concept of knowledge.
Other epistemological models feature various other concerns but none brings them all

17
together. Creating a comprehensive model of the process of belief and believing is an
endeavor for the future.
Personal epistemology, a subject still being formalized, maps out how individuals
conceive of and use logic, evidence and other people to assemble and fortify their belief
systems. Empirical studies have supported that there are degrees of maturity and
effectiveness in using epistemological reasoning (Perry, 1970). This research has
evaluated participants on a variety of levels corresponding to the constructs in RJM and
shown that a large degree of interpersonal variability in skill with belief exists. This
research led its principle investigator, William Perry (in his scheme of intellectual
development), to point out that mature people realize that not all questions have verifiable
answers, that some contentious issues are truly only a matter of opinion and that even
distinguished authorities can disagree on certain topics (1970). It is clear that some
statements can be proven, others can be strongly supported, others can only be bolstered
and that judicious and discerning individuals can perceive and apprehend the difference.
Every believer should benefit from being exposed to these enlightening epistemological
considerations. Other informative doctrines of epistemology that potentially could and
perhaps should be reconciled with the notion of belief include agnosticism, determinism,
fatalism, nihilism, skepticism and solipsism. Personal epistemology is a topic that will
pervade our discussion of beliefs for the remainder, especially our examination of the role
of evidence and logic.

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Chapter 4: Empirical Evidence and Logical Reasoning

“The wise know too well their weakness to assume infallibility; and he who knows most,
knows best how little he knows.”
-Thomas Jefferson

The use of evidence and reason in guiding belief has been a topic of foremost
concern in scientific methodology. For thousands of years philosophers of science have
been active in rationally examining the nature of belief derived from observational
research (Bechtel, 1988). Aristotle contributed appreciably to the understanding of how
data could lead to classification, theory and knowledge. His ideas on the matter were
preserved and conformed to for over a millennium despite the fact that they were less
than comprehensive. Aristotelian science was subject, in a haphazard way; to the rules of
natural philosophy where naturalistic observations could be analyzed using the
philosophical method of one’s choosing. Since the 17
th
century, Francis Bacon, Rene
Descartes, John Stuart Mill and the Logical Positivists have greatly improved upon the
old philosophical methods of syllogism, transitive inference, metaphysics and ontology
with more algorithmic methods of science. The modern scientific method espouses the
view that empirical evidence is indispensable for knowledge of the world and that
scientific beliefs must be justified by strong physical evidence, materialistic induction
and deduction and the systematic testing of alternative hypotheses.
Although the scientific method acts as a good model of belief epistemology, its
methodology is too rigorous and exhaustive to be practical for personal beliefs. People
need a quicker more direct way to justify their beliefs. It is probably a safe bet to base

19
one’s beliefs on the beliefs of scientists but much scientific thought takes voluminous
reading to uncover and many things that people want to form beliefs about have not been
subjected to scientific inquiry. Instead people often rely on personal observations, the
opinions of secondary sources, authority claims, social or cultural consensus and the
coherence of argumentation (Irving et al., 1998). Personal observations are usually
trustworthy unless the perception involved was illusory or the person attempts to
generalize an observation inappropriately. Secondary source evidence such as photos,
videos, or reports are often credible except when they are manipulative or misleading.
Authority claims and social consensus can differ but are both taken as reliable by most
people (Ross & Anderson, 1982). Logic, reason and the coherence of arguments are
usually, at least, taken into account by people deciding what to believe. But precise logic,
which involves the forming of premises and deducing valid conclusions from them, is
laborious (Abelson, 1979). Every person probably has their own idiosyncratic methods of
using logic and evidence, and these methods themselves are probably used inconsistently.
Most people think that their unique way of justifying beliefs is valid. They assume
the beliefs that they choose to espouse are those that are consistent with sensory
perceptions, sociologically accepted systematizations and dedicated reasoning. These
people may be sincere and even sensible in thinking that the manner in which they
choose what to believe is logically permissible but there is a good deal of research
suggesting that most people hold a multitude of beliefs that are not supported by evidence
or well-reasoned argument (Kida, 2006).

20
Empirical studies have examined the role of evidence and reason in guiding
personal opinion and have demonstrated that they are often used inconsistently and
inappropriately (Schommer, 1990). Schommer administered an epistemological
questionnaire to undergraduates and found that students that simplify their searches for
evidence too much tend to be overconfident in their comprehension and tend to reach
oversimplified conclusions. Further, she discovered that students that frequently use
irrational epistemological reasoning are more likely to reach inappropriately absolute
conclusions when asked to write a concluding paragraph to a passage about scientific
findings. In fact, a growing body of literature indicates that our beliefs, and or certainty in
them, may be guided more strongly by emotional construals, transient motivations,
subjective biases, subconscious objectives and constructs tied to self-identity (Tversky &
Kahneman, 1974). This can be good or bad, depending on the belief in question.
Many researchers advocate that emotions (in the form of conditioned visceral
reflexes, amygdalar responses or orbitomedial prefrontal cortex biases) can cause people
to jump to accurate conclusions without need of employing the intervening cognitive
steps (Damasio, 1994a,b). Other research in this area shows that evidence may not be
necessary when it has already been gathered, when it is implicit in an emotional response,
when good evidence cannot be found, or when too much evidence leads to “analysis
paralysis” (Gladwell, 2005). Spontaneous decisions or snap judgments can be helpful in
such situations, but this is more often true of one-time decisions than permanent beliefs.
A decision is usually particular to a situation; whereas, a belief is often formed because of

21
its utility in multiple situations. When beliefs are constitutional, when it is clear that they
will guide future behavior, they should be made deliberately and not spontaneously.
The philosophical literature on “evidential belief” makes a distinction between
core beliefs and dispositional beliefs. Core beliefs are propositions that have been
considered or decided upon in the past. Any core belief has been thought about actively at
one point. A dispositional belief is a belief that someone might ascribe to if confronted
with a topic but has never considered the topic before and therefore has not come to a
belief about the topic in the past (Bell et al., 2006). Dispositional beliefs have not yet had
logic or evidence brought to bear on them. It is thought that dispositional beliefs, when
formed, are more likely to be contrived hastily and, relative to beliefs that have some
kind of precedent, are not as adequately supported. Another similar view of belief
revision explains that keeping consistency among our beliefs is a basic human need and
an urgent concern during belief formulation (Schick & Vaughn, 1995). Pencil and paper
studies evince that people tend to reject facts or statements that are at odds with core
beliefs that they have chosen to espouse or support in the past (Schick & Vaughn, 1995).
For this reason, many people will embrace evidence that supports a held belief and
disregard evidence that conflicts – regardless of merit - in order to maintain cognitive
consistency (Dancy, 1991).
The philosophy of belief source has elaborated on two approaches: the foundation
model and coherence model (Doyle, 1992). According to foundations theory, beliefs are
maintained if they are reasonable, rational and justified, and beliefs are abandoned as an
individual adopts evidence to the contrary. The coherence approach, in contrast, contends

22
that an individual will accept a belief if it logically coheres with other closely held beliefs
pertaining to self. Some beliefs may be more important, or psychologically central, for a
person than others and so new beliefs are probably tested for coherence with these first
(Pehkonen, 1994). Core beliefs are usually affected by both. The foundational and
coherence models are thought to be able to coexist and lead to the following situation: the
availability of rational and justified evidence will combine with personal relevance of the
belief to determine certainty strength or degree of conviction. Like DBR and RJM, these
approaches can be used to inform predictions about how humans will make decisions
under different evidentiary conditions (Doyle, 1992).
Mathematicians have contributed to the debate about human beliefs and proposed
prescriptive models of how a person’s belief should change in strength when they are
presented with new evidence supporting or refuting a belief. Bayes’ Theorem has been
used to describe how the strength of a rational person’s beliefs should change when they
combine new evidence with previously accumulated evidence (von Winterfeldt &
Edwards, 1986). In fact, the field of Decision Analysis was born in 1954 when Ward
Edwards, asking participants to revise their existing beliefs after being exposed to new
evidence, demonstrated that human decision makers depart greatly from the mathematical
predictions of Bayes’ Theorem (Edwards, 1954). Most people were never instructed how
to use evidence rationally and we cannot expect them to operate under mathematically
optimal conditions. Also, people do not normally calculate probabilities, they compare an
imagined scenario employing a given belief to a scenario without the belief.

23
Descartes and Spinoza had different ideas about how evidence plays a role in
belief. Rene Descartes described beliefs as involving two mental representations, one
regarding the claim at stake and another that exposes this claim to assessment and
scrutiny. He thought that evidence played a major role in this assessment. Importantly
though, he maintained that beliefs are held and analyzed objectively until the person
chooses to accept or reject it (Clarke, 2006). This view dominated until Baruch Spinoza
argued that in order to assess a belief we must first comprehend it and in order to
comprehend it we must accept it (Boucher, 1999).
Some functional magnetic resonance imaging (fMRI) data have supported this
notion that in order to question a belief we must, at least momentarily, accept it as true
(Harris et al., 2007). Others have taken this idea further and pointed out that because we
must believe a belief in order to understand and analyze it, perhaps sometimes we believe
falsely because we have begun, but not finished, the process of belief formation (Gilbert,
1991). Studies have shown that merely being exposed to a statement, like leading
questions from an unethical lawyer, can induce belief. Other studies have shown that
distraction or time pressure can make people prone to accepting a falsehood (Schick &
Vaughn, 1995). Ironically, failing to properly bring good evidence to a claim, in some
circumstances, can make us more likely to believe it.
Wimmer and Pemer (1983) have elaborated on Spinoza’s position and asserted
that in order to analyze an incoming belief we must construct two completely separate
models of the world: one in which the information is true and one in which the
information is false. It is not clear if this is true or not but certainly, the ability to create a

24
different model of the world can act as a frame of reference helping us to better
understand, interpret and predict the actions of someone whose beliefs differ from our
own. It would be interesting to find out more about how individuals employ working
memory to represent, model and test probationary beliefs. That very little research has
been done here and that few have attempted to verify or disprove these philosophical
ideas is exciting for younger generations of researchers.
When we act on intuition, instead of employing working memory, we may be
relying on evidence that was acquired in the past but is now preconscious. Certain
behaviors, even ones that we are not aware of, can become routinized and automated to
reflect entrenched beliefs that, in the past, were based on true evidence. For example, one
might have a predilection to treating strangers kindly without having to reactivate
previously held convictions about altruism. Just as good posture can be maintained by
muscle memory, personality, general demeanor, belief propensity and even decision
styles can be maintained unconsciously. Many beliefs that have influenced behavior in
the past probably become phased out of consciousness as they are incorporated into
automatic subroutines. When held accountable for explaining why they acted in a certain
way, one may not be able to invoke the original belief despite the fact that it did
powerfully, albeit indirectly, influence behavior. It is probable that young children
become explicitly aware of some of the cognitive protocol involving belief formation, but
after repetition and practice in using beliefs, these formal rules become procedural, and
thus, lost to conscious awareness. These explicit rules of belief and knowledge
acquisition are effectively retained in the sense that they continue to determine belief

25
outcome, but, because they have been made implicit, they are unavailable for personal or
even scientific scrutiny. For this reason, attempting to pry loose the integral elements of
belief, especially in early life, should help us attain a comprehensive model for belief
dynamics. The section on the influence of other people will consider from whom and
how we extract evidence.
Research shows that individuals will often maintain a belief in spite of
overwhelming amounts of contradicting evidence and this tendency is termed
“unwarranted theory perseverance.” After performing several survey studies and an
extensive literature review, Anderson et al., (1980) concluded that people frequently cling
to beliefs to a, “considerably greater extent than is logically or normatively warranted.”
Their findings and the findings of others suggest that evidence is often not measured
judiciously and that competing beliefs and counter explanations are too often ignored or
overlooked (Kida, 2006; Schick & Vaughn, 1995). The ability to guard against hasty
belief has been called “source monitoring” by Marcia Johnson (Johnson, 1999). This
ability is thought to be multifaceted and proficiency is said to take experience and
practice (Johnson, 1999). Ability at source monitoring is thought to be a function of a
person’s awareness of and refusal to commit the common mistakes of belief formation.

26
Chapter 5: How Believing Goes Wrong

"I do not consider it an insult, but rather a compliment to be called an agnostic. I do not
pretend to know where many ignorant men are sure -- that is all that agnosticism means."
- Clarence Darrow

When forming beliefs, people use processing shortcuts, or heuristics, which work
in some situations, but also lead to mistakes if they are used inflexibly. Several popular
books have been written on the topic of cognitive blunders and it seems that the public
has an appreciation for, or at least an interest in how to recognize and correct common
mental lapses. According to Thomas Kida (2006), these mistakes include human
tendencies to: prefer stories or anecdotes to statistics; be confused by superficial
similarities; give preferential treatment to concepts that come to mind more readily; seek
to confirm though not to question ideas; disregard alternative explanations for
phenomena; accept flimsy evidence to support an extraordinary claim; underemphasize
the role of chance and coincidence in shaping events; misperceive; oversimplify; and
have faulty memories. Several of these mistakes are congruent with specific logical
fallacies identified by philosophers. These are a somewhat arbitrary and motley grouping
of blunders, but because psychologists (Kida, 2006; Tversky & Kahneman, 1982) have
emphasized them routinely, we shall briefly consider each in an attempt to glimpse how
beliefs go wrong.
People have a tendency to prefer stories or anecdotes to statistics. Stories are
probably easier for us to understand; they seem more salient and more reliable even
though they are usually less reliable than statistics garnered by intensive experimentation.

27
Cognitive science has evinced that people often find themselves in situations where it is
necessary to employ statistical reasoning to solve problems or make intelligent estimates.
Most people have difficulty using statistical information effectively; consequently, they
will often use other “heuristics” to help solve problems. Kahneman and Tversky (1974)
studied these phenomena in depth by measuring people’s performances on carefully
devised assessments. They wanted to see what rationale people used to make decisions,
especially decisions related to determining the relative frequency of specific events.
Representativeness heuristic and the availability heuristic were two heuristics they found
thought to have a substantial bearing on beliefs. The representativeness heuristic is used
when we judge two things as being similar only because they share prima facie
characteristics, or a superficial resemblance. People using this heuristic ignore statistical
rules and assume that if one concept shares a specific quality with another concept that
these two concepts are sure to share many other qualities and should be categorized
together (Tversky & Kahneman, 1982). This heuristic is very similar to the fallacy of
“faulty analogy” mentioned earlier. Both are thought to be responsible for why many
people see illusory relationships in a series of random events. In addition, when applied
incorrectly, the representativeness heuristic is known to lead to the creation of damaging
stereotypical beliefs (Kahneman & Tversky, 1973).
The availability heuristic is similar but distinct from the representativeness
heuristic. Many psychological experiments have shown that people regularly use
“available” or easily accessible memories to make judgments about the likelihood of
events. This is probably because it is natural for us to use concepts that readily spring to

28
mind rather than complete and unbiased information. We can easily remember recent
experiences or reports from friends and the news, and we often use these types of
information instead of using statistical information to estimate probabilities (Tversky &
Kahneman, 1973). The fact that prejudiced information is more readily available to
memory causes us to discard more reliable empirical knowledge and thus, leads to
unobserved, hasty beliefs. Researchers have pointed out that throughout its evolutionary
history, our species has gained knowledge from personal anecdotes or memorable
occurrences, not from statistics or experimental studies. Many researchers believe that
this partly underlies our penchant to pay close attention to information coming from a
story, a personal account, or an associated experience (Shermer, 1997). This tendency
has the effect of making us believe in causes that are really only partial causes, accept
things that are unsubstantiated, and trust small sample sizes (Sagan, 1995).
We also seek to confirm our beliefs. People have a strong penchant for
committing the confirmation bias or the positive-test strategy, where they are prejudiced
towards confirming their speculations. This is a common cognitive error that biases us
toward confirming our ideas by making us seek out cases that support our hypotheses and
disregarding cases that question them (Shermer, 2003). This proclivity acts to reinforce
existing beliefs and plays a large role in the maintenance of delusion, in attitude
polarization, and in illusory correlation (Charles & Lodge, 2006; Lee & Anderson, 1982).
Related, the behavioral confirmation effect, also known as the self-fulfilling prophecy,
occurs when a person’s expectations influence their own behavior, which can lead to
disastrous decisions in organizational, military, and political contexts (Darley & Gross,

29
2000). These examples show how powerful expectations can be in influencing our
decision-making strategies.
Expectations have even been shown to influence perceptions. When a newsflash
in a small town reported that a large bear had escaped from a local zoo, the 911
switchboards lit up. People reported seeing the bear all over town, despite the fact the
bear never wandered more than 100 yards from the zoo (Harter, 1998). In a similar way,
sports fans have been shown to be functionally blind to infractions committed by their
own team (Hastorf & Cantril, 1954). People expecting to deduce the rules used in a
video of a ball-passing game have an attentional scotoma for the appearance of a man in a
gorilla suit, simply because his presence was not expected and thus, was not attended to
(Simons & Chabris, 1999). Other experiments with selective attention have shown that
people can be functionally blind to highly salient stimuli if they are concertedly attending
to other stimuli (Knudsen, 2007). These and many similar anecdotes and experimental
outcomes embody the lyric, “what a fool believes… he sees.” Expectations can have
powerful effects on perception, and it is thought that misguided perceptions also have the
capacity to lead to false beliefs (Kida, 2006).
The hindsight bias is another, related mistake that has the potential to impinge on
both memory and belief. It is common for people to recall their correct predictions but to
forget about the faulty ones (Fischhoff & Beyth, 1975). The dramatic fervor that fans
display for their team is rekindled after a win but quickly forgotten about after a loss.
When a situation is playing out, an individual might throw in a quiet remark about their
prediction for a certain event. If they lose, the remark is forgotten. If they win, they can

30
then speak vociferously about their “uncanny” prediction. Often an attempt to gain
credibility, this tactic can confuse even the speaker because it gives them an erroneous
conception of probability and of their own ability to predict random events.
Many psychological models of memory impairment attempt to explain how this
type of cognitive error might stem from a few different causal factors. Some
psychologists think that knowledge about the outcome of an event might alter or erase
previous memories related to the event before it played out (Fischhoff & Beyth, 1975).
Motivational factors and factors related to the heuristics used in recalling events might
make the original judgments or beliefs less easy to activate (Morson, 1994). The
hindsight bias, much like many of the phenomena described by psychologists, to many
people seems to be trite or “common sense.” This view is influenced by the hindsight
bias, the tendency to see things as obvious, but only after the fact.
Faulty memory can lead to mistakes in belief formation. Memory recall was once
thought to be a highly accurate and automatic process in the sense that it ran to
completion via subconscious mechanisms, and thus was hard, if not impossible, to bungle
up. Now, recall is often conceived as a subjective process, where people use working
memory and executive functions to piece together past events. Memory recall thus
involves conscious deliberation and, because of this, is open to all sorts of processing
errors. Memory is often thought to be patently veridical, but when it is not – when it is
reconstructive – it is fallible.
Confabulation is a common error that can be made during recollection.
Confabulation is the spontaneous and unintentional narrative report of events that never

31
happened. When confabulations involve recollection, it is the confusion of imagination
with memory or a confusion in the application (or integration) of true memories (Berrios,
1999). Confabulation is an indicator of psychosis or frank delirium but is thought to
occur in a less prominent and less understood way in all people. Daniel Schacter’s
(2001) book The Seven Sins of Memory points out seven common problems with memory
or its use that can result in mistaken thinking. These involve the transience of many
memories; the consequences of absent-minded thinking; the tendencies of certain
memories to interfere with or block the recall of other related memories; the
misattribution of source; the intrusive persistence of memories that are impertinent,
unwanted or disturbing; and the corruptibility of memory by suggestion; and bias.
Beliefs are necessarily predicated on memories, and thus, when memory is obscured or
blatantly erroneous, belief accuracy can be made especially vulnerable.
It has been shown that a wide variety of memories can be falsely created, either
inside or outside of a therapist’s office, through the use of suggestion, guided imagery,
and hypnosis. Though these techniques do not always result in false memories,
experiments suggest that a significant proportion of people will believe in and actively
defend the existence of fabricated events, even after they are told that the events were
false and deliberately implanted (Reyna & Lloyd, 1997). False memories involving
childhood sexual abuse have gained significant attention because, even when it is clear
the accused is innocent, the accuser can be irrationally convinced to the contrary (Loftus
& Ketcham, 1994). Not just the victims of guided imagery believe in its efficacy.
Surveys indicate that most Americans believe psychologists or hypnotherapists can free

32
up traumatic events that were previously inaccessible or repressed, even though research
does not support this (Loftus & Loftus, 1980). False memories can even be created by
suggestions that are much more subtle.
Eyewitness testimonies, for instance, were thought to be highly reliable at one
time until cognitive psychologists were able to show that the memories that these
testimonies rely on are highly volatile and heavily vulnerable to contaminating
information. It is worth mentioning that false testimony is thought to be a common
occurrence despite the fact that the witness, who is under oath, often believes resolutely
in their testament (Loftus & Loftus, 1980). It is becoming clear that our conscious mind
can come to believe things that are patently false because its reality constructing
mechanisms often act in prefabricated and obstinate ways. Inflexibility in our memory
and thought has been shown to affect our ability to understand even our own intentions.
Neuroscientist Michael Gazzaniga (1998) has a paradigm that explicates why we
are so susceptible to mistaken thought and how it is intimately tied to the way the
conscious mind pieces the world together. Gazzaniga formulated this paradigm after
working with split-brain patients with callosotomies. These patients have had their
corpus callosum cut in half (sagitally), effectively isolating the left and right hemispheres
from each other. Gazzaniga observed the speechless, right hemispheres of these patients
command the left half of the body in ways that were inconsistent with the wishes of the
speaking, left hemisphere. One might expect that the left hemisphere would report that it
could not explain these actions and that it was not responsible for them. However,
Gazzaniga (1998) found that often the person would confabulate; they would make up

33
false reasons for why the right hemisphere did what it did as if they had been in control
all along. It was disconcertingly clear that otherwise sensible people were not at all
aware of this conspicuous subterfuge. This led Gazzaniga to posit that much of our
immediate behavior must be mediated by unconscious, habitual, or procedural brain
systems, and that we often only have the capacity to analyze our decisions after we act
on. He purports that what he calls the interpreter, the language center in the left
hemisphere, does its best to provide rationale for decisions and actions after the fact, and
that this has the potential to result in blustering, duplicitous distortion (Gazzaniga, 1998).
Rigorous experimentation on normal people without callosotomies have supported this
conclusion, showing that spontaneous cerebral initiative to action, involving no
preplanning, precedes conscious awareness of the will to act by more than 300 ms (Libet,
1985).
When we respond quickly to an environmental stimulus, the conscious mind does
not have the time to be considerate and reflective. Often we act simply because we trust
an intuition. Since subconscious brain modules perform these cursory actions (behaviors
that can often seem complex), our conscious mind never has the opportunity to
understand what was done or why until afterwards. As it is not involved in the planning
of many fast responses (and because much of the cortex does not have direct connections
to many subconscious motor areas, such as the basal ganglia), it can only infer from what
it can gather through the senses, why the lower areas did what they did. Studies of the
neuroscience of free will have shown that a person’s brain can commit to certain
decisions from a half a second, to several seconds, before the person is consciously

34
apprised of the decision (Soon, et al., 2008). Some researchers have inferred that, because
our conscious selves are updated independent of the unconscious guidance mechanisms,
most people may confuse the correlation of conscious experience with movement for
causation (Schlinger, 2009). Not only the creation of motor movement but the immediate
creation of sensory imagery – thinking itself – may be highly guided by determined by
unconscious processing. This impels one to wonder how often our beliefs are predicated
on thoughts that are invalid or uninformed attempts at explaining unconscious
phenomena. At first glance it appears to trivialize the role of beliefs, as we have said that
beliefs are mediated by conscious thought. Upon further inspection we remember that
beliefs can become deeply ingrained and that perhaps a large amount of unconscious
action can reflect past conscious belief.
When we have the time to think before acting, we often employ preconceived
models, or schemata, to help orient ourselves conceptually. Schemas are learned
conceptual models that people impose on their experiences to aid them in information
processing, decision-making, and memory (Bartlett, 1932). A schema for a certain social
situation might contain the sequence of events normally associated with that situation.
Our schema for visiting a friend may include calling ahead of time, greeting our friend,
interacting with them, and finally thanking them. Examples of schemata include
academic scripts, social worldviews, stereotypes, and archetypes. Schemas can help to
make certain routines become second nature and help us to develop mental
representations or “theories” about how our world operates. Sometimes we use schemas,
mental frameworks, for commonly occurring things, to help us organize current

35
knowledge, and to provide structure for future understanding (Bartlett, 1932). We can
utilize our schemas to prod us into remembering events or hard-to-recall facts (Brewer &
Treyens, 1981). They help make processing less effortful. For example, I might forget
what I wore last Sunday, but remembering that I attended church might help to expedite
my information search.
Using schemas incorrectly, however, can easily lead to cognitive errors. The
misapplication of a schema is very similar to confabulation. Sometimes one has to think
outside the box and consider the possibilities of other less normative routines coming into
play in order to avoid these errors. Many people with self-limiting schemas, even those
that have demonstrated insight into the questionable origin of the schema, still adhere
closely to them, and continue to act on them, even when it would be far easier to abandon
or even temporarily ignore them (Hoffer, 2002). Since beliefs constitute habitual ways of
perceiving the environment, they are, at least in many ways, comparable to schemas. It
seems that like beliefs, the repeated utilization of a schema increases its consolidation and
makes it less susceptible to disruption (Hoffer, 2002).
This list of common mistakes of belief formulation includes both conscious
oversights and unconscious inadvertences. We search for meaning in the wrong places,
connect the dots in the wrong ways, and adopt frames of mind that miss the big picture.
The solutions to correcting most of these mistakes appear to be common sense, but so
many of us fall prey to them and others like them, on a daily basis. It is clear that once
informed, people can make efforts to resist some of these pitfalls (Tversky & Kahneman,
1982). At best, these mistakes can lead to extreme views in matters of opinion, but at

36
their worst, they can cause people to adopt beliefs that are contrary to what most people
know, causing them to be ridiculed, pitied, or at very worst, institutionalized.
Interestingly, the delusions of a person with pronounced schizophrenia or drug-induced
psychosis appear to be formed under the same conditions as false beliefs.

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Chapter 6: False and Delusional Beliefs

“The heart has its reasons of which reason knows nothing.”
-Blaise Pascal

It seems that the literature on delusions can be brought to bear informatively on
the literature on beliefs and vice versa. There are important differences between delusions
and normal false beliefs. Most false beliefs can be challenged, modified or brought to
extinction if they prove erroneous or unsupported. Delusions though, persevere even in
the absence of support and in the face of strong contradictory evidence. The American
Psychiatric Association defines a delusion as a “false belief based upon an incorrect
inference about external reality,” one “that is firmly sustained despite what almost
everyone else believes and despite what constitutes incontrovertible and obvious proof or
evidence to the contrary (APA, 1994).”
It is thought that it is possible to describe delusions accurately in terms of
associative learning (Miller, 1989). According to this interpretation, in a delusion, one
concept is linked somehow with another, but under a fallacious association. Given this,
the concept of extinction, the uncoupling of two formerly associated things, may be an
appropriate construct to represent the resolution of a delusion (Miller, 1989). If a delusion
is resolved, the explanatory, causal associations that held the delusion together are
disentangled so that the concepts are no longer coactivated together. This is thought to be
similar to the decline of a salivary response to a bell that was formerly, but no longer,
paired with food. During associative learning, also known as conditioning, an organism

38
learns to associate a previously neutral stimulus (such as a tone, referred to as the
conditioned stimulus) to a reinforcer (such as food or an electric shock, referred to as the
unconditioned stimulus). Once a dog is exposed to the ringing of a bell several times
without being given food it learns not to expect the food in this new situation (Pavlov,
1927). At first it associates the absence of food with some new (misleading) contextual
cues that allow it to differentiate between the original situation where the bell predicted
food, and the new association. Eventually though, with enough pairings, the dog learns
that the bell is not associated with food and the extinction of this association is thought to
involve an inhibitory mechanism that overrides the midbrain dopamine neurons
responsible for maintaining the strength of the original association (Pan et al., 2008).
It is thought that in schizophrenia (a disorder marked by dopamine dysregulation);
individuals are relatively insensitive to this type of extinction. They do not learn to inhibit
the previously reinforced response. Delusions, however, do more than persist in the
absence of confirming evidence; they also persist in the face of contradictory evidence
(Rubin, 1976). When faced with clear, counterfactual indications against their delusions,
the deluded often confabulate, make further erroneous suppositions or preposterously
transform disconfirming information into confirming information (Joseph, 1986).
Attempting to question the delusion of a deluded person is often futile. Simply
brining up their delusion activates it, strengthens it and makes it more available in the
future. This process is called reconsolidation and it makes it so that the two concepts
being associated are more likely to be coactivated again in the future. In the same way
that beliefs can be weakened by extinction, they can be strengthened by reconsolidation

39
(Eichenbaum & Bodkin, 2000). Depending on how the memory for a belief is reactivated,
it may be opened up to condemnation or simply made highly salient so that it is highly
associable and reconsolidated. Just being reactivated may make the memory traces
responsible for the false belief more stable and more likely to be activated by related
memories in the future. Hence, the salience of reactivation may matter more than whether
it was confirmatory or disconfirmatory in fixing the belief. Salience probably plays a
large role in determining which memory traces are reconsolidated into knowledge and in
turn, are enshrined as beliefs.
As the deranged, confirming memories become less available and the delusion
becomes weakened and less salient, the deluded person experiences ambivalence between
belief and disbelief. Such double-bookkeeping occurs when a delusion persists but the
person does not act on it consistently (Sass, 2004). When psychotic individuals go on
antipsychotic medication, the memory trace mediating belief in the delusion is not erased
completely; it is merely overshadowed by extinction learning. This explains why
delusions often return once medication is ceased (Chadwick, 2001). It has also been
shown that confronting deluded patients with reasons for why their delusion is unrealistic
often actually strengthens the delusional belief not only because of reconsolidation but
sometimes because the patient is so inflexible that they incorporate the inconsistent
information into their delusional schema (Milton et al., 1978).
In the discussion of delusions and extreme false beliefs, two very important
concepts come into play: motivational salience and prediction error. Motivational
salience is a quality of objects or events that affects a person’s interest in them and affects

40
the person’s relevant actions. Salient stimuli command attention and direct goal-driven
behavior (Berridge & Robinson, 1998). People who are delusional often have a skewed
sense of what is salient and might become motivated by superficial or misleadingly
important things. This may be continuous with the “utilization behavior” seen in bilateral
frontal lobe damage, where a patient’s behavior is obligatorily linked to the most obvious
“affordances” presented by the objects in their immediate environment. When the frontal
damage is extensive, the patient may display the “environmental dependency syndrome”
where they have no capacity to inhibit pre-potent motor programs that are procedurally
linked to the presence of certain objects (Lhermitte, 1986). A delusional person with
skewed motivational salience does more processing between input and output but has
limited capacity to inhibit pre-potent salience programs. A distorted sense of importance
causes them to attend to minor, emotionally laden stimuli at the expense of the bigger
picture. It causes them to act on these impulses but, unlike environmental dependency,
they have enough cognitive reserve to actually analyze and formulate beliefs about them.
These beliefs are usually simplistic and often paranoid. The beliefs themselves are likely
to be faulty. The emotionally salient aspects of the situation overpower other, often more
causal, considerations and they contaminate subsequent conclusions.
The second valuable concept in delusory thinking, prediction error, represents the
mismatch between what we expect to experience in a given situation and what we
actually encounter. Prediction error has been shown to be a fundamental parameter in
associative learning models, and it often determines the strength of perceived salience
(Smith et al., 2006). Only a limited subset of factors is considered making the prediction

41
inaccurate. Efforts that are made to reduce this mismatch result in a clearer and more
accurate worldview. It is thought that prediction errors and salience are interrelated, and
together, greatly affect the formation of delusions seen in individuals with schizophrenia
(Murray et al., 2008). To someone with psychosis, events that are insignificant and
merely coincidental can be perceived as significant, can command attention and, after
analysis, can seem to relate to each other in meaningful ways. Clearly, both false beliefs
and delusions have mistaken or meretricious associations at their crux.
A person does not have to be delusional to be deluded about certain statements.
Studies have shown that, when asked, most people indicate that they believe that low
self-esteem is a cause of aggression, that crime in America is steadily increasing and that
cosmetic implants cause major disease. They believe these things even though research
indicates that these are all false and it is highly unlikely that the respondents had ever
been exposed to good evidence for them (Kida, 2006). But false beliefs are not all bad.
Perhaps, under certain circumstances, it can be harmless, or even beneficial to formulate
a false belief or two. The best way to test a hypothesis is to take it seriously for a while.
Humans learn through the process of trial and error and erring sometimes allows the
learning of important life lessons and demonstrates how and why certain strategies are
not preferable.
Normal people are more likely to accept a false belief if other have accepted it.
Collective false beliefs, often called mass delusions, have been documented several times
in the United States in only the last 50 years. In the spring of 1954, tens of thousands of
people were convinced that a windshield-pitting epidemic had broken out. All scientific

42
investigations of this phenomena reported that no increased window pitting had occurred
at all and that because of simple suggestion- the salience changed- people were looking at
their windshield, searching for pits, instead of what they usually do, looking through
them (Medalia & Larsen, 1958). Historically, Homo sapiens have convinced each other
(despite good, available evidence to the contrary) in animal spirits, astrology, ghosts,
psychic powers, witches and demons. Even today, superstition, religious mythology and
magical thinking play a large role in our culture and in many peoples’ everyday life.
Chadwick and Lowe (1994) reported that four main principles have emerged from
the application of cognitive therapy toward delusion: a) Belief modification should begin
with the least strongly held beliefs; b) Patients should be encouraged to consider the
alternative to the delusional belief rather than encouraged to try to accept the alternative
immediately; c) Evidence for the belief should be challenged before the belief itself; and
d) The patient should be encouraged to voice the arguments against the belief his or
herself. These principles demonstrate that treatment of delusion is a tender and touchy
matter that many patients exhibit high levels of reactance against. These also show that it
is important to try to undermine the foundation of the beliefs before attempting to topple
it directly. It can be extremely difficult to alter beliefs even in non-delusional people.
Some questionnaire work done measuring plasticity in occupational-related beliefs
concluded that most beliefs could not be changed in the short time span of a few hours
(Harris & Daniels, 2005). It seems that the dopaminergic pressure reinforcing the
associations between concepts, even in non-patients, can be very difficult to overcome.

43
Uncovering the neuroscience of belief should help to elucidate causes and treatments for
false belief but should also tell us much more.

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Chapter 7: The Neuroscience of Belief

“One must marry one’s feelings to one’s beliefs and ideas. That is the only way to
achieve a measure of harmony in one’s life.”
-Napoleon Hill

Beliefs form and change in the brain. However, not much has been said about
where beliefs reside in the brain, what brain processes are responsible for them or what
changes in the brain when beliefs change. That almost no literature addresses this topic
forces the present author to offer speculation about the neural underpinnings of belief.
First we must consider the important question of whether different beliefs can be
said to share neurological characteristics. Clearly, no two beliefs are the same, and thus,
the brain basis for any two beliefs must be different. Recognizing that there are many
kinds of beliefs makes it clear that it is a difficult task to attempt to use the reductive
method to pinpoint where and how beliefs form in the brain. Many singular concepts can
be reduced to their component parts in the way that our brain can be reduced to individual
cells or the way cells can be reduced to molecules. Not all concepts have to be singular to
be broken down to their constituent parts though. Scientists have been keen on
explicating molecular and neuroscientific reductionist accounts of memories, which, like
beliefs, consistently differ from one another in many ways. These individual differences
have not stopped memory researchers from dissecting and classifying memories on
neurological grounds and likewise, should not impede our progress. Indeed, from a
neuroscientific perspective, belief and memory overlap substantially with each other, it is
just not yet entirely clear how.

45
Memories are recorded in the brain as alterations that modify the firing patterns
between neurons. These modifications are mediated by either physical or chemical
changes in cellular structures. One of the most plastic components of the neuron is the
synapse which takes advantage of protein synthesis either to increase or decrease the
sensitivity of the postsynaptic neuron to the presynaptic neuron (Kandel et al., 2000).
Small networks of neurons that “fire together” to create representations of things in the
environment, become “wired together.” After they are “wired up” they comprise a stable
representation of some feature in the environment that can be activated to contribute to a
sensory perception or to mental imagery. The smallest and most localized of these
networks code for the most basic stimulus features and are commonly called neural
assemblies (Kandel et al., 2000). When a number of features held by different assemblies
are coactivated, they bind together to create representations of objects and concepts
(Baars & Gage, 2007). Most memories involve coactivations across large numbers of
these neural assemblies; building features into complex structures. The more the
assemblies responsible for a memory are coactivated together, the more entrenched the
memory becomes and the stronger the affinity between the coactivated assemblies.
Beliefs, like memories, must be composed of neural networks and their constituent
assemblies.
Conscious, associative memories and the networks responsible for them, are
commonly thought to be etched into the synapses of neurons of the cerebral cortex
(Thompson, 2005). The cortex is a wrinkled sheet of neural tissue covering the brain that
(because of the distribution and plasticity of its synapses) exhibits a more profound

46
capability for learning than any other area. The connections that form outside of the
cortex, in subcortical areas of the brain, are responsible for automatic and reflexive
behaviors (and even perhaps for behaviors that reflect ingrained beliefs) but probably
belief formation or change. Activity in the cortex, especially in frontal and parietal fields
is responsible for conscious thought- the kind of thought necessary for belief dynamism.
At first, like all new memories, freshly generated beliefs are stored in the hippocampus
along with other contextual elements that surrounded the belief at its inception (Baars &
Gage, 2007). As the information is gradually transferred from the hippocampus to other
cortical areas it becomes separated from its episodic context making it difficult to recall
how and when it was first learned (Smith & Mizumori, 2006). This phenomenon, called
source amnesia, probably contributes to the difficulty in recalling whether memorable
information had factual merit (Schacter et al., 1984). Again limitations inherent in human
memory retrieval impact the accuracy of beliefs. But, in much the same way that beliefs
are more than knowledge, they are also more than just activated memories.
Beliefs are memories that have associative meaning relative to other memories.
This associative or propositional meaning allows them their utility, their applicability in
problem solving, self-directed action and day-to-day life. The prefrontal cortex (PFC), the
“central executive” of the cortex, probably contributes heavily to our ability to piece
together simple memories to create beliefs (Kandel et al., 2000). The PFC sits above and
in front of the other brain areas and fine tunes our actions by inhibiting, overriding and
commanding posterior-cortical, subcortical and spinal areas to modify their tendencies
and reflexes (Sylvester, 1993). The PFC has this ability because it is wired up to receive

47
fully processed information from a large number of different areas giving it the
perspective to make multimodal, cross-conceptual associations. It also has the ability to
inhibit these other areas, allowing it to replace impulsive responses with better informed
responses and to orchestrate the efforts of separate brain modules. The cerebral cortex,
guided by the PFC, is probably the only part of the brain that has an overt capacity for
logic and the weighing of evidence but as we have seen, the thoughts and behaviors that
it directs are often approximations that may result in faulty beliefs. Conversely, reflexive,
subcortical areas, such as the brain stem, the midbrain, the basal ganglia and the
cerebellum operate outside of consciousness yet still, they can administrate behaviors and
decisions that are functional and that can appear logical (Baars & Gage, 2007). For
example, in most animals the cortex is proportionally very small relative to these
subcortical areas, yet animal behavior is highly functional and purposeful (Alcock, 2001).
Does this mean that animals are guided by instinct and implicit learning but not belief? It
can be difficult, especially in animals that cannot report on their experiences, to
determine whether the response to a stimulus is mediated by conscious or unconscious
brain activity. The association responsible for the salivation response shown by Pavlov’s
dogs is contingent on an association in the brain; however, it is not easy to determine if
this association is an unconscious, automatic reflex (Moscovitch et al., 2007). If the dog
salivates after it hears a bell, and it has no access as to why, the response would be
indicative of an associative memory in the animal’s subcortex without a corresponding
belief. A conscious, propositional association between the bell and the provisioning of
food could mediate the response though, in which case, it would constitute a belief. The

48
response could also be both reflexive and conscious depending on the given animal’s
mental state.
If the response exhibited by a Pavlovian dog is only mediated by a subcortical
reflex then it does not constitute a belief. But, if the response involves cortical processing
it may constitute a belief. Especially if the dog is intelligent enough to become aware of
the association and to use memories of it to inform other behaviors, then perhaps it
should be seen as a true belief.
As we pointed out earlier, many human beliefs probably start unconsciously as
gut feelings, acquired through classical or operant conditioning, that we later came to be
conscious of. Like other animals, through trial and error, reward and punishment, we are
conditioned by our environment to have certain tendencies. If we can become aware of
these tendencies (associate the association to other associations), they are no longer
simply behavioristic and can be called true beliefs. Once a belief becomes highly
associated to other memories, it is reconsolidated, made salient and potentiated for use in
guiding behavior. Like the simplest of animals, humans have tendencies that they never
become aware of. The difference between a human and most invertebrates though is that
humans can become aware of most of their tendencies because their attention can be
directed to the high-level abstractions necessary for introspection.
Animals, which have sense organs to receive stimuli and muscles to react to them,
are continuously bombarded by sensory stimuli from their environment. Overtime,
guided by reflexes, instincts, innate behavioral tendencies and prepared learning, they
develop complex ways of interpreting the perceptions that stream through their senses.

49
Some coopt this process, drawing conclusions about experiences to form subjective
knowledge (Greenough, et al., 1987). This process uses knowledge to build knowledge.
New learning interacts with, and is perceived in terms of, old learning as no belief ever
appears in isolation. Swiss psychologist Jean Piaget (1977) viewed learning in terms of
two basic processes: assimilation and accommodation. He defined assimilation as the
process whereby an individual interprets their environment in terms of their own
internalized model of the world that they have been forming since their creation.
Accommodation is the process of changing the internalized model to accommodate the
new information. These two processes were intended to be applied to knowledge but can
also be applied to belief. Beliefs involve a good deal of assimilation and accommodation-
mental work that requires working memory, active representation and modeling,
comparison of alternative scenarios and conscious, cortical deliberation.
How much consciousness, or alternatively, how much cortex do you need in order
to have the processing power to truly believe things? There is very little research on
belief in animals, although it is assumed that most animals are relatively limited in what
they can believe but that humans, with large brains and language, are fully equipped to
acquire and personally manufacture beliefs about virtually anything (Damasio, 2000). It
seems clear that many vertebrates, especially mammals, are rational agents that can be
understood not only from a behaviorist but also from a cognitivist perspective (Dennet,
1991).
Daniel Dennett (1998) has affirmed that many species of animals can hold beliefs-
especially if one is using a liberal definition of belief. The capacity to entertain explicit

50
beliefs and to evaluate and reflect on them though, is probably a recently evolved
innovation, rare or absent in other species. Humans alone embellish beliefs using
language. Beliefs held by intelligent animals, although not implicit, are less explicit than
those of humans because animals associate their beliefs to a much smaller number of
concepts. For instance, an animal may recognize a belief - knowing that it used this belief
in the past - but may not be equipped with the right conceptualizations or vocabulary to
know how to doubt or question their belief. It has been made clear that humans have can
also have trouble questioning beliefs but humans are shown informally by others about
how to believe – lessons animals rarely have.
At the outset of this section, we asked three questions about beliefs that we still
have not answered sufficiently. Beliefs probably reside, like memories, within networks
of neurons and (in a psychological sense) within the mental imagery that these networks
create. The cortex, especially the PFC and Wernicke’s and Broca’s language areas, are
probably responsible for the human ability to manipulate, question and be aware of
beliefs. Belief change probably involves the dissociation of shared activity between the
networks responsible for two previously associated memories. We have seen that this
dissociation is made more easily if the midbrain dopamine neurons, that tie the
association in with biological drives, release the associated networks from each other.
Clearly, these answers are of limited practical use. Allow me to share a personal anecdote
that may help shed light on these issues.
Recently I heard a rustle in the top of a tree followed by loud chirping that
continued for a number of seconds. A large leaf fell from the tree, but for at least a full

51
two seconds I mistook the large leaf for the bird that was making the chirping sound. I
didn’t have my glasses on and I did not realize that the leaf was not the bird until I
noticed that it was falling in a way that was very characteristically, stereotypically leaf-
like. But for hundreds of milliseconds I “believed” that I was seeing the bird despite the
fact that the real bird was totally obscured by foliage the entire time.
It became apparent to me that this illusion was caused by an error in perceptual
binding. The neural networks responsible for two different constructs, in two different
sensory modalities, were activated. Then these two perceptions were bound together, in
the jargon of “operational architectonics,” they were integrated in synchronous
oscillatory processing. Because they were visually striking and loud enough, the sight and
sound gained privileged access to the cortex after being judged for relevance by the
thalamus. At first, these stimuli were processed for content at the level of primary sensory
cortex: primary visual (striate) cortex for the sight of the leaf and primary auditory cortex
for the sound of the bird. Here, their spatial and temporal frequencies were given the
chance to excite existing neural networks in order to determine if their features mapped
on to anything I had experienced before. This message was passed from the primary
sensory areas to secondary sensory areas, where they excited assemblies that
corresponded to their unique traits, allowing more detailed identification of structure and
form. Then the messages traveled from the secondary sensory areas to higher order, more
globally communicative areas.
The information was allowed to spill into the brain regions responsible for
processing experiences outside of a single modality like the prefrontal, occipitotemporal

52
and intraparietal cortex. These “association” or “convergence” areas, which are equipped
with the right inputs to consider multisensory information, have networks that are able to
accommodate the binding of visual with auditory stimuli. According to the neural binding
hypothesis, brain areas with different neuronal assemblies fired in synchrony to unite
different features of these neuronal representations together. In my opinion, these higher-
order interpretations are sent back to the earlier (primary and secondary) sensory
processing areas just mentioned, creating visual imagery that corresponds to the
interpretations of the association areas. In other words, bottom-up mental imagery evoked
by a top-down interpretation, of a bottom-up perception caused a picture of a bird in my
mind’s eye to be superimposed over the falling leaf.
I heard chirping, saw a large leaf begin to fall and failed to question whether the
two stimuli might represent different entities. This perception was automatic in the sense
that my brain fused the two stimuli before I could question the association consciously.
After I had the time to do so (it takes tens of milliseconds for the frontal lobe to have
access to the output of the sensory areas), I still did not change my immediate perception
and found myself consciously expecting to see the leaf fly away. It probably was not until
motion neurons, located in visual area 5, identified a familiar pattern in the motion of the
leaf that I became aware that a bird would never fall as slowly and as waveringly as a
leaf. This mistake in binding, a common occurrence underlying everyday mistakes, is
sometimes called an illusory conjunction. It is clear that I fully believed that this leaf was
a bird, and this was due to binding between stimuli that should not have been bound. This
association took place automatically and had to be questioned deliberately in order to be

53
fixed (or perhaps it was superseded by the subsequent automatic perception of the leaf’s
motion).
This example can be thought of as a bottom-up belief. Incongruous sensory
elements were fused or bound before higher-order areas could intercede. This can
probably take place with emotional learning (such as conditioned fear) and procedural
learning (such as the salivation reflex). Binding can also be controlled by higher-order
association areas and the resultant associations could be thought of as top-down beliefs.
It seems rational to assume that many false beliefs occur because the wrong
concepts are bound in association areas where different features can converge into one.
Such perceptual illusions are rare because people become experts at perceiving physical
events without error from an early age. Higher-order perceptions and representations
though, ones that are not seen but imagined, are probably much more fallible. Such
cognitive perceptions consist of judgments involving many moving parts and can be
extremely difficult to parse apart, collect evidence for and question systematically. These
perceptions, held in association areas, involve concepts like existence, import and relative
efficacy, whereas the lower-order perceptions, held in sensory areas, involve things like
contour, color and timbre. Higher-order beliefs probably work much like sensory ones,
and go wrong for the same reasons. Both involve perceptual elements, that, after limited
information is considered are bound together to create a new, higher-order perceptions.
Sometimes, like a superstitious belief, these are chimerical contrivances that have no
basis in the real world. Once the binding of memories that should never have been bound
happens, mental imagery corresponding to the conjunction is created. Once this imagery

54
is analyzed and acted upon a few times, aspects of it become implicit, making it difficult
to regain conscious insight into the reason for the underlying belief. Happily, with
experience and concerted practice, we become accurate and proficient in the way that we
conjoin higher-order concepts.

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Chapter 8: Ontology of Belief

“For those who believe, no proof is necessary. For those who don't believe, no proof is
possible.”
-Stuart Chase

The objective existence of belief and similarly indefinite concepts in psychology
has been questioned. Ontology, the philosophical study of being, involves determining
what things can be said to exist in reality and what kinds of existence there are. Some
philosophers, most notably of the Platonic school, contend that to exist, something must
be referred to, or referable to, by a noun. In fact, according to some, all abstract nouns are
thought to refer to existent entities (Griswold, 2001). Beliefs then, by this criterion, do
exist. Other philosophers contend that nouns do not always refer to entities but that they
often refer to collections of entities or events that do not necessarily sum to an objectively
existent whole. Thus, beliefs may not be real, only nominal.
There do not seem to be any established methods of determining the existence of
many non-physical entities such as beliefs, minds, communities, thoughts or happiness.
Beliefs certainly cannot be easily scrutinized or manipulated as concrete, physical objects
can. That a belief can be “held” but not touched tells us that we can bring some schemas
to bear on beliefs but that many schemas fail to be compatible with them. When some
schemas work with an abstract noun, it implicitly appears to be real. Beliefs may be
defensible in some instances, but if it is indefensible in most, if it is incompatible with
most scientific schemas, can it really be said to exist? Habitual, unobserved use of the
word belief probably makes people implicitly assume that they are as real as any physical

56
object. This reveals that beliefs can be conceived as patchwork of explanations and
abstractions that has a place in lay discourse but very limited scientific utility. One could
even go so far as to say that many common concepts such as the self, love, attitude,
consciousness, the soul and beliefs can be seen as inadequately specified, indefensible
fictions.
Many in this area of research contend that if belief is a defensible, adequately
specified psychological construct then it should be possible to identify the underlying
neural processes that support it (Baker, 1989). However, if beliefs are not equivalent to
mental states, are incoherent or ultimately indefensible, then any attempt to identify their
underlying neural substrates will fail. Much of the contemporary literature on beliefs in
philosophy has been devoted to the validity of the term belief as a natural or
neuroscientific phenomenon.
Jerry Fodor published well-received work supporting the notion that the most
people’s common-sense understanding of belief is correct (1985). This is sometimes
called the “mental sentence theory,” which perceives beliefs as simple statements and
purports that the way that people talk about beliefs in everyday life is more or less
complete and scientifically valid (Baker, 1989). Three twists on this conception exist.
Stephen Stich argued that our common-sense understanding of belief might not be
entirely correct but that it is useful until we can devise a more scientifically accurate
understanding. Paul and Patricia Churchland advocate a view called eliminativism, or
eliminative materialism, which argues that the common-sense understanding of beliefs is
not scientifically accurate and will eventually be replaced by a different and

57
neuroscientifically accurate account. These philosophers of mind argue that no coherent
neural basis will be found for many everyday psychological concepts such as belief,
desire, or even thought. Daniel Dennet (1998) and Lynne Rudder Baker (1989) take the
third position on the common-sense understanding of beliefs, in what Dennett has called
the “intentional stance.” Dennet says that our current conceptualization of what beliefs
are is entirely wrong but that it does have some redeeming value such as its utility in
generating testable hypotheses about intent, motivation and logic. It may never be
completely clear, even with definitive and comprehensive knowledge of neuroscience,
whether and when belief is an ontologically valid construct. Beliefs, like consciousness
carry crucial subjective aspects that science may never be able to capture or explicate.
Certainly, however, the term, belief, is functional and instructive for learners, children
especially. Imagine growing up without the concept of belief. During early cognitive
development the concept of belief is instrumental in the creation of mental models
concerning empathy, decision and knowledge acquisition. Even beliefs flout ontology, at
least they facilitate ontogeny.

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Chapter 9: The Neuroscience of Thought

“Believe none of what you hear, and half of what you see.”
-Benjamin Franklin

Introduction
This article presents an analogy meant to integrate known information into a
theoretical interpretation of the neurocognitive events that underlie transitions between
mammalian brain states. Gradual changes in a pool of simultaneously coactivated
neurons occur as cortical assemblies that continue to receive sufficient activation energy
are maintained, assemblies that receive reduced energy are released from activation, and
new assemblies that are tuned so as to receive sufficient energy from the current
constellation of coactivates are converged upon, recruited and incorporated into the
remaining pool of active assemblies from the previous cycle. This neurophysiological
process is presented here as analogous to the locomotive behavior of a many-armed
octopus that grabs and releases footholds as it pulls itself from place to place.
The stride of an octopus that plants the majority of its arms temporarily while actively
repositioning arms that have let go of their footholds, represents the uninterrupted,
nonlinear, spatio-temporal pattern of assembly activation, deactivation and coactivation
in the brain. This analogy uniquely describes a system where certain nodes are conserved
through time as others come and go.
The fact that some assemblies within association areas remain active for
prolonged periods (i.e. the octopus arms remain planted), during reciprocal top-down to

59
bottom-up communications, is taken to account for the continuity found between
successive brain states. The longer assemblies in association areas can be continuously
activated - over a series of states - the longer they can influence sequences of bottom-up
imagery in a sustained and consistent way allowing modeling, planning and working
memory in general. The result is a stream of consciousness where each thought is
quantitatively different from the ones preceding, as newly relevant assemblies are added
and the least relevant ones are removed. Highly intelligent mammals have a larger group
of available assemblies to select from, can coactivate a larger number of assemblies
together simultaneously and have the capacity to prolong activation in goal-relevant
association assemblies for extended periods. Prolonged activation of association
assemblies (made possible by the tonic firing of prefrontal and parietal neurons) allows
the topological imagery created in early sensory areas to reflect, not only the bottom-up
inputs from the immediate present, but also top-down inputs from the recent past. In the
most highly intelligent animals motor output (decisions) and early sensory activity
(imagery) reflects several seconds worth of overlapping association activity.
Relevant Literature
Some of the important questions in cognitive neuroscience today include: 1) How
can the thought process, the sensations of consciousness and the functionality of working
memory be described in terms of brain events? 2) How do elemental features (fragments)
of long-term memory combine together to represent entities and episodes? 3) What
neurological events take place when mammals transition from brain state to brain state or
thought to thought? 4) What is the nature of communication between association and

60
sensory areas? 5) How does the human brain permit such sophisticated working memory
relative to other animals? 6) What processes in the brain gives rise to the mental
continuity that humans experience? Without being able to tie together all of the
neurological, psychological and philosophical loose ends necessary to answer these
questions comprehensively, this paper will attempt to address them using novel
approaches based on a simple analogy.
There are currently many illustrative and biologically plausible theories that
address the issues listed above. Some do a fine job of tying together a large number of
relevant phenomena into a cohesive picture. Models such as Baar’s global workspace
theory (Baars, 1997; 2002), Baddeley’s theory of working memory (Baddeley, 2000;
2007), Damasio’s convergence-divergence paradigm (Damasio, 1989; Meyer &Damasio,
2009), Edelman’s theories of reentrance and neural Darwinism (Edelman, 1987; 2006),
Edelman and Tononi’s conceptualization of a“functional cluster” or “dynamic core”
(2001), Tononi’s conception of integration of information (Tononi, 2004), and Grossberg
and Carpenter’s adaptive resonance theory (Carpenter and Grossberg, 2003) have done
much to lend perspective and insight into the mechanics of perception, attention, working
memory and consciousness. Despite much progress, most scientists report that current
theory is unsatisfying because it cannot yet bridge the gaps between epiphenomenal
consciousness, brain processes and neural connectionism (Chalmers, 1995; Chalmers,
2010; Shear, 1997). Further, even though many contemporary models largely agree with
empirical data, little has been done to reconcile their disparate approaches (Pereira &
Ricke, 2009; Vimal, 2009).

61
This work presents an analogy that intends to integrate current knowledge about
neurocognition, while remaining consistent with other theory in the literature. It
approaches the questions posed above from what is perhaps a much neglected
perspective, that of the biological basis for transition between brain states. An analogy
involving the ambulatory behavior of an octopus is offered here to provide perspective.
The octopus holds localized assemblies of active cortical neurons in its arms and its
pattern of locomotion is taken to resemble the pattern of cortical activation and
deactivation. The analogy will grow to encompass several phenomena related to
cognitive neuroscience and will eventually inform a theoretical interpretation of the
generation and subsequent embellishment of mental representations as they pass between
sensory and association areas. Simple, localized assemblies of cortical neurons are taken
to be the building blocks of these representations and the cornerstone of the framework so
let us define them first.
Microscopic, Localized Assemblies Hold Fragments of LTM
Like several other models of mind-brain processes, this model views cognition as
a system responsible for using representations in long-term memory (LTM) to guide
goal-directed processing (Moscovich, 1992). The present model is consistent with
connectionism and parallel distributed processing in that it conceptualizes mental
representations as being built from decentralized, interconnected networks of nodes that
have multiple inputs and outputs (Gurney, 2009). Like other biological models, it
envisions these nodes as modular neural units in the cerebral cortex and assumes that
each individual unit represents an elementary feature or fragment of LTM (Meyer &

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Damasio, 2009). To construct a higher-order LTM-based representation, the cortex must
combine a subset of the numerous units at its disposal (which each represent stable,
preexisting microrepresentations) into an improvised composite representation.
Importantly, representations are constrained to being built from preexisting units and the
brain does not attempt to represent what it experiences in the environment using anything
but combinations of these “receptive field units.” Thus working memory, thought and
consciousness consist of the activation and intricate copresentation of fragments of long
term memories.
Here this fundamental unit of cognition is taken to be comprised of a number of
similarly-tuned neurons that are synaptically bound to create a functionally discrete
assembly or “cognitive atom” (Lansner, 2009). Because the neurons of such an assembly
share similar (or nearly the same) receptive field, they all respond to a particular
conjunction of stimuli and can be said to have a unique although primitive “window on
the world.” A single assembly then, has an aspect of irreducibility in the sense that its
constituent neurons often fire together when they fire maximally. These assemblies are
capable of being activated and spreading their activation energy to assemblies that they
have been associated with in a Hebbian fashion. An assembly is activated by the
simultaneous firing of multiple other assemblies that have come to be associated with it.
Thus the assemblies, like the neurons that compose them, function as coincidence
detectors (Fujji et al., 1998).
These assemblies mostly probably closely correspond to cortical minicolumns of
cells. This is so because minicolumns consist of neurons with highly similar receptive

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fields that are thought to map onto a specific, elementary, perceptual feature (or fragment
of such a feature). Exactly how these discrete fields of cells function and interact has
been relatively mysterious since the columnar organization of the cerebral cortex was
first delineated by Vernon Mountcastle (1978). This article will continue to refer to the
previously described “receptive field units” (meant to represent a mental building block)
as assemblies, however, it is meant to be implied that the cortical minicolumn is a likely
candidate for this construct despite some reservations regarding its internal consistency
and presumed unitary nature. Importantly, minicolumns contain microscopic and
conceptual structural inconsistencies making their boundaries fuzzy in numerous
respects. However, minicolumns are somewhat spatially distinct, contain neurons with
highly qualitatively similar receptive fields and contain the necessary communicative
properties as they span each of the cortical layers. Neurons are not equally good
candidates for a “receptive field unit” (cognitive atom) because, despite the fact that each
neuron has a distinct and singular receptive field, their functional properties vary widely
depending on their cell type and the layer in which they are found. Hypercolumns are
also not good candidates as they can be subdivided into units with various, qualitatively
different, receptive fields. Given that assemblies are equated with minicolumns here, we
can think of each assembly as having its own subcortical and cortical inputs and outputs.
Unlike subcortical areas, strictly one-to-one, linear activation is probably rare in
the cortex. Also, unlike subcortical areas, information processing in the cortex is not
compartmentalized into individual nuclei that are relatively isolated from processing
occurring elsewhere. A given assembly will affect other assemblies that are nearby but

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will also readily pass activation energy via axonal projections to distant assemblies
depending on the combination of early axonal migration and experientially determined
connectivity. Cortical assemblies work together by spreading the activation energy
necessary to recruit, or converge upon, the next set of assemblies that will be coactivated
with the remaining assemblies from the previous cycle. An assembly is released from
coactivation when it no longer receives sufficient activation energy from its inputs i.e. its
relevance to the processing demands diminishes. An assembly may also be released from
coactivation if a number of inhibitory neurons converge on it. This pooling of activity
could be referred to as multiassociativity. To differentiate from the established
multiassociative neural networks we can abandon the prefix “multi” and call this brand of
associativity among neurons, polyassociative.

Figure 1: Polyassociativity Explained
Gradual additions to and subtractions from a pool of simultaneously coactivated
neurons occur as:
1. assemblies that continue to receive sufficient activation energy are maintained
over subsequent points in time
2. assemblies that receive sufficiently reduced energy are released from activation
3. new assemblies, that are tuned so as to receive sufficient energy from the current
constellation of coactivates, are converged upon, recruited and incorporated into
the remaining pool of active assemblies from the previous cycle.

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Outlining the process of polyassociativity in this way is meant to show that
“computation” in the brain is primarily directed at determining which neurons (or
assemblies) should be activated next. Polyassociativity describes how spreading
activation selects neurons. At one point, it felt to me that psychological concepts are
related to one another causally through one relationship or another, but how neurons
could record these relationships seemed infathomable. The concept of polyassociativity
purports that all of the psychological complexity can be reduced to a simple process. The
important concept here that has not been emphasized adequately in other research is that
the next assembly or cognitive unit that will become active is actually selected by not one
but several coactive assemblies. In other words, nothing becomes active that is not
converged upon from multiple directions. This may be the secret, unconscious algorithm
of the brain that is difficult to infer from psychology or neuroscience alone. We do not
willfully choose our next thought, the components of our next thought are selected by the
cooperative firing efforts of the components of the previous thought. WM can be thought
of as the active portion of LTM but clearly it performs some kind of work in sensory
gating, maintaining information, updating information, sense making, and goal direction.
In this author’s view, this work is accomplished via polyassociativity.
This process and the way it is outlined is highly derivative of known
neuroscientific processes. The way it is presented here should have some value though. It
is meant to communicate that processing in the cortex is not compartmentalized, and that
most processing trajectories do not lead up to dead ends due to the fact that the
assemblies of the cortex are massively interconnected. It is also meant to illustrate that at

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any one time there are a complement of simultaneously active neurons that can be split
into three groups, those that are newly activated, those that are being deactivated and
those that have been active over a succession of time intervals. Clearly, the longer the
interval of time between two moments, the fewer the number of assemblies that have
been reactivated or conserved. For instance if the distance between moment A and
moment B is 10 milliseconds, then a very large proportion of assemblies will be
conserved over this period. If the time between A and B is 5 seconds then the proportion
of assemblies in moment B that remain from moment A will be much smaller. This
concept of neural polyassociativity is taken here to be scalable towards thought in the
sense that our next thought will be based on representations that are closely related to the
mix of previously active representations.
It is probably very common that more than two assemblies are each synaptically
wired to a common assembly but have never each fired at that common assembly
together in the past at the same time. When these assemblies become active their activity
will summate to activate the common assembly. In other words, the node most wired with
the currently firing nodes will fire next, even if it has never responded to this unique set
of activators before.
This model posits that groups of these vertical or columnar cortical assemblies
can be bound by coordinated activity to create the neurological instantiations of mental
representations. These mental representations are here called ensembles. Ensembles
stretch laterally through the cortex and are comprised of assemblies (minicolumns) with

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disparate but experientially related receptive fields. It is taken that these ensembles span
areas of the cortex from early sensory to association areas.
Cortical Assemblies Unite to Create Ensembles Which Are Mental Representations
It is unlikely that individual assemblies represent consciously perceptible
constructs. In fact if only one assembly was systematically removed from a complex
representation its absence could probably not be distinguished. We will refer to groups of
assemblies that represent a whole consciously perceptible construct as an ensemble. This
is a highly theoretical proposition but it will allow us to continue in our systematization
of neurocognition. The ensemble is a helpful distinction that will lead us to conclude that
when a psychologically perceptible construct is displaced from working memory (and the
ensemble associated with it loses activation), each of the individual assemblies that
constitute the ensemble have a tendency to stop firing as well. Thus there is a many to
one correspondence and when a consciously perceptible ensemble is removed from the
train of thought, many of its constituent assemblies are often removed as well – unless an
assembly has sources of activation independent of the first ensemble (i.e. it belong to
more than one active ensemble). In other words, assemblies (which are neural units) can
be bound by experience to constitute an ensemble (which is a neuropsychological unit).
Assemblies are bound together in a Hebbian manner due to approximately simultaneous
activation during experience.
Assemblies are relatively discrete and immutable whereas ensembles are fuzzy
with boundaries that change each time they are activated. The vertical or columnar
assemblies correspond to specific, very primitive, conjunctions and are required in great

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numbers to compose composite representations of complex, real-world objects and
concepts. Ensembles are these composite representations and of course have variable,
indefinite boundaries as the experience of no two objects or concepts are exactly the
same. Assemblies are preexisting, are found in microscopic, fixed locations and are
selected as activation energy passes through structurally-descriptive hierarchical
networks that function on neural convergence. Ensembles, on the other hand, span these
networks (from early sensory to association areas) on a macroscopic scale, are mutable
and are inherently improvised. The behavior of an ensemble can be reduced to the
behaviors of its constituent assemblies just as the behavior of a population can be reduced
to the behavior of people. This is in some ways consistent with Joaquin Fuster’s concept
of cognits – distributed memories or items of knowledge defined by patterns of
connections between neuron populations associated by experience. Fuster emphasizes
that his cognits are hierarchically organized, link noncontiguous neurons and overlap and
interconnect profusely (Fuster, 2009).
It is known that object recognition involves two-way traffic of signal activity
between various neural maps that stretch laterally through the cortex from early sensory
areas to late association areas. This activity involves feedforward and reentrant
connections in the corticocortical and thalamocortical systems that bind retinotopic
information from early neural maps about the perceived object with higher-order
information from later maps forming a somewhat stable constellation of activity (Crick
and Koch, 2003) that we conceptualize here as an ensemble. The assemblies that
correspond to an ensemble are coactive, presumably are locally synchronous and are

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bound together through reentrant connections in the corticocortial and thalamocortical
system representing constellations of activity that can remain stable for tens or hundreds
of milliseconds (Crick and Koch, 2003). Moreover, activity within these ensembles tends
to reciprocate on hierarchically structured pathways between cortical sensory areas and
cortical associations areas on the order of brain oscillations (Klimesch, Freunberger,
Sauseng, 2010). It is important to note that ensembles are not static but are constantly
reincarnated and transmuted as additional information is injected into them during the
reciprocations between bottom-up and top-down areas. Because of this, delineating the
borders of an ensemble is fundamentally arbitrary and subjective.
Feedforward activation from bottom-up sensory areas selects among potential
assemblies in association cortex. Conversely, feedback activation from top-down
association areas modulates and drives the selection of assemblies in early sensory
cortex. Thus, the oscillation of information between abstract association areas and
veridical sensory areas allows these two types of cortex to converse, learning from the
other’s unique brand of content, like two people in a conversation. As stated, the present
model addresses aspects of attention, working memory and consciousness but is largely
about how neural networks, built of these assemblies, combine their features to create
cycles of mental imagery and thus thought. The octopus analogy will introduce the
concept that some of these assemblies are conserved through time and can thus affect
imagery for sustained periods bringing continuity to thought. Again, the model proposed
will attempt to accomplish two main objectives: 1) offer an analogy between the
locomotive behavior of an octopus, and the behavior of neural assemblies; and, 2) offer

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explanations for how imagery is generated in sensory cortex, subsequently interpreted by
association cortex, and then is sent back to sensory cortex, resulting in the creation of
updated imagery. After elaborating on the nature of these processes it subsequently
attempts to integrate the customary approaches of attention, working memory and
consciousness.
The Hypothesis
The nature of the pattern of assembly activation in the cortex is addressed by an
analogy, which involves a many-armed octopus grabbing and releasing footholds
(ensembles made of cortical assemblies) as it pulls itself from place to place. The analogy
captures several neurophysiological phenomena but also fails to capture others. It is
meant to illustrate that the thought process involves the simultaneous coactivation of
several clusters of cortical assemblies at a time (multiple footholds held by an octopus) as
well as the activation of previously inactive assemblies (the placement of an arm on a
new foothold) and the deactivation of previously active ones (the removal of an arm from
a foothold). This analogy may be valuable because it depicts a system where specific
nodes are conserved through time as others are actively repositioned.
In the present analogy, each octopus arm corresponds to an active ensemble, the
suction cups on one arm can be taken to correspond to the assemblies that make up the
ensemble, and the grains of sand under each suction cup on an arm represent cortical
neurons. This analogy is apt because, like the grains of sand on the sea floor, cortical
neurons do not move, only the pattern of activation – the octopus and its appendages -
moves. The same assembly can contribute its representational content to different

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ensembles just as ensembles can be combined in different ways to create different
thoughts. Neurons, however, may be relatively restricted to the confines of the assembly
that they are found within and just how restricted they are may depend on how cohesive
individual cortical columns prove to be. For the most part, humans have some ability to
guide thought on the level of the selection of ensembles but have almost no control over
combinations of assemblies. In other words, humans may have reportable insight into
what ensembles they are combining but selection on the level of assemblies is automatic,
rigidly biological and perhaps closed to working memory.
When the assemblies of a single ensemble are activated at the same time, the
features that they code for are amalgamated into composite mental imagery in whatever
way prior probability and previous experience dictate. They sum their component
features together to portray mental images and this occurs in both sensory and association
areas. How assemblies sum their features to create composite imagery is largely
uncharacterized, is related to the binding problem and will be explored here later. When
an ensemble is deactivated, the perceptual or conceptual element corresponding to it
decays rapidly over time (along with each of its component assemblies) and dissipates
until it no longer impacts present experience. Whatever new ensemble is introduced will
inform the present sum of coactivates in a unique and informative way. Thus, imagery
changes plastically over time as assemblies that continue to be useful are maintained,
assemblies that are rendered less useful are released from activation, and assemblies that
are newly recognized as useful are activated and incorporated into the remaining
amalgamation of useful coactivations. This is analogous to the “seafloor walking”

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behavior of an octopus that plants the majority of its arms temporarily and actively
repositions arms that lie behind it, toward the front, in the direction of its movement. The
fact that the placement of some of its arms are conserved, over sequential moments, gives
the octopus balance and stability just as the conservation of some assemblies, during
these transitions, provides the physical basis for the continuity of thought. The assemblies
that are conserved reside mostly in association areas whereas assemblies in early sensory
areas are activated much more transiently. This is as if the arms in back of the octopus
(corresponding to posterior sensory cortices) move much more quickly than the forelimbs
(which find firm, reliable footholds in anterior association cortices).
The following sections will elaborate on this interchange pointing to the
consequences of the octopedal patterning and relating other models to this one. Before
this is done, the next section will consider how this octopus analogy was first conceived
and how it was revised.
The Old Analogy: An Ape Swinging From Cortical Branches
I have developed and evaluated a few different models for representing the
workings of the mind. While doing so I realized that a good model would have to satisfy
certain criteria. The original allegory that I used was of an ape swinging from branch to
branch (hand over hand) where each branch represented a group of neural assemblies in
the cortex that coded for a new thought. This early analogy tried to convey the idea that
the branches, or concepts, from the immediate past determine what branches will be held
in the future. It was meant to convey that we move from one thought to the
neurologically nearest, most appropriate thought in a deterministic manner. The next

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chosen branch in the cortical canopy represented to me, the probabilistically most likely
association given the person’s current thought, given their past and given the structure of
their memory. This model of the thinking process is limited because it is linear. I came to
understand that localized groups of neural assemblies cannot code for complete thoughts,
images, or memories and that assemblies are not activated and then deactivated, one set at
a time, in linear sequence. This caricature of memory was limited, vague and failed to
capture the polyassociative and unintermitting nature of thought.
Once I started to think nonlinearly, I concluded that mental activities must involve
the simultaneous coactivation of numerous assemblies from multiple locations each that
code for different features of long-term memories. I since replaced the branch-swinging
ape with a walking octopus. I changed the animal because the octopus has more arms and
can simultaneously possess more footholds. The many arms introduced important and
divergent features to the locomotive behavior that I think creates instructive analogies
when superimposed on the neural processes of thought. For example, the octopus analogy
has the advantage of demonstrating how several interacting elements combine to allow
thought. Also because these elements remain active for different durations, thought does
not stop and go in discrete steps but is continually “carried along” by those elements that
endure through time. All elements, or neural assemblies, will deactivate within a few
seconds, but the intermingling of assemblies of some temporal stability with those of
more fleeting persistence sustains the associative bridges that allow the thematic and
unifying consistency that is a hallmark of cognition.

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The routine of assembly activation and deactivation is very similar to “polypedal
locomotion” or movement in animals with many legs. It is not much like the locomotion
of an insect such as a millipede or a centipede though because these animals move their
legs in stereotypical, repetitive ways where the placement of each leg is not actively
influenced by the placements of other legs or of the qualities of the footholds. The pattern
of activations in the brain is more like the polypedal locomotion of an octopus that is
“seafloor walking” because it is asymmetrical, dynamic and the placement of the next
legs is influenced by the octopus’ stance, posture and the characteristics of the footholds
themselves. Most importantly, this model can accommodate nonlinear aspects of
neurodynamics. One neural assembly does not activate the next in sequence. Several
assemblies are coactivated together and they pool their activation energy to determine
which assemblies will be activated next.
The New Analogy: An Octopus Walking on a Cortical Sea Floor
Individual cortical assemblies fire maximally only when they are converged upon
by the neurons present in many others. However, many can be found working together, in
concert, at any one time. In the present analogy, the octopus’ footholds represent
simultaneously activated assemblies. But as thoughts do not hold still, the octopus is
constantly repositioning its arms. The assemblies that are used are constantly cycling as
the “octopus” releases some to free up resources (arms) in order to grab new ones.
Even though assemblies are constantly being deactivated, we take many
assemblies with us through time. If we did not do this, we could not be informed of what
we were just thinking, and we could not have a progressive train of thought. Because

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some assemblies and their associates can remain active for a number of seconds we are
able to transition between closely-related thoughts. Thus, mental continuity has a neural
basis. When an assembly receives sufficient activation energy from its inputs it will fire
at its targets (its projective field), often firing recurrently at the sources that targeted it (its
receptive field), until the configuration of assemblies changes to the point where it no
longer receives sufficient activation from either the bottom-up or top-down assemblies
that converge on it.
Figure 2: A Depiction of Polyarticulated Neurocognition

Some assemblies can probably be retained even after the transitions between a
number of thoughts. This happens when your thoughts cycle and change but hold a
common element or theme constant. When we attempt to solve a novel and complex
problem we try to keep the majority of our octopus arms firmly planted so that we can
keep the problem set in mind. Some aspects of creative thinking or free association, on

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the other hand, might involve strategically pivoting around a smaller set of continually
active assemblies and using these to determine the next set of coactivates.
It is not always the case that the majority of assemblies are conserved from one
thought to another. Most assemblies can be dropped or abandoned at the same time, i.e.
when they become a lower priority. This readily happens when we are exposed to a new,
salient, perhaps emotionally laden, stimulus. When this occurs, the octopus “jumps,”
taking all of its arms with it, and reorients to the new stimulus and its accompanying set
of features. Such a jump would constitute a disruption of mental continuity. So clearly
mental continuity can be viewed on a continuum where a high proportion of assemblies
are conserved between brain states on one end of the continuum and a low proportion are
conserved on the other end. Disruptions in continuity might occur due to a stimulus in the
environment, or from an internally generated stimulus. Evolution has probably
programmed the octopus to jump and reposition its arms quickly in order to respond to
important sensory stimuli, so that mammals react to them with all of their cognitive
resources. Mental continuity is less easily disrupted in humans than it is in other
mammals, although perhaps more easily disrupted in people with habituation deficits.
Attention and distraction must be intimately related to the temporal conservation of
assemblies. In fact, the extent of attention deficit and distractibility should be inversely
related to the neurological capacity to conserve assemblies in association areas from
second to second. Creating an operational definition for this proportion and ways to
measure it (on a scale of neurons per millisecond) may prove informative and may
represent a biological measure of general intelligence.

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Another analog of this analogy is the idea that the octopus will “topple” if it loses
its grip on a sufficient number of assemblies. This makes the body of the octopus
analogous to consciousness because brains become unconscious once coactivation
(especially in the frontal and parietal fields) is sufficiently diminished. Thus anterior-
posterior balance and coordination are important for our allegorical octopus.
Subsequent sections will continue to explore this analogy in an attempt to relate it
to hierarchical processing and other concerns. However, a number of things will remain
unclear: 1) How is the extent of assembly activation limited in the cortex? 2) How close
is the relationship between neocortical minicolumns and the present assemblies? 3) What
organization of nearby, similarly-tuned neurons constitutes an assembly? 4) How
localized or spread out are assemblies? 5) What is the role of rhythmic binding in
assembly coactivation? 6) How do synaptic changes associated with learning affect
ensembles or their assemblies? 7) What is the role of neocortical layers and
hypercolumns?
Working Memory: The Coordination of 7 Plus or Minus 2 Arms
The total number of cortical ensembles (or assemblies) that can be coactivated
must besomewhat stable given known limitations on things like neural excitability,
cortical hemodynamics and working memory. In our analogy the number of available
octopus arms is very stable and this represents our fixed, innate capacity for working
memory. Even though the number of chunks (psychologically perceptible units of
perception and meaning) that can be held in working memory, 7 plus or minus 2,
coincidentally coincides with the number of arms that a living octopus has (8), this is not

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a reliable indication of the number of ensembles that can be coactivated in association
cortex. This is true because even though chunks and ensembles may be relatively
congruent, the exact relationship between them is currently unclear. It seems clear though
that the octopus has a relatively invariant number of arms and that perhaps, in order to
bring a new ensemble into the train of thought it must first let go of some other ensemble.
Surely the number of activatable assemblies/ensembles differs from area to area and from
task to task, but it probably remains relatively constant within tasks. Relative to
Baddeley’s model of working memory(Baddeley, 2000; 2007), active ensembles
spanning from association cortex: to visual areas can be equated with the visuospatial
sketchpad, to auditory and language areas can be equated with the phonological loop and
to the prefrontal cortex (PFC) equated with the central executive.
It may be correct to say that someone with a working memory deficit (because of
mental retardation, intoxication, psychosis or dementia) has fewer of these allegorical
octopus arms. Someone with a general mental deficit, temporary or chronic, probably
cannot bring as many assemblies with them through time and may not be able to
synchronouslycoactivate as many of them together simultaneously. Because assemblies
work cooperatively to select the next brain state, having fewer assemblies of less duration
will reduce network searching power and specificity. In other words, it may be the case
that, the larger the number of active assemblies, the more vivid and precise the mental
imagery created in the mind’s eye; whereas, fewer means less accurate, less precise
perceptions (and less specific and pertinent memory recall). Similarly, the longer certain
assemblies are activated, the more new thoughts are informed by recent thinking. An

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intelligent person, endowed with a large working memory, can prolong activation of
certain assembliesallowing detailed priming and filtering of memory that allows the
person to be perceptive and keen.
A deficient working memory (or one lower on the phylogenetic scale) may have
the following characteristics: 1) fewer assemblies to select from (based on reduced
cortical surface area and the resulting smaller number of cortical columns with unique
receptive fields); 2) fewer assemblies bound during instances ofcoactivation; and, 3) the
activation period of association assemblies is less long-lasting. The extended activation
ofassemblies in association areas changes the learning process as well.
Prolonged activation causes synaptic changes to reflect higher-order, temporally-
structured representations; altering the weights of receptive fields, tuning ensembles and
their assemblies to be able to respond to even more temporally complex features in the
future. Thus, fluid intelligence derives from the number and duration of assemblies,
whereas crystallized intelligence derives from the connections between assemblies and
their tuning properties.
The Selection of New Assemblies: Where The Octopus Sends Its Free Arm
The way that new assemblies are primed in this model is consistent with
connectionism and spreading activation theory. In spreading activation theory,
associative networks can be searched by labeling a set of source nodes which spread their
activation energy to closely associated nodes. Nodes correspond to concept and
knowledge units which may be congruent with our conceptualization of the unitary
conceptual fragment embodied by a cortical assembly’s receptive field. The propagation

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of activation follows weighted links to other nodes. Several alternate paths through these
links can reach a specific end node. When enough of these alternate links reach the same
node this node is likely to be activated. In the brain, these links are thought to represent
connections between neurons or assemblies and the weights are found in the synapses. In
other words, when a particular number of cortical areas are active they will converge on
particular other areas according to the weights found in the network. Many areas will be
converged upon weakly, other areas will be converged upon by inhibitory potentials, but
a few areas will be converged upon enough to increase the frequency of action potential
firing, which in turn increases metabolic activity.
A cortical cell has many inputs (in the form of synapses) and a large number of
these inputs must be actively sending it neurotransmitters (creating excitatory post
synaptic potentials) in order for the cell’s firing rate to increase appreciably. Cells in the
cortex are open to being activated maximally, but they remain at a resting level until just
the right complement of inputs takes place. Once the cell becomes activated sufficiently,
the cell will send outputs to other cells within its projective field. Further increases in
activation may increase it firing rate. Increased firing of the cells that constitute an
assembly will lead to “ignition” of the assembly itself. On an assembly level, this
happens when cells within the input layers become excited enough to activate the
pyramidal projection neurons associated with the assembly, which fire out rapidly to the
cells of other assemblies in the cortex. Inhibitory interneurons determine what neurons
will be inhibited from contributing to their assemblies, and pyramidal neurons determine
what assemblies will become active relative to the rest of the brain. I believe that the

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outputs of pyramidal projection neurons in sensory areas broadcast mental imagery by
converging on downstream assemblies. Projection neurons in association areas, far
downstream, recurrently direct and modulate this imagery through top-down,
backpropagating retroactivation.
Because only a small minority of cells become highly active at any one time,
many memories and much imagery remains dormant. Only the precisely appropriate cells
are chosen and this creates the specificity of thought. It takes just the right combination of
inputs from other primed areas (anywhere in the cortex and even subcortical areas) to pull
these cells into the octopus’ embrace. Input from four associated assemblies may not
activate a new assembly without the contribution of a fifth assemblies’s EPSPs. From a
psychological viewpoint, we may not be able to recall a particular memory unless just the
right combination of related memories are coactivated.
By the same account, sometimes arbitrarily associated assemblies are coactivated
(and superfluous memories are recalled) because the brain cannot know beforehand if the
memory will be applicable (unless it has had experience with this particular confusing set
of coactivates). The brain uses a blind heuristic, summoning up the memories with the
largest numbers of related inputs. In other words, the precise combination of active
assemblies determine together (by spreading activation) which assemblies will be
activated next. This reasoning is consistent with the conclusion of psychologists that the
thinking process works associatively.The implications of this neural “polyassociativity”
are taken here to map on to psychological associationism meaning that reportable
psychological states are a product of associations between the elements found in previous

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states. On a millisecond timescale, we do not pick and choose our thoughts, they are
chosen for us based on how the currently active assemblies interact with the associative
network. In this way, thinking appears haphazard because ultimately the way new
assemblies are selected is not overseen by any rational process other than the historically
selected architecture of the brain and thepolyassociative algorithm described. There may
be no other hidden logic or computation aside from that found in the epigenetic structure
of memory due to past learning. Thus, the fewer the number of assemblies coactivated to
choose the next set assemblies, the more random, mercurial, deterministic and unguided
this process appears. Again, such localized, well-connected assemblies are analogous to
the suction cups on an octopus arm. The sum of these assemblies (which code for
elementary features that alone do not capture attention) produces an ensemble (a trait
which can capture psychological attention) that is then summed with other active
ensembles to create the complete thought.
Ensembles can also be viewed as “microconstellations,” theoretical or statistical
subsets of existing “macroconstellations.” A constellation of brain activity involves each
of the firing neurons that contribute to cortical activity in a moment’s time. This
constellation will hold a number of microconstellations, which are groups of neurons that
ordinarily belong to a discrete function, process or concept but are now, in this moment,
firing with the neurons of other functions, processes or concepts. Each of these
microconstellations will belong to a much larger group of neurons that ordinarily fire
during a particular function, process or concept, this would be a macroconstellation.
Macroconstellations, again are a group of neurons that have shown a past tendency to fire

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together (and are thus wired together). However, all of these statistically correlated
neurons never all fire at once. In fact, only their most relevant elements are picked and
chosen by the other miniconstellations present in the brain. In other words, ensembles or
microconstellations are fleeting subsets - temporary and limited instantiations of larger,
theoretical macroconstellations. A certain macroconstellation would involve a unique
group of brain neurons where some were more heavily involved than others. A
macroconstellation could be expressed mathematically with a frequency distribution that
delineating how many times each neuron in the brain fired with it. The tonic or persistant
activity of PFC neurons allows larger, more complicated macroconstellations that span
longer time domains. Getting back to the topic, as microconstellations coactivate they
flesh each other out, forcing each other to change by adding and subtracting components
of their respective macroconstellations. Most microconstellations pivot around the
microconstellation that is given the most dopamine. The stream of thought then, takes
temporally discontiguous macroconstellations and weaves them together creating new
ones.
The allegory of an interactive TV: How the octopus guides mental imagery
This model is also consistent with the consolidation hypothesis which states that
memory is stored in the same areas that allow active, real-time perception and function
(Moscovitch et al., 2007). Relatedly, it assumes that remembering (or imagining) a
particular sensory image largely activates the same neural networks that are involved in
actually perceiving the imagery in the environment (Crick and Koch, 2003). Thus, the
available population of assemblies in sensory cortex act as an active canvas for either the

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environment (via feedforward connections), or expectation (via feedback connections) to
paint on.
This section will attempt to explain how coactivated assemblies combine to create
mental imagery, how features of images come together and how subsequent images are
chosen. To do this we will consider hierarchical brain processing. [To a certain extent,
the cortex is organized hierarchically. The primary visual area (V1) (along with the
thalamus) processes the stream of information sent to it from the retina allowing it to
distinguish dots. The secondary visual area (V2) puts these dots together to form lines-
the edges and curves that make up the visual scenery. Even higher-order, “downstream”
areas put these lines and curves together to discern more complex visual features
amounting to the recognition of movement, color and even objects, scenes and faces.
Imagery is created in sensory areas as sets of assemblies pool their activation energy and
converge on assemblies with higher-order, more specific receptive fields. Now we turn to
how the cycling of information between lower-order, bottom-up areas and higher-order,
top-down areas is accomplished and worked into our conceptual schema of the octopus.
In other words, this section will address how imagery is created, interpreted and then
modified.
Here, their spatial and temporal frequencies were given the chance to excite
existing neural networks in order to determine if their features mapped on to anything I
had experienced before. This message was passed from the primary sensory areas to
secondary sensory areas, where they excited assemblies that corresponded to their unique
traits, allowing more detailed identification of structure and form. Then the messages

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traveled from the secondary sensory areas to higher order, more globally communicative
areas.
We will now describe the brain events involved in a cycle between internally
generated imagery and our higher-order perception of it, i.e. the- reciprocal activation
between sensory and association areas. This process is similar to what it would be like to
watch a television program, one that can be controlled with ideas, conceptions and
conceptualizations. Similarly, the early auditory area can be equated with a tape recorder
that can be recorded upon and played back. The early visual areas constitute the TV in
this analogy because, unlike association areas, they map imagery that is spatially or
retinotopically bound to the visual field. Early visual areas take inputs from higher
association areas and, given these specifications, paint metric imagery. Importantly,
things that follow from our abstract conceptualizations, but that we did not expect to see,
are routinely rendered in imagery. For instance, our sensory areas might pull up the
imagery specified by association areas, but elaborate on it with closely associated but
unforeseeable embellishments. Thus, the cyclical oscillations of information between
sensory and association areas allow them to learn from each other, and to integrate their
knowledge like two people in a conversation. The fact that they have fundamentally
different perspectives on the world makes the “conversation” between them dynamic and
informative for both because of the lack of redundancy. The crosstalk is similar to that
between two specialists in related areas, speaking the same language and interrogating
each other about the nature of their common interests.

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This analogy of the “TV you control with your mind,” represents the process
whereby higher-order (top-down) association areas interpret and then control sensory
imagery in early (bottom-up) sensory areas. The higher-order association areas influence
this imagery through their outputs to sensory areas, and then receive feedback from the
sensory areas (as if the created imagery is actively “watched” to permit feedback). At this
point, some of the association areas remain activated because they are restimulated by the
imagery. Other association areas that are not restimulated or those that are stimulated by
inhibitory neurons might deactivate. We create imagery in our minds, but we do not
necessarily pay attention to every aspect of the imagery, as we do not necessarily notice
every aspect of the perceptions created regarding our environment. When an association
assembly contributes to mental sensory imagery in a way that is not noticed by
association areas in the next cycle, that assembly is not reactivated and does not
contribute to subsequent imagery until reactivated. The sensory imagery that is generated
is not seen as a whole as we might like to think. In fact, many features of the mental
imagery that is created probably remainpreattentive. Thus this analogy of the “TV you
control with your mind” can be combined with the octopus analogy because the elements
of the TV that are noticed drive the placements of the octopus’ free arms. Technically,
the imagery in early association areas also involves assemblies and thus the concept of
the octopus arms as well.
Antonio Damasio has proposed that early sensory cortices construct image space
and that association cortices construct dispositional space that does not hold any imagery
itself. In my opinion, association areas do hold imagery. They hold imagery of higher-

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order concepts that are disoriented from spatial mapping or retinotopic coordinates. In
other words, visual sensory areas hold spatially-oriented optical imagery, auditory
sensory areas hold temporally-oriented sound imagery and association areas hold
abstracted, multimodal, conceptually-oriented imagery that is relatively free of reality-
imposed, unimodal, spatio-temporal constraints. Contrary to Damasio’s notion that
association areas only guide the construction of imagery, I think that association areas
can hold true imagery in the sense that they can invoke high-level perceptions of things
that the person can become conscious of. However, consistent with Damasio, this model
agrees that association areas do not possess all of the information held in the early
sensory cortices that converge upon them. The firing of a grandmother neuron in the
anterior temporal cortex alone does not produce a conscious visual depiction of a
grandmother in the mind’s eye. In other words, you cannot visualize a spatial, line-bound
image of your grandmother without early visual cortex. However, without early visual
cortex, you can still hold associative, conceptual imagery about her as long as your
anterior temporal cortex is intact. In this sense, the imagery (its construction, and
manipulation) is truly in the process of bottom-up to top-down reciprocal oscillations.
How the dynamic pathways between assemblies interact with one another and the extent
to which historically unrelated assemblies cooperate to drive new images is still poorly
understood. Higher order association areas are less useful if early visual cortex is
destroyed because they were meant to interact with the early areas. The same goes for
early sensory cortex without higher association centers, the two were designed to interact
and have interacted together throughout development so each is lame without the other.

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I propose that the early visual cortex activation creates vibrant and specific
internal imagery simply because it has become correlated with the appearance of this
imagery in the environment. Brain cells create a theatre of the mind because they have
“taken on” certain external properties. Imagery is held everywhere because each part of
the brain has become correlated with some type of environmentally induced experience.
Like the neurons responsible for the sensations in a phantom limb, early visual neurons
“hold” the experiential properties of experiences that they have been correlated with in
the past. Surely anterior association areas have been correlated with experiences, albeit
abstract ones. Thus purporting that association areas do not hold true imagery is like
saying that imagery is held in the dots of primary visual cortex but not in the lines of
secondary visual cortex. –This is wrong. The firing of neurons is not correlated with
sensory experience, it is sensory experience. When you imagine something, you
experience it again, in a half-baked way, you fire the same neurons that fire when it is
experienced.
Even though the brain is a very dark place and neurons themselves cannot
generate light, early visual areas have been tuned by experience to represent variations in
brightness, color and form, so that when they become active, from either retinal or top-
down inputs, they display vibrant imagery. It may be correct to say that only sensory
areas constitute this TV, and that association areas hold higher-order concepts that drive
image construction within the TV. On the contrary, the association areas may just be
extensions of the TV because they, like the sensory areas, are turned on every time a
specific visual concept is invoked so there is no reason to assume that they do not hold

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their own form of imagery. Arguing that association areas do not hold imagery is tenuous
and is akin to saying that only V1 holds imagery, and that V2 simply modulates the
imagery. Thus cortical areas responsible for visual processing - from the posterior
occipital pole to the dorsolateral prefrontal cortex – lie together on a continuum with
retinotopic imagery on one side and abstract, conceptual imagery on the other.
The progression of thought involves oscillatory messaging between bottom-up
sensory imagery and top-down interpretations of that imagery where none of the brain
areas know exactly what they are going to invoke in the areas they are communicating
with until they receive feedback. The brain probably does not make any plans about how
sensory areas will integrate the various association inputs, it simply does so reflexively
based on prior probabilities. At first it seems that early sensory areas would have a
difficult time integrating multiple concepts from association areas into a single,
meaningful image. However, the ability to take incongruous elements and integrate them
has become the sensory cortex’s specialty as, over developmental time, it has been
trained to do this with environmental perceptions.
The process of reconstituting diverse association specifications into sensory
imagery is probably identical in many ways to the way that sensory areas combine
features of the sensory environment to create early sensory perceptions. When sensory
areas create perceptions based on inputs from the retina they construct scenes by
conjoining dissimilar elements into a cohesive interpretation based on what they have
been rewarded for creating in the past. Sensory areas must do the same thing with inputs,
not from the retina, but from the association areas to create imagined imagery. This

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suggests that one can only perceive the relationship between two abstract ideas if one
already has implicit information in the sensory cortex (and its hierarchical network of
structural descriptions) about how to co-represent them in an image. If the person is
missing instrumental conceptual knowledge in their sensory areas then these areas will
not be able to create the image (although it is possible that association areas could
manipulate a series of images to lead up to a final image where the important elements
are depicted retrospectively). In turn, things that had not been considered prior are
brought up on the screen, important things that help to guide our train of thought, things
that only our unconscious visual memory system can conjure up. Again, our higher-order,
abstract, more highly evolved associative memory systems inject their own unique take
on things, that are still very much mechanical and deterministic, but more reflective of
what we have done and learned, instead of what we have been passively exposed to.
The reciprocal activity allows sensory and association areas to learn from each
other. Association areas might as well be saying to themselves: “Well, it will be
interesting to see how the visual system will combine this unique set of higher-order
coactivations into a composite, lower-order, feature-based image.” This sensory image
does what association areas cannot do on their own – take various components and
integrate them into a visage that is environmentally veridical. The way that sensory areas
integrate when they construct images is informed directly by reality, as they have been
tuned directly by real environmental inputs, unlike associative areas, which are tuned
indirectly by reality due to the intervening effects of motivation, temporal delay and
inference. The whole reason that these coactivations make a perception that is good is

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that this early visual system has been exposed to so much sensory information, it has a
huge repertoire, accumulated over time, of tricks and insights into how physical and
psychological things work. The sensory activations potentiated by the sum of coactivated
association areas determine the next group of association assemblies that will be
activated- the placements of the octopus’ free arms.
What happens psychologically when multiple sensory assemblies are coactivated
together to form an imagined perception? I believe that, when this happens, mental
imagery is created of the sort that we are all accustomed to seeing in our mind’s eye. The
cortical assemblies that we have been talking about, that can be primed by internal inputs,
also correspond to brain areas that are primed by external inputs during sensory
perception. In fact, early sensory assemblies were first tuned by external inputs- sensory
experiences that we have been having since before birth. When these assemblies are
activated by the environment we have a sensory perception - a rich and vivid experience
that can involve any sense but the most vivid and highly processed are probably visual
and auditory. As an association assembly is converged upon from sensory assemblies
upstream, it becomes active and in turn divergently reactivates the upstream sensory
nodes that just converged on it, and additionally may also activate the other nodes that
ordinarily converge upon it. The resultant sensory imagery is then either superimposed
over objects perceived in the environment (during perception) or combined with other
features in the mind’s eye (during imagination).
When we think and imagine, we activate early perceptual networks. The PFC and
other associative areas do not appreciably influence processing in V1 or the LGN of the

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thalamus (the earliest of visual processing areas) via recurrent projections, but can
profoundly influence the activity in extrastriate visual areas. Thus, contemplative thought
takes place on the same Cartesian stage that the sensory experience takes place on. To me
there is far less mystery left about the origin of conscious imagery after this is taken into
account. Sense, remembered or reactivated is the substrate of thought. The unique pattern
of coactivations that are primed in the cortex by a particular external visual scene is
perceived as a sensory image. An internally driven sensory image is a pattern of
coactivations in sensory cortex that is not primed by information from the retina, but
information from higher-order association areas. The internally driven imagery does not
activate sensory areas as much as a true sensory experience does, and this probably
accounts for why imaginary imagery is not as vivid as actual imagery. Internally driven
imagery, on the other hand, probably activates higher-order, top-down, association areas
more than does true sensory experience. Both externally- and internally-generated
imagery have the capacity to activate higher-order sensory areas. In other words, we can
alternate sequentially from externally-generated imagery to our higher-order perception
of it; we can alternate reciprocally between internally-generated imagery and our higher-
order perception of it, or combinations of both.
Our visual sensory cortex is composed of primary and secondary sensory areas
known to have their own very short-term memory called “sensory memory.” Sensory
memory has been shown to hold more than working memories’ 7 plus or minus 2 chunks,
although it does so very fleetingly (2.5 seconds for auditory sensory memory, and 250
milliseconds for visual sensory memory). Mental imagery probably works in much the

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same way in the sense that the imagery holds more information than we can consciously
attend to and that it fades within a quarter of a second if it is not bound to, or reactivated,
by higher-order associations. In other words, there are many association assemblies
watching the fast-paced TV but these will only respond if they are adequately activated.
Importantly, this cycle demonstrates how Baddeley’s visuospatial sketchpad and
phonological loop operate. Psychologically this feels as if new concepts evoke apposite
imagery which we then analyze and modify. Our sensory areas conjure up their best
sensory representation of the concepts the higher areas developed and, given the
specifications handed down to them, use receptive fields and prior probabilities to present
this visage. This ability is probably fine-tuned during early visual development, and
probably makes use of the vast architecture of recurrent (back-propagating) pathways and
is accomplished rapidly and automatically. The nervous system is wired in a way that
almost all cortical-to-cortical connections can go both ways. The sensory imagery
generated causes association areas to think of something else, something that could be a
slight modification on what we saw last, or seemingly a paradigm shift away from it. The
next configuration of octopus arms may seem wildly different, because it evokes a
different sensory image, but unless emotions were evoked, it is likely that many of the
high-order arms are still in place even when the early sensory imagery changes
profoundly. Hence, sequential images created on the TV may look very different but they
are likely to be highly interrelated.
One might ask then, where and when does consciousness happen? Most
neuroscientists agree that, when considering sensory information from the environment,

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lower-order, primary areas are activated, then it takes a quarter of a second to become
aware of the information. This time is thought to correspond to the time it takes for these
lower areas to activate the most important high-order area, the PFC. Once the PFC
becomes aware, we are thought to be conscious of the sensory stimulus. This makes some
sense that associative, convergence areas such as the PFC, hippocampus, and the angular
gyrus must be activated for us to become aware of something. But these areas do not
contain vivid sensory imagery, at least not the kind we associate with sight for instance.
Because of this, we probably have to wait for the PFC and other high-order areas to
contact sensory areas in order to experience our response to a stimulus. What does it
mean to be conscious of something if we have not yet responded with imagery? Maybe
consciousness is the ability to form new imagery from previous imagery. Then there is
the question of how many times we have to re-experience our response to lower-order
imagery for us to be “aware” of it. It seems that this happens as soon as assemblies
associated with imagery related to self-awareness are activated. We may be conscious of
the early sensory imagery, that is the experience that we remain in through time. But the
association cortex is the buffer of coactivating concepts that actively selects what mental
imagery we see next. Only personal insight allows us to see this.
The octopus’ momentum moves it inexorably, mechanistically and
deterministically to the nearest associated concepts, these connections are hard-wired like
a fixed action pattern. The PFC and hippocampus though can modulate the octopus
movement in order to reflect a past experience (the hippocampus), or a past motivation
(the PFC). Hippocampal pattern completion helps the octopus place a number of its arms

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on a constellation of assemblies that were activated at some point in the past. The PFC
helps the octopus maintain its position, in the sense that it trains and controls the spatio-
temporal layout of coactivations, it also pins a few arms down at a time to allow planning
and modeling. The hippocampus and PFC work together too. The PFC keeps several
things active long enough so that when the hippocampus takes a snap shot, the snap shot
contains several different activates.
How Thought Propagates
Thought is made up of the comingling of several concepts at once. In the brain,
this involves the simultaneous firing of all of the neurons that represent each of these
comingling concepts. The various neurons involved coactivate together and spread their
activation energy leading to the activation of neurons corresponding to the concept that is
the most closely linked (associatively or causally) to this particular set of concepts. Every
second, as thoughts change, old concepts are removed, new ones are added, but yet a
large number persist. Figure 3 includes a diagram attempting to show how concepts are
displaced, newly activated, and coactivated in working memory to form the “stream” or
“train” of thought.

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Figure 3: Selection, Inclusion and Displacement in Working Memory
Note: Each concept is represented by a letter and the order in which they are pulled into consciousness
follows alphabetical order. 1) Shows that concept A has already been displaced from working memory and
that now B, C and D are coactivated. When coactivated, these concepts combine (or spread) their activation
energy to activate a new concept, E. Once E is active it immediately becomes a coactivate, restarting the
cycle. 2) Shows that concept B has been displaced from working memory, C, D and E are coactivated, and
F is newly activated. 3) Shows that concept E, but not C has been displaced from working memory. In
other words, what is displaced is not necessarily what came first, but what has proven, within the network,
to be the most valuable to the given set of coactivates. C, D and F coactivate to make G active.
Importantly, it would be almost impossible to break down the activation dynamics in the brain into discrete
time frames as is shown here. Also, this model makes it seem that only three concepts are coactivated at a
time, whereas this number would be larger (maybe “7 plus or minus 2”). Further, the concepts that are
displaced from working memory may not be in immediate cortical memory, but may still be held in another
form of working memory that involves cortical priming or hippocampal memory. This scheme makes it
seem that it is only the concepts that we consciously experience that activate subsequent concepts, but the
concepts (or nodes) that are unconsciously primed also contribute to the activation of subsequent concepts.

Consciousness and working memory are driven by the reciprocating cross-talk
between fleeting bottom-up imagery in early sensory cortex and lasting top-down
priming in late association cortices. The figures below attempt to explore how this
reciprocal interaction works.

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Figure 4: Frontal Association Areas Selectively Preserve Sensory Features

Note: Each concept held tonically active in association cortex is represented by a letter. The order these
letters are pulled into consciousness arbitrarily follows alphabetical order. 1) Shows that concepts A,B,C,D
and E all diverge onto posterior visual sensory cortex. 2) Shows that neurons involved in the retinotopic
imagery from figure 1 converges on the association neurons responsible for holding concept F. It also
shows that concept B drops out of activation, and that A, C, D, E and F diverge back onto visual cortex. 3)
Shows that this same process leads to G being activated and A being deactivated in association cortex. 4)
Shows that H is activated and that E is deactivated.

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Figure 5: Features Cycle Between Association and Sensory Cortex

Note: Sensory areas can only create one sensory image at a time, whereas association areas are capable of
holding the salient or goal-relevant features of several sequential images at the same time. In other words,
neurons in association cortices can remain tonic for a number of seconds, making activity there progressive
because it can reflect the most important aspects of several successive instances of sensory activity.
Dopamine is key to this progressive process because it helps to determine which features are
relevant/motivating.

The process described in Figure 5 can be described by the following list:

1) Sensory imagery is made in early sensory cortex. This is either a product of
feedforward sensory information from sense receptors or from downstream
retroactivation.

2) Salient features are extracted and communicated with higher association areas

3) Salient features that cohere with already active features in association areas (that are
consistent with the present set of concerns) are added to the active features there, whereas
the least relevant features in association areas are dropped from activation.

4) The important features of the last few images are maintained active in association
areas.

5) The new set of features is handed down to early sensory areas.

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6) This new set of features is used by early sensory areas to construct topographic
imagery. In reality, sensory cortex from each sense modality feeds their inputs to
association cortex (and to each other).

7) The process starts over.

If it were possible to pause a mind at a single instant and look carefully inside the
brain, we would see that some brain cells are active and others are inactive. How long
these neurons continue to fire after the mind is unpaused is determined by how much
input they are getting from other active neurons. If they are not being sent more than the
requisite number of messages from their peers, they slow down or turn off. Some of the
currently active neurons will remain active for only a few milliseconds, others for large
fractions of a second and others for several seconds. None remain active indefinitely, but
rather they each persist for different durations. The pattern of activity in the brain is
constantly changing, but because some individual neurons persist during these changes,
particular features of the overall pattern will be conserved over time. In other words, the
distribution of active neurons in the brain transfigures gradually from one configuration
to another, instead of continually changing all at once. I believe that the persistence of
certain neurons allows the temporary maintenance of mental imagery which is a central
hallmark of consciousness and working memory. I also believe that this persistence lends
continuity to the train of thought.
Six years ago I was waiting at a bus stop wondering how my mind is different
from that of other animals. I realized that my thoughts can extend further in the sense that
I can carry a complex concept out to its logical conclusion. I can take more information
with me through time before I lose it and forget what it was I was just thinking about.

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Psychologists agree that working memory, or the ability to preserve information and
perform manipulations on it, is more highly developed in humans. Influenced by the
various lengths of different pine needles on a Douglas fir at the bus stop, I concluded that
human thoughts were somehow “longer.” But if thought has a length associated with it,
then it must have a beginning and an end too. I wondered for a while if thoughts really do
begin and end, and if so, on what time scales. I now believe that it is possible to answer
these questions using the reasoning in the previous paragraph.
Thoughts have length in a sense, but thoughts do not have a clear beginning or an
end. Thoughts are “longer” in humans because they are composed of elements (that
correspond to individual neurons, or neural assemblies) that remain active for longer
periods than they do in other animals. Our large prefrontal cortex and association areas
keep some neurons online for several seconds at a time, whereas in our pets, for example,
most neurons remain active only very briefly. So it is not that individual human thoughts
are longer, it is that our thoughts are composed of elements that remain coactivated for
longer. The neurons that persist stop and go at different intervals. It is not the case that all
of the neurons that persist turn on and off simultaneously. In fact, the beginning of the
activity of one neuron will actually overlap with the tails of others. The neurons act like
racecars that join in and drop out of a race intermittently. Their behavior is staggered,
insuring that we continually have a cascade of cognitive elements that persist through
time. Thus there is no objective stopping or starting point of thought. Instead, thought
itself is composed of the startings and stoppings of huge numbers of individual elements
that, when combined, create a dynamic and continuous whole.

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Sensory neurons in the back of the brain do not usually remain active for long. It
is the anterior, association areas, especially the prefrontal cortex that contains neurons
that stay online for seconds and even minutes at a time. These neurons, by remaining
active, can mete out sustained signaling to other neurons, insisting that the
representations that they code for are imposed upon the processing of other neurons that
are firing during their span of activity. This is why the prefrontal cortex is associated with
working memory, mental modeling, planning and goal setting. The longest, most
enduring element or neuron would correspond to what the individual is most focused on,
the underlying theme or element that stays the same as other contextual features fluctuate.
Thought changes incrementally during its course. We picture one scenario in our mind’s
eye and this can often morph into a related, but distinctly different scenario. Our brain is
constantly keeping some elements online whether they are representations of things that
are concrete and tangible or abstract and conjunctive. I think that neural continuity as
described here is an integral element of consciousness and may be a strong candidate for
the “neural correlate of consciousness.” Philosophers and neuroscientists have identified
many different elements of brain function (thalamocortical loops and reentrant cortical
projections) and attempted to explain how these may lead to conscious experience. I think
that the present concept of “continuity through differential temporal persistence of
distributed neural activity” is instructive and I even feel that it is the core aspect of
conscious experience, qualia and phenomenality.

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Conclusions
Described herein is a low-level model that proposes some general patterns of
mental activity in terms of neuroanatomical space and the spreading of activation
between processing units over time. These writings are exploratory and at times
superficially reductionistic. Many important concerns were left out of the discussion.
Mental activity was likened to the “polypedal locomotion” of an octopus that is “seafloor
walking.” This octopus leaves the majority of its arms where they are and only moves an
arm when the foothold it is placed on is distant. This is meant to show that we drop
neural assemblies when their relevance to the processing demands diminishes. When one
arm is removed, the concept corresponding to that branch becomes deactivated and no
longer informs present thought.
How is the mind like an interactive television? It is a continuous cycle between
coactivations in higher order areas and those in lower-order (primary and secondary
sensory) areas. The lower order imagery automatically activates a new set of higher-order
nodes that are tuned to the different features of the lower order imagery. These newly
activated higher-order nodes are added to the remaining previous nodes (or octopus arms
that remained on the past branches) to create a unique set of coactivations that again (via
recurrent connections) activates lower order sensory neurons that are responsible for
pulling up a new scene of mental imagery. It is a continuous cycle between coactivations
in higher-order areas and those in lower-order (primary and secondary sensory) areas.
The important point is that the priming lasts longer in the higher-order areas and the
lower order sensory areas can be wiped clean quickly to accommodate a new picture. The

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lower-order sensory areas probably last just as long as sensory memory whether they are
activated by the environment or by higher-order areas. The octopus is always straddling
the line between bottom-up and top-down and the arms (or assemblies) on the bottom-up
side move faster.
The vast majority of mammals have small prefrontal cortices so they rarely have
assemblies that remain active over a number of brain oscillations. It is probably
maladaptive for animals to prolong the influence of a particular feature that is not found
in the environment. The persistence of assemblies probably causes animals to react
slowly to their environment because their imagery is influenced by past concerns instead
of very present concerns in real-time. Mental continuity slows the octopus down. The
continuity provided by the PFC allows systemization of the environment and higher-
order learning, things that our species adapted to necessitate. Most other vertebrates do
not need this kind of continuity to create adaptive behavior, they probably find the
persistence of working memory distracting, noisy, and task-irrelevant. It all depends on
the nature of the tasks that the animal is confronted with in their neuroecological setting.
This is why most mammals learn from trial and error more than from mental modeling.
Assembly activation in most animals is due to external stimuli in the environment:
sensory areas are activated by environmental inputs, these then activate association areas,
which in turn activate motor areas, then the animal waits for the next group of stimuli.
Humans can activate their sensory areas using only internal, associative stimuli, allowing
a powerful reciprocal loop between association and sensory areas.

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The process whereby neural assembly activation fluctuates spatio-temporally is
taken to be analogous to the nonlinear stride of an octopus that plants the majority of its
arms temporarily, while actively repositioning arms that have let go of their footholds.
Here, each individual cortical assembly is thought to be tuned to code for a discrete
element of long-term memory. When multiple assemblies are coactivated the individual
elements can be united into composite, mental representations. These representations
fluctuate back and forth between early, bottom-up sensory cortex (where they are metric
and topographic) and late, top-down association cortex (where they are abstract and
conceptual) on the order of brain oscillations. Sensory areas and association areas
continually stimulate each other into building interpretations of the other’s outputs
resulting in a conversational interchange with minimized informational redundancy.
Humans hold, in their memories, countless examples of non-reconciled
conceptual representations. You may have knowledge of two facts, that when put
together (when their elements are coactivated together in the automatic creation of
imagery) create new knowledge. For example when one coactivates a present scenario
with a psychological schema, the schema will inform their interpretation of their present
predicament. If the schema was not recalled and its nodes were notcoactivated with the
current nodes, they would never be reconciled, no imagery depicting their integration
would be created, and the person would continue acting without the knowledge provided
by the schema. When you forget something, you failed to integrate an important memory.
In other words, memories are discrete and are usually only integrated during conscious,
associative thought. Two pieces of related knowledge are only reconciled if they are

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pulled into the octopus’ embrace, and are corepresented in an image, or in a related
sequence of imagery. Thus, thought is the process of the construction of imagery in early
sensory cortices in response to a particular set of coactivated assemblies in association
areas. The thought changes once association assemblies respond to the elements of this
imagery that they are sensitized to, and in turn, project recurrently to early sensory areas
for the creation of modified imagery.
Maybe all of these bottom-up to top-down reciprocations are organized into
oscillations that propagate in regularly timed intervals, across the brain so that they do
not interfere with each other. The oscillations reciprocate back and forth at just the right
speed so that each area has the time to process its inputs before re-projecting so that they
have time to finish processing before they get the next complement of inputs. Messaging
would be muddled if areas were to get information while they are processing, or if they
didn’t receive all of their inputs at the same time. Perhaps these bottom-up to top-down
cycles of imagery map neatly to the synchronized oscillations of neural populations
known to give rise to macroscopic oscillatory electric fields, which can be observed in
the electroencephalogram.
A longstanding debate in this field has been between connectionism and
computationalism. The present hypotheses have been largely connectionist as they
emphasize the importance of interconnected networks of simple and often uniform units
rather than modeling the “computational manipulation of explicit symbols.” Scientists
comparing human brains to computers sometimes assume that the brain accomplishes
what it does by performing vast numbers of calculations or computations, using hidden

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logic and special algorithms to process inputs into outputs. Are neurons logic gates that
perform Boolean algebra like any other Universal Turing Machine? The present
arguments would not directly support the notion that the brain computes solutions to
problems, but that it retrieves them from memory. It seems clear to me that this is not
going on. Life experience gives the functional structure to these processes that illusorily
appears to be computation. Surely much of the function can be quantified and turned into
math, but I believe that the connectionistic function must be understood before insights
into the processing will be gained from the mathematics. Unlike a CPU, the knowledge
and memories of a brain, like that of a neural network, are distributed throughout its
connectivity. Understanding the world by finding invariant structure in the constantly
changing stream of input.
Crick and Koch have advocated that neuroscientists should concentrate on finding
the neural correlates of consciousness, defined as the smallest set of brain mechanisms
and events sufficient for some specific phenomenal state. This article has suggested that
node transience/persistence is a key correlate of consciousness. Also, here the significant
neural correlate of consciousness (NCC) is taken to be open and rapid communication
between sensory and association areas where sensory areas are creating imagery, and
association areas are attending to aspects of the imagery by activating the higher-order
assemblies that best correspond to these aspects, given the association assemblies already
active. Binding then does not occur due to coactivation alone, but via the convergence of
coactivated features in both association and early sensory areas.

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Chapter 10: The Unconscious Mind and Belief

“We hear and apprehend only what we already half know.”
-Henry David Thoreau

Modern psychology and neuroscience routinely operate under the assumption
that, apart from the mental processes that we have conscious access to, there are many
other brain processes that contribute to our thoughts and behaviors that we cannot
consciously access. Some of these unconscious processes are accessible to consciousness
but ordinarily go unnoticed, other processes are not accessible to consciousness at all but
can be inferred from behavior. An example of the former is when we do something but
never examine the preceding thoughts that lead us to do this thing. An example of the
latter is when we do something and there are no directly preceding thoughts. Pinpointing
unconscious processes and understanding their causes can be very difficult despite the
fact that they are ubiquitous and constant. Unconscious processes are thought by most
scientists to guide and scaffold not only our physical coordination, but also perception,
memory, decision making, motivation and even consciousness itself. All aspects of our
behavior, because they are influenced by either innate tendencies or forgotten past
experiences, are thought to be affected by unconscious factors.
There are a large number of mental phenomena recognized by cognitive
neuroscience as unconscious. Related phenomena include unnoticed emotions,
underappreciated motives, subliminal perceptions, unfinished thoughts, hidden phobias,
concealed desires, automatic skills, procedural habits and bodily reflexes. A popular

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example of an unconscious process is the way that people respond to some subliminal
messages without being aware of the influence. This is a prototypical example of an
unconscious process that most people can relate to. Subliminal messages do not just come
from external, environmental stimuli, they also come from within. We will explore a
number of such processes, analyze the similarities and differences between them and
attempt to create a kind of taxonomy of unconscious phenomena.
We will conclude that behavior is unconscious when neural structures in the brain
influence thought or behavior without sharing the full content of their processes globally,
making it unavailable to the spotlight of consciousness. I have posited before that when
someone becomes conscious of something, prefrontal and parietal association areas,
guide the construction of mental representations (usually visual or auditory imagery in
sensory areas) of this thing. Therefore, any process that takes place without being
recognized by association areas, and subsequently depicted in sensory areas through
mental imagery, goes unnoticed and is unconscious.
But this definition is not sufficient to account for all of the various related
unconsious phenomena that we will encounter. Because science does not have a thorough
definition of consciousness, attempting to delineate unconscious processes proves
extremely difficult. Perhaps, though, a thorough definition of unconscious processes will
help us define consciousness. In turn, because beliefs cannot be unconscious, this survey
into the unconscious will help us to better understand what neurological forms beliefs can
and cannot take.

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Background
The science of psychology has made tremendous advancements in the past
century in its understanding of the unconscious mind and of the automatic processing
which underlies it. These advancements have been made in response to a very large
amount of evidence that has shown that human behavior is highly influenced by brain
procedures that are not recognized by or perceivable to conscious thought. Modern
psychological research has thoroughly examined and recorded many observable effects of
these unconscious, automatic influences but it has only begun to define the psychological
and neurobiological nature of them. Currently the unconscious is viewed by science and
philosophy as somewhat enigmatic. Although this chapter does not hope to fully
explicate this enigma, it does hope to detail a paradigm for use in analyzing its periphery.
The anthropomorphic, clinically oriented ideas that were established by theorists
who initially developed the concept of the unconscious have strongly affected the modern
view. Intellectuals such as Sigmund Freud and Carl Jung personified the unconscious as
something that had a mind of its own. Because of the limitations of their day, these
theorists were not able to analyze the mind or the unconscious from a rigorously
biological perspective. As unconscious processes are primarily biological in origin, this
perspective is necessary. British psychologist C. Lloyd Morgan famously stated that “In
no case is an animal activity to be interpreted in terms of higher psychological processes,
if it can be fairly interpreted in terms of processes which stand lower in the scale of
psychological evolution and development.” I believe that Morgan’s canon should

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similarly be applied to the unconscious. Scientists should avoid anthropomorphizing it
unnecessarily.
Some modern neuroscientists, psychologists and philosophers seem to think that
all unconscious processes can be attributed to an unconscious mind that itself is a
mysterious entity that is highly analogous to the conscious mind. This is probably
because some unconscious behaviors such as the unfolding of dreams, the telling quality
of free associations and the curious validity to many Freudian slips appear to be guided
by an intelligent source. These examples will be framed as the results of brain areas
acting autonomously - in intelligent ways - but only because they have been programmed
with these simple forms of intelligence through their interaction with the conscious brain
areas over the span of many years. Not only have brain areas that act automatically and
autonomously been programmed by higher-order brain areas, but they have also been
programmed by the environment and thus they contain veridically associated elements of
objective reality. The primary visual area by itself is dumb and deaf and practically blind,
even to its own visual representations, but because it has been programmed by real visual
phenomenon in the environment it has its own form of intelligence in the structure of the
representations that it is capable of building. In other words, lower brain areas that serve
as slave units to consciousness can create quasi-conscious outcomes when they act on
their own. These outcomes, even when they seem to involve intentionality or
epiphenomenality are merely phantoms of volitional behavior. It is only the effects of the
complex interactions between brain processes and memory then, that take true
unconscious processes and weave them into the apparition of an unconscious mind.

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If the cognitive unconscious was not guiding conscious thought, scaffolding it and
handing it relevant associations, it could not exist on its own. It is important to mention
that even though conscious thought guides the mental imagery that is created, it is
painting it with experiences and memories that mostly remain preattentive. Everything
implicit in the mental imagery that we create is unconscious, only the aspects of the
imagery that we notice and attend to, become conscious. Associations that were made
conscious in the past are often not noticed or attended to directly but still “feel” like they
are more than implicit simply because we could make them conscious if we attended to
them.
It is the position of this author that it is only the conscious mind that engages in
elaborate, meaningful analysis and what is thought to be the unconscious is actually a
non-thinking byproduct of memory that enables both animals and humans to streamline
processing and conserve cognitive and metabolic resources. The unconscious may be
programmed in an intelligent way, but is not itself intelligent because it does not have the
capacity to deliberate over prolonged time periods. Most of its processes carry to
completion quickly and although this facilitates intuition, snap-judgments and loose
associations, it obviates dedicated reasoning, extended analysis and algorithmic logic.
The high-level conclusion that we are approaching here is that unconscious processes do
not involve prolonged, persistent activations of cortex and thus are simpler than those that
do because transient activations do not persist long enough to allow the global
coactivations necessary for conscious thought.

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For over a decade, I have had an urging intuition that consciousness can be
reduced to unconscious processes. Finally, I have come to the conclusion that this is true
on neurobiological grounds. As discussed in the chapter on the octopus analogy,
unconscious perceptions and associations are bottom-up processes that can be activated
for a prolonged duration if they are selected by the PFC due to their relevance in goal
direction. When a module (brain area responsible for a feature of memory) is activated
for more than one cycle of reperception it creates a certain amount of continuity
(uninterrupted global persistence) between subsequent thoughts that unconscious
processes cannot maintain. In other words, consciousness is simply unconscious,
associative processing with the continued activation of some processes (modules or
neural assemblies) over successive thoughts (neural oscillations). This feature of
continued activation augments associative searches by allowing specific features to be
used as function parameters (to serve as coactivates) for more than one bottom-up to top-
down oscillation.
History of the Unconscious
The concepts of consciousness and unconscious influences originated in antiquity
and have been contributed to by many different cultures. Hindu texts known as the Vedas
(Alexander, 1990), Shakespeare (Faber, 1970), Paracelsus (Harms, 1967) and western
philosophers such as Kierkegaard, Leibniz, Nietzsche and Spinoza have all contributed to
the concept of the unconscious. One of the first psychologists to contribute substantially
to the concept was Sigmund Freud. Freud was a clinical psychologist whose ideas about
personal conflicts and the therapies necessary to treat them had a huge impact on the

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psychology of his time and continues to influence modern clinical psychologists. One of
the most seminal parts of Freudian psychology was his conception of the unconscious (he
abandoned the currently unscientific term “subconscious” early on).
Freud proposed that every person came across thoughts that troubled and
frustrated them. He believed that people often tried to suppress these unsettling thoughts
and ideas and that usually these resided in the unconscious. When these thoughts became
active again they most often did so without conscious action or knowledge. These
suppressed cognitions, many of which were thought to be formed at a very young age,
were related to socially unacceptable ideas or desires, jealousy, guilt, inadequacy and
traumatic memories. Unconscious thoughts were not available to introspection but could
be “tapped” by the use of dream analysis, free association or verbal slips and then
“interpreted” by a therapist trained in psychoanalytic methods. Freud also coined the term
preconscious which described thoughts which are currently unconscious though available
for recall at anytime. One of his most popular conclusions about the unconscious is that it
can be taken to represent a tremendous influence on thought and behavior and that the
conscious mind is only the “tip of the iceberg.”
John Searle in the “Rediscovery of the Mind” has written a critique of Freudian
unconscious. Here he contends that “unconscious thoughts” are untenable constructs and
that for thoughts to exist there must be a thinker. Loftus and Klinger have challenged this
argument stating that such a thinker could exist silently, culminating in the idea of a
dumb unconscious. In my opinion, if what the unconscious does is taken to constitute
thinking it should be on a continuum with conscious thinking where unconscious

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“thoughts” are more discrete, less reconciled with other thoughts and less lasting in the
brain. It certainly would not if we were using the previous definition of thought: cyclical
oscillations of information between imagery and association areas.
Contrasting Freud’s view, modern psychology does not hold that the information
that lies within the unconscious is necessarily repressed and it does not focus so closely
on negative emotions. It simply posits that we do not have access to, notice or understand
many of the processes underlying thought and behavior (Kihlstrom 1987). Much of
Freud’s work is still valuable to modern psychologists and therapists but psychology has
changed much since his time. Much of Freud’s work was based on speculation and
observation but today’s psychologists are more apt to favor orientations based around
research and experimentation. Freud was opposed by critics claiming that his ideas about
the unconscious were not falsifiable or scientific and this criticism still lingers.
Today, most research on unconscious processing is done in the academic tradition of the
information processing paradigm and not Freud’s psychodynamic one. Speculative
concepts such as the Oedipus and Electra complexes, the death wish and the centrality of
libidinous impulses are no longer thought to be useful and instead the cognitive tradition
minimizes theoretical assumptions and rests on data-driven, empirical research. Even
today though, nonscientists commonly use the concept of the unconscious haphazardly to
discuss speculative, mystical or occult phenomena.
Prior to the ‘70s most formal psychological research failed to address unconscious
or automatic processing as a scientific phenomenon. Since the 1970s a great number of
psychologists have conducted studies to help define exactly what affect the unconscious

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mind and its automatic processing have on behavior and mental processes. Subsequent
work in the area developed a clear framework that carefully defines automatic processing
and delves deeply into its implications on different aspects of psychology. Some see the
unconscious mind as a limited metaphor that is not cohesive enough to be thoroughly
refined. Neuroscientists are more apt to study unconscious processes than the more
literary and psychoanalytic concept of the unconscious mind (Westen, 1998). For
example, Timothy Wilson’s idea of an adaptive unconscious describes unconscious
processes that are not lowly and simple but that involve more complicated, even goal-
directed activities. The modifier, adaptive, holds the connotation that the unconscious has
been fine-tuned by evolution to respond to organismal and environmental concerns.
Keeping these things in mind we will take a look at a few different forms of unconscious
processing in order to better understand how they work.
Unconscious and Automatic Processing
Reminiscent of Freund’s iceberg analogy, scientists today believe that a great deal
of human behavior is affected by automatic processing. Automatic processing occurs
anytime a person’s behavior is influenced without them being consciously aware of it.
This type of processing affects what we like, what we are uncomfortable with, what we
are motivated by and how we act. It would seem very difficult at times to distinguish
conscious from automatic processes. Experts largely agree though that unconscious,
automatic processes can be associated with specific, distinguishing elements (Shiffrin and
Schneider 1977). Automatic processes are those that 1) occur outside of awareness; 2) are
very efficient in that they require very few cognitive resources and can be completed in

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parallel with other processes; 3) they are uncontrollable in that they cannot be stopped or
inhibited once they have been started and 4) they are never intentional. Finally,
consciousness is often assessed with verbal reports whereas completely unconscious
processes are unable to be perceived or reported upon verbally (Ericsson and Simon,
1993). Conscious processes are available globally (decentralized and distributed) so they
can be easily directed toward the areas responsible for language and verbal imagery
whereas unconscious processes are localized, have limited outputs to other areas, and
information about their content cannot be directed to language areas. Thus, unconscious
processes are insular, but at the same time very fast. In fact, the conscious mind is
thought to be hundreds of milliseconds behind unconscious processes (Shiffrin and
Schneider 1977).
Actions that involve automaticity do not require conscious control to be
completed. The ability to learn or acquire these automatic processes though, often
requires practice or trial and error. In fact, it is thought that all actions conscious or
unconscious involve parallel processing which is found when the conscious and the
unconscious are working in unison. Walking, driving a car and many other complex
functions that necessitate practice involve such parallel or dual processing. It is now
thought that very few, if any, high-order cognitive processes use one or the other
exclusively (Bargh 1989, 1994; Zbrodoff and Logan 1986). Reading is a good example
of dual processing because learning how to read takes a great deal of practice yet with
time the difficulties involved in phonetics, spelling, and sentence recognition become
second nature. After much practice one automatically delegates the technical aspects

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involved in reading to the unconscious and can concentrate his or her conscious thought
on the content of the writing. Studies have shown that many forms of motoric, perceptual
and cognitive processing can become highly automatic through extensive practice
(Underwood 1974).
It is not understood whether the unconscious can create new associations; whether
it can take one concept, rationalize it and associate it to another without conscious
awareness. This makes one wonder whether most associations, within the neural
association areas, were at one time rationalized by the conscious. One might find
themself trying to hurt someone that they have been consciously jealous of in the past
without having to become consciously jealous of them again in the present. This points to
the idea that the further an association has been elaborated upon in the past, the more
conscious it can be taken to be in the present. The unconscious was programmed with
certain motivations in mind, so when we use some of these old automatisms we have to
ensure that the motivation or emotion that was there when it was created fits the current
scenario, otherwise we might be planning behavior with unintended consequences.
Subliminal Perception
Stimuli that are never perceived by consciousness are able to profoundly affect
behavior. This is known as subliminal perception. There are many ways to be exposed
unconsciously to outside information without being aware of it. Information that is
available for very brief periods or available among a lot of “noise” can be “hidden” from
focused attention but still have access to the mind (Vokey and Read, 1985). Visual
stimuli that are flashed on a screen very quickly can enter early visual cortex without

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being processed sufficiently to reach consciousness. Visual stimuli can also be presented
and then masked, thereby interrupting the processing. Auditory stimuli can be subliminal
if they are played below an audible volume, masked by other stimuli or recorded
backwards. Contrary to superliminal stimuli, these forms of subliminal stimuli are below
an individual’s absolute threshold for conscious perception.
Studies examining subliminal stimuli have shown that emotionally arousing
pictures can be flashed on a screen for a duration too short for conscious attention to be
directed to them. Nonetheless, early processing areas in the visual cortex begin to
perceive this imagery and can send outputs to emotional areas increasing physiological
arousal without awareness. The person that witnessed the shocking imagery might report
that they feel shocked or uneasy but cannot explain why. Perception without awareness
(Ortells, Juan et al, 2002) can influence many different types of behavioral consequences
including complex decision making. The scientific literature on the phenomenon of
priming explores the effects of stimuli that are consciously perceived but that affect
behavior on an unconscious level because exposure to them has been forgotten.
Priming
Priming, or the implicit memory effect, takes place when superliminal exposure to
a stimulus is forgotten about completely but still influences the response to a similar
stimulus later. Word-stem completion tests are an excellent example. These demonstrate
that if someone is shown a long list of words that includes the word misguided, they will
be more likely to use the word when asked later to complete a word starting with the
letters mis. Priming works best for stimuli within the same modality. This means that

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visual priming works best with visual cues and verbal priming with verbal cues. But
priming can also occur between modalities (Zurif, 1995). Priming can be perceptual
(where tab primes table) or conceptual (where tab primes the word chair). Similarly fox
can prime the recollection of the word wolf because of their perceptual resemblance. The
word fox can also prime the adjective sly, conceivably because of their semantic or
conceptual associations (Matsukawa et al., 2005). Multiple primed concepts have been
shown to interact together to prime (or speed up processing of) an associated concept in
what has been called context priming. This happens when one reads written text. The
grammar and vocabulary of a sentence provide contextual cues for words that occur later
in the sentence. These later words are processed more quickly than if they had been read
alone (Stanovich & West, 1983).
Lexical decision tasks are interesting tests where participants are asked to quickly
indicate whether a set if letters is a word or nonword (e.g., “fishing” versus “lishing”). It
takes time and processing resources to determine if the word in known vocabulary. But
there is a very easy way to speed this process up. These tests show that priming of a
related word can increase reaction time. For example if you are shown the word “nurse”
and asked if it is a word or nonword, your reaction would be faster if nurse was preceded
by “doctor” than if it was preceded by “butter.” Showing people the word “water,” even
minutes before, will speed up their recognition of drink as a valid word. This is
interesting because since water activates the word drink, it also must activate many other
similar words such as “pool,” “splash” and “wet.” This suggests that past associative
linkages between concepts may cause us to have a predilection for activating a related

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concept without having a conscious rationale. Interestingly, primed concepts have been
shown to illogically effect decisions. In fact, completely uninformative, only nominally
related stimuli can prime networks that result in illogically biased estimations and
decisions and this is known as primed contamination (Chapman & Johnson, 2002).
Priming is thought to occur because the neural networks of closely related
representations activate and disinhibit each other. The priming phenomenon is not
conscious and is unstoppable, an artifact of neural architecture. There are two types of
priming, positive and negative. Positive priming, which can occur even if the stimulus is
not seen, speeds up the processing of a stimulus. It is thought to be caused by spreading
activation where encountering a stimulus makes the representations of it in memory (and
other closely associated representations) more active (Mayr & Buchner, 2007). This
increased activity makes it so that a related task can fully activate this representation
making it consciously accessible. Negative priming occurs when someone experiences a
stimulus and chooses to ignore it. The act of ignoring something that is brought to mind
makes it less accessible in the future (Reisberg, 2007). The distractor inhibition model
asserts that ignored stimuli are actively inhibited in the brain (Mayr & Buchner, 2007).
Studies of patients with anterograde amnesia due to damage to the medial
temporal lobe and hippocampus, show that patients retain the ability for perceptual
priming and some abilities related to conceptual priming (Cermak et al., 1985). This
indicates that the priming phenomenon is independent of the declarative memory system
which is controlled by the medial temporal lobe and hippocampus. In the same way, the
polypedal pattern of coactivation described in the last chapter is hippocampal

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independent. For the most part, the dynamics of activation, coactivation and deactivation
are driven by the same neural logic that underlies priming phenomena.
Priming and stimulus repetition improves performance and it also decreases
neural processing in the cerebral cortex. Studies utilizing a number of different brain
imaging techniques indicate that perceptual priming reduces processing (and the energy
expenditure and bloodflow associated with it) in early sensory areas (Wig et al., 2005).
This is probably because earlier activation sharpens representational networks reducing
the efforts needed to reactivate these networks. Conceptual priming has been linked to
reduced blood flow in the prefrontal cortex, indicating that its involvement in the
semantic processing of words is reduced by prior exposure (Demb et al., 1995). That a
fundamental part of the process of conceptual priming, a largely unconscious
phenomenon, takes place in the PFC demonstrates that the involvement of the PFC and
other association areas does not ensure attention, awareness or consciousness.
Unconsciousness, Processing Resources and Habit
Concepts in the brain can become interrelated and they can maintain their
closeness even when the reason that they have been paired is no longer able to be recalled
by the conscious mind. We do our learning by associating new concepts to old ones, or
old concepts to old ones in new ways. Concept pairings that are rationalized or used
routinely are often taken as knowledge, beliefs or mental schemas. When we make
associations with our conscious mind, the brain changes physically, neuronal pathways
between associated nodes become more used and therefore more accessible. I have called

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this gradual process “implicitization” and this is one way for processes to become
unconscious.
Our behavior in any situation can be thought of as mostly a patch-work of
different responses that we have in our repertoire that are inflexibly and somewhat
arbitrarily applied in our everyday activities. At one point, these behaviors (especially
when they were first conceived, thought through or imitated) were open to analysis,
insight and change. Over time, the behaviors became closed off to new information, to
introspection and even to consciousness. After a behavior or a reaction or a tendency or a
frame of mind has been open to consciousness, it transitions toward becoming
unconscious. The pathways in the brain become more and more ingrained. The
psychological avenues in the brain become more and more familiar to us, more trusted
because they have proven effective, or at least not harmful. A temporary solution
becomes a tendency, becomes a habit, becomes a way of life.
This automation of learned behaviors can be highly beneficial because it allows us
to use our limited processing resources to attend to other, incipient behaviors and allows
us to pool together a number of automated activities to accomplish more complex,
conglomerated activities. The down side of this is that sometimes we have a tendency to
put automated activities together in ways that they were not meant to be combined and
this can lead to confused behavior, mistakes and even misinformed thought. We combine
these automatisms according to instinct, impulse, intuition and, probably to a more
limited extent, reason.

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So the fact that much of our behavior is a patchwork of inflexible actions is good
thing; however, it can be bad if we have little insight into how we are sewing the patches
together. But would it be helpful to question our driving route at each and every
intersection? When we stop questioning ourselves and the applicability of our actions it is
very easy to fall into entrenched routines. After years of driving, heavy traffic or a major
obstruction on our favorite route won’t deter us, we are willing to wait it out instead of
expending the mental energy and exercising the mental discipline it takes to devise an
alternate path. What we think now and how we allowed ourselves to think in the past is
going to affect our ability to make decisions in the future. If the thoroughfares that are
being employed are not numerous, pertinent or well-functioning, behavioral complexity
and functionality can be expected to decline. The less variability we seek, the less new
knowledge we procure and the less we try to adjust our behavior to closely account for
small variations in our environment the simpler our behavior becomes.
Becoming conscious of a distinct association between two things happens when a
particular association between two concepts is coactivated with other associations.
Together these coactivates create imagery in early sensory areas that depicts some kind of
interaction between the associated concepts. This imagery is reappraised by association
areas, which maintain the two concepts as coactivates but adds new ones, which are sent
back to early sensory areas to create more imagery and so on. In other words, the longer
the cortical octopus holds two concepts in its embrace during alternating cycles of
imagery and reperception, the more the relationship between those two concepts will
become conscious. Ironically, the more closely associated these two concepts come to be,

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the more their imagined aspects become chunked together and the more the association
between them is an implicit assumption in subsequent bouts of coactivation.
Conclusions
We have considered here that perhaps the unconscious is not an entity, separate
from consciousness, meant to be anthropomorphized. It is part of our mental tool set, part
of the wiring of the brain, involved in the simplification of physical action, memory
recall, motivation, and other behavior. This review has allowed us to form some general
conclusions about how unconscious or automatic effects operate:
1) It is not necessary to be aware of incoming information for it to affect performance
2) automatic processing, unlike controlled processing, cannot be reported about verbally
3) automatic activity involves activation that is too transient to be noticed or recalled
4) automatic activity involves activation in an area that either does not communicate
appreciably to the PFC, or at least the PFC has not been tuned to recognize its
communications
5) perhaps conscious activity is generally composed of the same types of processes as
unconscious activity, only conscious activity is temporally prolonged
This discussion of unconscious processes may have a powerful bearing on how
beliefs are formed and how they can go wrong. Many of the mistakes of belief formation
involve a mix of conscious and unconscious thinking-gone-wrong. The true cognitive
factors involved in belief determination may be relatively cognitively impenetrable to
many because they involve implicit assumptions that go mostly unnoticed. Certain
aspects of belief evaluation probably become automated over time until a point is reached

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where it is very difficult to have introspective insight into a process that illusorily appears
conscious and deliberate. But even early beliefs are probably muddled and disarrayed.
The first beliefs, formed in early childhood, must come about without being scrutinized
rationally or explicitly. An infant does not have the capacity to search for justification for
its beliefs, a process that probably involves life experience and even proficiency with
language. This tells us that, as infants grow older, they probably implicitly maintain some
of their criteria (which was never exposed to declarative criticism) for accepting beliefs.
Infants certainly form strong expectations and attitudes - two processes that appear very
much like beliefs. But how do they do this? Looking at beliefs ontogenetically, blurs the
line between beliefs and attitudes. Looking at beliefs from the perspective of unconscious
processing, blurs the line between implicit and explicit belief.
The neuroscience or neuropsychology of belief is a contentious topic. Some
researchers have argued that beliefs must be represented in the mind by consistent,
recognizable patterns of neural activity, whereas others have argued that scientists should
not expect there to be a coherent neurological substrate or physical embodiment of a
belief. To explore this, we must first concede that although beliefs must be physical
phenomena and that different beliefs must share some neurological similarities, they are
not unitary. There must be several types of beliefs and to some degree every belief must
have a different physical, or neuroanatomical makeup. This literature review has pointed
out that there are accepted beliefs, rejected beliefs and beliefs that are in the process of
being entertained or tried-out. Such “candidate beliefs,” that might be held in suspended
disbelief, would probably have a different basis in the brain than a belief that is firmly

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entrenched in the psyche. New beliefs are usually more tentative whereas the oldest
beliefs are unlikely to be overturned easily because of emotional and implicit factors that
are difficult to overcome. The neural “networks” that correspond to personally important,
long-held beliefs must involve extensive, wiry masses of neurons and axons that branch
out and interact with sensory, language and even subcortical systems. On the other hand,
a short-lived belief may only amount to a small number of synaptic changes in temporal
or prefrontal cortex. Because beliefs come in so many different flavors, one would
assume that they each must have different neural bases. We certainly wouldn’t expect the
following types of beliefs to involve the same neuroanatomical infrastructure: uncertain
beliefs, religious beliefs, beliefs about motor praxis, superstitious beliefs, implied beliefs,
well-contemplated beliefs, common misconceptions, dogmatic beliefs, iconoclastic
beliefs, make-beliefs, self-serving beliefs, self-defeating beliefs, etc. Neuroscience does
not nearly seem to be ready to attempt to reduce belief from the mysterious, personal
experience that we all know, to the cooperation and interactions of the molecules that
build and organize neurons, their circuits and their emergent mental processes.
We can no longer blame the “malicious” unconscious mind for stereotyping
individuals against our wishes. We must blame the reemergence of associations that we
made hyperaccessible in the past for this automatic stereotyping. When we dance we can
no longer attribute the complex movements which seem so “natural” yet so intricate to
the dexterity of the conscious mind. We should attribute this to the many complex
memories for movement that we have created within our brain and spine throughout our
lives. The unconscious is not a mind, is not an entity and anthropomorphizing it using

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human adjectives may be fun but is misleading. Or is it? Is it equally as anthropomorphic
to attribute beliefs and other high-order cognitive states to our conscious mind?
When I was very young, the material I read about the unconscious led me to
believe that it was a mysterious and intelligent entity that connived and planned with
foresight and its own set of goals. After reading the psychological literature about
unconscious processes more recently, I have come to see them as inadvertent reflexes,
misunderstood impulses, and generally just a side effect of the way memory interacts
with consciousness. The unconscious is more a series of discrete, unrelated processes
than anything that has the cohesion and sophistication to be comparable to consciousness.
To me, this illusion of an unconscious entity dissipated to become no longer a mind, no
longer another entity that shares my head. However, another major theme that has
emerged in this discourse is the piecemail, fragmentary, irrational, unsystematic,
unreliable nature of conscious processes. Many studies have shown that we are just a
bundle of instincts and impulses and that there is often very little true continuity even in
our conscious lives. These findings along with things like the cohesiveness and
meaningfulness of my dreams, my Freudian slips and my intuition has urged me to
reconsider. Perhaps if I am going to consider my consciousness to constitute a mind
despite the fact that it is insubstantial in many ways, then it is only fair to permit
unconsciousness the same nominal privilege. Withholding this distinction from
unconsciousness could be considered existential hypocrisy.
How can we pursue distinctions between conscious and unconscious processes if
conscious processes are actually composed of unconscious ones as discussed above?

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Earlier I argued that temporal persistence of unconscious processes creates
consciousness. Unconscious, associative linkages are responsible for priming all of the
things that come to consciousness and unconscious motivational-neurons in the subcortex
decide to keep these things primed. “We” don’t pick and choose our associations or
motivations, we can inhibit some, but the rationale for this inhibition was, again,
summoned unintentionally by associative networks. Remember, when you coactivate a
number of different features, concepts that you could never anticipate, or even summon
voluntarily, are invoked.
The unconscious allows the conscious mind to skip from conception A to
conception C when it used to have to go through B first. A is now sufficient to pull up C
by itself and B is habituated to. In most cases B can be recalled declaratively (meaning it
is preconscious), but is implicit in the process of moving from A to C. In terms of the
octopus we introduced in the last chapter, B is a module that can be left out entirely from
the coactivation process and the modules that comprise A can activate module C on their
own. Thus, a module becomes implicit, and its features become unconscious, when it is
no longer needed - during coactivation with its normal coactivates - to recruit another
particular module. In this way, the octopus of consciousness is constantly obviating the
need for modules.

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Chapter 11: Natural Selection and Belief

"Nothing in biology makes sense except in the light of evolution."
-Theodosius Dobzhansky

Weak points in our abilities to formulate realistic beliefs may have genetic,
biological bases that have their origins in evolution. It is clear that we were naturally
selected to think clearly about and ponder certain things but these probably did not
involve the same types of concepts or activities that we contemplate today (Nesse &
Williams, 1995). Today our beliefs rarely involve foraging, ranging, tracking, food
processing, hunting, protection from the elements or from predators; the concepts that we
were designed to test, systemize and understand (Cosmides & Tooby, 1992). Natural
selection may not have ever been responsible for selecting us to formulate functional
beliefs about philosophical, sociological, religious and scientific concepts. We were
never selected on the basis of our ability to have epistemologically sound, high-order
beliefs. Much of the thinking done by our ancestors, even the higher-order thinking,
probably controlled procedural processes - the capacity to learn, store and recall
information about physical tasks and movements. When we use the same brains used to
forage and hunt to contemplate academic phenomena with complex natural backgrounds
we are liable to make errors. It is a little disconcerting, given the extent of human
dominion over the earth, that we may not be well-adapted to contemplating and forming
opinions about modern issues.
In the ancestral past, humans were selected to respond to physical feedback from
the environment as to whether the plan of action that they chose was efficacious or not.

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Today, our activities involve topics that are difficult to test by ourselves because we do
not get the same instant physical feedback from them. Few of our beliefs involve survival
or even true behavior; more often they simply involve further thought. Since many of our
beliefs are about intangible or abstract concepts, and because they often do not translate
into behavior, we probably get less feedback from the environment about the accuracy of
our beliefs than we used to. Further, the dangers of prehistoric environments may have
selected us to unhesitatingly choose one belief over another without concerted
deliberation. Unfortunately this pressure to react quickly may lead us to be hasty,
especially with stressful or emotionally arousing beliefs. In other words, beliefs about
issues that are the most important may be formed rapidly because the brain is tricked into
thinking that there is limited time due to portending danger.
The brain that we inherited from our reptilian and “paleomammalian” ancestors
was designed to process procedural and emotional behaviors. These types of memory had
much more time to develop evolutionarily than did declarative memory. Moreover,
procedural and emotional expertise is gained easily and rapidly and leaves little room for
mistakes. Coordinating suitable movements and properly assessing danger is much less
error-prone than forming accurate beliefs about abstract topics. Expertise with language-
driven belief making is hard-won and still much more susceptible to error and
miscalculation.
Everyone knows the feeling of being assured and confident in a specific
statement. It seems that evolution endowed us with a sensation of surety, a “feeling of
being certain.” In order to feel this certainty we must be fairly sure about the belief in

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question (Burton, 2008). Evolution must have contributed to the set point of how easy it
is to have this feeling of certainty. Today this may cause us to accept beliefs that sound
right but are factually inaccurate, and because they are not testable, this leads us to an
increased proclivity for believing the wrong stuff. Evolutionary shortcuts, the inability to
ensure that our beliefs are systematic and the reliance of our beliefs on a faulty memory
system have rendered us rather gullible and susceptible to the influence of minutiae that
we may not even vaguely be aware of. The manner in which natural selection fine-tuned
us to believe may involve a mismatch between the environment that our brains are
expecting and the 21
st
century environment that they are experiencing.

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Chapter 12: Déjà vu, Emotion and False Feelings of Certainty

"You got to be careful if you don't know where you're going, because you might not get
there."
- Yogi Berra

Déjà vu is the experience of a new situation that feels like it has been previously
witnessed or experienced. I think that my theoretical explanation for the phenomenon
sheds light on the experience of belief certainty, so I will share it here. I argue that the
feeling of déjà vu is a delusion created by the coactivation of several memories each
corresponding to familiar features that the person has experienced before. These features
have truly been experienced before, but never all together. People are always
experiencing features that they have seen elsewhere, but when enough of these features
come together during one experience, the person may have a strong feeling that they have
done this thing before- and this feeling is usually correct. Sometimes this feeling is
wrong. When it is wrong it results in déjà vu and this is probably related to the false
alarm principle. Alarms, like human minds, are calibrated to detect coincidences. Usually
the presence of a large number of smoke molecules within the internal detection chamber
of a fire alarm indicates to the alarm that something is burning. In the same sense, a large
number of familiar coactivations must tell some neurological module, responsible for
coincidence detection, that it should invoke the mental sensation of having “already seen”
the present experience. Like the alarm, this module is calibrated to minimize error, but
more importantly, to maximize detection. A false alarm for a fire is usually not costly, but
an undetected fire can be tremendously costly. In the same sense, the experience of déjà

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vu may not be costly to reproductive success, but failing to integrate a set of important
behavioral cues certainly might be. It seems that this coincidence detection module in the
brain works relatively well because déjà vu, although common, is very infrequent.
The human brain must also be equipped with a belief certainty detector module
that tells us that a sufficient amount of rationale and evidence seems to have been
compiled to justify the feeling of certainty. This module must be innate because everyone
seems to have it, but it also must be programmed or calibrated by environment learning.
Trial and error teach us when it is safe to feel certain and when it is not. By childhood,
this detector works very well for simple physical and social activities. Most children can
be around a new person for a few minutes and gauge how safe or hostile this person is.
Different features of hostility or amiability combine to create an overall impression and if
the amiability greatly overwhelms the hostility the child might feel certain that the person
is safe. A child might feel certain that a building of blocks is stable enough not to topple,
or elaborate enough to engender praise from their parent. Despite how certain the child
feels this, the certainty could be delusive.
In adulthood this detector (and especially its inputs) becomes much more
experienced and reliable for use with complex situations. It is still possible though, for an
adult to feel quite certain about a belief even though the belief is false. This is probably
most common when it takes place in an area that the person is inexperienced in.
Especially in an unfamiliar domain, the certainty module is likely to misfire when a
number of contributing criteria indicate that the person should feel certain even though
they should not, and even though the criteria are misleading. For instance, a social worker

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not accustomed to dealing with convicted criminals might fully believe that a client is
trustworthy even when they are not. Further, a person might feel that they have all of the
rationale in the world to be convinced conclusively in the existence of free will even
though this stance may not currently be philosophically or scientifically tenable. As we
said about beliefs earlier, this belief module may have its own logic that is not accessible
to consciousness. The feeling of certainty (like déjà vu, anger, excitement and other
emotions), however, is accessible to consciousness and feels authoritative and
dependable.
It feels good to be able to come to a conclusion about the veracity or falsity of a
premise and it feels bad to remain uncertain. We are probably given satisfaction by our
pleasure and reward systems for thinking a problem through to the point where we feel
we have satisfied the necessary epistemological demands of certitude. The environment
must interact with some, currently unknown, neurological system to calibrate this scale
between truthfulness and falseness. A very precise scale of weight only shows that two
things are equal in weight when both weight the exact same amount. The antecedents that
contribute to the mental scales of certainty though, (measuring the difference between the
truthfulness and the falseness of a proposition) are rarely precisely measured, and thus,
most people rarely judge truth and falseness equal and decide to be uncertain. Our scales
for certainty are usually tipped one way or another because balance or uncertainty is an
aversive state. One side usually weighs more and largely our emotional system ultimately
makes the determination that this option “must be right.” That we are emotionally
programmed to loathe the feeling of uncertainty may explain why we often “have to

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know,” why we believe resolutely in mere hypotheses and why aboriginal cultures (and
industrialized nations) necessitate “just so stories” to explain the origins of humans and
the creation of the universe.
There is an intriguing body of literature on the therapeutic benefits of the
appraisal of emotions. This clinical research – often related to cognitive behavioral
therapy or mindfulness meditation - shows that negative emotions can be quelled and
controlled simply by identifying them (Hofmann et al., 2010). When a person is able to
recognize a negative emotion like sadness or frustration, certain areas of the PFC become
active and reframe the feeling from a compelling sensation to a harmless concept
reducing its negative emotional impact (Kabat-Zinn, 2005). The negative, misleading
aspects of the emotion of certainty might also be ameliorated from similar appraisals.
Identifying feelings of certainty as the fallible approximations that they are may help
people to dissociate from dangerously subjective opinions. The simple act of appraising
felt certainty as mere belief should help people to question unsupported assumptions and
foster objectivity.
The feeling of certainty, like other feelings, arises from involuntary sensory
systems that are highly fallible but have been tuned by both evolution and the
environment to help us make up our mind. No isolated circuitry in the human brain can
think without input from emotional, involuntary and undetectable influences. Even
though certainty can feel very much like a conscious conclusion that we reach
purposefully and intentionally, certainty is often a mental sensation that happens
mechanistically, deterministically and preattentively.

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Chapter 13: Other People’s Beliefs

“It is the mark of an educated mind to be able to entertain a thought without accepting it.”
-Aristotle

As with other forms of learning it seems that learning what to believe starts with
adopting or modeling the beliefs of others. Understanding how others introduce us to
beliefs and how we conceptualize them in an ontogenetic sense should prove integral to
understanding not only how beliefs develop but also how they change with time. James
Alcock famously argued that our beliefs have their origins in the influences of authority
figures; specifically pinpointing parents as the main influence for most people early in
life (Alcock, 1995). Authority figures may also, according to Alcock, influence one’s
“gut feelings” because, at a young age, one does not necessarily have sufficient empirical
evidence to support his or her beliefs (Alcock, 1981). Literature on the development of
social theories supports the idea that children primarily adopt beliefs from parents, peers,
teachers and other social agents and then create a larger proportion of their own beliefs
later in life as experiences accumulate (Alcock et al., 1998; Anderson & Sechler, 1986).
Beliefs adopted or borrowed during childhood and adolescence create scaffolding for
personal belief building in adulthood. In young childhood, when many core beliefs are
first being formulated, children actively learn about what to believe and how to believe
from their parents, teachers and friends. Many of these early beliefs are formative and
resistant to change (Anderson & Sechler, 1986). These findings and others like them may
also indicate that beliefs that have emotional concomitants, like many religious and
sociological beliefs, have a tendency to become fixed early in life for the same complex

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reasons that emotional responses and personality become fixed early on. One survey
study has shown that people, regardless of age, have a strong tendency to believe in the
religion that they were inculcated into during childhood (Argyle, 1997). In the book,
Parental Belief Systems: The Psychological Consequences for Children, Irving Sigel
claimed that belief systems should change with respect to parents’ influences over time.
He pointed out that if one were to measure similarities in the beliefs of parents and
children, it would be important to note how old the children are during the time of the
study. Survey and interview-based research has found that young children tend to blindly
accept whatever their parents tell them, and may never question these beliefs or even
have a full understanding of them (Sigel, 1992). By the time they reach adolescence, they
tend to be more rebellious and may want to distance themselves from their parents, in
which case they may form their own beliefs and reject the stances taken by their parents.
Once individuals reach young adulthood, the similarities in beliefs become less
predictable.
David A. Murphey’s (1992) research has shown that children are highly likely to
adopt the beliefs of people they find charismatic, interesting and inspiring. He points out
that the parents that are most successful in effectively transferring their heartfelt belief
systems are those that are accepted by their children as role models. Charismatic and
loving parents that avoid inciting dissent, seem to be the most influential. Similarly, a
longitudinal, questionnaire-based study performed by Allan Wigfield (1994) revealed that
the more involved a parent is and the more they combine warmth with reasonable levels
of control, the more likely their child is to share their general attitudes and beliefs about

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the world. Interestingly, parenting style has been shown to be highly conserved across
generations (Simons et al., 1992).
Religious affiliation provides a set of core beliefs that have been proven
predictable, in some ways, to psychologists (Argyle, 2000). Scott Myers of Penn State
University published one study examining the heredity of religious beliefs in 1996. The
study, which interviewed 471 parents and their adult offspring, attempted to determine
the degree to which parental religious beliefs affect their child’s chosen religion. The
study also attempted to identify some family characteristics that make intergenerational
transmission of religious beliefs more likely. The study found that the children’s religious
beliefs were strongly correlated with the beliefs of their parents. The study also found
that religious beliefs are particularly similar amongst close-knit families in which the
mother did not work (Myers, 1996). Previous work conducted by Cynthia Clark and
colleagues (1988) looking at transmission of religious beliefs to first-born sons resulted in
somewhat similar findings. This work featured 68 mother-father-son triads, where each
family member was interviewed about their beliefs. The study incorporated and
controlled a large number of potential predictors. Mothers were found to influence son’s
religious application and practice; whereas, fathers influenced son’s church attendance.
The authors point out that parents hope to transmit values to their children that are not
interfered with by transmission from schools, media and other adolescents. They imply
that this is not always easy. The findings also suggested that parents that served as
dedicated, consistent role models were the most influential.

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It seems that it is difficult to be an effective role model if your actions
hypocritically contradict your verbal messages. In a study featuring 192 mother-
adolescent pairs, there was a strong correlation between a mother’s smoking-related
beliefs and the likelihood that her child becomes a smoker (Chasin et al., 1998).
However, the strongest predictor of whether a child will smoke was whether the mother
herself smoked, suggesting that parental actions have a larger impact on children than
beliefs do.
Another important set of beliefs that have been examined across generations are
political beliefs. A study consisting of 1,440 college students asked to present their
political beliefs and to estimate the beliefs of their parents showed that nearly two-fifths
of the participating students rebelled actively against their parents’ political beliefs
(Middleton & Putney, 1963). Rebellious students tended to hold more liberal beliefs than
their parents did and tended to report far more interest and involvement in politics than
the students who mirrored their parents’ political views.
A questionnaire study performed in 1983 by Allan Wigfield calculated
correlations between a child’s belief in their mathematical abilities and the child’s
perceptions of their parents’ belief about them. His analyses caused him to conclude that
the actual beliefs of parents do not have powerful, direct influences on children’s beliefs.
Instead children interpret whatever messages they receive from their parents (which may
not necessarily coincide with the true beliefs of the parent), and these interpretations
influence the child’s beliefs (Wigfield, 1994). He concluded that the study demonstrates
that parents’ beliefs affect their children far less than the direct, explicit messages that

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they provide, regardless of how few or how many. This is probably true for most beliefs,
not just those transferred by parents. Children and adults should be more aware of this
disseverance between true beliefs and communicated messages so that they can better
monitor both the way that they construct their own messages and the way they think
about the messages of others.
Sara Harkness (1997) wrote about the importance of cultural factors on parent’s
influence on beliefs. She discusses the case of second-generation immigrants and of how
they struggle with the clashing between their parent’s traditional views and those of the
culture that they were born into and immersed in. Her studies have found that, although
they often maintain that they respect their parents’ belief systems, these people tend to
favor the views of the culture into which they have been indoctrinated. People also tend
to favor culture when their parents, families or friends have beliefs that can be taken by
others to be superstitious, old-fashioned or insular (Harkness, 1997).
Attitudes and beliefs are known to be transmissible from parent to child but it is
not known if this due to imitation or genetics (Underbill, 1988). We know that nurture
effects attitudes and beliefs, but what about nature? Hereditary variables are thought to
affect attitudes, but probably do so indirectly (Tesser, 1993). For example, if a person
inherits a disposition to become an introvert, this may also indirectly affect their attitude
towards other people, towards music or towards taste in apparel. Interestingly, the same
principle may apply for beliefs. For example, a person with a genetic disposition for
introversion may be more likely to believe that approaching strangers is dangerous. It
would be interesting to employ twin studies (comparing beliefs between identical and

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fraternal twins) to determine the contribution of environment and genetics to belief
although many methodological issues may arise. There has been little study of innate,
heritable beliefs but it is clear that most people believe that a meal of food is satisfying,
that healthy mates are preferable and that kin and offspring are worth making some
sacrifices for.
The discipline of evolutionary psychology looks at these kinds of common
psychological traits and interprets them as adaptations to the ancestral, hunting and
gathering environment (Cosmides & Tooby, 1992). Evolutionary psychologists interpret
behaviors that are near universal, such as the predisposition to fear snakes and insects, to
reflect the experiences of our ancestors (Bjorkland & Pelligrini, 2000). The role of
natural selection in belief is uncertain. Although it is certain that our brains and nervous
systems have preexisting pathways that strongly influence how we respond to our culture
and environment. Further, some seemingly innate behaviors are not universal among
humans, and these may correspond to particular alleles of a certain gene. For instance,
people that are extroverted- due to certain genes diminishing the responsiveness of their
amygdale, locus coeruleus, paraventricular hypothalamus or adrenal glands- may be more
likely to espouse the belief that strangers are trustworthy and that people are inherently
good. Thus, there are pathways through which genes could affect belief and these are
generally congruent with the hypothesized pathways thought to exist for the heritability
of attitudes (Tesser, 1993).
Another twist on the parental impact on belief is the viewpoint taken by physical
anthropologists that refer to it as “intergenerational resource flows of cognitive capital

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(Lancaster, 1997).” It takes resources, such as time and energy, to instruct offspring;
resources that are that better spent elsewhere in most animal species. Human mothers,
and often fathers, in hunter-gatherer groups instruct their young for nearly two full
decades (Blurton Jones & Marlowe, 1999). This is done to ensure that the youth has the
knowledge- about foraging, about social conventions and about survival- that it needs to
survive and pass on its genes (Kaplan et al., 2000). Humans are unique in this regard.
Virtually no other animals – even great apes - instruct their offspring for more than a
decade. Thus, human brains may have evolved, within this didactic environment, to be
neurologically suited for belief acquisition more than any other animal (Reser, 2006).
Richard Dawkins (1976) conceived individual units of this cultural information as
self-propagating entities he calls memes. Dawkins defines a meme as an idea, practice or
belief that can be transmitted from one mind to another through speech, writing, gestures
or other imitable phenomena. Dawkins and other supporters of the meme concept regard
memes as analogous to genes in that they self-replicate and respond to natural selection.
For instance, memes are inherited from parent to child, memes mutate and change as they
are passed along and only useful, or seemingly useful memes spread. Some memes
become extinct and others, that replicate effectively- like religions, catch-phrases and
dance movements- proliferate, even sometimes to the detriment of their host. Dawkins
(1976) has said that memes can be thought to parasites the brains of their host and this
has been called “thought contagion.” The meme concept and the accompanying discipline
of memetics continues to be an influential, reductionist account of beliefs that shows us
that beliefs can be envisioned as active replicators that invade and inhabit our passive

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nervous systems. It also shows us that perhaps we have less control over the beliefs we
find ourselves entertaining than we would like to think.
It is known that adults, in addition to children, tend to internalize the beliefs of the
people that they are surrounded by. Beliefs espoused by one’s community group, place of
work, church, neighborhood, city or country often have powerful, polarizing effects on
people. For instance, a neighborhood-based survey study found that one’s political beliefs
are effected the most by the political views in the community where one lives (Gelman et
al., 2008). Studies like these make the process of believing seem quite deterministic.
Some have questioned whether belief is voluntary or just a product of memes and the
social environment. Like so many other psychological phenomena, the conclusion is
generally that it can be either or both (Kida, 2006). As is thought to be the case for free
will, it is clear that with beliefs the environment plays a significant role in a person’s
choices but it does not necessarily play the only role. In addition, certain environments
can be more influential than others can, and certain people may be better able to exercise
control and determination over their environmental inputs.
An instance where belief formation seems to be the most deterministic is when
someone adopts the beliefs of a charismatic leader, even if the beliefs are contrary to their
prior beliefs and even if the belief produces consequences detrimental to their self-
interest (Hoffer, 2002). This phenomenon seems like it would be rare but has been
documented to be a commonly occurring political and sociological phenomenon (Kida,
2006). Authoritative and especially authoritarian leaders have demonstrated this power
many times during history. In fact, the pressure to be obedient to an authority, even when

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it involves following untoward orders, has the capacity to modify beliefs profoundly
(Blass, 1999). In other words, people change their beliefs to fit the expectations put on
them by their environment, often doing so in ways that minimize cognitive dissonance or
social reproach. Charisma, persuasiveness and frequency all play major factors in beliefs
formed under pressure from others (Hoffer, 2002).
Scientists, technical specialists and other professional investigators are thought to
have the capacity to influence and change the beliefs of the abecedarian. It is not
currently known when or why people take the word of such experts on faith, but the
appeal to scientific authority is extremely common. When one judges that they do not
have the knowledge to model a belief correctly, they often turn to someone that does.
One reason that people believe authorities is because they assume that the authority has
considered more evidence. In this way, looking to an authority can be seen as a strategy
for obtaining lots of evidence without having to search for it or organize it. Thomas Kuhn
(1970) proposed that to evaluate the correctness of a scientific belief one must assess the
assent of the scientific community, not merely the amount of supporting empirical
evidence for the belief. This could be due to the fact that a layperson does not have the
knowledge or expertise to judge the relative importance of the available evidence. The
scientific community may adopt some beliefs because they deem the evidence for it to be
particularly convincing despite the fact that it might appear scant or unconvincing to a
novice. For example, the theory of evolution seemed outlandish to many of Darwin’s
contemporaries but to modern biologists, who have a much wider breadth of knowledge
about biology, evolution is a logical necessity. Authorities are not always right and even

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when they are it is not clear that their conclusions will be informative or helpful to
laypeople in their day-to-day lives. It is fortunate that the opinions of authorities, on a
multitude of important issues, are largely available to people willing to do a little research
and reading although it is disappointing that few are willing to invest the time necessary
to do it.
Other people, whether through their spoken or written communications, do much
to influence our beliefs. Parents seem to be the biggest factor, especially in early life, and
because their influence is formative, it is also lasting. We have seen that charisma,
kindness, authority, expertise, frequency and cultural morays all act as contributory social
influences on belief. Uncovering more about how beliefs are guided in the early years,
and what influences these beliefs to be maintained should help inform scholastic and
pedagogical efforts. In the next section, we will consider how other people’s assessments
of us, and our thoughts about this, can influence our beliefs.

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Chapter 14: Importance to Self-identity

“I can believe anything provided it is incredible.”
-Oscar Wilde

Abelson and Prentice (1989) maintained that beliefs interact extensively with self-
identity concerns. They emphasized that beliefs help us to make decisions and solve
problems in our own idiosyncratic ways - ways that help to define our social identity.
Beliefs serve as identifiers and we use them to align ourselves with social groups and to
advertise our central values and attitudes. It has been argued that extreme stands in an
attitude are commonly a result of high ego-involvement (Levy, 1997). If a statement
concerns a construct that contributes to a person’s pride, uniqueness or self-identity, it
will rarely be regarded with detachment and will usually arouse intense attitude.
Beliefs that are strongly tied to one’s self-identity can be extremely difficult to
change. Since our beliefs can do much to define us as individuals, we often defend them
when they are questioned. Instead of changing our beliefs to fit with encountered
evidence or subjecting them to criticism, we often protect them with defense
mechanisms. People strive to maintain their sense of self-identity for many reasons: it
gives them pride, a feeling of individuality and gives them a stable view of the world
(Markus, 1977).
Information consistent with one’s self-identity is seen by most people as more
credible than information that is inconsistent (Levy, 1997). Self-concept maintenance
results in the reinforcement of one’s character, and causes people to try to reject feedback

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that conflicts with their ideas about themselves (Sutherland, 1992). People will go to
great lengths to maintain their sense of identity. They will engage in certain cognitive
strategies (that can be largely unconscious) that include selective attention, selective
memory, and selective interpretation (Shermer, 2003). Furthermore, consonant with the
elaboration likelihood model discussed below, statements that arouse polarized attitudes
are likely to have been evaluated heavily in the past and will be more stable (Sherif &
Sherif, 1968). It is clear that the desire to keep our beliefs in line with our self-concept
determines what kind of feedback we seek from others and from our environment
(Gilovich, 1990). At times it may not be clear if people are more concerned about
believing things that are true or about believing things consistent with egoic concerns.
Also, things that involve pride, vanity or self-worth, often also involve the types of
emotion that can cloud judgment.
Self-identity causes us to espouse beliefs that cohere with our values without
respect to fact. For example, someone who considers themselves an environmentalist
might believe that all re-cycling activities, natural alternative energy sources and forms of
conservation are good and that all materialism, waste and pollution should be avoided at
any cost. This tendency to align with a cause and to reject anything that, at first glance,
appears inconsistent with the cause is a common strategy used by people with ties to a
political party, religion, secular initiative, philosophical movement or strong personal
conviction. Especially in domains such as politics, there is often so much information
about the issues that people are forced to choose sides based on slogans or catchwords
instead of knowledge building. Unlike knowledge systems, belief systems can tend to

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hold conceptions that cluster together based on specious or frivolous assumptions.
Knowledge systems are formed logically and objectively whereas belief systems are
formed in whatever way the believer sees fit at the time. All of this points back to the
existence of central beliefs, which are often egocentric and which spawn derivative
beliefs in a quasi-rational fashion (Abelson, 1979).
An article by Castelfranchi (1996) offered a substantial review of the literature on
self-concept and concluded by suggesting three measures of self-identity: relevance,
permanence and likeability. Castelfranchi argued that these three constructs are the main
emotional determinants that guide us through the process of accepting or rejecting beliefs
and that they ultimately contribute strongly to certainty. Changes in the relative
permanence, likeability and relevance are thought to predict swift changes in belief
strength (Castelfranchi, 1996) and further underscore the role of self-identity concerns in
belief formation. Research shows that these three measures fluctuate with respect to one
another, depending on the belief in question. Questionnaire studies performed by the
present author indicate that permanence is often rated highly by experimental subjects
faced with beliefs that relate to self-image. Likeability has been shown to be similarly
related to attitude, as has relevance to lifestyle (Reser, 2009).
Evidence also points toward the fact that our beliefs and attitudes are often
determined by past behavior. When our past behavior does not coincide with our beliefs,
an aversive state of “cognitive dissonance” arises forcing us to reappraise our beliefs in
terms of who we are and what we have done in the past (Festinger & Carlsmith, 1959).
People will often accept beliefs that they feel they have a vested interest in. They will be

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more likely to believe things that are consistent with their lifestyle or in-line with their
political leanings (Taber & Milton, 2006). People can probably have a cognitive vested
interest in certain leanings as well. If past thinking is generally inconsistent with a new
belief it can serve as a barrier to entry for the belief. Some people remain open to new
ideas and this “cognitive liquidity” can be beneficial or subversive depending on how
conscientiously and prudently it is used. Bem’s (1967) self perception theory and
Gazzaniga’s (1998) interpreter theory echo this sentiment and reinforce the notion that
people, sometimes unwittingly, search for reasons to side with previous decisions.

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Chapter 15: Attitude Change, Persuasion and Belief

“The Church says that the Earth is flat, but I know that it is round. For I have seen the
shadow on the moon and I have more faith in the Shadow than in the Church.”
- Ferdinand Magellan

Attitudes represent a major construct with a heavy bearing on beliefs that modern
psychology has given a full treatment. This literature shows that not all human judgments
can be assessed as either veridically wrong or right. Attitudes, unlike beliefs cannot be
false or delusional in an objective sense, but like beliefs, are sentiments that are
frequently held with conviction. An attitude is a hypothetical construct that represents a
person’s measure of like or dislike for something (Eagly & Chaiken, 1995). Attitudes are
thought to represent stable, learned predispositions to respond to a stimulus in a fixed
way. Beliefs often tend to be more cognitive and attitudes tend to be more affective and
conative in the sense that they involve motivated reactions to the environment. Like
beliefs, attitudes are formed either from observational learning or through direct
experience (Fazio, 1986). Due to the similarities between attitudes and beliefs, we will
take some time surveying the popular literature on attitudes to see how they form and
change and to see how they overlap with beliefs. Happily, research on how attitudes form
has been a topic of a great deal of purposeful research.
Because of the similarities that they share, attitudes have been said to be
congruent with but not necessarily equal to beliefs (Underhill, 1988). Some researchers
argue that beliefs are a type of attitude, and others contend that the two represent opposite
ends of a spectrum (Bassarear, 1989). More recently, like beliefs, attitudes have been

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characterized as being impacted by conscious and unconscious factors and subject to
continuous evaluation and change (Furinghetti & Pehkonen, 2002). Generally attitudes
are either positive or negative but they can be ambivalent or conflicted such as when
someone holds a positive and negative attitude toward an item simultaneously. A person
can hold conflicting or mutually incompatible beliefs yet this state is probably less
common than ambivalent attitudes. Attitudes, like beliefs, are judgments, although
attitudes take on a strong emotional aspect and designate preference; two qualities that
beliefs may have but do not necessitate. Unlike personality, but like beliefs, attitudes are
very plastic and expected to change as a function of adult experience. The knowledge
garnered by psychologists in the area of attitudes is thought to have useful applications
for things like therapeutic attitude change, marketing and personal persuasion (Sherif &
Sherif, 1968).
There are several theories about the formation of attitudes, which hold important
implications for the formation of beliefs and these include the self-perception theory, the
persuasion theory, the elaboration likelihood model and the social judgment theory. The
self-perception theory is an account of attitude formation or change developed by
psychologist Daryl Bem (1972). It asserts that people develop attitudes by analyzing their
own behavior and making conclusions about what attitudes must have caused them.
Conventional wisdom dictates that attitudes cause behavior, but there is much evidence
now to support the idea that causality can go both ways.
Bem (1972) took this idea further when he propounded that often people
determine what their attitude was, after the fact, without accessing or recalling the

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cognitive states that led to them. He claimed that in this way, people often explain their
own behaviors in the same way that they might explain the behavior of another person. In
essence, often unknowingly, they look at themselves from an observer’s standpoint. The
same process might drive the formation of some beliefs. If so, many beliefs may arise
simply when a person commits to a certain act once and then feels pressured to act in a
way consistent with this act in the future. Future scenarios may only superficially
resemble an initial scenario but the desire to be seen as consistent might cause people to
create beliefs that are hasty and poor. Bem’s (1972) theory and the self-perception theory
of attitudes are very similar to Gazzaniga’s (1998) theories regarding the “interpreter”
(the language area of the left hemisphere) mentioned in a previous section.
The self-perception theory, which has been supported by a good deal of empirical
and clinical research (Robak et al., 2005), suggests that individuals with psychological
problems such as anxiety or depression infer their inner feelings from their behaviors.
This has become a cornerstone of cognitive-behavioral therapy where the therapist works
with the client to gain insight into maladaptive behaviors and the thoughts that are caused
by them. The attitudes characteristic of anxiety and depression can play a large role in
guiding what is believed. Looking through a list of common personality traits it is clear
that a number of dispositions could predispose people to accept certain beliefs over
others. It is easy to see that traits such as optimism, neuroticism, openness, introversion,
conscientiousness, cynicism, agreeableness or paranoia could each foster very different
leanings in belief. Further, as with attitudes and personality styles, beliefs should be more

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closely analyzed by therapists because of their potential bearing on therapeutic coping
strategies.
Another relevant model of attitude formation and change is the elaboration
likelihood model of persuasion (Petty & Cacioppo, 1986). This model makes a distinction
between the central route to persuasion which involves the persuaded person thoughtfully
deliberating and scrutinizing the persuasive communication, and the peripheral route
which involves the persuaded person ignoring the merit and logic of the persuasive
communication and instead focusing on secondary factors such as how the message is
being delivered or presented (Petty & Cacioppo, 1981). Whether the person is able to
take the central route and actually think for themselves is determined by their motivation
and ability (Petty & Cacioppo, 1986). Interestingly, attitudes (and presumably beliefs)
formed using the central route that were elaborately analyzed are more likely to be stable
over time, more likely to be predictive of behavior and more resistant to subsequent,
contradictory persuasion (Petty & Wegener, 1999). Again, the literature on attitudes
provides a valuable framework for the understanding of beliefs. Certainly one’s
motivation and ability to analyze the merit and logic of a belief can compete with their
inclination to take a “peripheral route” to belief formation.
Social judgment theory, proposed by Muzafer Sherif and Carl Hoveland (1961), is
yet another model of attitudes that contributes to the effort of gaining perspective on
belief. These theorists argued that the amount of attitude change expected could be shown
to be directly proportional to the message receiver’s level of interest, the nature of
alternative attitudes and the credibility of the source. These researchers asked participants

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to categorize statements into piles of most acceptable, unacceptable and neutral in order
to observe their decision making behavior. Sherif and Hoveland (1961) concluded that
categorization is an observable judgment process that must play a major role in attitude
formation. They also concluded that categorization and thus attitude acquisition are
products of past experience, prior knowledge, emotion and the current situation (Sherif et
al., 1965). This study, by requiring participants to sort statements into categories also
uniquely allowed the experimenters to notice another feature of attitudes: even small
quantitative differences between two attitudes can be perceived as constituting a
qualitative difference. For example, two people that both believe in natural selection may
disagree as to the specifics of the role of natural selection in nature. There are many
factors that contribute to both attitudes and beliefs and this makes it so that people can
agree on a subject, yet disagree as to its parts.
A number of concepts derived from the social psychology of attitudes are
particularly relevant to the discussion of beliefs: self-serving bias, false consensus,
unrealistic optimism, the fundamental attribution error, saying becomes believing, prior
commitment, channel of communication, indoctrination, attitude inoculation, group
polarization and deindividuation. That there is little work coordinating or even
contrasting these lines of research with the present topic is another indication that beliefs
are undertheorized.
Attitude accessibility refers to the degree to which an attitude can be quickly
recalled from memory (Fazio et al., 1986). The more accessible an attitude, the more
resistant it is to change and the more likely it is to influence behavior (Fazio et al., 1986).

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It is thought that some attitudes are not accessible at all. Implicit attitudes, thought to be
unacknowledged or outside of awareness, can actually play a large role in behavior
despite being inaccessible. Sophisticated methods measuring people’s response times to
specific stimuli can dissociate implicit attitudes from those that we can report on. The
relationship between implicit and explicit attitudes however is thought to be poorly
understood.
Persuasion is a form of social influence that guides people toward the adoption of
a belief, attitude or action due to the power of appeal. Forms of persuasion that should
have a strong effect on beliefs include liking, social proof, and some coercive approaches.
“Liking” happens when people are easily persuaded by people or things that they find
attractive, amusing or appealing. “Social proof” is similar to the conformity principle
discussed earlier and is the process whereby people can be persuaded to believe
something or do something that they observe others doing (Cialdini, 1993). Finally the
coercive techniques of persuasion recognized by psychologists include: deception,
hypnosis, subliminal influence, brainwashing, mind control and torture (Cialdini, 1993).
Studies have shown that if there is not enough emotional appeal, attitudes will not
change, whereas, if there is too much and the emotional appeal is overdone, the receiver
will exhibit reactance and reject the appeal altogether (Fazio et al., 1986). There is no
reason to think that appeals to belief would not work this way.
Other methods of attitude persuasion recognized by psychology involve
constructs such as reciprocity, commitment and scarcity. “Reciprocity” involves the
strategy of indebting someone to you because you know that people tend to return favors

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(Fehr & Gachter, 2000). This is thought to contribute to the pervasiveness of free samples
seen in many marketing campaigns. “Commitment” involves taking advantage of
someone’s prior commitment in order to ensure their continued commitment or
patronage. “Scarcity” involves people being successfully persuaded to try something
when it is pointed out that this thing will only be available for a short time. These kinds
of persuasion have a high likelihood of influencing behavior and attitude but probably do
not strongly effect beliefs.
Philosophers recognize different methods of persuasion towards the appeal to
reason, namely: logical argument, rhetoric, scientific method and proof (Nelson et al.,
1987). They also recognize methods of persuasion that appeal to emotion: advertising,
faith, imagination, propaganda, seduction, tradition, pity, hope, jealousy, disgust,
indignation, fear and anger. Studies in marketing and advertising have generally shown
that whether the appeal is to reason or emotion, repetitious exposure is key (Kilbourne &
Pipher, 2000).
Persuasion by authority is an interesting and important appeal, recognized by
psychologists and philosophers, which can appeal to reason or emotion. The “argument
from authority” or “appeal to authority” is known in psychology to be very persuasive but
known in philosophy as a logical fallacy. Such an appeal involves arguing that a
statement is true because a person or source that is regarded as authoritative makes the
statement. It is regarded as a fallacy because the truth of a statement is not necessarily
dependant on personal qualities of the claimant. The argument from authority, though, is
embraced by “informal logic” which takes a utilitarian approach to logic. Because no

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person can have expert knowledge on every topic, people are forced to rely on the
judgments made by people who are experts. Under informal logic, there is no fallacy in
appealing to authority except when it is claimed or implied that the authority is infallible
(Copi & Cohen, 1998). The more relevant the expertise of an authority, the more
compelling their judgments, or their efforts at persuasion, is deemed to be (Bachman,
1995).
Many people are easily persuaded by authorities and can even be impelled by
them to perform objectionable acts (Shepard & Greene, 2003). In advertising appeal to
authority can be unreasonable because often the person chosen for the endorsement or
sponsorship is not qualified as an expert. However, experts can be mistaken, can have
views that differ markedly from other experts in their field and sometimes can be
willfully deceptive because of pressure from peers, employers or from financial interests
(Damer, 1995). All things considered, believing in the knowledge or even opinion of a
true authority (assuming their field is legitimate) is often a person’s best recourse when it
comes to beliefs outside their domain of expertise.
It is probably very common that people acquire beliefs because of persuasive
messages. Most information that children receive about the world, from their parents or
from school teachers can be described as persuasive. It is probably rare that people
acquire beliefs from persuasive advertisements. Advertisements, like other peripheral
routes to persuasion, have the ability to change immediate behaviors and even have
access to changing attitudes but probably do not change beliefs with much consistency.
What is Next?

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This review of the literature shows that beliefs determine what we think, how we
behave and largely who we are. Throughout history, thoughtful people have debated the
nature of human beliefs and speculated about their sources, stability and substance.
Modern psychology, relative to advances on other topics, has paid only very little
attention to why individual people believe the things they do and to the factors that
influence them to do so. Very little work has recruited participants to contemplate and
report on factors that influence their disposition to believe.
Instead, the research has focused on either the semantics of belief; on showing
how “arational” or irrational human beliefs can be (Kahneman & Tversky, 1973); on
closely related topics such as knowledge and attitudes; or on how to develop decision
aids that will circumvent the “irrational” aspects of human beliefs that depart from
“rational” models (von Winterfeldt & Edwards, 1986). New work must be proposed to
remedy the dearth of empirical, social science research on human beliefs by studying
their psychological, sociological and biological foundations. What should this much
needed work investigate?
Our research has shown that contributing evidence and the stance of parents may
play the most formative roles in the search for justification of belief. The present
literature review seems to echo this sentiment, that parents and evidence are
determinative, interesting constructs that, unlike so many of the other factors we have
considered here, are not opaque to psychological investigation. But what kinds of beliefs
are we likely to turn to our parents for? Are these different from those that we search for
evidence to support?

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Our past research shows that people value what they think their parents believe
and what they estimate is good evidence. This data, though unique in scope, does not tell
us if children believe what their parents actually believe. To answer this question, we
would need to have the parents report their belief strength. We have garnered some ideas
for why parents and evidence are so important, but we have not resolved how either
makes its unique contribution to certainty strength.
This brief review of past thought about beliefs leads to three important research
questions for which there are, surprisingly, few answers:
• What is the role of empirical evidence in determining the strength of an
individual’s belief?
• What is the role of parents in forming and shaping beliefs?
• What role does a person’s sense of self-identity play in belief?

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Chapter 16: Psychological Correlates of Belief: Measuring the Relative Influence of
Parents, Evidence, Source, and Importance to Self-Identity

Chapter Abstract
Study 1 examined the psychological determinants of personal belief by measuring the
contribution of various constructs to certainty strength. The study collected data from
over 250 child-parent pairs regarding how physical, social and religious beliefs are
formulated. Participants rated their strength of belief in statements within these domains
relative to the following determinants: the importance of substantiating evidence, the
perceived logic inherent in a belief, the importance to self-identity, the influence of
parents, the social community and authority figures. The present research found that
substantial correlations exist between the beliefs of children and their parents but that the
strongest correlation is the one between a child’s belief and the child’s estimate of their
parent’s belief. The study found that strength of certainty can be best predicted by one’s
estimate of their family member’s belief, the quality of empirical evidence that the person
can offer to support the belief, and the perceived importance of the belief to their sense of
self-identity.

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Introduction
The purpose of the 2 studies presented here was to achieve a better understanding
of the determinants and functions of belief. Study 1, the topic of this section, sought to
examine the determinants of belief formulation by exploring relationships between the
beliefs of children and the beliefs of their parents. Study 2, detailed in the next section,
examined the role of belief in behaviors and behavioral outcomes by focusing on whether
people's self-reported beliefs predicted weight management behaviors and whether these
behaviors in turn predicted BMI. These two studies were designed to complement each
other. In order to inform behavioral intervention strategies and to better conceptualize
belief from ontological, epistemological and critical perspectives, it is necessary to
understand both the contributors to belief strength and the role of belief strength in
complex behavior and decision making.
Our previous research attempted to determine, which of several different factors
had the largest influence on belief strength. Although parents turned out to be the most
significant and compelling factor in this research, important questions remained
unanswered. Because we did not get data directly from the parents we were not able to
discern whether the parents’ actual beliefs, or simply their children’s estimations of them,
influenced the children’s beliefs. Study 1 recruited child-parent pairs to determine
whether children’s beliefs match, not only their estimates of their parents’ beliefs, as we
have found previously, but also their parents’ true beliefs. Study 1 parsed the influence of
parents into separate components: children’s beliefs, children’s estimates of their parents’
beliefs, the parents’ beliefs and the parents’ estimates of their children’s beliefs. This

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study adds to the knowledge garnered by previous studies by examining the role of
parents’ actual, self-reported beliefs.
The present study also measured evidential, logical, emotional and environmental
determinants of certainty strength so that influence of parents can be considered in the
context of competing determinants. The logical determinants examined included
rationale, personal account, firsthand and secondhand evidence; the emotional
determinants included personal likeability, permanence and relevance and the
environmental determinants included opinion of parents and friends, opinions of
authorities and other sources of influence. These determinants were gathered from a
review of the literature on the components of belief certainty, which is both scarce and
divided. Many different contributors to certainty strength have been recognized by
previous theorists and researchers over the years and the present study has attempted to
examine the most meaningful and compelling of these constructs and to compare their
influence to that of parents.
The Treatment of Beliefs in Previous Literature
Much of the existing literature on belief relies on speculation about belief
formulation without support from empirical evidence (e.g., Paglieri, 2005; Doyle, 1992;
Pennington, 1993). This literature has hypothesized that factors, including our attitudes,
our goals, our moods, our sense of self-identity and our stereotypes, have the power to
moderate belief strength (Gilovich, 1990; James, 1958). It seems that only a few
researchers have attempted to measure certainty strength in specific beliefs as a function
of the beliefs of others (Castelfranchi, 1996; Colby, Tesler & Enea, 1969; Becker, 1973).

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This research has explored how existing beliefs affect decision making and cognition
(e.g., Geraerts et al., 2008; Anderson & Sechler, 1986, Anderson et al., 1980; Costa et al.,
1995); however, it has not investigated how individuals appraise different components of
belief strength relative to each other. Again, little to no research has attempted to
determine which factors are the most powerful in determining belief. Awareness of this
dearth of information about the relative value of different contributors to certainty
strength influenced us to attempt to identify and then measure several different
components of belief formulation in order to use multiple regression techniques to
determine how much variation in certainty strength each component can be shown to
account for.
A large number of factors have been demonstrated to affect belief strength
(Gilovich, 1990). Evaluation of competing beliefs can be affected inadvertently by our
liking for them, by our goals, by our moods, and by our stereotypes, all with or without
conscious deliberation (James, 1958). According to the Data-oriented Belief Revision
(DBR) model (Paglieri, 2005), the number of potential logical, emotional, and
developmental determinants that play roles in whether beliefs are accepted or rejected is
large and can vary between people and circumstances (Paglieri, 2005). Moreover, it has
been shown that evidence, conscious goals and rational thinking influence beliefs. Under
different scenarios, transient motivations, subjective biases, and the expectations of
others can be more influential (Travis & Aronson, 2007; Tversky & Kahneman, 1974).
Because unconscious responses can manifest themselves in behavior without
conscious intent, they can largely affect our “feeling of being certain” without our

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awareness (Burton, 2008). For this reason, various determinants that are often not
apparent until one takes the time to reflect can influence beliefs (Burton, 2008).
Knowledge of how the determinants of beliefs can be transient, unstable, and unavailable
to conscious introspection influenced our method for analyzing the determinants of
belief. It became clear that we needed to expose participants to multiple beliefs
representing different domains and come up with ways to measure several different
components of belief formulation in order to see how much variation in belief strength
each component can be taken to account for.
The Influence of Parents and Others
As with other forms of learning it seems that learning what to believe starts with
adopting or modeling the beliefs of others. Understanding how others introduce us to
beliefs and how we conceptualize them in an ontogenetic sense should prove integral to
understanding not only how beliefs develop but also how they change with time. James
Alcock famously argued that our beliefs have their origins in the influences of authority
figures; specifically pinpointing parents as the main influence for most people early in
life (Alcock, 1995). Authority figures may also, according to Alcock, influence one’s
“gut feelings” because, at a young age, one does not necessarily have sufficient empirical
evidence to support his or her beliefs (Alcock, 1981). Literature on the development of
social theories supports the idea that children primarily adopt beliefs from parents, peers,
teachers and other social agents and then create a larger proportion of their own beliefs
later in life as experiences accumulate (Alcock et al., 1998; Anderson & Sechler, 1986).

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Beliefs, adopted or borrowed during childhood and adolescence, create scaffolding for
personal belief building in adulthood.
In young childhood, when many core beliefs are first being formulated, children
actively learn about what to believe and how to believe from their parents, teachers and
classmates. Many of these early beliefs are formative and resistant to change (Anderson
& Sechler, 1986). These findings and others like them may also indicate that beliefs that
have emotional concomitants, like many religious and sociological beliefs, have a
tendency to become fixed early in life for the same complex reasons that emotional
responses and personality become fixed early on. One survey study has shown that
people, regardless of age, have a strong tendency to believe in the religion that they were
inculcated into during childhood (Argyle, 1997).
In a 1992 book, Parental Belief Systems: The Psychological Consequences for
Children, Irving Sigel claimed that belief systems should change with respect to parents’
influences over time. He pointed out that if one were to measure similarities in the beliefs
of parents and children, it would be important to note how old the children are during the
time of the study. Survey and interview-based research has found that young children
tend to blindly accept whatever their parents tell them, and may never question these
beliefs or even have a full understanding of them (Sigel, 1992). By the time they reach
adolescence, they tend to be more rebellious and may want to distance themselves from
their parents, in which case they may form their own beliefs and reject the stances taken
by their parents. Once individuals reach young adulthood, the similarities in beliefs
become less predictable.

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David A. Murphey’s (1992) research has shown that children are highly likely to
adopt the beliefs of people they find charismatic, interesting and inspiring. He points out
that the parents that are most successful in effectively transferring their heartfelt belief
systems are those that are accepted by their children as role models. Charismatic and
loving parents that avoid inciting dissent seem to be the most influential. Similarly, a
longitudinal, questionnaire-based study performed by Allan Wigfield (1994) revealed that
the more involved a parent is and the more they combine warmth with reasonable levels
of authoritative control, the more likely their child is to share their general attitudes and
beliefs about the world. Interestingly, beliefs about interpersonal interaction, like
parenting style, have been shown to be highly conserved across generations (Simons et
al., 1992).
Religious affiliation provides a set of core beliefs that have been shown by
psychological experimentation to remain highly stable (Argyle, 2000). Scott Myers of
Penn State University published one study examining the heredity of religious beliefs in
1996. The study, which interviewed 471 parents and their adult offspring, attempted to
determine the degree to which parental religious beliefs affect their child’s chosen
religion. The study also attempted to identify some family characteristics that make
intergenerational transmission of religious beliefs more likely. The study found that the
children’s religious beliefs were strongly correlated with the beliefs of their parents. The
study also found that religious beliefs are particularly similar amongst close-knit families
in which the mother did not work (Myers, 1996). Previous work conducted by Cynthia
Clark and colleagues (1988) looking at transmission of religious beliefs to first-born sons

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resulted in somewhat similar findings. This work featured 68 mother-father-son triads,
where each family member was interviewed about their beliefs. The study incorporated
and controlled a large number of potential predictors. Mothers were found to influence
son’s religious application and practice; whereas, fathers influenced son’s church
attendance. The authors point out that parents hope to transmit values to their children
that are not interfered with by competing transmissions from schooling, media and other
adolescents. They implied that this is not always easy. The findings also suggested that
parents that served as dedicated, consistent role models were the most influential.
The literature has compared children and parents’ beliefs in religious and political
domains but has not compared their beliefs to their estimates of each other’s beliefs or to
other epistemic influences. This motivated us to gather data on the influence of parents in
our studies in hopes that we could compare parents, peers, authorities and other social
agents in terms of relative influence.
The Influence of Evidence
Once children are able to accumulate evidence from experience to support their
beliefs, parents become less important. Theorists have speculated that young people often
work concertedly to expose their beliefs, acquired from their parents, to systematic
testing. Another popular perspective explains that keeping consistency among our beliefs
is a basic human need and an urgent concern during belief formulation (Schick &
Vaughn, 2002). People tend to reject facts or statements that are at odds with their current
beliefs (Schick et al., 1995). For this reason, many people will embrace evidence that

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supports their belief and disregard conflicting evidence in order to maintain cognitive
consistency (DeNoma, 2001).
Research also shows that individuals will often maintain a belief in spite of
overwhelming amounts of conflicting evidence, and this tendency is termed
“unwarranted theory perseverance.” After performing several studies and an extensive
literature review, Anderson et al. (1980) concluded that people frequently cling to beliefs
to a “considerably greater extent than is logically or normatively warranted.” Their
findings and the findings of others suggest that evidence is often not measured
judiciously and that competing beliefs and counter explanations are too often ignored or
overlooked (Kida, 2006; Schick & Vaughn, 2002).
These findings influenced us to change the way we gathered data on the
importance of evidence. Instead of asking subjects to provide evidence to support their
stance on a subject, we asked them to provide evidence to support the truthfulness and
falseness of each belief. Subjects were then asked to rate each point of evidence in terms
of perceived quality. This allowed us to gather data on how people weigh evidence in
favor of and against beliefs.
The Influence of Self-identity
Beliefs that are strongly tied to one’s self-identity can be extremely difficult to
change. Most people perceive information that is consistent with one’s self-identity as
more credible than the information that is inconsistent (Levy, 1997). People strive to
maintain their sense of self-identity for many reasons. It gives them pride, a feeling of
individuality, and a stable view of the world (Markus, 1977). Self-identity maintenance

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reinforces one’s self-concept and encourages people to reject feedback or information
that conflicts with their ideas about themselves (Sutherland, 1992). People will go to
great lengths to maintain their sense of identity. They will engage in certain cognitive
strategies (most of which they are not fully conscious of) that include selective attention,
selective memory, and selective interpretation (Shermer, 2003). It is clear that the desire
to keep our beliefs in line with our self-concept determines what kind of feedback we
seek from others and from our environment (Gilovich, 1990). Therefore, this study
needed to include measures to assess the importance to self-identity.
An article by Castelfranchi (1996) offered a substantial review of the literature on
self-concept and broke self-identity into three determinants, relevance, permanence, and
likeability. Castelfranchi argued that these three constructs are the main emotional
determinants that guide us through the process of accepting or rejecting beliefs and that
they ultimately contribute to certainty. These three constructs seemed compelling and
represented an apt way to divide self-identity into separate components; therefore, we
included them in our research in order to analyze their relative roles in determining
certainty strength.
The Influence of Logical Reasoning
An extensive literature on the subject of “belief revision” has elaborated on the
differences between two models, the foundations and coherence models (Doyle, 1992).
According to foundations theory, beliefs are maintained if they are reasonable, rational,
and justified and beliefs are abandoned when an individual obtains evidence to the

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contrary. The coherence approach, in contrast, contends that an individual accepts a
belief if it logically coheres with other closely held beliefs pertaining to self.
Most people tend to think that the manner in which they choose what to believe is
logical; however, the evidence suggests that many people hold beliefs that are not
supported by evidence or well-reasoned argument (Kida, 2006). One might think that
people derive their beliefs from experience and that the beliefs that they choose to adopt
are those that are consistent with sensory perceptions, rational reasoning, and careful
deliberation. But how does a reasoned consideration of evidence measure up to the other
determinants of belief? Many studies have looked to see what role reasonably convincing
evidence plays in certainty strength; yet these studies consider evidence by itself
(Schoomer, 1990).
Our Previous Research
Many of the articles cited above rely on speculation about belief formulation
without support from empirical research (e.g., Paglieri, 2005; Doyle, 1994; Pennington,
1993). Some of these articles collect data to show that a single determinant of belief
affects certainty (e.g., Anderson & Sechler, 1986; Anderson et al., 1980; Geraerts et al.,
2008) but fails to consider them relative to each other. In order to build on this
knowledgebase about belief strength, we carefully designed our previous research to
assess how several important determinants affect people’s beliefs when taken together.
In order to identify the determinants most predictive of strong beliefs, we
conducted two multivariate studies (Reser, 2009). Together, the two studies attempted to
examine the fundamental question “Why do people hold the beliefs they do?” by delving

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into the physical, social and religious beliefs of college students. We intended to analyze
how the students support, rationalize, think and feel about the beliefs they held. The
studies measured the relationships between logical, emotional and environmental
determinants of certainty strength.
In the first study, subjects considered 12 individual beliefs that were grouped into
three conceptual categories: 1) beliefs about physical existence, 2) beliefs about religion
and 3) beliefs about social issues. Subjects were asked to consider the 12 belief
statements relative to each of several determinants to assess the contribution of each to
the strength of certainty in the statement. The determinants were: 1) the quality of the
evidence that the subject can generate to support the truthfulness of the belief statement;
2) the quality of the evidence that the subject can generate to support the falseness; 3) the
importance of the belief to the subjects’ sense of self-identity if it was true; 4) the
importance of the belief to self-identity if it was false.
One question we wanted to address with this study is whether people are logical
and rational deliberators who rely on what they take to be empirical evidence to
formulate their beliefs. The findings suggested that this is partly true. Subjects wrote
what evidence they could offer to support the truthfulness and falseness of each belief.
Then they used a 7-point scale to rate their judgment of the strength of each piece of
evidence they provided. We used the sum of their evaluation scores in our analyses. The
participants’ ratings of the quality of evidence (for the truthfulness of the belief) had a
strong, positive relationship with their certainty strength (R square values varied from a
low of .02 to a high of .11). This relationship was stronger than the positive relationship

204
between importance to self-identity and certainty strength (These R square values varied
between .02 and .29). The best indicator of the strength of certainty though, was the
difference between the quality of evidence for the truthfulness and that of the falseness (R
square values varied from .08 to .37).
In the second study, we introduced new measures in an attempt to determine how
the perceived beliefs of others would compare, in predictive capacity, to quality of
evidence and self-identity. This time, subjects gave estimates for the certainty strength of
their parents, personal contacts and the average American for each belief and these data
were entered into a regression equation. The estimates for certainty strength of the
average American varied negatively with participants’ estimates, whereas the estimated
certainty strength for personal contacts and parents were strong, positive predictors. We
were surprised to see that the estimates of parents’ beliefs proved to be the strongest
predictor of all (R square values varied from a low of .40 to a high of .69) across the six
belief investigated. In fact, the second study showed that the influence of personal
contacts was generally a better predictor than quality of evidence and that the influence
from one’s parents was actually far better than either was. See the R-square values in
Table A below.
Table A summarizes the major findings from our earlier research. It shows the
betas (βs) and R-square (R²) changes when beliefs were used as dependent variables to
predict the independent variables of belief determinants in a stepwise multiple regression
analysis. The values shown in the cells of Table A are: (1) the standardized regression
coefficients, βs; (2) the R-square change associated with the predictor shown in the

205
parentheses; (3) the superscript numbers following the parentheses indicate the order of
entry in the stepwise regression. The R-square shown in the last column is the total
variance accounted for by the predictors that entered the equation. The table highlights
the relative importance of the various determinants we measured on the certainty strength
of subjects for the six beliefs we assessed in the study. For example, the first row reports
that the participants’ estimates of the belief strength of their parents predicted 69% of the
variance in their belief in the existence of gorillas as a real species of primate. The table
also shows that parents’ belief was the most important predictor - indicated by the
superscript of 1. Similarly, the first row of Table A shows that the perceived belief of
scientists was the second strongest predictor of belief in gorillas, accounting for 2% of
the variance in the participants’ certainty strength. The right most column of Table A
shows that the total R-square was 71%. The other beliefs relate to similar questions about
the existence of bigfoot, heaven, God, communal laws and the importance of social
interaction. The findings reported by this table largely corroborate the speculations of
other theoretical writers (Anderson & Sechler, 1986; Alcock, 1981, 1995, 1998; Estes et
al., 2003; Fine, 2006) that emphasize the influential effects of personal contacts and
especially parents, in decision making and belief formulation.

206
Table A: Determinants of Belief Strength From Our Previous Study
Note. Numbers before the parentheses are the standardized regression coefficients (β). Numbers in parentheses are the R square
change associated with the predictor. Superscript numbers indicate the order of entry in the stepwise regression. The R
2
shown in the
last column is the total ratio of variance explained by all the predictors. ** p < .001

In both of our early studies, the strength of certainty in God held a strong positive
relationship with self-identity. In fact, the majority of people reported that their sense of
self-identity would be strongly affected both if God were proven to exist and if God were
proven to not exist. Furthermore, the findings of the second study suggested that most of
this importance to self-identity can be explained by the relevance to self. In both studies,
people were certain of their beliefs in the social and religious domains, regardless of
whether quality empirical evidence could be presented to support their particular belief.
In the domain of physical existence, we found that people were only certain of the beliefs
that could actually be bolstered by empirical evidence (such as gorillas and gravity), but,
with uncertain beliefs (such as Bigfoot and UFOs), individuals seemed to lack certainty
because of the absence of physical or anecdotal evidence.
Belief Parent
Personal
contact
Average
Amer. Scientist Evid. Relevant R
2

Gorilla .72(.69)
1
.18(.02)
2
.71**
Bigfoot .51(.59)
1
.18(.02)
3
.19(.04)
2
.65**
Heaven .49(.45)
1
.15(.02)
5
-.18(.03)
3
.25(.07)
2
.17(.02)
4
.59**
God .37(.46)
1
.19(.03)
4
-.14(.02)
5
.14(.02)
6
.22(.06)
3
.30(.10)
2
.67**
Laws .40(.43)
1
.29(.07)
2
-.11(.01)
5
.24(.04)
3
.11(.01)
4
.56**
Social .58(.40)
1
.21(.02)
3
-.20(.02)
2

.44**

207
Both of our previous studies showed that self-reported empirical evidence is a
stronger factor in predicting the strength of the belief than the importance to self-identity,
which is only moderately contributive. In the first study, even though each of the
determinants was important in specific instances, the best indicator of the strength of
certainty was the difference between the quality of evidence for the truthfulness and that
of the falseness (R square values varied from .08 to .37). The second study generally
replicated these findings. Again, in the second study, the quality of evidence proved to be
a stronger predictor than the importance to self-identity, with the exception of belief in
God. This finding can be interpreted as showing that there may not be much verifiable
evidence for the existence of God, and religious beliefs may weigh more heavily in one’s
sense of self-identity. These empirical findings coincided with the theoretical
expectations of other writers that people will first try to base their beliefs on what they
think is solid, quality evidence before they are influenced by other factors (Sutherland,
1992; Kida, 2006).
The results from these two studies supported our expectations that strength of
certainty in belief is predicted by the quality of empirical evidence participants can offer,
the importance of the belief to their self-identity, and what they think their parents and
other close associates believe. These factors acted in additive ways to account for the
variation in strength of certainty in physical, social and religious areas of thought.
Moreover, we found differences in the importance of these predictors across physical,
social and religious beliefs that were in line with our predictions.

208
These studies demonstrated that more research in this area is needed in order to
understand how people justify their beliefs and how people’s beliefs are influenced by
those of their parents. In order to build on the current knowledge and our past findings
we designed a new research effort: a questionnaire study that assessed the role of parents’
beliefs. This will build on our previous research conceptually to determine the
correspondence between children’s beliefs and their parents’ actual beliefs.
Study 1 focused on five beliefs and attempted to determine the correlation or
correspondence between the beliefs of children and their parents. The data and findings
from our previous studies were used as a guide to revise our previous questionnaire for
use in this study. Despite the fact that the strongest predictor in our previous study was
the perception of the parents’ beliefs, we could not know if participants were truly
influenced by their parents’ actual beliefs; instead, they may have simply assumed that
their parents share their beliefs. We needed to ascertain the actual beliefs of participants’
parents directly from the parents themselves in order to assess whether children: 1)
accurately estimated the belief of their parent and 2) shared the belief with their parent.
The study includes this key new determinant, the true belief of the child’s parent. We
planned to use multiple regression to determine how much of the variance in children’s
belief can be predicted by the parents’ actual belief. In addition, we sought to replicate
the findings of our two previous studies on the role of self-reported evidence and self-
identity in determining certainty strength.

209
Methods
Participants
Participants were student volunteers recruited from a private research university,
the University of Southern California (USC). Individuals from this sample of 532 people
participated in our study on “The Foundations of Personal Beliefs” and students received
credit toward a course requirement.
A USC institutional review board application, which required us to meet strict
guidelines in terms of respect and confidentiality, was submitted and approved. A
consent form described the study, its intentions, and the associated risks (see Appendix
A). The participants indicated that they understood and agreed to the details of the study.
After reading the consent form participants began the questionnaire (see Appendix B)
which started with a demographics section.
Demographics
The demographics section of the questionnaire asked participants about their age,
gender, level of educational achievement, academic major, hobbies, ethnicity, religious
affiliation, and their scientific, social, and religious background. The background
questions inquired about the extent to which participants consider themselves
scientifically minded, the extent to which they are socially minded, and the extent to
which they are religious. Participants were asked to circle a number, 0 through 6, that
best describes their response to each question.
Procedure

210
Participants were given an information sheet with a URL linked to a qualtrics.com
questionnaire. There were two online questionnaires, one designed for students and a
separate questionnaire designed for the parents. The questionnaire was completed after
participants read over a consent form and completed a demographics survey. The survey
took participants approximately 25 minutes to complete.
Beliefs Questionnaire
Participants were told to take as much time as they need to finish the
questionnaire but were be asked to refrain from discussing the study with their personal
contacts until both they and their family member completed the questionnaire. Next,
participants were asked to rate five belief statements on a seven-point scale ranging from
0 to 6, indicating the degree to which they think the statements are either true or false.
Participants were asked to mark a 0 if they were confident that the statement is false, a
three if they were not sure, and a six if they were confident it is true. Next, the
participants that were children were asked to make a similar rating to estimate the beliefs
of both of their parents, the one taking the questionnaire as well as the non-participating
parent. The participating parents were similarly asked to estimate the belief of their child
and the other parent for each of the belief statements. We included the estimate of the
belief strength of the other parent in order to introduce a third factor that could be
compared to the estimates of the parents and children taking the questionnaire. We
expected that the estimated ratings of the other parent might serve as a unique predictor
that captures unique variance in belief strength. Also, we wanted to determine how much
the parent taking the questionnaire reports being affected by the other biological parent

211
of their child (presumably this other parent was most commonly their spouse or ex-
spouse). The five beliefs considered are listed in Table 1 below.

Table 1: List of Belief Statements
1. Bigfoot or Sasquatch is a large animal found on Earth.
2. A Supreme Being or “God” exists in some form.
3. Women have extremely limited access to the highest leadership positions in
society.
4. Every American should purchase a home as early in adulthood as possible.
5. Every adult should exercise, from youth to old age, at least 5 times every week for
30 minutes or more, performing a combination of aerobic and strength training
activities.

The next section of the questionnaire asked participants to reconsider the beliefs
and provide the reasons to support the truthfulness for each. After giving up to five
reasons, or points of evidence to support the veracity of the belief statement, they were
asked to rate each of their reasons in terms of its explanatory strength. The reasons were
rated on a scale from 0 to 6, with zero indicating an insignificant reason, three indicating
a significant reason, and six indicating a very significant reason. After rating each belief
statement, participants were asked to list and then rate reasons to support the falseness for
each. As a result, we obtained 10 sets of evidence, 5 supporting the truthfulness and 5
supporting the falseness.

212
In the third section, as in our previous study, participants were asked to rate the
influence of different sources of information on their beliefs. These sources included
personal accounts, secondary source evidence, social consensus, logic and reason, and
authorities, such as scientists, political scholars, and religious leaders. Respondents used
a seven-point scale, ranging from 0 indicating “a very insignificant source” to 6
indicating a “very significant source”, to rate the importance of each of these sources of
information in supporting their belief.
The final section of the questionnaire assessed the importance of the belief to the
individual or their self-identity. Importance was divided into three subcategories,
likeability, permanence, and relevance, according to Paglieri (2005). These items were
measured on a 7-point scale, with 0 indicating very unlikeable, impermanent, or
irrelevant and 6 indicating very likeable, permanent, or relevant. The three facets of
personal importance were defined on the questionnaire as follows. Likeability: How
much you personally like your belief. Permanence: How stable, and unlikely to change,
is your belief. Relevance: How important or relevant the belief is to your sense of self-
identity; the degree to which your world would be turned upside down if you were to find
out that your belief was wrong.
Results and Discussion
We had a total of 532 participants and 256 complete child-parent pairs. The
average child was 22 years old (with a standard deviation of 5.0 and median age of 21)
and the average parent was 52 years old (with standard deviation of 7.7 and median age
of 51). The data contained 85 male children, 171 female children, 96 fathers, and 160

213
mothers. The child’s group was 33% male whereas the parents’ group was 38% male.
The sample was roughly 43% White, 33% Asian, 10% Hispanic, 5% Black, 3% Indian,
1% Pacific Islander and 6% Other.
We were interested to see if the averages in belief strength between the two
groups, children and parents, differed, possibly due to cultural or cohort effects. The
group averages are summarized in Table 2.

Table 2: Averages of the Certainty Strength of Parents and Children
Child’s
Rating
Parents’
Rating
Child’s
Estimate of
Parent
Parents’
Estimate of
Child
Child’s
Estimate of
other Parent
Parents’
Estimate of
other Parent
Bigfoot 2.28 2.48 2.09 2.79 2.23 2.56
God 5.04 5.42 5.41 5.04 5.22 5.25
Women 4.19 4.13 4.28 4.04 4.29 4.19
Home
Ownership
4.30 4.55 4.35 4.41 4.38 4.89
Exercise 5.88 5.73 5.60 5.70 5.40 5.37
Certainty strength was measured on a Likert scale ranging from 0 to 6

The parents had slightly higher average belief in God, home ownership and
Bigfoot. For instance, parents’ belief strength in God was significantly larger (M = 5.42,
SD = 1.11) when compared to that of children (M = 5.04, SD = .80), t(521) = 4.00, p <
.05. When estimating each other’s beliefs, both parents (M = 5.41, SD = 1.31) and
children (M = 5.04, SD = 1.56), t(524) = 3.21, p<.05, seemed to recognize this
discrepancy. Further, parents and children had very similar average estimates concerning
the other parents’ belief in God (5.22 and 5.25, not significantly different), which would
be expected if families have knowledge about each other’s religious attitudes.

214
Interestingly, this same general pattern of relationships were revealed when we
assessed these beliefs using a completely different format. Another measure introduced
later in the questionnaire, asked participants to indicate their certainty by either judging
the belief true or false. This gave us a dichotomous measure to assess certainty strength
which closely matched the continuous data reported in Table 2. The results of this
discontinuous measure are reported in Table 3.

Table 3: Percentage of Participants Judging the Belief Statements as Either True of False
Children True Children False Parents True Parents False
Bigfoot 17% 83% 21% 79%
God 73% 27% 82% 18%
Women 63% 37% 54% 46%
Home 51% 49% 65% 35%
Exercise 90% 10% 78% 22%

The preceding belief averages are interesting relative to the information collected
about extent of scientific, religious and social thinking found in Table 4. These data
indicate that children are more likely to rate themselves as scientifically minded (M= 5.4,
SD=1.21) relative to the parents’ (M = 4.8, SD = .79), t (527) = 4.31, p<.05. Children
also rated themselves as more socially minded as indicated by a mean score of 5.24 (SD
= 2.3) relative to the parents’ score of 5.10 (SD = 1.2), t(526) = 3.21, p < .05). Finally,
importance of religious faith was rated more highly by parents (M = 4.87, SD = 2.44)
compared to children (M = 3.93, SD = .86), t(526) = 4.56, p < .05 . These findings appear

215
to be consistent with the parents’ higher belief in God, lower belief in the benefits of
exercise and their relative willingness to believe in Bigfoot.

Table 4: Averages of Self-Rated Orientations for Parents and Children
Children Parents
Scientifically Minded 5.41
(1.08)
4.8
(1.67)
Socially Minded 5.24
(1.25)
5.10
(1.53)
Religious Minded 3.93
(2.07)
4.87
(1.05)
Self-rated scientific, religious and social orientations were measured with a Likert scale ranging from 0 to
6. Numbers in parentheses are standard deviations.

Table 5 offers the mean and standard deviation for the components of self-identity
for each of the five beliefs. The table suggests that parents found their belief in Bigfoot,
God, women, and home ownership more permanent, likable, and relevant compared to
children. The differences between the means for parents and children were significant at
α=.05 for all except the likeability of exercise. For example, the mean permanence rating
by children for the importance of owning a home (M = 4.56, SD = 1.49) was significantly
lower than the same mean rating for parents (M = 5.09, S = 1.84) t(522) = 4.56, p < .05.

216
Table 5: Mean Ratings of Permanence, Likeability, and Relevance by Belief
Child’s
Rating of
Likeability
Child’s Rating
of
Permanence
Child’s
Rating of
Relevance
Parents’
Rating of
Likeability
Parents’
Rating of
Permanence
Parents’
Rating of
Relevance
Bigfoot 4.37
(2.11)
4.43
(2.10)
2.29
(2.74)
4.63
(1.73)
4.64
(2.33)
2.95
(1.74)
God 5.50
(1.66)
5.27
(1.75)
5.31
(1.76)
5.86
(1.05)
5.79
(1.61)
5.63
(1.99)
Women 4.36
(1.92)
4.54
(1.47)
4.78
(1.78)
4.84
(1.34)
4.94
(1.48)
5.03
(1.28)
Home 4.71
(1.62)
4.56
(1.49)
4.39
(1.71)
5.23
(1.95)
5.09
(1.84)
4.58
(1.11)
Exercise 5.96
(1.48)
5.83
(1.47)
5.84
(1.53)
5.97
(1.74)
5.96
(1.26)
5.70
(1.07)
Self-identity factors were measured with a Likert scale ranging from 0 to 6. Numbers in parentheses are
standard deviations.

Tables 6 and 7 present the averages for the ratings of source of influence. The
numbers show that there was a high amount of variability in ratings. These tables show
that first hand evidence is generally stronger than second hand evidence, and social
consensus except in the case of Bigfoot. This seems reasonable considering that
participants from our sample probably did not have first-hand experience with Bigfoot.
Rational argument rivals 1st hand evidence in importance in providing credibility for a
belief. Also, the opinions of scientists were rated the most important of the three expert
opinion factors (scientists, political leaders, religious leaders) for belief in Bigfoot (M =
3.37, SD = 2.16 for children, and M = 3.25, SD = 2.11 for parents), and for the benefits of
exercise (M = 5.75, SD = 1.71 for children, and M = 5.77, SD = 1.37 for parents),
whereas opinion of political leaders was most important for the belief in the rights of
women (M = 4.39, SD = 1.78 for children, and M = 4.14, SD = 1.95 for parents) and
opinion of religious leaders was the most important for belief in God (M = 4.15, SD =
2.26 for children, and M = 4.88, SD = 2.30 for parents).

217
Table 6: Mean Ratings of Significance of Source by Belief for Children
1
st

Hand
2
nd

Hand
Social
Consensus
Rational
Argument
Opinion of
Scientists
Opinion of
Political
Leaders
Opinion of
Religious
Leaders
Bigfoot 2.35
(2.23)
2.83
(1.93)
2.61
(1.64)
2.97
(2.06)
3.37
(2.16)
2.24
(1.64)
1.88
(1.36)
God 4.03
(2.45)
3.21
(2.20)
4.26
(2.06)
4.03
(1.99)
3.41
(2.09)
2.98
(1.87)
4.15
(2.26)
Women 5.01
(1.81)
4.92
(1.71)
4.81
(1.55)
4.60
(1.69)
3.88
(1.83)
4.39
(1.78)
2.96
(1.81)
Home 4.13
(2.03)
3.84
(1.9)
4.01
(1.7)
4.57
(1.85)
2.97
(1.82)
3.09
(1.84)
2.37
(1.72)
Exercise 5.84
(1.67)
5.79
(1.48)
5.43
(1.63)
5.81
(1.56)
5.75
(1.71)
3.48
(2.20)
2.97
(2.12)
Sources of influence were measured with a Likert scale ranging from 0 to 6. Numbers in parentheses are
standard deviations.

Table 7: Mean Ratings of Significance of Source by Belief for Parents
1
st

Hand
2
nd

Hand
Social
Consensus
Rational
Argument
Opinion of
Scientists
Opinion of
Political
Leaders
Opinion of
Religious
Leaders
Bigfoot 2.43
(2.27)
3.00
(2.09)
2.61
(1.81)
2.93
(2.06)
3.25
(2.11)
1.98
(1.49)
2.07
(1.67)
God 4.30
(2.45)
3.74
(2.35)
4.58
(2.09)
4.31
(2.09)
3.52
(2.15)
3.00
(2.11)
4.88
(2.30)
Women 4.95
(1.98)
4.75
(1.79)
4.79
(1.72)
4.76
(1.71)
4.01
(2.02)
4.14
(1.95)
3.32
(1.97)
Home 4.85
(2.00)
4.17
(2.01)
4.59
(1.69)
4.82
(1.73)
3.40
(1.91)
3.18
(1.88)
2.58
(1.71)
Exercise 5.75
(1.67)
5.73
(1.59)
5.47
(1.58)
5.64
(1.54)
5.77
(1.37)
3.19
(2.09)
3.06
(2.01)
Sources of influence were measured with a Likert scale ranging from 0 to 6. Numbers in parentheses are
standard deviations.

Table 8 is a summary table that offers the Pearson’s correlation coefficients
associated with children’s and parents’ beliefs and their estimates of each other’s beliefs.
Squaring these correlations gives us R-square values, which indicate the percentage of
variance accounted for. Column three of Table 8 offers the correlations between the
children’s ratings and the children’s estimates of the parents’ ratings. This is the only
column in the table that directly mirrors the data collected in our previous study as each

218
of the other columns feature data that was not collected previously. As in our previous
study, this relationship appears strong. The R-square values associated with this
relationship vary between a low of .43 for belief in God to .56 for the belief in women’s
access to leadership positions.
The correlations in column three constitute the highest in the table and in fact
were consistently the highest out of all correlations, for each belief. Column three
partially replicates the findings related to parents in Table A above. For instance, in Study
A children’s estimate of the parents’ belief accounted for 59% of the variance in
children’s certainty in Bigfoot, whereas in Study 1, it accounted for 52%. Similarly, in
Study A children’s estimates accounted for 46% of the variance in certainty in God, and
in Study 1 it accounted for 43%.
The R-square values in column three can be compared to those in column six,
which provides the correlation between the parents’ ratings and the children’s estimates
of the parents’ ratings. These R-square values are actually much lower and range between
.08 and .38 (average = .20). We designed this study to assess these correlations so that we
could determine if parents actually believe what their children think they believe. This is
the first suggestion that children’s beliefs are less closely related to their parents’ actual
beliefs than they are to the children’s estimates of them.
Another interesting comparison is between columns two and three. Column two
gave the correlations between children’s ratings and parents’ ratings. For each belief, the
R-square values in column two were smaller than those in column six. The R-squares
found in column two ranged from a low of .06 for the belief in home ownership to a high

219
of .17 for the belief in God (average = .10). We initially sought to determine if children’s
and parents’ beliefs correlate better with their estimates of the others’ beliefs or with the
others’ actual beliefs. As column two demonstrates, the correlations between the actual
beliefs of parents and children are consistently the lowest in the table.
The fourth column holds the correlations between the parents’ ratings and the
parents’ estimates of the children’s ratings. The R-square values in this column were
relatively high and resemble those in column 3. The values in this column suggest that
the parents think that the child believes what they do. In other words, columns three and
four, when compared to column two, indicate that both children and parents overestimate
the degree of shared belief.
Row two in column five gives the correlation (.644) and R-square (.415) for the
relationship between children’s rating and the parents’ estimate of the children’s rating
for belief in God. This relationship, compared to the others in the column, is strong. It
suggests that parents in our sample, relative to the other beliefs, had reliable knowledge
of their children’s beliefs in God. This correlation was significantly higher than the
correlation (.417) between the parents’ rating for God and the children’s rating (Fisher’s z
= 3.57, p = .0002).

220
Table 8: Summary Table for Correlations Between the Beliefs of Parents and Children
Child’s
Rating vs.
Parents
Rating
Child’s
Rating vs.
Child’s
Estimate
of Parent
Parent’s
Rating vs.
Parents
Estimate
of Child
Child’s
Rating vs.
Parent’s
Estimate
of Child
Parent’s
Rating vs.
Child’s
Estimate
of Parent
Child’s
Rating vs.
Child’s
Estimate
of Other
Parent
Parent’s
Rating vs.
Parent’s
Rating of
Other
Parent
Bigfoot .278
(.077)
.718
(.516)
.717
(.514)
.382
(.146)
.398
(.158)
.344
(.118)
.677
(.458)
God .417
(.174)
.658
(.433)
.584
(.341)
.644
(.415)
.619
(.383)
.454
(.206)
.481
(.231)
Women .286
(.082)
.751
(.564)
.739
(.546)
.290
(.084)
.292
(.085)
.705
(.497)
.657
(.431)
Home .262
(.069)
.720
(.518)
.670
(.449)
.296
(.088)
.366
(.134)
.781
(.670)
.679
(.461)
Exercise .300
(.090)
.675
(.456)
.612
(.375)
.379
(.144)
.494
(.244)
.665
(.442)
.582
(.339)
Numbers before parentheses are Pearson’s correlation coefficients. All correlations were significant at the
.01 level, r(530), p<.01. Numbers in parentheses are R-square values.

The last two columns of Table 8, columns 7 and 8, show that both the children
and the parents expected that the other parent that was not part of the questionnaire,
generally shared their beliefs. For example the R-squares for the correlation between the
children’s ratings and the children’s ratings of the other parent ranged between .18 and
.67. The data in the following tables also give the correlations between the children’s
estimate of one parent and their estimate of the other parent. These correlations were
among the highest values in these tables indicating that, on average, children estimate
that their parents have relatively similar beliefs (R-squares ranged between .09 and .45).
Tables 9-13 below provide the original correlation matrices from which Table 8
was constructed and contain all possible correlations between certainty and estimates of
certainty for children, the parent that took the questionnaire and the child’s other parent.
We designed this study to assess these correlations, and the related multiple regression
equations, so that we could determine if parents actually believe what their children think
they believe. The tables below offer glimpses at the answers.

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Table 9: Correlations Between the Beliefs of Parents and Children for Belief in Bigfoot
Child’s
Rating
Parents’
Rating
Child’s
Estimate of
Parent
Parents’
Estimate of
Child
Child’s
Estimate of
other Parent
Parents’
Estimate
of other
Parent
Child’s
Rating
x .278 .718 .382 .344 .377
Parents’
Rating
x .398 .717 .344 .677
Child’s
Estimate
of Parent
x .378 .612 .402
Parents’
Estimate
of Child
x .435 .706
Child’s
Estimate
of other
Parent
x .413
All correlations were significant at the .01 level, r(530), p<.01.

Table 10: Correlations Between the Beliefs of Parents and Children for Belief in God
Child’s
Rating
Parents’
Rating
Child’s
Estimate of
Parent
Parents’
Estimate of
Child
Child’s
Estimate of
other Parent
Parents’
Estimate of
other Parent
Child’s
Rating
x .417 .658 .644 .454 .344
Parents’
Rating
x .619 .584 .438 .481
Child’s
Estimate
of Parent
x .472 .481 .587
Parents’
Estimate
of Child
x .356 .565
Child’s
Estimate
of other
Parent
x .587
All correlations were significant at the .01 level, r(530), p<.01.

222
Table 11: Correlations Between the Beliefs of Parents and Children for Belief in Women
Child’s
Rating
Parents’
Rating
Child’s
Estimate of
Parent
Parents’
Estimate of
Child
Child’s
Estimate of
other Parent
Parents’
Estimate of
other Parent
Child’s
Rating
x .286 .751 .290 .705 .238
Parents’
Rating
x .292 .739 .288 .657
Child’s
Estimate
of Parent
x .209 .632 .168
Parents’
Estimate
of Child
x .300 .688
Child’s
Estimate
of other
Parent
x .337
All correlations were significant at the .01 level, r(530), p<.01.

Table 12: Correlations Between the Beliefs of Parents and Children for Belief in Home
Child’s
Rating
Parents’
Rating
Child’s
Estimate of
Parent
Parents’
Estimate of
Child
Child’s
Estimate of
other Parent
Parents’
Estimate of
other Parent
Child’s
Rating
x .262 .720 .296 .781 .258
Parents’
Rating
x .366 .670 .302 .679
Child’s
Estimate
of Parent
x .271 .665 .288
Parents’
Estimate
of Child
x .343 .719
Child’s
Estimate
of other
Parent
x .372
All correlations were significant at the .01 level, r(530), p<.01.

223

Table 13: Correlations Between the Beliefs of Parents and Children for Belief in Exercise
Child’s
Rating
Parents’
Rating
Child’s
Estimate
of Parent
Parents’
Estimate
of Child
Child’s
Estimate
of other
Parent
Parents’
Estimate
of other
Parent
Child’s
Rating
x .300 .675 .379 .665 .345
Parents’
Rating
x .494 .612 .378 .582
Child’s
Estimate
of Parent
x .373 .667 .321
Parents’
Estimate
of Child
x .359 .661
Child’s
Estimate
of other
Parent
x .512
All correlations were significant at the .01 level, r(530), p<.01.

Again, for every belief the correlation between the children’s rating and the
parents’ rating is lower than the correlation between the children’s rating and the
children’s estimate of the parents rating (Fisher comparison, p < .05). With the belief
about exercise for example, the correlation between the children’s rating and parents’
rating is .300 and the correlation between the child’s rating and the child’s estimate of the
parents rating is .675. A Fisher r-to-z transformation reveals a z value of 5.81(p = .0001)
indicating that the former is significantly smaller. Fisher r-to-z transformations reveal the
same for the other four beliefs as well.
Without exception, the highest correlations found were those between the child’s
rating and the child’s estimate of parents’ rating. Close behind these correlations were the
correlations between the parents’ ratings and the parents’ estimates of the children’s

224
ratings. That these two sets of correlations were close in magnitude suggests that parents
think that their children believe what they do, nearly as strongly as children think that
their parents’ believe what they do. In order to reveal firm answers to the questions about
child and parent beliefs that this study was designed to analyze, we turn to multiple
regression. Regression is helpful specifically because it will allow us to bring the other
variables that we measured into the conversation and determine the relative role of each
in predicting belief strength.
Multiple regression analyses were used to analyze the influence of the different
determinants on belief strength. Five stepwise multiple regression equations were
generated - one for each of the five belief statements - in order to uncover the influence
of evidence, others’ beliefs, and self-identity on certainty strength. Each of the regression
analyses used the strength of respondents’ belief as the dependent variable. Six
independent variables were entered into each stepwise analysis in Table 14 below. These
variables were: (1) the estimate of the parents’ certainty strength; (2) the estimate of the
other parents’ (not taking the questionnaire) certainty strength; (3) the difference between
the strength of the evidence offered for the truthfulness and falseness of the belief; (4) the
likeability; (5) permanence; and (6) relevance of the belief. These regression equations
were repeated for the parents (Table 15) and then again for the children with the parents’
true certainty strength included in the equation (Table 17). We expected to replicate our
findings from the previous studies by showing that estimates of parents’ belief have the
highest betas followed by the significance of evidence, self-identity, and finally source of

225
evidence. In fact, source of evidence did not enter as a significant predictor in any of the
regression analyses and thus has not been included in any of the tables.
Table 14 shows the betas (βs) and R-square (R²) changes in a stepwise multiple
regression analysis. The values shown in the cells of Table 14 are: (1) the standardized
regression coefficient associated with each predictor; (2) the R-square change, shown in
the parentheses; (3) the order of entry in the stepwise regression, indicated by the
superscript numbers following the parentheses. The R-square shown in the last column is
the total variance accounted for by the predictors that entered the equation. Only
statistically significant findings at p < .05 are included in the table; blank table cells were
not statistically significant contributors to the prediction.
Table 14 highlights the relative influence of the determinants we measured on the
certainty strength of subjects for the five beliefs we assessed in the study. For example,
the second row reports that the participants’ ratings of the importance of evidence (β= .47
and R² = .45) was the most important predictor for belief in God (indicated by the
superscript of 1) and predicted 45% of the variance in their belief in the existence of God.
Similarly, the second row also shows that the relevance to self-identity was the second
strongest predictor of belief in God (β= .38 and R² = .16), accounting for 16% of the
variance in the participants’ certainty strength. Further, the children’s estimate of parent
A’s belief strength (β= .26 and R² = .05) came in third, accounting for 5% of the
variance. The right most column of Table 14 shows that 67% of the variance in belief in
God is explained by the study’s factors.

226
Table 14: Stepwise Regression Analyses for Variables Predicting Child’s Belief Strength

Note. The values are the standardized regression coefficients or βs, in parentheses is the R-square change
associated with the predictor. Superscript numbers indicate the order of entry in the stepwise regression.
The R-square shown in the last column is the total ratio of variance explained by all the predictors.
1
The
parent who took the survey.
2
The other parent who did not take the survey.** p < .001

We were interested to see if the multiple regression analyses would mirror our
previous results and what role the actual belief of parents would play. Table 14 largely
replicates our previous results. The children’s estimate of the parents’ belief strength,
shown in the second column of Table 14, entered as the first predictor of belief for all
beliefs (except for the belief in God where difference in evidence entered first). The
children’s estimate of the parents’ belief strength, for the other four beliefs, captured
variance ranging between a low of 38% for belief in exercise and 51% for belief in
women’s rights.
Difference in evidence, found in column four of Table 14, was the only other
variable that captured variance for each of the five beliefs, ranging from 2% for the belief
in exercise to 45% for belief in God where it entered first. It either entered second or third
Statement
Estimated
Parent A’s
Belief
Strength
1
Estimated
Parent B
Belief
Strength
2
Difference
in
Evidence Likability Permanence Relevance R
2

Bigfoot .39(.47)
1
.26(.03)
3
.20(.05)
2
-.11(.01)
4
.56**
God .26(.05)
3
.47(.45)
1

.38(.16)
2
.67**
Women .47(.51)
1
.30(.05)
3
.22(.06)
2
.62**
Home .55(.47)
1

.23(.04)
3
.24(.08)
2
.59**
Exercise .51(.38)
1
.16(.02)
3

.30(.11)
2
.52**

227
for the other beliefs indicating that it was typically the second best predictor of belief
strength. Each of the other variables entered into at least one of the regression equations
where they captured significant but not large amounts of variance. It is interesting to note
that likeability entered second for the regression for belief in home ownership (β= .24 and
R² = .08) and that relevance entered second for the beliefs in God ( β= .38 and R² = .16)
and for the importance of exercise (β= .30 and R² = .11). Perhaps this showed that God
and exercise were judged as personally relevant, and were given credence due to this, by
our participants. However, very few of the self-identity items entered as significant
predictors and only relevance accounted for any substantial amount of variance.

Table 15: Stepwise Regression Analyses for Variables Predicting Parents’ Belief Strength

Note. The values are the standardized regression coefficients or βs, in parentheses is the R-square change
associated with the predictor. Superscript numbers indicate the order of entry in the stepwise regression.
The R-square shown in the last column is the total ratio of variance explained by all the predictors.
Nothing entered for the variable permanence.
1
The other parent who did not take the survey.** p < .001

Statement
Estimated
Child’s
Belief
Strength

Estimated
Parent B
Belief
Strength
1
Difference
in Evidence Likability Relevance R
2

Bigfoot .34(.10)
2
.47(.48)
1
.20(.04)
3
.62**
God .25(.04)
3
.35(.16)
2
.34(.31)
1
.51**
Women .38(.12)
2
.45(.51)
1
.14(.02)
3
.64**
Home .23(.02)
3
.59(.68)
1
.23(.06)
2
.77**
Exercise .32(.09)
2
.33(.49)
1
.23(.05)
3
.20(.03)
4
.66**

228
Table 15 recreates Table 14 but instead of predicting children’s belief strength it
predicts that of the parents. Table 15 largely echos Table 14 in the sense that parent’s
estimates of children’s beliefs (column two) and difference in evidence (column four)
were major factors. Both entered either second or third in the regression equations. Table
14 and 15 were very similar with one exception. Parents’ beliefs were related to their
estimates of their children’s beliefs but were more strongly related to their estimates of
the other parents’ beliefs (potentially their spouse or ex-spouse). In fact, parents’
estimates of the other parents’ belief entered first for each belief except for the belief in
God. Estimates of the other parents’ beliefs captured between 68% (home) and 48%
(Bigfoot) of the variance in parents’ ratings. This suggests that even though children are
likely to believe what they think their parents believe, that parents are not equally as
influenced by their estimates of their children’s beliefs as they are by the beliefs of their
children’s other parent. For instance, in the multiple regression predicting parents’ belief
in the importance of owning one’s own home, parents’ estimate of the parent not taking
the questionnaire came in first (β= .59 and R² = .68), the difference in evidence came in
second (β= .23 and R² = .06), and the estimate of the child’s belief strength came in third
(β= .23 and R² = .02). Together these predictors accounted for 77% of the variance in
belief strength.
The other parent’s estimate did not enter into the regression equation for the belief
in God and it is unclear why this is so. Likeability entered first for belief in God (β= .34
and R² = .31), capturing 31% of the variance. Difference in evidence entered second ( β=
.35 and R² = .16) capturing 16% followed by the estimate of the children’s belief which

229
entered last (β= .25 and R² = .04) and captured 4%. Overall, these three variables
accounted for 51% of the variability in parents’ belief strength for God.

Table 16: Stepwise Regression Analyses for Variables Predicting Child’s Belief Strength

Note. The values are the standardized regression coefficients or βs, in parentheses is the R-square change
associated with the predictor. Superscript numbers indicate the order of entry in the stepwise regression.
The R-square shown in the last column is the total ratio of variance explained by all the predictors.
1
The
parent who took the survey. ** p < .001

Table 16 shows the regression equations by belief for children’s belief strength
again, but this time we removed the child’s estimates from the regression equation to see
if parents’ actual belief strength can capture variance when entered alone. It captured a
small amount of variance, entering third, for belief in both Bigfoot (β= .24 and R² = .06)
and God (β= .13 and R² = .02) but not entering for the other beliefs. When the estimate of
the parents’ belief is dropped, the difference score for evidence entered first for each
belief, with the exception of exercise, where difference in evidence did not enter at all.
For the final regression equation we added all of the predictors simultaneously in order to
see which would enter significantly.

Statement
Parent A’s
Actual
Belief
Strength
1
Difference
in
Evidence Likability Permanence Relevance R
2

Bigfoot .24(.06)3 .36(.19)
1

-.26(.08)2

.32**
God .13(.02)3

.50(.43)
1

.42(.18)2

.63**
Women .41(.17)
1

.17**
Home .47(.29)
1

.24(.06)2

.34**
Exercise

.37(.32)1

.29(.04)2

.37**

230
Table 17: Stepwise Regression Analyses for Variables Predicting Child’s Belief Strength

Note. The values are the standardized regression coefficients or βs, in parentheses is the R-square change
associated with the predictor. Superscript numbers indicate the order of entry in the stepwise regression.
The R-square shown in the last column is the total ratio of variance explained by all the predictors.
1
The
parent who took the survey.
2
The other parent who did not take the survey.** p < .001

Table 17, the final regression analysis, includes all of the same independent
variables as Table 14, with the addition of the parents’ actual belief strength. The
correlations in Tables 8-13 showed that parents’ actual belief is not as strongly related to
children’s belief as the children’s estimate of the parents’ belief. Table 17 indicates that
the parents’ actual beliefs did not have any predictive value or significance as a variable
even though its addition as a 7
th
independent variable slightly altered the significance of
some of the other independent variables when added to the equation. Besides this, Table
17 is very similar to table 14. In Table 17, the values for total R-square range from .55 for
belief in Bigfoot to .69 for belief in God. As Table 16 shows, these high R-square values
shrink when estimates of belief strength are removed as predictors. Overall, children’s

Estimated
Parent A’s
Belief
Strength
1
Estimated
Parent
B’s
Belief
Strength
2
Parent
A’s
Actual
Belief
Strength
Diff. in
Evidence Likabil. Perman. Relevance R
2

Bigfoot .43(.47)
1
.21(.02)
3

.20(.06)
2
-.12(.01)
4
.55**
God .27(.06)
3

.45(.44)
1

.41(.19)
2
.69**
Women .46(.47)
1
.25(.03)
3

.26(.08)
2
.58**
Home .53(.44)
1

.26(.08)
2
.18(.03)
3
.55**
Exerc. .51(.47)
1

.13(.02)
4
.19(.02)
3

.22(.13)
2
.61**

231
estimates of their parents’ beliefs turned out to be the most influential predictors of
children’s beliefs, even greater than the difference score in evidence and the parent’s
actual beliefs.
Conclusions
The results from the study supported our predictions that strength of certainty in a
belief can be predicted by the quality of empirical evidence participants can offer, the
importance of the belief to their self-identity, and what they think their parents believe.
These relationships were found in the simple correlation matrices and multiple regression
analyses, both of which largely corroborated each other. Moreover, in line with our
predictions, we found consistent differences in the importance of these determinants
across beliefs from different domains. The strongest relationship in previous studies was
between the estimate of parents’ belief and the child’s personal belief strength. However,
the present investigation was designed to assess if people are actually influenced by the
parents true beliefs, or if they attribute their own beliefs to their parents. We expected
that individual child-parent pairs would tend to show a significant correlation in their
personal ratings, they do, but it is not as significant as the majority of the other
relationships that we measured.
With Study 1, we were interested in whether we could replicate the results of our
previous study showing that children believe what they think their parents believe. This
relationship actually represented the highest correlation, out of all correlations, for each
belief and was further borne out by the regression analyses. We found that children do
inflate the similarity between their beliefs and their parents’ beliefs. Thus, participants

232
seem to reliably agree with what they estimate their parents believe, more so than they
agree with or understand what their parents actually believe. Tables 16 and 17 made it
clear that children and parents’ beliefs corresponded more closely with their estimates of
the others’ belief than they corresponded with the others true belief. In fact, Table 17
shows that the parents’ actual beliefs did not even enter significantly into the regression
equations as predictors of their children’s beliefs once estimates were entered along with
them. Further, Table 16 showed that parents’ actual beliefs accounted for only very little
variance, in only two of the beliefs, when they were entered without the estimates of
parents’ beliefs.
It is unclear why the children in our study overestimated the similarity between
their beliefs and the beliefs of their parent. Could it be a misperception, or perhaps a
misattribution? It might be due to the fact that children treat their parents as closely
connected individuals; therefore, they either explicitly or implicitly assume that their
parents should share their opinions. Perhaps children know that they inherited many of
their earliest beliefs from their parents and this knowledge causes them to implicitly
assume that they and their parents share more in common than they actually do. If this
hypothesis is correct, then the parents’ belief as estimated by children are just a distorted
proxy of the children’s own belief and thus a deceptive predictor of the children’s actual
beliefs. How much do close associates discuss the details of their own belief systems and
how often do they merely assume that others around them share their beliefs? These
questions could be topics of future research in this area.

233
Another question we wanted to address with this study is whether people are
logical and rational deliberators who rely on what they take to be empirical evidence to
formulate their beliefs. The findings suggest that this is partly true. As with our past
studies, the participants’ ratings of the quality of evidence had a strong, positive
relationship with their belief strength. Again, the effect of the weight of evidence on
belief strength was significantly stronger than the effect of self-identity and source of
influence. We know that perceived high-quality of evidence turned out to be the strongest
predictor of belief strength in our data, after the estimate of parents’ beliefs. In fact,
evidence emerges as the strongest predictor of beliefs once the illusory contribution of
the children’s perceptions regarding their parents’ beliefs is left out of the equation.
These findings corroborate the speculations of other researchers that emphasize the
influential effects of parents (Anderson & Sechler, 1986; Alcock, 1995; Fine, 2006;
Levy, 1997; Lewis et al., 2001; Markus, 1977) and evidence (Sutherland, 1992; Kida,
2006) in decision making and belief formulation. However, past belief researchers
extolling the importance of parents may not have anticipated or accounted for the fact
that parent’s beliefs may make their impact on children’s beliefs indirectly.
The findings show that the effects of evidence and parents are nuanced. What the
child thinks the parent believes affects them the most, followed by evidence, followed by
self-identity concerns, followed by what the parent actually believes, followed finally by
other sources of influence. Overall, the results of the study showed that the variables
measured acted in additive ways to account for the variation in strength of certainty in
belief.

234
Future Directions
The reasons that people use to justify their beliefs should be analyzed more
carefully in order to better understand how people support their beliefs, if their beliefs are
justifiable and how they are related to the beliefs of their close contacts. A wider group of
beliefs should be investigated to determine the range of factors that predict belief
strength. Furthermore, experimental studies are needed to examine whether or not the
variables identified are indeed causal factors in influencing beliefs.
Experimental Analysis of The Influence of Evidence and Others
We have designed and received IRB approval for an experimental investigation
designed to examine the effect of evidence and social interaction on belief strength. This
study seeks to determine how belief strength is modified by a presentation of clearly
articulated evidence for or against: 1) the physical existence of Bigfoot as a species, and
2) the efficacy of geothermal power as an efficient and renewable energy solution. We
will first expose the participants to a ten minute video presentation, set as a lecture
format. The video will either be about Bigfoot or Geothermal energy and will discuss
good evidence for the topic, or evidence against the topic. We will collect the data
regarding our participants’ level of certainty about the topics using a questionnaire. Our
purpose is to test the hypothesis that experimental manipulations of evidence for and
against a topic produce changes in people’s beliefs about those topics.
The study will also determine if a lively discussion with a close social contact,
about the presentation seen, has an effect on belief strength. Participants will bring a
close contact into the lab with them, they will watch the presentations individually on

235
separate monitors and then they will engage in a 10 minute conversation about the topic.
The conversation will be mediated by an experimenter who will attempt to guide them
toward comparing and contrasting their viewpoints and evidentiary arguments.
Participants will be placed randomly in one of two conditions: they and their contact both
received evidence for a belief; one received evidence for the belief and the other against.
We will create separate, equivalent groups for this study as we are planning on
conducting between group comparisons. We will use a 2 way analysis of variance to
examine group differences in belief strength following these manipulations. We believe
that these analyses will provide further insight into the role of evidence and close contacts
in the formation and retention of belief. Does positive evidence increase belief strength,
increase the number of points of evidence people can offer and increase the rating of the
quality of that evidence? In our previous studies we didn’t know if belief strength was
related to evidence because people believe more strongly in things that they have good
evidence for or if they simply produce more evidence for things that they are firmly
convicted about. We hope that this study, paired with the methodology outlined in the
next section below will help us glimpse aspects of the causal relationships involved.
Objective Assessments of Participants’ Evidence
In our past research we have focused on the strong relationship between the
amount of evidence that participants can offer for and against a belief, and the ability of
the weight of their subjective judgments about the quality of that evidence to predict their
own belief strength. We have not; however, sought to measure the objective quality of
participants’ self-generated evidence. The evidence that participants self-generated could

236
have been faulty or biased, and it is possible that their beliefs are unwarranted on the
basis of the “objective quality” of that evidence, a phenomenon that is thought to be
widespread in belief research (Anderson et al., 1990). We believe an important next step
in our research is to examine the objective quality of the evidence that participants offer
for their beliefs. It is important to point out that even though our present research does
not do this, it does have a reliable record of how each subject perceives the quality of
their evidence. Furthermore, we know that perceived high quality of evidence turned out
to be one of the strongest predictors of belief strength in our data. In other words, we are
interested in these measurements but do not think that its omission from Study 1 is a
problem for our research focus, given that our goal was to examine the relationship
between evidence subjects could offer and their own beliefs. Furthermore, it is not clear
that a third parties objective assessment of a person’s evidence will be able, or would be
predicted, to meaningfully predict variance in that person’s belief strength.
We would like to see if people’s ratings of the quality of the evidence they were
able to produce is positively related to the actual objective quality of the evidence. In
order to do this we plan to develop objective, scientific evaluations of the most common
forms of evidence offered by participants in response to our past queries for evidence.
We have already compiled extensive lists of evidence generated by participants for and
against belief in God, gorillas, Bigfoot and women’s access to the highest leadership
positions. Next we plan to contact experts in these areas and have them rate the quality of
the most frequently recurring points of evidence. With these ratings as a measure we can
use correlation techniques to determine the extent to which participants’ ratings of

237
evidence quality correlate with those of qualified experts. In other words, we will be able
to address the question of whether or not the participants who think they have good
evidence, really do, and thus are entitled to feel that they have been objective.
The Influence of Beliefs on Health Behaviors
Better understanding of the determinants of belief should encourage researchers to
examine the role that beliefs play in behavior and the relationships between belief
strength, behaviors and outcomes. In fact, we think beliefs are important because we
think they influence and guide behavior. This led us to examine the relationship between
belief strength in the importance of certain health behaviors and the habitual performance
of those behaviors. In Study 2, reported next, we examined such relationships in the
consequential domain of human weight management. We explored relationships between
belief strength in the importance of diet and exercise and the self-reported level of
engagement in those behaviors, as well as the relationship between the beliefs and
behaviors and the outcome variable of body mass index (BMI).

238
Chapter 17: Health Beliefs and Their Relationship to Behavior and BMI

Chapter Abstract
This study investigates whether people's self-reported weight management beliefs
predicted diet and exercise behaviors and whether these behaviors in turn predicted BMI.
These expected results were strongly supported by the data gathered from 996
participants, who responded to a questionnaire, reporting their height, weight, beliefs
about various aspects of weight management, and personal weight-management
behaviors, including exercise activities and eating habits. Body Mass Index (BMI) and
total number of minutes of weekly exercise were computed from the reports.
Relationships were found between strong beliefs about the importance of specific health
practices and the performance of the practices. More specifically, multiple regression
analyses revealed significant relationships between total weekly exercise and belief in the
importance of exercise; between total weekly exercise and healthy diet; and between BMI
and unhealthy diet. Overall, 40% of the variance in BMI within our sample, including
49% of the variance in BMI in individuals older than 25, could be predicted by a
combination of health beliefs and their associated eating and exercise behaviors.

239
Introduction
Study 1 intended to contribute to previous literature by focusing on specific
relationships between belief strength and its determinants. But why should researchers
study beliefs unless they relate to important behaviors and behavioral outcomes? Study 2
intended to follow up with the concept of belief strength, to analyze its role in predicting
behavior patterns and chose to do this in the important domain of health decision making.
To what extent do beliefs about health management affect behavior? Could the behaviors
driven by these beliefs actually influence health as measured by body mass index (BMI)?
The present study was designed to analyze these questions by determining whether the
strength of belief in health-related statements predicts habitual engagement in health
related activities and the important health outcome of BMI. We hypothesized that belief
in the importance of monitoring weight, maintaining a healthy diet, maintaining a healthy
exercise regimen, and maintaining discipline in weight management would all relate
positively to healthy behaviors and negatively to unhealthy behaviors and BMI.
Conversely, we expected that the belief in the role of genetics as a determinant of weight
would show the opposite pattern of relationships.
Identifying the psychological factors involved in effective diet and exercise has
long been an area of interest and research (Popkin, 2006). Motivational aspects have been
extensively examined as sustained regimens of diet and exercise are known to lead to
tangible, health-promoting results. Exercise has been shown to increase both mental and
physical health whereas poor diet and sedentary lifestyle have been closely associated
with a number of health complications and diseases (Pardo Silva, De Laet, Nusselder,

240
Mamun, & Peeters, 2006). Health problems that result from or become exacerbated by
both improvident eating habits and sedentary lifestyle include coronary heart disease,
cardiovascular disease, hypertension, diabetes mellitus, obesity, osteoporosis, and some
cancers (Ekelund, Franks, Sharp, Brage, & Wareham, 2007). Consistent regimens of
vigorous exercise paired with proper eating habits have been shown to significantly
improve self-image (Bowen, Fesinmeyer, Yasui, Tworoger, Ulrich, Irwin, Rudolph,
LaCroix, Schwartz, & McTiernan, 2006), cognitive functioning and mental health
(Taylor, Sallis, & Needle 1985), as well as symptoms associated with mild to moderate
depression (Annesi, 2008); they have also been shown to serve as powerful adjuncts for
the treatment of alcoholism and substance abuse (Koeppl, Heller, Bleecker, Meyers,
Goldberg & Bleecker, 1992). Furthermore, exercise alone has been shown to reduce
symptoms of anxiety, reduce extreme physiological responses to stressors, and palliate
aspects of coronary prone (Type A) behavior (Bowen, Fesinmeyer, Yasui, Tworoger,
Ulrich, Irwin, Rudolph, LaCroix, Schwartz, &McTiernan, 2006). Despite the benefits of
weight management and the significant costs of mismanagement, the prevalence of
obesity has reached epidemic proportions in the United States as 75 % of Americans are
overweight, 30% are clinically obese, and only 25% of Americans are at a medically
healthy weight with a BMI of 25 or less (Flegal, Carroll, Ogden, & Curtin, 2010).
Self-regulation of weight is known to incorporate at least two sub-tasks: self-
regulation of diet and self-regulation of physical activity. Although self-regulation and
health management have been widely researched and promoted in the United States, most
overweight people do not engage in exercise or self-regulatory dietary practices (Kruger,

241
Yore, & Kohl, 2008). This situation leads to an important question for consideration: Are
current programs and methods of intervention targeting appropriate psychological
factors?
Given the importance of health self-management, much research has studied the
relationship between health-promoting behaviors and demographic, lifestyle, and
cognitive factors. Psychologists, exercise physiologists, public health experts, and others
have reported on hundreds of controlled studies examining the cognitive factors,
including awareness of disease risk (Naslund, 1997), propensity for goal-setting
(Macdonald &Palfai, 2008), influence of body image (Paxton, Wertheim, Gibbons,
Szmukler, Hillier, &Petrovich, 1991), capacity to self-monitor (Nothwehr & Peterson,
2005), and knowledge of medical recommendations (Morrow, Krzewinski-Malone,
Jackson, Bungum, & FitzGerald, 2004). However, research on the relationship between
individuals’ dietary and exercise behaviors and their personal beliefs about these factors
is lacking.
Studies have shown that survey respondents are generally well aware of
traditional activities that provide health benefits, but are less aware of specific exercise
and lifestyle guidelines (Morrow et al., 2004). Morrow et al. found that accurate
knowledge about the benefits of physical activity was not significantly related with
exercise activity sufficient for a health benefit. This research as well as the work of others
suggests that simply being knowledgeable about healthy exercise behavior is not
sufficient to elicit healthy exercise behavior. Based on the understanding that knowledge

242
alone does not incite healthy behaviors, the present authors speculate that beliefs might
play a role in health management even if proper knowledge does not.
Targeting belief may be a more efficient and efficacious way to influence health
behaviors than targeting knowledge. It is important to point out that belief is not
equivalent to knowledge (Abelson, 1979; 1986; Hay, 2008). Belief is usually described as
a psychological state in which a person holds a proposition, perception, inference,
judgment, or premise to be true (Green, 1971). Belief is also influenced by factors like
self-identity and personal history that do not play commensurate roles in knowledge
formation (Reser, 2009). Therefore, in the absence of veridical knowledge, an overly
strong belief about benefits of diet or exercise regulation may be highly motivational,
although it may be factually incorrect, whereas a belief that downplays the benefits may
be an unfortunate handicap. Beliefs come to be accepted and are reformulated differently
than knowledge, and past research has targeted belief as a factor in health behaviors. In
fact, previous studies have measured the role of belief in certain aspects of medical health
management.
Studies on asthma, hypertension, diabetes, and hypercholesterolemia have
documented relationships between patient outcomes and beliefs about medical
compliance (Lynch, Birk, & Weaver, 1992). Belief in the importance of seeking
treatment, perceived benefits of treatment compliance, and perceived seriousness of
disease and treatment risks have all been associated with positive results (Chambers,
Markson, Diamond, Lasch, & Berger, 1999; Edman, Diamond, Wortman, & Carballo-

243
Sayao, 2001; King, 1982). Beliefs have also previously been considered, to a limited
extent, relative to certain aspects of diet and exercise.
According to the health belief model (Becker, 1974; Rosenstock, 1974), a person
will embrace health behaviors if he/she understands the benefits of a particular practice as
well as negative consequences from avoiding the practice. The Health Beliefs
questionnaire (Diamond, Becker, Arenson, Chambers, & Rosenthal, 2007) is a 15-item
instrument measured on a 5-point Likert scale ranging from “strongly agree” to “strongly
disagree.” It assesses (a) center of control (“Being healthy is largely a matter of good
fortune.”), (b) certainty (“I am often confused about what to do to stay healthy.”), (c)
self-awareness of health (“I have an objective perspective on my health.”), and (d)
importance of health (“I think about my health a lot.”). The literature in this area shows
that certain beliefs detract from healthy behavior while others promote it. This research
has shown that believing that exercise or dieting is unhealthy, risky, or personally
irrelevant forms a barrier to exercise and dieting (O’Brien, Cousins, & Gillis, 2005).
Individuals also experience such barriers if they believe that they are not athletic, lack
confidence or feel embarrassed to be seen exercising, and believe that exercise does not
provide much pleasure (Jewson, Spittle, & Casey, 2008). Thus, previous studies have
analyzed beliefs concerning barriers to health behaviors and medical compliance, but
have not looked at individuals’ belief strength in statements about the efficacy of specific
health management behaviors and have not looked to see if these can be predictive of
actual behavior and health outcomes, such as BMI.

244
In an effort to gain understanding of the relationships between beliefs and health
outcomes, the present exploratory study aimed to determine the relationship between
belief strength in self-regulative statements about health behaviors and reported health
behaviors and their outcomes. The study focused on five core beliefs we identified in the
weight management literature and attempted to determine the correlation between the
health beliefs of individuals and their behavior. Furthermore, we sought to use multiple
regression analyses to determine whether people’s self-reported beliefs in weight
management behaviors predict their exercise and dieting behaviors as well as BMI.
Methods
Participants
Participants were student volunteers and their close contacts recruited from a
private research university, the University of Southern California (USC). Overall, 996
participants, age 18 or older, participated in the study. Self-reported data was collected
online using a Qualtrics survey tool. No personal identifiers were collected. A USC
institutional review board application, which requires researchers to meet strict guidelines
in terms of protection and confidentiality, was submitted and approved. The participants
indicated that they understood and agreed to the details of the study by continuing with
the survey after reading an information sheet that described the study, its intentions, and
the associated risks (see Appendix C).
Procedure
Participants were given an information sheet with a URL linked to an online
questionnaire. The questionnaire (see Appendix D) was designed to identify individual

245
variability across our participants in BMI, health beliefs, and health behaviors. The
survey took approximately 5-10 minutes to complete.
Beliefs Questionnaire
Participants first responded to the demographics section of the questionnaire,
which asked participants about their age, gender, level of educational achievement, race,
height, and weight. Next, participants were asked to rate five belief statements on an
eleven-point scale ranging from -5 to 5, indicating the degree to which they think the
statements are either true or false. Participants were asked to mark a -5 if they were
confident that the statement is false, a zero if they were not sure, and a 5 if they were
confident it is true. The five beliefs considered are listed in Table 18. Participants were
asked to read and respond to 11 individual questions about health behavior also found in
Table 18. The questions asked participants to respond with numerical estimates.

Table 18: Lists of Questions Responded to with Numerical Estimates
List of Belief Statements
1. Every adult should exercise at a moderate to intense level for at least 30 minutes a
day, five times a week.
2. Every adult should eat a healthy, nutritious diet containing lots of fruit,
vegetables, and fiber.
3. Every adult should monitor their weight and keep it in a normal healthy range.
4. Managing your weight and keeping it in a healthy, normal range is a practiced
skill that requires attention and effort.
5. Genetic factors play a large role in how much a person weighs and make it
difficult for many people to keep their weight in a normal, healthy range.

List of Behaviors:
1. How many times a week do you perform moderate to intense exercise, such as
running, cycling, swimming, basketball, weight training, etc.?

246
2. How many minutes of moderate to intense exercise do you do, in a typical
session?
3. How many servings of fruits and vegetables do you eat in a typical day?
4. How many servings of fish do you eat in a typical week?
5. How many servings of lean meats such as chicken or turkey do you eat in a
typical week?
6. How many servings of red meat do you eat in a typical week?
Table 18, continued

7. How many servings of pork do you eat in a typical week?
8. How many servings of desserts do you eat in a typical week?
9. How many servings of junk food (potato chips, candy bars, etc.) do you eat in a
typical week?
10. How many fast food meals (hamburgers, cheese burgers, fries, tacos, etc.) do you
eat in a typical week?
11. How many regular soft drinks (non-diet, Coke, Pepsi, etc.) do you drink in a
typical week?

The answers to these behavioral questions were used to construct composite measures.
Total weekly exercise for each participant was computed by multiplying the number of
reported minutes of exercise per session by the total number of sessions per week. The
eating behaviors reported by participants were used to calculate positive, neutral, and
negative measures of diet. Positive diet was calculated by summing the total number of
reported weekly servings of fruit and vegetables, fish, and lean meat. Neutral diet was
computed by summing together weekly servings of red meat and pork. Negative diet was
calculated by summing weekly servings of dessert, junk food, fast food, and soft drinks.
BMI was determined by taking the weight in pounds, dividing it by height in inches
squared, and then multiplying it by a conversion factor of 703 (West, 1980).
The weight and height data were based on self-reports, not on direct
measurements. This was a limitation in our study. However, self-reported and measured
weight are highly correlated, with a correlation as high as 0.98 (Larsen, Geenen, van

247
Ramshorst, Brand, Hox, Stroebe, & Doornen, 2006; McAdams, Van Dam, & Hu, 2007),
thus the fact that we used self-report data to calculate BMI shouldn’t have strongly
affected the results.
Results
We collected 1,222 responses to the questionnaire in total. After removing
pregnant women (14 or 1.4% of the sample), individuals who reported they had a medical
complication affecting their weight (142 or 13%), and individuals whose responses
contained data out of the range of plausible values, we obtained 996 valid responses. The
average participant was 24 years old (with a standard deviation of 9.3 and median age of
23). The youngest was 18 and the oldest was 80. The sample was 61% female with 662
female respondents. The sample was roughly 60% White, 23% Asian, 7% Hispanic, 4%
Black, 2% Indian, 1% Pacific Islander and 3% other. The average number of years of
education completed was 14.83. The mean height was 66 inches and the weight was 149
pounds with a standard deviation of 32.3. The mean BMI was 23.6 with a minimum of
16.1 a maximum of 43.8 and a standard deviation 4.2.
Table 19 shows correlations between pertinent variables. The correlations support
our hypotheses, indicating that strong beliefs in health statements relate positively with
healthy behaviors and negatively with unhealthy behaviors and BMI. BMI correlated
negatively with total exercise (-.25), positive diet (-.33), and four health management
beliefs (belief in exercise (-.37), belief in healthy diet (-.47), belief in monitoring weight
(-.44), and belief that weight management is a practiced skill (-.41)), whereas BMI
correlated positively with negative diet (.44), neutral diet (.25), and belief in genetics as a

248
determinant of weight (.13). The highest correlations seen in the table (.36 to .69) were
among the four health management beliefs: the importance of exercise, the importance of
a healthy diet, the importance of weight monitoring, and the belief that weight
management is a practiced skill.

Table 19: Correlations among All Study Variables

BMI Total
Exer.
Belief
in
Exer.
Belief
Health
& Diet
Belief
Mon.
Weight
Belief
Pract.
Skill
Belief
Genet
ics
Pos.
Diet
Neut.
Diet
Neg.
Diet
BMI -.25** -
.37**
-.47** -.44** -.41** .13** -.33** .25** .44**
Total
Exercise
.35** .24**

.24** .20** -.03 .29** -.10** -.27**
Belief in
Exercise
.45** .44** .36** .02 .16** -.22** -.35**
Belief in
Healthy
Diet
.69** .65** .05 .31** -.13** -.30**
Belief in
Monitor
Weight
.66** .04 .31** -.11** -.31**
Belief in
Practiced
Skill
.08* .30** -.04 -.23**
Belief in
Genetics
-.17** .02 .075*
Positive
Diet
.15** -.13**
Neutral
Diet
.41**
Negative
Diet

Note.*Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-
tailed).

Table 20 shows the betas (βs) and R-square (R²) changes when beliefs were used
as independent variables to predict the dependent variables of health behaviors and BMI
in a stepwise multiple regression analysis. The values shown in the cells of Table 20 are:
(1) the standardized regression coefficients, βs; (2) the R-square change associated with

249
the predictor shown in the parentheses; (3) the superscript numbers following the
parentheses indicate the order of entry in the stepwise regression. The R-square shown in
the last column is the total variance accounted for by the predictors that entered the
equation.

Table 20: Stepwise Regression Analyses of Contribution of Health Beliefs on Health
Behaviors and BMI.
Note. The values are the standardized regression coefficients, β (R-square change associated with the
predictor). Superscript numbers indicate the order of entering in the stepwise regression. The R
2
shown in
the last column is the total ratio of variance explained by all the predictors. ** p < .001

As seen in Table 20, total exercise was predicted best by the belief in exercise (β=
.30 and R² = .12) and then by the belief in the importance of monitoring weight ( β= .11
and R²= .01). The positive betas indicated that the stronger our participants belie ved in
the importance of exercise and monitoring weight the more likely they were to report that
they engage in regular exercise.
Positive diet was predicted best by the belief in the importance of monitoring
weight (β= .15 and R²= .11), and the relationship was positive. Next positive diet was
negatively related with the belief in the role of genetics (β= -.19 and R²= .03), positively
B: Exercise

B: Healthy
Diet

B: Monitor
Weight
B: Practiced
Skill B: Genetics R
2

Total Ex. .30(.12)
1
.11(.01)
2

.13**
Pos. Diet .13(.01)
4
.15(.11)
1
.13(.02)
3
-.19(.03)
2
.16**
Neu. Diet -.23(.05)
1

.05**
Neg. Diet -.25(.12)
1
-.09(.01)
4
-.14(.03)
2
.09(.01)
3
.17**
BMI -.17(.03)
2
-.24(.23)
1
-.12(.01)
5
-.14(.02)
4
.17(.03)
3
.31**

250
related with the belief that managing weight is a practiced skill (β= .13 and R²= .02), and
finally, positively related with the belief in healthy diet (β= .13 and R²= .01). Neutral diet
was only predicted by belief in exercise (β= -.23 and R²= .05). The negative beta here
indicated that as the belief in the importance of exercise increases, the less red meat and
pork is consumed.
Negative diet was predicted best by the belief in exercise (β= -.25 and R²= .12).
This relationship was negative indicating that the higher the belief in exercise, the lower
the consumption of unhealthy foods. Negative diet was also predicted by the belief in the
importance of monitoring weight (β= -.14 and R²= .03), the belief in genetics ( β= .09 and
R-square = .01), and finally the belief in healthy diet (β= -.09 and R²= .01). These results
show that the less participants believed in the importance of exercise, monitoring weight
and healthy diet, and the more they believed in the contribution of genetics, the more
unhealthy food they reported to consume.
BMI was predicted best by the belief in a healthy diet (β= -.24 and R²= .23)
followed by the belief in exercise (β= -.17 and R²= .03), the belief in genetics ( β= .17 and
R²= .03), the belief that weight management is a practiced skill ( β= -.14 and R²= .02), and
the belief in the importance of monitoring weight (β= -.12 and R²= .01). The results of
these stepwise multiple regressions are highly consistent with our predictions, and
provide further understanding of the unique variances explained by the pattern of simple
correlations shown in Table 19.
As Table 20 shows, the belief in the importance of exercise entered into the
equation first in three of the five regression equations, those predicting total exercise,

251
neutral diet, and negative diet. The beta between belief in the importance of exercise and
total exercise performed was positive (.30), as we predicted. Similarly as predicted, the
beta was negative between the belief in the importance of exercise and BMI (-.17) and
both neutral (-.23) and negative (-.25) dietary habits. The beta for the belief in genetics
was exactly the opposite of those found for exercise. The beta for belief in genetic
determinants of weight was negative for positive dietary habits (-.19) but positive for
negative diet (.09) and BMI (.17). Aside from the belief in the importance of exercise,
neutral diet was not predicted by any other belief and it had the lowest, albeit significant,
R-square of .05. The regression equation using beliefs to predict BMI accounted for the
most variance. Table 20 shows that 31% of the variance in BMI can be predicted directly
from the five beliefs we chose for this study. Each of these beliefs makes a unique,
significant contribution to the total R-square.
In the next stage of our analysis, we examined the extent to which health
behaviors predict BMI. The results in Table 21 show that the behaviors reported by our
sample did predict BMI and accounted for 28% of the variance. Table 21 shows that
negative diet entered first into the regression equation (β= .32 and R²= .19), followed by
positive diet entering second (β= -.29 and R²= .07), neutral diet entering third ( β= .15 and
R²= .02), and total exercise entering last ( β= -.10 and R²= .01). The valence of these betas
closely corresponds with our predictions. Negative dietary practices predicted larger
BMIs and positive dietary practices and exercise predicted smaller BMIs.

252
Table 21: Stepwise Regression Analyses of BMI on Behavioral IVs

Note. The values in the table are standardized regression coefficients β (the R-square change associated
with the predictor). Superscript numbers indicate the order of entering in the stepwise regression. The R
2
in
the last column is the total ratio of variance explained by all the predictors. ** p< .001

Table 22 shows the results of regression analyses performed with BMI as the
dependent variable and each of the beliefs and behaviors allowed to enter as independent
variables. When comparing this Table with Tables 20 and 21, it is clear that some
variables drop out of the equation when beliefs and behaviors are entered together. In
fact, Table 22 was constructed to investigate if beliefs and behaviors are unique and
additive determinants of BMI or merely overlapping predictors. Table 22 shows that BMI
was predicted best by belief in a healthy diet (β= -.20 and R²= .23), followed by negative
diet (β= .23 and R²= .10), positive diet ( β= -.17 and R²= .03), neutral diet ( β= .13 and R²=
.01), the belief that weight management is a practiced skill (β= -.15 and R²= .01), the
belief in the role of genetics (β= .11 and R² = .01), and the belief in exercise ( β= -.10 and
R²= .01). When both beliefs and behaviors were allowed to enter the regression equation
together, they accounted for 40% of the total variance in BMI. In other words, together
they account for 9% more variance in BMI than either accounted for alone. There is
considerable independent variance in BMI being predicted by beliefs and behaviors –
they are not complete proxies for each other.

Neg. Diet Pos. Diet Neu. Diet Total Exercise

R
2

BMI

.32(.19)
1
-.29(.07)
2
.15(.02)
3
-.10(.01)
4
.28**

253
Table 22: Stepwise Regression Analyses of BMI on all IVs.

Note. The values in the table are standardized regression coefficients β (the R-square change associated
with the predictor). Superscript numbers indicate the order of entering in the stepwise regression. The R
2
in
the last column is the total ratio of variance explained by all the predictors. ** p< .001

Supplementary Analysis
There was very little inter-individual variability for many of the variables in our
sample. For instance, most participants had very low BMIs; however, older participants
had more variability in BMI. Overall, 742 individuals were younger than 25 years old and
253 individuals were older than 25. For individuals under 25, the mean BMI was 23.23
with a standard deviation of 3.98 (with a minimum of 16.10 and maximum of 41.73). For
individuals 25 and over, the mean was 23.80 with a standard deviation of 4.70 (with a
minimum of 17.54 and maximum of 43.92). This difference in standard deviation, 3.98
versus 4.70 motivated us to repeat the same regression analyses for the older group.
Knowing that low variability in the dependent variable decreases statistical power in
regression analyses, we decided to redo the regression equations using the 253 members
who were older than 25 years. The results are summarized in Tables 23, 24, and 25. the
betas and the R-square values were higher in the older group.
Table 23 shows the same analyses as Table 20 but was conducted with older
participants. The beta and R-square values are higher for this sub-sample. The total R-
square predicting exercise behavior from beliefs increased from .13 for the total sample

B: Healthy
Diet Neg. Diet Pos. Diet
Neu.
Diet
B:
Practiced
Skill
B:
Genetics
B:
Exercise R
2

BMI -.20(.23)
1
.23(.10)
2
-.17(.03)
3
.13(.01)
4
-.15(.01)
5
.11(.01)
6
-.10(.01)
7
.40**

254
to .19 for the 253 participants who were over 25 years of age. Similarly, the total R-
square predicting positive diet from beliefs increased from .16 to .19 for the older sample.
The total R-square predicting neutral diet from beliefs increased from .05 to .18, and the
R-square for predicting negative diet from beliefs increased .17 to .24. Similarly, the
total R-square for predicting BMI from beliefs increased from .31 to .40 for the older
sample, alone. Aside from the magnitude of the findings, the general pattern of
relationships is the same.

Table 23: Stepwise Regression Analyses of Contribution of Health Beliefs on Health
Behaviors and BMI for Individuals over 25.

Note. The values are the standardized regression coefficients, β(R-square change associated with the
predictor). Superscript numbers indicate the order of entering in the stepwise regression. The R
2
shown in
the last column is the total ratio of variance explained by all the predictors. ** p< .001

The findings in Table 24 (reporting on the sample of individuals 25 and over) are
very similar to those in Table 21 but show that the beta and R-square values are
considerably higher in individuals over 25. Tables 21 and 24 both show that positive diet,
negative diet, and exercise behaviors significantly predicted BMI. In the older sample,
B: Exercise

B: Healthy
Diet

B: Monitor
Weight
B:
Practiced
Skill
B:
Genetics R
2

Total Ex. .30(.15)
1
.14(.02)
3

-.15(.02)
2
.19**
Pos. Diet .18(.02)
3
.20(.13)
1
-.21(.04)
2
.19**
Neu. Diet -.37(.18)
1

.18**
Neg. Diet -.36(.20)
1
-.22(.04)
2

.24**
BMI -.26(.10)
2
-.19(.02)
3

-.30(.27)
1
.14(.02)
4
.40**

255
however, these three variables accounted for 37% of the variance in BMI, whereas they
only accounted for 28% of the variance in the total sample.

Table 24: Stepwise Regression Analyses of BMI on the Behavioral IVs for Participants
over 25.

Note. The values in the table are standardized regression coefficients β (the R-square change associated
with the predictor). Superscript numbers indicate the order of entering in the stepwise regression. The R
2
in
the last column is the total ratio of variance explained by all the predictors. ** p< .001

Tables 22 and 25 show the regression equations in which both behavior and
beliefs were jointly entered to predict BMI for the total and over 25 subsample. A
comparison of Tables 22 and 25 shows individuals younger than 25 differed from
individuals older than 25 in terms of the order in which variables entered into the
equation and in terms of which variables did and did not enter the equation. Also there
was a considerable difference in the total variance accounted for: When beliefs and
behaviors entered the regression equation together, the independent variables accounted
for 40% of the variance in BMI for the total sample (see Table 22), whereas a similar set
of variables accounted for 49% of the variance in BMI for individuals over 25 years of
age (see Table 25).

Neg. Diet Pos. Diet Total Exercise R
2

BMI

.46(.31)
1
-.18(.04)
2
-.15(.02)
3
.37**

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Table 25: Stepwise Regression Analyses of BMI on All IVs for Individuals Over 25
Note. The values in the table are standardized regression coefficients β (the R-square change associated
with the predictor). Superscript numbers indicate the order of entering in the stepwise regression. The R
2
in
the last column is the total ratio of variance explained by all the predictors. ** p< .001

Discussion
This study intended to address and quantify the influence of 5 core beliefs on
habitual engagement in diet and exercise. We hypothesized that belief in the importance
of monitoring weight, maintaining a healthy diet, maintaining a healthy exercise regimen,
and maintaining discipline in weight management would all relate positively to healthy,
self-regulative behaviors and negatively to unhealthy behaviors. We also expected that
healthy behaviors would relate negatively to BMI and that unhealthy behaviors would
relate positively to BMI. The findings supported our predictions: Health management
beliefs were found to be strong predictors of health behaviors, which in turn were strong
predictors of BMI. When all of the variables were allowed to enter into a regression
equation together, health beliefs and behaviors captured 40% of the variance in BMI. To
our knowledge, this is the first time that belief strength has been shown to be strongly
predictive of diet, exercise, and health outcomes. Furthermore, the predictive value of
these beliefs highlights their importance as psychological factors in the modern obesity
epidemic.
The results were highly consistent with our hypotheses, and also remained highly
consistent with each other in terms of the direction of the betas and the magnitude of the
Neg. Diet
B:
Practiced
Skill
Total
Exercise
B: Healthy
Diet B: Exercise R
2

BMI .33(.31)
1
-.26(.13)
2
-.12(.03)
3
-.14(.02)
4
-.12(.01)
5
.49**

257
R-square changes. The relationships found in the simple correlation matrix largely
coincided with those found in the multiple regression analyses and the individual
regression analyses were highly consistent with one another. As predicted, the belief in
the role of genetics related negatively to positive health outcomes and positively to
negative health outcomes and BMI. The other four beliefs exhibited the opposite pattern
in a manner that was consistent throughout the analyses. In other words, beliefs in the
importance of exercise, healthy diet, weight monitoring, and practiced skill positively
related to total exercise and positive diet but inversely related to negative diet and BMI.
Furthermore, multiple regression analyses revealed that the three most significant
relationships were between BMI and unhealthy diet, between total weekly exercise and
belief in the importance of exercise, and between total weekly exercise and healthy diet.
These findings indicate that belief in the negative impact of an unhealthy diet and belief
in the importance of exercise may be among the most important psychological factors for
health intervention strategies to tackle.
Table 19 shows that every variable we measured, the five beliefs and four
behaviors, were all significantly correlated with BMI, suggesting that all beliefs and
behaviors chosen in this study can be taken to predict BMI. The multiple regression
analyses show that many of these variables share considerable variance in common, and
thus, compete with each other to enter into the regression equations. Table 19 though,
demonstrates the high intercorrelations among the predictors. The highest correlations
were found among three health beliefs; specifically, a correlation of .688 was found
between the belief in the importance of a healthy diet and the importance of weight

258
monitoring, a correlation of .646 was found between the belief that weight management
is a practiced skill and the belief in the importance of a healthy diet, and a correlation of
.656 was found between the belief that weight management is a practiced skill and the
belief in the importance of weight monitoring. These highly significant correlations
suggest that the four health management beliefs were highly interrelated, creating a
“health-conscious lifestyle factor.” Belief in genetics was uncorrelated with these beliefs
and again, consistently showed the opposite pattern of relationships.
Both Tables 20 and 23 support our prediction that the beliefs of our participants
predict their behaviors. Tables 21 and 24, in turn, support our prediction that weight
management behaviors predict BMI. Tables 22 and 25 show that a combination of health
beliefs and health behaviors, when taken together, are strong predictors of BMI.
Interestingly, the variables in Table 25 accounted for approximately 9% more of the
variance in R-square (R²= .49) than variables in Table 22 (R²= .40), demonstrating that -
for people over the age of 25 - these factors are better predictors of BMI. This increase in
R-square may be attributable to higher variance in BMI within the older group. There
may have simply more variance to predict. Another interpretation of this result may be
that the longer you live, the longer the cumulative consequences of your health beliefs
and health behaviors on BMI and the 25 and older group is reflecting this effect.
Study Limitations
There are a number of limitations that should be addressed. First, there is a debate
as to whether conscious thoughts can guide behavior. Some researchers argue that
conscious thoughts—such as beliefs—can’t influence behavior and claim consciousness

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is an epiphenomenon (Dijksterhuis, Chartrand, & Aarts, 2007; Wegner, 2003; Wilson,
2004; Wyer & Bargh, 1997). Others argue that conscious thoughts can cause behavior
(Bandura, 1995; Locke, 1995), and there is empirical evidence demonstrating conscious
causality (for review see Baumeister, Masicampo, & Vohs, 2011). To the extent that
conscious thoughts can guide behaviors, our research shows that health beliefs may be a
powerful determiner of health behaviors, and ultimately BMI.
A second limitation is that we are unsure how health beliefs and health behaviors
are related. We believe that strong health beliefs motivate healthy behaviors; however,
there are other possible interpretations. The relationship may be in the opposite direction;
people who participate in healthy behaviors might be more likely to report them as
important (Festinger, 1957). It is also conceivable that the relationship is reciprocal:
health beliefs and behaviors simultaneously influence one another (Infurna, Gerstorf, &
Zarit, 2011; Lachman, 2006; Skaff, 2007). Finally, a third variable, such as genetics, may
be mediating the relationship between health beliefs and health behaviors.
There is evidence in support of our opinion that health beliefs are driving
behavior. For instance, Infurna, Gerstorf, and Zarit (2011) found that health beliefs
precede health outcomes. Using a bivariate dual change score model, they found that
perceived control—the belief that one has control over their environment—predicted
changes in health, but found no evidence that health predicted perceived control. More
research is needed on the particular health beliefs used in this study to determine how
they relate to health behaviors.

260
Despite these limitations, health beliefs predicted a large amount of variance in
BMI and health related behaviors, and constitutes an interesting new avenue of research.
To the extent that these beliefs can be changed, it may offer a new strategy for increasing
motivation to self-regulate behavior.
Conclusions
The results highlight the importance of people’s health beliefs about weight
management in shaping their weight management behaviors, and in turn, their BMI. The
data suggest that belief in genetics as a determinant of weight may detract from certainty
in the other beliefs, be self-limiting and demotivational, and should perhaps be
downplayed in public communication. However, the other beliefs, especially the belief
that keeping a healthy weight is a practiced skill, appear to be empowering and should be
promulgated in health promotion efforts and programs.
Undoubtedly, the current epidemic of obesity and general metabolic disease is
largely a product of our modern environment (Flegal, Carroll, Ogden, & Curtin, 2010).
Artificially high levels of sugars, fats, and processed foods along with sedentary behavior
make us more susceptible to obesity today compared to our hunting-and-gathering
evolutionary ancestors. It will not be easy to restructure the modern environment without
restructuring people’s beliefs. If all people strongly believed in the four positive beliefs
concerning weight management featured in this study and were disinclined to espouse the
belief in the role of genetics, consumer choices and market forces would inevitably
precipitate a global reengineering of our current “obesogenic” conditions.

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Yet how can belief strength in the positive notions about diet and exercise be
increased in the general population? Perhaps this can be done if people are given clear,
quality evidence for these beliefs, the beliefs feel permanent, likeable and relevant for
them and they were inculcated early, by their parents, to espouse proper notions
regarding diet and exercise. Study 1 showed that the estimated opinions of parents,’
quality of empirical evidence and importance to self-identity worked together to account
for variation in strength of certainty. That study examined the reasons that people use to
justify their beliefs and found that, for most people, beliefs are justifiable and closely
related to the beliefs of their personal contacts. Study 2 found that people’s beliefs
closely predict behavior patterns and even the outcomes of those behaviors in the domain
of health management. It is probable that this is true of beliefs in other important domains
such as how to: save money, parent children, maintain proper hygiene, remain ethical in
the face of adversity and foster personal happiness. Together Studies 1 and 2 reinforce
previous research and speculation, with new empirical data - long missing in this area of
research, about belief and its guiding role in human life.
Belief is distinct from knowledge, memory, and attitude and can be affected by
factors such as persuasion, social contacts, self-identity, and personal history (Schacter &
Scarry, 2000; Reser, 2009). The key aspects involved in the formation of enduring and
actuating beliefs are early inculcation (Anderson & Sechler, 1986; Argyle, 1997),
repetitious exposure (Kilbourne & Pipher, 2000), involvement of parents and significant
others (Sigel, 1992), expected permanence, perceived relevance, and personal likeability
(Paglieri, 2005; Reser et al., 2011). Intervention programs that target belief strength and

262
focus on these avenues toward persuasion and psychological certainty should achieve
significant results in the effort to promote better diet, increased exercise, and healthier
lifestyles.

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Chapter 18: Final Conclusions

"Whatever we learn has a purpose and whatever we do affects everything and everyone
else, if even in the tiniest way… And it's much the same thing with knowledge, for
whenever you learn something new, the whole world becomes that much richer."
- Norton Juster

Practically all human thinking involves belief, a modified version of a belief, or a
cogitation that can be reframed as a type of belief. For this reason, it is very difficult to
give a comprehensive treatment to belief unless one is willing to give a comprehensive
overview of cognitive psychology. It seems that by using an interdisciplinary approach,
we have uncovered a great deal about beliefs in a short time. Beliefs, although often
vaguely defined in discourse, can be conceptualized in many different ways: as
associative memories; as coactivations between multiple neural assemblies; as functional
cognitive instruments; as self-propagating entities that parasitize minds; as the firings of a
certainty module in the brain; as placeholders for self-identity; or as means by which to
make sense out of the world. Not only is the concept of belief multifacted but it is also
multifactorial, meaning that beliefs can be affected by a large number of different factors.
Evidence, rationale, intuition, attitude, persuasion, friends, family, authorities, the social
community and the importance to self-identity all make unique contributions to how, and
how much, we believe. Other concepts that have proven helpful in the endeavor to
explicate the origins and dynamics of belief include personal epistemology, how beliefs
can go wrong, delusional thinking, the neuroscience of belief, the ontology of belief, the
analogy with déjà vu and speculation about the evolutionary pressures on belief accuracy.

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Despite the fact that it is not clear how much of what we have considered can be
reconciled, it does seem clear that much of what we have considered is not incompatible
or contradictory. The viewpoints we have garnered, although they come from disparate
fields, appear to interface in many ways and taken together, paint a rich portrait of the
mechanics of belief.
We have come to see that belief, knowledge, memory and attitude are very
different and yet tied together inextricably. At this point, we can practically define belief
in terms of knowledge, memory and attitude. Belief, it seems, is a type of
metaknowledge, or knowledge that particular knowledge is useful and trustworthy. A
belief may also be thought of as a type of knowledge that is resistant to correction or
modification by subsequently learned knowledge. Because an attitude is a position or
leaning on a subject, it seems reasonable to claim that a belief could also be
conceptualized as an attitude about knowledge. Further, memories can be seen as beliefs
about past events and beliefs can be seen as composed of and reinforced by memories.
Redefining beliefs in this way is instructive but many facets of the meaning of belief
cannot be captured by these formulae.
Belief seems to imply a state of metapsychological awareness more so than
memory, knowledge or attitude and seems to relate that the bearer must be, in some way,
aware of their belief in order for it to be a belief. Some of the most common ways to be
aware of a belief are to: be aware of its effect on your behavior or thought; be cognizant
of the fact that you selected this belief among competing beliefs; or realize that other
people may hold a belief that is an incompatible alternative to yours. It is hardly a belief

265
if you are not aware of it in some way, whereas, knowledge, memories and attitudes can
be held without any awareness. Surely, there are many potential ways to be aware of a
belief, not all of which involve full or objective appraisal. What kind of
metapsychological appraisal must occur, if any, for trusted knowledge to qualify as belief
has remained unclear. In fact, much has remained unclear about the ontology and
semantics of belief.
Because the word “belief” has never been operationally defined, many other
words can be used interchangeably with it. Until a more clear synthesis of work on belief
emerges, it will be difficult to discern the difference between belief and similar words
like knowledge, attitude, apperception, conception, assumption, conviction, impression,
opinion, presumption, supposition or understanding. If anything, belief seems to evoke –
more so than these other words – connotations of faith, personal investment and hope.
To some, issues like these are of utmost importance in understanding beliefs, to others
they are linguistic and pedagogical (Audi, 1988). Semantic issues like these have not
been prominent in the present discussion but may help to contribute eventually to a well-
rounded understanding of belief formation and change. Here, instead, we have relied on
the literature on predictors of belief, and mistaken belief, to help inform this
understanding.
Knowledge structures are filled with holes. People are inquisitive and determined
enough to want to fill in the holes that they are able to notice, even if they can only do so
with guesswork. This is partly because we crave the feeling of certainty. People adopt
beliefs often because they feel that, for emotional reasons, they need an answer and they

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are willing to convince themselves of something that they are not totally sure about. Too
often, we believe something because we want it to be true and not because we know it to
be so. This effect is especially problematic because many people have trouble discerning
the difference between what they want and what they know. When wish fulfillment plays
a large role in the existence of a belief, it is usually not subjected to judicious criticism
and it is often protected with the use of defense mechanisms. It is curious that we have a
strong propensity to shield beliefs from and impose beliefs on others; even ones that we
have not exposed to thoughtful critique.
People must realize on their own, at least to some degree, that they have a limited
capacity for analysis, that they are susceptible to making cognitive mistakes and that they
are not appropriately informed to make all of the decisions that they would like, about
what to believe. When people realize that they do not have the substantiation necessary to
formulate a belief, they generally do one, or a combination, of three things: search for
evidence, attempt to think rationally or search for an authority. The trouble with this is
that people do not do these things exhaustively; they often proceed sloppily and hastily.
The tendency to make use of heuristics, to skim, to wing it and to use a finite amount of
reasoning inevitably leads to mistakes. Unfortunately, we are never formally taught how
to identify these mistakes or how to compensate for them. Courses in logic and critical
thinking are rare in high school and can often be misguided in college (Paul, 2005).
Without the fundamentals it is difficult for many adults to discriminate between justified
contention and specious sophistry. To compound matters, our innate neurological belief
system is susceptible to haphazard associations and contaminated inferences. In our

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ceaseless attempt to discover meaning, we connect the dots in ways that they were not
meant to be connected, and we have a tendency to become fixed in comfortably familiar
or just-plain-wrong frames of mind.
People use beliefs inflexibly and apply established beliefs to situations that appear
like something they have seen before, based on superficial and irrelevant similarities.
Much of what we know about the world is drawn from inferences based on prior
probabilities. Often the concepts invoked to make these inferences - the nodes that are
primed or coactivated to determine the prior probability - are unrelated to the true factors
involved in the causal process. This type of thinking creates a false reality, which can
lead to superstitious, inefficient behavior or to drastic consequences. A tendency for
forming rash and unsubstantiated beliefs can become habitual, especially when no
negative consequences of this tendency are imposed by the environment.
When there are inconsistencies between what people believe and what they
experience, this is an indication that their existing world view is not accurate and that
they must question their belief (Kelman & Baron, 1968). It is too bad we have a tendency
to ignore these inconsistencies (Abelson, 1986a). It is also unfortunate that we have a
tendency to inherit these inconsistencies, and even individual beliefs caused by them,
from our parents.
However, all is not lost, because the simple use of the term belief can be
redemptive. It is customary for people to refer to their working assumptions as beliefs,
even if they hold the assumption very dear. The term is an admission of doubt and

268
because it is honest and self-deprecating, use of it can be forthright, virtuous and
empowering.
Beliefs can be empowering in many other ways. Beliefs steer everything from
simple behaviors to decision making to goal-directed activities. Belief in religion gives
people transcendental comfort; belief in a cause provides purpose and meaning; belief in
placebo or medicine creates substantial therapeutic benefits; belief in others results in
sociability, camaraderie and intimacy; and belief in one’s self drives dedication,
discipline, and ultimately, prosperity. It is clear that the ability to believe is essential to
being human and can be highly adaptive in many contexts.
It seems humans may have been naturally selected to form certain types of beliefs
effectively but not others. We are prepared to believe things that involve foraging
activities, tool making and primitive social exchange. Relative to the ancestral past, few
of our beliefs today are related to testable strategies and more of them are related to
higher-order concepts that are difficult for a single person to falsify. In the modern world,
much of what we believe is not directly observable and most of the beliefs that we
entertain in day-to-day thought were not acquired first hand but from friends, teachers,
books or the media. This process, on a large scale, functions well and clearly enables us
to create elaborate cultural and technological advancements that would have been
impossible if everyone demanded first-person experience for every belief. On a smaller,
personal scale though, we formulate our beliefs within a world very different from that
we evolved to believe within and because of this our feeling of certainty might be

269
miscalibrated. It, in fact, may predispose us to being more naïve and credulous than our
wits should allow us to be.
This evolutionary or historical perspective gives us valuable insight into how
better to understand other people’s reasoning about beliefs, the explanatory relationships
among them or the lack thereof. Such an understanding of beliefs can influence us to
have a more compassionate outlook on those that hold beliefs that are different from ours.
It should also help us identify sociologically accepted paradigms that need more reality
testing and areas of human inquiry that deserve more thought and rumination. They may
be the most difficult to change but cultural beliefs that are based on emotions like fear,
desire, hate or excitement may be the ones that most desperately need to be reconsidered.
Most people have belief systems that are not founded entirely on facts about
reality. This is true because so many of us remain very much uninformed about the causal
processes that control the world around us. It can be argued that better understanding of
these processes affords an individual increased confidence, a more profound sense of
self-awareness and a more sophisticated vantage point. The best way to come to
understand these processes is to identify matters of importance and then to search for
credible documentation on these matters. Initiating and organizing such searches is not
always easy, even for intelligent and inquisitive people. Not only can evidence be
difficult to unearth but it can also be difficult to identify consequential issues to consider,
contemplate and believe in. This is especially so today because commercial media
attempts to convince us that trivial and transient things are important. To be introduced to
new, interesting information that will embellish and enrich one’s worldview is time

270
intensive and probably involves discriminative television watching, discerning friendship
and dedicated reading.
It can be very difficult for people to alter their fundamental beliefs about the
world. This is partly because, as the pertinent literature has shown, many people are
dogmatically protective of their ideological systems and derive much of their own sense
of identity and security from them. Also, people cling tenaciously to their beliefs due to
the fact they have been formulating many of them since they were very young. Heavily
elaborated beliefs become ingrained in the psyche. Ideas radically different from
longstanding beliefs can be threatening, confusing and difficult to compensate for.
Most people do not want to engage in the mental work involved in accepting a
new idea because it can be a very difficult task. The more fundamental the belief, the
more difficult it is to replace because it is likely to be interrelated with other beliefs that
may be contingent upon it. Unconscious, automatic brain circuitry, which is programmed
by repeated, habitual actions, is involved here. The more a belief is invoked, the more
ingrained it becomes, the more likely the brain is to trust it and use it unquestioningly.
Once we routinize the automatic schemas, perceptual associations and emotional
concomitants implicitly attached to a familiar belief it may become very difficult to
reevaluate or even question them. Questioning a belief requires working memory, time
and cognitive resources, and it is clear that, as the cognitive misers that we are (Fiske &
Taylor, 1991), humans are reluctant to do this.
In addition, one cannot expect a modified belief to inform decision making in
every applicable situation. A new belief must be applied consciously and intentionally

271
under various circumstances until it gradually becomes implicit itself. Thus, embracing a
new belief often involves two things: changing many previous, related beliefs, and
reprogramming unconscious reactions. Viewed in this way, new beliefs seem discrete and
isolated from those already established and regularly instituted. Belief formation and
change then, are gradual, thought-intensive processes that create continuity and
integration. This leads us to propose that beliefs are estimations of certainty that people
formulate when a situation deems it necessary and this formulation is not based on a
comprehensive review of pertinent information, but rather based on whatever criteria
happen to come to mind at the time.
If the reader is anything like the present author, a good deal of this literature on
beliefs resonates with their own experiences of believing. Looking back years ago, I have
a hard time believing how convinced I could be of certain things with only shreds of
supporting evidence. I was especially gullible about things that were tied up with my self-
concept. Now I know to take precaution with beliefs that involve my ego because my
neurological systems may be poorly prepared to deal equitably with these. I also take
extra care with beliefs that I borrowed from others without analyzing them on my own.
Overall, I feel that the most important thing that I have learned about beliefs is to
question my feeling of certainty. Like many emotions, feelings of certainty can be
aroused involuntarily. Just because this unwilled, unthinking sensation has proven
trustworthy and dependable in some situations does not mean that it is unimpeachable
and can be relied on without reservation. It may not be coincidental that because the
preparation of this manuscript has caused me to undergo extensive personal, epistemic

272
inquisition of my feeling of certainty, that I now feel like I am certain much less
frequently and like my beliefs are less egocentric, less intense and overall less polarized.
This review was not meant to instruct the reader as to what to believe, but was
meant to show a little about how to believe and hopefully how to think about belief. To
summarize the previous conclusions about how to believe, it seems advisable to:
- Reconsider propositions a few times before exercising belief in them
- Consider beliefs as subjective knowledge
- Take note of the context within which a belief is considered
- Acknowledge that all beliefs should be open to change and modification
- Keep in mind that peers, parents, social consensus and even authorities can be
wrong
- Remember that disempowering beliefs can be handicaps and empowering ones
springboards
- Be wary of the feeling of certainty and its influence on jumping to conclusion
- Remember that not all questions have verifiable, or even objective answers.

It would be interesting to speculate about how a person’s behavior would change if
their beliefs could be made more discerning and more objective. One might assume that
decision making ability, goal-setting and personal productivity would be positively
affected. On a larger scale, one might wonder how personal improvements in fluency,
expediency and proficiency in the operations of believing might affect larger institutions
such as classrooms, companies, states and nations. Because beliefs either actively or

273
passively manage almost all human activity we should assume that improved believing
should result in more well-functioning behavior - for everyone.
There are certainly many alternative views about how one might conceptualize
the field of belief and how it might be situated into larger psychological traditions. This is
partly due to the fact that beliefs can be conceptualized, validly, under several different
contexts and circumstances. There are clearly many circumstances under which a belief
can be formed and many ways that a belief can change. We have come to see that there is
not a failsafe algorithmic method to evaluate the validity of a potential belief. Believing
is certainly not an exact science, as almost any belief will require the believer to have
faith in the estimations of others, in the veridical nature of their perceptions, in the
accuracy of their memory and in their powers of reason. It is amazing that this process
works as well as it does. People’s underlying beliefs about knowing and believing
mediate the life-long processes of knowledge-acquisition and knowledge-construction.
How this process is executed, constrained by biology, influenced by culture and
supervised by consciousness will probably be seen as important considerations for a long
time to come.

274
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292
Appendix A: Informed Consent for Study 1

University of Southern California Psychology Department
INFORMATION SHEET FOR NON-MEDICAL RESEARCH

The Psychological Foundations of Personal Beliefs: Child’s Version

PURPOSE OF THE STUDY
This research study examines the psychological foundations of personal beliefs in both
scientific and social domains. Please take as much time as you need to read the
information sheet. You may discuss this study with your family or friends. Completion of
the questionnaire will constitute consent to participate in this research project.

PARTICIPANT INVOLVEMENT
If you volunteer to participate in this study, you will be asked to complete a
questionnaire. All procedures will be done over the Internet. You must be at least 18
years of age to participate.

PARTICIPATION AND WITHDRAWAL
Your participation is voluntary. You may choose to not answer individual questions or
stop participating at any time, without penalty.

CONFIDENTIALITY
There will be no identifiable information obtained in connection with this study.
The members of the research team and the University of Southern California’s Human
Subjects Protection Program (HSPP) may access the anonymous data. The HSPP reviews
and monitors research studies to protect the rights and welfare of research subjects.

INVESTIGATOR CONTACT INFORMATION
This research study is being conducted by Dr. David Walsh and Jared Reser, from the
Psychology Department at the University of Southern California. If you have any
questions or concerns about the research, please feel free to contact Professor David
Walsh by phone at 213-740-2275 or email at dwalsh@usc.edu.

IRB CONTACT INFORMATION
University Park IRB, Office of the Vice Provost for Research Advancement, Stonier
Hall, Room 224a, Los Angeles, CA 90089-1146, (213) 821-5272 or upirb@usc.edu

293
Appendix B: Questionnaire for Study 1

Beliefs Questionnaire:

The following questionnaire examines people’s beliefs about a few elements of the
physical, social and religious world. We estimate that it will take only 30 minutes for you
to complete and all of your answers will be anonymous. There are four sections to the
questionnaire and each has detailed directions of what we would like you to do. It is
important that you complete the four sections in the order in which they are presented, so
please don’t jump ahead to a latter section before completing an earlier one.

You participation in this study is completely voluntary and you are free to stop
participating at anytime, even though you may have initially agreed to participate or you
may choose to not answer individual items. However, we will only be able to use your
responses if you complete every item and carefully follow directions.

If you have questions about completing any of the items we ask that you first go back and
reread the instructions for that section and review the example items. If you are unclear
after reviewing the directions, then ask the person who is present and overseeing this
study for clarification.

Carol Brown, Jian Li, Edward Lin, Jared Reser and Professor David Walsh of the
University of Southern California are conducting this study. Professor Walsh can be
reached by phone at 213-740-2275 or email at dwalsh@usc.edu.

Demographics Questionnaire:

Please answer the following questions to provide us with useful information about
yourself. Remember, your information will remain completely anonymous, as we are not
asking for your name or any other identifying information.. However, we do need to
match responses from family members. The last four digits of your family residence
phone number will allow us to do this without being able to uniquely identify you.

Last 4 digits of family residence phone: ______

Age: ______

Sex: ______

Years of Education (where a high school diploma is equivalent to 12 years): __________

Academic Major: __________

294
Please list your top two hobbies/interest areas: _______________, ________________
(ie: Cars, Science)

Ethnicity: _______________________

Religious Affiliation: ____________________

Please answer the following question circling the number that best describes your position:

1. How well has your education prepared you to think scientifically?

0 1 2 3 4 5 6
Very Moderately Very
Unprepared Prepared Prepared

2. To what extent are you a social person?

0 1 2 3 4 5 6

Very Moderately Very
Unsociable Sociable Sociable

3. How important is religious faith to you?

0 1 2 3 4 5 6
Very Moderately Very
Unimportant Important Important

295

Truth or Falseness of Beliefs.
Below are 3 statements that describe beliefs that some people think are true, while others
think are false. We would like you to use the 7-point scale below to indicate the degree
to which you think each is either true or false. For example, you would choose the
number “0” if you are confident that a belief is false or you would choose “6” if you are
confident the belief is true. You would write the number “3” if you think it is equally
likely the belief is true or false. You would use other numbers to express your opinion if
it is intermediate to our examples. There are no right or wrong answers.

We would also like you to use the 7-point scale below to indicate the degree to which you
estimate other people think these 6 statements are true or false. First estimate how your
parents might evaluate the statement (an average). Then estimate how your five closest
personal contacts (friends, family members or peers) might evaluate the statement (again
an average). Also estimate how you think the average American would evaluate it, and
finally, how a scientist, or an expert on the topic at hand, would do so.

We have provided two examples of how to use the given scale to evaluate the statements
that follow:
0 1 2 3 4 5 6
Confident it Not sure if Confident It
Is False True or False Is True

Example Belief 1: The sun is the center of the solar system.

You: _5__

Parents: _5__

Personal Contacts: _5__

Average American: _4__

Scientist: _6__

The person that filled out this example question responded to the “you” line with a 5,
indicating that they were relatively confident that the sun is the center of the solar system.
They also responded with a 5 for “parents” and “personal contacts.” The 4 for the
“average American” tells us that they estimate that many Americans are not as confident
as they and their family and friends are about this belief. Finally, the 6 for scientist tells
us that they think most scientists believe confidently that the sun is in the center of the
solar system.

296

0 1 2 3 4 5 6
Confident it Not sure if Confident It
Is False True or False Is True

Example Belief 2: The moon is made of Swiss cheese.

You: _0__

Parents: _0__

Personal Contacts: _0_ _

Average American: _1_ _

Scientist: _0__

For this second example it is clear that the person is highly confident that the moon is not made
of Swiss cheese and that their parents and personal contacts and scientists would agree. The
fact that they entered a 1 for the average American shows that they may not be so confident in
the analytical abilities of Americans in general. If you are clear on how to use the 7-point scale
to express your confidence in the truth or falseness of beliefs, please proceed to do so for each
of the 6 beliefs found on the next 2 pages. If you are not clear, please review the above
instructions and examples again.

Please use this scale to evaluate each of the statements that follow:

0 1 2 3 4 5 6
Confident It Not Sure If Confident It
Is False True Or False Is True

1. Bigfoot or Sasquatch is a large animal found on Earth.

You: ___

Parents: ___

Personal Contacts: ___

Average American: ___

Scientist: ___

297

2. A Supreme Being or “God” exists in some form.
You: ___

Parents: ___

Personal Contacts: ___

Average American: ___

Scientist: ___

3. Women have extremely limited access to the highest leadership position in society.

You: ___

Parents: ___

Personal Contacts: ___

Average American: ___

Scientist: ___

4. Every American should purchase their own home as early in adulthood as possible

Reasons you use to support your belief:
You: ___

Parents: ___

Personal Contacts: ___

Average American: ___

Scientist: ___

298
5. Every adult should exercise, from youth to old age, at least 5 times every week for 30
minutes or more, performing a combination of aerobic and strength training activities.

You: ___

Parents: ___

Personal Contacts: ___

Average American: ___

Scientist: ___

299

Reasons Behind Your Beliefs
We now want you to tell us the reasons you have to support or justify your opinion about
the truth and falseness of the 3 beliefs you just evaluated. For some beliefs you may only
have reasons that support the truth of the belief, while for others you may have only
reasons that support the falseness of the belief. For other beliefs you may have reasons
that support both the truth and falseness. Please respond to each statement by writing the
reasons you have to support the truth and/or falseness of each belief.

After you list a reason to support your opinion, we want you to also rate how strong you
think that reason is “as good evidence”. For example, write the number “0” beside a
reason you have listed if this reason is quite insignificant, but write the number “6”
beside the reason if you think it is very significant evidence supporting the truth or
falseness of the belief. Below is an example of how you should use the scales to indicate
the strength of the evidence you provided. Note that after each reason is a number in bold
type that we have entered to indicate how strong we think each reason is as evidence for
the truth and falseness of the belief.

Example: The moon is made of Swiss cheese.
Reasons to Support the Truthfulness Reasons to Support the Falseness
I have not been to the moon myself to see
that it is not made out of Swiss Cheese. 1
Astronauts have been to the moon and have
not found Swiss cheese on the moon. 6

Lunar landing craft have brought back
samples of soil and rock from the moon
and no Swiss cheese has been found. 6

0 1 2 3 4 5 6
Very Moderately Very
Insignificant Significance Significant
Evidence Evidence Evidence

If you understand what is expected of you, proceed to write in the reasons you have to support
the truth or falseness of the beliefs on the next page. Be sure to write a number (between 0 and
6) after each reason you write to indicate how strong you think that reason is.

300

1. Bigfoot or Sasquatch is a large animal found on Earth.
Reasons to Support the Truthfulness Reasons to Support the Falseness

Please use this scale to evaluate the quality of your reason as evidence:
0 1 2 3 4 5 6
Very Moderately Very
Insignificant Significance Significant

301
Evidence Evidence Evidence

2. A Supreme Being or “God” exists in some form.
Reasons to Support the Truthfulness Reasons to Support the Falseness

Please use this scale to evaluate the quality of your reason as evidence:
0 1 2 3 4 5 6

302
Very Moderately Very
Insignificant Significance Significant
Evidence Evidence Evidence
3. Women have extremely limited access to the highest leadership position in society.
Reasons to Support the Truthfulness Reasons to Support the Falseness

Please use this scale to evaluate the quality of your reason as evidence:
0 1 2 3 4 5 6
Very Moderately Very

303
Insignificant Significance Significant
Evidence Evidence Evidence

4. Every American should purchase their own home as early in adulthood as possible

Reasons to Support the Truthfulness Reasons to Support the Falseness

Please use this scale to evaluate the quality of your reason as evidence:
0 1 2 3 4 5 6

304
Very Moderately Very
Insignificant Significance Significant
Evidence Evidence Evidence
5. Every adult should exercise, from youth to old age, at least 5 times every week for 30
minutes or more, performing a combination of aerobic and strength training activities.

Reasons to Support the Truthfulness Reasons to Support the Falseness

Please use this scale to evaluate the quality of your reason as evidence:
0 1 2 3 4 5 6

305
Very Moderately Very
Insignificant Significance Significant
Evidence Evidence Evidence
Sources That Effect Your Beliefs
We now want you to rate a list of sources according to how much they influence your
belief about the statements. For example, write the number “0” beside a source if it does
not contribute to your belief, but write the number “6” beside the source if it does. Below
is an example of how you should use this scale to indicate the strength of each source.
Note that before each source in the example is a number in bold type that indicates how
strongly we think each source supports our belief.

This scale will be used to evaluate the example that follows:

0 1 2 3 4 5 6
Very Moderately Very
Insignificant Significant Significant
Source Source Source

Example Belief 1: The Sun is the center of the solar system.
Reasons you use to support your belief:
_1__ I have personally witnessed this.
_4__ I have seen photos, video, a written report, or other secondary source evidence.
_5__ There is a socio/cultural consensus in support of this.
_5__ The best argument in support of this is rational and logically coherent.
Many authoritative sources maintain this position, such as:
_5__ Scientists
_0__ Political scholars
_0__ Religious leaders

The 1 in the first blank indicates that the person has no personal observations of the sun
as the center of the solar system and thinks that personal experience is a weak source of
support for the belief. This makes sense, as does the 4 they entered for secondary source
evidence, because it is not possible to get outside the solar system to see that the sun is in
the center. The 5s indicate that the person is highly influenced by the rationality of the
argument for the belief, as well as the social and scientific consensus for it. This person
entered 0s for political scholars and religious leaders telling us that they are not
influenced at all by the opinions of political scholars or religious leaders on this matter.
If you are clear on how to use the 7-point scale to indicate how you have been influenced
by different sources, please proceed to do so for each of the 6 beliefs found on the next 3
pages.

306

Please use this scale to evaluate each of the statements that follow:

0 1 2 3 4 5 6
Very Moderately Very
Insignificant Significant Significant
Source Source Source

1. Bigfoot or Sasquatch is a large animal found on Earth.
Reasons you use to support your belief:
___ I have personally witnessed that this is true
___ I have seen photos, video, a written report, or other secondary source evidence.
___ There is a socio/cultural consensus in support of this.
___ The best argument in support of their existence is rational and logically coherent.
Many authoritative sources maintain this position, such as:
___ Scientists
___ Political scholars
___ Religious leaders

2. A Supreme Being or “God” exists in some form.
Reasons you use to support your belief:
___ I have personally witnessed that this is true
___ I have seen photos, video, a written report, or other secondary source evidence.
___ There is a socio/cultural consensus in support of this.
___ The best argument in support of their existence is rational and logically coherent.
Many authoritative sources maintain this position, such as:
___ Scientists
___ Political scholars
___ Religious leaders

3. Women have extremely limited access to the highest leadership position in society.

307
Reasons you use to support your belief:
___ I have personally witnessed that this is true
___ I have seen photos, video, a written report, or other secondary source evidence.
___ There is a socio/cultural consensus in support of this.
___ The best argument in support of their existence is rational and logically coherent.
Many authoritative sources maintain this position, such as:
___ Scientists
___ Political scholars
___ Religious leaders

4. Every American should purchase their own home as early in adulthood as possible

Reasons you use to support your belief:
___ I have personally witnessed that this is true
___ I have seen photos, video, a written report, or other secondary source evidence.
___ There is a socio/cultural consensus in support of this.
___ The best argument in support of their existence is rational and logically coherent.
Many authoritative sources maintain this position, such as:
___ Scientists
___ Political scholars
___ Religious leaders

5. Every adult should exercise, from youth to old age, at least 5 times every week for 30
minutes or more, performing a combination of aerobic and strength training activities.

Reasons you use to support your belief:
___ I have personally witnessed that this is true
___ I have seen photos, video, a written report, or other secondary source evidence.
___ There is a socio/cultural consensus in support of this.
___ The best argument in support of their existence is rational and logically coherent.
Many authoritative sources maintain this position, such as:
___ Scientists
___ Political scholars

308
___ Religious leaders

Personal Importance of Beliefs.
Now we would like to know more about why you hold the beliefs that you do. This
section will ask you to indicate the degree to which your opinion of the 3 beliefs is
likeable, permanent and relevant to you as a person. For each belief first enter a T or an F
to indicate whether you think the belief is true or false. Then use the 7-point scales to
indicate how likeable your opinion about the belief is to you (how agreeable and pleasant
the concept is to you), then indicate how permanent your conviction is (how unlikely
your stance is to change in the future). Finally indicate how relevant this belief is to you
(how important the belief is to your sense of self-identity). Write the number “0” if your
opinion is not likeable, permanent or relevant at all, but write “6” if it is very likeable,
permanent or relevant. Below is an example of the scale and how to use it to indicate
how important each belief is to you.

This scale will be used to evaluate the example that follows:
0 1 2 3 4 5 6
Very Moderately Very
Unlikeable Likeable Likeable
Impermanent Permanent Permanent
or Irrelevant or Relevant or Relevant

Example Belief 1: The sun is the center of the solar system.

True or False: __T__

Likeability: __2__

Permanence: __5__

Relevance: __6__

The “T” in the first blank indicates that the person who filled out this example believes
that the Sun is the center of the solar system. In this example the person wrote a 2 for
likeability, they must not find this belief particularly likable, perhaps they would make
the Earth the center of the solar system if they could. The person expected that their
belief about the sun was unlikely to change in the future and so they wrote a 5 for
permanence. Finally, the person believes the sun is the center of the solar system and
must find this to be an important organizing principle of how they think about the world
around them. Thus, they used a “6” to indicate that the belief is relevant to their sense of
self-identity.

309

Example Belief 1: The moon is made of Swiss cheese.

True or False: __F__

Likeability: _1_

Permanence: 6__

Relevance: __0__

The “F” in the first blank indicates that the person who filled out this example believes
that the moon is not made of Swiss cheese. In this example the person wrote a 1 for
likeability indicates they are not happy with the belief that the moon is NOT made of
Swiss cheese. The person used a 6 for permanence to indicate they are certain to never
expect the moon is made of Swiss cheese. Finally, the person used a 0 to indicate the
moon NOT being made of Swiss cheese has no relevance to their life. Below is a review
of the concepts we want you to consider and rate:

True or False: Whether you think this belief is true or false.

Likeability: How much you personally like your belief.

Permanence: How stable, and unlikely to change, is your belief.

Relevance: How important or relevant the belief is to your sense of self identity.

If you are clear on how to use the 7-point scale to express how likeable, permanent and
relevant each belief is to you, please proceed to rate the beliefs on the next 2 pages.

Please use this scale to evaluate each of the statements that follow:
0 1 2 3 4 5 6
Very Moderately Very
Unlikeable Likeable Likeable
Impermanent Permanent Permanent
or Irrelevant or Relevant or Relevant

1. Bigfoot or Sasquatch is a large animal found on Earth.

310
True or False(T/F): _____

Likeability: _____

Permanence: _____

Relevance: _____

2. A Supreme Being or “God” exists in some form.

True or False(T/F): _____

Likeability: _____

Permanence: _____

Relevance: _____

3. Women have extremely limited access to the highest leadership position in society.

True or False(T/F): _____

Likeability: _____

Permanence: _____

Relevance: _____

4. Every American should purchase their own home as early in adulthood as possible

True or False(T/F): _____

Likeability: _____

Permanence: _____

Relevance: _____

311
5. Every adult should exercise, from youth to old age, at least 5 times every week for 30
minutes or more, performing a combination of aerobic and strength training activities.

True or False(T/F): _____

Likeability: _____

Permanence: _____

Relevance: _____

312

Appendix C: Informed Consent for Study 2

University of Southern California Department of Psychology
College of Letters, Arts & Sciences, 3551 Trousdale Parkway

INFORMATION/FACTS SHEET FOR NON-MEDICAL RESEARCH

Beliefs About Health and their Affect on Health Behaviors

PURPOSE OF THE STUDY
The purpose of this study is to see if people’s beliefs about a healthy weight predict their
weight management behaviors.

PARTICIPANT INVOLVEMENT
If you volunteer to participate in this study, you will be asked to complete a
questionnaire. You will be asked for demographics information, your beliefs about a
healthy weight, and behaviors you use to help manage your weight. This will only take 5
to 10 minutes. All procedures will be done over the Internet. You must be at least 18
years of age to participate.

PARTICIPATION AND WITHDRAWAL
Your participation is voluntary. You may choose to not answer individual questions or
stop participating at any time, without penalty.

CONFIDENTIALITY
There will be no identifiable information obtained in connection with this study.
The members of the research team and the University of Southern California’s Human
Subjects Protection Program (HSPP) may access the anonymous data.

The HSPP reviews and monitors research studies to protect the rights and welfare of
research subjects.

INVESTIGATOR CONTACT INFORMATION
If you have any questions please contact Jared Reser (reser@usc.edu)

IRB CONTACT INFORMATION
University Park IRB, Office of the Vice Provost for Research Advancement, Stonier
Hall, Room 224a, Los Angeles, CA 90089-1146, (213) 821-5272 or upirb@usc.edu

313

Appendix D: Questionnaire for Study 2

Beliefs About Health and their Effect on Health Behaviors

Last name of the person that recruited you to take this study: _______

Demographic Information:
1. Age _______ in years
2. Sex _______ male or female
3. Race _______
4. Education _______ years after high school
5. Height _______ in inches
6. Weight _______ in pounds

Beliefs: Please indicate your belief strength in the following beliefs by circling a number
from -5 to 5 on the scale.

1. Every adult should exercise at a moderate to intense level for at least 30 minutes a
day, five times a week.

-5 -4 -3 -2 -1 0 1 2 3 4 5
Completely Neither True Completely
False nor False True

2. Every adult should eat a healthy, nutritious diet containing lots of fruit, vegetables
and fiber.

-5 -4 -3 -2 -1 0 1 2 3 4 5
Completely Neither True Completely
False nor False True

3. Every adult should monitor their weight and keep it in a normal healthy range.

-5 -4 -3 -2 -1 0 1 2 3 4 5
Completely Neither True Completely
False nor False True

314

4. Managing your weight and keeping it in a healthy, normal range is a practiced
skill that requires attention and effort.

-5 -4 -3 -2 -1 0 1 2 3 4 5
Completely Neither True Completely
False nor False True

5. Genetic factors play a large role in how much a person weighs and make it
difficult for many people to keep their weight in a normal, healthy range.

-5 -4 -3 -2 -1 0 1 2 3 4 5
Completely Neither True Completely
False nor False True

Behaviors: Please indicate your eating and exercise habit by filling in the following
blanks with whole numbers.

1. How many times a week do you perform moderate to intense exercise, such as
running, cycling, swimming, basketball, weight training, etc.? _________
2. How many minutes of moderate to intense exercise do you do, in a typical
session? _________
3. How many servings of fruits and vegetables do you eat in a typical day?
_________
4. How many servings of fish do you eat in a typical week? _________
5. How many servings of lean meats such as chicken or turkey do you eat in a
typical week? _________
6. How many servings of red meat do you eat in a typical week? _________
7. How many servings of desserts do you eat in a typical week? _________
8. How many servings of junk food (potato chips, candy bars, etc.) do you eat in a
typical week? _________
9. How many fast food meals (hamburgers, cheese burgers, fries, tacos, etc.) do you
eat in a typical week? _________
10. How many regular soft drinks (non-diet, Coke, Pepsi, etc) do you drink in a
typical week? _________
11. How many times do you weigh yourself in a typical month? _________

Abstract (if available)

Abstract This dissertation examines the psychological foundations of personal belief by conducting a review of classical and contemporary thought about belief, by hypothesizing about ways to conceptualize belief and by presenting new evidence about belief from empirical studies. Two studies measured the contributions of various constructs to belief strength in an effort to examine the determinants and functions of personal belief. Study 1 collected data from over 250 child-parent pairs regarding how beliefs are formulated. Participants rated their strength of belief in statements relative to the following determinants: the importance of substantiating evidence, the perceived logic inherent in a belief, the importance to self-identity, the influence of parents, the social community and authority figures. Study 1 found that strength of certainty can be best predicted by one’s estimate of their family member’s belief, the quality of empirical evidence that the person can offer to support the belief, and the perceived importance of the belief to their sense of self-identity. Study 2 investigated whether people's weight management beliefs predicted diet and exercise behaviors and whether these behaviors in turn predicted BMI. These expected results were strongly supported by the data gathered from 996 participants, who responded to a questionnaire, reporting their height, weight, beliefs about various aspects of weight management, and personal weight-management behaviors, including exercise activities and eating habits. Overall, 40% of the variance in BMI within our sample, including 49% of the variance in BMI in individuals older than 25, could be predicted by a combination of health beliefs and their associated eating and exercise behavior.

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Asset Metadata

Creator Reser, Jared Edward (author)

Core Title Assessing the psychological correlates of belief strength: contributing factors and role in behavior

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Publication Date 04/26/2012

Defense Date 03/28/2012

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Attitude,Belief,believe,BMI,brain,certainty,cognitive neuroscience,Community,consciousness,correlation,delusions,diet,empirical evidence,epistemology,Evolution,Exercise,exercise behavior,health beliefs,idea,multiple regression,Neuroscience,OAI-PMH Harvest,Parents,persuasion,Philosophy,polyassociativity,prefrontal cortex,Psychology,questionnaire,self-identity,thinking,thought,unconscious,working memory

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Walsh, David A. (committee chair), McClure, William O. (committee member), Read, Stephen J. (committee member), Wood, Justin N. (committee member)

Creator Email jared@jaredreser.com,reser@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-15374

Unique identifier UC11289357

Identifier usctheses-c3-15374 (legacy record id)

Legacy Identifier etd-ReserJared-662.pdf

Dmrecord 15374

Document Type Dissertation

Rights Reser, Jared Edward

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA