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The development of electrodermal fear conditioning from ages 3 to 8 years and relationships with antisocial behavior at age 8 years

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The development of electrodermal fear conditioning from ages 3 to 8 years and relationships with antisocial behavior at age 8 years

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THE DEVELOPMENT OF ELECTRODERMAL FEAR CONDITIONING FROM
AGES 3 TO 8 YEARS AND RELATIONSHIPS WITH ANTISOCIAL BEHA VIOR
A T AGE 8 YEARS

by

Yu Gao

_____________________

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

May 2008

Copyright 2008 Yu Gao

ii
TABLE OF CONTENTS
List of Tables iii
List of Figures iv
Abstract v
Chapter One: Introduction 1
1. Fear Conditioning 1
2. Development of Fear Conditioning 6
3. Factors Associated with Development of Fear Conditioning 7
4. Fear Conditioning and Antisocial Behavior 8
5. Latent Growth Curve Modeling and Latent Class Growth Analyses 14
6. Aims 15
Chapter Two: Method 17
1. Participants 17
2. Experiment Design 17
3. Skin Conductance Recording and Data Reduction 19
4. Antisocial Behavior Assessment 21
5. Social Adversity Assessment 21
6. Statistical Analyses 23
Chapter Three: Results 28
1. Development of Fear Conditioning 28
2. Role of Arousal, Orienting, and Unconditional Responses
in Explaining Individual Differences in Conditionability 33
3. Fear Conditioning and Antisocial Behavior 34
Chapter Four: Discussion 40
1. Development of Fear Conditioning in Childhood 40
2. Arousal, Orienting, Unconditional Responses, and Conditioning 44
3. Fear Conditioning and Antisocial Behavior 45
4. Employing Electrodermal Conditioning in Children 50
5. Limitations and Strengths 51
6. Future Directions 53
7. Conclusions 56
References 58
Appendix A: Auditory Stimuli Type and Intensity 68

iii
List of Tables
Table 1. Observed Descriptive Statistics for Average Classical
Conditioning, FIR (First Interval Conditioned
Response), SIR (Second Interval Conditioned
Response), and TIR (Third Interval Conditioned
Response) from Ages 3 to 8 Years 28

Table 2. Numerical Results from Latent Growth Curve Models
Fitted to Average Conditioning, FIR, SIR and TIR from
Ages 3 to 8 Years (N = 200) 31

Table 3. Correlations between Average Electrodermal Fear
Conditioning and OR, UCR, and SCL from Ages 3 to 6
(upper half) and Linear Regression Results (lower half) 34

Table 4. Frequencies of Aggressive, Nonaggressive, Total
Antisocial Behavior, and Social Adversity Measures (N
= 143) 35

Table 5. Correlation Coefficients between Average Classical
Conditioning at Each Age, Antisocial Behavior, and
Social Adversity 36

Table 6. Fit of the Latent Class Growth Analyses Models with
Different Classes 36

Table 7. Multivariate and Univariate Analyses of Variance for
Antisocial Measures 38

Table 8. Means and Standard Deviations for Aggressive,
Nonaggressive, and Total ASB in Good and Poor
Conditioners 38

Table 9. Means and Standard Deviations for Measures of
Antisocial Behavior as a Function of Gender, Social
Adversity, and Conditioning Group 39

iv
List of Figures

Figure 1. Schematic diagram illustrating stimuli durations,
latency windows (in seconds from the onset of the CS),
and the three conditioning components. FIR = first
interval response, SIR = second interval response, TIR
= third interval response. 19

Figure 2. Predicted developmental profiles of average classical
conditioning (CC) and the three components of
conditioning (FIR, SIR, and TIR) from ages 3 to 8. The
conditioned response is the square root of the difference
between CS+ and CS-. 30

Figure 3. Predicted developmental profiles of classical fear
conditioning for Class 1 (good conditioners) and Class
2 (poor conditioners). 37

v
Abstract
Poor fear conditioning has been found in adult psychopaths and criminals, but
little is known about how fear conditioning develops early in life, how changes in
conditioning across age are related to antisocial behavior (ASB), and whether
the psychophysiology – antisocial association is moderated by gender or social
adversity. Using a differential, partial reinforcement conditioning paradigm with a
10 second inters-stimulus interval, skin conductance responses to auditory
aversive tones were recorded from 200 male and female Mauritian children at
five occasions at ages 3, 4, 5, 6, and 8 years. Aggressive and nonaggressive ASB
data were assessed at age 8 by teachers. Measures of social adversity were
collected from the caregiver when the children were aged 3 years. Latent growth
curve modeling was used to map the development of fear conditioning
throughout childhood and to define homogenous clusters of children based on
their developmental fear conditioning trajectories over time. Group differences in
ASB and the moderating effects of gender and social adversity were then
examined. Results from skin conductance tests showed that fear conditioning
increases from ages 3 to 8 years. The three components of fear conditioning (first
interval response, second interval response, and third interval response) exhibited
somewhat different developmental trajectories. Two latent classes were identified
based on average conditioning: good conditioners (N =32, 47% male) and poor
conditioners (N = 111, 52% male). Poor conditioners scored significantly higher
on the total antisocial scale and both aggressive and nonaggressive antisocial
subscales. Results are consistent with the hypothesis that the relationship between
poor fear conditioning and ASB occurs early in childhood and demonstrate that

vi
this association applies to all children across gender and social backgrounds. It is
suggested that reduced electrodermal fear conditioning may represent an early
risk factor for future antisocial behavior, and that it has potentially important
implications for early identification and intervention attempts which can target
these high-risk children.

1
Chapter One: Introduction
For decades fear conditioning has been a critically important construct in the
fields of cognitive science, clinical science, and neuroscience. Fear conditioning
has been applied to a number of clinical conditions, the most salient being anxiety,
phobias, and antisocial behavior (ASB; Field, 2006; Raine, Venables, & Williams,
1996). Despite its potential importance, there has been surprisingly little research
on the development of fear conditioning in normal children.
Though deficits in fear conditioning have been found in adult psychopaths
and criminals, little is known about how changes in conditioning across age are
related to ASB and whether the psychophysiology-antisocial association is
moderated by gender or social adversity. A better understanding of the
neurobiological correlates of antisocial and aggressive behavior will prove
essential for the prevention and treatment of persistent ASB in children and
adults.
1. Fear Conditioning
Fear conditioning is a form of Pavlovian conditioning through which
individuals learn the hedonic value of previously neutral stimuli via a process of
association. In a typical classical conditioning paradigm, the neutral stimulus
initially elicits no emotional reaction, but after repeated pairings with the
unconditioned stimulus (UCS), the neutral stimulus becomes a conditioned
stimulus (CS) and obtains the potential to elicit a conditioned response (CR).
Normally, after a number of trials the, the CS signals the UCS onset and induces
emotions associated with the anticipation of the aversive UCS. In this context,

2
conditioning may indicate an abnormality when emotional reactivity to a CS is
reduced or absent in the presence of a CS/US contingency.
1.1. Electrodermal Fear Conditioning
Fear conditioning in humans has been most typically studied using
autonomic measures, including eye-blink conditioning, cardiovascular
conditioning, and electrodermal conditioning (Boucsein, 1992; Ingram &
Fitzgerald, 1974; Woodruff-Pak, Jaeger, Gorman, & Wesnes, 1999). Among the
above measures, electrodermal reactivity or skin conductance responses (SCRs)
has been the most frequently used psychophysiological variable (Boucsein, 1992).
Furthermore, in functional magnetic resonance imaging (fMRI) studies of
classical conditioning SCRs have been used to confirm the presence of
conditioning (Büchel & Dolan, 2000).
When classical conditioning is assessed using SCRs, a neutral, nonaversive
tone (CS) is presented to the subject, followed a few seconds later by either a
loud tone or an electric shock (UCS). The key measure is the size of the SCRs
elicited by the CS after a number of CS-UCS pairings.
There have been two basic paradigms used for the conditioning assessment.
In the first paradigm, researchers have used the average SCRs from a single,
repeatedly-presented CS during acquisition as an index of conditioning. However,
the use of a single stimulus has several important weaknesses, including the
inability to assess the specificity of the fear response to the CS. In other words,
responses to the CS could reflect a generalized increase in reactivity to the
“threatening” context and/or to any stimulus presented within that context. A
second approach for the assessment of conditioning has been to measure the

3
difference between the response to a cue that signals the UCS (designated as the
CS+) and the response to a second cue that is not paired with the UCS
(designated as the CS-). This method assesses the differential learning of “fear”
and “safety” to the CS+ and CS-, respectively, where the magnitude of
conditioning may depend as much on the acquisition of a “safety” response in
relation to the CS- as the learning of “fear” responses to the CS+. As suggested
by Lissek et al. (2005), this differential aversive conditioning paradigm has the
advantage of controlling for nonspecific reactivity, as would be reflected by large
SCRs to both CS+ and CS-. As such, the second approach was employed in this
thesis for the assessment of conditioning.
Three separate components have traditionally been delineated in
electrodermal fear conditioning in humans. These three components have usually
been referred to as the first interval response (FIR), the second interval response
(SIR), and the third interval response (TIR; Prokasy & Ebel, 1967, see below). In
order to allow sufficient time to assess separable SCRs, the CS-UCS interval has
to be lengthened with 4 seconds argued to be the minimum (Bitterman &
Holtzman, 1952; Prokasy & Ebel, 1967). A 10 second ISI was used in the current
study.
In this information processing model of the orienting response (OR), Öhman
(1979) provides a theoretical interpretation of the origin of different components.
In this model, the properties of the UCS and the temporal relations between the
CS and UCS are stored in short-term memory. The OR is elicited when the
signaling CS is identified, which is manifested by the occurrence of the FIR.
Afterwards, the UCS occurrence is checked continuously, as indicated by the

4
appearance of the SIR. If the UCS does not occur during the expected time
window, a TIR may be elicited.
In this context, the three components are believed to be independent
responses. The conditioned FIR and TIR are thought to be related to the process
of orienting (Öhman, 1971, 1979) and have been argued to occur without the
awareness of the CS-UCS contingency (Boucsein, 1992). In contrast, cognitive
awareness of the CS-UCS association has been argued to be essential for the
elicitation of the SIR (Dawson & Schell, 1987; Grings, 1969). Dawson and his
colleagues (Dawson, Catania, Schell, & Grings, 1979; Dawson & Schell, 1987;
Dawson, Schell, Beers, & Kelly, 1982) have conducted several studies and
concluded that classical conditioning is due to complex information processing,
which includes CS perception and recognition, memory of the UCS occurrence
and encoding, and the storage of this information at the end of each trial. In this
context, the autonomic responses in the classical conditioning paradigm can be
regarded as peripheral correlates of the outlined central cognitive processes.
1.2. Neural Circuitry Underlying Fear Conditioning
In terms of the functional neuroanatomy of classical conditioning, one key
structure consists of the amygdala. Animal (Davis, 1992), human lesion (LaBar,
LeDoux, Spencer, & Phelps, 1995), and fMRI studies (Critchley, Mathias, &
Dolan, 2002; Knight, Nguyen, & Bandettini, 2005; Pine et al., 2001) have all
implicated this structure in classical fear conditioning. In a synthesis of the
animal literature, Knight et al. (2005) conclude that sensory input projects to the
lateral amygdala where synaptic plasticity takes place and that projections from
the amygdala’s central nucleus to the brain stem regulate the learning of

5
autonomic fear responses (Davis, 2000; LeDoux, 2000a). While there appears to
be no functional imaging research on amygdala activation in children, adult
research has shown significant amygdala activation during the development of
the conditioned fear response (Büchel, Morris, Dolan, & Friston, 1998; Cheng,
Knight, Stein, & Smith, 2003; Knight, Smith, Cheng, Stein, & Helmstetter, 2004;
LaBar, Gatenby, Gore, LeDoux, & Phelps, 1998).
It has been found that different amygdala nuclei or pathways are involved in
different forms of conditioned fear behavior. The acquisition of fear in the simple
conditioning paradigm, for example, is thought to be mediated primarily by a
sub-cortical amygdala system involving direct and different sensory projections
from thalamic nuclei and efferent connections to various brainstem and
hypothalamic nuclei that mediate the behavioral and physiological fear responses
(LeDoux, 1998; LeDoux, 2000b). In contrast, in more complex differential fear
conditioning paradigms in which participants acquire fear to only one of several
different stimuli (e.g., CS+), the acquisition of fear has been shown to rely on
cortical modulation of the sub-cortical amygdala system (Jarrell, Gentile,
Romanski, McCabe, & Schneiderman, 1987; Morris, Friston, & Dolan, 1997).
This cortical modulation is thought to be important for the regulation of fear
responses according to the specific conditioning contingencies (i.e., responding to
the CS+ but not to the CS-). Taken together, research indicates that the amygdala
is one of the most important structures in regulating autonomic fear conditioning
and that cortical modulation is involved in this psychological process.
Accordingly, conditioning ability may represent a peripheral measure of
amygdala function.

6
2. Development of Fear Conditioning
The first issue addressed in this thesis concerns how fear conditioning
develops across age in humans. Babies as young as three months show skin
potential aversive conditioning (Ingram & Fitzgerald, 1974), but to date, there
have been extremely few studies on the development of any autonomic measure
of conditioning in either infants or children. In the only developmental autonomic
cross-sectional study that appears to have been conducted to date, Block, Sersen,
and Wortis (1970), using a 95 dB UCS and a 600 Hz difference between CS+ and
CS-, found that 6-11 year old children (N = 38) showed evidence for cardiac
discriminant conditioning. In contrast, 4-6 year olds (N = 17) showed only partial
evidence for conditioning, while 2-4 year olds (N = 22) showed no conditioning;
degree of conditioning showed a linear increase with age. Conditioning in the
latter two groups was statistically non-significant, but this may be due to the
smaller samples sizes in these groups; the 6-11 year olds showed a 36% increase
in conditioning compared to the 2-4 year olds.
Furthermore, there appears to be no developmental studies of the differential
trajectories of the three components of electrodermal fear conditioning (FIR, SIR,
and TIR). This developmental issue is of potential importance because
understanding how fear conditioning develops in normal children can provide a
foundation to understanding abnormal development in conditionability, which
may in turn, aid in the understanding of the etiology of later childhood and early
adolescent clinical disorders, such as ASB (Field, 2006; Raine et al., 1996). This
important issue was addressed in the second part of this thesis.

7
3. Factors Associated with Development of Fear Conditioning
Another issue concerns the role of individual differences in arousal, OR, and
defensive responding (i.e. the unconditional response, UCR) in contributing to
individual differences in fear conditioning. Sokolov (1963) theorized that OR
facilitates the formation of the conditioned response and was indeed a
prerequisite for conditioning. Based on Sokolov’s theory, adult research has
shown that individual differences in OR correlate with individual differences in
conditioning (Ingram, 1978), and studies have also demonstrated this to be true in
infants (Ingram & Fitzgerald, 1974). In contrast, there appears to be little research
on this issue in young children. With respect to the UCR, it is known that
experimentally increasing the strength of the UCS increases conditionability
(Davey, 1992; Hosoba, Iwanaga, & Seiwa, 2001; Prokasy & Kumpfer, 1973).
Individual differences exist in the degree to which children respond to the UCS,
with some giving a greater UCR than others, at least in part reflecting the
psychological impact of the UCS on the child. It is unknown to what extent these
individual differences within a group are associated with individual difference in
the degree of conditioning in that group. Similarly, experimental manipulations of
arousal in adults have shown that increased arousal causes increased
conditionability (Critchley et al., 2002). Taken together, research suggests that
increased arousal, OR, and UCR may be associated with increased classical
conditioning across ages.

8
4. Fear Conditioning and Antisocial Behavior
4.1. Fear Conditioning and Antisocial Behavior
In several theories of crime, reduced classical fear conditioning has been a
key concept (Eysenck, 1977; Lykken, 1957; Trasler, 1987). Based on Eysenck’s
theory (1977), individuals develop a conscience that deters antisocial responding
through successful classical conditioning. The greater the individual’s capability
to develop classical fear conditioning, the greater the conscience development,
and the less likely will be the probability of becoming antisocial.
As stated above, classical conditioning is an associative learning process
whereby a neutral stimulus (CS) takes on the properties of an unconditioned
stimulus (UCS) and comes to elicit a conditioned response (CR) following
repeated pairings of the CS with the UCS. In the cases of children, punishment
(UCS) naturally causes feelings of distress, an UCR for most children. Whenever
the child is caught stealing, he/she is presumably punished, therefore leading to
feelings of distress. The repeated pairing of stealing with punishment would
eventually cause the child to feel distress when thinking of stealing things. It is
the association between stealing and feelings of distress caused by punishment
that will deter most children from stealing. However, if a child shows deficits in
conditioning, the association between stealing and feelings of distress is weak or
absent. Therefore he/she will not be able to inhibit the act of stealing, because the
consequences of engaging in that act are not salient to the child.
To test Eysenck’s theory of poor classical conditioning in criminals and
antisocials, many studies have been conducted in adult populations in relation to
electrodermal, cardiovascular, and eye-blink fear conditioning. In an earlier

9
systematic review by Hare (1978), poorer classical conditioning and
quasi-conditioning have been consistently found in psychopaths, criminals,
delinquents, and antisocials as compared to control groups in 12 out of 14 studies.
Specifically, antisocials compared to controls have been found to exhibit lower
electrodermal response amplitude elicited by the CS after a number of CS-UCS
pairings. In a later review by Raine (1993) on six studies conducted from 1978 to
1993, the author found some evidence indicating significantly poorer SC
conditionability in antisocials.
However, only two prospective studies have been conducted to test
Eysenck’s theory. In an earlier study performed by Loeb and Mednick (1977),
electrodermal classical conditioning data were collected from 60 male and 44
female subjects in 1962. The UCS was a 4.5 s 96dB irritating noise and appeared
0.5 s after the CS onset, which was a 1k Hz, 54dB tone. There were 14 partial
reinforcement trials (9 CS-UCS parings and 5 CS alone), and the inter-trial
interval (ITI) varied from 14 to 77 seconds. Ten years later, seven of the male
subjects had become delinquent based on police reports and were compared with
seven non-delinquent counterparts from the original sample on their SCRs. It was
found that later delinquents showed smaller SCR amplitudes and a reduced
degree of conditioning. Therefore, the authors concluded that deficits in
autonomic reactivity, as measured by SCRs, may have contributed to the
development of an antisocial personality in delinquents. Specifically, this
prospective study has shown that the deficits in electrodermal measures may be
one of the possible factors that might have predisposed delinquents to antisocial
acts. However, as noted above, the use of a single stimulus conditioning paradigm

10
in this study made it hard to tell whether or not the increased SCRs reflects a
generalized increase in reactivity to the “threatening” context and/or to any
stimulus presented within that context.
In a study by Raine et al. (1996), individuals classified as antisocial during
adolescence, but who had desisted from engaging in criminal activity in
adulthood, showed enhanced conditioning relative to those classified as antisocial
during adolescence and convicted of an offence in the intervening period. The
authors argued that this suggests that intact or superior conditioning ability may
act as a psychophysiological protective factor from ASB development.
However, little is known about the temporal characteristics of the autonomic
fear conditioning dysfunction observed in these individuals. Specifically, it would
be instructive to understand whether or not impairment of the autonomic
reactivity to fear precedes the onset of ASB, or whether it is a consequence of
engaging in such behavior. Longitudinal studies, in fact, would allow further
identification of neurobiological markers that would aid prediction of life
outcomes and enable us to implement early interventions that are effective in
altering the life-course of a persistent offender (van Goozen, Fairchild, Snoek, &
Harold, 2007). The poor fear conditioning – antisocial relationship may reflect a
stable, trait-like risk factor for later antisocial behavior. Alternatively, it may
reflect a neurodevelopmental abnormality whereby children who later become
antisocial show normal early fear conditioning, but fail developmentally to
manifest the age increase in conditioning found in normal children. To date, there
have been no developmental studies on this issue. In the current study, these two
competing hypotheses were tested using a longitudinal design with equal

11
numbers of male and female children from a wide variety of social environments.
Moreover, the possibility of the prospective nature of the conditioning – ASB
association was investigated.
4.2. Gender Differences
An important question concerns whether the same abnormal developmental
trajectory of fear conditioning is present in both male and female antisocials. In
influential reviews, Moffitt and Caspi (2001) and Moffitt, Caspi, Rutter, and Silva
(2001) argue that the causes of life-course persistent ASB in females and males
are essentially the same. Specifically, genetic studies on the development of ASB
have shown that the magnitudes of genetic and environmental influences on
adolescent and adult antisocial behavior are approximately equal across the sexes
(Baker, Mack, Moffitt, & Mednick, 1989; Jacobson, Prescott, & Kendler, 2002).
However, other studies have argued that there is a significant gender
difference. For example, in contrast to the perspective of Moffitt and colleagues,
Silverthorn, Frick, and Reynolds (2001) found that girls in a secure detention
facility, in contrast to boys, had an absence of childhood-onset ASB and are
universally adolescent-onset. Furthermore, several genetic studies have indicated
that there is a gender difference in the gene-environment interactions predicting
ASB. For example, an adoption study by Langbehn, Cadoret, Yates, Troughton,
and Stewart (1998) has found that conduct disorder among adopted males was
predicted by adoptive family environment alone, while biological background
and gene-environment interactions predicted conduct disorder among females. In
the current study, given the equal numbers of male and female participants, the

12
gender differences in the association between deficits in fear conditioning and
ASB were explored.
4.3. Biosocial Interactions
In a review by Raine (1993), relevant studies conducted after 1978 were
assessed. The author found that all six studies reviewed showed some evidence
indicating significantly poorer electrodermal conditioning in antisocials. However,
it is important to notice that in some studies, poor conditioning was only found in
certain subgroup of criminals. For example, Hemming (1981) observed less
discriminant conditioning among criminals from relatively good social
backgrounds. Similarly, Raine and Venables (1981) found poor conditioning
specifically in antisocial children from higher social class, but not in those from
lower social class. Additionally, Raine and Venables (1981) found that antisocials
from lower social classes showed relatively good conditioning. According to
Eysenck (1977), children who are highly conditionable and who have antisocial
parents will become “antisocialized”, whereas children who condition poorly
would avoid becoming antisocial in such an aversive social environment. In
contrast, in a benign home environment (i.e., high socioeconomic status),
children who are highly conditionable will be socialized successfully, whereas
children who condition poorly will fail to develop conscience and become
antisocial.
Evidence for the social environment interacting with biological factors in
predicting ASB has been demonstrated in a few genetic studies. For example,
studies have found that up to 50% of variance in ASB can be accounted by
genetic effects (see review by Miles & Carey, 1997) and genetic influences on

13
ASB were more important in individuals in socioeconomically more advantaged
environments (Tuvblad, Grann, & Lichtenstein, 2006). In parallel, a functional
polymorphism in the monoamine oxidase A (MAOA) gene has been found to
moderate the effects of a risk environment on the development of ASB (Caspi et
al., 2002; Foley et al., 2004; Kim-Cohen et al., 2006). The meta-analysis
conducted by Kim-Cohen and colleagues (2006) has indicated that the association
between maltreatment and mental health problems, such as ASB, is significantly
stronger in the group of males with the genotype conferring low vs. high MAOA
activity. In a recent study by Jokela, Lehtimäki, and Keltikangas-Järvinen (2007)
on a similar but somewhat different behavioral construct, the T allele of the
T102C polymorphism of the serotonin receptor 2A gene was associated with a
low level of the adult temperament trait of harm avoidance, which indicates
disinhibited and fearless behavior and hyporeactivity to aversive stimuli; this
association was most pronounced among those with high parental socioeconomic
status (SES).
In a review by Raine (2002) on biosocial studies of ASB, the author noted
that there are two main themes in the relevant studies. Specifically, when social
and antisocial variables are grouping variables and biological functioning is the
outcome, then the social variable invariably moderates the antisocial – biology
relationship such that these relationships are strongest in those from benign home
backgrounds. In contrast, when biological functioning and social factors are
grouping variables and antisocial measures are outcomes, social factors may
sometimes moderate the antisocial – biology association. In this thesis, it was

14
hypothesized that the deficits in autonomic fear conditioning in antisocials would
be found only in children from social environments with a low level of adversity.
5. Latent Growth Curve Modeling and Latent Class Growth Analyses
Whereas the traditional analysis of variance (ANOV A) assumes homogeneity
of the underlying covariance patterns for the repeated measurements, the
structural equation methodology offers an alternative strategy, the latent growth
curve models (LGC). These models describe not only a single individual’s
developmental trajectory, but also capture individual differences in the intercept
and slopes of those trajectories. LGC has been established as one of the effective
modeling techniques and its objective is to capture information about
interindividual differences in intraindividual change over time (Nesselroade,
1991).
The main foci in this thesis have been (1) to model the longitudinal
developmental trajectories of individuals to understand how electrodermal fear
conditioning develops over time in childhood, (2) to explore distinctive
subpopulations each with different trajectory, and (3) to relate development
patterns of conditioning to later ASB. In a typical sample of individual growth
trajectories, conventional LGC approaches map the general developmental
changes in a group, giving a single average growth estimate (i.e., intercept and
slope), a single estimation of variance of the growth parameters, and assuming a
uniform influence of covariates on the variance and growth parameters.
However, it is possible that there may be a subset of individuals whose
growth trajectories are significantly different from the overall estimate, and there
may be clinical implications underlying this differentiation. A latent class growth

15
analysis (LCGA) was therefore used in this thesis in order to identify the
subgroups and fully capture the psychophysiology - antisociality association.
LCGA allows for differences in growth parameters across unobserved
subpopulations and assumes that all individual growth trajectories within a class
are homogeneous. In this context, separate growth models for each latent class,
each with its unique estimates of variances and covariate influences, are
generated. This class assignment information could then be used to test mean
differences in ASB measures across the classes and the possible moderating
effects of gender and social adversity using multivariate analysis of variance
(MANOV A).
6. Aims
Five specific aims were addressed in this thesis. The first aim was to explore
the developmental trajectory of electrodermal fear conditioning in the same
children from ages 3 to 8 years. A second aim was to examine whether arousal,
OR, and UCR are associated with development of conditioning. A third aim was
to investigate whether the reduced fear conditioning relates to childhood
aggression, which has been indicated as a reliable risk factor for later ASB. A
fourth aim was to investigate whether the above association is moderated by
social adversity. A fifth aim was to explore the gender differences in any
psychophysiology - antisocial association in early childhood.
Specifically, the following hypotheses were tested in this thesis:
1. Electrodermal fear conditioning in a differential aversive conditioning
paradigm would generally increase from ages 3 to 8 years. Specifically, the
developmental profile for conditioning was hypothesized to follow the profile

16
previously established for OR (Gao, Raine, Dawson, Venables, & Mednick,
2007).
2. Individuals differences in arousal, OR, and UCR were associated with
individual differences in conditioning, with higher conditioning in those with
increased arousal, OR, and UCR.
3. Distinctive subgroups would be identified based on the profiles of fear
conditioning development in early childhood. Specifically, poor conditioners
were expected to exhibit lower initial levels and slower rate of change in the
growth curve of electrodermal fear conditioning from ages 3 to 8 years,
indicating a developmental delay in conditioning. Compared to good
conditioners, poor conditioners would score higher on aggressive and
nonaggressive antisocial behavior measures at age 8.
4. There would be a biosocial interaction such that the association between poor
electrodermal fear conditioning and increased ASB would be stronger in
children who come from benign home environments.
Finally, given the equal numbers of male and female children in the current
study, the gender differences in the psychophysiology - ASB association would
be explored in the current thesis.

17
Chapter Two: Method
1. Participants
The population from which the participants were drawn consisted of 1,795
children from the country of Mauritius, a tropical island in the southwest Indian
Ocean. All children born in 1969–1970 in two towns (Quatre Bornes and Vacoas)
and were recruited at age 3 years. The ethnic makeup of the sample was as
follows: 68.7% Indian, 25.6% African, and 5.6% other (Chinese, English, or
French descent). Females made up 45.9% of the sample. Two hundred children
(100 boys and 100 girls) were selected from the original sample based on their
sex, race, and electrodermal activity. Psychophysiological measures were taken at
ages 3, 4, 5, 6, and 8 years in these 200. Informed consent was obtained from the
parents of the children. To assess whether this population of 200 was
representative of the initial population (N = 1795), comparisons were made
between those will be included in the study and the rest of the sample on
demographic measures. Participants and non-participants did not differ on sex, χ
2

(1) = 0.490, p > .488, ethnicity, χ
2
(4) = 0.465, p > .501, and social adversity, t
(1,110) = 0.730, p > .327. Specifically, original electrodermal activity did not
affect later behavior measures (p > .150), therefore it was not studied further in
the following analyses.
2. Experimental Design
The experimental procedure consisted of three sections presented without
interruption. The first section comprised 6 stimuli selected to elicit the OR; the
second was a set of 12 stimuli (9 CS+ and 3 CS-) in an SCR conditioning
paradigm; and the final section used 6 further stimuli (3 CS+ and 3 CS-) in a test

18
for extinction. In the conditioning section, the CS+ tone was reinforced by a loud
noise UCS, and a different tone served as CS- which was unreinforced. A 66%
partial-reinforcement schedule was used, with 6 of the 9 presentations of the CS+
randomly reinforced. The CS+ was a 1000 Hz, 60 dB, 12.5 sec tone with rise and
fall time of 25 msec whereas CS- was a 500 Hz, 60 dB, 12.5 sec tone with rise
and fall time of 25 msec. The UCS was an auditory stimulus of 90 dB intensity,
4.5 sec duration recorded from white noise played in a tin can with metal jangling
objects (e.g. keys) thus adding low and high frequency components. From a
human subject standpoint, this stimulus was used in order to produce as noxious a
sound as possible without causing unnecessary upset to the child and avoiding the
use of a higher intensity sound which might cause physiological damage. The
onset of the UCS was 10 s after the onset of the CS+. The ISI was 10 s (onset to
onset), with a randomized ITI (inter-trial interval) of 38 s (range 34-42 s). Three
CS+/CS- pairs (trial 9/10, 13/14, and 16/18) among the 12 trials were used in the
current study. A schematic diagram illustrating the stimuli durations and SCR
onset latency windows is provided in Figure 1. Further details of the complete
standard auditory stimuli used are given in Venables (1978) and Raine et al.
(2002).

19

Figure 1. Schematic diagram illustrating stimuli durations, latency windows (in seconds
from the onset of the CS), and the three conditioning components. FIR = first interval
response, SIR = second interval response, TIR = third interval response.

For the first four of the five ages (3, 4, 5, and 6), data were also available on
electrodermal measures that all preceded the time of the onset of conditioning
trials: (1) initial skin conductance level (SCL) recorded prior to the onset of the
first tone, (2) skin conductance orienting responses (SCORs) averaged across the
initial six orienting trials, and (3) the UCR to the very first UCS (see Gao et al.
(2007) for full details).
3. Skin Conductance Recording and Data Reduction
SC data were collected using a Grass Type 79 polygraph with a constant
voltage system, Beckman miniature silver/silver chloride electrodes, and an
electrolyte consisting of 0.5% KCl in 2% agar–agar (for further details, see Raine,

20
Venables, Dalais, Mellingen, Reynolds, & Mednick, 2001; Venables, 1978). SC
was recorded from the medial phalanges of the index and middle fingers of the
left hand.
Three types of classically conditioned SCRs were scored: (a) FIR, which was
elicited by the CS, starting within 1 to 3 s after CS onset, (b) SIR which was
elicited prior to the UCS, with latency between 6 and 10.5 s after CS onset, and (c)
TIR starting at 11 and ending at 13 s after CS onset (see Figure 1). Responses
greater than 0.05 μS within the above latency windows were scored as SCRs. To
create conditioned SCRs, difference scores were calculated in which responses to
CS- were subtracted from those to the CS+ for the FIR, SIR, and TIR,
respectively (Dawson et al., 1982; Ingram & Fitzgerald, 1974). According to this
measure, difference scores of zero reflect no differential learning, and difference
scores above zero reflect greater responses to the CS+ relative to the CS-.
Conditioned SCRs were collapsed across the three CS+/CS- conditioning pairs
for each of the three components (FIR, SIR, and TIR). In addition, a global
measure of conditioning was formed by averaging the above three components.
All subjects were tested in a sound-insulated cubicle with a controlled
temperature of 30°C. A dehumidifier was used to minimize fluctuations in
humidity. The child was seated on a chair at each phase except at age 3, when the
subject was seated on the mother’s lap throughout testing. It was established by
asking the mothers at age 3 that the mother could not hear any of the stimuli
presented to the child.

21
4. Antisocial Behavior Assessment
ASB was assessed at age 8 years by teachers using the Children’s Behavior
Questionnaire (CBQ; Rutter, 1967; Venables, Fletcher, Dalais, Mitchell,
Schulsinger, & Mednick, 1983). The eight-item Antisocial Behavior scale from
the CBQ consists of two subscales: aggressive and nonaggressive antisocial
subscales. The aggressive subscale had an alpha value of 0.79 and was comprised
of the following four items: “often destroys own or others belongs,” “frequently
fights with other children,” “irritable and quick to ‘fly off the handle’,” and
“bullies other children.” The nonaggressive subscale had an alpha value of 0.67
and was comprised of the following four items: “truants from school,” “is often
disobedient,” “often tells lies,” and “has stolen things on one or more occasions.”
The antisocial scores were summery of the aggressive and nonaggressive
subscales and had an alpha value of 0.88. High scores on these measures
indicated relatively more ASB problem at age 8. Complete ASB measures were
collected for 143 children only, therefore in this thesis the psychophysiology –
ASB association and potential moderating effects of social adversity and gender
were tested among this subgroup. Analyses were conducted to assure that this
subgroup was not different from the 200 in terms of the gender, ethnicity, and
social adversity. Further details of the ASB assessment used are given in Raine,
Yaralian, Reynolds, Venables, and Mednick (2002).
5. Social Adversity Assessment
The age 3 years social adversity index was formed based on nine variables
collected by social workers who visited the homes of the children at age 3 years
(Raine, Yaralian, et al., 2002). The nine variables were created along lines similar

22
to Rutter’s (1978) adversity index (see Raine, Yaralian, et al., 2002 for details).
One point was added for adverse factors for each of the following variables:
father uneducated (no schooling, 30%), mother uneducated (no schooling, 29.4%),
semiskilled or unskilled occupation (occupational status 3 or less on an 8-point
occupational scale: 0, unemployed; 4, factor worker; cook; 8, academic, head of
large business; 55.5%), teenage mother (age 19 years or younger when child was
born, 14.2%), single-parent status (2.1%), separation from parents (orphaned or
raised by substitute mother, 0.9%), large family size (sibling order fifth or higher
by age 3 years, 30.0%), poor health of mother (coded 1 on a 3-point scale: 3,
above average; 2, average; 1, below average; 3.3%), and overcrowded home (five
or more family members per house room, 28.8%). Scores ranged from 0 to 5
(Median = 2). High scores on this index indicated relatively high adversity at age
3. No gender or ethnicity differences were found on social adversity, χ
2
(5) =
3.447, p = .631 for gender, and χ
2
(15) = 16.173, p = .371 for ethnicity.
Following the procedures of prior publications on this sample (Raine, Reynolds,
Venables, & Mednick, 2002; Raine, Reynolds, Venables, Mednick, & Farrington,
1998), a median split on the social adversity index was used and all individuals
were categorized into two subgroups: high (social adversity ≥ 2, 51.5% of total
sample, 33 boys and 41 girls) versus low (social adversity < 2, 40 boys and 29
girls) social adversity.

23
6. Statistical Analyses
6.1. Development of Fear Conditioning
6.1.1. ANOV A
To elucidate the development of electrodermal fear conditioning between
ages 3 to 8 years, repeated measures analysis of variance (ANOV A) was first
carried out to test for conditioning for the FIR, SIR, TIR, and average
conditioning, respectively. In each of these analyses, Age (3, 4, 5, 6, and 8), Trial
(trial 1, 2, and 3), and Stimulus (CS+ vs. CS-) were within-subject factors and
Sex was be the between-subjects factor. Main effects and interaction terms were
evaluated using Wilk’s lambda. Significant discrimination in classical
conditioning would be demonstrated by the significant main effect of stimulus in
the direction that the SCRs to the CS+ were larger than those to the CS-.
6.1.2. Latent Growth Curve Modeling
LGC was then employed to analyze the developmental trajectory of
conditioning using this longitudinal data. First, a set of unconditional growth
analyses were conducted in which a linear individual change trajectory was
posited for overall electrodermal conditioning and the three components (FIR,
SIR and TIR), respectively.
In a classical growth curve model, the score of each individual (Y[t]) is
composed of the following components:
Y[t] = y
0,n
+ y
s,n
× Age[t]
n
+ e[t]
n

(1) the y
0,n
(intercepts), which are latent scores representing the individual’s
initial level, (2) the y
s,n
(slopes), which are latent scores representing the
individual’s linear change over time, (3) the Age[t]
n
, a set of basis coefficients

24
that define the timing or shape of the trajectory over time, and (4) the e[t]
n
, which
are unobserved but independent errors of measurements. On the next level, the
latent scores are decomposed into parameters representing a mean and a variance
term:
y
0,n
= μ
0
+ d
0,n
and y
s,n
= μ
s
+ d
s,n

where μ
0
and μ
s
represent the fixed group means for intercept and slope, d
0,n

and d
s,n
imply individual deviations around these means, and a set of random
variance and covariance terms ( σ
0
2
, σ
s

2
, σ
0s
) are used to describe the distribution
of individual deviations. Usually only one random error variance (σ
e
2
) is
assumed.
Three models were fitted respectively for overall conditioning and each
conditioning component to determine the shape of trajectories. The first model
was a no-growth model (M
0
), which assumed no slope component and only three
parameters were estimated: an initial level mean ( μ
0
), an initial variance ( σ
0

2
),
and an error variance ( σ
e

2
). Second, a linear growth model (M
1
) was fitted, in
which a linear pattern of change over time was assumed and a fixed basis
coefficient was formed by taking A[t] = [(Age[t] − 3)/5], or fixed values of A[t] =
[0, .2, .4, .6, 1]. This linear scaling was chosen to permit a practical interpretation
of the slope parameters in terms of a per-5-year change. Three more parameters
were estimated: a slope mean ( μ
s
), a variance ( σ
s

2
), and a correlation ( ρ
0s
). The
third model was a latent growth model (M
2
), in which some of the loadings, A[t],
were free to vary. In the current study, A[3] = 0 and A[8] = 1 were fixed, but the
three other coefficients were estimated from the data.

25
The likelihood ratio ( χ
2
) and root mean square error approximation (RMSEA)
were used to evaluate the fit of each model. An RMSEA value of .05 or smaller
suggests that the absolute magnitude of the discrepancies between the models and
the data is small (i. e., the model is a good fit, Hu & Bentler, 1998). Values in the
range of .05 to .08 indicate a fair fit, .08 to .10 a moderate and values above .10 a
poor fit (MacCallum, Browne, & Sugawara, 1996).
6.2. Arousal, Orienting, Unconditioned Responses, and Conditioning
To assess the possible role of individual differences in arousal, OR, and UCR
in explaining individual differences in conditionability, measures of these three
processes (which preceded conditioning in time) were correlated with the average
conditioning score for each of the four years where these data were available
(ages 3, 4, 5, and 6). In addition to correlational analyses, linear regression using
the stepwise method was used to assess the relative importance of the three
processes at each age.
6.3. Development of Conditioning, Antisocial behavior, Social adversity, and
Gender
6.3.1. Latent Class Growth Analyses
In addition to examining processes of stability and change of SC fear
conditioning over age in individuals using LGC, LCGA was further conducted to
investigate inter-individual differences and similarities in change patterns. LCGA
is a semi-parametric, group-based approach, which uses a multinomial modeling
strategy to identify a set of homogeneous clusters of individual trajectories and to
test the effects of covariates on those profiles (Nagin, 1999).

26
Three different latent class models of fear conditioning were estimated: the
first fitting only one latent profile, the next, two profile groups, and the last, three
profile groups. Each model contained initial level ( μ
0
) and slope ( μ
1
) terms. Since
the likelihood ratio comparing k-1 and k-class model does not have the usual
large scale χ
2
distribution because they are not nested (Muthén, 2004), Akaike
information criterion (AIC), Bayesian information criterion (BIC), and adjusted
BIC were used to evaluate models. The model with the smaller AIC or BIC was
accepted within model comparisons. Finally, part of this decision included the
relative size of each resulting profile so that, ideally, no one cluster held less than
approximately 5% of the total sample.
Data were analyzed using Version 2 of Mplus (Muthén & Muthén, 1998).
One of the benefits of its use is a maximum likelihood estimation procedure that
uses all observations within the data set, including those with data missing at one
or more waves. This reduces the sampling bias that occurs in approaches such as
listwise or pairwise deletion that excludes participants with incomplete data.
6.3.2. MANOVA
Once the optimal number of latent profiles was established, ASB measures
were compared between the classes using MANOVA. An N (number of
conditioning groups) × 2 (gender) × 2 (social adversity) MANOVA with total
antisocial score, aggressive, and nonaggressive antisocial scores as dependent
measures was conducted using SPSS 15.0.
Consistent with previous studies (Boucsein, 1992; Venables & Mitchell,
1996), mean magnitude (including zero responses) rather than amplitude was
analyzed in this thesis: mean magnitudes of the SCRs to each stimulus were

27
recorded at ages 3, 4, 5, 6, and 8 years. A square root transformation (using
SQRT (magnitude + 0.0001)) was used before conducting the inferential
statistical analyses to help attain normality (Venables & Christie, 1980).

28
Chapter Three: Results
1. Development of Fear Conditioning
Attrition in this 5-year longitudinal study was low, as indicated by the fact
that complete data were available on 172 (86.0%) of the 200 children originally
tested at age 3. Information about the pattern of missing data has been reported in
Gao et al. (2007). Analyses indicated no biases in drop-out of subjects (Gao et al.,
2007). Descriptive statistics for electrodermal classical conditioning and each
component from ages 3 to 8 are listed in Table 1.
Table 1
Observed Descriptive Statistics for Average Classical Conditioning, FIR (First Interval
Conditioned Response), SIR (Second Interval Conditioned Response), and TIR (Third
Interval Conditioned Response) from Ages 3 to 8 Years

Average Classical
Conditioning
FIR SIR TIR

N M SD M SD M SD M SD
Age 3
Age 4
Age 5
Age 6
Age 8
200
195
190
184
172
.067
.057
.069
.096
.100
.071
.059
.068
.081
.075
.073
.034
.073
.105
.101
.106
.071
.112
.111
.122
.060
.060
.080
.085
.088
.097
.080
102
.132
.099
.067
.076
.053
.099
.112
.096
.107
.083
.106
.128

1.1. ANOV A
ANOV A based on 172 complete cases revealed a significant main effect for
Stimulus for the FIR and TIR, respectively, F (1, 170) = 10.982, p < .01, F (1,
170) = 3.890, p < .05, indicating significant conditioning for these two
components. In contrast, a main effect of Stimulus was not observed for the SIR,
F (1, 170) = 0.339, p = .561. However, a significant Age × Stimulus interaction
was found for the SIR, F (4, 167) = 2.594, p < .05. Further analysis showed that
CS+ elicited significantly larger responses than CS- at age 8 but not other ages, t
(171) = 2.483, p = .014, indicating significant conditioning at age 8 years. No

29
other significant effects involving Stimulus were found (p > .275). A main effect
of Age was also found for FIR, SIR, and TIR, respectively, F (4, 167) = 90.471, p
< .001, F (4, 167) = 19.426, p < .001, and F (4, 167) = 33.027, p < .001, with
SCRs magnitudes increasing across ages for each component.
Similar analyses were conducted on the average classical conditioning
measure (FIR, SIR, and TIR combined). Significant main effects were observed
for Stimulus, F (1, 170) = 10.503, p = .001, and Age, F (4, 167) = 38.352, p
< .001. An Age × Stimulus interaction was found, F (4, 167) = 2.954, p = .022.
Post-hoc analyses showed that CS+ elicited significantly larger responses than
CS- at age 3, t (199) = 2.695, p = .008, and age 8, t (171) = 3.562, p < .001, but
not other ages. No other significant effects were found, p > .102.
1.2. Latent Growth Curve Modeling
1.2.1. Average Classical Conditioning
The no-growth model (M0) yielded a model likelihood of L
2
= 86. The linear
growth model (M1) resulted in a new likelihood of L
2
= 34 and showed an
improvement in fit over the baseline model (M1 vs. M0: Δ χ2 = 52 on Δdf = 4, p
< .05). The latent growth model (M2) produced an improvement in the model
likelihood (L
2
= 11) and was substantially better than both the baseline model (M2
vs. M0: Δ χ2 = 75 on Δdf = 7, p < .05) and the linear growth model (M2 vs. M1: Δ χ2
= 23 on Δdf = 3, p < .05). Further analyses showed that the basis coefficients at
ages 6 and 8 are equal. The predicted trajectory of the FIR from ages 3 to 8 is
displayed in Figure 2. Both intercept ( μ
0
= .064, SE = .004, p < .05) and slope
mean ( μ
s
= .032, SE = .006, p < .05) were significant, indicating that in general
fear conditioning was present age 3 and increased across ages (see Table 2). Sex

30
was not significantly related to intercepts ( γ
0
= .001, SE = .003, p > .05) or slope
( γ
s
= -.006, SE = .005, p > .05).

Figure 2. Predicted developmental profiles of average classical conditioning (CC) and
the three components of conditioning (FIR, SIR, and TIR) from ages 3 to 8. The
conditioned response is the square root of the difference between CS+ and CS-.

31
Table 2
Numerical Results from Latent Growth Curve Models Fitted to Average Conditioning, FIR, SIR and TIR from Ages 3 to 8 Years (N = 200)
Parameters
Average Classical
Conditioning
FIR SIR TIR
Fixed effects
Basis α [3] 1, 0 1, 0 1, 0 1, 0
Basis α [4] 1, -0.158 (0.183) 1, -0.476 (0.216) 1, 0 1, 0.866 (0.240)
Basis α [5] 1, 0.008 (0.152) 1, 0.259 (0.188) 1, 0 1, -0.596 (0.538)
Basis α [6] 1, 1 1, 1 1, 2.154 (0.566) 1, 1
Basis α [8] 1, 1 1, 1 1, 1 1, 1
Level μ
0
0.064 (0.004) 0.061 (0.006) 0.067 (0.004) 0.066 (0.008)
Slope μ
s
0.032 (0.006) 0.041 (0.007) 0.011 (0.006) 0.029 (0.011)
Random effects
Error σ
e
2

0.004 (0.001) 0.010 (0.001) 0.009 (0.001) 0.011 (0.001)
Level on Sex γ
0
0.001 (0.003) 0.007 (0.004) 0.001 (0.004) -0.006 (0.004)
Slope on Sex γ
s
-0.006 (0.005) -0.006 (0.006) -0.003 (0.005) -0.003 (0.006)
Correlation ρ
0s
0.001 (0.001) 0.001 (0.002) 0.001 (0.002) 0.001 (0.001)
Fit Indices
Num para. 14 14 14 14
Log likelihood 11 24 31 45
RMSEA .000 0.050 0.067 0.099

32

1.2.2. First Interval Response
The no-growth model (M0) yielded a model likelihood of L
2
= 2073. The
linear growth model (M1) resulted in a new likelihood of L
2
= 1606 and showed
an improvement in fit over the baseline model (M1 vs. M0: Δ χ2 = 467 on Δdf = 4, p
< .05). The latent growth model (M2) was substantially better than both the
baseline model (M2 vs. M0: Δ χ2 = 2049 on Δdf = 7, p < .05) and the linear growth
model (M2 vs. M1: Δ χ2 = 1582 on Δdf = 3, p < .05). Further analyses showed that
the basis coefficients at ages 6 and 8 were equal and the coefficient at age 3 was
significantly higher than that at age 4.
The predicted trajectory of the FIR from ages 3 to 8 is displayed in Figure 2.
Both intercept ( μ
0
= .061, SE = .006, p < .05) and slope mean ( μ
s
= .041, SE
= .007, p < .05) were significant, indicating that the FIR appeared at age 3 and
increased over time (see Table 2). Boys and girls did not differ in the intercept ( γ
0

= .007, SE = .004, p > .05) or the slope ( γ
s
= -.006, SE = .006, p > .05).
1.2.3. Second Interval Response
The linear growth model (M1) fit better than the baseline model (M1 vs. M0:
Δ χ2 = 2047 on Δdf = 4, p < .05). The latent growth model showed an
improvement in fit over the baseline model ( Δ χ2 = 2066 on Δdf = 3, p < .05), and
the linear growth model ( Δ χ2 = 18 on Δdf = 3, p < .05). Further analyses showed
that the basis coefficients at ages 3, 4, and 5 were equal, whereas SIR at ages 6
and 8 were higher than previous ages. The predicted trajectory of SIR from ages
3 to 8 are displayed in Figure 2.
Based on the final latent growth model, both intercept ( μ
0
= .067, SE = .004,
p < .05) and slope mean ( μ
s
= .011, SE = .006, p < .05) were significant from zero,

33

indicating that SIR appeared at age 3 and increased thereafter (see Table 2). Sex
effects were not found to be significant, γ
0
= .001, SE = .004, γ
s
= -.003, SE
= .005, all p > .05.
1.2.4. Third Interval Response
The linear growth model (M1) fit better than the baseline model (M1 vs. M0:
Δ χ2 = 2060 on Δdf = 4, p < .05). The latent growth model showed an
improvement in fit over the baseline model ( Δ χ2 = 2084 on Δdf = 7, p < .05) and
also the linear growth model ( Δ χ2 = 24 on Δdf = 3, p < .05). Further analyses
showed that the basis coefficient at age 6 was equal to that at age 8. See Figure 2
for the predicted trajectory of the TIR across ages.
In the final latent growth model, both intercept ( μ
0
= .066, SE = .008, p < .05)
and slope mean ( μ
s
= .029, SE = .011, p < .05) were significant from zero,
indicating that the TIR appeared at age 3 and increased thereafter (see Table 2).
No significant effects of Sex were observed, γ
0
= -.006, SE = .004, γ
s
= -.003, SE
= .006, all p > .05.
2. Role of Arousal, OR, and UCR in Explaining Individual Differences in
Conditionability
Table 3 shows the results of correlational (upper half) and regression (lower
half) analyses for ages 3, 4, 5, and 6 years. Increased arousal, orienting, and UCR
were all significantly associated with increased conditioning in each age group.
Linear regression using the stepwise method showed that the amount of variance
explained increased from age 3 (17.2%) to 6 years (28.8%), with an exponential
increase occurring from age 5 (19.5%) to age 6 (28.8%).

34

Table 3
Correlations between Average Electrodermal Fear Conditioning and OR, UCR, and SCL
from Ages 3 to 6 (upper half) and Linear Regression Results (lower half)
Age 3 Age 4 Age 5 Age 6
OR .281*** .366*** .362*** .453***
UCR .280*** .389*** .407*** .412***
SCL .375*** .282*** .302*** .438***
Total R
2
17.2% 19.3% 19.5% 28.8%
OR 3.2% n.s. 13.1% 25.4%
UCR n.s. 19.3% 4.1% n.s.
SCL 14% n.s. 2.3% 3.4%
Note. OR = orienting responses, UCR = unconditioned response, SCL = skin
conductance level.
*** p < .001.

3. Fear Conditioning and Antisocial Behavior
3.1. Descriptive Statistics
Frequency of ASB and social adversity measures are listed in Table 4. No
ethnic differences were found with respect to aggressive, nonaggressive, and total
ASB scores, χ
2
(4) < 1.529, ps > .203. Correlations between overall
electrodermal classical conditioning at each age, ASB, and social adversity
measures are shown in Table 5.

35

Table 4
Frequencies of Aggressive, Nonaggressive, Total Antisocial Behavior (ASB), and
Social Adversity Measures (N = 143)
Measures n %
Aggressive ASB
0 93 65.0
1 25 17.5
2 13 9.1
3 4 2.8
4 4 2.8
5 3 2.1
6 1 0.7
Nonaggressive ASB
0 97 67.8
1 21 14.7
2 13 9.1
3 6 4.2
4 3 2.1
5 2 1.4
7 1 0.7
ASB
0 76 53.1
1 23 16.1
2 13 9.1
3 13 9.1
4 8 5.6
5 3 2.1
6 1 0.7
8 4 2.8
10 1 0.7
11 1 0.7
Social Adversity
0 26 18.2
1 43 30.1
2 35 24.5
3 28 19.6
4 7 4.9
5 4 2.8

36

Table 5
Correlation Coefficients between Average Classical Conditioning at Each Age,
Antisocial Behavior, and Social Adversity
Average Classical Conditioning

Age 3 Age 4 Age 5 Age 6 Age 8
Social
Adv.
Agg. Nonagg.
Age 3 1
Age 4 .192* 1
Age 5 .186* .255** 1
Age 6 .095 .167* .014 1
Age 8 .100 .147 .116 .482*** 1
Social
Adv.
-.124 -.056 -.140 -.106 -.154 1
Agg. -.098 -.070 -.125 -.146 -.126 -.064 1
Nonagg. -.022 -.045 -.095 -.118 -.059 -.032 .499*** 1
ASB -.070 -.067 -.127 -.153 -.107 -.056 .868*** .864***
Note. Social Adv. = Social Adversity; Agg. = Aggressive antisocial score; Nonagg. =
Nonaggressive antisocial score; ASB = Total antisocial behavior score (aggressive and
nonaggressive).
* p < .05; ** p < .01; *** p < .001.

3.2. Latent Class Growth Analyses
Model evaluations are displayed in Table 6. In comparing model fit among
the one-, two-, and three-class models, a two-class model had the smallest AIC,
BIC, and adjusted BIC and was thus selected. For descriptive purposes the latent
classes were labeled as good conditioners (N = 39) and poor conditioners (N =
161). Predicted developmental trajectories of fear conditioning for each class are
shown in Figure 3. Class 1 members (19.5% of sample) started higher on fear
conditioning at age 3 but did not differ than the class 2 members in the rate of
change. The two classes did not differ on SCL, SCOR, and UCR, all ps > .05.
Table 6
Fit of the Latent Class Growth Analysis Models with Different Classes
Test C1 C2 C3
AIC -2323.430 -2431.309 -2429.513
BIC -2293.479 -2381.391 -2377.145
Adjusted BIC -2321.995 -2428.917 -2420.875
Note. C1 = One class; C2 = Two classes; C3 = Three classes. AIC = Akaike information
criterion; BIC = Bayesian information criterion.

37

Figure 3. Predicted developmental profiles of classical fear conditioning for Class 1
(good conditioners) and Class 2 (poor conditioners).

Among the 143 subjects with complete ASB data, 32 children were good
conditioners and 111 were poor conditioners. Good and poor conditioners did not
differ on social adversity, t(141) = -1.244, p = .216; gender, χ
2
(1) = 0.287, p
= .592; and ethnicity, χ
2
(4) = 2.967, p = .563.
3.3. MANOVA
A 2 × 2 × 2 between-subjects MANOV A was performed on three dependent
variables: antisocial total score, aggressive antisocial score, and nonaggressive
antisocial score. Independent variables were conditioning group (good vs. poor
conditioners), gender (boys vs. girls), and social adversity (high vs. low social
adversity). Results of these analyses are summarized in Table 7.

38

Table 7
Multivariate and Univariate Analyses of Variance for Antisocial Measures
ANOV A
MANOV A ASB Agg. Nonagg.
Variable F (2, 134) F (1, 135) F (1, 135) F (1, 135)
Conditioning group (C) 3.472* 6.950** 5.586* 4.514*
Gender (G) 1.172 2.286 2.053 1.300
Social Adv. (S) 0.500 1.002 0.798 0.657
C × G 0.761 1.252 0.462 1.513
C × S 0.473 0.932 0.557 0.808
G × S 0.518 0.970 0.964 0.480
C × G × S 0.314 0.350 0.052 0.613
Note. F ratios are Wilks’ λ approximation of Fs. MANOV A = multivariate analysis of
variance; ANOV A = analysis of variance; Social Adv. = Social Adversity; ASB = Total
antisocial behavior score (aggressive and nonaggressive); Agg. = Aggressive antisocial
score; Nonagg. = Nonaggressive antisocial score.
* p < .05; ** p < .01.

Total N of 200 was reduced to 143 with the deletion of cases missing scores
on antisocial measures. With the use of Wilks’ criterion, the combined dependent
variables were significantly affected by conditioning group, F (2, 134) = 3.472, p
< .05, but not by gender, F (2, 134) = 1.172, p = .313, or social adversity, F (2,
134) = 0.500, p = .608. No two- or three- way interaction was found, ps > .469.
Univariate analyses revealed that poor conditioners scored significantly
higher on total antisocial, F (1, 135) = 6.950, p < .01, aggressive, F (1, 135) =
5.586, p < .05, and nonaggressive antisocial subscales, F (1, 135) = 4.514, p < .05.
Mean scores on ASB measures of the two conditioning groups are displayed in
Table 8.
Table 8
Means and Standard Deviations for Aggressive, Nonaggressive, and Total ASB in Good
and Poor Conditioners
Good Conditioners (n = 32) Poor Conditioners (n = 111)
Measures M SD M SD
ASB 0.500 0.916 1.604 2.313
Agg. 0.250 0.508 0.829 1.354
Nonagg. 0.250 0.570 0.775 1.333
Note. ASB = Total antisocial behavior score (aggressive and nonaggressive); Agg. =
Aggressive antisocial score; Nonagg. = Nonaggressive antisocial score.

39

Finally, means and standard deviations for antisocial measures as a function
of conditioning group, gender, and social adversity group are displayed in Table
9.
Table 9
Means and Standard Deviations for Measures of Antisocial Behavior as a Function of
Gender, Social Adversity, and Conditioning Group
ASB Agg. Nonagg.

N M SD M SD M SD
Boys
Good home
Good conditioners 10 1.000 1.054 0.600 0.699 0.400 0.699
Poor conditioners 30 2.433 3.308 1.233 1.716 1.200 1.901
Bad home
Good conditioners 5 0.000 - 0.000 - 0.000 -
Poor conditioners 28 1.750 1.917 0.893 1.257 0.857 1.268
Girls
Good home
Good conditioners 9 0.667 1.118 0.222 0.441 0.444 0.726
Poor conditioners 20 0.650 1.136 0.444 0.821 0.250 0.444
Bad home
Good conditioners 8 0.000 - 0.000 - 0.000 -
Poor conditioners 33 1.303 1.811 0.667 1.267 0.636 0.994
Note. ASB = Total antisocial behavior score (aggressive and nonaggressive); Agg. =
Aggressive antisocial score; Nonagg. = Nonaggressive antisocial score.

40

Chapter Four: Discussion
In this longitudinal study, it was observed that: (1) fear conditioning
increases with age from ages 3 to 8 years, (2) somewhat different growth curves
were observed for the three components of conditioned fear response, (3) for
average conditioning and all three conditioning components, there was a
noticeable increase from age 5 to 6, (4) individual differences in arousal, OR, and
UCR were correlated with individual differences in conditioning at each age, with
a substantial increase in the influence of these three processes from age 5 to 6, (5)
poor conditioners scored higher on aggressive, nonaggressive, and total antisocial
scales at age 8, and (6) the psychophysiological – antisociality association was
not moderated by gender or social adversity. Findings constitute the first
developmental study of electrodermal fear conditioning in early childhood and
indicate that early deficits in autonomic fear conditioning predispose individuals
to later aggressive and antisocial behavior. It is suggested that greater utilization
of this electrodermal paradigm in future studies of young children may help
elucidate the etiology of both child and adult clinical disorders.
1. Development of Fear Conditioning
A key finding of this study was the age-related changes in fear conditioning.
As expected, conditioning significantly increased with age (see Figure 2).
Perhaps the most surprising finding was that conditioning was observed as early
as age 3, despite the facts that (1) the UCS was not particularly intense (90 dB),
(2) a partial reinforcement schedule was used, (3) the difference between the CS+
and CS- involved only a modest frequency change of 500 Hz, and (4) a short
number of conditioning trials were used. The implication for future

41

developmental studies is that researchers can employ modestly aversive and
temporally short (7.5 minutes) paradigms to obtain a measure of fear conditioning
in children, with minimal risk from a human subjects perspective. The belief that
this is not entirely possible with young children may have deterred developmental
researchers from implementing fear conditioning paradigms with vulnerable child
populations.
The different components of electrodermal conditioning showed somewhat
different growth patterns across age, with more erratic and complex development
of the FIR and TIR at early ages compared to the relatively more linear
development of the SIR. In attempting some initial understanding of these
differences, the FIR and TIR are thought to reflect conditioned orienting
responses, whereas the SIR is thought to reflect a partially separate expectancy or
preparatory learning process (Öhman, 1979; in press). In an fMRI study
measuring SCRs to fear stimuli, Williams et al. (2000) found that fear stimuli that
elicit an SCR were associated with activation of the amygdala and medial frontal
cortex, whereas fear stimuli that did not evoke SCRs activated a
hippocampal-lateral network. They argued that while the amygdala-medial frontal
network was preferentially involved in the visceral experience of threat, the
hippocampal-lateral frontal network reflected activation of declarative and
contextual processing of threatening stimuli. FIR and TIR may therefore more
reflect amygdala-medial frontal automated processing, while the SIR may reflect
more controlled hippocampal-lateral prefrontal activation.
Although conditioning generally increased with age for the SIR (see Figure
2), this was not true for the FIR. In particular, conditioning of the FIR was

42

unexpectedly higher at age 3 than at age 4 (see Figure 2). One possible
explanation for this finding is that the first test session was particularly stressful
for the young children. Anecdotal reports from adult participants of this
longitudinal study to research staff indicate that they particularly remembered
testing in the small cubicle at age 3 where conditioning took place, and that this
was not a positive experience. Animal and human research has shown that stress
facilitates the development of classical conditioning (Cordero, Venero, Kruyt, &
Sandi, 2003; Jackson, Payne, Nadel, & Jacobs, 2006). In contrast, during testing
the following year at age 4, some habituation to the stress of the laboratory
testing would be anticipated, resulting in a reduction in conditioning (FIR – see
Figure 2). Alternatively, the child sat on the mother’s lap at age 3 years, but not
thereafter, and this may have facilitated arousal, which in turn, facilitated
conditioning. It is noted, however, that this stress / arousal explanation cannot be
applied adequately to the TIR, which increased from 3 to 4 years.
The findings of different developmental trajectories for FIR and SIR in the
current study are consistent with previous studies showing the disassociation of
these two components. For example, Cheng and colleagues (Cheng, Richards, &
Helmstetter, 2007) used fMRI and concurrent SC measurements and found that
the FIR is associated with activation, while the SIR is associated with
deactivation of amygdala. In addition, the deactivation of amygdala during the
SIR has been argued to represent strategies that attenuate responsiveness to the
aversive UCR (Cheng et al., 2007; Petrovic, Carlsson, Petersson, Hansson, &
Ingvar, 2004). Similarly, Hare argued that psychopaths give a larger-than-normal
cardiovascular anticipatory fear response that attenuates the responsiveness of the

43

UCR (Hare, 1978). The SIR may be another autonomic indicator of capability to
cope with stress, and therefore, the increased SIR across ages may suggest that
this emotion regulation capability improves gradually in children during this age
range.
For conditioning in general, it can be seen from Figure 2 that conditioning
showed a marked increase from ages 5 to 6 years. Indeed, inspection of Figure 2
indicates that this increase was the most consistent part of the growth curve
across the three conditioning components. The increase in conditioning from age
5 to 6 years is partly consistent with findings documenting the growth of the
cerebral hemispheres across the first ten years of life, with a very pronounced
increment in EEG phase present in left temporal-frontal electrode sites from ages
4 to 6 years (Thatcher, Walker, & Giudice, 1987) and the highest level of rate of
EEG coherence growth between frontal and posterior lobes at age 6 (Thatcher,
1992). Furthermore, SC orienting was also found to show a pronounced increase
from ages 5 to 6 in this same sample (Gao et al., 2007). In the current sample at
ages 5 to 6 years, children began to enter state schools. This environmental
change may constitute a mild stressor, which could facilitate fear conditioning at
this age. In addition, given that environmental enrichment in animals has been
shown to result in neurogenesis in the hippocampus (Kempermann & Gage, 1999)
and given the role of the hippocampus in both fear conditioning (Bast, Zhang, &
Feldon, 2003; Knight et al., 2004) and orienting (Williams et al., 2000; Critchley,
2002), the novelty of the school experience at age 5-6 may partly explain why
association learning markedly increased at this age. Conceivably, neurogenesis in
the hippocampus caused by novelty and environmental enrichment may

44

contribute to both the periods of major change in EEG coherence, orienting, and
conditioning during this age period.
2. Arousal, Orienting, Unconditioned Responses, and Conditioning
A subsidiary question of this study concerned what psychophysiological
factors are associated with fear conditionability at each age. The availability of
pre-conditioning measures of arousal, OR, and UCR data at four of the five ages
allowed for an examination of the extent to which these processes are correlated
with conditioning. Results demonstrated that all three processes are positively
associated with individual differences in conditioning. The amount of variability
they explained in conditionability increased from 17.2% at age 3 to 28.8% at age
6 (see Table 3). While it cannot be concluded that higher OR, arousal, and UCR
are causally associated with better conditioning, the fact that in the current study
all three measures precede the onset of the first conditioned response rules out the
possibility that higher fear conditioning causes increases in the three processes.
Instead, it is conceivable that higher arousal, orienting, and UCR partly facilitate
better conditioning. For example, because two of the three conditioned responses
(FIR and TIR) are known to have a significant orienting component, increased
orienting could reasonably be expected to facilitate better conditioning.
Furthermore, children who give a larger UCR may be expected to be more fearful
of the UCS, because a more aversive UCS is known to give rise to stronger fear
conditioning (Prokasy & Kumpfer, 1973). Alternatively, third factors (e.g.
individual differences in brain processes that regulate arousal and
information-processing) may result in across-the-board increases in all processes.

45

One further developmental finding is that stepwise linear regression showed
that while SCL (at age 3) and UCR (at age 4) had significant influence at these
early ages, their influence decreased over time and were replaced by the strong
influence of orienting whose contribution to conditioning increased from
explaining 3.2% of the variance at age 3 to 25.4% at age 6 (see Table 3, lower
half). This may be due to the developmental increase in orienting previously
observed in this sample (Gao et al., 2007). Indeed, the developmental trajectories
of the OR (Gao et al., 2007) and conditioning are strikingly similar. This may be
partly due to the fact that two of the three components of conditioning (FIR and
TIR) are orienting in nature, reflecting the potential importance of orienting in the
facilitation of conditioning.
3. Fear Conditioning and Antisocial Behavior
It has been suggested that a well-functioning nervous system serves a
protective function by facilitating learning of adaptive behaviors (Damasio, 1994).
As Eysenck (1977) argued, socialization occurs through the process of being
conditioned to avoid punishment. If the nervous system is not functioning
properly, in other words, if one cannot fully experience the fear or discomfort
associated with punishment, normal socialization will be impaired. Findings of
poor electrodermal fear conditioning in antisocial children lend some support to
this view by showing that (1) autonomic fear conditioning collected
longitudinally from ages 3 to 8 predicts ASB at age 8 years, (2) the effects hold
for both boys and girls, (3) the effects generalize across social adversity groups,
and (4) the effects appear to apply to aggressive as well as nonaggressive ASB.
From a neurobiological perspective, the current finding that poor conditioning

46

precedes antisocial behavior suggests that ASB may have occurred as a
consequence of suffering from chronic psychophysiological deficits as early as
age 3.
Consistent with this hypothesis, early poor fear conditioning is associated
with childhood ASB. Research conducted on SC activity among various
antisocial populations reveals that such individuals are characterized by
electrodermally underarousal (i.e., have a reduced frequency of NSFs), SC
orienting deficits, and reduced responsiveness to aversive stimuli (see review by
Raine, 1996). It has been argued that underarousal may predispose individuals to
stimulation seeking, and therefore, risky situations and antisocial environments
involving threats, whereas impaired orienting may indicate lack of attentional
processing to initially neutral stimuli that warn of impending punishment (Raine,
1996). Furthermore, reduced UCR may indicate inefficient ability to process
aversive stimuli (Raine, 1996). Given the significant relationships between
classical conditioning with arousal, OR, and UCR (see Table 3), it is possible that
the above three factors (i.e., sensation seeking, deficits in attention allocation, and
inefficient capability to process aversive stimuli) combine to contribute to the
poor conditioning – ASB association.
Alternatively, impairments in certain brain structures or functions may
underlie both fear conditioning deficits and ASB. This view is supported by
neuropsychological and brain-imaging data on the neuroanatomy and
neurophysiology of SC conditioning showing that the prefrontal cortex and
amygdala appear to be crucial for both the acquisition and expression of
conditioned fear in humans (Bechara, Tranel, Damasio, Adolphs, Rockland, &

47

Damasio, 1995; Büchel et al., 1998; Cheng et al., 2003; Knight et al., 2004, 2005;
LaBar et al., 1998; Kruesi, Casanova, Mannheim, & Johnson-Bilder, 2004). For
example, Bechara et al. (1995) found that a patient with selective bilateral
damage to the amygdala could not acquire conditioned SC responses to visual or
auditory stimuli, which were paired with the unconditioned stimulus. Knight et al.
(2005) found that increases in amygdala activity were associated with the
production of conditioned, but not orienting, nonspecific, and unconditioned SC
responses to fearful stimuli using fMRI in normal adults. Furthermore,
neuroimaging studies have reported prefrontal and amygdala deficits in antisocial
adults (e. g., Birbaumer et al., 2005; Raine, Lencz, Bihrle, LaCasse, & Colletti,
2000). Recent neuroimaging studies conducted by Sterzer and colleagues have
shown that compared to normal children, children with conduct disorder (CD)
showed reduced grey matter volume in the left amygdala (Sterzer, Stadler,
Poustka, & Kleinschmidt, 2007), and less activation in the left amygdala when
viewing negatively valenced pictures (Sterzer, Stadler, Krebs, Kleinschmidt, &
Poustka, 2005). In addition, an fMRI study conducted on children with
early-onset CD has reported that prefrontal cortical volumes may be reduced in
this sample (Kruesi et al., 2004). It is suggested that the prefrontal cortex may
less effectively regulate amygdala activity in individuals with ASB, thereby
preventing them from exercising cognitive control over their emotional behavior
(van Goozen et al., 2007). Taken together, compared to normal children,
antisocial children exhibit a similar pattern of impairments as observed in adults
and are characterized by both less sensitivity to cues signaling threat and
inefficient emotion regulation.

48

Inconsistent with the hypothesis, these data suggest that children with a
particular deficit – in the current study, poorer fear conditioning – appear to be
relatively more susceptible to the later occurrence of behavior problems,
regardless of their gender and environmental adversity. In addition, the fact that
low and high biological risk groups did not differ with regard to profiles of
psychosocial risk suggests that environmental adversity does not exert direct
effects on electrodermal fear conditioned responses.
This finding, on one hand, extends the psychophysiology – ASB association
to early childhood and indicates that conditioning deficits are a potentially
important, non-artificial risk factor for antisocial behavior, which may have its
roots in genetic or nongenetic, early biological influences. On the other hand, it
may suggest a different profile underlying the pathway to ASB in children. As
suggested by Venables (1987), biological concomitants of ASB in adulthood may
be epiphenomena of the relatively stressful social conditions associated with
antisociality and delinquency (West & Farrington, 1977), rather than being
directly implicated in a causal sense. In contrast, psychophysiological precursors
of ASB may be very different in childhood from the pattern shown in later
adolescence/adulthood (Venables, 1987). Therefore, it is possible that the
biosocial bases of ASB observed in adults are absent in early childhood because
the influences of environmental factors are less salient and the biological factors,
such as poor conditioning, are therefore stronger determinants in predicting ASB.
In fact, the current findings may suggest that this psychophysiological deficit may
independently, or combined with adverse social factors, contribute to later
behavior problems. Long-term developmental studies, which monitor the

49

interplay between psychophysiology, antisociality, and social factors during the
transition period from early childhood to adolescent are needed to replicate this
finding.
In the current study, poor conditioning has been found to predict both
aggressive and nonaggressive ASB. Recent theory proposes that aggressive and
nonaggressive ASB have different pathways toward delinquency (Loeber & Hay,
1997; Achenbach, 1991) and that the aggressive pathway is more stable than the
nonaggressive pathway (Stanger, Achenbach, & Verhulst, 1997; Tolan &
Gorman-Smith, 1998). Twin studies, for example, have generally found that
aggressive ASB is highly heritable, whereas nonaggressive ASB is more
influenced by shared environment (e. g., Ghodesian-Carpey & Baker, 1987;
Hudziak, et al., 2003), though some studies reported a roughly equal influence of
genes and shared environment (Edelbrock, Rende, Plomin, & Thompson, 1995;
Eley, Lichtenstein, & Stevenson, 1999). Taken together, it is hypothesized that the
conditioning – aggressive ASB will hold in adolescent and adulthood, whereas its
relation with nonaggressive ASB may dissipate over time. Future studies are
needed to test this hypothesis.
It is worth noting that in the current study, ASB measures were collected
when children were age 8 years. Therefore, children with ASB in the current
sample may actually resemble the life-course-persistent antisocials, defined by
Moffitt (1993), who show childhood-onset of severe conduct problems that lead
to more criminal violence convictions in adulthood (Moffitt, Caspi, Harrington, &
Milne, 2002), although some of them desist from crimes (Raine et al., 1996).
Previous studies have indicated that compared to adolescent- limited ASB,

50

life-course-persistent ASB is more heritable and less influenced by environmental
factors (Lyons et al., 1995; Silberg, Rutter, Tracy, Maes, & Eaves, 2007; Taylor,
Iacono, & McGue, 2000). The observed psychophysiology – ASB association in
the current study therefore tentatively supports the notion that
life-course-persistent ASB reflects an underlying temperamental trait likely to
have at least some biological basis (Moffitt, 1993).
4. Employing Electrodermal Conditioning in Children
Understanding the development of the amygdala-related processes, such as
the conditioned fear response in children, is critically important in understanding
the etiology of adolescent and adult clinical conditions. Rich et al. (2006) have
argued that a paradigm shift is occurring in clinical neuroscience whereby
psychiatric illnesses are increasingly being viewed as neurodevelopmental in
nature, and that assessment of amygdala dysfunction can help elucidate the
neurodevelopmental basis of bipolar disorder in children. Similarly, Pine et al.
(2001) have argued strongly that fMRI paradigms should be developed to assess
amygdala functioning in children and that there is a critical need for the
assessment of fear conditioning in childhood as a risk factor for anxiety disorders
in adulthood. As stated above, deficits in electrodermal fear conditioning has
been repeatedly linked to antisocial and aggressive behavior. Together with
current findings of this autonomic dysfunction in antisocial children, evidence
suggests that electrodermal conditioning may be a reliable measure to identify
those at high risk for later behavior problems. Until functional imaging studies
are more easily able to scan young children in the 3-8 year age range, a relative
strength of the electrodermal fear conditioning paradigm for child development

51

researchers is that it allows for an indirect assessment of the neural networks
subserving different forms of information-processing, in particular, the specific
circuit generally implicated in fear conditioning. Given the technical ease with
which it can be measured and its low cost, it is recommended that electrodermal
fear conditioning should be increasingly assessed in both laboratory and field
studies of antisocial and aggressive behavior where it can been integrated with
other variables. Such future studies will help address a variety of questions as to
how and why deficits in electrodermal conditioning predispose to clinical
disorders.
5. Limitation and Strengths
One of the limitations of this study is that this sample is composed of
community-recruited children. Moreover, the subjects are culturally and
ethnically a somewhat unique sample. Thus, the trajectories of electrodermal fear
conditioning and its relation to childhood ASB obtained in this sample may not
necessarily generalize to Western samples or to clinical populations.
Secondly, ASB measures were based on teachers’ rating only. One study has
specifically shown that the biosocial interaction underlying aggression was
significant when parent, but not teacher reports were involved (Marks, Miller,
Schulz, Newcorn, & Halperin, 2007). Further studies using multiple-informant
measures of the construct of ASB are needed to replicate any findings from the
current study (e. g., Baker, Jacobson, Raine, Lozano, & Bezdjian, 2007).
Finally, the use of median split procedures to establish low and high social
adversity groups may have limited group discriminability and could have
potentially attenuated the association between psychosocial adversity and ASB

52

for children in the high biological risk group. Although alternative subgrouping
methods (e. g., tertiles or quartiles) might bolster discriminability, such methods,
by reducing the overall sample size, may limit the power to detect group
differences.
As noted previously, strengths of this study include the following. Firstly,
this is the only study known to the author’s knowledge investigating the
development of autonomic measures of conditioning using a longitudinal design.
This longitudinal, rather than cross-sectional assessment of conditioning provides
a fuller and clearer picture of how fear conditioning develops among antisocial
individuals, which helps shed light on identifying a biological marker of ASB at
very early ages. As stated above, the majority of studies examining the
neurobiological bases of childhood ASB are cross-sectional and correlational in
nature, thereby limiting any conclusions regarding the causal nature of
neurobiological influences on behavior. Prospective longitudinal studies,
including the current study, permit investigation representing different causal
relations between variables and tests of the mediating and moderating factors that
underlie early adverse influences on ASB in childhood. The current findings
support Eysenck’s theory that early childhood conditioning deficits act as
precursors to disruptive behaviors. In addition, these biological factors predispose
ASB in children across all socioeconomic classes and both gender groups.
Secondly, the biosocial approach was used in this study in order to examine
the presumed moderating effects of social adversity on the relationship between
physiological deficits and ASB. As noted above, there is increasing need to

53

understand the role played by environmental factors on the genesis of ASB to
fully understand how it develops.
Thirdly, both boys and girls were examined in the study. To date few studies
of antisocial behavior include female subjects and more attention should be given
to the investigation of the variance in this behavior due to gender differences.
Finally, given the limitations on conducting fMRI in young children and the
increasing needs for understanding amygdala dysfunction in children (Pine et al.,
2001; Rich et al., 2006), there may be continuing advantages in applying the
traditional electrodermal measures in children to provide an indirect assessment
of the neural networks subserving different forms of information-processing, in
particular, the specific neural circuit implicated in fear conditioning.
6. Future Directions
In the current study, abnormal development of electrodermal fear
conditioning from ages 3 to 8 years has been found to be a biological risk factor
for ASB at age 8. Although childhood aggression and ASB are predictors for
adult crime, not all of the antisocial children would grow up being criminals.
Indeed, only a small portion of children with behavior problems would break
laws in adolescence or adulthood (Moffitt, 1993). Thus, research is needed to test
whether deficits in electrodermal fear conditioning in early childhood will predict
adult crime. It is critical to note that prospective longitudinal studies are needed
to follow these high-risk children into adulthood to determine what percentage of
these children will show significant levels of ASB and/or commit crime. It is left
open to question whether or not poor fear conditioning in early childhood should

54

be considered a “developmental precursor” to crime until such predictive utility
has been established.
This abnormal conditioning-ASB finding is consistent with Eysenck’s theory
in which conditioning has been argued to be the key concept of crime
development (Eysenck, 1977). However, it cannot explain why some children
who are at risk for later violence by virtue of having a negative family and peer
environment or by showing ASB in adolescence do not become violent adults.
According to Eysenck’s theory, children with good conditioning would easily
learn ASB when they are in high risk environments. Alternatively, good
psychophysiological functioning could serve as a protective factor against later
ASB and crime. Empirical studies have suggested that some psychophysiological
characteristics, such as higher SCOR, SCL, and better SC conditioning, may
contribute to desistence to later crime (Brennan et al., 1997; Raine, Venables, &
Williams, 1995; Raine et al., 1996). These competitive hypotheses should be
tested on children with antisocial families in future studies in order to fully
understand ASB and to explore what can retard its development.
The current study focused on a limited range of psychosocial risk factors
purportedly related to adverse outcomes. Other psychosocial risk factors, such as
child abuse, parental responsivity, parental warmth, and parental support have
been shown to mediate the outcome of early-onset conduct disturbances
(Beauchaine, Webster-Stratton, & Reid, 2005) and should be explored in future
studies to examine biosocial models of ASB.
This study also cannot clarify whether deficits in brain circuits associated
with autonomic fear conditioning are specific to antisocial populations and

55

whether or not the methodology used in the current study can be extended to
other clinical populations, such as those with depression, anxiety, or social phobia.
Some data suggest that individuals with these disorders show enhanced
frontolimbic (e. g., orbitofrontal cortex and amygdala) reactivity and autonomic
response to aversive stimuli (Birbaumer et al., 1998; Davidson, Marshall,
Tomarken, & Henriques, 2000; Veit et al., 2002). Further studies can be
conducted on children using the electrodermal conditioning paradigm in order to
investigate the etiology of these psychopathological disorders.
Another issue concerns whether this psychophysiology – ASB association is
specific to electrodermal measures. It has been argued that various autonomic
measures are somewhat independent and that different underlying brain circuits
may be associated with each measure (Boucsein, 1992). Therefore, future studies
using other conditioning measures, such as heart rate and eyeblink, are needed to
address this issue.
The biggest questions left unanswered are what causes abnormal autonomic
conditioning and how this causal relationship can help shed light on the
understanding of prevention and intervention programs for psychopathology. As
noted above, although brain imaging studies have been conducted in an attempt
to discover the brain circuits underlying conditioning, no such studies have been
performed in children. Furthermore, it is unknown at what age the abnormal brain
functioning can be detected. Future imaging research is urgently needed to
address these issues. In addition, behavior genetic research can help reveal the
genetic and environmental basis of psychophysiological measures and the extent

56

to which genes and environment mediate the abnormal development of
psychophysiological measures in early childhood.
One prediction arising from the current findings is that reinstatement of
normal autonomic nervous system functioning should protect children from or
ameliorate some forms of ASB and potentially enhance the response to
therapeutic interventions. An environmental preschool enrichment program, for
example, has resulted in significant increases in SC and EEG activity and
attention 9 years later (Raine et al., 2001). It is possible that directly altering
biological functioning through interventions like good prenatal care, nutrition and
health programs, biofeedback training, or psychotropic medication may also have
some utility.
7. Conclusions
In this study, a prospective, longitudinal approach was adopted to
demonstrate the utility of psychophysiological measures in predicting aggressive
and antisocial behavior. This is the first study that documents a relatively linear
increase in classical fear conditioning in early childhood from ages 3 to 8 years
and the first study that demonstrates early deficits in fear conditioning in
antisocial children. Some differences in the curves of the different conditioned
responses suggest that while the more controlled expectancy and preparatory
processes reflected in the SIR develop relatively late, the stress of first testing
could have produced the accentuated conditioning observed in the more
orienting-related FIR. The marked increase in average conditioning from 5 to 6
years may be accounted for by the increasing roles of arousal, orienting, and the
UCR (see Table 3) and the stress and novelty / environmental enrichment

57

associated with transition from home to formal state schools at this age.
Furthermore, the data clearly show that the impairments in electrodermal fear
conditioning constitute early biological risk factors for ASB and that these
deficits are present to a similar degree in those exhibiting aggressive and
nonaggressive antisocial behavior. This psychophysiology – antisociality
association generalizes across various psychosocial and gender populations. The
future promise for child development research is that the utilization of the
electrodermal fear conditioning paradigm in a longitudinal context offers a
relatively unique window into the interface between developmental science,
neuroscience, and clinical science, which can significantly elucidate the etiology
of child, adolescent, and adult problem behavior.

58

References
Achenbach, T. M. (1991). Manual for the Child Behavior Checklist/4-18 and
1991 profile. Burlington, VT: University of Vermont Department of
Psychiatry.
Baker, L. A., Jacobson, K. C., Raine, A., Lozano, D. I., & Bezdjian, S. (2007).
Genetic and environmental bases of childhood antisocial behavior: A
multi-informant twin study. Journal of Abnormal Psychology, 116,
219-235.
Baker, L. A., Mack, W., Moffitt, T. E., & Mednick, S. (1989). Sex differences in
property crime in a Danish adoption cohort. Behavior Genetics, 19,
355-370.
Bast, T., Zhang, W. N., & Feldon, J. (2003) Dorsal hippocampus and classical
fear conditioning to tone and context in rats: Effects of local
NMDA-receptor blockage and stimulation. Hippocampus, 13, 657-675.
Beauchaine, T. P., Webster-Stratton, C., Reid, M. J. (2005). Mediators,
moderators, and predictors of 1-year outcomes among children treated for
early-onset conduct problems: A latent growth curve analysis. Journal of
Consulting and Clinical Psychology, 73, 371-388.
Bechara, A., Tranel, D., Damasio, H., Adolphs, R., Rockland, C., & Damasio, A.
R. (1995). Double dissociation of conditioning and declarative knowledge
relative to the amygdala and hippocampus in humans. Science, 269,
1115-1118.
Birbaumer, N., Grodd, W., Diedrich, O., Klose, U., Erb, M., Schneider, F., et al.
(1998). fMRI reveals amygdala activation to human faces in social phobias.
NeuroReport, 9, 1223-1226.
Birbaumer, N., Veit, R., Lotze, M., Erb, M., Hermann, C., Grodd, W., et al.
(2005). Deficient fear conditioning in psychopathy. Archive of General
Psychiatry, 62, 799-805.
Bitterman, M. E., & Holtzman, W. H. (1952). Conditioning and extinction of the
galvanic skin response as a function of anxiety. Journal of Abnormal and
Social Psychology, 47, 615-623.
Block, J. D., Sersen, E. A., & Wortis, J. (1970). Cardiac classical conditioning
and reversal in the mongoloid, encephalopathic, and normal child. Child
Development, 41, 771-785.
Boucsein, W. (1992). Electrodermal activity. New York: Plenum Press.

59

Brennan, P. A., Raine, A., Schulsinger, F., Kirkegaard-Sorensen, L., Knop, J.,
Hutchings, B., et al. (1997). Psychophysiological protective factors for
male subjects at high risk for criminal behavior. American Journal of
Psychiatry, 154, 853-855.
Büchel, C., & Dolan, R. J. (2000). Classical fear conditioning in functional
neuroimaging. Current Opinion in Neurobiology, 10, 219-223.
Büchel, C., Morris, R. J., Dolan, R. J., & Friston, K. J. (1998). Brain systems
mediating aversive conditioning: An event-related fMRI study. Neuron, 20,
947-957.
Caspi, A., McClay, J., Moffitt, T. E., Mill, J., Martin, J., Craig, I. W., et al. (2002).
Role of genotype in the cycle of violence in maltreated children. Science,
297, 851-854.
Cheng, D. T., Knight, D. C., Stein, E. A., & Smith, C. N. (2003). Functional MRI
of human amygdala activity during Pavlovian fear conditioning: Stimulus
processing versus response expression. Behavioral Neuroscience, 117,
3-10.
Cheng, D. T., Richards, J., & Helmstetter, F. J. (2007). Activity in the human
amygdala corresponds to early, rather than late period autonomic responses
to a signal for shock. Learning and Memory, 14, 485-490.
Cordero, M. I., Venero, C., Kruyt, N. D., & Sandi, C. (2003). Prior exposure to a
single stress session facilitates subsequent contextual fear conditioning in
rats: Evidence for a role of corticosterone. Hormones and Behavior, 44,
338-345.
Critchley, H. D. (2002). Electrodermal responses: What happens in the brain?
Neuroscientist, 8, 132-142.
Critchley, H. D., Mathias, C. J., & Dolan, R. J. (2002). Fear conditioning in
humans: The influence of awareness and autonomic arousal of functional
neuroanatomy. Neuron, 33, 653-663.
Damasio, A. (1994). Descartes' error: Emotions, reason, and the human brain.
New York: Avon Books.
Davey, G. C. L. (1992). Classical conditioning and the acquisition of human fears
and phobias: A review and synthesis of the literature. Advances in
Behaviour Research and Therapy, 14, 29-66.
Davidson, R. J., Marshall, J. R., Tomarken, A. J., & Henriques, J. B. (2000).
While a phobic waits: Regional brain electrical and autonomic activity in
social phobias during anticipation of public speaking. Biological
Psychiatry, 47, 85-95.

60

Davis, M. (1992). The role of the amygdala in conditioned fear. In J. P. Aggleton
(Ed.), The amygdala: Neurobiological aspects of emotion, memory, and
mental dysfunction (pp. 255-305). New York: Wiley.
Davis, M. (2000). The role of the amygdala in conditioned and unconditioned fear
and anxiety. In J. P. Aggleton (Ed.), The amygdala: A functional analysis
(2nd ed., pp. 213-287). New York: Oxford University Press.
Dawson, M. E., Catania, J. J., Schell, A. M., & Grings, W. W. (1979). Autonomic
classical conditioning as a function of awareness of stimulus contingencies.
Biological Psychology, 9, 23-40.
Dawson, M. E., & Schell, A. M. (1987). Human autonomic and skeletal classical
conditioning: The role of conscious cognitive factors. In G. Davey (Ed.),
Cognitive processes and Pavlovian conditioning in humans (pp. 27-55).
New York: Wiley & Sons.
Dawson, M. E., Schell, A. M., Beers, J. R., & Kelly, A. (1982). Allocation of
cognitive processing capacity during human autonomic classical
conditioning. Journal of Experimental Psychology: General, 111, 272-295.
Edelbrock, C., Rende, R., Plomin, R., & Thompson, L. A. (1995). A twin study of
competence and problem behavior in childhood and early adolescence.
Journal of Child Psychology and Psychiatry, 36, 775-785.
Eley, T. C., Lichtenstein, P., & Stevenson, J. (1999). Sex differences in the
etiology of aggressive and nonaggressive antisocial behavior: Results from
two twin studies. Child Development, 70, 155-168.
Eysenck, H. J. (1977). Crime and personality (3rd ed.). St. Albans, England:
Paladin.
Field, A. P. (2006). Is conditioning a useful framework for understanding the
development and treatment of phobias? Clinical Psychology Review, 26,
857-875.
Foley, D. L., Eaves, L., Wormley, B., Silberg, J., Maes, H., Kuhn, J., et al. (2004).
Childhood adversity, monoamine oxidase a genotype, and risk for conduct
disorder. Archives of General Psychiatry, 61, 738-744.
Fowles, D. C. (2000). Electrodermal hyporeactivity and antisocial behavior: Does
anxiety mediate the relationship? Journal of Affective Disorders, 61,
177-189.
Gao, Y., Raine, A., Dawson, M. E., Venables, P. H., & Mednick, S. A. (2007).
Development of skin conductance orienting, habituation, and reorienting
from ages 3 to 8 years: A longitudinal latent growth curve analysis.
Psychophysiology, 44, 855-863.

61

Ghodesian-Carpey, J., & Baker, L. A. (1987). Genetic and environmental
influences on aggression in 4- to 7- year-old twins. Aggressive Behavior,
13, 173-186.
Grings, W. W. (1969). Anticipatory and preparatory electrodermal behavior in
paired stimulation situations. Psychophysiology, 5, 597-611.
Hare, R. D. (1978). Electrodermal and cardiovascular correlates of psychopathy.
In R. D. Hare and D. Schalling (Eds.), Psychopathic behavior: Approaches
to research (pp. 107-144). New York: Wiley.
Hemming, J. H. (1981). Electrodermal indices in a selected prison sample and
students. Personality and Individual Differences, 2, 37-46.
Hosoba, T., Iwanaga, M., & Seiwa, H. (2001). The effect of UCS inflation and
deflation procedures on ‘fear’ conditioning. Behaviour Research and
Therapy, 39, 465-475.
Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling:
Sensitivity to underparameterized model misspecification. Psychological
Methods, 3, 424-453.
Hudziak, J. J., van Beijsterveldt, C. E. M., Bartels, M., Rietveld, M. J. H., Rettew,
D. C., Derks, E. M., et al. (2003). Individual differences in aggression:
Genetic analyses by age, gender, and informant in 3-, 7-, and 10-year-old
Dutch twins. Behavioral Genetics, 33, 575-589.
Ingram, E. M. (1978). The interstimulus interval in classical autonomic
conditioning of young infants. Developmental Psychology, 11, 419-426.
Ingram, E. M., & Fitzgerald, H. E. (1974). Individual differences in infant
orienting and autonomic conditioning. Developmental Psychology, 7,
359-367.
Jackson, E. D., Payne, J. D., Nadel, L., & Jacobs, W. J. (2006). Stress
differentially modulates fear conditioning in healthy men and women.
Biological Psychiatry, 59, 516-522.
Jacobson, K. C., Prescott, C. A., & Kendler, K. S. (2002). Sex differences in the
genetic and environmental influences on the development of antisocial
behavior. Development and Psychopathology, 14, 395-416.
Jarrell, T. W., Gentile, C. G., Romanski, L. M., McCabe, P. M., & Schneiderman,
N. (1987). Involvement of cortical and thalamic auditory regions in
retention of differential bradycardia conditioning to acoustic conditioned
stimuli in rabbits. Brain Research, 412, 285-294.

62

Jokela, M., Lehtimäki, T., & Keltikangas-Järvinen, L. (2007). The serotonin
receptor 2A gene moderates the influence of parental socioeconomic status
on adulthood harm avoidance. Behavior Genetics, 37, 567-574.
Kempermann, G., & Gage, F. H. (1999). Experience-dependent regulation of
adult hippocampal neurogenesis: Effects of long-term stimulation and
stimulus withdrawal. Hippocampus, 9, 321-332.
Kim-Cohen, J., Caspi, A., Taylor, A., Williams, B., Newcombe, R., Craig, I. W.,
et al. (2006). MAOA, maltreatment, and gene-environment interaction
predicting children’s mental health: New evidence and a meta-analysis.
Molecular Psychiatry, 11, 903-913.
Knight, D. C., Nguyen, H. T., & Bandettini, P. A. (2005). The role of the human
amygdale in the production of conditioned fear responses. NeuroImage, 26,
1193-1200.
Knight, D. C., Smith, C. N., Cheng, D. T., Stein, E. A., & Helmstetter, F. J. (2004).
Amygdala and hippocampal activity during acquisition and extinction of
human fear conditioning. Cognitive, Affective, and Behavioral Neuroscience,
4, 317-325.
Kruesi, M. J., Casanova, M. F., Mannheim, G., & Johnson-Bilder, A. (2004).
Reduced temporal lobe volume in early onset conduct disorder. Psychiatry
Research, 132, 1-11.
LaBar, K. S., Gatenby, J. C., Gore, J. C., LeDoux, J. E., & Phelps, E. A. (1998).
Human amygdala activation during conditioned fear acquisition and
extinction: A mixed-trial fMRI study. Neuron, 20, 937-945.
LaBar, K. S., LeDoux, J. E., Spencer, D. D., & Phelps, E. A. (1995). Impaired
fear conditioning following unilateral temporal lobectomy in humans.
Journal of Neuroscience, 15, 6846-6855.
Langbehn, D. R., Cadoret, R. J., Yates, W. R., Troughton, E. P., & Stewarts, M.
A. (1998). Distinct distributions of conduct and oppositional defiant
symptoms to adult antisocial behavior: Evidence from an adoption study.
Archives of General Psychiatry, 55, 821-829.
LeDoux, J. E. (1998). The neural circuits underlying anxiety and fear. Biological
Psychiatry, 44, 1229-1238.
LeDoux, J. E. (2000a). The amygdala and emotion: A view through fear. In J. P.
Aggleton (Ed.), The amygdala: A functional analysis (2nd ed., pp. 289-310).
New York: Oxford University Press.
LeDoux, J. E. (2000b). Emotion circuits in the brain. Annual Review of
Neuroscience, 23, 155-184.

63

Lissek, S. M., Powers, A. S., McClure, E. B., Phelps, E. A., Woldehawariat, G.,
Grillon, C., et al. (2005). Classical fear conditioning in the anxiety
disorders: A meta-analysis. Behaviour Research and Therapy, 43,
1391-1424.
Loeb, J., & Mednick, S. A. (1977). A prospective study of predictors of
criminality: Electrodermal response patterns. In S. A. Mednick and K. O.
Christiansen (Eds.), Biosocial bases of criminal behavior (pp. 245-254).
New York: Gardener Press.
Loeber, R., & Hay, D. (1997). Key issues in the development of aggression and
violence from childhood to early adulthood. Annual Review of Psychology,
48, 371-410.
Lykken, D. T. (1957). A study of anxiety in the sociopathic personality. Journal of
Abnormal and Social Psychology, 55, 6-10.
Lyons, M. J., True, W. R., Eisen, S. A., Goldberg, J., Meyer, J. M., Faraone, S. V.,
et al. (1995). Differential heritability of adult and juvenile antisocial traits.
Archives of General Psychiatry, 52, 906-915.
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis
and determination of sample size for covariance structure modeling.
Psychological Methods, 1, 130-149.
Marks, D. J., Miller, S. R., Schulz, K. P., Newcorn, J. H., & Halperin, J. M.
(2007). The interaction of psychosocial adversity and biological risk in
childhood aggression. Psychiatry Research, 151, 221-230.
Miles, D. R., & Carey, G . (1997). Genetic and environmental architecture of
human aggression. Journal of Personality and Social Psychology, 72,
207-217.
Moffitt, T. E. (1993). Life-course-persistent and adolescence-limited antisocial
behavior: A developmental taxonomy. Psychological Review, 100,
674-701.
Moffitt, T. E., & Caspi, A. (2001). Childhood predictors differentiate life-course
persistent and adolescence- limited antisocial pathways among males and
females. Development and Psychopathology, 13, 355-375.
Moffitt, T. E., Caspi, A., Harrington, H., & Milne, B. (2002). Males on the
life-course persistent and adolescence-limited antisocial pathways:
Follow-up at age 26. Development and Psychopathology, 14, 179-206.
Moffitt, T. E., Caspi, A., Rutter, M., & Silva, P. A. (2001). Sex differences in
antisocial behavior: Conduct disorder, delinquency, and violence in the
Dunedin longitudinal study. Cambridge, UK: Cambridge University Press.

64

Morris, J. S., Friston, K. J., & Dolan, R. J. (1997). Neural responses to salient
visual stimuli. Proceedings: Biological Sciences, 264, 769-775.
Muthén, B. O. (2004). Latent variable analysis: Growth mixture modeling and
related techniques for longitudinal data. In D. Kaplan (Ed.), The Sage
handbook of quantitative methodology for the social sciences (pp.
345-368). Thousand Oaks: Sage.
Muthén, L. K., & Muthén, B. O. (1998). Mplus user’s guide. Los Angeles, CA:
Muthén & Muthén.
Nagin, D. S. (1999). Analyzing development trajectories: A semi-parametric,
group-based approach. Psychological Methods, 4, 139-157.
Nesselroade, J. R. (1991). Interindividual differences in intraindividual change. In
L. A. Collins and J. L. Horn (Eds.), Best methods for the analysis of
change (pp. 92-106). Washington, DC: American Psychological
Association.
Öhman, A. (1971). Interaction between instruction-induced expectancy and
strength of unconditioned stimulus in GSR conditioning. Journal of
Experimental Psychology, 88, 384-390.
Öhman, A. (1979). The orienting response, attention, and learning: An
information- processing perspective. In H. D. Kimmel, E. H. van Olst, and
J. F. Orelebeke (Eds.), The orienting reflex in humans (pp. 443-471).
Hillsdale, NJ: Erlbaum.
Öhman, A. (in press). Human fear conditioning: Focus on the amygdala. In P.
Whalen and E. Phelps (Eds.), The human amygdala. New York: Guilford.
Petrovic, P., Carlsson, K., Petersson, K. M., Hansson, P., & Ingvar, M. (2004).
Context-dependent deactivation of the amygdala during pain. Journal of
Cognitive Neuroscience, 16, 1289-1301.
Pine, D. S., Fyer, A., Grun, J., Phelps, E. A., Szeszko, P. R., Koda, V., et al.
(2001). Methods for developmental studies of fear conditioning circuitry.
Biological Psychiatry, 50, 225-228.
Prokasy, W. F., & Ebel, H. C. (1967). Three components of the classically
conditioned GSR in human subjects. Journal of Experimental Psychology,
73, 247-256.
Prokasy, W. F., & Kumpfer, K. L. (1973). Classical conditioning. In W. F.
Prokasy and D. C. Raskin (Eds.), Electrodermal activity in psychological
research (pp. 197-202). New York: Academic Press.
Raine, A. (1993). The psychopathology of crime: Criminal behavior as a clinical
disorder. San Diego: Academic Press.

65

Raine, A. (1996). Autonomic nervous system activity and violence. In D. M.
Stoff and R. B. Cairns (Eds.), Neurobiological approaches to clinical
aggression research (pp. 145-168). Mahwah, New Jersey: Lawrence
Erlbaum.
Raine, A. (2002). Biosocial studies of antisocial and violent behavior in children
and adults: A review. Journal of Abnormal Child Psychology, 30, 311-326.
Raine, A., Lencz, T., Bihrle, S., LaCasse, L., & Colletti, P. (2000). Reduced
prefrontal gray matter volume and reduced autonomic activity in antisocial
personality disorder. Archives of General Psychiatry, 57, 119-127.
Raine, A., Reynolds, C., Venables, P. H., & Mednick, S. A. (2002). Stimulation
seeking and intelligence: A prospective longitudinal study. Journal of
Personality and Social Psychology, 82, 663-674.
Raine, A., Reynolds, C., Venables, P. H., Mednick, S. A., & Farrington, D. P.
(1998). Fearlessness, stimulation-seeking, and large body size at age 3
years as early predispositions to childhood aggression at age 11 years.
Archives of General Psychiatry, 55, 745-751.
Raine, A., & Venables, P. H. (1981). Classical conditioning and socialization - A
biosocial interaction? Personality and Individual Differences, 2, 273-283.
Raine, A., Venables, P. H., Dalais, C., Mellingen, K., Reynolds, C., & Mednick,
S. A. (2001). Early educational and health enrichment at age 3-5 years is
associated with increased autonomic and central nervous system arousal
and orienting at age 11 years: Evidence from the Mauritius Child Health
Project. Psychophysiology, 38, 254-66.
Raine, A., Venables, P. H., & Williams, M. (1995). High autonomic arousal and
electrodermal orienting at age 15 years as protective factors against
criminal behavior at age 29 years. American Journal of Psychiatry, 152,
1595-1600.
Raine, A., Venables, P. H., & Williams, M. (1996). Better autonomic conditioning
and faster electrodermal half-recovery time at age 15 years as possible
protective factors against crime at age 29 years. Developmental Psychology,
32, 624-630.
Raine, A., Yaralian, P. S., Reynolds, C., Venables, P. H., & Mednick, S. A. (2002).
Spatial but not verbal cognitive deficits at age 3 years in persistently
antisocial individuals. Development and Psychopathology, 14, 25-44.
Rich, B. A., Vinton, D. T., Roberson-Nay, R., Hommer, R. E., Berghorst, L. H.,
McClure, E. B., et al. (2006). Limbic hyperactivation during processing of
neutral facial expressions in children with bipolar disorder. Proceeding of
the National Academy of Sciences of the United States of America, 103,
8900-8905.

66

Rutter, M. (1967). A children's behavior questionnaire for completion by teachers:
Preliminary findings. Journal of Child Psychology and Psychiatry, 8, 1-11.
Rutter, M. (1978). Family, age, and school influences in the genesis of conduct
disorders. In L. A. Hersov, M. Berger, and D. Shaffer (Eds.), Aggression
and antisocial behavior in childhood and adolescence (pp. 95-113). New
York: Wiley.
Silberg, J. L., Rutter, M., Tracy, K., Maes, H. H., & Eaves, L. (2007). Etiological
heterogeneity in the developmental of antisocial behavior: The Virginia
Twin Study of Adolescent Behavioral Development and the Young Adult
Follow-Up. Psychological Medicine, 37, 1193-1202.
Silverthorn, P., Frick, P. J., & Reynolds, R. (2001) Timing of onset and correlates
of severe conduct problems in adjudicated girls and boys. Journal of
Psychopathology and Behavioral Assessment, 23, 171-181.
Sokolov, E. N. (1963). Perception and the conditioned reflex. Oxford: Pergamon
Press.
Stanger, C., Achenbach, T. M., & Verhulst, F. C. (1997). Accelerated longitudinal
comparisons of aggressive versus delinquent syndromes. Developmental
Psychopathology, 9, 43-58.
Sterzer, P., Stadler, C., Krebs, A., Kleinschmidt, A., & Poustka, F. (2005).
Abnormal neural responses to emotional visual stimuli in adolescents with
conduct disorder. Biological Psychiatry, 57, 7-15.
Sterzer, P., Stadler, C., Poustka, F., & Kleinschmidt, A. (2007). A structural
neural deficit in adolescents with conduct disorder and its association with
lack of empathy. NeuroImage, 37, 335-342.
Taylor, J., Iacono, W. G., & McGue, M. (2000). Evidence for a genetic etiology
of early-onset delinquency. Journal of Abnormal Psychology, 109,
634-643.
Thatcher, R. W., Walker, R. A., & Giudice, S. (1987). Human cerebral
hemispheres develop at different rates and ages. Science, 236, 1110-1113.
Thatcher, R. W. (1992). Cyclical cortical reorganization during early childhood.
Brain and Cognition, 20, 24-50.
Tolan, P. H., & Gorman-Smith, D. (1998). Development of serious and violent
offending careers. In R. Loeber and D. P. Farrington (Eds.), Serious and
violent juvenile offenders: Risk factors and successful interventions (pp.
68-85). SAGE publications.
Trasler, G. (1987). Biogenetic factors. In H. C. Quay (Ed.), Handbook of juvenile
delinquency (pp. 184-216). New York: Wiley.

67

Tuvblad, C., Grann, M., & Lichtenstein, P. (2006). Heritability for adolescent
antisocial behavior differs with socioeconomic status: Gene-environment
interaction. Journal of Child Psychology and Psychiatry, 47, 734-743.
van Goozen, S. H. M., Fairchild, G., Snoek, H., & Harold, G . T. (2007). The
evidence for a neurobiological model of childhood antisocial behavior.
Psychological Bulletin, 133, 149-182.
Veit, R., Flor, H., Erb, M., Hermann, C., Lotze, M., Grodd, W., et al. (2002).
Brain circuits involved in emotional learning in antisocial behavior and
social phobia in humans. Neuroscience Letters, 328, 233-236.
Venables, P. H. (1978). Psychophysiology and psychometrics. Psychophysiology,
15, 302-315.
Venables, P. H. (1987). Autonomic and nervous system factors in criminal
behavior. In S. A. Mednick, T. E. Moffitt, and S. Stack (Eds.) The causes of
crime: New biological approaches. New York: Cambridge University
Press.
Venables, P. H., & Christie, M. J. (1980). Electrodermal activity. In I. Martin and
P. H. Venables (Eds.), Techniques in psychophysiology (pp. 3-67). New
York: Wiley & Sons.
Venables, P. H., Fletcher, R. P., Dalais, J. C., Mitchell, D. A., Schulsinger, F., &
Mednick, S. A. (1983). Factor structure of the Rutter 'Children's behavior
questionnaire' in a primary school population in a developing country.
Journal of Child Psychology and Psychiatry, 24, 213-222.
Venables, P. H., & Mitchell, D. A. (1996). The effects of age, sex and time of
testing on skin conductance activity. Biological Psychology, 43, 87-101.
West, D. J., & Farrington, D. P. (1977). The delinquent way of life. Heinemann,
London.
Williams, L. M., Brammer, M. J., Skerrett, D., Lagopolous, J., Rennie, C., Kozek,
K., et al. (2000). The neural correlates of orienting: An integration of fMRI
and skin conductance orienting. NeuroReport, 11, 3011-3015.
Woodruff-Pak, D. S., Jaeger, M. E., Gorman, C., & Wesnes, K. A. (1999).
Relationships among age, conditioned stimulus-unconditioned stimulus
interval, and neuropsychological test performance. Neuropsychology, 13,
90-102.

68

APPENDIX A: AUDITORY STIMULUS TYPE AND INTENSITY

Stimulus type Intensity UCS ISI(secs)

ORIENTING
1 sec duration
1. 1000hz 75db - 42
2. 1000hz 75db - 40
3. 1000hz 75db - 36
4. 1311hz 75db - 42
5. 1311hz 75db - 34
6. 1311hz 75db - 37

CONDITIONING

12.5 sec duration 4.5 sec duration
7. 1000hz cs+ 60db 90db 42
8. 1000hz cs+ 60db 90db 38
9. 500hz cs- 60db - 34
10. 1000hz cs+ 60db - 34
11. 1000hz cs+ 60db 90db 42
12. 1000hz cs+ 60db 90db 42
13. 1000hz cs+ 60db - 38
14. 500hz cs- 60db - 38
15. 1000hz cs+ 60db 90db 34
16. 500hz cs- 60db - 38
17. 1000hz cs+ 60db 90db 42
18. 1000hz cs+ 60db - 34

EXTINCTION

19. 1000hz cs+ 60db - 42
20. 1000hz cs+ 60db - 38
21. 500hz cs- 60db - 34
22. 1000hz cs+ 60db - 34
23. 1000hz cs+ 60db - 42
24 1000hz cs+ 60db - -

Abstract (if available)

Abstract Poor fear conditioning has been found in adult psychopaths and criminals, but little is known about how fear conditioning develops early in life, how changes in conditioning across age are related to antisocial behavior (ASB), and whether the psychophysiology antisocial association is moderated by gender or social adversity. Using a differential, partial reinforcement conditioning paradigm with a 10 second inters-stimulus interval, skin conductance responses to auditory aversive tones were recorded from 200 male and female Mauritian children at five occasions at ages 3, 4, 5, 6, and 8 years. Aggressive and nonaggressive ASB data were assessed at age 8 by teachers. Measures of social adversity were collected from the caregiver when the children were aged 3 years. Latent growth curve modeling was used to map the development of fear conditioning throughout childhood and to define homogenous clusters of children based on their developmental fear conditioning trajectories over time. Group differences in ASB and the moderating effects of gender and social adversity were then examined. Results from skin conductance tests showed that fear conditioning increases from ages 3 to 8 years.

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Prenatal air pollution exposure, newborn DNA methylation, and childhood respiratory health

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Examining parent responsiveness to sensory reactivity and regulation cues of infants with developmental risk factors

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Examining the associations of respiratory problems with psychological functioning and the moderating role of engagement in pleasurable activities during late adolescence

Asset Metadata

Creator Gao, Yu (author)

Core Title The development of electrodermal fear conditioning from ages 3 to 8 years and relationships with antisocial behavior at age 8 years

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Publication Date 03/27/2008

Defense Date 02/27/2008

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

Tag antisocial behavior,Child,conditioning,Development,electrodermal,Fear,OAI-PMH Harvest

Language English

Advisor Raine, Adrian (committee chair), Baker, Laura A. (committee member), Dawson, Michael E. (committee member), Manis, Franklin R. (committee member), Trickett, Penelope K. (committee member)

Creator Email yugao@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m1059

Unique identifier UC1272400

Identifier etd-Gao-20080327 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-39828 (legacy record id),usctheses-m1059 (legacy record id)

Legacy Identifier etd-Gao-20080327.pdf

Dmrecord 39828

Document Type Dissertation

Rights Gao, Yu

Type texts

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

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu