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The role of good habits in facilitating long-term goals

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The role of good habits in facilitating long-term goals

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Content Running head: GOOD HABITS IN FACILITATING LONG-TERM GOALS 1

The Role of Good Habits in Facilitating Long-Term Goals
Pei-Ying Lin
University of Southern California

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Table of Contents
I. Title …………………………………………………………………………... ........... 1
II. Abstract…………………………………………………………………….. .............. 3
III. Introduction…………………………………………………………………… ........ 4
IV. Study 1………………………………………………………………………. ......... 14
V. Study 2………………………………………………………………………. .......... 26
VI. General Discussion………………………………………………………….. ......... 35
VII. References………………………………………………………………….... ....... 36

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Abstract
Habits remain a largely unrecognized mechanism of goal adherence in current research.
Because habitual behaviors are elicited automatically by the presence of contextual cues
and require little cognitive effort to perform, we predicted that both good and bad habits
should have a stronger impact on behavior when people do not have sufficient motivation
or capacity to exert self-control. The first study manipulated self-control strength and the
habit to approach or avoid eating chocolates. The result revealed a significant interaction
between habit and self-control strength on chocolate consumption. Participants trained to
approach chocolates ate more chocolates when self-control strength was low, whereas
self-control strength did not significantly affect chocolate consumption among those
trained to avoid eating chocolates. In the second study, the impact of habits was
examined in a more explicit decision making context where participants had to choose
between a healthy habitual option and an unhealthy non-habitual option. No significant
effect was found. Overall, good habits can prevent violation of long-term goal pursuit
and people may benefit from habits that outsource behavior control to the environment.
Keywords: habit, habit formation, self-control, ego-depletion, decision-making

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The Role of Good Habits in Facilitating Long-Term Goals
People make many decisions every day and act on their decisions to achieve their
goals. Often times, decisions involve two goals that cannot be fulfilled at the same time
point. People may have goals to obtain long-term rewards such as health, sufficient
retirement money, or high productivity while simultaneously hold goals to enjoy short-
term rewards, such as high calorie food, spending money freely, or watching TV shows.
Self-regulation is a fundamental capacity because it allows us to forego short-term
pleasure and successfully implement a behavior that is consistent with our long-term
goals. Self-regulation failure, on the other hand, has been associated with obesity,
substance abuse, poor academic performance, and poor quality social interactions
(Heatherton & Wagner, 2011; Vohs & Baumeister, Ciarocco, 2005).
In this paper, we first discuss effortful self-control, the most widely investigated
strategy for self-regulation, and the condition in which it is more likely to be recruited
and succeed. Next, different forms of automatic self-regulation (i.e. automatic attitudes
and habits) will be reviewed and compared with each other. Although habits and goals
both influence self-control behaviors, they are most impactful in different situations.
Because effortful self-control requires more self-control capacity and motivation than
habit-directed responses, habits are expected to exert greater impact on behavior when
self-control strength is relatively low. In addition, because habits are largely independent
of current evaluation of behavioral outcomes, habits should be more impactful under low
self-control strength regardless of their congruency with goals. The first study examined
this hypothesis by manipulating self-control strength and habits of eating chocolates. The
second study tested the idea further in an explicit decision making context.
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Effortful Self-control
People use a variety of self-regulation strategies, and current research has focused
mostly on effortful self-control. Indeed, when people have a long-term goal, they may
pursue that goal by coming up with a plan or a strategy, executing the plan, examining
consistency between their performance and the goal, and adjusting the plan or even the
goal accordingly (Rangel, Camerer, & Montague, 2008). In addition, when there is a
short-term temptation that can distract them from the goal, people may need high-level
control in order to continue performing behaviors that are consistent with their long-term
goals (Heatherton & Wagner, 2011; Hofmann, Friese, & Strack, 2009). However, when
cognitive resources are unavailable (e.g., attention is elsewhere or motivation is low),
active self-control is more likely to fail.
Many studies have shown that after people exert self-control effort on one task,
they usually perform worse on the subsequent self-control task, regardless of similarities
between the domains of the first and the second tasks. This effect is called ego-depletion
(Muraven & Baumeister, 2000), and some researchers believe that this depletion
represents reduced control motivation (Inzlicht, & Schmeichel, 2012; Muraven &
Slessareva, 2003; Kurzban, in press). For example, after participants had exerted effort to
control their emotions from a first task, their performance on a Stroop task was worse
than the performance of those who had not controlled their emotions (Inzlicht & Gutsell,
2007). It was argued that participants’ motivation to control was reduced by performance
at the first task. Thus, when they were asked to further exert self-control, the low
motivation resulted in poorer performance. This argument is also supported by studies
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that manipulated motivation levels. For example, increasing incentives to perform the
second task was found to mitigate the ego-depletion effect (Muraven & Slessareva).
Automatic Self-Regulation
Although active self-control has been extensively studied over the past decade,
less attention has been paid to the role of automaticity in self-regulation. A few
researchers have studied the influence of implicit attitudes and implicit goals on self-
regulation (Friese, Hofmann, & Wänke, 2008; Webb & Sheeran, 2003; Bargh &
Morsella, 2008). Implicit attitudes were found to have more predictive power when
participants had low cognitive capacity or when they were depleted. Friese, Hofmann,
and Wänke (2008) asked participants to inhibit their emotional responses when watching
a video, measured their implicit attitudes toward potato chips by the Implicit Association
Test (IAT), and then gave participants a bag of potato chips to sample and evaluate. The
study showed that the predictability of implicit and explicit attitudes was dependent on
participants’ self-control resources. Participants who reported positive attitudes toward
potato chips ate more when they had high self-control resources, but their explicit
attitudes were unrelated to consumption when self-control resources were low.
Conversely, the implicit attitude was positively related to potato chip consumption when
self-control resources were low and was unrelated to consumption when the resources
were high. Thus, people are more likely to act on their implicit attitudes when they have
low self-control resources.
In addition to attitudes, implicit goals have also been shown to guide goal pursuit.
Implementation intention is a form of implicit goals and follows the statement: “As soon
as situation y occurs, I will initiate goal-directed behavior x.” (pp. 280, Webb & Sheeran,
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2003). By increasing the accessibility and sensitivity to contextual cues, implementation
intentions have been shown to increase the likelihood of performing a behavior (Sheeran,
2002) and modify the depletion effect (Webb & Sheeran, 2003). Implementation
intentions are believed to function relatively automatically, and are assumed to require
less self-control resources. For example, when participants were asked to form the
implementation intention to name the color and ignore the meaning of words in a Stroop
task, they were more persistent in the second self-control task, compared with those who
did not form implementation intention (Webb & Sheeran, 2003). This finding indicates
that implicit goals can guide behavior through mechanisms that require less self-control
resources.
Habits are different from implicit attitudes and implicit goals in many aspects
(Wood & Neal, 2007), and very few studies examine the role of habits in self-regulation.
Habits are learned cognitive associations between cues and responses. As people repeat a
response in a particular context over time, the behavior becomes associated with
recurring features of that context in memory. After sufficient repetition, perception of the
cue can automatically bring to mind the associated habitual response. Unlike implicit
attitudes and implicit goals, which feature multifinality and can be satisfied or
accomplished through multiple methods, habitual responses are restricted to a specified
behavior. For example, one’s implicit goal or positive attitude to stay healthy may cause
one to engage in different exercises and eat various kinds of healthy food. However, if
one has a habit of eating brown rice at home, that habit would not be translated into other
healthy behaviors.
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Many studies suggest that habit performance is largely independent of goals.
Tricomi, Balleine, and O’Doherty (2009) either over- or under- trained participants to
associate a cue with obtaining chocolates and orange flavored food, and then fed
participants with one type of food to satiation. At testing, unlike under-trained
participants who avoided food that was satiated, over-trained participants continued to
press the buttons for the satiated, now unpleasant food. Additionally, Neal, Wood,
Labrecque, and Lally (2012) found that priming habitual stadium-goers with stadium
pictures made them speak more loudly, whereas priming the goal to go to the stadium did
not have such an effect. These dissociations in which habits do not conform to current
goals support the idea that goals and habits are relatively independent. In the present
studies, we trained participants’ consumption habits and investigated the effect of habits
on food choices. We predicted that our habit training should influence consuming
behavior independent of food evaluation.
Optimal Conditions for Habit Performance
Goals and habits both exert influence on self-control behavior, but their impacts
vary depending on circumstances. Unlike active self-control, which requires cognitive
effort and motivation, habits can be cued automatically and not require as much
involvement of higher cognitive processes. Therefore, habits should have greater
influence on behaviors when people do not have sufficient deliberative capacity or when
they do not intend to use their rule-based, controlled system. Much evidence from
neuroimaging also supports that the habit system (i.e. striatum) is more active and related
to performance when self-control capacity or motivation is low (Foerde, Knowlton, &
Poldrack, 2006; Schwabe & Wolf, 2012; Marchette, Bakker, & Shelton, 2011).
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Many studies demonstrated double dissociations between the habit system and the
rule-based system by interrupting one or the other. Double dissociation is a method to
show independence between two systems. If one of the systems is impaired, only the
associated psychological function will be undermined whereas the other function should
remain normal. For instance, stress, distractions, and lesions in the hippocampus in
humans and rats have been shown to selectively impair deliberate performance, whereas
performance on implicit tasks remained intact. Conversely, impairment in caudate and
putamen influences only implicit learning, but does not degrade explicit learning (Yin,
Knowlton, & Balleine, 2004). In Schwabe and Wolf’s (2012) study, participants were
put under stress (or not) and then performed a classification learning task in which they
gradually learned the associations between cues and correct responses through training.
For the non-stressed group, task performance was positively related to hippocampal
activation but not striatal activation. On the contrary, for the stressed group, task
performance was positively related to striatal activation but negatively related to
hippocampal activation. This is consistent with the idea that the habit system exerts
stronger influence when their rule-based system is interrupted, in this study by stress. A
similar effect was found when the rule-based system was interrupted by a secondary
distraction task (Foerde, Knowlton, & Poldrack, 2006). Participants’ task performance
was related to the medial temporal lobe activity when they could concentrate, but their
performance was related to the striatal activity when they were distracted by the
secondary task. Therefore, when people’s motivation and capacity to control is
undermined by stress, distraction, or a previous demanding task, habits should have
stronger impact on their behavior. In the present studies, we used an ego-depletion
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paradigm and predicted that habit should have greater influence on behavior if
participants’ self-control motivation and capacity had been undermined.
Habits and Long-Term Goal Pursuit
The effect of ego-depletion on self-regulatory behaviors has elicited a lot of
interests over the past decade (Hagger, Wood, Stiff, & Chatzisarantis, 2010). However,
when researchers discuss habits in self-control, they focus mostly on bad habits,
especially that people tend to revert back to bad habits when distracted or depleted.
Because habits are automatic associations between contextual cues and behavioral
responses, whether a habit is good or bad should not alter its influence under limited
motivation or capacity (Neal, Wood, & Drolet, 2013). Hence, we predicted that people
should be more likely to perform their good habits, the ones that are consistent with long-
term goals, when their motivation or capacity to control behavior is low. In other words,
people should be more likely to perform good habits when they are stressed, depleted, or
distracted. In the present study, we tested this hypothesis using depletion as a mechanism
to interrupt people’s deliberative control.
The idea that good as well as bad habits have greater influence on people when
their decision making resources are low is supported by a recent study by Neal, Wood,
and Drolet (2013). Before or after a difficult mid-term exam, MBA students made
various snack choices for both healthy and unhealthy snacks, and it was assumed that
students have more self-control resources before the exam than after. Students also
reported how often they consumed each snack in similar circumstances, and this question
was used to measure the habit strength of their snack choices. The results showed that
depleted participants were more likely to make snack choices based on their strong
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habits, compared with those who were not depleted. In contrast, self-control resources
did not influence snack choices when habits were weak. Importantly, whether they
considered a snack to be healthy or unhealthy did not modify this relationship. In other
words, participants were more likely to choose habitual healthy as well as unhealthy
snacks when their self-control resources were low.
Unlike Neal, Wood, and Drolet’s (2013) study, which used self-report to measure
habit strength, the present studies attempted to form participants’ habits in the lab and
investigated the influence of habits on self-regulatory behaviors. In addition, the self-
control challenge in the studies by Neal, Wood, and Drolet (2013) was between
performing or not performing several goal-congruent and goal-incongruent behaviors,
and their participants were not explicitly asked to choose between goal-congruent and
goal-incongruent options. Self-control challenges in real life often happen when people
make explicit choice among multiple mutually exclusive options. Hence, Study 2
examined whether habits can still impact self-control behavior even under a context of
explicit decision-making.
Present Studies
In the first study, we manipulated habits and self-control resources to test the
hypothesis that both good and bad habits should have stronger influence on behavior
under depletion. Participants were trained to either approach (i.e. bad habit) or avoid (i.e.
good habit) eating chocolates by a joystick, and half of them were depleted. All
participants in the studies liked chocolate yet believed that eating chocolates is bad for
health, thus approaching eating chocolates was considered a bad habit and avoiding
eating chocolates was considered a good habit. We expected participants who had formed
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the good habit would eat fewer chocolates when depleted whereas participants with the
bad habit would eat more chocolates when depleted.
The second study sought to test the effect of habits in a context of multiple
alternatives. Self-control challenges in real-life often occur when people encounter
multiple behavioral possibilities that satisfy different desirable goals such that performing
one excludes the opportunity to achieve another at the moment. A common self-control
struggle occurs when a person has a goal to enjoy a short-term reward on one hand while
also having a goal to achieve long-term good. Self-control challenges in such a situation
(e.g. facing both healthy and unhealthy food at a buffet) may be more difficult than when
only a temptation is present (e.g. eating chocolate or not). Hence, we presented both
options to participants in Study 2 and examined the effect of habits in explicit food
choices.
Another goal of Study 2 was to find further evidence of habit training. Habits are
cognitive associations between contextual cues and responses. In Study1, the images of
people eating chocolates were taken in the lab so that the context shown in the images
was the same as the context where participants evaluated and consumed chocolates.
However, in Study 1, the contextual cue was not manipulated, and we did not have an
appropriate manipulation check of habits. Therefore, given that perception of an
associated cue should elicit habitual performance, we trained participants in Study 2 to
associate a specific cue with the response to obtain healthy food and manipulated the
presence of the cue in a following food choice task. We hypothesized that participants
who developed a strong habit through over-training would be more likely to choose
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healthy food over unhealthy food when an associated cue was present, but food selection
by undertrained participants would not differ by the contextual cues.
The two studies reported here used a joystick to manipulate habitual responses to
approach or avoid healthy or unhealthy food. In past research, pushing a joystick away
was considered an avoidance response, and pulling a joystick close was considered an
approach response (Cacioppo, Priester, & Berntson, 1993; Chen & Bargh, 1999,
Krieglmeyer & Deutsch, 2009). Participants in previous studies were faster at pulling a
joystick or a lever close to themselves in response to positive stimuli than pushing it
away, and were faster at pushing away a joystick or a lever away in response to a
negative stimuli than pulling it close. A manipulated chocolate eating habit in Study 1
was established through repeatedly pairing chocolate eating images with an approach or
an avoidance behavior. A baby carrot eating habit in Study 2 was similarly established
by repeatedly pairing a fractal cue, which served as a conditioned stimulus, with the
response to pull a joystick for baby carrots.
We believe the our joystick manipulation reflects habit training instead of goal
training even though joysticks have been used in past research to manipulate approach or
avoidance goals (Fishbach & Shah, 2006). In a study by Fishbach and Shah (2006), one
group of participants was instructed to pull a joystick in response to healthy food and
push it away in response to unhealthy food, and the other group did the opposite. After
training, participants trained to pull a joystick for healthy food were more likely to
choose healthy snacks than those trained to push it away. Although the habit training in
the present studies was similar, we did not instruct participants to approach a healthy
goal, and the rule for pulling or pushing a joystick in our studies was not based on health.
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Instead, an irrelevant criterion (i.e. whether the actions in the images require a lot of
facial muscle movement or not, see Method) was used in the joystick task in first study.
Unlike the study by Fishbach and Shah (2006), in which participants formed an approach
goal of healthy foods, participants in the present study either approach or avoid images
that contained facial muscle movement. Therefore, we do not consider the training to be
a manipulation of implicit goals, although we still included goal-related measurements in
the studies to test for any goal effects. The instruction in the second study did not involve
the goal to be healthy, and participants were trained to pair a cue that was either present
or absent during testing with pulling a joystick for baby carrots during training.
We selected participants in the two studies based on prescreening to increase the
chance that participants experienced a self-control struggle during the lab session as they
might experience in real life. Specifically, participants in the first study had both the goal
to enjoy chocolates and the goal to be healthy. Thus, our habit manipulation (i.e. either to
approach or avoid chocolates) was consistent with one of the goals and inconsistent with
the other, and the participants’ goals should be constant across conditions. In line with
this reasoning, eligible participants in the second study all liked M&Ms as well as baby
carrots.
Study 1
The first study used a 2 (training: approach/avoid) x 2 (depletion or not) design in
addition to a neutral training non-depleted group. Participants signed up for a study on
how emotions influence product evaluation. Participants were trained to approach or
avoid chocolates by either pulling the joystick close to themselves or pushing it away
when seeing images of people eating chocolates. Because habits should dominate when
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people’s motivation to control is low, it was predicted that depleted participants would be
more likely to perform their good as well as bad habits. In other words, we anticipated a
two–way interaction between depletion and training: Participants who were trained to
approach chocolates (bad habit) would eat more chocolates when depleted, whereas
participants who were trained to avoid would eat fewer chocolates when depleted.
Method
Participants.
Seventy-five participants (63 women), with average age of 19.56 (1.06) and body
mass index (BMI) of 21.91 (2.47) were tested individually.
Participants were selected from the psychology subject pool from the University
of Southern California or from flyers distributed around the campus.
Potential subject pool participants completed a batch of pre-screening
questionnaires online (along with other pre-screening questions from other research
groups), and the prescreening questions were (a) Are you currently on a diet? (b) How
much do you like to eat chocolates? (c) How much are you concerned about health when
deciding what to eat? (d) Are chocolates healthy or unhealthy food for you? Selection
criteria included: (a) non-dieters, (b) liked to eat chocolates, (c) concerned about health,
and (d) considered chocolates to be unhealthy. Qualified participants received a study
invitation and could sign up for the study voluntarily. Subject pool participants received
course credits and summer subjects received $10 for their participation
Procedure.
Participants were instructed to abstain from food for three hours before coming to
the lab session. Upon arriving at the lab, participants were given an overview of the
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procedure and then completed the followings tasks in order:
Joystick training.
Participants were randomly assigned to the approach, avoidance, or control group
during training. The goal of this task was to train participants’ habitual responses toward
the chocolate eating cues by practicing pushing or pulling the joystick repeatedly.
The experimenter told the participants that the task was to assess their image
processing skills, given that previous research had shown a relationship between imaging
and product evaluation. In this task, participants viewed images of different actions
during the task. For the approach and avoidance groups, half of the images (60 trials)
were people eating chocolates (critical trials), and the other half were people performing
various actions (i.e., playing the piano, violin, or guitar). There were 6 different images in
total, three chocolate eating and three non-chocolate eating images. Each image appeared
20 times in a random order. Participants were instructed to move the joystick in response
to the images. Specifically, those in the approach group were instructed to “pull the
joystick towards you” if the image shows an action that requires a lot of facial muscle
movement and “push the joystick away from yourself “ if the action in the image did not
require a lot of facial muscle movement. In other words, participants in the approach
group pulled the joystick close to themselves when seeing people eating chocolates and
pushed the joystick away when seeing non-chocolate relevant images. Participants in the
avoidance group were told to do the opposite. Therefore, they would push the joystick
away from themselves when seeing people eating chocolates.
The instructions for the neutral training group were the same as the approach
group. Participants were instructed to pull the joystick if the images depicted an action
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with a lot of facial muscle movement and push it away if the images did not. However,
the chocolates images were replaced with people making a face, kissing, and
singing. Thus, the control group was not exposed to chocolate eating images.
For all groups, the task took about twenty minutes to complete.
Implicit evaluations: manikin task.
The manikin task was adapted from Krieglmeyer and Deutsch (2009).
Participants had to decide whether a given combination of words made sense or not by
pressing the computer keyboard. At the start of a trial, a manikin figure appeared either
at the upper or lower part of the screen, which would then be right above or below the
word string. A word string then appeared in the middle of the screen. The word strings
made sense in half of the trials (i.e. 'Eating chocolates' and 'Doing dishes') and did not
make sense in the other half (i.e. 'Walking a book' and 'Mopping poker'). In 2 blocks,
participants were required to press the keyboard three times to move the manikin close
(approach) to the word strings that made sense and move the manikin away (withdraw)
from the words that did not make sense. In the 2 other blocks, participants were
instructed to do the opposite: press the keyboard three times to move the manikin away
(withdraw) to the word strings that made sense and move the manikin close to (approach)
the words that did not make sense.
For the critical trials that had “eating chocolates” in the middle of the screen, we
computed participants’ reaction time to move the manikin toward or away from “eating
chocolates” in order to capture their implicit approach and withdrawal motivation toward
eating chocolates. According to Krieglmeyer and Deutsch (2009), reaction time in the
manikin task is driven by people’s dispositions, evaluation, and behavioral
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approach/avoidance schemata. Because habit training is not expected to influence
evaluation, reaction time to approach or avoidance the term ‘eating chocolate’ should not
vary by groups.
Depletion.
Participants watched a 7-minute funny video that contained three sections: an
interview clip of Louis Szekly, a Japanese prank show, and a German
commercial. Participants in the depletion condition were told to inhibit their facial
emotional expression to these funny videos, although they could feel and experience their
emotions. Participants in the control condition were told to watch the video as they
would normally do. This emotional inhibition paradigm has been widely used in the ego-
depletion literature (Hagger, Wood, Stiff, & Chatzisarantis, 2010).
Chocolate consumption.
In this task, participants were instructed to sample and evaluate three kinds of
chocolates (Hershey®, Dove®, Russell Stover®). The chocolates were all sugar free but
the sugar free labels on the packages were covered. We used sugar free chocolates
because of the possibility raised in previous research that sugar might replenish self-
control strength (Gailliot, Baumeister, DeWall et al., 2007; although see Molden, Hui,
Scholer, Meier, Noreen, D’Agostino, & Martin, 2012).
Participants were seated at a table with three plates, each containing a different
kind of chocolate, with 5 chocolates on each plate. Participants were told to sample and
evaluate the chocolates for 10 minutes. To further engage participants in the task, the
experimenter asked participants to memorize the taste of chocolates since there might be
a recognition test at the end (actually bogus). Participants were all given 10 full minutes
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during this task, even if they stopped eating. While sampling the chocolate, they
completed an evaluation form and rated each kind of chocolate along 5 criteria. After
participants left the lab, the experimenter counted how many pieces participants
consumed. The results combined across the consumption of all three kinds of chocolates.
Dessert choice.
The experimenter showed participants 4 cupcakes and 4 granola bars in a basket
and asked them to choose one to bring back as a token of appreciation for participating in
the study. Participants had the choice of one healthy and one unhealthy option, so that
we could examine whether our manipulation of chocolate eating habit would affect a
non-chocolate related food decisions.
Survey and interview.
After evaluating the chocolates, participants completed an online questionnaire
about their demographic background, height, weight, depletion manipulation check,
everyday chocolate eating habits, experiences during chocolate sampling (e.g. I felt
reluctant to take an extra piece of chocolate), a trait measure of self-control (Tangney,
Baumeister, & Boone, 2004), conscientiousness (Big Five Inventory, John & Srivastava,
1999), and drive for thinness (Garner, Olmstead, & Polivy, 1983). We used self-reported
height and weight to calculate body mass index (BMI).
Finally, participants indicated what they thought was the purpose of the joystick
task and whether they suspected other purposes to the study than emotions and product
evaluation. Sixteen participants identified the purpose of the study to be self-control, but
no one could correctly recognize the purpose of the joystick task.
Results
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To
test
our
primary
hypotheses,
data
were
analyzed
with
a
Depletion

Condition(depleted
vs.
not)
x
Training
(approach
vs.
avoid)
design.
The
neutral

training
group
was
included
as
an
external
control
group
only
in
analyses
to

compare
chocolate
consumption
in
the
non-‐depletion
condition
across
the
three

training
conditions
(approach
vs.
avoid
vs.
neutral).
Depletion manipulation check.
Six items were used to examine the effectiveness of the depletion manipulation.
A principle component analysis from the approach and the avoidance groups revealed 2
factors with eigenvalues greater than one, with the positively and negatively phrased
items loading on the first (α= .61) and second factors (α = .88) respectively. The four
negatively phrased items were: Think back to the video watching task you completed
earlier (a) How hard did you try to complete this task? (b) I felt drained when I was done.
(c) I felt mentally exhausted when I was done. (d) When done, I was in a bad mood. The
two positively phrased items were: (a) When done, I felt like I could accomplish my goals.
(b) When done, I felt confident in my abilities. Composite measures were formed from the
factor scores to represent effort expended (4 negative items) and goal confidence (2
positive items).
Depletion had significant influence on reported expending effort, F(1, 56) = 4.20,
p = .045. Compared with the non-deletion condition, participants in the depletion
condition considered the task to be more challenging and exerted more effort in the task.
Neither training nor interaction effects between training and depletion were found (ps
> .05). In terms of goal confidence, a 2 (depletion) x 2 (habit training) analysis did not
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reveal any significant effects, ps > .05. The approach and the avoidance groups did not
differ in how positively they felt about themselves after watching the video.
Chocolate consumption.
To test our prediction that participants fell back on their good or bad habits when
depleted, a 2 (depletion) x 2 (habit training) ANOVA and a resampling procedure were
performed. A multiple regression analysis examined whether habit training and depletion
interacted to influence chocolate consumption above and beyond the effect of gender and
participants’ attitudes toward the chocolate. To test our prediction that habit training
should have a weaker effect on behavior when people had sufficient self-control
resources, an additional one-way ANOVA tested the effect of habit training
(approach/avoid/control groups) on consumption within the non-depletion condition.
Three participants provided nonoptimal data. One had an unrealistically high
survey response concerning weekly chocolate consumption, and two had high error rates
in the joystick task. To increase sensitivity of testing, the interaction effect between
depletion and training was analyzed both with and without these three
participants. Without the three participants, a significant interaction emerged between
training and depletion on chocolate consumption, F(1, 53) = 6.88, p = .011, partial
!
!
= .12 (see Table 1). Simple effects tests to decompose the interaction revealed that the
approach group consumed more chocolates when depleted, F(1, 53) = 8.34, p = .006, and
depletion did not have a significant effect on the avoidance group, p = .385 (see Figure 1
and Table 1).
Analysis including the three participants was also computed using a permutation-
resampling technique (Anderson, 2001). The analysis was performed by randomly
GOOD HABITS IN FACILITATING LONG-TERM GOALS

22

distributing all cases into the 4 conditions 10, 000 times and computing their pseudo Fs.
The p-value was obtained by comparing the observed F(1, 56) = 5.24 with the random
10,000 Fs. In this resampling analysis, the interaction between depletion and training
remained significant, p < .03.
A multiple regression tested whether the interaction between habit training and
depletion remained after adjusting for gender and chocolate evaluation, factors that
should also affect chocolate consumption. Chocolate evaluation from one participant was
missing so the sample size was 59 in this analysis. As expected, men tended to eat more
chocolates, ! = .32, t(53) = 2.74, p = .008. Chocolate evaluation also had a marginally
significant effect on chocolate consumption. People who liked chocolates tended to eat
more, != .23, t(53) = 1.96, p = .055. The interaction effect between depletion and
training remained significant after adjusting for gender and evaluation, ! = 1.23, t(53) =
2.43, p = .019. Thus, the finding that depletion tended to amplify both good and bad
habit performance could not be explained by people’s attitudes toward eating more or
fewer chocolates. In other words, depletion and habit did not change self-control
behavior through increasing liking toward the chocolate in the approach group or
decreasing liking in the avoidance group.
An additional analysis using a one-way ANOVA tested whether the approach,
avoidance, and control groups had similar chocolate consumption when they were not
depleted. As expected, no effect of training was found when participants had sufficient
control motivation or capacity, F(2, 42) = 1.76, p = .185.
The habit training also showed an interesting effect on the self-reported
approach/avoidance disposition toward chocolates. In the survey following the
GOOD HABITS IN FACILITATING LONG-TERM GOALS

23

evaluation of chocolates, compared with the approach group, the avoidance group tended
to reported greater reluctance to eat chocolates, t(58) = 1.79, p = .079. The effect did not
interact with depletion.
Implicit evaluation: manikin task.
The manikin task was performed right after the training. To test whether the habit
training affected implicit evaluation toward eating chocolate, a t-test analysis was
performed. Reaction times to avoid or approach the phrase “eating chocolate” did not
significantly vary by habit training, p > .05.
Dessert choice.
As expected if the joystick task established a habit and not a broader preference
for unhealthy desserts, a 2 x 2 logistic regression revealed that dessert choice was
unaffected by depletion, habit manipulation, or their interaction, ps > .05.

No significant 2 (depletion) x 2 (training) interaction effects were found on
chocolate evaluation, goals to be healthy, BMI, trait self-control, conscientiousness, or
drive for thinness. Regarding prescreening items, we also did not find preexisting group
differences in attitudes toward chocolate, concern about health, and belief in
healthfulness of chocolate, ps >.05.
Discussion
Habits and depletion interact to influence consumption.
Study 1 manipulated people’s good and bad habits in an experimental setting and
examined the effect of depletion on habit performance. Our hypothesis that habits will
exert stronger influence regardless of their congruency with long-term goals was partially
GOOD HABITS IN FACILITATING LONG-TERM GOALS

24

supported. Depleted participants who were trained to approach chocolates consumed
more chocolates than non-depleted participants. The direction was consistent with
previous depletion studies that showed increased unhealthy consumption after depletion
(Inzlicht & Kang, 2010; Vohs & Heatherton, 2000). On the other hand, this commonly
reported depletion effect was not significant among those who were trained to avoid
chocolates. Participants trained to avoid did not eat more or less chocolate because of
depletion. Moreover, the interaction between habit and depletion was robust, given that
the resampling analysis also yielded this pattern. Furthermore, this stronger influence of
the habit system when people are unable to deliberate echoes Neal et al.’s (2013) research
using self-report measures of habit strength. Much like their naturally-occurring habits,
participants tended to perform their experimentally manipulated habits when self-control
strength was low.
Although participants trained to approach ate more chocolates after depletion,
participants trained to avoid did not eat significantly less when depleted. Thus, the
hypothesis that habits have a greater impact on depleted people was only supported in the
approach group. Because all participants in this study liked chocolates, they might have a
stronger predisposition to approach chocolates than to avoid. Training participants to
develop a habit incongruent with the current predisposition may require more trials than
training them to have a congruent one. Given the same amount of approach and
avoidance training in the study, the experimentally manipulated habit to avoid eating
chocolates might be weaker than the habit strength to approach.
Habits and goals are relatively independent.
GOOD HABITS IN FACILITATING LONG-TERM GOALS

25

Both goals and habits can guide self-regulation, and in this study we tested
whether they exerted influence on behavior through independent mechanisms. We found
that chocolate rating did not differ according to whether participants had undergone
approach or avoidance training, and, after controlling for rating of the chocolate, the
predicted interaction between habit and depletion remained significant. Habit training
and depletion also did not affect self-reported goals to be healthy or implicit evaluation of
eating chocolates. Therefore, participants with the habit to approach chocolates did not
eat more chocolates because they liked them more or because they were less concerned
about health. These findings are consistent with the notion that action control is
modulated by goals as well as habits, and these two sources of influence are relatively
independent (Balleine & O’Doherty, 2009). In the devaluation paradigm, participants’
attitudes toward a kind of food changed from positive to negative (devalued) due to
overeating that food; yet, if they had a strong habit to obtain the devalued food, they
continued to do so (Tricomi et al., 2009). Hence, unlike goals, habits are relatively
inflexible to value changes in outcomes. Reducing control motivation or capacity should
render people more likely to continue a well-learned habit even when they have also a
goal to obtain appealing and desirable short-term rewards.
The study also captured another notable difference between habits and goals,
involving goal multifinality. Goals can be pursued through various behaviors, whereas
habits are based on a specific S-R association and do not spill over to other behaviors that
support the same goal. A habit to recycle would not contribute to a habit of taking public
transportation even though they both serve a goal of environmental sustainability. In line
with this reasoning, the manipulation in the present study was specific to eating
GOOD HABITS IN FACILITATING LONG-TERM GOALS

26

chocolates. Accordingly, when participants chose between cupcakes and granola bars at
the end of the study, their choices were not influenced by habit training.
Study 1 showed that habits could modify people’s self-control behavior. People
do not always behave inconsistently with their long-term goals when their control system
is undermined. Instead, their good habits can moderate this relationship and protect them
from seeking out a tempting alternative that is harmful in the long-run. People without a
good habit are more likely to yield to temptation especially when they are not motivated
or are incapable of exerting control, such as when they are depleted, stressed, or
distracted. On the contrary, good habits can facilitate long-term goal pursuit even under
those difficult situations.
Although the above evidence suggests that the training was unlikely to influence
participants’ valuation of chocolates or goal to be healthy, it remains possible that the
training influenced these goals implicitly. To address this possibility, Study 2 included
an implicit attitude measure.

Study 2
The induced self-control conflict in Study 1 was between either eating chocolates
or not. When people encounter only one tempting item, the idea that avoiding the
temptation is better in the long term may not be salient or reach awareness. Thus, people
sometimes might not experience a self-control struggle when only one tempting stimulus
is present. On the other hand, when multiple alternatives varying in long-term and short-
term values are simultaneously presented to people, knowing that another alternative has
a greater short-term or long-term value than the other should create a stronger self-control
GOOD HABITS IN FACILITATING LONG-TERM GOALS

27

struggle. Study 2 examined whether habits could influence decision making in a context
of multiple alternatives, in spite of their attitudes toward the alternatives.
Previous research has examined people’s self-control behavior and neural activity
when people simultaneously encountered two food options that differed in health and
taste values. Hare, Camerer, and Rangel (2009) showed that selecting a healthy food
option was related to activity in the dorsal lateral prefrontal cortex (dlPFC), and
activation in dlPFC was negatively related with activation in the ventral medial prefrontal
cortex, a region implicated in valuation. This finding seems to suggest that active and
deliberate processes are involved in making a healthy choice, and the exertion of self-
control is associated with reduced value of temptation. However, as discussed in Study 1,
habits and deliberate control can function independently. Although it is often assumed
that people select what they like rationally and need deliberative control to inhibit
temptation, habits may be another source of influence on healthy behavior that is largely
independent of short-term and long-term values of the alternatives.
Whether a healthy behavior is goal-directed or habit-driven should depend on the
presence of associated cues. Because habits are formed through repeated pairings of
contextual cues and responses, people should be more likely to perform habits when
perceiving an associated cue. No study has directly examined whether perception of
relevant contextual cues can moderate the impact of habits on self-control performance.
To test this in Study 2, the habit to get healthy food was trained with a specific cue, and
food choices were performed in the context of the trained cue or novel cues. Because
habitual responses can be elicited by associated cues, we expected participants to show
more habit-consistent behavior when the cue to select healthy food was present than
GOOD HABITS IN FACILITATING LONG-TERM GOALS

28

when it was absent. This pattern of results would additionally bolster our claim that
habits are activated by cues relatively independently of goals.
Study 2 used a 2 (training: over-trained or under-trained) x 2 (cue: trained/novel)
design. Participants were either over- or under-trained to learn an association between a
fractal cue and the response to pull the joystick for baby carrots. Then, all participants
performed an attention control task after training. This was to undermine their
subsequent control motivation and capacity. At the testing phase, participants chose
between baby carrots and M&Ms while in the presence of the trained cue or novel cues.
We hypothesized that over-trained participants would be more likely to select baby
carrots when seeing the trained cue than when seeing the novel cues. However, given
that the association between the cue and the response was weaker for undertrained
participants, the cues should not impact their food choice as much. Thus, in the analysis
of healthy choices, we predicted a significant interaction between training and cues:
Over-trained participants should be more likely to choose baby carrots in the trials with
the associated cue than in the trials with novel cues. In contrast, the likelihood of
choosing baby carrots should not vary by cues for under-trained participants.
Method
Participants and prescreening.
Twenty-three participants, 12 in the under-training group and 11 in the over-
training group, completed the study (one additional was deleted due to a computer
malfunction). Participants were selected based on prescreening from the subject pool at
University of Southern California based on the following criteria (a) non-dieters (b) liked
to eat baby carrots (c) liked to eat M&Ms (d) concerned about health (e) cared about taste
GOOD HABITS IN FACILITATING LONG-TERM GOALS

29

of food (f) considered M&Ms to be unhealthy (g) considered baby carrots to be healthy
(h) female. Studies have shown gender difference in eating behavior (Horstmann, Busse,
Mathar, et al., 2011) and male participants also consumed more chocolates in Study1.
Because it was more difficult to recruit male participants from the psychology subject
pool, Study 2 only recruited female students to control for gender.

Procedure.
Participants were told that we were interested in how colors influence food
choices. Participants completed a pre-lab survey and abstained from food for 4 hours
prior to the study. Participants were either over- or under- trained on the habit of getting
baby carrots, completed an attention control task, and selected between baby carrots and
M&Ms. After all of the food choice tasks, participants completed an Affective
Misattribution Procedure (AMP) and post-experiment surveys and answered interview
questions before collecting all winnings.
Pre-lab survey.
The pre-lab survey was sent to participants after they signed up for the study. If
the survey was not completed by the night prior to the lab visit, the lab visit was
cancelled. The prescreening questions were repeated in this pre-lab survey, along with
other irrelevant questions to disguise the study purpose.
Training.
During training, participants saw 5 different fractal pictures, one of which was
randomly picked as a participant’s food cue (CS+). Participants were instructed to pull
the joystick close toward themselves in order to get baby carrots when the CS+ fractal
GOOD HABITS IN FACILITATING LONG-TERM GOALS

30

was present, and to rest when they saw other fractals (CS-). The length of each food
block was 30 seconds on average, and the length of each rest block was 12 seconds.
During the food blocks, participants could pull the joystick as frequently as they liked.
The food and the rest blocks were intermixed randomly so that participants would learn
to differentiate fractals and learn to pull the joystick only when a specific cue was present.
The baby carrots were delivered on a variable interval schedule (using geometric
distribution with probability = .1), and feedback was given right after they pulled the
joystick. If participants won the reward, they saw a 3-second animation of baby carrots,
and the timer for the reward was reset. If the reward was not available when the
participants responded, they saw a gray dot, but the timer was not reset. Each rewarded
response resulted in an additional 1/4 of a baby carrot, and participants collected all
winnings at the end of the lab session. We used a variable interval schedule because
interval schedules were shown to facilitate habit formation more rapidly than ratio
schedules in previous research (Yin & Knowlton, 2006; Dickinson, Nicholas, & Adam,
1983). Training was about 23 minutes for the over-training group and was about 4
minutes for the undertraining group.
To equalize the time and the total number of baby carrots won in each condition,
the under-trained participants completed another 20-minute keyboard task, in which
twenty-four baby carrot blocks were mixed with sixteen rest blocks. Both baby carrot
and rest blocks were 30 seconds. During each baby carrot block, participants were asked
to press the keyboard as frequently as they liked when seeing the text “Now you can press
the space bar for baby carrot”. The reward was delivered on a fixed ratio schedule: 1/4
of a baby carrot was delivered with every 10 presses. Fixed ratio schedules were used to
GOOD HABITS IN FACILITATING LONG-TERM GOALS

31

keep participants’ attention on reward and thus inhibit habit formation. Baby carrots
were collected at the end of the session.
Attention control.
All participants in the present study underwent a depleting manipulation.
Attention control has been widely used as a depletion manipulation in previous research
(Hagger, Wood, Stiff, & Chatzisarantis, 2010). Controlling attention requires monitoring
and inhibitory control, so people who had experienced attention control are assumed to
have lower self-control motivation and resources.
Participants were told that another researcher from the lab had collected interview
video clips and needed participants to form an impression of the interviewee in the video.
The 6-minute video clip, which was muted, depicted a woman being interviewed.
Meanwhile, some everyday words (e.g. tree, glue, crutch) appeared and disappeared on
the bottom right corner of the computer screen. To deplete control motivation and
resources, participants were instructed not to look or read the words.
Testing.
At testing, participants could choose between M&Ms and baby carrots while in
presence of their associated food fractals and four other novel fractals. When the
associated food fractal was on the computer screen, participants pulled the joystick close
to themselves for baby carrots (same as their training), and pushed the joystick away for
M&Ms. Similar to training, baby carrots and M&Ms were delivered on variable interval
schedules, and feedback was given after each attempt. Participants were told to use the
joystick to get food as frequently as they liked when they saw any fractals. Each food
block was 30 seconds, and rest block was 5 seconds. Participants experienced a total of
GOOD HABITS IN FACILITATING LONG-TERM GOALS

32

32 food blocks (16 with the trained fractal and 16 with the novel fractals) and 8 rest
blocks, with total length about 16 minutes.
Affect misattribution procedure (AMP).
AMP (Payne, Cheng, Govorun, & Stewart, 2005) was used to measure implicit
attitudes toward M&Ms and baby carrots. We believed habit training would not affect
participants’ implicit attitudes, so we used the AMP task to test this hypothesis.
Stimuli in the AMP task were pairs of images that were flashed one after another.
The first picture, either M&Ms or baby carrots, was flashed briefly, and then followed by
a random inkblot. Participants were told that this task measured their preferences for
basic patterns and the first image was just to signal the start of a trial. Participants
reported, based on their gut feelings, whether they liked or disliked the inkblots by
pressing one of the two corresponding buttons. Their implicit attitudes toward M&Ms
and baby carrots were assumed to be reflected in their preferences for the subsequent
random inkblots. Implicit attitudes were calculated by the proportion of inkblots
participants judged as pleasant after the baby carrot and M&M primes.
To test whether different fractals influenced people’s explicit attitudes and desire
for baby carrots, we mixed 3 explicit attitude and motivation questions within AMP
because both tasks involved chocolate and baby carrot primes. The three questions were
“How much are you willing to pay for a bag of 10 baby carrots?” “How much do you feel
like having a baby carrot now?” and “How tempting is eating a baby carrot for you right
now?” Before each explicit question appeared on the screen, participants saw a brief
flash of the trained fractal or one of the novel fractals. Thus, each question appeared
twice, each time paired with one of the fractals. The order of fractals and questions was
GOOD HABITS IN FACILITATING LONG-TERM GOALS

33

randomized, and the explicit questions were given every 10 trials of AMP. Given that
habit and goal-directed values should be relatively independent, we did not expect habit
training to influence explicit attitudes toward baby carrots, but habit training might
induce a stronger urge to choose baby carrots.
Survey.
Survey questions included demographic backgrounds, experience with depletion,
habits for eating M&Ms and baby carrots, general mindfulness of eating (e.g. “in general,
how much thought do you give to what you eat”), goals for eating healthily (e.g. “eating
healthy food is an important of mine right now”), trait self-control (Tangney, Baumeister,
& Boone, 2004), drive for thinness (Garner, Olmstead, & Polivy, 1983), ten-item-
personality-inventory (Gosling, Rentfrow, & Swann Jr., 2003), and a manipulation check
concerning food abstinence prior the lab session.
Interview.
Finally, participants reported whether they suspected that the study had other
purposes, and their ideas about the joystick task. Although 8 disbelieve that the
experimenter told them the true purpose, no one identified the purpose of the joystick
task correctly.
Results
The dependent variable was constructed from the total number of attempts to get
baby carrots over the sum of attempts to get baby carrots and M&Ms, averaged across
blocks. Results were analyzed using a cue (habit fractal vs. other) x training (extensive,
habit vs. minimal) ANOVA. Although we anticipated that the relevant cues would have
a stronger impact on food choices in the over-trained participants than the undertrained
GOOD HABITS IN FACILITATING LONG-TERM GOALS

34

participants, neither cue, F(1, 21) = 1.23, p = .28, condition, t(21) = 1.59, p = . 126, nor
their interaction, F(1,21) = 1.08, p = .311, influenced the percentage of choosing baby
carrots (Table 2).
Discussion
We did not find evidence to support the hypothesis that habits exert stronger
impact on behavior when an associated cue is present. Overall, the cue did not influence
how participants chose between baby carrots and M&Ms.
Multiple reasons may explain the null findings. First, the sample size of 24
subjects was small, and the design might be underpowered. Second, when participants
were told that the food selection rule (i.e. pull for baby carrots and push for M&Ms) was
the same across all kind of fractals, participants could essentially ignore all the fractals
and become inattentive to all of the stimuli on the screen. To rectify that, future studies
should increase participants’ engagement in the task. For example, we can ask
participants to count ten seconds once they see an irrelevant fractal on the screen and
interweave the time-counting fractals, the habit-associated fractal, and the novel fractals
at testing.
In the current study, both habit-associated and novel fractals engaged the same
response program (i.e. pull and push), and it is possible that the learned association
quickly spilled over to the novel fractals due to the similarities among them. Given that
habits are learned associations between cues and responses, habit performance should
depend on not only the perception of associated cues but also on the associated responses.
Both unfamiliar cues and unfamiliar response patterns can be used to interrupt habit
GOOD HABITS IN FACILITATING LONG-TERM GOALS

35

performance. One potential approach to dissociate habit-driven and goal-directed
performance can be manipulating both factors at the same time.
General Discussion
Little research has investigated how good habits guide self-control choices. The
present studies, to our knowledge, were the first research attempt to train participants’
habits in an experimental setting and examined the effect of habits on self-control. We
found that trained habits, just as their naturally-occurring ones, could guide self-control
behavior. People with bad habits are more likely to perform their bad behaviors when
they are unwilling or unable to exert self-control. However, having a good habit can
prevent people from poor behavior even when self-control motivation or capacity is low.

GOOD HABITS IN FACILITATING LONG-TERM GOALS

36

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effortful self-presentation depletes regulatory resources. Journal of Personality
and Social Psychology, 88, 632-657.
Vohs, K. D., & Heatherton, T. F. (2000). Self-regulatory failure: A resource-depletion
approach. Psychological Science, 11(3), 249-254.
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depletion? Journal of Experimental Social Psychology, 39, 279-286.
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interface. Psychological review, 114(4), 843-863.
Yin, H. H., Knowlton, B. J. & Balleine, B. W. (2004). Lesions of dorsolateral striatum
preserve outcome expectancy but disrupt habit formation in instrumental learning.
GOOD HABITS IN FACILITATING LONG-TERM GOALS

41

European Journal of Neuroscience, 19, 181-189.
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formation. Nature Reviews Neuroscience, 7(6), 464-476.

GOOD HABITS IN FACILITATING LONG-TERM GOALS

42

Table 1
Summary of Chocolate Consumption

Depletion Non-depletion
Approach Avoid Approach Avoid Control
Mean 4.333 3.231 2.933 3.679 3.500
Standard
Deviation
1.332 1.130 .998 1.739 1.225
N 15 13 15 14 15

Note. The mean and standard deviation of chocolate consumption. The two-way
interaction between depletion and training was significant (p=.011). In terms of simple
effect, depletion effect was significant within the approach group (p=.006) and training
effect was significant within the depletion condition (p=.033).

GOOD HABITS IN FACILITATING LONG-TERM GOALS

43

Table 2.
Percentage Attempt to Obtain Baby Carrots
Over-training group, n=11 Under-training group, n=12
Learned fractal .52 (.24) .32 (.27)
Novel fractal .43 (.21) .32 (.27)

Note. The percentage of attempts to get baby carrots within each block was computed for
each block and then averaged across blocks. Standard deviation is presented in
parenthesis.

GOOD HABITS IN FACILITATING LONG-TERM GOALS

44

Figure 1. The two-way interaction between depletion and training was significant, p=.011.

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Creator Lin, Pei-Ying (author)

Core Title The role of good habits in facilitating long-term goals

School College of Letters, Arts and Sciences

Degree Master of Arts

Degree Program Psychology

Publication Date 07/11/2014

Defense Date 07/11/2014

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

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