© 2018 melissa r. cervantezufdcimages.uflib.ufl.edu/uf/e0/05/26/14/00001/cervantez_m.pdf ·...
TRANSCRIPT
CORTICAL PROCESSING DURING REWARD BASED DECISION-MAKING
By
MELISSA R. CERVANTEZ
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2018
© 2018 Melissa R. Cervantez
To my parents, Tony and Irma Cervantez
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ACKNOWLEDGMENTS
I would like to thank my advisors, Drs. Neil Rowland and Andreas Keil for
supporting me throughout my time in the BCN program. I appreciate and value all of the
advice, guidance, and knowledge that was shared with me. I am truly grateful that I was
able to train and work with you both and that I had wonderful mentors to help shape my
academic career.
I would also like to acknowledge my family for the constant love and support. I
would not be who or where I am without you all believing in me. I want to acknowledge
how wonderful it was to know that everyone back home was rooting for me and sending
me love. Moving across the country to pursue my education would not have been
possible without all your love, prayers, and support.
Additionally, I would like to thank my friends and colleagues for their constant
help and encouragement. I want to thank everyone who was helpful to me while I
worked on this project, especially my dissertation committee. I am extremely grateful for
all your contributions to my project.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS ...................................................................................................... 4
LIST OF TABLES ................................................................................................................ 7
LIST OF FIGURES .............................................................................................................. 8
LIST OF ABBREVIATIONS ................................................................................................. 9
ABSTRACT ........................................................................................................................ 10
CHAPTER
1 INTRODUCTION ........................................................................................................ 12
Obesity ........................................................................................................................ 12 Homeostatic and Hedonic Models of Food Intake ..................................................... 14 Food Environment ....................................................................................................... 15 Impulsivity.................................................................................................................... 16
Reward sensitivity ................................................................................................ 17 Disinhibition to Foods ........................................................................................... 18 Attention................................................................................................................ 20 Treatments ........................................................................................................... 21
Three Factor Eating Questionnaire ............................................................................ 22 Biased Competition Model .......................................................................................... 23 ssVEPs ........................................................................................................................ 24 Significance and Innovation ........................................................................................ 25 Study Aims .................................................................................................................. 27
Specific Aim 1 ....................................................................................................... 27 Specific Aim 2 ....................................................................................................... 28
2 METHODS .................................................................................................................. 29
Participants ................................................................................................................. 29 Behavioral Task .......................................................................................................... 30 Trials ............................................................................................................................ 32 Stimuli and Cues ......................................................................................................... 32 EEG Recording and Processing ................................................................................. 33 Statistical Methods ...................................................................................................... 34 Variables ..................................................................................................................... 35
3 RESULTS .................................................................................................................... 41
Demographic and Behavioral Results ........................................................................ 41 Food and Money ......................................................................................................... 42
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ssVEPs for Food ......................................................................................................... 43 ssVEPs for Money ...................................................................................................... 44
4 DISCUSSION .............................................................................................................. 67
Demographic and Behavioral Correlates ................................................................... 67 ssVEPs ........................................................................................................................ 69
Visual Processing ................................................................................................. 69 Behavioral Relevance .......................................................................................... 70
Limitations ................................................................................................................... 71 Future Directions ......................................................................................................... 72
LIST OF REFERENCES ................................................................................................... 74
BIOGRAPHICAL SKETCH ................................................................................................ 81
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LIST OF TABLES
Table page 3-1 MANOVA results for demographic and behavioral variables ................................ 46
3-2 Correlation table for the behavioral and demographic variables .......................... 47
3-3 Repeated Measures ANOVA ssVEP results ......................................................... 48
3-4 Repeated Measures ANOVA ssVEP results for the food block ............................ 48
3-5 Repeated Measures ANOVA ssVEP results for the money block ........................ 48
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LIST OF FIGURES
Figure page 2-1 Image presented during instructions ...................................................................... 36
2-2 Example of a trial shown during the behavioral task ............................................. 37
2-3 Time sequence and presentation of a trial ............................................................ 38
2-4 Grand mean ssVEPs. ............................................................................................. 39
3-1 Correlation between body measurements ............................................................. 49
3-2 Correlation between uncontrolled eating and emotional eating ............................ 52
3-3 Correlation between BMI and cognitive restraint................................................... 53
3-4 Correlation between BMI and money impulsivity .................................................. 54
3-5 Correlation between uncontrolled eating and food impulsivity .............................. 55
3-6 Correlation between minutes without food and cognitive restraint ....................... 56
3-7 Correlation between uncontrolled eating and reaction time .................................. 57
3-8 Food block main effect of reward type ................................................................... 58
3-9 Food block main effect of other option shown ....................................................... 59
3-10 Correlation between minutes without food and ssVEP difference between large and small rewards ......................................................................................... 60
3-11 Money block main effect of reward type ................................................................ 61
3-12 Money block main effect of other option shown .................................................... 62
3-13 Money block interaction between reward and other option shown ....................... 63
3-14 Correlation between cognitive restraint and ssVEP amplitude ............................. 64
3-15 Correlation between cognitive restraint and ssVEP amplitude ............................. 65
3-16 Correlation between cognitive restraint and ssVEP amplitude ............................. 66
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LIST OF ABBREVIATIONS
ACC Anterior cingulate cortex
ADHD Attention deficit/hyperactivity disorder
BMI Body mass index
BOLD Blood-oxygen-level-dependent
DD Delay discounting
DG Delay of gratification
EEG Electroencephalography
EFT Episodic future thinking
fMRI Functional magnetic resonance imaging
OFC Orbitofrontal cortex
PFC Prefrontal cortex
ssVEP Steady-state visual evoked potential
TFEQ-18 Three-factor eating questionnaire r18
WC Waist circumference
WHR Waist to hip ratio
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
CORTICAL PROCESSING DURING REWARD BASED DECISION-MAKING
By
Melissa R. Cervantez
August 2018
Chair: Neil Rowland Cochair: Andreas Keil Major: Psychology
Research on the underlying mechanisms of eating behavior is vital in order to
help prevent and reverse the rates of obesity. While there is not a single causal factor
that contributes to overeating and obesity, decision-making research has shown that
obese individuals lack inhibition and control. Decision-making may impact eating
behaviors, such as choosing a particular food or eating despite being sated. These
behaviors can lead to greater caloric excess and increases in adipose tissue, therefore
contributing to the current obesity epidemic. The biased competition model refers to the
competition of neuronal resources that allows behaviorally relevant stimuli to be favored
over irrelevant stimuli in the visual field. The allocation of cortical resources to
behaviorally relevant stimuli may be an important component of decision-making
behavior. Steady-state visual evoked potentials (ssVEPs) allow us to measure cortical
activity to concurrent stimuli thereby enabling quantification of attention selection when
observers are presented with multiple options simultaneously.
The current study used ssVEPs to measure cortical activity to two separate
reward cues during a decision-making task for 51 participants. The reward cues were
varied by amount and time delay with two separate blocks to measure ssVEP
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amplitudes to food or monetary rewards. Participants also provided physiological
measures, demographic and behavioral information, and completed the Three Factor
Eating Questionnaire 18 (TFEQ-18). Physiological measures did not correlate with the
ssVEP amplitudes. However, repeated measures ANOVAs revealed a significant main
effect of reward size and time delay for the food and monetary blocks. Average ssVEP
amplitudes for the larger, later reward cue were significantly smaller than the smallest
and medium sized rewards for the food block. ssVEP amplitudes for the larger, later
reward was significantly smaller than the medium reward option for the monetary block.
Findings suggest that a smaller, sooner reward may be more behaviorally relevant than
larger, later rewards across all participants, regardless of health status. Future research
with a larger range of obese subjects is needed to fully understand the relationship
between obesity and reward-based decision-making.
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CHAPTER 1 INTRODUCTION
Obesity
In the United States, more than a third of adults are considered obese and
another one third are considered overweight (Ogden et al., 2014; Flegal et al., 2012).
Obesity is one of the top leading causes of preventable death and is associated with
many health problems including diabetes, hypertension, and dyslipidemia (Malnick &
Knobler, 2006). Due to the high comorbidity with other diseases, roughly 280,000
people in the United States die yearly due to obesity related illnesses (Allison et al.,
1999). Despite the well-known adverse effects associated with obesity, the prevalence
rates are still rising, and obesity is currently considered a global epidemic.
In clinical practice, the most widely used method to measure obesity is based on
the measurement of body mass index (BMI) (World Health Organization, 2011). BMI is
calculated by using measurements of weight and height, by taking the weight in
kilograms divided by height in meters squared (kg/m2). BMI scores fall on a continuum
with ranges used to describe health status. A BMI score greater than or equal to 25 is
considered overweight, a BMI of 30 or greater is considered obese class I, 35 or greater
considered obese class II, and 40 or greater is considered obese class III (World Health
Organization, 2011). While BMI is a widely used method to classify health status, there
are some caveats that should be noted. BMI scores do not measure fat or muscle mass
and do not fully provide insight on adipose tissue distribution (Prentice & Jebb, 2001).
Additionally, there have been disagreements on the classification ranges in regard to
gender and age (Deurenberg et al., 1991). Lastly, certain ethnicities have different body
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fat distributions and percentages, which may not accurately explain their health status
when using BMI (Lear et al., 2007).
A method that measures visceral central abdominal fat is waist circumference
(WC) and waist to hip ratio (WHR) measurements. These measurements have been
shown to have a strong association with the development of metabolic disorders, in
which central obesity is a risk factor, such as cardiovascular disease, type 2 diabetes,
and hypertension (Nicklas et al., 2004; Nicklas et al., 2006; Visscher et al., 2001).
Additionally, WC and WHR can be gender specific with the World Health Organization
having classifications based on gender. To have a comprehensive measure of health
status, all three measurements, BMI, WC, and WHR will be collected and used in the
current research project.
Currently, there are gender disparities in the development of obesity in many
countries around the world, with women having a higher prevalence for obesity than
men (Popkin & Doak, 1998). There are many theories to explain this disparity which
include differences in body composition, effects of hormones and pregnancy, and
differences in energy metabolism (Lovejoy et al., 2009). Previous research indicates
that men have greater amounts of weight loss and benefits from physical exercise
programs than women. It was found that women increase their energy intake and food
consumption more than men after exercise, indicating that there may be sex differences
in response to metabolic response to physical activity (Westerterp et al., 1992).
Additionally, research has shown that the distribution of body fat differs by gender: post-
menopausal women and men tend to accumulate more abdominal and visceral fat
compared to premenopausal women who typically have more lower body fat (Lovejoy et
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al., 2009). The differences between women and men on the development and
maintenance of obesity suggests that research should investigate gender differences in
obesity further. Additionally, gender differences in obesity should also be explored and
may tailor sex-specific treatment options for obesity in the future.
Homeostatic and Hedonic Models of Food Intake
The development of obesity occurs when there is an excess of energy
consumption (food intake) in relation to energy expenditure (physical activity or
metabolic energy loss). The increased energy intake typical in obesity has not been
attributed to a single causal factor but rather a combination of genetics, environment,
socioeconomic status, physiology, and psychology (Aronne et al., 2009). There are
homeostatic systems that regulate energy balance and hedonic systems which respond
to the rewarding properties of taste and food (Saper et al., 2002). The relationship
between these two systems are responsible for the control and reinforcing effects of
food consumption.
Previous models of homeostatic energy intake focused on physiological signals
that control hunger and satiety to keep weight stable. Earlier models focused on a
simple feedback system where the hypothalamus directly controlled food intake and
kept body weight around a set point (Anand & Brobeck, 1951; Kennedy, 1953; Hervey,
1959). Set point theory discussed by Harris (1990) mentions how many complex
systems such as the roles for nutrients, hormones, dietary composition and organoleptic
properties, neural pathways, brain nuclei, and neurotransmitters regulate body weight to
keep weight relatively stable around a set point. It is postulated that for body weight to
remain stable, that undereating or overeating will occur after a period of forced weight
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gain or loss. However, previous homeostatic theories alone do not accurately explain
how obesity rates are steadily rising.
Foods, especially those high in fat and sugar, are considered natural rewards
that can promote eating. Hedonic systems rely on the rewarding properties associated
with food, which may allow them to override the homeostatic systems and lead to
increased food consumption and weight gain. Berridge (2009) discusses the hedonic
reaction to pleasurable food that can lead to “liking” and the motivational states that can
lead to “wanting.” Where “liking” refers to the pleasurable sensory aspects of a food
reward and “wanting” refers to the craving and incentive salience towards obtaining a
food reward. Berridge considers that impairments in brain reward function may lead to
reward dysfunctions, which can ultimately result in eating related problems. For
example, if food becomes hedonically “liked” too much or there is overactivation in brain
regions important for “wanting,” that can lead to increased food consumption. Similarly,
Saper et al., (2002) mentions how humans will spend large sums of money for a
pleasurable meal and that most mammals will eat beyond need when palatable food is
present. Lowe and Butryn (2007) mention that food consumption is being driven by the
palatability and pleasure of food rather than a metabolic need to consume food. These
examples indicate that the hedonic system can increase food intake when palatable
foods are available regardless of homeostatic need.
Food Environment
There are many environmental food cues which allow the hedonic system to
override physiological, homeostatic signals of hunger and encourage eating behaviors.
In our current obesogenic environment, we are continually exposed to rewarding food
stimuli and have relatively easy access to high caloric food items. Levitsky (2005) states
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that environmental factors such as portion sizes and variety are more likely than
biological factors to effect food intake. The constant presentation of food cues on
television commercials, pictures on social media, and easily accessible restaurants can
stimulate the hedonic aspects of liking and motivational aspects wanting, which may
lead to increased food consumption and result in obesity. Among restrained eaters, pre-
exposure of food cues increased food intake while those not exposed to food cues did
not increase food intake (Fedoroff et al., 1997). Not everyone exposed to environmental
food cues overeat, indicating that certain behaviors or individual characteristics may be
more likely to increase food intake when opportunities are present. Therefore, it is
important to investigate those traits and behaviors that can allow one to overeat and
override physiological signals of hunger.
Impulsivity
Impulsivity is characterized by behaviors that relate to disinhibition, reward
seeking, novelty seeking, risk aversion, and the inability to delay gratification (Eysenck,
1993). The characteristics associated with impulsive behaviors have been shown to
result in negative health related outcomes. Many pathologies such as substance abuse
disorders, personality disorders, attentional deficits, aspects of obesity, food addiction,
and eating disorders all present impulsive behaviors (Evenden, 1999). Overall,
impulsive individuals are suggested to have a heightened sensitivity to rewards,
reduced sensitivity to negative outcomes, make risky decisions, and be more motivated
by immediate rewards (Ainslie, 1975). The following three measurable constructs of
impulsivity: reward sensitivity, disinhibition, and attentional deficits will be discussed in
regard to reward-based decision making, increased food intake, and their implications in
the development and maintenance of obesity.
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Reward sensitivity
Reward sensitivity is a domain of impulsivity that has been observed among
obese individuals. Neuroimaging studies in obese populations found differences in
response to food or cues, which indicates that obese individuals have hyperactive
reward systems to the anticipation of highly palatable foods. When obese women are
shown rewarding food cues, fMRI results indicate that they have greater blood oxygen
level dependent (BOLD) activity in the nucleus accumbens core/ventral striatum, medial
and lateral orbitofrontal cortex (OFC), medial prefrontal cortex (PFC), anterior cingulate
cortex (ACC), amygdala, hippocampus, and insula compared to normal weight controls
(Stoeckel et al., 2008). When viewing pictures of high caloric sugar-based foods, there
is greater BOLD activity in the insula, ACC, hippocampus, caudate, and putamen
compared to pictures of savory food items. Sugar based foods typically have rewarding
properties and increased BOLD activity may be an indicator of reward sensitivity for
obese women. Those brain regions are implicated in reward and motivation circuits
indicating that obese individuals have higher reward sensitivity to food cues compared
to normal weight subjects.
When obese adolescent girls consume high caloric milkshakes, there is a
decrease in BOLD activity in reward (caudate) and taste (gustatory cortex) regions of
the brain (Stice et al., 2008). The anticipation of rewarding food stimuli increases activity
in those regions, while the actual receipt of the milkshake decreases activity.
Additionally, those with higher BMIs had less BOLD activity in the caudate during
receipt of a sucrose solution compared to their baseline BOLD activity (Green et al.,
2011). The decrease in BOLD activity while eating may help to explain how overeating
can occur. Obese individuals may consume more palatable foods in order to
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compensate for the decreased activity in reward and taste regions in the brain that are
normally overactive during the anticipation of eating. This can lead to increased reward
seeking and overindulging in palatable foods, which can ultimately lead to increased
weight gain.
Elfhag and Morey (2008) states that there are many food cues and stimuli
present in the current environment and that being sensitive to rewarding food stimuli
alone is not necessarily a characteristic typical of overeating. They mention that
disinhibition to food stimuli and having a conditioned response to eat when food cues
are present is a stronger correlate for external eating than reward sensitivity. Giel et al.,
(2017) also mention that reward sensitivity is an aspect of impulsivity that may
contribute to a heightened response to food cues but that problems in resisting
temptation of an immediate reward leads to increased food consumption. Lastly, Dawe
and Loxton (2004) have a framework for impulsivity in clinical populations in which
reward sensitivity and rash-spontaneous impulsivity contribute to problems in behavior.
These studies highlight that there may be an important interaction between reward
sensitivity and disinhibition that contributes to overeating. This suggests that when
rewarding food stimuli is present, it requires cognitive restraint to prevent eating during
satiation. The inability to inhibit eating behavior is an important impulsive behavior that
may lead to overindulging in palatable foods and ultimately result in increased weight
gain.
Disinhibition to Foods
Neuroeconomic theories explain the process that occurs when an individual is
presented with choices with different subjective values. It requires a cognitive process to
assign values to two competing stimuli to make a selection. The decision-making
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process should lead to behavior that reflects the preferred outcome, where learning can
modify the process in the future (Rangel et al., 2008). Delay of gratification (DG) and
delay discounting (DD) are two procedures used to measure impulsive behavior in
regard to reward-based decision making (Reynolds & Schiffbauer, 2005). Previous
literature has used them interchangeably and Rachlin (2000) asserts they measure the
same construct. However, there are discrete differences between them (for full review
see Reynolds & Schiffbauer, 2005). DG is thought to measure the behavioral process
for receiving a delayed reward and can be thought to measure willpower. While DD
relies on choice preference, which emphasizes the stimulus and pattern of devaluation
rather than internal cognitive processes. A similarity between them is that they both
provide ecological validity to behavior inhibition and choice, especially for food intake. In
regard to eating behavior and weight loss, one must choose to restrict food intake for a
long-term goal of losing weight, which measures choice preference and resembles DD.
Additionally, they must also forgo pleasurable high caloric foods that are immediately
available and sustain that behavior for longer periods of time, this behavioral inhibition
closely resembles DG. The attentional processes that are used to evaluate reward
choices involved in decision-making may affect DD while disinhibition to rewards and
being more reward sensitive may affect DG. The current study uses a reward based
decision-making task which uses both principles of DD (choice behavior) and DG
(sensitivity to rewards) to assess impulsivity.
Previous research using delay discounting paradigms among obese subjects
allow us to measure the valuation of rewards and choice behavior, which may be an
important component of health-related decision making. Weller et al., 2008 found that
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obese women have greater delay discounting on a computer controlled hypothetical
reward discounting paradigm compared to control women. This indicates that there is a
relationship between health status and the valuation of rewarding stimuli. However,
Weller and colleagues mention that this significant difference in discounting behavior
was not observed between obese and control men, so there may be gender specificity.
Appelhans et al., (2011) gave obese and overweight women a delay discounting task to
measure inhibitory control and found that those with greater discounting consumed
more palatable foods compared to those who had less discounting of rewards. This
indicates that inhibitory control measured by DD affects food intake behavior.
Behavioral inhibition towards food related rewards is also impaired among obese
subjects. Houben et al., (2012) reports that those who are more successful at
maintaining a healthy weight exhibit better inhibitory control towards palatable food
consumption. They reported that healthy weight and unhealthy weight subjects exposed
to food cues increased reported craving in both groups. However, healthy weight
subjects are better at inhibiting food related responses on a stop signal task and have
less food consumption in a taste test compared to overweight subjects. These findings
indicate that inhibitory behavior is important to prevent increased or excessive food
intake. The ability to inhibit choosing a palatable food item in the absence of hunger
requires strong cognitive control and disruptions in attention may interfere with this
ability.
Attention
Attention and inhibitory control may be required to resist readily available food
stimuli, especially among those who are more sensitive to rewards and environmental
food cues. The problems associated with overeating and food related decisions can
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begin early in the lifespan and the domains of impulsivity that lead to poor health related
outcomes have been measured in young children. Braet et al., (2007) found that
overweight boys report more problems with attention and focus on a questionnaire,
while overweight boys and girls self-report having a harder time with shifting their
attention. Clinical interviews found that overweight boys scored higher on impulsivity,
hyperactivity, and inattentive symptoms. Additionally, overweight children have more
errors and faster response times on the matching familiar figure test compared to
normal weight children. This research indicates that there may be impulse control
problems in childhood obesity and that gender differences in the constructs of
impulsivity may also exist. Even among adult samples, there is a higher prevalence for
obesity among those with adult ADHD. In a German clinical sample, de Zwaan et al.,
(2011) reported that after adjusting for sociodemographic variables, depression, anxiety,
and purging behaviors that adults with ADHD have significantly greater prevalence for
obesity compared to controls without adult ADHD. Similar findings from the
Collaborative Psychiatric Epidemiology Surveys found that ADHD is also associated
with overweight and obese subjects in the US after controlling for demographics and
depression (Pagoto et al., 2009). Research supports that there is a relationship between
weight and deficits in attention.
Treatments
It is well known that obesity is due to an energy imbalance; weight loss programs
that focus on reducing energy intake and increasing energy expenditure thus should be
effective. However, there are many difficulties with sustaining this behavior and overall
long-term success is not easily attained (Lowe, 2003). Riggs et al., (2007) used a
program called PATHS (promoting alternative thinking strategies) which addresses
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emotional regulation, impulse control, and decision making in elementary school aged
children in two pilot schools. Pre-test post-test results found significant positive attitudes
towards self-regulation of appetitive behavior and physical activity.
Children with impulsive tendencies, measured on the stop signal task, lost less
weight after obesity treatment compared to children who were able to inhibit behavior
during the stop signal task (Nederkoorn et al., 2007). Additionally, the
hyperactivity/inattention subscale of the parent-rate strengths and difficulties
questionnaire negatively predicted weight loss success, where children who reported
higher inattention lost less weight (van Egmond-Froehlich et al., 2013). This indicates
that programs related to weight loss should include behavioral interventions specific to
impulsive behaviors.
Programs that focus on future rewards have been shown to have success with
reducing bias towards immediate rewards (Peters & Büchel, 2010; Benoit et al., 2011),
which may be applied to obesity interventions. Episodic future thinking (EFT) has been
used to combat impulsivity during decision making by relying on episodic memories and
self-projections of the future to drive behavior. Daniel et al., (2013) used EFT to reduce
DD of money rewards for obese and overweight subjects. Their task reduced DD for
money related rewards rather than food related rewards, which may not necessarily be
translatable to obesity treatment options. Therefore, more research on the choice
behavior for food rewards in relation to health status may provide more insight into
possible future treatment methods.
Three Factor Eating Questionnaire
The Three Factor Eating Questionnaire developed by Stunkard and Messick
(1985) was developed to assess dimensions of human eating behavior. The three
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domains on the 51-item questionnaire are cognitive restraint of eating, disinhibition, and
hunger. Karlsson et al., (2000) developed the TFEQ-18 which used 18 items based on
the original TFEQ to assess eating behavior. Similar to factor analysis findings from
Hyland et al., (1989), they found that three different domains more accurately explained
eating behavior. The domain of cognitive restraint remained the same, however two
new domains were introduced: uncontrolled eating and emotional eating. These
domains were formed from some of the original items from the disinhibition and hunger
scales. Previous research on the TFEQ found that disinhibition and hunger formed one
global factor (Hyland et al., 1989) and have been shown to have a high correlation (r =
.69) among obese subjects (Karlsson et al., 2000). The new domains have been shown
to be a reliable indicator of eating behavior among obese (Karlsson et al., 2000) and
non-obese samples (Hyland et al., 1989; de Lauzon et al., 2004).
The domain of cognitive restraint is thought to measure dietary restraint where
control over food intake is used to control body weight. The domain of cognitive restraint
has been positively associated with BMI, where cognitive restraint may have a
counterproductive effect and contribute to weight gain when restraint gets interrupted
due to stressors or life events (de Lauzon-Guillain et al., 2006). The domain of
uncontrolled eating is most similar to disinhibition where aspects of inhibition and control
are needed to regulate eating behavior. This domain also relies on external
environmental cues that may contribute to overeating. The domain of emotional eating
is used to assess how much mood and emotion contribute to eating behavior.
Biased Competition Model
Desimone and Duncan (1995) proposed a model of biased competition where
visual input and cortical processing favors behaviorally relevant stimuli over irrelevant
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stimuli. This model is based on a tenet that visual input to the brain can process limited
amounts of stimuli. Therefore, behaviorally relevant stimuli and irrelevant stimuli must
compete for limited cortical resources. During competition, selectivity is used to filter out
the irrelevant information, so that cortical resources are allocated towards the
behaviorally stimuli. Ultimately, this results in being aware of the attended stimuli and
unaware of the filtered information.
The biased competition model relies on both bottom-up and top-down neural
mechanisms to evaluate the competing stimuli in the visual field. Bottom-up processing
allows the attended stimuli to be separated from the surrounding background while top-
down processing provides information about the behavioral relevance and significance
of stimuli in the visual field. Object recognition, which is processed in the ventral stream,
is needed for the top-down neural processing to resolve the competition between
different objects in the same visual field. Therefore, items in the visual field are
evaluated by behavioral importance and cortical resources are allocated more towards
the behaviorally relevant stimuli.
ssVEPs
Electroencephalography (EEG) detects and records electrocortical activity non-
invasively from the brain. EEG is used to record steady-state visual evoked potentials
(ssVEPs), which is an electrophysiological oscillatory response produced by visual
neurons in the cortex. These visual neurons respond to flickering stimuli and have the
same temporal frequency of the stimuli, which can then be separated to quantify visuo-
cortical engagement to the stimuli. ssVEPs paradigms that use binocular rivalry allow
for cortical activity to be recorded to two separate flickering stimuli by assigning
separate flickering frequencies to the stimuli. ssVEPs are characterized by having
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excellent signal to noise-ratios, less susceptibility to eye movement artifacts, can be
collected non-invasively from the scalp, are sensitive to semantic stimuli, and provide a
measure of visuo-cortical activity to attended stimuli (Kaspar et al., 2010; Vialatte et al.,
2010; Regan, 1989). ssVEP amplitudes are higher towards attended stimuli (Müller et
al., 1998) or emotionally relevant stimuli (McTeague et al., 2011), and can be used to
record visuo-cortical activity to two competing stimuli in the visual field. Therefore, this
methodology would allow for cortical activity to be recorded to two separate stimuli
presented concurrently during a reward-based decision-making paradigm and provide
information on the cortical resources towards separate stimuli during decision-making.
Significance and Innovation
While previous research in obese populations has focused on performance in
delay discounting paradigms and neural circuitry, such as reward circuitry and executive
functioning, the current study is the first to use ssVEPs to capture the cortical
processing of competition during concurrently presented stimuli consisting of immediate
and delayed rewards and to correlate the data with physiological measures of health
such as BMI, WC, and WHR. A strength of using an electrophysiological methodology
such as ssVEPs is that it is a non-invasive technique that will allow us to quantitatively
and continuously characterize differences in brain activity to reward stimuli at different
stages of the decision-making process while also allowing for the collection of
behavioral decision-making data. The proposed project is innovative since it is the first
use ssVEPs to investigate the cortical processing of competing visually rewarding
stimuli and correlate physiological measures of health.
Currently, the literature is inconsistent on the question of whether greater delay
discounting shown among obese populations applies to all rewarding stimuli or is
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reward specific (i.e., food). The notion that reward type influences delay of gratification
is supported by the drug addiction research, which consistently finds that substance
users overall discount more than non-users, but discount much greater to their drug of
choice than money (Madden et al., 1997). Previous work has found that obese women
show greater discounting to monetary rewards compared to healthy weight women
(Weller et al., 2008). Rasmussen et al., (2010) found that there were significant
differences in delay discounting among obese individuals specific for food rewards
compared to healthy weight individuals. However, there were no significant differences
between the two groups in delay discounting to monetary rewards. Therefore, the
proposed work is significant since it will use ssVEPs to determine if cortical processing
and biased competition is specific to reward type by using monetary and food rewards.
This information can be used to tailor behavioral interventions to specific reward type or
general rewards.
The long-term goal of this research is to determine if there are differences in
cortical processing to immediate and delayed rewards and types of rewards and
whether this varies as a function of physiological measures of health. The objective of
this study, which is a crucial step towards my long-term goal, is to quantify the extent to
which biased competition towards rewards with various time delays differs across
physiological measures of health and whether there are differences in cortical activity
specific to reward type.
The primary hypothesis is that there will be differences in ssVEP activity to a
small reward given today, a medium reward given in a week, and a large reward given
in a month. Additionally, it is hypothesized that ssVEP activity will correlate with
27
measures of BMI, WC, and WHR, where those with higher measurements of obesity will
show more sensitivity to rewards. The secondary hypothesis is there will be differences
in biased competition specific to reward type, and specifically that immediate food
rewards will have a stronger neuronal bias compared to immediate monetary rewards in
subjects with higher BMI, WC, and WHR. The rationale for the proposed study is that
neuronal resources during a reward based decision-making task may contribute to
health related decisions. This information may provide benefits for behavioral
interventions and strategies to combat overeating.
The proposed research will also provide us with novel information related to the
cortical activity to two separate stimuli at the same time, which will allow us to quantify
stimulus-specific visuo-cortical engagement during a decision-making task. This
contribution may provide support for a mechanism that underlies decision-making and
choice behavior, and thereby influence health related decisions. Additionally, this
research on the role of cortical engagement during decision-making may impact,
influence, and enhance how behavioral interventions for overeating and obesity are
conducted. Lastly, if this measure proves to be sensitive and specific, it can then be
applied to other populations who tend to have higher impulsivity scores and show
greater discounting of rewards, such as drug addicts and smokers.
Study Aims
Specific Aim 1
Test the hypothesis that cortical processing of the competition between smaller,
sooner rewards and later, larger rewards correlates with physiological measures of
health, where those with higher BMIs, WCs, and WHRs will show higher stimulus driven
bias towards the smaller, sooner reward.
28
Specific Aim 2
Test the hypothesis that cortical processing of competition will show a stronger
bias towards immediate food rewards than immediate monetary rewards among those
with higher BMIs, WCs, and WHRs.
29
CHAPTER 2 METHODS
Detailed information for the participants, behavioral task, data acquisition, and
data analysis is discussed below. All procedures were approved by the University of
Florida Institutional Review Board (IRB) on protocol 2010-U-0713. All participants gave
informed consent and received psychology course credit for participating. Subjects had
their weight, height, waist and hip circumferences recorded, completed a self-report
questionnaire of demographic and health history information, completed the Three-
Factor Eating Questionnaire-18 (TFEQ-18) (Karlsson et al., 2000), and then participated
in a behavioral task during EEG recording.
Participants
Participants were 51 adults (Males = 24, Females = 27) between the ages of 18-
23 (M= 18.92, SD=1.02) recruited from the University of Florida Psychology
undergraduate participant pool. Via self-report, the majority of the participants were right
handed (92.2%), most had no current psychological disorders (88.2%, 3.9% ADHD,
3.9% anxiety, and 3.9% with depression and anxiety), and most did not engage in
alcohol, tobacco or drug use (76.5%, 72.5%, and 84.3%, respectively). All participants
reported normal or corrected to normal vision, no known neurological or eating
disorders, or history of epilepsy. Roughly, 55% of participants identified as Caucasian,
8% as African American, 14% as Hispanic/Latin, 22% as Asian, and 2% as Indian.
Additionally, information on the time of last meal or drink of beverage other than water
was also collected. Two participants were excluded from the study due to high artifact
content during the electrophysiological recording of data.
30
Height, weight, waist and hip measurements were measured at the time of
testing and then used to compute BMI, WC, and WHR. BMI scores were calculated
using height and weight (kg/m2), WC used the raw waist measurement, and WHR was
calculated by dividing the waist measurement by the hip measurement. The shorter
version of the Three Factor Eating Questionnaire (TFEQ) (Stunkard & Messick, 1985),
TFEQ-18, developed by Karlsson et al., (2000), was given to all participants to assess
the domains of cognitive restraint of eating, uncontrolled eating, and emotional eating.
The responses for each domain were added to form a raw score which was then used
to compute a domain score on a 0-100 scale. The following formula was used to
compute the domain scores: [(raw score- lowest possible raw score)/ possible score
range] x 100 (de Lauzon et al., 2004).
Behavioral Task
Participants completed a behavioral task on a computer while their brain activity
was recorded non-invasively from the scalp using a sensor net. Participants were
seated about 1.5 m from a 53 cm monitor used to display visual cues. Each participant
completed two testing blocks with each block associated with either a monetary or food
reward with instructions and training for each block. The order of the blocks was
randomized across subjects so that the monetary block did not always precede the food
block or vice versa.
Before the first block, participants were notified that they would be completing a
computer controlled decision-making task while cortical activity was recorded from their
scalp using EEG. The following instructions were verbally given to participants before
they began a training period. While viewing Figure 2-1, the participant was told that,
“during the behavioral task, you will have the option to choose between two of the three
31
options presented. The pie charts above the options presented can be used during the
behavioral task to help you identify the options presented. The pie chart with the two
larger sections, represents the largest reward amount (10 dollars) given after the
longest time delay (one month), the smallest pie sections represents the smallest
reward (2 dollars) given after the shortest time delay (today), and the middle sections
represent the middle reward (4 dollars) given after the middle time delay (one week).”
All participants were required to verbally identify the reward amounts and time delays
correctly before moving on to the next screen. The next screen shows a sample trial
(see Fig. 2-2) and it was explained that the flickering patches are angled towards a pie
chart above, which symbolizes the reward amount and time delay. All participants were
required to verbally identify both of the options shown on the left and right side of the
screen and practice making a response by clicking either the left or right arrow key on a
computer keyboard. The left arrow indicates choosing the left presented option and the
right arrow indicates choosing the option on the right. All participants were told that they
should choose which option they would prefer and that they should be choosing their
preferred response at that moment throughout the entire behavioral task. Participants
were instructed to maintain gaze at the fixation cross in the center and to minimize eye
movements during the presentation of the flickering (Gabor) patches. Detailed
information on the Gabor patches and computer is discussed further below.
During the training period, all participants were required to verbally identify the
two stimuli options presented in 4 trials correctly before beginning the first block. If
errors were made, the participant had to continue training until 4 trials were completed
correctly. MATLAB recorded the number of training trials completed and the average
32
training trials for all participants was M= 4.96, SD = .57; there was not a significant
difference between the number of training trials for the food and monetary blocks. After
the training, participants were told that the first block will begin and that each trial needs
a response before the next one begins. After the first block ended, the instructions were
repeated using the other reward type and the same training with the same criteria for
the other reward type was given before the second block of trials. Each block took
approximately 30 minutes to complete depending on the individual average speed of
response.
Trials
In order to elicit ssVEPs (see later), two of the previously learned Gabor patch
cues were presented side by side for 3000 ms on each trial; one patch phase-reversed
at 15 Hz and the other at 20 Hz. These reversal frequencies were randomly assigned
for each trial so that one cue or side of the screen was not associated always with one
particular flicker rate. After the 3000 ms with the Gabor patches flickering, the trial
remained on the screen until the participant made a computer-controlled response.
Then, a randomized 2.5-3 s inter-stimulus interval was programmed before the next trial
began (see Fig. 2-3). Participants were shown 180 randomized trials, incorporating 15
trials each counterbalanced for the 3 different reward and time delays (smallest reward
today vs. middle reward in one week, smallest reward today vs. largest reward in one
month, middle reward in one week vs. largest reward in one month), side of screen
presentation, and frequency of the Gabor patch.
Stimuli and Cues
Visual stimuli were displayed on a computer monitor with a 120 Hz refresh rate.
The Gabor patches and pie charts used in the program were gray-scaled to not have
33
high contrast (see Fig. 2-2). Three Gabor patches were used to indicate a left, middle,
or right option and angled at either -50, 5, and 55 degrees.
The food stimulus of chocolate was chosen since it is the most craved food item
in Western society and is high in fat and sugar (Weingarten & Elston, 1991; Zellner et
al., 1999). The rewards associated with each cue were based on reward amounts and
time delays from previous literature and choices (Hendrickson & Rasmussen, 2013;
Blackburn et al., 2012). The monetary cues were $2 today, $4 in a week, or $10 in a
month while the food cues were 2 bars of chocolate today, 4 bars of chocolate in a
week, or 10 bars of chocolate in a month. Thus, given that the current price of a
chocolate bar is of the order of $1, the money rewards were approximately equivalent to
the amounts of chocolate.
EEG Recording and Processing
The MATLAB Psychophysics Toolbox was used to program and run the
behavioral task and electrical recordings. ssVEPs were recorded from the scalp using
an Electrical Geodesics high-density sensor net with 257 electrodes (EGI, Eugene, OR).
The electrode site of Cz was used as reference. A bandpass filter was applied to 0.1-50
Hz and impedances were kept below 70 kΩ.
The EMEGS (ElectroMagnetoEncephalography) software in MATLAB was used
for offline processing of the data. The recordings were filtered using a 45-Hz low pass
and a 5-Hz high pass filter. Epochs were extracted 200 ms pre-stimulus and 1900 ms
post-stimulus from the raw EEG data. Artifact contaminated channels and trials were
identified and removed using the statistical parameters in Junghöfer et al., (2000) based
on the absolute value, standard deviation, and maximum of differences of the extracted
EEG epochs. Contaminated channels were replaced by statistically weighted, spherical
34
spline interpolated values from the channel set. After artifact rejection, for the food
block, an average of 89.33% trials remained for the condition when a small reward was
shown next to a medium reward, 86.67% of trials remained for the condition when a
small reward was shown next to a large reward, 84.90% of trials remained for the
condition when a medium reward was shown next to a small reward, 86.50% of trials
remained for the condition when a medium reward was shown next to a large reward,
86.13% of trials remained for the condition when a large reward was shown next to a
small reward, and 87.43% of trials remained for the condition when a large reward was
shown next to a medium reward. For the money block, an average of 80.90% trials
remained for the condition when a small reward was shown next to a medium reward,
76.90% of trials remained for the condition when a small reward was shown next to a
large reward, 77.80% of trials remained for the condition when a medium reward was
shown next to a small reward, 81.87% of trials remained for the condition when a
medium reward was shown next to a large reward, 80.07% of trials remained for the
condition when a large reward was shown next to a small reward, and 78.60% of trials
remained for the condition when a large reward was shown next to a medium reward.
Artifact free averages were then averaged by blocks for each participant and submitted
to Fourier transformation so that ssVEP amplitudes were obtained across the frequency
spectrum (see Fig. 2-4). Spectral power was extracted at the two tagging frequencies
and averaged across a cluster of 8 parieto-occipital midline electrodes (124, 125, 135,
136, 137, 138, 148, 149) to form the dependent variables.
Statistical Methods
ssVEP data were averaged so that there were 12 variables for each block (food
and money): The side of screen presentation (left or right), reward size and time delay
35
(smallest reward today, middle reward in a week, and largest reward in a month), and
other option presented (smaller or larger reward option). There was not a significant
main effect for side of presentation (p > .05) so further analyses were averaged over the
side of screen presentation. A 2x3x2 repeated measures ANOVA was conducted using
SPSS to explore differences between block (food or money), reward size (small,
medium, and large), and other option shown (smaller or larger). A 3x2 repeated
measures ANOVA was then used for the food and money blocks. Exploratory
correlations were conducted for the demographic and behavioral variables. A
significance level of .05 (two-tailed) was used for all analyses.
Variables
Impulsivity percentages were calculated by totaling the number of times a
smaller, sooner reward was chosen over a larger, later reward and forming an average
percentage. This was done separately for each block so that each participant has an
average food impulsivity and money impulsivity score. Additionally, differences in
ssVEP amplitudes were calculated to compare the difference in amplitude between
rewards.
36
Figure 2-1. Image presented on the computer monitor while instructions were given to the participant during the behavioral task for the monetary block.
37
Figure 2-2. Example of a trial shown during the behavioral task. On this trial, the largest reward with the longest delay is the left option and the smallest reward with the shortest delay is the right option.
38
Figure 2-3. Time sequence and presentation of a trial. Gabor patches flickered for 3000 ms before the participant could make a response (not timed), after which a variable inter-stimulus interval of 2.5-3 s elapsed before another randomized trial was presented.
39
A
Figure 2-4. Grand mean ssVEPs. The grand mean steady state at 15 Hz to illustrate the scalp topography (A). The robust oscillatory response from the stimuli recorded from an electrode located along the midline of the back of the head (Oz). The two tagging frequencies of 15 Hz and 20 Hz are shown (B).
40
B
Figure 2-4. Continued.
41
CHAPTER 3 RESULTS
Demographic and Behavioral Results
The results of the demographic variables and behavioral scores are summarized
in Table 3-1. Multivariate analyses of variance (MANOVA) on mean age, BMI, WC,
WHR, the three domains of the TFEQ-18, impulsivity percentages and reaction times for
the food and money blocks, and minutes since the last time food was consumed
revealed significant differences for WC and WHR between male and female
participants. Males had significantly larger WC and WHR measurements than females.
All other demographic and behavioral variables did not differ between male and female
participants (p > .05).
Pearson correlation tests were performed between pairs of behavioral and
demographic scores, and the results are summarized in Table 3-2. WHR and BMI were
positively correlated, where those with larger WHR measurements also had higher BMI
ratings. WC measurements were also positively correlated with BMI and with WHR (see
Fig. 3-1). Scores on the uncontrolled eating and emotional eating domains of the TFEQ-
18 were also positively correlated (see Fig. 3-2). The cognitive restraint domain of the
TFEQ-18 was not significantly associated with the scores on either uncontrolled eating
or emotional eating domains (ps > .05). The cognitive restraint domain was positively
correlated with BMI but not with WC or WHR (see Fig. 3-3). The other domains of the
TFEQ-18 did not correlate with BMI, WC, or WHR.
Impulsivity percentages were calculated for each participant for the food and
money blocks. For the money block, there was a significant negative Pearson
correlation between BMI and the impulsivity scores (see Fig. 3-4). For the food block,
42
the correlation between BMI and impulsivity was not significant (p > .05). There was not
a significant relationship between WC or WHR and impulsivity scores for either the food
or money blocks.
Individual scores on the uncontrolled eating domain of the TFEQ-18 was
positively correlated with the mean food impulsivity score (see Fig. 3-5). In contrast, the
impulsivity score on the money block was not so correlated. Further, scores on the
emotional eating and cognitive restraint domains of the TFEQ-18 were not correlated
with impulsivity scores in either food or money blocks. The domain of cognitive restraint
on the TFEQ-18 was negatively correlated with the number of minutes since food was
last consumed. (see Fig. 3-6).
Mean individual reaction times during the food and money blocks were positively
correlated (Pearson r(51) = .538, p < .001). In contrast, mean individual impulsivity
scores during the food and money blocks were not significantly correlated. Individual
scores on the uncontrolled eating domain of the TFEQ-18 were negatively correlated
with mean individual reaction times during the food block (see Fig. 3-7).
Food and Money
A 2x3x2 repeated measures ANOVA was computed to compare the ssVEP
amplitudes for the food and money blocks (see Table 3-3). There was not a significant
main effect of block. There were significant main effects of reward and other option
shown. There were not any significant interactions with block with either reward or other
option shown. There was a significant interaction between reward and other option
shown. Additionally, there was also a three-way interaction between block, reward, and
other option shown.
43
ssVEPs for Food
For the food block, a 3x2 repeated measures ANOVA indicated a significant main
effect of reward size on spectral power F(2, 51) = 6.599, p = .002 (see Table 3-4 and
Fig. 3-8). Follow up analyses indicated that the ssVEP amplitudes for the largest reward
at the longest time delay was significantly smaller than for the medium sized and
smallest reward options given sooner [t(50) = 3.349, p = .002; t(50) = 2.968, p = .005,
respectively]. A main effect of other option presented was also significant F(1, 51) =
13.801, p = .001 (see Fig. 3-9). When small or large reward cues were presented with a
medium reward option there was significantly smaller ssVEP amplitudes than when
medium and large reward cues were presented with the smallest reward option [t(50) =
2.079, p = .043]. There was no significant difference when rewards were presented with
the largest reward option. There was not a significant interaction between reward size
and other options shown for the food block.
A Pearson correlation indicated that there was not a significant relationship
between the health and behavioral variables with the ssVEPs for the smallest reward
option given today, the medium sized reward option given in a week, and the largest
reward option given in a month. Differences in ssVEP amplitudes for the food block
were computed for the significant effects and correlated with the health and behavioral
variables. The amplitude difference between the large and small rewards for the food
block was positively correlated with the number of minutes since food was last
consumed (see Fig. 3-10). There were not any significant correlations between the
health and behavioral variables for the computed ssVEP amplitude difference between
the large and medium sized rewards. Additionally, there were not significant correlations
44
between the health and demographic variables with the computed ssVEP amplitude
difference between the small other option shown and medium other option shown.
ssVEPs for Money
For the money block, a 3x2 repeated measures ANOVA indicated a significant
main effect of reward size on spectral power F(2,51) = 3.969, p = .022 (see Table 3-5
and Fig. 3-11). Follow up analyses indicate that the middle reward given in a week had
significantly higher ssVEP amplitudes than the larger reward given in a month t(50)=
3.243, p = .002. There was not a significant difference between the smallest reward
given today with either the middle reward in a week or largest reward in a month. A
main effect of other option shown was also significant F(1, 51) = 4.511, p = .039 (see
Fig. 3-12). When choices were presented with the medium sized options, there was
significantly greater ssVEP amplitudes for the smaller or larger reward [t(50)= 2.748, p =
.008; t(50) = -3.044, p = .004, respectively]. A Pearson correlation indicated that there
was not a significant relationship between the measures of BMI and WHR with the
ssVEPs for the smallest reward option given today, the medium sized reward option
given in a week, and the largest reward option given in a month.
There was also a significant interaction between reward magnitude and time
delay with the other option presented F(2, 51) = 12.127, p < .001 (see Fig. 3-13).
Newman-Keuls procedure was utilized to perform a post hoc analysis to determine
differences among means. Post hoc tests revealed that there was significantly smaller
ssVEP amplitudes when the largest reward given in a month was presented alongside a
medium reward option given in a week (M = .2083) compared to the following: The
largest reward option given in a month was presented alongside the smallest reward
option given today (M = .2662), the smallest option given today was presented
45
alongside the medium reward option given in a week (M = .2572), and when the
medium sized reward given in a week was presented alongside the largest reward
option given in a month (M = .2820).
A Pearson correlation indicated that there was a significant positive correlation
between the small reward when shown with a large reward and the TFEQ-18 domain of
cognitive restraint (Pearson r(51) = .287, p = .041) (see Fig. 3-14). There was not a
significant relationship between the other health and demographic variables with the
ssVEPs for the smallest reward option given today, the medium sized reward option
given in a week, and the largest reward option given in a month. Differences in ssVEP
amplitudes for the money block were computed for the significant effects and correlated
with the health and behavioral variables. The difference between the amplitudes for the
large and medium rewards were negatively correlated with the TFEQ-18 domain of
cognitive restraint (see Fig. 3-15). The difference between the ssVEP amplitude when
the medium option was shown and when the large option was shown was also
negatively correlated with the TFEQ-18 domain of cognitive restraint (see Fig. 3-16).
The computed difference between ssVEP amplitude when the medium option was
shown and when the small option was shown did not correlate with any health or
behavioral variables.
46
Table 3-1. MANOVA results for age, BMI, WHR, the three domains of the TFEQ-18, impulsivity percentages and reaction times for the food and money blocks, and minutes since the last time food or beverage was consumed for male and female participants (n = 51). Males had significantly larger WC and WHR than women.
Males Females
M SD M SD F df p
Age 19.00 1.18 18.85 0.86 0.27 1 0.608
BMI 25.11 4.19 23.88 4.25 1.09 1 0.302
WC 32.18 3.82 29.70 4.67 4.21 1 0.046*
WHR 0.83 0.05 0.79 0.05 12.48 1 0.001**
Cognitive Restraint 31.25 19.85 36.48 18.54 0.95 1 0.335 Uncontrolled Eating 36.83 16.77 34.96 16.78 0.16 1 0.693
Emotional Eating 23.50 25.03 32.04 20.21 1.81 1 0.184
Food Impulsivity 45.97 43.51 49.05 39.68 0.07 1 0.793
Money Impulsivity 48.71 42.59 38.44 38.98 0.81 1 0.373 Food Reaction Time 1293.48 828.85 1205.44 725.99 0.16 1 0.688 Money Reaction Time 1439.51 980.39 1242.97 784.72 0.63 1 0.431
Last Food (mins) 144.17 206.78 131.30 180.73 0.06 1 0.813
47
Table 3-2. Pearson correlation table between the behavioral and demographic variables (n = 51). P < .05 is signified by * with P < .001 signified by **.
1 2 3 4 5 6 7 8 9 10
1. BMI - 2. WC .852** -
3. WHR .411** .612** -
4. Cognitive Restraint .292* .258 .133 -
5. Uncontrolled Eating -.068 -.055 .020 .170 -
6. Emotional Eating -.076 -.033 -.024 -.068 .361** -
7. Food Impulsivity -.049 -.126 .119 .159 .302* .074 -
8. Money Impulsivity -.301* -.271 -.169 -.084 .251 .107 .261 -
9. Food Reaction Time -.080 .064 -.090 -.122 -.295* -.144 -.254 -.097 -
10. Money Reaction Time -.134 .026 .064 .139 .036 .125 -.063 -.022 .538** -
11. Last Food (mins) -.234 -.243 -.139 -.311* -.082 .067 -.082 -.075 .089 .007
48
Table 3-3. Repeated Measures ANOVA ssVEP results (n = 51). P < .05 is signified by * with P < .001 signified by **.
F df p
Block .001 1 .971
Reward 9.528 2 <.001**
Option 18.10 1 <.001**
Block X Reward .575 2 .564
Block X Option 3.326 1 .074
Reward X Option 11.173 2 <.001**
Block X Reward X Option 3.101 2 .049*
Table 3-4. Repeated Measures ANOVA ssVEP results for the food block (n = 51). P <
.05 is signified by * with P < .001 signified by **.
F df p
Reward 6.599 2 .002**
Option 13.801 1 .001**
Reward X Option .990 2 .375
Table 3-5. Repeated Measures ANOVA ssVEP results for the money block (n = 51). P <
.05 is signified by * with P < .001 signified by **.
F df p
Reward 3.969 2 .022*
Option 4.511 1 .039*
Reward X Option 12.127 2 <.001**
49
A Figure 3-1. Correlation between body measurements. Scatterplot showing the
significant positive correlation between WHR and BMI for all participants (Pearson r(51) = .411, p = .003)(A). Scatterplot showing the significant positive correlation between WHR and WC for all participants (Pearson r(51) = .612, p < .001) (B). Scatterplot showing the significant positive correlation between BMI and WC (Pearson r(51) = .852, p < .001) (C) (n = 51).
0.7 0.8 0.9 1.0 1.115
20
25
30
35
40
WHR
BM
ICorrelation between WHR and BMI
r = .411
50
B Figure 3-1. Continued.
0.7 0.8 0.9 1.0 1.120
30
40
50
WHR
WC
Correlation between WHR and WC
r = .612
51
C
Figure 3-1. Continued.
15 20 25 30 35 4020
30
40
50
BMI
WC
Correlation between BMI and WC
r = .852
52
Figure 3-2. Correlation between uncontrolled eating and emotional eating scores on the TFEQ-18. Scatterplot showing the significant positive correlation between the domains of disinhibition and hunger on the TFEQ-18 for all participants (n = 51) (Pearson r(51) = .361, p = .009).
0 20 40 60 800
20
40
60
80
100
Uncontrolled Eating
Em
oti
on
al E
ati
ng
Correlation between Uncontrolled Eating and
Emotional Eating Scores on the TFEQ-18
r = .361
53
Figure 3-3. Correlation between BMI and cognitive restraint. Scatterplot showing the significant positive correlation between BMI and the cognitive restraint domain of the TFEQ-18 for all participants (n = 51) (Pearson r(51) = .292, p = .037).
15 20 25 30 35 400
20
40
60
80
100
BMI
Co
gn
itiv
e R
estr
ain
tCorrelation between BMI and
Cognitive Restraint
r = .292
54
Figure 3-4. Correlation between BMI and money impulsivity. Scatterplot showing the significant negative correlation between BMI and impulsivity percentages during the money block for all participants (n = 51) (Pearson r(51) = -.301, p = .032).
15 20 25 30 35 400
20
40
60
80
100
BMI
Mo
ney Im
pu
lsiv
ity
Correlation between BMI and
Money Impulsivity Scores
r = -.301
55
Figure 3-5. Correlation between uncontrolled eating and food impulsivity. Scatterplot showing the positive correlation between the uncontrolled eating scores and the food impulsivity percentages on the TFEQ-18 for all participants (n = 51) (Pearson r(51) = .302, p = .031).
0 20 40 60 800
50
100
Uncontrolled Eating
Fo
od
Im
pu
lsiv
ity
Correlation between Uncontrolled Eating and Food Impulsivity
r = .302
56
Figure 3-6. Correlation between minutes without food and cognitive restraint scores. Scatterplot showing the negative correlation between the number of minutes since the participant last ate with scores on the cognitive restraint domain of the TFEQ-18 (n = 51) (Pearson r(51) = -.311, p = .026).
0 200 400 600 800 10000
20
40
60
80
100
Minutes without Food
Co
gn
itiv
e R
estr
ain
tCorrelation between Minutes without Food
and Cognitive Restraint Scores
r = -.311
57
Figure 3-7. Correlation between uncontrolled eating and reaction times for the food block. Scatterplot showing the negative correlation between the uncontrolled eating scores on the TFEQ-18 and average reaction times during the food block for all participants (n = 51) (Pearson r(51) = -.295, p = .036).
0 20 40 60 800
1000
2000
3000
4000
Uncontrolled Eating
Fo
od
Blo
ck R
eacti
on
Tim
e (
ms)
Correlation between Uncontrolled Eating and
Food Block Reaction Time
r = -.295
58
Figure 3-8. Food block main effect of reward type. Mean ssVEP amplitudes and SEM for the three rewards (small, medium, and large) for the food block. During the food block, there was a significant main effect of reward magnitude and time delay. Overall, the largest reward had significantly smaller ssVEP activity compared to the smallest reward given today and the medium sized reward given in a week (n = 51) (F(2,100)= 6.599, p = .002).
59
Figure 3-9. Food block main effect of other option shown. Mean ssVEP amplitudes and SEM for the three other options shown for the food block. For the food block, there was a main effect for the other option shown. There was significantly smaller ssVEP activity to stimuli presented with a medium reward option given in a week compared to when they were presented with the smallest reward option given today (n = 51) (F(1,50)= 13.801, p = .001).
0.15
0.20
0.25
0.30
Other Option
ssV
EP
Am
plitu
de (µ
V)
Food Block
Main Effect of Other Option Shown
Small
Medium
Large
*
60
Figure 3-10. Correlation between minutes without food and ssVEP difference between large and small rewards. Scatterplot showing the positive correlation between the number of minutes since food was last consumed and the computed differences between ssVEP amplitudes for the large rewards minus the small rewards for the food block (n = 51) (Pearson r(51) = .283, p = .045).
200 400 600 800 1000
-0.2
-0.1
0.0
0.1
Food Block Correlation between the
Number of Minutes without Food
and the ssVEP Difference
between Large and Small rewards
Minutes since Food
ssV
EP
Dif
fere
nce L
arg
e
min
us S
mall R
ew
ard
s
r = .283
61
Figure 3-11. Money block main effect of reward type. Mean ssVEP amplitudes and SEM for the three rewards (small, medium, and large) for the money block. For the money block, there was a significant main effect of reward magnitude and time delay. Overall, the largest reward given in a month had significantly smaller ssVEP activity compared to the medium sized reward given in a week (n = 51) (F(2,100)= 3.969, p = .022).
0.15
0.20
0.25
0.30
Rewards
ssV
EP
Am
plitu
de (µ
V)
Money Block
Main Effect of Reward Type
Small
Medium
Large
*
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Figure 3-12. Money block main effect of other option shown. Mean ssVEP amplitudes and SEM for the other option shown for the money block. For the money block, there was a significant main effect of the other option presented. When stimuli were presented alongside the medium reward option given in a week, they had significantly smaller ssVEP activity compared to stimuli presented alongside the smallest and larger rewards (n = 51) (F(1,50)= 4.511, p = .039).
0.15
0.20
0.25
0.30
Other Option
ssV
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plitu
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V)
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Main Effect of Other Option Shown
Small
Medium
Large*
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Figure 3-13. Money block interaction between reward and other option shown. Mean ssVEP amplitudes and SEM for the interaction between the three rewards (small, medium, and large) and other option shown during the money block. During the money block, there was a significant interaction between the reward type and other option shown. The largest reward option given in a month presented alongside the medium reward option given in a week had significantly smaller ssVEP activity compared to the medium option when presented alongside a larger option, the larger option when presented alongside the smallest option, and when the smallest option was presented alongside a medium option (n = 51) (F(2,100)= 12.127, p < .001).
Oth
er S
mal
ler
Oth
er L
arger
0.15
0.20
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0.30
0.35
Other Option Shown
ssV
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V)
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Interaction between Reward
and Other Option Shown
Reward Small
Reward Medium
Reward Large
*
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Figure 3-14. Correlation between cognitive restraint and ssVEP amplitude. Scatterplot showing the positive correlation between the cognitive restraint domain of the TFEQ-18 with the money block ssVEP amplitudes for small rewards when shown next to the large option (n = 51) (Pearson r(51) = .287, p = .041).
0 20 40 60 80 1000.0
0.2
0.4
0.6
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Money Block Correlation between the
ssVEP Amplitudes for the Small Rewards
when Shown with Large Rewards and
Cognitive Restraint Scores
Cognitive Restraint
ssV
EP
Am
plitu
de (µ
V)
r = .287
65
Figure 3-15. Correlation between cognitive restraint and ssVEP amplitude. Scatterplot
showing the positive correlation between the cognitive restraint domain of the TFEQ-18 with the money block ssVEP amplitudes for large rewards minus medium rewards (n = 51) (Pearson r(51) = -.306, p = .029).
20 40 60 80 100
-0.3
-0.2
-0.1
0.0
0.1
Money Block Correlation between the
Cognitive Restraint Scores and the
ssVEP Difference between Large and Medium rewards
Cognitive Restraint
ssV
EP
Dif
fere
nce L
arg
e
min
us M
ed
ium
Rew
ard
s
r = -.306
66
B
Figure 3-16. Correlation between cognitive restraint and ssVEP amplitude. Scatterplot showing the positive correlation between the cognitive restraint domain of the TFEQ-18 with the money block ssVEP amplitude difference between the medium other option shown minus large other option shown (n = 51) (Pearson r(51) = -.343, p = .014).
20 40 60 80 100
-0.3
-0.2
-0.1
0.0
0.1
Money Block Correlation between the
Cognitive Restraint Scores and the
ssVEP Difference between Medium and
Large Other Shown
Cognitive Restraint
ssV
EP
Dif
fere
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ed
ium
Min
us L
arg
e O
ther
r = -.343
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CHAPTER 4 DISCUSSION
The aim of the present study was to provide novel information about visuo-
cortical activity during reward-based decision making towards food and monetary
rewards as a function of health status. The first hypothesis that cortical processing of
smaller, sooner rewards would correlate with physiological measures such as BMI, WC,
and WHR was not supported. The secondary hypothesis that there would be differences
in cortical processing between food and monetary rewards was also not supported. The
implications of the significant findings for the demographic, behavioral, and ssVEP data
will be further discussed.
Demographic and Behavioral Correlates
The significant finding that the physiological measures were positively correlated
was expected as BMI, WC, and WHR are all different measures of health status. Males
had larger WC and WHRs than women, which is also expected as the World Health
Organization has separate cutoff values for men and women (World Health
Organization, 2011). The TFEQ-18 had a significant positive correlation between the
uncontrolled eating and emotional eating domains. This is expected because previous
studies consider emotional eating as a subgroup of uncontrolled or disinhibited eating
(Ozier et al., 2008; Konttinen et al., 2009). Those who have uncontrolled eating
behaviors are more likely to overeat when stressors and emotional feelings occur
(Jansen & Van den Hout, 1991). Additionally, the TFEQ-18 domain of cognitive restraint
was positively correlated with BMI, where those with higher restraint scores also have
larger BMIs. This finding replicates previous research that found a positive correlation
between cognitive restraint and BMI (Lluch et al., 2000). This suggests that those who
68
spend more cognitive effort to maintain a diet or control of food intake are more likely to
disrupt their diet when stressors or adverse life events occur. Additionally, those who
severely restrict or restrain from certain foods may be more likely to binge them later.
The impulsivity percentage for the money block was negatively correlated with
BMI; however, BMI did not correlate with the food block impulsivity scores. This finding
indicates that those with higher BMIs were less likely to choose smaller, sooner
monetary rewards. Rasmussen et al., (2010) found that impulsivity measured by a delay
discounting paradigm was associated with food rewards but not monetary rewards
among obese participants.
Those with higher scores on the TFEQ-18 domain of uncontrolled eating had
higher impulsivity scores during the food block but not for the money block. This
indicates that uncontrolled eaters are more likely to choose a smaller food reward that is
readily available instead of waiting for a larger food reward. In an obesogenic
environment, uncontrolled eaters may be more likely to consume palatable foods
regardless of internal or physiological states of hunger. Additionally, those with higher
uncontrolled eating scores had slightly faster decision-making reaction times for the
food block, but not for the money block. This indicates that uncontrolled eaters were
more impulsive towards the food rewards by making faster decisions tending to choose
the smaller, sooner reward.
There was a negative correlation between the number of minutes since food was
last consumed and with the TFEQ-18 domain of cognitive restraint. Those with higher
cognitive restraint scores ate more recently to the testing session than those with lower
cognitive restraint scores. This again supports previous research that those who
69
practice higher amounts of cognitive restraint are more likely to overeat or eat more
frequently (Lluch et al., 2000). Additionally, those with higher cognitive restraint may rely
less on internal signals of hunger and satiation, which can allow for stressors to override
their restraint and ultimately lead to unwanted eating.
There was a positive correlation between the average reaction times for the food
and money blocks. Participants who took longer to respond during the decision-making
process for the food block also took longer to respond for the money block. Since there
were not differences in behavioral performance of decision-making between the food
and money blocks, this suggests that differences in cortical processing during the food
and money blocks illustrates the sensitivity of the ssVEP data to detect neural
differences towards rewards.
Average impulsivity scores food the food and money blocks were not significantly
correlated, indicating that a participant who was impulsive towards food may not have
been impulsive towards money. Previous research has also suggested that decision-
making towards rewards is specific to the type of reward (Odum et al., 2006).
ssVEPs
Visual Processing
The visuo-cortical processing towards the different reward cues indicates that
this methodology and paradigm has the sensitivity and specificity to detect differences
in the evaluation of rewards. The data support the premise that biased competition to
two rewarding stimuli compete for cortical resources in the visual cortex. This indicates
that aspects of decision-making rely on top-down processing so that visuo-cortical
responses can be allocated towards the more behaviorally relevant stimuli. Doya (2008)
described four basic steps to the decision-making process: the subject first recognizes
70
the options, then evaluates the options, selects the option or appropriate action, and
lastly, reevaluates the decision based on the possible outcomes. The current study
recorded ssVEPs while two stimuli were presented simultaneously and captured the
cortical resources while options were being recognized, evaluated, and selected. The
differences in ssVEP amplitudes to the different rewarding stimuli indicate that the visual
cortex is an active component in the evaluation of rewards and decision-making. Further
suggesting that the visual system is part of an early sensory system that is important for
decision-making.
Heekeren et al., (2008) discuss perceptual decision-making where decisions are
based on the sensory information provided and available. This sensory information is
then processed and used to make decisions that affect behavior. They mention that
different options for rewards might affect sensory representations in the brain, but how
this occurs in the brain has not yet been fully answered, and that future studies may
help explain economic decisions based on reward outcomes at basic sensory
processing levels. The current study provides information on the role of the visual cortex
during the evaluation of behaviorally relevant rewards. Greater visuo-cortical resources
were allocated towards the stimulus cue, which provides information about the
behavioral relevance of the rewards.
Behavioral Relevance
The finding that ssVEP amplitude was smaller to the larger, later reward for the
food block indicates that smaller, sooner food rewards may be more behaviorally
relevant. This could be due to the biological aspect that food is relevant for survival and
resources should be spent trying to obtain food. A small amount of food given today
could be more advantageous for survival compared to a larger amount of food later. For
71
the money block, there was not a significant difference in ssVEP amplitudes for the
smallest reward given today and the larger reward given in a month. This could be due
to the way money is valued and viewed. If money is not immediately needed to
purchase resources for survival, then waiting a little bit longer to receive a larger amount
may be more advantageous. Additionally, we do not typically receive money
instantaneously but rather wait for paychecks or paydays to receive money. Overall, we
may be behaviorally trained towards waiting for larger amounts of money.
The number of minutes since the last time food was consumed positively
correlated with the difference in ssVEP amplitude from the large rewards minus the
smallest rewards. Those who ate more recently before the beginning of the testing
session were more likely to have greater visuo-cortical activity towards smaller, sooner
food rewards. This suggests that there is a relationship between those who view
immediate rewards as more behaviorally relevant and the frequency of eating.
The TFEQ-18 domain of cognitive restraint negatively correlates with the
difference in ssVEP amplitude for large rewards minus medium rewards. Those who
had greater scores on cognitive restraint were more likely to have greater visuo-cortical
activity towards medium sized monetary rewards given in a week than larger rewards in
a month. This suggests that those who have greater cortical activity towards sooner
monetary rewards may have to expend more cognitive restraint towards rewards in
general. Those who are not as easily tempted by immediate rewards do not need higher
levels of restraint to prevent them from obtaining those rewards.
Limitations
There are a few limitations to the current study that must be discussed. The first
is that the sample only contains 6 participants who would be classified as obese on the
72
BMI scale. With a small number of obese individuals, the measures of obesity cannot be
used as a categorical variable to determine differences between groups of participants
based on health measures. The majority of the participants have health scores in an
average range, which does not provide a large amount of variance in the BMI, WC, and
WHR measurements. Therefore, the implications of health status are not robust despite
having a large sample size. Additionally, the exploratory correlations may have
introduced type 1 errors. Another limitation is that the methodology requires specific
cues to be trained and used as the stimuli, so the number of cues used to represent
reward size and time delays were limited. The current paradigm does not fully capture
DD to rewards, where the reward amount and time delay are varied to assess patterns
of reward devaluation. Only 3 different cues were taught to keep the task simple for all
participants, but this does not fully allow us to explore visuo-cortical differences towards
many different reward sizes as a function of various time delays. Lastly, the information
on time of last meal was self-reported by all participants and differences in caloric
content or food type may influence the decision-making process towards the food
rewards. Therefore, we were not able to control for hunger and it may have influenced
the decision-making process.
Future Directions
The current results indicate that ssVEPs have the sensitivity and specificity to
detect differences in visuo-cortical processing in a reward-based decision-making task.
This protocol can be used to further build research in this area. First, a larger variance
in the BMI, WC, and WHR scores are needed to better determine the role of health
status on reward-based decision making while recording ssVEPs from the visual cortex.
Additionally, including a larger number of cues and varying the cues so that the reward
73
amounts and time delays have greater ranges would provide additional information
about the visuo-cortical processing of rewards. Lastly, controlling for the amount of food
consumed before the task would better equate satiety levels at the start of the testing
session. Additional information could also be collected between participants who
consume food before the session and those who are forced to fast before the testing
session to assess the role of hunger on reward-based decision-making.
Previous research indicates that certain populations such as drug addicts,
gamblers, and smokers are more likely to choose sooner, smaller rewards (Kirby et al.,
1999; Dixon et al., 2003; Andrade & Petry, 2012). Research using this methodology and
protocol can be used to assess visuo-cortical processing of rewards among those
populations and use reward specific stimuli for those populations. Therefore, this
protocol may provide additional information about the visuo-cortical processing among
other populations who have difficulty with reward-based decision making.
74
LIST OF REFERENCES
Ainslie, G. (1975). Specious reward: a behavioral theory of impulsiveness and impulse control. Psychological bulletin, 82(4), 463. doi:10.1037/h0076860
Allison, D. B., Fontaine, K. R., Manson, J. E., Stevens, J., & VanItallie, T. B. (1999). Annual deaths attributable to obesity in the United States. Jama, 282(16), 1530-1538. doi:10.1001/jama.282.16.1530
Anand, B. K., & Brobeck, J. R. (1951). Hypothalamic control of food intake in rats and cats. The Yale journal of biology and medicine, 24(2), 123.
Andrade, L. F., & Petry, N. M. (2012). Delay and probability discounting in pathological gamblers with and without a history of substance use problems. Psychopharmacology, 219(2), 491-499. doi: 10.1007/s00213-011-2508-9
Appelhans, B. M., Woolf, K., Pagoto, S. L., Schneider, K. L., Whited, M. C., & Liebman, R. (2011). Inhibiting food reward: delay discounting, food reward sensitivity, and palatable food intake in overweight and obese women. Obesity, 19(11), 2175-2182. doi:10.1038/oby.2011.57
Aronne, L. J., Nelinson, D. S., & Lillo, J. L. (2009). Obesity as a disease state: a new paradigm for diagnosis and treatment. Clinical cornerstone, 9(4), 9-29. doi:10.1016/S1098-3597(09)80002-1
Benoit, R. G., Gilbert, S. J., & Burgess, P. W. (2011). A neural mechanism mediating the impact of episodic prospection on farsighted decisions. Journal of Neuroscience, 31(18), 6771-6779. doi:10.1523/JNEUROSCI.6559-10.2011
Berridge, K. C. (2009). ‘Liking’and ‘wanting’food rewards: brain substrates and roles in eating disorders. Physiology & behavior, 97(5), 537-550. doi:10.1016/j.physbeh.2009.02.044
Blackburn, M., Mason, L., Hoeksma, M., Zandstra, E. H., & El-Deredy, W. (2012). Delay discounting as emotional processing: An electrophysiological study. Cognition And Emotion, 26(8), 1459-1474. doi:10.1080/02699931.2012.673478
Braet, C., Claus, L., Verbeken, S., & Van Vlierberghe, L. (2007). Impulsivity in overweight children. European child & adolescent psychiatry, 16(8), 473-483. doi:10.1007/s00787-007-0623-2
Daniel, T. O., Stanton, C. M., & Epstein, L. H. (2013). The future is now: comparing the effect of episodic future thinking on impulsivity in lean and obese individuals. Appetite, 71, 120-125. doi:10.1016/j.appet.2013.07.010
Dawe, S., & Loxton, N. J. (2004). The role of impulsivity in the development of substance use and eating disorders. Neuroscience & Biobehavioral Reviews, 28(3), 343-351. doi:10.1016/j.neubiorev.2004.03.007
75
de Lauzon-Guillain, B., Basdevant, A., Romon, M., Karlsson, J., Borys, J. M., Charles, M. A., & FLVS Study Group. (2006). Is restrained eating a risk factor for weight gain in a general population?–. The American journal of clinical nutrition, 83(1), 132-138. doi: 10.1093/ajcn/83.1.132
de Lauzon, B., Romon, M., Deschamps, V., Lafay, L., Borys, J. M., Karlsson, J., ... & Charles, M. A. (2004). The Three-Factor Eating Questionnaire-R18 is able to distinguish among different eating patterns in a general population. The Journal of nutrition, 134(9), 2372-2380. doi:10.1093/jn/134.9.2372
de Zwaan, M., Gruß, B., Müller, A., Philipsen, A., Graap, H., Martin, A., ... & Hilbert, A. (2011). Association between obesity and adult attention-deficit/hyperactivity disorder in a German community-based sample. Obesity facts, 4(3), 204-211. doi:10.1159/000329565
Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual review of neuroscience, 18(1), 193-222. doi: 10.1146/annurev-psych-122414-033400
Deurenberg, P., Weststrate, J. A., & Seidell, J. C. (1991). Body mass index as a measure of body fatness: age-and sex-specific prediction formulas. British journal of nutrition, 65(2), 105-114. doi:10.1079/BJN19910073
Dixon, M. R., Marley, J., & Jacobs, E. A. (2003). Delay discounting by pathological gamblers. Journal of applied behavior analysis, 36(4), 449-458. doi: 10.1901/jaba.2003.36-449
Doya, K. (2008). Modulators of decision making. Nature neuroscience, 11(4), 410. doi: 10.1038/nn2077
Elfhag, K., & Morey, L. C. (2008). Personality traits and eating behavior in the obese: poor self-control in emotional and external eating but personality assets in restrained eating. Eating behaviors, 9(3), 285-293. doi:10.1016/j.eatbeh.2007.10.003
Evenden, J. L. (1999). Varieties of impulsivity. Psychopharmacology, 146(4), 348-361. doi:10.1007/PL00005481
Eysenck, H. J. (1993). The nature of impulsivity. doi:10.1037/10500-004
Fedoroff, I. D., Polivy, J., & Herman, C. P. (1997). The effect of pre-exposure to food cues on the eating behavior of restrained and unrestrained eaters. Appetite, 28(1), 33-47. doi:10.1006/appe.1996.0057
Flegal, K. M., Carroll, M. D., Kit, B. K., & Ogden, C. L. (2012). Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010. Jama, 307(5), 491-497. doi:10.1001/jama.2012.39
76
Giel, K. E., Teufel, M., Junne, F., Zipfel, S., & Schag, K. (2017). Food-Related Impulsivity in Obesity and Binge Eating Disorder—A Systematic Update of the Evidence. Nutrients, 9(11), 1170. doi:10.3390/nu9111170
Green, E., Jacobson, A., Haase, L., & Murphy, C. (2011). Reduced nucleus accumbens and caudate nucleus activation to a pleasant taste is associated with obesity in older adults. Brain research, 1386, 109-117. doi:10.1016/j.brainres.2011.02.071
Harris, R. B. (1990). Role of set-point theory in regulation of body weight. The FASEB Journal, 4(15), 3310-3318. doi:10.1096/fasebj.4.15.2253845
Heekeren, H. R., Marrett, S., & Ungerleider, L. G. (2008). The neural systems that mediate human perceptual decision making. Nature reviews neuroscience, 9(6), 467. doi: 10.1038/nrn2374
Hendrickson, K. L., & Rasmussen, E. B. (2013). Effects of mindful eating training on delay and probability discounting for food and money in obese and healthy-weight individuals. Behaviour research and therapy, 51(7), 399-409. doi:10.1016/j.brat.2013.04.002
Hervey, G. R. (1959). The effects of lesions in the hypothalamus in parabiotic rats. The Journal of physiology, 145(2), 336-352. doi:10.1113/jphysiol.1959.sp006145
Houben, K., Nederkoorn, C., & Jansen, A. (2012). Too tempting to resist? Past success at weight control rather than dietary restraint determines exposure-induced disinhibited eating. Appetite, 59(2), 550-555. doi:10.1016/j.appet.2012.07.004
Hyland, M. E., Irvine, S. H., Thacker, C., Dann, P. L., & Dennis, I. (1989). Psychometric analysis of the Stunkard-Messick Eating Questionnaire (SMEQ) and comparison with the Dutch Eating Behavior Questionnaire (DEBQ). Current Psychology, 8(3), 228-233. doi: 10.1007/BF02686751
Jansen, A., & Van den Hout, M. (1991). On being led into temptation:“Counterregulation” of dieters after smelling a “preload”. Addictive Behaviors, 16(5), 247-253. doi: 10.1016/0306-4603(91)90017-C
Junghöfer, M., Elbert, T., Tucker, D. M., & Rockstroh, B. (2000). Statistical control of artifacts in dense array EEG/MEG studies. Psychophysiology, 37(4), 523-532. doi:10.1111/1469-8986.3740523
Karlsson, J., Persson, L. O., Sjöström, L., & Sullivan, M. (2000). Psychometric properties and factor structure of the Three-Factor Eating Questionnaire (TFEQ) in obese men and women. Results from the Swedish Obese Subjects (SOS) study. International journal of obesity, 24(12), 1715. doi:10.1038/sj.ijo.0801442
Kaspar, K., Hassler, U., Martens, U., Trujillo-Barreto, N., & Gruber, T. (2010). Steady-state visually evoked potential correlates of object recognition. Brain research, 1343, 112-121. doi: 10.1016/j.brainres.2010.04.072
77
Kennedy, G. C. (1953). The role of depot fat in the hypothalamic control of food intake in the rat. Proc. R. Soc. Lond. B, 140(901), 578-592. doi:10.1098/rspb.1953.0009
Kirby, K. N., Petry, N. M., & Bickel, W. K. (1999). Heroin addicts have higher discount rates for delayed rewards than non-drug-using controls. Journal of Experimental psychology: general, 128(1), 78. doi: 10.1111/j.1360-0443.2003.00669.x
Konttinen, H., Haukkala, A., Sarlio-Lähteenkorva, S., Silventoinen, K., & Jousilahti, P. (2009). Eating styles, self-control and obesity indicators. The moderating role of obesity status and dieting history on restrained eating. Appetite, 53(1), 131-134. doi: 10.1016/j.appet.2009.05.001
Lear, S. A., Humphries, K. H., Kohli, S., & Birmingham, C. L. (2007). The use of BMI and waist circumference as surrogates of body fat differs by ethnicity. Obesity, 15(11), 2817-2824. doi:10.1038/oby.2007.334
Levitsky, D. A. (2005). The non-regulation of food intake in humans: hope for reversing the epidemic of obesity. Physiology & behavior, 86(5), 623-632. doi:10.1016/j.physbeh.2005.08.053
Lluch, A., Herbeth, B., Mejean, L., & Siest, G. (2000). Dietary intakes, eating style and overweight in the Stanislas Family Study. International journal of Obesity, 24(11), 1493. doi: 10.1038/sj.ijo.0801425
Lovejoy, J. C., Sainsbury, A., & Stock Conference 2008 Working Group. (2009). Sex differences in obesity and the regulation of energy homeostasis. Obesity Reviews, 10(2), 154-167. doi:10.1111/j.1467-789X.2008.00529.x
Lowe, M. R. (2003). Self‐regulation of energy intake in the prevention and treatment of obesity: Is it feasible?. Obesity, 11(S10). doi:10.1038/oby.2003.223
Lowe, M. R., & Butryn, M. L. (2007). Hedonic hunger: a new dimension of appetite?. Physiology & behavior, 91(4), 432-439. doi:10.1016/j.physbeh.2007.04.006
Madden, G. J., Petry, N. M., Badger, G. J., & Bickel, W. K. (1997). Impulsive and self-control choices in opioid-dependent patients and non-drug-using control patients: Drug and monetary rewards. Experimental And Clinical Psychopharmacology, 5(3), 256-262. doi:10.1037/1064-1297.5.3.256
Malnick, S., & Knobler, H. (2006). The medical complications of obesity. QJM: An International Journal of Medicine, 99(9), 565-579. doi:10.1093/qjmed/hcl085
McTeague, L. M., Shumen, J. R., Wieser, M. J., Lang, P. J., & Keil, A. (2011). Social vision: sustained perceptual enhancement of affective facial cues in social anxiety. Neuroimage, 54(2), 1615-1624. doi: 10.1016/j.neuroimage.2010.08.080
Müller, M. M., Picton, T. W., Valdes-Sosa, P., Riera, J., Teder-Sälejärvi, W. A., & Hillyard, S. A. (1998). Effects of spatial selective attention on the steady-state
78
visual evoked potential in the 20–28 Hz range. Cognitive Brain Research, 6(4), 249-261. doi: 10.1016/S0926-6410(97)00036-0
Nederkoorn, C., Jansen, E., Mulkens, S., & Jansen, A. (2007). Impulsivity predicts treatment outcome in obese children. Behaviour research and therapy, 45(5), 1071-1075. doi:10.1016/j.brat.2006.05.009
Nicklas, B. J., Cesari, M., Penninx, B. W., Kritchevsky, S. B., Ding, J., Newman, A., ... & Harris, T. B. (2006). Abdominal obesity is an independent risk factor for chronic heart failure in older people. Journal of the American Geriatrics Society, 54(3), 413-420. doi:10.1111/j.1532-5415.2005.00624.x
Nicklas, B. J., Penninx, B. W., Cesari, M., Kritchevsky, S. B., Newman, A. B., Kanaya, A. M., ... & Harris, T. B. (2004). Association of visceral adipose tissue with incident myocardial infarction in older men and women: the Health, Aging and Body Composition Study. American journal of epidemiology, 160(8), 741-749. doi:10.1093/aje/kwh281
Odum, A. L., Baumann, A. A., & Rimington, D. D. (2006). Discounting of delayed hypothetical money and food: Effects of amount. Behavioural processes, 73(3), 278-284. doi: 10.1016/j.beproc.2006.06.008
Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and adult obesity in the United States, 2011-2012. Jama, 311(8), 806-814. doi:10.1001/jama.2014.732
Ozier, A. D., Kendrick, O. W., Leeper, J. D., Knol, L. L., Perko, M., & Burnham, J. (2008). Overweight and obesity are associated with emotion-and stress-related eating as measured by the eating and appraisal due to emotions and stress questionnaire. Journal of the American Dietetic Association, 108(1), 49-56. doi: 10.1016/j.jada.2007.10.011
Pagoto, S. L., Curtin, C., Lemon, S. C., Bandini, L. G., Schneider, K. L., Bodenlos, J. S., & Ma, Y. (2009). Association between adult attention deficit/hyperactivity disorder and obesity in the US population. Obesity, 17(3), 539-544. doi:10.1038/oby.2008.587
Peters, J., & Büchel, C. (2010). Episodic future thinking reduces reward delay discounting through an enhancement of prefrontal-mediotemporal interactions. Neuron, 66(1), 138-148. doi:10.1016/j.neuron.2010.03.026
Popkin, B. M., & Doak, C. M. (1998). The obesity epidemic is a worldwide phenomenon. Nutrition reviews, 56(4), 106-114. doi:10.1111/j.1753-4887.1998.tb01722.x
Prentice, A. M., & Jebb, S. A. (2001). Beyond body mass index. Obesity reviews, 2(3), 141-147. doi:10.1046/j.1467-789x.2001.00031.x
Rachlin, H. (2000). The science of self-control. Harvard University Press.
79
Rangel, A., Camerer, C., & Montague, P. R. (2008). A framework for studying the neurobiology of value-based decision making. Nature reviews neuroscience, 9(7), 545. doi:10.1038/nrn2357
Rasmussen, E. B., Lawyer, S. R., & Reilly, W. (2010). Percent body fat is related to delay and probability discounting for food in humans. Behavioural Processes, 83(1), 23-30. doi:10.1016/j.beproc.2009.09.001
Regan, D. (1989). Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine.
Reynolds, B., & Schiffbauer, R. (2005). Delay of gratification and delay discounting: A unifying feedback model of delay-related impulsive behavior. The Psychological Record, 55(3), 439-460. doi:10.1007/BF03395520
Riggs, N. R., Sakuma, K. L. K., & Pentz, M. A. (2007). Preventing risk for obesity by promoting self-regulation and decision-making skills: Pilot results from the PATHWAYS to health program (PATHWAYS). Evaluation Review, 31(3), 287-310. doi:10.1177/0193841X06297243
Saper, C. B., Chou, T. C., & Elmquist, J. K. (2002). The need to feed: homeostatic and hedonic control of eating. Neuron, 36(2), 199-211. doi:10.1016/S0896-6273(02)00969-8
Stice, E., Spoor, S., Bohon, C., Veldhuizen, M. G., & Small, D. M. (2008). Relation of reward from food intake and anticipated food intake to obesity: a functional magnetic resonance imaging study. Journal of abnormal psychology, 117(4), 924. doi:10.1037/a0013600
Stoeckel, L. E., Weller, R. E., Cook III, E. W., Twieg, D. B., Knowlton, R. C., & Cox, J. E. (2008). Widespread reward-system activation in obese women in response to pictures of high-calorie foods. Neuroimage, 41(2), 636-647. doi:10.1016/j.neuroimage.2008.02.031
Stunkard, A. J., & Messick, S. (1985). The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. Journal of psychosomatic research, 29(1), 71-83. doi:10.1016/0022-3999(85)90010-8
van Egmond‐Froehlich, A., Claußnitzer, G., Dammann, D., Eckstein, E., Bräuer, W., & de Zwaan, M. (2013). Parent reported inattention and hyperactivity/impulsivity as predictor of long‐term weight loss after inpatient treatment in obese adolescents. International Journal of Eating Disorders, 46(1), 39-46. doi:10.1002/eat.22043
Vialatte, F. B., Maurice, M., Dauwels, J., & Cichocki, A. (2010). Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. Progress in neurobiology, 90(4), 418-438. doi: 10.1016/j.pneurobio.2009.11.005
80
Visscher, T. L. S., Seidell, J. C., Molarius, A., van der Kuip, D., Hofman, A., & Witteman, J. C. M. (2001). A comparison of body mass index, waist–hip ratio and waist circumference as predictors of all-cause mortality among the elderly: the Rotterdam study. International journal of obesity, 25(11), 1730. doi:10.1038/sj.ijo.0801787
Weingarten, H. P., & Elston, D. (1991). Food cravings in a college population. Appetite, 17(3), 167-175. doi:10.1016/0195-6663(91)90019-O
Weller, R. E., Cook III, E. W., Avsar, K. B., & Cox, J. E. (2008). Obese women show greater delay discounting than healthy-weight women. Appetite, 51(3), 563-569. doi:10.1016/j.appet.2008.04.010
Westerterp, K. R., Meijer, G. A., Janssen, E. M., Saris, W. H., & Ten Hoor, F. (1992). Long-term effect of physical activity on energy balance and body composition. British Journal of Nutrition, 68(1), 21-30. doi:10.1079/BJN19920063
World Health Organization. (2011). Waist circumference and waist-hip ratio: report of a WHO expert consultation, Geneva, 8-11 December 2008.
Zellner, D. A., Garriga-Trillo, A., Rohm, E., Centeno, S., & Parker, S. (1999). Food liking and craving: A cross-cultural approach. Appetite, 33(1), 61-70. doi:10.1006/appe.1999.0234
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BIOGRAPHICAL SKETCH
Melissa Cervantez was an undergraduate at Las Positas College when she
discovered and developed an interest in psychology. She received her Associate of Arts
degree in liberal arts and sciences in 2008. After, she transferred to San Diego State
University to study psychology and began working as an undergraduate research
assistant in Dr. Claire Murphy’s Lifespan Human Senses Laboratory. She graduated
with a Bachelor of Arts in psychology in 2010 and began her graduate work in the same
laboratory. She received her Master of Arts degree in psychology with an emphasis in
behavioral and cognitive neuroscience in 2013. Her master’s thesis was titled, “Obesity
Measures Predict Differences in Olfactory Event Related Potential Latencies Between
Apolipoprotein E Ԑ4 Carriers and Non-Carriers.” After graduating, she moved to Florida
to begin a PhD program at the University of Florida. She joined Dr. Neil Rowland’s
laboratory researching animal models of feeding behavior. Later, she collaborated with
Dr. Andreas Keil’s laboratory to work on her dissertation research. With the guidance of
Drs. Rowland and Keil, she was able to develop her research idea into a complete
project. She graduated from the University of Florida with a PhD in psychology with an
emphasis in behavioral and cognitive neuroscience in 2018.