BenHonorsPaperFINAL (1)

1PHYSIOLOGICAL INDICATORS OF PROJECTION BIAS
PHYSIOLOGICAL INDICATORS OF PROJECTION BIAS
Benjamin H. Olson
Spring 2016
William M. Hedgcock
Business Administration
Standard economic theory assumes that when making important decisions whose
consequences occur in the future, individuals can accurately estimate future benefits and
costs, enabling themselves to maximize their intertemporal utility. However, past studies
have observed that this idealized assumption does not hold in practice: individuals tend to
exaggerate the extent to which future tastes or states (like hunger, anger, and weather
conditions) will resemble current tastes or states, leading to suboptimal decisions. This
projection bias has many practical applications, particularly in settings where hungry
individuals must make food choices whose consequences extend into the future. The
current study aims to understand howindividuals display projection bias when choosing
healthy or unhealthy snacks to consume at a future date and how physiological responses –
eye patterns – and a key trait – interoceptive awareness – relate to how subjects value these
snacks. In the present study, in an incentive-compatible lab environment, researchers
manipulate hunger levels of subjects and ask their willingness to pay (bids) for five healthy
and five unhealthy snacks. I ask subjects to give their bids to consume each snack in one
week’s time, and upon returning one week later, I ask subjects for bids to consume each
snack that day. Physiological measurements are taken at the first lab visit, including
heartbeat analyses, the speed with which subjects recognize snack images on a computer,
pupil dilation when viewing the images, and distance to screen when viewing the images. I
predict that hungry subjects will display higher degrees of heartbeat awareness, faster
fixation times, increased pupil dilation,and closer screen distance than satiated subjects do.
In addition, I predict that hungry subjects, during their advanced choice, will be willing to pay
more for the ten snacks than satiated subjects will. Data from 21 subjects (six male and 15
female) with a mean age of 20.7 years (SE = 0.29) reveal: hungry subjects bid higher, on
average, on all ten snacks than satiated subjects during the advanced choice (p = 0.04);
examining log bids, hungry subjects bid higher, on average, on the five healthy snacks than
satiated subjects during the advanced choice (p = 0.05); hungry subjects visually fixate on
the largest snack images of all snacks faster, on average, than satiated subjects (p = 0.01);
hungry subjects visually fixate on the largest snack images of healthy snacks faster on
average, than satiated subjects (p = 0.00). Overall, this study calls attention to the
effectiveness of physiological tools – particularly eye-tracking technology – in revealing
unobservable processes that facilitate the interaction between hunger and decision making.
The study can also link to possible real-world marketing solutions, such as improvements in
grocery store layout and store-shelf design.

I. Introduction
When making summer plans in the cold of December, deciding whether to buy a
convertible or pickup truck in cold or warm weather, or deciding to smoke cigarettes during final
examination week at a university, consumers are often faced with decisions whose consequences
are delayed over time. Unfortunately, standard economic theory does not often lend itself to
individuals’ actual behavior in these scenarios.
Standard economic theory contains many rigid assumptions, namely that individuals’
preferences are selfish, coherent, and situation independent. Coherent means that individuals’
preferences are stable over time and unchanging. Furthermore, standard theory assumes that
when making important decisions, individuals are able to accurately estimate future benefits and
costs, enabling themselves to make decisions that maximize intertemporal utility. Experimental
evidence, however, shows that people make systematic errors in decision making when the
results of a choice are separated from the choice itself by time. These errors violate standard
economic assumptions: consumers often make decisions that depend on their personal or
surrounding environments and do not accurately estimate future benefits and costs.
Common settings where these decision errors occur include investment decisions for
durable goods, such as cars, that will be used for long durations of time (Busse et. al, 2014). In
this scenario, a consumer’s state of being at the time of purchase, and even the weather at
purchase time, can be vastly different from these states one week or one year after the purchase.
Similarly, when purchasing groceries for the upcoming week, shoppers may be overly influenced
by their hunger levels at purchase time and fail to consider their hunger the next day or at the end
of the week (Kanouse & Nisbett, 1969).

More specifically, in the above settings, individuals display projection bias – they
exaggerate the extent to which future tastes or states (like hunger, anger, jealousy, and weather)
will resemble current tastes or states. In their literature review of projection bias, Loewenstein,
O’Donoghue, and Rabin (2003) remark that “people tend to understand qualitatively the
directions in which their tastes will change, but systematically underestimate the magnitudes of
these changes” (p. 1210). Therefore, in the case of shopping on an empty stomach, the hungry
shoppers implicitly assume that they will be just as hungry a few days from the food purchase as
they are during the food purchase, causing the shoppers to buy more food than they would if they
could correctly predict their hunger levels each successive day.
Defining Projection Bias
Projection bias contains two main components: intrapersonal empathy gaps and dynamic
inconsistency. Lowenstein, Prelec, and Shatto (1998) call the inability for people to picture
themselves in a different emotional, or visceral, state than they are presently in as an
intrapersonal empathy gap. “Hot-to-cold” empathy gaps arise when a “hot” self – a person who
is currently experiencing a visceral state, like hunger – cannot accurately predict the needs of a
“cold” self – a person who is not experiencing the heights of a visceral state. A main setting
where “hot-to-cold” empathy gaps arise is during the aforementioned shopping-on-an-empty-
stomach scenario (Read & van Leeuwen, 1998).
In addition, based on the work of Strotz (1956) and Thaler (1981), Read and van
Leeuwen note, “[D]ynamic inconsistency occurs when consumers’ ‘preferences for goods
reverse predictably as a function of the delay between choice and consumption’” (1998, p. 191).
Due to dynamic inconsistency, when consumption of food is far in the future, consumers might

plan to eat healthy food, but when the consumption date finally arrives, people are likely to
change their minds and eat unhealthy food – food that has “immediate appeal” but is harmful in
the long run.
Foundational Study
For this current study, I aimed to observe projection bias in the scenario where students
made choices between consuming healthy and unhealthy snacks. When designing this study, I
relied heavily on Read and van Leeuwen’s Predicting Hunger: The Effects of Appetite and Delay
on Choice (1998). In their study, the authors visited a Dutch office building of workers between
the ages of 20 and 40 and asked subjects to make snack selections to consume one week later.
Specifically, subjects made advanced choices either while hungry or satiated (based on time of
day) for snacks to consume one week later when they would be either hungry or satiated. The
snack options were either healthy or unhealthy selections. When one week passed, the
researchers returned to the workplace and allowed the subjects to re-pick a snack to consume at
that moment – in essence, the advanced choice was not binding.
In this manner, the study contained four experimental groups: HS, currently hungry
subjects choosing a snack to consume while satiated one week later; SS, HH, and SH. Overall,
from 200 subjects, Read and van Leeuwen (1998) found that the proportion of unhealthy snacks
initially chosen by HS subjects was greater than that of SS subjects, which is what would be
expected given the properties of intrapersonal empathy gaps. Moreover, all subjects chose more
unhealthy snacks for immediate choice than for advanced choice due to dynamic inconsistency.
Through using their four experimental groups, Read and van Leeuwen (2008) were able to
measure both “hot-to-cold” empathy gaps by comparing the advanced choices of HS subjects

with those of SS subjects, and “cold-to-hot” empathy gaps by comparing the advanced choices of
SH subjects with SS subjects.
Notably, Fisher & Rangel (2014) discovered that both types of empathy gaps are
symmetric in magnitude. In the laboratory environment where 101 Cal-Tech students – in the
HS group or SH group – bid on 50 different snacks, “hungry subjects overbid 20 cents for a
snack they would eat later when they were satiated, and satiated subjects underbid 19 cents for a
snack they would eat later when they were hungry” (Fisher & Rangel, 2014, p. 120).
Through elements of Fisher and Rangel (2014) and Read and van Leeuwen (1998), I
designed a lab experiment that asked subjects to bid on healthy and unhealthy snacks in two lab
sessions separated by one week. Due to the symmetry in “hot-to-cold” and “cold-to-hot”
empathy gaps, I chose to only include two experimental groups in my design, HS and SS, to limit
my required sample size, as including the other two experimental groups from Read and van
Leeuwen (1998) would, in essence, be redundant. In addition to using bids to measure projection
bias, I also aimed to measure subjects’ physiological responses to these snacks.
Physiological Responses
An eye-tracking study was used to measure subjects’ visual physiological responses to
snack stimuli. Specifically, I set out to measure subjects’ physiological responses to images of
snacks through measuring time to first fixation, how long it took subjects to visually fixate on a
snack image; pupil dilation, how large pupils became when subjects looked at one preview
image of each snack; and distance to screen, how close subjects leaned toward the preview image
of each snack. Eye-tracking technology, an advanced camera that monitors subjects’

physiological responses when viewing stimuli, was used to obtain these measurements while
snack images were displayed on a screen of a Dell Workstation computer.
In addition, I measured subjects’ interoceptive awareness – how accurately they
perceived their body’s physiological processes, through using Schandry’s (1981) heartbeat
counting task. This task asked subjects to sit still, and without touching themselves, estimate the
number of times their hearts beat during given time intervals. Simultaneously, an EKG
measured subjects’ actual heartbeats during these intervals. Through this design, Schandry
calculated error scores and found that subjects with high interoceptive ability (low error scores)
possessed high levels of experienced emotion. I thought it would be an interesting angle of my
study to see if subjects with high hunger levels (possibly related to high levels of experienced
emotion) possessed high interoceptive ability as well. In this sense, interoceptive awareness is a
trait rather than a physiological response.
Eye Tracking Capabilities
Eye-tracking technology is increasingly prominent in the academic literature, particularly
in consumer behavior and marketing, as a means to measure subjects’ in-the-moment
subconscious reactions to stimuli in decision-making scenarios or during tasks. In general, table-
mounted eye-tracking devices, such as the Tobii X2-60 model, utilize small head- and eye-
tracking cameras embedded in a computer’s LCD monitor, allowing subjects to freely move their
heads during the eye-tracking study (Malhotra, 2008, p. 124). Naresh K. Malhotra, in the fourth
volume of Review of Marketing Research (2008), noted that low costs of eye-tracking
technology, short calibration time, and natural exposure settings have increased the feasibility for
marketing professionals and academics to utilize eye-tracking studies for real-world applications

and for contributions to consumer behavior theory (p. 124). Moreover, in his working paper
entitled “Pupil Dilation and Eye-tracking,” Joseph Tao-yi Wang of National Taiwan University
mentioned that “using the eye tracker, we can investigate how fixations (looking at the same
place for a while), saccades (fast eye movements) and pupil dilation responses (changes in pupil
sizes) are related to the information on the screen and behavioral choices during an experiment”
(2009, p. 1).
Overall, Malhotra (2008) summarized many studies that point to a key benefit of eye-
tracking technology – that a person’s attention “might even be closer to actual behavior than
intuition informs us, and eye movements could be more than the tip of the iceberg” in terms of
revealing the processes that dictate where a person focuses attention (p. 126). At a more
important level for consumer behavior, studies have shown that eye movement has predictive
validity. As Malhotra (2008) reviews, prior studies demonstrate that a relationship exists
between subjects’ attention to brands on store displays, as measured by eye-tracking, and
subsequent store purchasing decisions (p. 127). Malhotra (2008) also cites a study by Treistman
and Gregg (1979), who found that the advertisements that subjects looked at longer during an
eye-tracking study obtained higher sales than other ads (p. 127). Clearly, these observations are
related to marketing scenarios, but in the present study, I wanted to use eye-tracking technology
to measure the unobservable processes that took place when subjects looked at snack images to
see if hunger had any impact on how fast subjects located snack images.
Pupil Dilation and Eye Tracking
Pupil dilation is a measurement influenced by many competing factors, such as eye angle,
amount of light, level of mental processing, or even emotion. Modern-day stationary eye-

trackers, such as the Tobii X2-60 model used in the present study, allow researchers to measure
pupil dilation while subjects engage in decision making, follow instructions when performing
tasks, or respond to stimuli of various forms, including touch, sound, and sight. Wang (2009)
described the technicality of measuring pupil dilation through eye-tracking devices: “…[E]ye-
trackers put cameras and infrared illuminators in front of subject’s eyes, and videotape eye
movements and corneal reflections….Since images of the pupil are recorded, the eye-tracker is
able to measure pupil dilation by…counting the number of pixels of the (dark color) pupillary
area” (p. 2). In the present study, I wanted to examine if subjects’ level of arousal – in this case,
hunger – had an effect on their pupil dilation while they viewed images of snacks.
Most literature has found that pupil dilation is a key measure of cognitive load – or how
much cognitive effort subjects display when they are completing a given task in an experimental
setting (Beatty, 1982). Much literature has also focused on the influence of emotion on pupil
dilation (Hess & Polt, 1960), as well as arousal from sounds of laughter or crying (Partala &
Surakka, 2003).
Because many causes can trigger pupil dilation, methods are devised to pinpoint an exact
cause of the pupil dilation in an experimental setting. For studies that involve specific
instructions and tasks, Wang (2009) asserted that distinguishing an exact cause of the pupil
dilation occurs through “designing control trials that are identical to the treatment trials except
for only one particular factor of interest” (p. 10). For example, when measuring pupil dilation
during a hearing task, Partala and Surakka (2003) compared the average pupil size of trials where
subjects heard a baby laughing with that of trials where subjects heard typical office noise.
Current literature shows a high degree of understanding about the outward effects of
projection bias – those outward effects of intrapersonal empathy gaps and dynamic

inconsistency. However, current literature does not display much understanding about these
unobservable, almost subconscious, physiological responses that subjects’ display when making
decisions and how these unobservable responses could actually be accurate predictors for
projection bias. Therefore, I wanted to measure subjects’ physiological responses in the moment
of the decision making to understand how they relate to projection bias.
II. Methods
Snack Selection and Pretests:
The foundational design of this study followed the study of Daniel Read and Barbara van
Leeuwen’s Predicting Hunger (1998), in terms of the focus on healthy and unhealthy snacks and
the designation of experimental groups, and of Fisher and Rangel (2014), in terms of the snack
bidding and auction method used. My present study centered on ten snacks – five healthy and
five unhealthy. The healthy and unhealthy snacks were classified and selected based on two
pretests conducted prior to the main lab experiment.
Pretest One:
The purpose of the first pretest was to compile a list of ten snacks to use in the final lab
study based on the preferences of subjects. Overall, 70 subjects completed the survey, including
34 males and 36 females. The mean age of the subjects was 21.7 years (SE = 0.37), with a range
between 18 and 38 years of age. This subject population was the same as the subject population
I used for the main study, though in the main study, I aimed to include subjects 20 years old and
above to better match Read and van Leeuwen’s (1998) subject population of individuals between
20 and 40 years old.

Using a Qualtrics online survey, subjects were presented a list of 29 snacks options and
asked to select five snacks they would most likely eat if given the opportunity, the five
unhealthiest snacks, and the five healthiest snacks. This list of snacks was largely generated
from my own reference point of healthy and unhealthy snacks and from the common products
and brands offered in grocery stores. Snacks included: traditional cheddar Goldfish crackers,
Honey Nut Cheerios, Almonds, plain milk chocolate M&Ms, Cheese cubes, Lays classic potato
chips, Lays baked potato chips, grapes, Skittles, red licorice bites, baby carrot sticks, small
marshmallows, unsalted peanuts, 94% fat-free microwavable popcorn, Oreo cookies, pretzels,
jelly beans, Starburst candies, trail mix (with peanuts, M&Ms, and raisins), WheatThin crackers,
Triscuit crackers, whole grain Goldfish crackers, plain Cheerios, Cheetos, traditional Chex Mix,
traditional Cheez-It crackers, gummy bears, Veggie Straws, and honey Teddy Graham crackers.
In order to arrive at a final list of five unhealthy and five healthy snacks, I used the
following rule: choose the five healthiest and five unhealthiest snack selections from the survey
subject to how likely the subjects would actually eat the snacks in their real lives and how
feasible it would be to photograph the snacks. In order to ensure that the lab study was incentive
compatible, I wanted to focus on snacks that subjects could envision themselves eating in their
real lives, though certainly variation in snack preferences still existed. In addition, because I
needed to take detailed photographs of the final list of snacks, I needed these snacks to be easily
measurable and distinguishable in photograph format. In this case, small, individualized units of
snacks worked better, like Skittles, than less distinguishable snacks, like trail mix that has many
crumbs and an uneven distribution of its components (some servings have more M&Ms and
pretzels than peanuts and raisins). Analyzing the data using this selection rule, the list of
unhealthy snacks consisted of original Skittles, Starburst candies, Mini Oreos, Cheetos, and

potato chips, and the list of healthy snacks consisted of 94% fat-free butter popcorn, cheese
cubes, baby carrots, plain Cheerios, and grapes (Figure 1).

Pretest 2:
The purpose of
the second pretest was to
determine appropriate
Figure1:Duringthefirstpretest,subjectswereaskedtoselectthefivehealthiestsnacks,fiveunhealthiest
snacks,andfivesnackstheywouldactuallyeatintheirreallivesfromaninitiallistof29snacks.Givingmore
weighttothehealthyandunhealthysnackselections,Imademyfinallistoftensnacksbya)confirmingthatthe
unhealthiestandhealthiestselectionsweresnackssubjectswouldactuallyeat,andb)ensuringthesnackswere
easilyphotographable–notmessyandeasilydistinguishable.Forexample,grapeswerethetopselectionfor
healthysnacks,andlookingatwhatsubjectswouldeatintheirreallives,grapeswerealsothetopselection.
Hence,grapeswerechosenasafinalhealthysnackchoice.

Pretest 2:
The purpose of the second pretest was to determine appropriate snack sizes to use in the
main study. Overall, 27 subjects, including 14 males and 13 females, completed the second
pretest. The overall mean age of subjects was 24.0 years (SE = 0.98).
First, I apportioned each snack into seven different sizes, ascending from extra-extra-small to
extra-extra-large based on multiples of one official serving size. I apportioned each snack so that
the “medium” size was equivalent to one official serving size. In Qualtrics, after presenting
subjects with the images of five different sizes for each snack, subjects selected which size they
would most like to consume one week later. From analyzing histograms of these snack size
preferences, I wanted to see if my apportioned sizes would be appropriate to use in the final eye-
tracking study. In general, a size was deemed appropriate if a respectably positive number of
subjects chose that size. Admittedly, this was a somewhat subjective task. If a particular snack
size had no subjects desire it, I would not use that snack size in the final eye-tracking study
(Figure 2). The goal was to choose five snack sizes, preferably maintaining the ascending order,
to use in the final study.

Figure 2: Above is the size histogramfor Skittles from the second pretest. Here, zero subjects desired to consume
the “XL” snack size image one week from the time of the survey. Therefore, for the final eye-tracking study, I
chose to use the “XXL” snack size image in place of the “XL” image. Overall, for Skittles, my final snack size
images were XS, S, M, L, XXL. The same change was adopted for Starburst and Mini Oreos.
To visually present these snacks to subjects in the survey, I needed to take photographs of
the snacks. My main goals were to ensure the different snacks had the same visual reference
point and size, and using the same circular baking pan and same distance between camera and
snack allowed this reference point to exist in all the pictures. I photographed seven images of
each snack using a Canon EOS 60D digital SLR camera set to the 18-135 mm zoom and held 24
inches from the table. The snacks were all housed within the perimeter of a circular baking pan
that is 8.25 inches in diameter. Because I ultimately wanted to utilize an eye-tracking device to
measure subjects’ physiological responses to these snack images, I needed to ensure that all
snacks appeared as the same size on a computer screen. So, I made sure that during photo
cropping, each photo had a width of 1.65 inches. Some of the heights of these cropped images
0
1
2
3
4
5
6
7
XXL XL L M S XS XXS None
Number of
Subjects
Snack Size
Skittles

differed by a small margin due to slight variations in the zoom on the camera, but at least all of
the images had the same baseline width. Regarding the snack sizes chosen, I used multiples of
one official serving size (judging by the U.S. Food and Drug Administration criteria on food
labels) to extend to the different snack sizes.
Main Lab Study:
The purpose of the main lab study was to create an incentive-compatible design in order
to observe projection bias and observe subjects’ physiological responses during their lab visit. I
designed my study using two lab visits separated by one week. Subjects’ hunger was
manipulated largely by time of day. During both lab visits, subjects revealed how much of their
$5 compensation they were willing to pay to consume each snack, either one week later (during
the first visit) or that very day (during the second visit). These bids allowed me to measure
projection bias. In addition, eye-tracking and heartbeat-monitoring software were used to obtain
physiological measurements.
Experimental Groups
Overall, 21 subjects completed both lab visits. The subjects’ mean age was 20.7 years
old (SE = 0.29), and the gender distribution was six males and 15 females. Subjects were
categorized into two experimental groups, HS (N = 11) and SS (N = 10). HS subjects were
hungry during their first lab visit and satiated during their second lab visit. SS subjects were
satiated during both lab visits. Hunger levels at the first lab visit were manipulated by time of
day and special disclaimers given to subjects during their lab sign-ups. I timed lab sessions
based on typical times of day when people are hungry and not hungry. Specifically, from 9 a.m.

to 11 a.m., from Noon through 2:30 p.m., and from 5:30 p.m. to 7:30 p.m., many people eat
breakfast, lunch, and dinner and are therefore satiated. At the intermediate intervals between
common meal times, such as 2:30 to 5:30 p.m., people may be hungry as they have not eaten
meals in some time. I tried to schedule lab visits around these assumed hunger patterns, but my
personal schedule did not always allow for this scenario. So, when subjects signed up for a lab
time slot, I included disclaimers: either 1) “Please do NOT eat anything (i.e. major meal or
snack) within 3 hours of your scheduled lab visit,” or 2) “Please eat something (i.e. major meal
or snack) within 1 hour of your scheduled lab visit.” When subjects showed up to the lab for
their first visit, I then had them sign up for their second lab visit, providing them the same
disclaimers. In this sense, subjects “knew” their current hunger level and their future hunger
level.
Bidding Procedure
During the first lab visit, using a Qualtrics survey with a an image of one official serving
size of each snack, subjects were asked how much of their $5 compensation, if any, they were
willing to pay to consume each snack at their lab time one week later. At this point, I reiterated
to subjects that: the survey acted as if they were allotted a separate $5 for each snack, they could
theoretically bid any value between $0 (no interest) and $5 (maximum interest) for each of the
ten snacks, and the survey focused on one official serving size of each snack. I also explained to
subjects that their bids mattered because they may have to pay, based on their bid, to consume a
snack the following week.
Here, the Becker-DeGroot-Marschak (BDM) method ensured the study was incentive
compatible. Using this method, a subject generates a bid. The bid is then compared to a price

given by a random-number generator. If the subject’s bid is greater than this random price,
he/she pays the price and receives the item up for auction. If the subject’s bid is less than this
random price, he/she pays nothing and does not receive the item (1964). In the context of this
study, I told subjects that at their next visit, I would randomly select a snack using a die. I would
then look back at their first-visit bid for that selected snack and compare it to a random number
between zero and five. The other BDM components then followed. To clarify the BDM
procedure, I also gave the subjects the same example involving carrots and emphasized that the
procedure gave them the incentive to provide their true willingness to pay for each snack – they
had nothing to gain by typing in a number less than their true value.
During the second lab visit, subjects completed the same survey, except it asked them
how much of their $5 compensation they were willing to pay, if any, to consume each of the ten
snacks that day. I also reminded subjects, verbally and through the same carrots example, that I
would be using the BDM procedure to determine if they must pay for a snack. However, I told
subjects that I would actually be conducting the BDM procedure based on their bids from that
day’s survey, not last week’s survey. This slight manipulation served to make the second lab
visit incentive compatible as well.
Through collecting bids from two separated lab visits for hungry and satiated subjects, I
was able to quantify projection bias. Moreover, on the surveys from both visits, I asked subjects
demographic questions (age, race, height, and diet status) as well as their objective hunger level
– time since last meal – and subjective hunger level – scaled ranking of hunger at the moment.
Through asking these hunger questions, I was able to determine if subjects followed directions to
not come to the lab having eaten or vice versa.

Physiological Measurements
Aside from the bidding procedure, I also collected physiological measurements from
subjects utilizing a Tobii X2-60 eye-tracking device (version 3.3) and a Biopac pulse oximeter.
In the Tobii device, I created a slideshow consisting of the five healthy and unhealthy snacks that
were defined by the pretest: original Skittles, Starburst candies, Mini Oreos, Cheetos, potato
chips, 94% fat-free butter popcorn, cheese cubes, baby carrots, plain Cheerios, and grapes. In
order to measure how fast subjects reacted to the image of these various snacks, I needed to
arrange five snack sizes in a circular formation on each slide with a thumbprint distance between
each image to act as the eye tracker’s margin of error. Preceding each circular slide, I included a
fixation slide with a plus sign in the middle followed by a snack preview slide with one image of
one official serving size of the snack in the middle (Figure 3). Prior to these three slides, a
general instruction screen appeared telling subjects that when the circular slide with multiple
images appears, find and stare at the largest snack size. From this eye-tracking task, I was able
to use the Tobii software to measure time to first fixation, how long it took subjects to fixate on
that largest snack image; pupil dilation, measured as the difference in dilation between the
fixation screen and preview screen; and distance to screen, measured as the difference in distance
between the fixation screen and preview screen.

Figure 3: This image displays the order of the slides for the eye-tracking task: a neutral plus sign appears for five
seconds,then a preview image of a snack for five seconds,and then a circular image with five different snack sizes
placed in a circle. Pupil dilation and distance to screen measurements can be calculated by comparing pupil dilation
and distance to screen during the plus sign to those measures during the preview image.
Finally, I measured subjects’ interoceptive awareness using Schandry’s (1981) heartbeat
counting task. Utilizing the Biopac pulse oximeter and an instructional counting mechanism
through E-prime, I had subjects estimate how many times their hearts beat during three time
intervals: 25 seconds, 35 seconds, and 45 seconds. The E-prime mechanism would beep to
denote the start of an interval and beep again to denote the stop of an interval. Then, I compared
subjects’ actual heartbeats, as measured by Biopac, to their estimates. From these comparisons, I
calculated an overall error score. It is important to note that interoceptive awareness is not a
physiological response itself, but rather, is a trait that describes how well subjects can perceive
their bodies’ underlying processes.
Overall, hunger’s influence on decision making may stem from a subject’s conscious
perception of hunger or from some subconscious effect of hunger. The current literature has
established that a subjects’ conscious perception of hunger affects decision making, but not so
much that subconscious effects of hunger affect decision making. As demonstrated Malhotra’s

(2008) sweeping literature review, eye tracking provides a window into the subconscious
processes that drive where subjects focus attention while hungry in the moment, rather than
through some post-hoc analyses that could yield biases.
III. Results
Hunger and Snack Bids
The “hot-to-cold” intrapersonal empathy gap, a main component of project bias, denotes
the scenario where hungry subjects (in a “hot” state) cannot “get-in-the-shoes” of their satiated
selves (in a “cold” state), resulting in these “hot” subjects choosing suboptimal amounts of food
to eat because they cannot accurately predict the needs of their “cold” selves. This intrapersonal
empathy gap lends itself to the following hypothesis:
Hypothesis: If HS subjects display projection bias, they should bid more for a) all snacks,
b) only unhealthy snacks and c) only healthy snacks, than SS subjects do during the
advanced choice.
To analyze the mean bids given by HS subjects and SS subjects, I used an ANOVA
method. Using a one-sided F test, HS subjects bid higher, on average, for all ten snacks than SS
subjects did during the advanced choice (p = 0.04). Said differently, hungry subjects were
willing to pay more than satiated subjects did when choosing snacks to consume one week from
their first lab visit (Figure 4).

Figure 4: During advanced choice, HS subjects bid, on average, 0.98 dollars (SE = 0.10) for all ten snacks, and SS
subjects bid, on average, 0.76 dollars (SE = 0.07).
This result conforms to the properties of projection bias. Hungry subjects, when making
snack decisions for which the consumption is removed by one week, project their current hunger
levels onto their lab visit one week away and implicitly assume they will be just as hungry then
as they were one week prior. Specifically, these results adhere to hypotheses I formed based on
the properties of “hot-to-cold” empathy gaps – that mean bids for all snacks would be higher for
the HS group than the SS group.
Notably, through more ANOVA analyses, HS subjects’ bids for all snacks did not
significantly differ from those of SS subjects during immediate choice.
Through further analyses, I categorically divided the data into the healthy snack category
and the unhealthy snack category. From ANOVA analyses using a one-sided F test, I found that
after taking the natural logarithm of mean healthy snack bids, hungry subjects, on average, bid
more for the healthy snacks than satiated subjects did during advanced choice (p = 0.05). In this
0
0.2
0.4
0.6
0.8
1
1.2
Advance Choice Immediate Choice
Bids in dollars
Mean Bids for All Snacks
HS
SS

scenario, HS subjects’ mean log bid for healthy snacks was 0.63 dollars (SE = 0.08), and SS
subjects’ mean log bid, was 0.47 dollars (SE = 0.05). In addition, HS subjects’ mean log bids for
healthy snacks during immediate choice did not significantly differ from those of SS subjects.
Contrary to expectations, HS subjects’ mean bids for unhealthy snacks were not
significantly different from those of SS subjects during advance choice or immediate choice.
Physiological Measurements
Eye-tracking literature shows that eye-movement closely aligns with individuals’
attention and that individuals’ unobservable processes for allocating attention are influenced by
arousal, or hunger (Wang, 2009; Malhotra, 2008). This general observation lends itself to
Hypothesis 1. In addition, Schandry’s (1981) experiment showed that subjects with high levels
of experienced emotion more accurately perceived their heartbeats than subjects without high
levels of experienced emotion did. If I consider the conscious perception of hunger as a pat of
experienced emotion, Hypothesis 2 arises:
Hypothesis 1: For the eye-tracking task, HS subjects will have a faster time to first
fixation, greater pupil dilation, and closer distance to the computer screen than SS
subjects do.
Hypothesis 2: For the heartbeat-counting task, HS subjects will have more accurate
heartbeat counting scores than SS subjects do.
Of all the physiological measurements currently analyzed at the publishing time of this
paper, time to first fixation had the most promising results. Through ANOVA analyses using a
two-sided F test, HS subjects, on average, fixated on the largest snack size images of all snacks
faster than SS subjects did (p = 0.01). HS subjects’ mean fixation time for all snacks was 1.09

seconds (SE = 0.12), and SS subjects’ mean fixation time was 1.57 seconds (SE = 0.14) (Figure
5). Adding robustness to this result, through using the generalized estimating equations method
that controls for repeated measures, HS subjects, on average, fixated on the largest snack size
images of all snacks faster than SS subjects did (p = 0.01).
Figure 5: HS subjects’ mean fixation time for all snacks was 1.09 seconds (SE = 0.12), and SS subjects’
mean fixation time was 1.57 seconds (SE = 0.14)
Upon analyzing the snacks by category, through ANOVA analyses using a two-sided F
test, I observed that HS subjects fixated faster, on average, than SS subjects did on the healthy
snack images (p = 0.00). Mean fixation time for HS subjects for healthy snacks was 0.87
seconds (SE = 0.08) while that of SS subjects was 1.81 seconds (SE = 0.21). However, the data
showed no mean difference in fixation time for unhealthy snacks between HS and SS subjects.
Here, the data conforms to my hypothesis that hungry subjects would locate all snacks faster than
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
HS SS
Time in
seconds
Mean Time to First Fixation for All
Snacks

satiated subjects did, but they do not conform to my hypothesis that hungry subjects would
specifically locate unhealthy snacks faster than satiated subjects did.
At this time, analyses for interoceptive awareness scores, pupil dilation, and distance to
screen are ongoing.
IV. Discussion
In the present study, I carried out a laboratory study of projection bias in the context of
snacking, based primarily on the experiments of Read and van Leeuwen (1998) and Fisher and
Rangel (2014). My study design borrowed largely from Read and van Leeuwen’s (1998)
experimental groups – HS, SS, SH, HH – and hunger manipulations based on time of day.
Additionally, I incorporated many design elements from Fisher and Rangel (2014), including the
use of snack bidding and the BDM procedure to make my experiment incentive compatible.
While Read and van Leeuwen (1998) measured projection bias by counting the
proportion of unhealthy or healthy snacks subjects chose to consume, and while Fisher and
Rangel (2014) focused on measuring empathy gaps through using 50 snack choices, I made a
hybrid experiment that measured projection bias vis-à-vis subjects’ bids in an incentive-
compatible setting for a set group of ten snacks. Moreover, I added the element of snack size
variation, hence why I included varying snack size images in the eye-tracking task. My study
also used innovative neuromarketing technology – eye-tracking and heartbeat monitoring – to
delve deeper than subjects’ cognition: to measure their underlying physiological responses when
making snack selections.
As Malhotra (2008) notes, “Eye movements are behavioral measures of the unobservable
visual attention process of prime interest” (p. 126). Said differently, physiological

measurements, such as eye tracking, were important to include in my study because they allowed
me to observe the otherwise unobservable processes behind subjects’ attention to large snack
sizes – something that post-hoc surveys could not directly reveal. Decision making consists of
both conscious and unconscious processes, as evidenced by the fields of neuroeconomics and
neuromarketing. Eye-tracking provides a window into the subconscious processes that drive
where subjects focus attention while hungry in-the-moment, allowing researchers to further
understand the mechanisms of projection bias.
Overall, this study showed that during an advanced snack choice, hungry subjects bid
higher, on average, for all snacks – healthy and unhealthy – than satiated subjects did; during an
advanced snack choice, hungry subjects bid higher, on average, for healthy snacks than satiated
subjects did; and hungry subjects, on average, fixated on the largest snack images of healthy
snacks, and the group of all snacks, faster than satiated subjects did.
The above observations align with the properties of projection bias and my hypotheses:
When making a choice whose outcome is not realized until the future – such as bidding on
snacks to consume in one week – hungry individuals over exaggerated the extent to which their
hunger during the snack consumption in one week would resemble their initial level of hunger.
This implicit line of thinking caused these hungry subjects to bid more for all snacks because
they thought they would have the same desire to satiate themselves in one week as they had in
the moment of advanced choice. Moreover, hungry subjects were physiologically aroused and
desired to satiate their hunger. Therefore, hungry subjects, when faced with images of snack
sizes, located certain snack images faster than satiated subjects who did not have the immediate
desire to satiate themselves.

Based on these findings, some real-world implications might exist. For example, because
hungry subjects, on average, bid higher for healthy snacks than satiated subjects, and because
hungry subjects, on average, fixated on healthy snacks faster than satiated subjects did, grocery
stores could rearrange their store shelfs and store layouts to attract consumers that shop on an
empty stomach. These stores could place packages of snacks, specifically king-size packages of
snacks, toward the middle region of store vertical store shelves to attract hungry customers’
gazes. In addition, instead of primarily stocking unhealthy snacks, like candy and cookies, near
checkout counters and at the front of aisles, these stores could also place packaged servings of
healthier snacks in these convenient locations.
My study’s results did not always match hypotheses, though. For example, I would have
expected that hungry subjects, on average, bid higher for unhealthy snacks than satiated subjects
and that hungry subjects, on average, fixated faster on unhealthy snacks than satiated subjects
did. A small sample size (N=21) is probably the culprit here, given that based on prior literature,
I desired to have 60 to 100 subjects complete my study given the two experimental groups.
Another weakness could have been the unequal distribution of male and female subjects (six
males and 15 females). In addition, though I used the statistical methods that many related
studies use, some of my analyses did not account for repeated measures, namely the ANOVA
analyses.
Future studies could further examine projection bias using neuromarketing technology
with a larger sample size to see if results are more robust and in line with the properties of
projection bias. Moreover, future projection bias studies could more strategically define
“healthy” and “unhealthy.” Though my final ten snack selections were based on findings from
two pretests that reflected the preferences of subjects who had similar characteristics as those in

the main laboratory experiment, the initial list of 29 snack options was based mostly on my
personal selections.

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