Slim by Design: Redirecting the Accidental Drivers of Mindless Overeating

Abstract

We first choose what to eat and then we choose how much to eat. Yet as consumer psychologists, we understand food choice much better than food consumption quantity. This review focuses on three powerful drivers of food consumption quantity: 1) Sensory cues (how your senses react), 2) emotional cues (how you feel), and 3) normative cues (how you believe you are supposed to eat). These drivers influence consumption quantities partly because they bias our consumption monitoring – how much attention we pay to how much we eat. To date, consumption quantity research has comfortably focused on the first two drivers and on using education to combat overeating. In contrast, new research on consumption norms can uncover small changes in the eating environment (such as package downsizing, smaller dinnerware, and reduced visibility and convenience) that can be easily implemented in kitchens, restaurants, schools, and public policies to improve our monitoring of how much we eat and to help solve mindless overeating. It is easier to change our food environment than to change our mind.

Full text

Research Dialogue
Slim by design: Redirecting the accidental drivers of mindless overeating☆
Brian Wansink
a
, Pierre Chandon
b,c,
⁎
a
Dyson School of Applied Economics and Management, Cornell University, 110 Warren Hall, Cornell University, Ithaca, NY 14853-7801, USA.
b
INSEAD, Boulevard de Constance, 77300 Fontainebleau, France
c
Institute for Cardio-metabolism and Nutrition, Paris, France
Received 11 January 2014; received in revised form 25 March 2014; accepted 30 March 2014
Available online 13 April 2014
Abstract
We first choose what to eat and then we choose how much to eat. Yet as consumer psychologists, we understand food choice much better than food
consumption quantity. This review focuses on three powerful drivers of food consumption quantity: 1) Sensory cues (how your senses react), 2) emotional
cues (how you feel), and 3) normative cues (how you believe you are supposed to eat). These drivers influence consumption quantities partly because they
bias our consumption monitoring—how much attention we pay to how much we eat. To date, consumption quantity research has comfortably focused on
the first two drivers and on using education to combat overeating. In contrast, new research on consumption norms can uncover small changes in the eating
environment (such as package downsizing, smaller dinnerware, and reduced visibility and convenience) that can be easily implemented in kitchens,
restaurants, schools, and public policies to improve our monitoring of how much we eat and to help solve mindless overeating. It is easier to change our food
environment than to change our mind.
© 2014 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved.
Keywords: Food; Obesity; Nutrition; Norm; Monitoring; Packaging
Introduction
Food choice decisions are different than food consumption
quantity decisions. The former determine what we eat (salad or
pasta); the latter determine how much we eat (half of it or all).
Consumer psychologists and health psychologists have often
focused on understanding the mechanisms that influence food
choice more than on understanding what influences food consump-
tion quantity. Yet at a time of increasing obesity, understanding
what influences how much we eat is as relevant as understanding
what we eat (Hall et al., 2011; Hill, 2009; Nestle & Nesheim, 2012;
Rozin, Ashmore, & Markwith, 1996; Young & Nestle, 2002).
Unfortunately, what consumer researchers have so far
discovered about consumption quantities has been largely
ignored by public health, nutrition, and medicine. Part of this is
due to our process-focus and their outcome-focus. As consumer
psychologists, we typically focus on causal antecedents,
process mediators, statistical significance, psychological indi-
vidual traits as moderators, and counter-intuitive short-term effects
on food choice. In contrast, public health and community nutrition
research largely focuses on outcomes, effect sizes, point estimates,
actionable interventions, demographic moderators, and long-term
effects on weight gain and health. This outcome-focus is really a
solution-focus. It ultimately places a premium on potentially
effective even if theoretically unsurprising interventions, such as
raising prices and nutrition education (e.g., Block, Chandra,
McManus, & Willett, 2010; Ni Mhurchu, Blakely, Jiang, Eyles,
& Rodgers, 2010).
A framework that organizes the drivers to overeating
(defined as eating more than one realizes) could help spotlight
and stimulate overlooked, creative, prescriptive solutions. As
seen in Fig. 1, we build on the important distinction between
☆
The authors are grateful to Michel Tuan Pham for his wonderful guidance
throughout the review process.
⁎Corresponding author.
E-mail addresses: wansink@cornell.edu (B. Wansink),
pierre.chandon@insead.edu (P. Chandon).
http://dx.doi.org/10.1016/j.jcps.2014.03.006
1057-7408/© 2014 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved.
Available online at www.sciencedirect.com
ScienceDirect
Journal of Consumer Psychology 24, 3 (2014) 413 –431

sensory and normative influences first made by Herman and
Polivy (2005,2008) and suggest that 1) sensory, 2) emotional,
and 3) normative drivers influence consumption quantity partly
because they either facilitate or interfere with consumption
monitoring. Although some consumption quantity research has
focused on sensory drivers and emotional drivers, these are
sometimes either individually specific or otherwise difficult to
change in a way that is scalable and cost-effective for public
health. Instead, increased attention needs to be given to
consumption norms and to the environmental interventions that
can influence them and improve the monitoring of how much we
eat (Chandon & Wansink, 2011; Wansink, 2004).
Consumption monitoring
We eat more than 1000 meals a year. We should expertly
know how much food we have eaten and it should be easy to
know when we are eating past the point when it is no longer
pleasurable. Yet when Americans are asked to recall the last
time they overate to the point of regret, 83% had done it within
the past 10 days (Wansink, 2014). Even after eating 1000
meals year after year, we are remarkably bad estimators of how
much we eat, but the errors are not random—they are biased
in a systematic way. Herein lie the seeds of a solution: While
these errors or biases often lead us to overeat, they can also be
leveraged to help us eat less.
Consumption estimation inaccuracies
When we eat standard-sized foods in small quantities—such
as two eggs for breakfast—it is relatively easy to monitor how
much we have eaten. It becomes much more difficult, however,
when we have eaten multiple foods or when the portion sizes
are not standard, such as a pasta entrée, a home-made cookie, or
a large, two-handed fountain drink with no size information.
Prior research describes a consumption range—amindless
margin—in which people can either slightly overeat or slightly
under-eat without being aware of it (Wansink, 2006). Over the
course of a meal, studies have suggested that a person can
appear to eat up to15–20% more or less than they typically do
without realizing they have relatively over- or under-eaten.
That is, if a person needs 2000 cal to stay in energy balance,
they could eat within the 1700–2300 cal without feeling they
had eaten less or more than typical. Yet over the course of one
year, even 100 fewer or extra calories per day (equivalent to a
tablespoon of peanut butter, 8 oz of soda, or 1 small glass of
wine) will make the difference between being 6 lb lighter or
6 lb heavier (Hall et al., 2011).
1
This inability to detect small differences partly explains
why people are generally unaware of their inability to monitor
their consumption and why so much of our consumption—
19.9%accordingtoameta-analysis(Trabulsi & Schoeller,
2001)—is under-reported. It also explains why people are
generally unaware of the influence of the accidental drivers of
mindless eating, whose effects tend to be within that 15–20%
range. Importantly, this bias repeatedly occurs regardless of
one's nutrition knowledge (Bellisle, Dalix, & Slama, 2004;
Tooze et al., 2004)anditisexacerbatedbythewayfoodis
packaged, displayed, and poured (Chandon, 2013). When a
person either selects or serves a food (before they eat), these
Fig. 1. Drivers of consumption quantity.
1
Forecast obtained from the NIH body weight simulator http://bwsimulator.
niddk.nih.gov.
414 B. Wansink, P. Chandon / Journal of Consumer Psychology 24, 3 (2014) 413–431

biases are perceptual. After a person has already eaten, these
biases are memory-related.
Perceptual biases of portion size (pre-intake)
The visual biases that lead to overeating begin as soon as
people pick up a package or plate. Even though volume and
weight information are mandatory on most packages, most
people visually infer the volume from the size of the package or
the size (medium versus large) mentioned on its label (Lennard,
Mitchell, McGoldrick, & Betts, 2001; Viswanathan, Rosa, &
Harris, 2005). In the case of restaurant portions or convenience
store cups, size information is not mandatory and consumers
have little choice but to estimate it visually.
Unfortunately, visual cues linked to sizes and shapes can
lead to dramatic estimation inaccuracies (Chandon & Wansink,
2007b; Folkes & Matta, 2004; Krider, Raghubir, & Krishna,
2001; Krishna, 2007). First, although people are relatively
accurate at estimating small quantities of a food, they under-
estimate large quantities of food by a surprising large margin
(Chandon & Wansink, 2007b). Just as we all underestimate
magnitude changes in volume, weight, or brightness, the subjective
estimate of an increasing meal or portion size appears much smaller
than it really is. As a rule of thumb, doubling the size of a fast food
portion or product packages only makes it appear to be 50–70%
bigger (Chandon & Ordabayeva, 2009). This helps explain why
obese people so dramatically underestimate their consumption—
they simply tend to choose larger meals and portion sizes. In other
words, meal size, not body size drives errors when estimating the
size of meals (Wansink & Chandon, 2006).
These biased perceptions are not a result of people
underestimating differences in the height, width, or length of
packages or portions. Instead, they are a result of people
intuitively believing that these changes in size are additive
instead of multiplicative (Ordabayeva & Chandon, 2013). This
mistaken use of an additive heuristic to solve a multiplicative
size estimation problem explains why elongated packages
appear bigger than packages with a lower height to width ratio
(Krishna, 2006; Raghubir & Krishna, 1999; Wansink & Van
Ittersum, 2003). For example, Ordabayeva and Chandon (2013)
showed that an object downsized by 24% appears to have been
downsized by only 2% when it has been elongated. Because of
the primacy of vision over other senses, elongation strongly
biases size perceptions even when people are asked to weigh
the product by hand. It even leads people to over-pour wine
when holding a glass (versus having it set on a table) or when
pouring into a wider red wine glass than a more narrow white
wine glass of the same capacity (Walker, Smarandescu, &
Wansink, in press).
Recall biases of consumption quantity (post-intake)
Estimating consumption becomes even more difficult once
the food has been eaten. Drawing attention to how much food
is eaten by leaving residual evidence of consumption in view
(such as discarded candy wrappers) facilitates monitoring and
decreases consumption quantity (Polivy, Herman, Hackett,
& Kuleshnyk, 1986). In one study, people ate a third fewer
stackable potato chips out of tube cans when every seventh chip
was colored red (Geier, Wansink, & Rozin, 2012). In another
setting, Super Bowl fans ate 27% fewer chicken wings when
waitresses did not remove the bones from the table compared to
when they did bus the table (Wansink & Payne, 2007). Adding
unobtrusive partitions (such as colored cellophane in between
the cookies inside the package, or having every seventh stacked
potato chip colored red) can reduce intake because it facilitates
consumption monitoring and because it offers an interruption or
a“pause point”for a person to ask themselves if they are really
that hungry (Cheema & Soman, 2008; Geier et al., 2012).
Impairing the ability to gage consumption from visual
cues increases consumption quantity. In a “dark restaurant”
(Dunkelbühne) in Berlin, diners were served regular or larger
dinner portions when either eating in total darkness or when
eating in a regularly-lit restaurant. Larger portions led to con-
sumption underestimation and to a 36% increase in food
consumption in the dark (versus only 22% in the light) yet
diners' subjective satiety was largely unaffected by how much
they had consumed (Scheibehenne, Todd, & Wansink, 2010).
Distractions that disrupt consumption monitoring
Given how difficult it is to accurately monitor our consump-
tion, it is unsurprising that consumers are as easily distracted
from how much they are consuming and this generally leads to
overeating. With the few exceptions discussed below, consumer
research has generally examined the effects of distraction on food
choice, not on consumption quantity (Shiv & Fedorikhin, 1999;
Shiv & Nowlis, 2004). For instance, when people are asked to
watch television during lunch, they eat 12% more without it
having any corresponding impact on their hunger, satiety, or
palatability (Bellisle et al., 2004). Another study found that
people ate 18% more when asked to eat lunch in front of the
television and ate 14% more when assigned to eat with a friend
(Hetherington, Anderson, Norton, & Newson, 2006). By
videotaping these lunches, it was found that these increases
were caused by reduced consumption monitoring. Whereas
people who were asked to eat alone spent 85% of the time
looking at their meal, this proportionwentdownto33%
(eating with a friend) and 28% (watching TV).
Distraction also has carryover effects, leading one to forget
what they have eaten and to again eat more after watching TV
(Higgs & Woodward, 2009). Conversely, enhancing memory
of one's last meal decreases later snack intake, which may be
one reason why keeping a food diary has been so effective in
weight loss. Not remembering what one has eaten is a major
reason why distractions promote overeating. It does not matter
whether they are in the form of television, video games, friends,
or a book. In a characteristically clever study, Rozin, Dow,
Moscovitch, and Rajaram (1998) even found that amnesiac
patients would repeatedly eat the same meal every hour if they
were told it was dinner time again.
Interestingly, there may even be an additional sensory ex-
planation for the pronounced impact distractions have on diets.
Recent studies suggest that distractions interfere with the
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monitoring of specific food attributes, such as flavor, variety,
and calorie density (Higgs, 2008), and they also delay the onset
of taste monotony or sensory specific satiety, which helps
determine when a person will stop eating a particular food
(Remick, Polivy, & Pliner, 2009). Its effects can be measured at
the physiological level of salivation rate (Epstein, Rodefer,
Wisniewski, & Caggiula, 1992).
Although it is clear that distractions can lead us to overeat,
they can also promisingly be used to distract us away from food
and to therefore eat less. In one snacking study, people were
given an average of one-quarter as much of an afternoon snack
as they typically ate and were then given a distracting task to do
(return a phone call or deliver an envelope to an office down the
hall). Fifteen minutes following their snack, they rated them-
selves equally sated and equally satisfied as a control group who
had eaten four times as much of the snacks (van Kleef, Shimizu,
&Wansink,2013).
Summary
In the absence of external stopping points, consumption
monitoring largely determines consumption quantity decisions.
To date, however, consumer psychology research has merely
focused on a) documenting the inaccuracies and systematic
biases of our consumption estimates, b) showing how they can
be explained by perceptual quantity estimation biases, and
c) showing that everyday distracting tasks like watching
television strongly interfere with monitoring. So while we know
a lot about a narrow slice of consumption monitoring, there are
many more promising opportunities. For instance, we have
typically focused on the negative consequences of distractions,
instead of thinking more creatively about how we can harness
these distractions to distract ourselves away from the temptation
of food before we overeat.
The next sections examine research on the sensory,
emotional, and normative drivers of overeating and highlight
how they influence consumption quantities by either directly or
indirectly interfering with—or facilitating—accurate consump-
tion monitoring.
Sensory drivers of consumption quantity
Most people wrongly believe that hunger is the biggest
determinant of consumption quantity (Glanz, Basil, Maibach,
Goldberg, & Snyder, 1998; Vartanian, Herman, & Wansink,
2008). In reality—except in the cases of extreme hunger or
extreme satiation—physiological cues (hunger, satiation, and
gastric distension) play a surprisingly limited role in how much
we eat. Instead of reviewing the physiology of hunger itself, we
focus on internal sensory cues (such as palatability) and on
ambient sensory cues (such as sounds, scents, lighting, and
temperature).
Hunger and satiation cues
A homeostatic model of eating assumes that people eat to
balance their energy inputs and output: They are driven to eat
because of declining energy resources (they feel hungry) and
they stop eating once they have replenished these resources
(they feel full). As a result, hunger and satiation (the opposite of
hunger) would naively appear to be the most obvious drivers of
consumption quantity (Herman & Polivy, 1983). This model
seems logically correct at the extremes—such as after a 24-hour
fast on one extreme, or after a Thanksgiving dinner on the other
extreme. Yet in between these extremes, the main impact of
being hungry seems to have less of an influence on how much
we eat than on what we eat. For instance, buffet goers who had
been deprived of food for 18 h (which is not uncommon when a
person skips breakfast) consumed no more food than those who
had eaten breakfast 3 h earlier. Instead, they ate more starches
(French fries and bread) than vegetables or fruit (Wansink, Tal,
& Shimizu, 2012b). The same is true when grocery shoppers
are hungry. They do not buy greater quantities of food when
they are hungry, they simply buy a greater proportion of less
healthy, ready-to-eat foods, including breakfast cereals, cook-
ies, crackers, and potato chips (Tal & Wansink, 2013).
To underscore this disconnect between one's hunger and
how much we eat, one seven-day study had people keep a food
diary and to also rate how hungry they were at various times
during the day (Mattes, 1990). There was no correlation
between how hungry people were and how much they decided
to eat. Their eating episodes often occurred when hunger
ratings were low or were constant, and very few people
displayed any correlation between hunger ratings and number
of eating occurrences. Most people eat even when they are not
hungry and when eating has stopped becoming pleasurable, and
they only stop eating when they reach the point of feeling
physically full but short of feeling physically uncomfortable
(Poothullil, 2002).
2
There is a growing body of research suggesting that hunger
and satiety are mostly psychological constructs determined
by memory and mental simulation (Morewedge, Huh, &
Vosgerau, 2010; Redden, 2008). For example, people satiate
less when they remember the variety of foods that they have
consumed in the past (Redden & Kruger, 2009). The worse
one's memory of what they just ate, or the perceived ease of
recalling past consumption, the less sated one feels and the
more desire they have to continue eating (Redden & Galak,
2013).
2
In a large part, this gap between when we feel full and when we stop eating
exists because these sensations of fullness or satiety are the outcome of a
complex integration of physiological, sensory, and contextual inputs influenced
by memory and expectations (Epstein, Temple, Roemmich, & Bouton, 2009).
The effects of the physical and chemical qualities of the ingested food and of
oral and gastric signals such as gastric distention on satiation are complex, vary
across people, and are highly interactive depending, for example, on the actual
location of the food is in the gastrointestinal tract (Cecil, 2001; Ritter, 2004).
For example, the formerly well-established result that liquid foods are less
satiating than solid foods has been shown to depend on characteristics such as
pre-load volume, the time lag between the pre-load and the next meal, and the
quantity and quality of sensory inputs (Almiron-Roig, Chen, & Drewnowski,
2003; de Graaf & Kok, 2010).
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Palatability
Palatability—the anticipated and the experienced pleasure of
eating or smelling tasty food—is one major reason why hunger
and satiation do not fully explain how much we eat. Palatability
is the result of complex multisensory interactions between the
smell, oral texture, temperature, viscosity, and the sound made
by food when they are being shown, served, or eaten (Auvray
& Spence, 2008; Rozin, 2009; Zampini & Spence, 2010).
Never before has a wider variety of tasty, affordable food
been more easily available to consume. These sensory cues of
palatability increase our subjective feelings of hunger and
decrease feelings of satiety (Rozin et al., 1998). Multiple
studies have also shown that they prime the goal of hedonic
eating while disrupting consumption monitoring (Stroebe,
Mensink, Aarts, Schut, & Kruglanski, 2008; Stroebe, van
Koningsbruggen, Papies, & Aarts, 2013). The mere sight or
smell of palatable food makes people simulate consumption,
with its pleasure and rewards, eroding a dieter's willpower and
leads to overeating (Papies, Barsalou, & Custers, 2012; Rogers
& Hill, 1989). Placing candies in clear (versus opaque) dishes
and close (versus 6 ft away) to people's desk was found to
double actual consumption quantity but not perceived con-
sumption (Wansink, Painter, & Lee, 2006).
Ambient sound, scent, lighting, and temperature
Consider four relevant ambient sensory cues of an eating
environment: Sounds, scents, lighting, and temperature. These
external (hence non-physiological) sensory cues can have a
large impact on consumption quantity because people typically
cannot block out, control, or avoid them (Krishna, 2009, 2012;
Zampini & Spence, 2010). With sound, for instance, loud
background music has been shown to increase the consumption
speed of food and drink (McCarron & Tierney, 1989; McElrea
& Standing, 1992; Stroebele & de Castro, 2006) and lead to up
to 18% increase in food consumption in a fast food restaurant
(Wansink & Van Ittersum, 2012). Beyond loudness, in nicer,
table-service restaurants, it has been shown that appealing
music leads to longer meals and higher calorie consumption
because people are more likely to order a dessert or another
drink (Caldwell & Hibbert, 2002; Milliman, 1986). At least part
of music's influence on overconsumption comes from con-
sumption monitoring failures. Pleasant and familiar background
music reduces the perception of time duration (Garlin & Owen,
2006; Morrin, Chebat, & Gelinas-Chebat, 2009) and music
distracts from the sensations coming from eating the food itself,
which further impairs monitoring (Woods et al., 2011).
Moving on to the other three features of eating environment,
the main effects are these: First, pleasant ambient odors that
complement a food can increase consumption quantity (Fedoroff,
Polivy, & Herman, 1997, 2003) and offensive or inconsistent
odors decrease consumption quantity (Wadhwa, Shiv, & Nowlis,
2008). Second, similar to loud sounds, harsh lighting makes
people eat faster and reduces the time they stay in a restaurant
whereas soft or warm lighting (including candlelight) generally
causes people to linger and likely enjoy an unplanned dessert or
an extra drink (Lyman, 1989; Stroebele & De Castro, 2004).
Third, people eat more when the ambient temperature is below
the thermo-neutral zone (Westerterp-Plantenga, van Marken
Lichtenbelt, Cilissen, & Top, 2002). Unlike for sound however,
it is not clear whether scents, lighting, and temperature impact
consumption quantities because they interfere with monitoring or
because they make food less attractive or less of a priority
compared with physical comfort (Scheibehenne et al., 2010;
Wansink, Shimizu, Cardello, & Wright, 2012a).
Individual differences and eating restraint
In the 1960s, Stanley Schachter cleverly demonstrated that
obese people (compared with normal weight people) are less
influenced by internal physiological cues like hunger and are
more influenced by external cues (Herman & Polivy, 2008). In
one classic study, obese people ate more food after Schachter
and Gross (1968) manipulated a clock to make them believe
that it was meal time. They repeatedly ate each time the clock
(an external cue) indicated it was the time they usually ate (such
as 12:00 and 5:00 PM), and still ate the full consumption
quantity eaten by the non-obese (Nisbett & Storm, 1974).
Later work has contended that these individual differences
have less to do with obesity and more to do with restrained
eating, which is often colloquially referred to as “dieting”but
more precisely defined as repeated restraint attempts and
failures (Fedoroff et al., 1997; Fishbach, Friedman, &
Kruglanski, 2003; Herman & Deborah, 1975). That is, instead
of determining when to start and stop eating based on hunger
and satiety, restrained eaters use cognitive “dieting”rules to
govern what, how much, or how often they should eat. These
dieting rules can easily be disrupted by one's mood, by cognitive
load, or by dietary violations such as accidently eating a
“forbidden”hedonic food. These “shocks”demotivate restrained
eaters from monitoring how much they eat, and make them more
likely than normal eaters to be influenced by external sensory,
emotional, and normative cues.
After a 40-year lull, researchers are again studying the
relative impact of internal versus external cues. For example,
Wansink, Payne, and Chandon (2007) found that when
Americans (versus French) report what led them to stop eating
dinner the previous night, they are more likely to report using
external cues such as whether their plate was empty or whether
the television show they were watching was over. In contrast,
the French reported using internal cues such as “I was no longer
hungry”or “The food no longer tasted as good.”The same
results were found when contrasting normal weight people with
obese people regardless of their nationality. Recent research on
self-control now distinguishes between successful and unsuc-
cessful restrained eaters or dieters (Fishbach et al., 2003).
For successful dieters, being exposed to palatable food cues
actually primed the opposite goal of restraint, enabling them to
successfully lose or maintain their weight when faced with
temptation. Repeated exposure to temptation actually inocu-
lates them from future self-regulation failures (Dewitte,
Bruyneel, & Geyskens, 2009; Geyskens, Dewitte, Pandelaere,
& Warlop, 2008).
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What is recently becoming of interest is how one's surround-
ings can help facilitate self-regulation—even in the face of
temptation. Environments that are devoid of food-related cues
(such as the workplace or church) help people better monitor their
consumption (Hofmann, Friese, & Roefs, 2009; Stroebe et al.,
2013). These environments are particularly helpful for people with
low-working memory capacity, again underscoring the critical
importance of consumption monitoring (Hofmann, Gschwendner,
Friese, Wiers, & Schmitt, 2008).
A new frontier is the one that shows how the impact of
different types of cues would change across different levels of
hunger. For example, among normal weight people, moderate
hunger actually increases reliance on some external cues (such
as social imitation) as well as reliance on sensory and emotional
cues (Jansen & van den Hout, 1991; Kaufmann, 1995; Spiegel,
Shrager, & Stellar, 1989). In contrast, normative cues such as
the size of the portion, the size of the container, or the
perception of the amount of food, tend to influence most people
more similarly (Herman & Polivy, 2008; Rolls, Morris, & Roe,
2002; Wansink & van Ittersum, 2007), but may have a slightly
exaggerated impact on extroverts compared to introverts (van
Ittersum & Wansink, 2013).
Summary
In the past, physiological cues have been the natural starting
point to study eating. Recently, however, researchers have
shown how seemingly straightforward physiological drivers
like hunger and satiation only account for a small percentage of
how much we eat. In their review paper, even Herman and
Polivy (2008) admit that their path-breaking boundary model—
which assumes that hungry individuals eat no matter what—
may only be true only in extreme cases and that other cues (like
palatability and external sensory cues) actually influence con-
sumption quantity to a much larger extent than previously
believed. Furthermore, palatability and sensory cues have been
shown to influence consumption quantities partially because
they short-circuit physiological signals.
Finally, these sensory cues appear to drive consumption
quantity more for restrained eaters or dieters than other
people. For over 30 years, this has been an unexplained
paradox. People who watch what and how much they eat—
and therefore should be better monitors of their consumption
—are repeatedly more impressionable than people who just
eat what they feel like eating. Once palatable food or ambient
scents (for example) throw them off balance, their cognitive
restraint and dieting rules are no longer effective; their
consumption becomes disinhibited, and they lose the motiva-
tion to monitor their consumption. This suggests that strict
consumption monitoring can actually backfire. On the other
hand, because another subset of dieters has found a way to
make these temptations actually increase their resolve
(Geyskens et al., 2008), there is a rich opportunity to better
understand what causes these dramatically different re-
sponses, and how new framing, rules of thumb, or in-home
interventions might be designed to better help restrained eaters
stay restrained.
Emotional drivers of consumption quantity
“Emotional eating”is a term often used to describe the interest
that some researchers and most of the popular press appear to
have with these eating bouts associated with mood or stress. Such
eating bouts are typically defined as involving three or more
times the amount of food a person would typically eat in this type
of situation (Wansink, 1994). Part of this interest with emotional
eating and eating bouts may have to do with the episodes being
memorable, definable, and dramatic. People can remember the
last time they binged on a pint of ice cream by the light of the
freezer door better than they can remember the slightly larger
portion of cereal they served themselves a couple days earlier.
It may be, however, that emotional eating is less common
than its media mention would lead one to believe. In one
self-reported study, when asked to identify the last time they ate
to the point of regret, only 12% attributed this overeating
episode to stress, mood, or other emotional cues (Wansink &
Chandon, 2014), whereas 49% attributed it to hunger and 39%
to the palatability of the food. In this section, we review the role
of affect valence (positive versus negative emotions) and the
impact of stress and ego depletion.
Affect valence
For many years, the key insight regarding mood and food
was that the worse you felt, the worse you ate. Nowhere was
this clearer than in one's choice of comfort foods. One study
found that people were 4.2 times more likely to eat a less
healthy food (mainly snack foods) when in a negative mood but
were 2.5 times more likely to eat a healthier food, such as a
meal-related food, when in a positive mood (Wansink, Cheney,
& Chan, 2003). People eat more popcorn and M&M's when
they are in a sad mood because they are watching a sad movie
but eat more raisins when they are watching a happy movie
(Garg, Wansink, & Inman, 2007).
Recently, however, it appears this tendency is most common
with restrained and stress eaters (Sproesser, Schupp, & Renner,
in press). With unrestrained eaters, there is less of an impact of
mood on food (Macht, 1999; 2008). Importantly, this helps
explain why most food and mood studies show much smaller
effect sizes if they look at a general population and must use an
eating restraint measure as a covariate in their analyses
(Gardner, Wansink, Kim, & Park, 2014).
More recent studies have looked beyond valence to examine
more specific aspects of emotions such as their temporal
orientation and function. Winterich and Haws (2011) found that
people experiencing hopefulness (a future-focused positive
emotion) consumed less of an unhealthy food than those
experiencing a past- (pride) or present-focused emotional state
(happiness). Finally, recent studies demonstrate the goal-
dependence of emotions (Andrade, 2005). In general, sadness
increases indulgent eating because it functions as a signal to
regulate negative emotions (Gardner et al., in press). When the
eating enjoyment goal is salient however, sadness functions as
a signal to be more vigilant about future losses and makes
418 B. Wansink, P. Chandon / Journal of Consumer Psychology 24, 3 (2014) 413–431

people less likely to indulge (Salerno, Laran, & Janiszewski, in
press).
Stress
Overeating research is dominated by studies of stress. Animal
studies have examined the effects of physical stressors—such as
extended immersion in ice water—and focused on identifying its
physiological pathways to subsequent behaviors (Adam & Epel,
2007). These studies suggest that stress increases the reward
value of palatable food because it stimulates opioid release which
decreases the stress response. Still, these theories assume that
stress uniformly leads to overeating.
Human studies have focused on why some people respond
differently to stress than others. These studies—which mostly
focus on restrained eaters—use a variety of creative manipu-
lations to induce feelings of stress, including threats of shock,
watching unpleasant videos, task failures, anticipated public
speaking, interpersonal rejection, and remembering negative
personal events. One consistent finding has been that restrained
(but not unrestrained) female students eat more when stressed,
regardless of the particular stressor used. Studies have also
found more stress-induced eating among women than men—
although this could be explained by the higher prevalence of
restrained eating among women (Greeno & Wing, 1994). As an
analog, consider the increasing levels of stress college students
face throughout the semester. One recent study showed the
sales of unhealthy foods sold on a university campus increases
across each semester but then drops dramatically back down at
the beginning of the next semester (Wansink, Shimizu et al.,
2012a; Wansink, Tal et al., 2012b). The opposite pattern was
found for healthier foods.
Some studies have examined the link between consumption
quantity and depression, which has been linked by some to an
extreme form of stress. Because of their strong link with
depression, lack of appetite and weight loss have long been
considered among the major symptoms of depression (Beck,
1972). Still, more recent studies have shown that between one
third and half of depressed people gain weight during
depression, showing that weight loss is not the useful
diagnostic symptom of depression that it was once thought to
be (Greeno & Wing, 1994).
Depletion
Consistent with stress, one of the more engaging and
novel streams related to consumption quantity is one
showing that threats to a person's identity and ego increase
consumption of indulgent, unhealthy foods (Baumeister,
Heatherton, & Tice, 1993; Lambird & Mann, 2006). For
example, Baumeister, DeWall, Ciarocco, and Twenge
(2005) found that people who were told that no one wanted
to work with them ate more cookies, and Inzlicht and Kang
(2010) found that the experience of stereotype threat—taking a
math test—led women who are highly stigma conscious to eat
significantly more ice cream. In a nationwide quasi-experiment,
Cornil and Chandon (2013) showed that people eat more, and
less healthily, after a narrow unexpected defeat of their favorite
football team, but they tend to eat less and better after a
victory.
Fortunately, recent insights suggest self-affirmation can
help counter the negative impact of vicarious losses to one's
ego as well as to their favorite football team (Cornil &
Chandon, 2013). Logel and Cohen (2012) even found that,
after two and half months, women who had been asked to
self-affirm their core values had a lower BMI than those who
did not. Now that the impact of depletion has been shown to
be robust across people and places, what would be most
promising is to discover what advice could be given to people to
lessen such an impact.
Summary
Despite its popular press presence, negative affect and stress
only reliably appear to increase consumption quantity among
restrained eaters. Moreover, despite its past clinical use, under-
eating is not the consistent indicator of depression as it was once
thought to be. In contrast to these two misperceptions, depletion
appears to influence consumption quantity reliably among both
restrained and normal eaters.
While the relationship between depletion and overeating is
robust, its explanations are not. Whereas some argue that ego
threats deplete people's self-regulation resources or motivate
them to escape self-awareness (Mandel & Smeesters, 2008),
others have argued it instead merely skirts attention away from
goal conflict and toward reward and gratification (Inzlicht &
Schmeichel, 2012; Stroebe et al., 2013). Supporting the idea that
depletion impacts attention, studies have found that goal conflict
influences the perceived size of food portions (Cornil,
Ordabayeva, Kaiser, Weber, & Chandon, 2014). All these
studies point to the role of attention, and hence of consumption
monitoring, to how emotional cues lead to overeating.
Normative drivers of consumption quantity
Whether it is Thanksgiving dinner or a tailgate party, there is
a flexible range as to how much food a person can eat and still
“make room for more”(Berry, Beatty, & Klesges, 1985; Ferber
& Cabanac, 1987; Herman & Polivy, 1984). To complicate this,
serving sizes are ambiguous. To many, the correct self-
serving size appears to be whatever a person thinks is
appropriate, normal, typical, and reasonable for them (Wansink,
2006). Consumption norm theory (Herman & Polivy, 2005;
Herman, Roth, & Polivy, 2003) suggests that the amount a person
serves oneself is determined by serving norms that can be
internally established, such as how much they usually serve, how
much they normally buy, or how much product they think they
have left in their pantry (Chandon & Wansink, 2006). These
norms can also be externally established by the eating behavior of
dinner companions (McDowell, 1988), by the size of food
packaging (such as the one bag of chips, or 20 oz of soft drinks),
or the size of dinnerware (Wansink, 2014).
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Social facilitation and social matching
We eat 30–60% more if we eat with others, according to lab
studies and food diary studies in free-living conditions (Herman
& Polivy, 2005; Herman et al., 2003), and this can increase to
as much as 75% when eating with friends or family (de Castro
& Brewer, 1992). These social facilitation effects do not impact
self-reported hunger, arousal, or emotionality (de Castro, 1990;
Patel & Schlundt, 2001) and influence consumption quantities
partly by extending how long people eat (Bell & Pliner, 2003;
de Castro, 1990; Feunekes, de Graaf, & van Staveren, 1995;
Hetherington et al., 2006) and partly by priming impression
management goals (Mori, Chaiken, & Pliner, 1987; Pliner,
Chaiken, & Flett, 1990). Eating with others also impairs
consumption monitoring. Hetherington et al. (2006) showed
that eating with familiar others increases consumption quantity
by 18% and that this is partly because, when people eat with
friends, they only spend 33% of their time looking at their food
versus 85% when they eat alone.
In general, people eat more when their eating companions
eat more and less when their eating companions eat less
(Brunner, 2012; Herman, et al. 2003; Romero, Epstein, &
Salvy, 2009). Social matching of food intake is consistent with
studies showing that obesity spreads across social networks
(Christakis & Fowler, 2007). One explanation is that the
amount eaten by others provides a social cue as to how much is
an appropriate amount to eat (Herman et al., 2003). Mimicry
also contributes to social matching. Real-time observations of
dyads of young females showed that they tend to take a bite of
their meal at similar times (Hermans et al., 2012). People
imitate others also in the belief that they will be more liked and
accepted. Ingratiation explains why matching is stronger when
people want to be socially accepted (Robinson, Tobias, Shaw,
Freeman, & Higgs, 2011) and when the eating companion is
similar to them. For example, people with a normal weight are
more likely to imitate the serving size of thin than obese people
(McFerran, Dahl, Fitzsimons, & Morales, 2010a) and dieters
are more persuaded by a heavy than thin server (McFerran,
Dahl, Fitzsimons, & Morales, 2010b).
Categorization cues and health halos
“Food is either healthy or unhealthy.”People spontaneously
categorize food as intrinsically good or bad, healthy or unhealthy,
regardless of how much is eaten (Rozin et al., 1996). This is why
people often determine their serving size based partly on whether
they categorized the food as healthy or unhealthy. This cate-
gorization, in turn, is influenced by the type of food, its health or
nutrition claims, its brand, packaging, price, promotion, and
distribution. If any of these marketing actions imply or lead one to
believe the food is healthier than they would otherwise think,
it can lead to a “health halo”(Chandon & Wansink, 2007a),
whereby people generalize that the food scores favorably on all
health and nutrition aspects (including it being lower in calories),
leading them eat more calories than they think (Andrews,
Netemeyer, & Burton, 1998; Carels, Konrad, & Harper, 2007).
When a food has such a health halo, choosing it can lead a person
to also choose more indulgent side dishes in the same meal or more
indulgent food in following consumption occasions (Chandon &
Wansink, 2007a; Finkelstein & Fishbach, 2010; Wilcox, Vallen,
Block, & Fitzsimons, 2009). Of course, as shown in Fig. 1 in
Chandon (2013), health inferences can also be more negative than
they should, an effect referred to as “health horn”by Burton, Cook,
Howlett, and Newman (2014).Furthermore,consumers—espe-
cially dieters—estimate that a combination of healthy and
unhealthy food (such as having a side salad with a hamburger)
has fewer calories than the unhealthy food (hamburger) alone.
Fortunately, this bias can be eliminated if a consumer can be
reminded or primed to think about food quantity (not just quality)
andwhentheyestimatecalories sequentially (Chernev, 2011;
Chernev & Gal, 2010).
Health halo effects happen for at least three reasons: 1)
health halos make people think that they can eat more without
breaking their dietary goals, 2) health halos make people
hungrier, and 3) health halos reduce guilt (for a review, see
Chandon, 2013). Health halos robustly operate independently
of a person's BMI, gender, or whether they are a restrained
or normal eater (Bowen et al., 2003; Provencher, Polivy,
& Herman, 2008). Neurological and behavioral responses
show that health halos influence the consumption experience
itself (and its neural and hormonal effects) and not just its
interpretation (Crum, Corbin, Brownell, & Salovey, 2011; Lee,
Frederick, & Ariely, 2006; Plassmann, O'Doherty, Shiv, &
Rangel, 2008).
Portion size cues
People can infer how much is appropriate to eat from the
portion size of the food they are served (Rolls et al., 2002),
from the size of the package it comes from (Wansink, 1996),
from how much they have left in their pantry (Chandon &
Wansink, 2006), and from the size of the dinnerware that is
being used—the plates, bowls, glasses, serving containers, and
serving spoons (van Ittersum & Wansink, 2012). Smaller
packages, smaller restaurant portions, and smaller dinnerware
all have one thing in common. They perceptually suggest to us
that it is more appropriate, typical, reasonable, and normal to
serve and to eat less food than larger versions would instead
suggest.
There is considerable evidence that—with perhaps the
exception of children under three—larger packages (Wansink,
1996) and serving sizes significantly increase consumption
(Chandon & Wansink, 2002; Devitt & Mattes, 2004; Fisher &
Kral, 2008; Geier, Rozin, & Doros, 2006; Marchiori, Corneille,
& Klein, 2012; Rolls, Engell, & Birch, 2000). These studies
have shown that the decrease in calorie intake due to down-
sizing can often be 30% less (Steenhuis & Vermeer, 2009). For
instance, it was recently found that the 104 calorie decrease in
the newly revised McDonald's Happy Meals did not result in
any corresponding within-meal increases in the selection of
more caloric options or in additional purchases (Wansink &
Hanks, 2014). Experimentally, Rolls, Roe, and Meengs (2006)
found no differences in hunger when people were served 50%
or 100% more food than usual, although their consumption had
420 B. Wansink, P. Chandon / Journal of Consumer Psychology 24, 3 (2014) 413–431

increased by 16% and 26%, respectively. Indeed, a recent meta-
analysis of 104 studies estimates that consumption quantity
increases by 35% when serving size is doubled (Zlatevska,
Dubelaar, & Holden, in press). Importantly, these changes
in consumption due to serving size increases or decreases are
typically not followed by caloric compensation for up to
10 days (Levitsky & Pacanowski, 2011; Rolls, Roe, & Meengs,
2007b; Steenhuis & Vermeer, 2009).
Recall earlier that when people were asked to consider the
last time they overate and to indicate why they did so, 49%
claimed to overeat because they were hungry and 38% said it
was because the food tasted really good (Wansink, 2014).
When it comes to portion sizes, package sizes, and serving
sizes, people overeat even when the food does not taste good
and when they are not hungry. Supersized servings can even
increase the consumption of bad-tasting foods, such as stale
14-day-old popcorn (Wansink & Kim, 2005). Consumption
increases of 30% are reported frequently, even for foods with
low palatability and even when the calorie count of the food
is also increased, suggesting that it is the perception of volume
that drives consumption—not eating enjoyment or actual
calorie content (Steenhuis & Vermeer, 2009; Wansink &
Park, 2001).
Manipulating the perceived size of the portion, not just its
actual size, also leads people to eat more. Labeling products as
“small”makes people eat more but think they are eating less
(Aydınoğlu & Krishna, 2011; Aydınoğlu, Krishna, & Wansink,
2009). In one study, when restaurant pasta servings were
labeled as “Regular”instead of “Double-size,”intake increased
from 305 to 463 cal (Just & Wansink, in press).
Even if the total amount of available food is the same,
changing the size of the food “unit”itself greatly influences
how much one takes. For instance, people served themselves
127% more candies and 69% more pretzels when the candies
and pretzels were in large units than when they were available
in small (e.g., half a pretzel) units (Geier et al., 2006). Even
“virtual”partitions such as placing a red potato chip every 7 or
14 regular chips, can serve as a cue for appropriate serving size
and influence consumption quantities (Cheema & Soman,
2008; Geier et al., 2012). Size perceptions and preferences can
also be manipulated simply by adding or removing extremely
large or small sizes even if nobody chooses either of them. By
virtue of the compromise effect, adding an extremely small or
large size alternative makes the middle size more attractive and
more frequently selected. Conversely, about two-thirds of the
people who chose a medium size beverage chose a larger size
when the small size was eliminated, thereby making their
previous “medium”size become the new “small”size of the
range (Sharpe, Staelin, & Huber, 2008).
People consume most of their food using serving aids such
as bowls, plates, glasses, or utensils (Wansink & Sobal, 2007).
Since people seem to serve in rough proportion to the size of
their bowl or plate, larger dinnerware leads to larger serving
sizes and larger calorie intake for at least the 54% of Americans
who say eat until they “clean their plate”(Collins, 2006). For
instance, even leading nutritional science professors who were
given 24 oz bowls of ice cream, served and consumed about
39% more ice cream than those given 16 oz bowls (Wansink,
van Ittersum, & Painter, 2006b).
Both packaging and dinnerware can serve as consumption
norms because most people, not just “plate-cleaners,”rely on
visual cues to stop eating. If a person decides to eat half a bowl
of cereal, the size of the bowl acts as a visual cue that influences
how much they serve, consume, and waste (van Ittersum &
Wansink, 2012). To illustrate this, when diners were served
tomato soup in bowls that were unknowingly being refilled
from tubing that ran through the table and into the bottom of the
bowls, they unknowingly ate 73% more soup (Wansink,
Painter, & North, 2005). The effects of perceived consumption
become stronger with delay. Another study manipulated both
actual and perceived intake by using refillable bowls and
showed that actual intake predicts hunger more strongly
than perceived intake immediately after consumption, but the
opposite occurs 2 h after consumption (Brunstrom et al., 2012).
Glass sizes and shapes also lead nearly all people—even
professional bartenders—to over-pour everything from milk to
juice to whiskey. Because elongated glasses appear to fill up
faster than short, fat glasses with the same volume (Krishna,
2006; Raghubir & Krishna, 1999), people pour less volume into
them and drink less from elongated glasses (Wansink & Van
Ittersum, 2003). This elongation bias caused summer campers
to unknowingly pour and drink 88% more juice or soft drinks
into a short, wide glass than into a tall, narrow one of the same
volume. Even Philadelphia bartenders poured an average of
32% more gin, vodka, and whiskey into tumblers than highball
glasses holding the same volume (Wansink and van Ittersum
2003). Even when shown their bias and asked to pour again
2 min later, they still exhibited an average 21% bias. Volume
perception biases also explain why cylindrical glasses (whose
volume increases with both the height and width poured)
appear to fill up faster than conical glasses, leading people to
over-pour when given “Martini”-shaped conical glasses
(Chandon & Ordabayeva, 2009). Visual illusions also operate
for plates. Because of the Delboeuf illusion, the same amount
of food seems smaller on larger plates or on plates with thin
rims (McClain et al., 2013) and this leads people to overserve
on larger plates and underserve on smaller ones (van Ittersum &
Wansink, 2012).
Although decreasing the size of packaging, portions, and
plates can appear to robustly decrease same-meal or within-meal
food intake without other forms of calorie compensation, there
were initially some questions about whether this would persist
long enough to have a measureable impact on weight loss
(Caine-Bish, Feiber, Gordon, & Scheule, 2007; Rolls, Roe,
Halverson, & Meengs, 2007a). Pedersen, Kang, and Kline (2007)
conducted a six-month trial with Type 2 diabetics who were
given portion-controlling dinner plates and cereal bowls.
Although people were aware of the manipulation, they still lost
4.4 lb more than the control condition. A study for an NIH trial
(Robinson & Matheson, 2014) showed that decreasing plate sizes
decreased average meat intake by 34% for adults and 5% for
children over a three month period. A second NIH study
investigated 216 households in Syracuse, New York, who had
been randomly given either 25 or 30.5-cm plates (Hanks,
421B. Wansink, P. Chandon / Journal of Consumer Psychology 24, 3 (2014) 413–431

Kaipainen, & Wansink, 2013). Those who used these plates 10 or
more times each week lost 3 lb or 1.4% of their BMI over the four
month study.
Summary
To understand the extent to which consumption monitoring
mediates the effects of normative cues on consumption quantity,
it is important to distinguish between visual and social cues.
Visual cues operate at an almost unknowing level, influencing
expert dieticians and novices alike (Chandon & Wansink,
2007b). Even when pointed out, people generally deny they
were influenced by such cues. For instance, when 1214 people in
six studies were told how they were biased by a manipulated
consumption norm (a package size, plate size, etc.), 94%
persistently and wrongly maintained that they were unaffected
(Wansink & Sobal, 2007). Moreover, even when they can
be convinced, the bartender data reported earlier show that
people are still biased the next time they serve themselves.
With misleading visual cues, it would appear that consumption
monitoring is almost hopeless. The easiest solution would simply
be to discreetly switch to smaller portions or to use taller glasses
or smaller plates which mislead people in the direction of
healthier, smaller portions.
In contrast, social cues—such as social facilitation and
social matching—provide people with a reference of how much
to consume. Eating with others appears to primarily influence
our consumption by impairing consumption monitoring by
drawing attention away from the food, outside our awareness.
However, because drawing attention to social influences
generally reduces their effects (Cialdini & Goldstein, 2004;
Hammond, 2010), alerting people that they are being influenced
by what others are eating should reduce these normative effects.
New directions for promising solutions
Nowhere in consumer psychology could a researcher have a
more immediate, measurable impact on a mother tomorrow
morning than when discovering solutions to eating problems.
Yet publishing our research results is not the same as solving
useful problems. Sometimes we solve interesting theoretical
problems that are not practical. Sometimes we solve practical
problems with non-scalable answers.
What we seldom do is to ask what implications our research
has for consumers who want to change a target behavior.
Consider many of the findings reviewed in this paper. One
group who would find these—or the derivation of their prin-
ciples—potentially useful are dieters who want to lose weight
or parents who want their family to eat better. Being very
specific about the changes they should make would be a useful
way to translate our discoveries into action. When we think in
terms of specific advice—how it could be used—it can change
how we design studies, discuss results, and disseminate the
related implications to consumers, companies, or policy makers
(Wansink, 2011).
As example, consider the main finding that when chocolate
candy dishes were moved off of desks of secretaries and put in
opaque containers, they ate half as many (Painter, Wansink, &
Hieggelke, 2002). Although the theoretical point of the research
was that food convenience and salience interfere with
consumption monitoring, the main relevance to a dieter is the
main effect: the closer the candy dish, the more you eat. As a
main effect, this basic principle can be extrapolated to provide
advice to these people in other areas of their life. For instance,
dieters could be advised to:
[] Place snacks in the TV room on a table 6 ft farther than
where you sit.
[] Eliminate the cookie bowl from the kitchen.
[] Move cereal boxes off of the kitchen counter.
[] Pre-plate entrées and starches in kitchen (don't serve them
family style).
[] Place a fruit bowl within 3 ft of your most traveled kitchen
pathway.
[] Serve salad and vegetables family style.
As an illustration, we took the evidence-based findings
reported in this paper—and relevant extrapolations of their
principles—and developed a Self-Assessment Scorecard with
100 simple tips for dieters (see Table 1—Wansink, 2014). The
goal of this scorecard is to be both diagnostic and prescriptive.
The recommended changes are unambiguous, binary, and
objectively measurable. This self-assessment would give a
person a score between 0 and 100 that shows whether they
control their eating environment to facilitate healthy eating or
whether their environment and habits negatively control their
eating. The lower the score, the more they are negatively
influenced by their eating environment; the higher their score,
the more they are using their environment to help them eat
healthier. But in addition to being diagnostic, this scorecard
also points out exactly what immediate changes they can make
to turn their immediate environment around, so that it works for
them rather than against them.
What prevents consumer psychologists from providing clear,
objective, simple advice to consumers based on their research?
Part of it may be that we cannot often imagine how our research
could be used. Because of our interest in theoretical explanations,
interactions, and mediations, we often overlook the power that
main effects can have in the lives of consumers. Whereas our
reputations benefit and our papers are published because of their
theoretical contributions, their value to consumers could come
from these simple, basic main effect findings that we typically
disregard as “uninteresting.”
Table 1 offers one possible take on what we consumer
psychologists have discovered and how it might easily fit into
people's lives as solutions. To move toward more refined
solutions, we will need to 1) view our research as a potential
solution to people's problems, but also to 2) conduct our
research in a theoretically rigorous way that yields general
principles. While the Self-Assessment Scorecard in Table 1
offers a first approximation at such solutions, it also suggests
dozens of follow-up opportunities for theoretical rigor that
would investigate boundary conditions and mediating mecha-
nisms. Knowing the mediating mechanisms will be useful in
422 B. Wansink, P. Chandon / Journal of Consumer Psychology 24, 3 (2014) 413–431

generating entirely new sets of interventions. Knowing the
boundary conditions of various interventions will be useful in
developing different assessment tools for different people and
situations.
Although Table 1 suggests basic changes that might work for
most people, some of these changes will work better for some
people than for others. For example, as noted earlier, smaller
portion sizes do not influence consumption of children under
three and do not reduce consumption when the sizes are so
small that restrained eaters view the food as healthy. Similarly,
although health halos generally increase consumption quantities,
Table 1
Slim by design in-home 100 self-assessment scorecard.
Dishware
[] Plates are between 9 and 10-in. in diameter
[] Plates have a colored rim
[] Plates are sectioned
[] Cereal bowls are smaller than 20 oz
[] Juice glasses are 8 oz
[] Glasses are tall and narrow
[] Water glasses are 16 oz or larger
Dining table
[] Children under 12 have smaller plates than parents
[] Children under 12 have smaller bowls than parents
[] Children under 12 have smaller glasses than parents
[] Serving bowls are small enough to have to be refilled
[] Salad and vegetables are served first
[] Salad and vegetables are served family style (on the table)
[] The serving bowls for starches and entrées are not setting on the table
[] The serving bowls for starches and entrées are located on the kitchen stove
[] Serving spoons tablespoon-sized or smaller
[] Serving tongs are not used
[] At least one person at the table is drinking milk
[] Everyone has a glass of water
[] No wine is being drunk or it is being drunk from tall narrow wine glasses
[] No soft drinks are being drunk
[] No food packages (other than condiments) are on the table
[] Lights are dimmed
[] Soft music is being played
[] Everyone stays seated until everybody is through eating
[] The family eats together at the same time
Kitchen
[] No television
[] No comfortable chairs
[] Earth-tone painted walls (neither too bright nor too dark)
[] Blender is on the counter
[] Toaster is not on the counter
[] Breakfast cereal is not visible
[] Cookies are not visible
[] Snacks are not visible
[] A full fruit bowl is visible
[] Fruit bowl contains 2 or more types of fruit
[] Fruit bowl is within 3 ft of the most common kitchen pathway
[] Kitchen has a floral scent
[] Kitchen is the major room you enter upon entering home
Refrigerator
[] Fruit and vegetables are on the center shelf
[] Cut fruit and vegetables are bagged or in a container
[] Healthiest leftovers are in transparent containers
[] Healthiest leftovers are wrapped in transparent wrap
[] Less healthy leftovers are stored in opaque containers
[] Less healthy leftovers are wrapped in aluminum foil
[] Refrigerator has at least 6 non-fat yogurts in it
[] A second low-calorie, high protein snack is available (e.g., string cheese)
[] Healthiest snacks are in the front middle
[] Less healthy snacks are in the back or the lower sides
[] Low-fat milk is in the refrigerator
[] Less healthy leftovers are stored in the produce drawers
[] No more than 2 cans of soft drinks
[] A full pitcher of cold water is always available
Freezer
[] Healthiest leftovers are in transparent containers
[] Less healthy leftovers are stored in opaque containers
[] Less healthy leftovers are wrapped in aluminum foil
Cupboards
[] Healthiest foods are in the front middle
[] Healthiest foods are eye level
[] Less healthy foods are in the back or the lower sides
Cupboards
[] Less healthy foods are stored on the bottom or the top
[] There is a designated snack cupboard that is inconvenient
[] Snack cupboard has a child-proof lock on it (even if no children)
Pantries
[] Healthiest foods are in the front middle
[] Healthiest foods are eye level
[] Less healthy foods are in the back or the lower sides
[] Less healthy foods are stored on the bottom or the top
[] Pantry is not located in the kitchen
Counters
[] Cookies are not visible
[] Snacks are not visible
[] Candy is not visible
[] Regular soft drinks are not visible
[] Diet soft drinks are not visible
[] Nuts are not visible
[] Breakfast cereal is not visible
TV room
[] Full glass or water or water bottle is always next to the chair
[] Snacks are located at least 6 ft from seating area
[] All snacks are eaten out of bowls, not bags or original containers
[] Snacks are eaten from small bowls, 8 oz or less
[] Candy wrappers are left on coffee table
[] Beverage containers—cans or bottles—are left on coffee table
Home office
[] Full glass of water or water bottle is always on the desk
[] Snacks are located at least 6 ft from seating area
[] All snacks are eaten out of bowls, not bags or original containers
[] Snacks are eaten from small bowls, 8 oz or less
[] Candy wrappers are left on coffee table
[] Beverage containers—cans or bottles—are left on coffee table
Car
[] Never take breakfast, lunch, or dinner in the car.
[] No candy.
[] No cookies.
[] No high-calorie snack.
[] Bag of nuts or other healthy snacks available for adults and children.
[] Always carry water bottle.
[] No soft drink.
[] Choose concentrated energy shots over energy drinks to avoid high-calorie
intake.
Night stand
[] No candy.
[] No cookies.
[] No high-calorie snack.
[] Glass or bottle of water handy.
Purse
[] No high-calorie snack.
[] Bag of nuts or other healthy snacks available for adults and children.
[] Water bottle.
(Reprinted, with permission from Slim by Design, Wansink, 2014).
Table 1 continued
423B. Wansink, P. Chandon / Journal of Consumer Psychology 24, 3 (2014) 413–431

their effectiveness is lower when health claims negatively
influence flavor expectations (Kiesel, McCluskey, & Villas-
Boas, 2011; Kozup, Creyer, & Burton, 2003), of people who care
more about taste than nutrition (Irmak, Vallen, & Robinson,
2011; Vadiveloo, Morwitz, & Chandon, 2013), of men (Bowen,
Tomoyasu, Anderson, Carney, & Kristal, 1992), and of familiar
brands or expert consumers (Hoegg & Alba, 2007). Future
research needs to examine the robustness of health halos
for diverse socio-economic groups and outside the US, where
the negative association between health and taste is less
pronounced because people associate “healthy”with “fresh”
and “high quality”(Fischler, Masson, & Barlösius, 2008; Werle,
Trendel, & Ardito, 2013).
More generally, research is needed to integrate food choice
and consumption decisions. Most of the studies on food choices
examine what to eat and not how much is eaten. Conversely,
most of the studies reviewed here examined how much to eat
after the decision of what to eat had already been made. Future
research must examine the effects of proposed interventions on
both what and how much to eat. For example, downsizing a
package—say a soft drink bottle—by elongating it can hide the
true extent of the size reduction and thus increase purchasing,
yet it could backfire if people finish it earlier than anticipated
and decide to consume a second.
Ultimately, learning how to change consumption norms—
particularly those resulting from visual cues—holds tremen-
dous promise for researchers for three reasons: 1) Their
impact is magnified because of repeated actions, 2) they
can be found in an endless number of forms, and 3) their
perceptual nature makes consumers more vulnerable to them
than they believe. From an intervention standpoint, changing
the size of a cafeteria tray or the size label on a restaurant
menu can change consumption in an automatic way that does
not necessitate willpower or an expensive public health
education campaign.
Implications for policy makers, companies, and consumers
Giving people objective nutrition knowledge about the
health costs of overconsumption is necessary but unlikely to be
sufficient to change behavior. Exhorting people to change their
dietary habits through moralizing and guilt-inducing appeals is
not a credible alternative solution. Both ideas fall short because
we cannot expect that most people will adopt a cognitively-
costly mindful eating approach for the over 200 automatic food
decisions that they make every day (Wansink & Sobal, 2007).
Even though mindful consumption strategies can be learned
(Papies et al., 2012; Peter & Brinberg, 2012), it is not clear that
most people will be willing to sustain this over the three years
that are usually required to lose weight and establish a new
equilibrium (Hall et al., 2011).
This suggests another complementary approach: focusing on
changing the choice environment at both the time of purchase
and the time of consumption (Thaler & Sunstein, 2003). This
relies less on persuasion and more on environmental inter-
ventions that lead consumers into making slightly better but
repeated food choices without thinking about each of them.
This is done mostly by altering the eating environment in the
ways suggested in Table 1 for one's home environment and in
similar ways in the four other places where people purchase or
consume food: their most frequented restaurants, their favorite
grocery store, where they work, and where their children go to
school (Wansink, 2014). This small-steps approach is not
designed to achieve major weight loss among the obese, but
rather to prevent obesity among the 90% of the population that
is gradually becoming fat by consuming an excess of less than
100 cal per day (Hill, Wyatt, Reed, & Peters, 2003).
Leveraging our research in medicine, nutrition, and public
health
Although consumer psychologists have been uncovering an
increasing number of insights about food consumption behavior,
many of these insights have not had their deserving impact on the
field of public health, nutrition, or medicine. In addition to what
has already been noted, consumer psychology research is often
overlooked because of our general lack of interest in consumer
heterogeneity. Public health researchers are keenly interested in
the differences between men and women, educated and less
educated people, old and young, rich and poor, both in the US
and abroad. These same researchers—and reviewers—are often
disconcerted when we acknowledge that we did not find these
distinctions theoretically interesting enough to analyze or even
collect. What we see as a conceptually uninteresting null effect
(such as the lack of differences between genders) or a confounded
or over-determined strong effect (such as strong differences
between income levels or ethnicities) can inform key interven-
tions they may be considering.
To have an impact beyond our field, we need to examine
how our short-term, cross-sectional results hold across time.
Longer time-horizons are particularly important because habit-
uation and compensation can offset short-term effects. Just as
the link between behavioral intentions and actual behavior is
not perfect, neither is the link between how much a person
decides to serve and how much they decide to subsequently eat.
Most of our studies measure what someone takes or how much
they take, but seldom how much they eat. While there is early
evidence that a large percentage of what a person self-serves is
eaten—perhaps as much as an average of 92% (Wansink &
Johnson, in press), this is not precise enough for the standards
of medicine, nutrition, and public health and may vary across
people and situations (such as school cafeterias versus lab
studies). Our results might make strong cases, but they do not
make precise cases. Because medicine, nutrition, and public
health are focused on measurable, tangible behaviors, they
consider many of these studies to be the equivalent of behavioral
intention studies with no clear proof of impact.
Ideally, new studies would combine the best characteristics
of consumer research (including rich psychological insights and
multi-method testing), nutrition (including longitudinal de-
signs, representative participants, biomarkers of calorie intake
and expenditures), and economics (including population-level
interventions and analyses, and policy implications).
424 B. Wansink, P. Chandon / Journal of Consumer Psychology 24, 3 (2014) 413–431

To summarize these concerns and the way forward, we can
go back to Michel Pham's (2013) presidential address about the
seven sins of consumer psychology. We must expand the scope
of our research from buyer behavior to actual (repeated)
consumption behavior (Sin 1). We need to adopt multiple
theoretical lenses (Sin 2). We must go beyond theory testing
and do more phenomenon-based and descriptive research and
empirical generalization (Sin 3). We must go beyond research
by convenience and do more grounded field work (Sin 6). And,
moregenerally,weneedtodoresearchthatismoreexternally
relevant (Sin 7). As noted throughout this review—this requires
moving our focus from statistical significance to effect size, from
theory testing to prediction models, and from tests of associations
to point estimates. Policy makers are not interested in counter-
intuitive findings that demonstrate consumer irrationality—these
findings are typically small and often occur under narrow, stylized,
“hot house”conditions.
Of all academics, consumer psychologists are perhaps in the
best position to have a real impact on how people, companies,
and legislators think about healthy eating. First, we understand
people's full motivations and choices better than nutritionists.
Second, we do not demonize the food industry in the same way
that many public health and medical researchers do (therefore
we have the ability to partner with the players with the biggest
impact on consumption). Third, thanks to the multidisciplinary
nature of most marketing departments, we can easily collaborate
with colleagues across the hall who know how to sophisticatedly
model supply—and not just demand—effects, who know how to
analyze archival data and estimate policy effects, who understand
the importance of consumer heterogeneity and who, most im-
portantly, understand and generally appreciate the contribution
of experimental research. Table 2 outlines the areas in which
we—as consumer psychologists—have a key methodological,
theoretical, or dispositional edge to powerfully change this
domain.
Helping companies make healthy profits
A wide range of people and institutions would like to better
control a person's consumption of food for a wide range of
reasons. Those in the hospitality industry want to decrease food
costs (via serving size) without decreasing satisfaction. Those
in public policy want to decrease waste, health expenditures,
and lost productivity which influence wellbeing. Those in health
and nutrition want to decrease obesity and its associated diseases.
Those in strenuous field situations, such as combat military and
deployed rescue workers, want to decrease under-consumption.
Those on restricted diets want to decrease calories, fat, or sugar
intake.
It is important to realize that food companies are not focused
on making people fat; they are focused on making money.
Take the notion of single-serving packaging. Although such
packaging can increase production costs, the $43 billion spent
in 2013 on diet-related products is evidence that there is a
portion-predisposed segment that would be willing to pay a
premium for packaging that enabled them to eat less of a food
in a single serving and to enjoy it more. For instance, results
Table 2
Value-added research opportunities of consumer psychologists studying consumption behavior.
Competitive advantages for consumer
psychology researchers
Competitive disadvantages for consumer
psychology researchers
Where are some of the greatest opportunities?
Consumption monitoring •Fits well into existing and comfortable
research paradigms
•Numerous theories and methods are available
to determine the source of cognitive and
perceptual biases
•Necessitates studies that involve actual eating
behavior (versus choice studies or computer
studies)
•To be most influential, studies will need to
involve realistic field situations and validation
•While most research has focused on bias, it has not focused
on the reasons behind why such cognitive or perceptual biases
exist.
•Generating rules of thumb and monitoring short-cuts would
be useful to legions of dieters and clinicians.
Sensory drivers of consumption quantity •We are well suited to conceive and test
psychological mechanisms that could interact
with physiological factors
•Tools and facilities for physiological research
are often costly and more accessible in medical
schools
•IRB delays
•High subject costs
•Isolating common or more general psychological states that
mediate and environmental stimuli and eating
•Articulating a greater range of environmental conditions that
could influence behavior (such as crowding, type of noise
level, lighting variation, and so on).
Affective drivers of consumption quantity •Wide range of theories are available
•Sophisticated tools/skills for investigating
relevance of theories to a behavior
•Too much focus on isolating a single mechanism
often makes the research question too narrow and
the research context too stylized and unrealistic
•Often results in studies with little apparent field
validity
•Determining how thought processes or feelings interact with
physiology to alter eating behavior
•Explaining general findings in the field and showing how the
interventions could be modified or targeted to be more
effective.
Normative drivers of consumption quantity •Strong conceptual training in both the
perceptual, behavioral, and social drivers of
consumption norms
•Tools and emphasis on internal validity
enable to disclose subtle effects
•An overemphasis on research precedent limits
the creativity of interventions
•An overemphasis on internal validity can
generate interventions that are not scalable or
generalizable
•Determine how internal norms are established and when
these are dominated by external norms
•Developing a new taxonomy for consumption norms would
aid in identifying a wider range of norms that could be useful
intervention points in personal life and in public health.
425B. Wansink, P. Chandon / Journal of Consumer Psychology 24, 3 (2014) 413–431

from a survey of 770 North Americans indicated that 57% of
them would be willing to pay up to 15% more for these portion-
controlled items (Wansink & Huckabee, 2005). Although
targeting this “portion-prone”segment will not initially address
the immediate needs of all consumers, it can provide the critical
impetus that companies need to develop profitable win–win
solutions.
There are many more of these win–win solutions that can
profitably benefit both companies and consumers; many of
which can offer a wide range of profitable segmentation
opportunities for companies. One answer to the obesity issue
lies in market-based changes that help consumers develop a
new appetite for healthy foods. Innovative solutions for de-
marketing obesity will be solutions that leverage the basic
reasons why we eat the way we eat. In this context, consumer
psychology can help companies develop a wide range of
solutions, just as has been done with the decrease of portion
sizes in Unilever's Seductive Nutrition program, or the refor-
mulation and relaunch of McDonald's Happy Meals (Wansink
& Hanks, 2014). In a previous article, we outlined dozens of
innovative actions taken by food producers, grocers, and res-
taurants to continue to grow without contributing to the obesity
epidemic (Chandon & Wansink, 2012).
Bringing research home . . . to consumers
Consumption is a context where understanding fundamental
behavior has immediate implications for consumer welfare
(Cutler, Glaeser, & Shapiro, 2003). People are often surprised
at how much they consume, and this indicates they may be
influenced at a basic level of which they are not aware or do not
monitor. Similar to the fundamental attribution error, this
explains why simply knowing these environmental traps does not
typically help one avoid them (Baranowski, Cullen, Nicklas,
Thompson, & Baranowski, 2003). Moreover, relying only on
cognitive control and on willpower is often disappointing (Boon,
Stroebe, Schut, & Jansen, 1998). Yet, consistently reminding
people to vigilantly monitor their actions around food is un-
realistic. Continued cognitive oversight is already difficult for
people who are focused, disciplined, and concentrated. It is
nearly impossible for those of us who are not. The studies
reviewed here—and their Scorecard manifestation in Table 1—
illustrate how an individual can alter his or her personal en-
vironment to help make their family slim by design.
Conclusion
As our consumer psychology studies show, our senses, affect,
and norms can all entice and contribute to our mindless
overconsumption of food. Yet, these studies also show that a
personally altered environment can help people more effortlessly
control their consumption in a way that does not necessitate the
discipline of dieting or the governance by someone else.
If changing the behavior of consumers, food marketers,
opinion leaders, and policy makers, is one objective of our
research, it is important to realize that it may not happen
naturally. Unfortunately, this has been the approach of consumer
psychologists over the last decades, and it has led to a dis-
appointing impact outside our field—especially as it relates to
changing consumers, companies, and policy. Instead it is
important to more actively visualize who will use our research
and how they will use it before we begin conducting our studies
(Mick, 2011). Consider the following example discussed by
Parmar (2007). Suppose researchers have a working hypothesis
that consumers pour more liquid into short, wide glasses than tall,
narrow glasses of the same volume. Before conducting that
research, the researchers might ask themselves the following
questions: 1) Who should use this? Managers of bar and
restaurant chains and the beverage companies that provide
glassware to them. 2) What change could they make? Replace
short, wide bar glasses with tall,thinonestoreducebeverage
consumption while improving margins. 3) What independent
variables are realistic? Barware in sizes and shapes most
commonlyusedbythelargestcasualdiningchains.4)What
would make this compelling? Real bartenders in real bars in a
real city who pour the four most commonly-poured drinks into
the most common glass sizes. Mapping out possible answers
to these questions—even though the results of the study are
not yet known—will direct the research design to be most
potentially impactful. Referred to as “activism research”
(Wansink, 2011), these answers can suggest a new context, a
different population, or overlooked independent variables
that can ignite unanticipated, but rewarding change. It is
easier to change our food environment than to change our
mind.
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