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Social Influence
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Modeling of food intake: a meta-
analytic review
Lenny R. Vartaniana, Samantha Spanosa, C. Peter Hermanb & Janet
Polivyb
a School of Psychology, UNSW Australia, Sydney, NSW2052,
Australia
b Department of Psychology, University of Toronto, Toronto,
Canada
Published online: 24 Feb 2015.
To cite this article: Lenny R. Vartanian, Samantha Spanos, C. Peter Herman & Janet Polivy
(2015) Modeling of food intake: a meta-analytic review, Social Influence, 10:3, 119-136, DOI:
10.1080/15534510.2015.1008037
To link to this article: http://dx.doi.org/10.1080/15534510.2015.1008037
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Modeling of food intake: a meta-analytic review
Lenny R. Vartanian
a
*, Samantha Spanos
a
, C. Peter Herman
b
and Janet Polivy
b
a
School of Psychology, UNSW Australia, Sydney, NSW 2052, Australia;
b
Department of Psychology,
University of Toronto, Toronto, Canada
(Received 14 September 2014; accepted 9 January 2015)
This meta-analysis provides a comprehensive quantitative assessment of research on
modeling of food intake. Thirty-eight articles met inclusion criteria. Overall, there was
a large modeling effect (r¼.39) such that participants ate more when their companion
ate more, and ate less when their companion ate less. Furthermore, social models
appear to have stronger inhibitory effects than augmenting effects. Moderator analyses
indicated that there were larger effects for correlational versus experimental studies,
and for women versus men. There was no difference in effect sizes for studies using a
live versus remote confederate, or for participants who were high or low in concern
with eating appropriately. Together, these findings point to modeling as a robust and
powerful influence on food intake.
Keywords: modeling; food intake; social influence; meta-analysis
Imagine that you are meeting a friend for lunch at a restaurant. You both order the daily
special and, when the food arrives, your friend eats almost everything on her plate.
The next day, you go for lunch at the same restaurant with another friend who eats almost
nothing. How much would you eat in each of those situations? Although factors such as
how hungry you are and how much you like the taste of the food will almost certainly play
a role, considerable evidence suggests that how much your companion eats will also play
an important role in determining how much you eat. In social situations, one’s eating
behavior can be influenced by the behavior of others in a variety of ways (see Herman,
Roth, & Polivy, 2003, for a review). For example, social-facilitation research finds that
people tend to eat more in larger groups than when eating alone (de Castro & Brewer,
1992; Herman, 2015), whereas the impression-management literature indicates that people
can use their eating behavior to convey a particular impression of themselves to others
(Vartanian, 2015; Vartanian, Herman, & Polivy, 2007). One of the most powerful social
influences on food intake is modeling: people adjust their food intake to that of their eating
companion, eating a little when their companion eats a little, and eating more when their
companion eats more.
According to the normative account of food intake, modeling occurs because other
people provide information about the appropriate amount of food to consume in a given
situation (Herman et al., 2003). This account follows from the fact that, in social situations,
the appropriate amount to eat is often ambiguous, and internal signals (i.e., hunger and
satiety) that one would expect to help guide food intake are often unreliable (Herman &
Polivy, 2005). Thus, in these situations, people may look to the example of others to help
them decide how much food is appropriate to consume. More specifically, Herman et al.
(2003) argued that people are often motivated to maximize their intake of palatable foods
q2015 The Author(s). Published by Taylor & Francis.
This is an Open Access articledistributed under the terms of the CreativeCommons Attribution-NonCommercial-NoDerivs License
(http://creativecommons.org/Licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any
medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
*Corresponding author. Email: lvartanian@psy.unsw.edu.au
Social Influence, 2015
Vol. 10, No. 3, 119–136, http://dx.doi.org/10.1080/15534510.2015.1008037
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without appearing to eat excessively, with “excess” defined as eating more than what
others are eating. Thus, a companion who eats very little can inhibit one’s own food
intake whereas a companion who eats a large amount can augment one’s own food intake
(or at least give one permission to eat an equally large amount). Support for the normative
account of modeling comes from recent research showing that perceived norms of
appropriate food intake mediate the influence of social models on food intake (Vartanian,
Sokol, Herman, & Polivy, 2013). Such a normative account has also been used by
some researchers to explain the spread of obesity in social networks (e.g., Christakis &
Fowler, 2007).
One of the most notable features of the modeling of food intake is how robust the effect
appears to be. Modeling is observed with unhealthy snack foods (Vartanian et al., 2013)
and healthy snack foods (Hermans, Larsen, Herman & Engels, 2009), and also during
meals (Hermans, Larsen, Herman, & Engels, 2012). Modeling occurs among people who
have been food deprived for up to 24 hours (Goldman, Herman, & Polivy, 1991), among
children (Bevelander, Anschu
¨tz, & Engels, 2012), and independent of individual
differences in body weight (Rosenthal & McSweeney, 1979) and dietary restraint (Roth,
Herman, Polivy, & Pliner, 2001). Modeling persists even when the other person is not
physically present and participants are exposed only to a written indication of the amount
of food eaten by supposed prior participants (a “remote” confederate; Roth et al., 2001;
Vartanian et al., 2013); indeed, Feeney, Polivy, Pliner, and Sullivan (2011) found no
difference in the strength of modeling whether the model was a live confederate or a
remote confederate.
Researchers have also examined a variety of individual difference and contextual factors
that should enhance or limit the extent to which people model the food intake of others. For
example, individuals who are high in trait empathy (Robinson, Tobias, Shaw, Freeman, &
Higgs, 2011) appear to model the behavior of their eating companion to a greater extent, as do
individuals high in expressiveness (Brunner, 2012), but individuals high in extraversion or
high in self-monitoring do not differ in the extent to which they model another person’s food
intake (Herman, Koenig-Nobert, Peterson, & Polivy, 2005). Furthermore, modeling tends to
be stronger when the person’s eating companion is an in-group member (Cruwys et al., 2012)
and when the experimental confederate is lean (as opposed to obese; e.g., McFerran, Dahl,
Fitzsimons, & Morales, 2010). Examining moderators of modeling is important in order to
elucidate any possible boundary conditions of the effect.
The purpose of the present meta-analysis is to quantify the effects of social models on
people’s food intake. Herman et al. (2003) provided a qualitative review of social
influences on food intake, but the literature was in its relative infancy at the time. Since
then, there has been a proliferation of research on the modeling of food intake, and there is
thus a critical mass of research allowing for a quantitative analysis of modeling effects.
One recent meta-analysis (Robinson, Thomas, Aveyard, & Higgs, 2014) examined the
impact of social norms on food choice and eating behavior and found that social norms
exerted a moderate effect on the eating outcomes. However, that review was limited by the
inclusion of only a small number of experimental studies (only eight studies that examined
actual food intake), and the authors were unable to quantitatively assess any moderators of
the modeling effect. Thus, we sought to provide a more comprehensive assessment of the
effects of social models on the amount of food that people eat. Furthermore, we sought to
develop a better understanding of the moderators of modeling effects by examining
variation as a function of experimental design (correlational vs. experimental studies), as
well as characteristics of the participant, characteristics of the model, characteristics of the
eating context, and factors that might influence the extent of people’s concern with eating
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appropriately. Doing so will not only provide a clearer picture of how, when, and why
social models influence people’s food intake, but should also provide direction for future
research aimed at filling the gaps in the existing knowledge base.
Method
Literature search strategy
The literature search involved a multi-step process and was completed in August 2014.
First, we searched three electronic databases (Scopus, PsycInfo, and Web of Science) for
articles published through the end of 2013 to identify studies containing the terms eating,
food intake,consumption,ingestion, and food choice in combination with modeling,social
influence,peer influence,matching, and confederate. Second, the reference section of each
retrieved article was searched to identify potentially relevant articles that were missed in
the initial searches. Third, citations to the retrieved articles were searched to identify any
recent relevant articles. Fourth, the electronic databases indicated above were further
searched using the names of the first author, last author, and corresponding author of each
eligible study. Fifth, the table of contents of key journals in the area (Appetite,Eating
Behaviors,Physiology & Behavior,Health Psychology) were searched for any relevant
articles that might have been missed through the other search steps.
The search process identified 110 articles which were then retrieved and reviewed in
detail by two independent judges (the first and second author) to determine their eligibility
for inclusion in this study. Note that, although the terms “modeling” and “matching” are
often used interchangeably in the literature, we suggest that the term “matching” should
refer specifically to the extent to which Person A eats an identical amount to Person B,
whereas the term “modeling” should refer more generally to the process of adjusting one’s
intake upward or downward in line with the intake of one’s eating companion (see also
Spanos, Vartanian, Herman, & Polivy, 2014). In this article, we focus on modeling (i.e.,
studies that assess the correlation between the amounts eaten by individuals, or studies that
examine how a confederate’s intake affects participants’ intake). Inclusion criteria for the
current meta-analysis were as follows: (1) the article must have been published in English
(1 article excluded); (2) the study must have measured the amount of food consumed by
participants (as opposed to food choice, self-reported food intake, behavioral intentions, or
other such measures; 28 articles excluded); and (3) for experimental studies, the study
must involve at least two norm conditions (i.e., at least two of the following: low-intake
model, high-intake model, no-intake model, or eat-alone/no-norm condition) and the norm
conditions must specify the amount eaten by the confederates (10 articles excluded). Other
reasons for excluding articles were that they were not modeling studies (e.g., social
facilitation studies; 15 articles excluded), were only abstracts without sufficient
information to code (9 articles excluded), were theoretical review papers (8 articles
excluded), or duplicated a sample from another study (1 article excluded). A total of 38
articles (representing 44 separate studies and 67 independent effects) met the inclusion
criteria and were included in the meta-analysis.
Effect size coding
We calculated effect sizes as r-values from the information available in the published
reports, including means and standard deviations, correlation coefficients or intraclass
correlations, F-values, and p-values. For experimental studies in which means for more
than two groups were reported (e.g., low-intake norm, high-intake norm, and a control
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condition), we used the formulas for one-way contrasts described by Rosenthal, Rosnow,
and Rubin (2000) to calculate an overall effect size. In each case, we ordered the norm
conditions as follows (from lowest to highest): no-intake confederate, low-intake
confederate, eat-alone control, and high-intake confederate. For studies that did not
provide sufficient information to calculate precise effect sizes, we contacted the authors to
obtain additional information (when possible), or used a conservative estimate of effect
sizes. For example, effects described as “significant” were assigned a p-value of .05, and
effects described as “non-significant” were assigned a p-value of .99. This conservative
effect-size estimate was used in only two cases and allowed us to include as many studies
as possible in the meta-analysis without potentially exaggerating any observed effects.
Effect sizes were weighted by sample size. We interpreted an effect size of .10 as small,
.25 as medium, and .40 or above as large (Lipsey & Wilson, 2001).
Meta-analytic procedures
To determine the magnitude and significance of the overall effect size, we fit a random-
effects model. In contrast to a fixed-effects approach, which assumes that individual effect
sizes differ from the population mean through sampling error alone, a random-effects
approach assumes that variability among effect sizes is due to sampling error as well as
unsystematic, random sources of error that vary across studies. Random-effects models are
also more conservative and have substantially reduced Type 1 error rates compared to
fixed-effects models (Field, 2003; Lipsey & Wilson, 2001), and allow for inferences to be
drawn beyond the studies included in the meta-analysis (Field & Gillett, 2010).
The primary analysis was an examination of the overall modeling effect across all
studies. Because there is considerable variability in the research design used in modeling
studies, we also tested whether mean effect sizes differed for experimental studies (those
in which the amount eaten by the model is determined by the experimenter) and
correlational studies (in which the researchers simply calculated the degree of
correspondence between the amounts eaten by two members of a dyad). For this
analysis, we excluded no-norm (eat alone) control conditions from the experimental
studies because there is no equivalent in correlational studies (the results are identical if
the eat-alone condition is included). Furthermore, past theory and research suggests that
social models might have an inhibiting effect more than they have an augmenting effect
(Herman et al., 2003; Vartanian et al., 2013); we therefore computed the mean effect size
separately for low-norm conditions versus no-norm/eat-alone control conditions, and for
no-norm/eat-alone control conditions versus high-intake conditions.
The potential for publication bias was assessed by calculating Orwin’s fail-safe N
(the number of studies with a correlation of r¼.00 that would need to be added to the
meta-analysis to bring the overall effect size to a negligible level, defined as r¼.05 in this
case; Orwin, 1983). The fail-safe Nwas 451 studies, suggesting that these effects are
robust to the so-called file-drawer problem.
Moderators
In addition to determining the overall effect size, we also evaluated the degree of
heterogeneity in effect-size distribution by calculating the Qstatistic, and followed up a
significant Qstatistic with a series of moderator analyses. Moderators were coded by two
independent coders with any discrepancies resolved through discussion with the first
author. The moderator coding for each study is displayed in Table 1.
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Table 1. Study characteristics for each moderator included in the meta-analysis.
Participant Model Eating context
Authors Year Design Gender Age Type Weight status Familiarity Food type Task type Concern
Addessi, Galloway,
Visalberghi, and Birch
2005 Exp. Mixed Children Live – Familiar Snack Eating –
Bevelander, Anschu
¨tz et al. 2013 Exp. Mixed Children Remote – Unfamiliar Snack Non-eating DNC
Bevelander et al. 2012 Exp. Mixed Children Live – Unfamiliar Snack Non-eating –
Bevelander, Meiselman,
Anschu
¨tz, and, Engels
2013 Exp. Mixed Children Live – Unfamiliar Snack Non-eating –
Brunner 2010; Study 1 Exp. Female Adults Live – Unfamiliar Snack Eating –
Brunner 2010; Study 2 Exp. Female Adults Live – Unfamiliar Snack Eating –
Brunner 2012 Corr. Female Adults – – Unfamiliar Snack Non-eating L: Low expressive
H: High expressive
Conger, Conger, Costanzo,
Wright, and Matter
1980 Exp. Mixed Adults Live – Unfamiliar Snack Eating –
Cruwys et al. 2012 Exp. Female Adults Live – Unfamiliar Snack Non-eating L: Out-group
H: In-group
Feeney et al. 2011 Exp. Female Adults Live – Unfamiliar Meal Non-eating –
Remote
Florack, Palcu, and Friese 2013; Study 1 Exp. Mixed Adults Live – Unfamiliar Snack Non-eating –
Florack et al. 2013; Study 2 Exp. Female Adults Remote – Unfamiliar Snack Eating –
Goldman et al. 1991; Exp. 1 Exp. Female Adults Live – Unfamiliar Meal Eating –
Goldman et al. 1991; Exp. 2 Exp. Female Adults Live – Unfamiliar Meal Eating –
Herman et al. 2005 Corr. Female Adults – – Unfamiliar Snack Non-eating L: Low extraversion
H: High extraversion
Hermans, Engels, Larsen,
and Herman
2009 Exp. Female Adults Live – Unfamiliar Snack Non-eating L: Unsociable model
H: Sociable model
(Continued)
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Table 1 – continued
Participant Model Eating context
Authors Year Design Gender Age Type Weight status Familiarity Food type Task type Concern
Hermans, Herman, Larsen,
and Engels
2010a Exp. Male Adults Live – Unfamiliar Snack Non-eating –
Hermans, Herman, Larsen,
and Engels
2010b Exp. Female Adults Live – Unfamiliar Meal Non-eating –
Hermans et al. 2008 Exp. Female Adults Live Thin Unfamiliar Snack Non-eating –
Normal
Hermans, Larsen, et al. 2009 Exp. Female Adults Live – Unfamiliar Snack Non-eating –
Hermans, Larsen, et al. 2012 Exp. Female Adults Live – Unfamiliar Meal Non-eating –
Hermans et al. 2013 Exp. Female Adults Live – Unfamiliar Snack Non-eating L: Low impulsivity
H: High impulsivity
Hermans, Salvy, et al. 2012; Exp. 2 Exp. Female Adults Remote – Unfamiliar Snack Non-eating –
Howland, Hunger, and Mann 2012; Study 2 Exp. Mixed Adults Live – Familiar Snack Non-eating –
Johnston 2002; Exp. 1 Exp. Female Adults Live Normal Unfamiliar Snack Eating –
Obese
McFerran et al. 2010; Exp. 1 Exp. Female Adults Live DNC Unfamiliar Snack Non-eating –
McFerran et al. 2010; Exp. 2 Exp. Female Adults Live Thin Unfamiliar Snack Non-eating –
Obese
Nisbett and Storms 1974; Exp. 1 Exp. Male Adults Live – Unfamiliar Snack Eating –
Pliner and Mann 2004; Exp. 1 Exp. Female Adults Remote – Unfamiliar Snack Eating –
Polivy, Herman, Younger,
and Erskine
1979 Exp. Female Adults Live – Unfamiliar Meal Eating –
Robinson et al. 2013 Exp. Female Adults Remote – Unfamiliar Snack Eating L: Low empathy
H: High empathy
Robinson et al. 2011; Study 1 Corr. Female Adults – – Unfamiliar Snack Non-eating L: Low empathy
H: High empathy
Romero, Epstein, and Salvy 2009 Exp. Female Children Remote – Unfamiliar Snack Non-eating –
Rosenthal and McSweeny 1979; Exp. 2 Exp. Male Adults Live – Unfamiliar Snack Eating –
Female
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Rosenthal and Marx 1979 Exp. Female Adults Live – Unfamiliar Snack Eating –
Roth et al. 2001 Exp. Female Adults Remote – Unfamiliar Snack Eating –
Salvy et al. 2009 Corr. Mixed Children – – Familiar
Unfamiliar
Snack Non-eating –
Salvy, Jarrin, et al. 2007 Corr. Male Adults – – Familiar
Unfamiliar
Snack Non-eating –
Female
Mixed
Salvy, Kieffer, and Epstein 2008 Corr. Mixed Children – – Unfamiliar Snack Non-eating –
Salvy, Romero, Paluch,
and Epstein
2007 Corr. Female Children – – Unfamiliar Snack Non-eating –
Salvy, Vartanian, Coelho,
Jarrin, and Pliner
2008 Corr. Mixed Children – – Familiar
Unfamiliar
Snack Non-eating –
Vartanian et al. 2013; Exp. 1 Exp. Female Adults Remote – Unfamiliar Snack Eating –
Vartanian et al. 2013; Exp. 2 Exp. Female Adults Remote – Unfamiliar Snack Eating –
Vartanian et al. 2013; Exp. 3 Exp. Female Adults Live – Unfamiliar Snack Non-eating –
Notes: Corr, correlational study; Exp, experimental study; DNC, did not code moderator because insufficient data was provided in the study or because the design precluded inclusion in
the moderator analysis; L, low concern with eating appropriately; H, high concern with eating appropriately.
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Characteristics of the participant
Participant gender (only male, only female, or male and female combined) and participant
age group (children vs. adults) were examined as potential moderators. Although the
weight classification of participants would be of interest, only three studies (Bevelander
et al., 2012; Nisbett & Storms, 1974; Salvy, Howard, Read, & Mele, 2009) provided data
separately for overweight and normal-weight participants, and thus participant weight
status was not included as a moderator.
Characteristics of the model
We compared effect sizes for studies involving live confederates versus remote
confederates. Remote-confederate studies included those that provided participants with a
list of the amount eaten by supposed previous participants (usually the 10 previous
participants; e.g., Roth et al., 2001), as well as studies using a video or social-media
presentation of the confederate (e.g., Bevelander, Anschu
¨tz, Creemers, Kleinjan, &
Engels, 2013; Hermans, Salvy, Larsen, & Engels, 2012). We also examined the
confederate’s weight status as a moderator of the overall effect size in those studies that
directly manipulated the confederate’s weight. Hermans, Larsen, Herman, and Engels
(2008) included a “slim” confederate (body mass index [BMI; kg/m
2
]¼20.9) and a
“normal-weight” confederate (BMI unspecified); McFerran et al. (2010) included a “thin”
confederate (BMI ¼19.2) and an obese confederate (apparent BMI ¼33); and Johnston
(2002) included a normal-weight confederate (BMI ¼24) and an obese confederate
(BMI ¼35). Because of the differences in how these weight groups were defined, we
compared effect sizes for thin (BMI ,21), normal-weight (BMI ¼21– 24), and obese
confederates (BMI .30). Finally, we examined whether familiarity with one’s eating
companion moderated the modeling effect.
Characteristics of the eating context
Studies were coded in terms of whether participants were served a meal or a snack. This
designation was established by considering the food provided to participants and the
description of the study context provided by the authors. Studies were also coded in terms
of whether the study was framed for participants as an eating task (e.g., a taste test, a meal)
or as a non-eating task in which they were given incidental access to food.
Concern with eating appropriately
Social models are thought to influence people’s eating behavior by providing a norm of
appropriate intake (Herman et al., 2003; Vartanian et al., 2013). A number of studies have
examined factors that might make participants more or less concerned about eating in an
appropriate manner and therefore more or less likely to model the intake of an eating
companion. These studies were coded, based on the description provided by the authors, in
terms of whether participants should experience high or low concern with eating
appropriately (see Table 1). Two other relevant characteristics (sociotropy and self-
esteem) were not included in the analysis because the specific study did not meet our
inclusion criteria (Exline, Zell, Bratslavsky, Hamilton, & Swenson, 2012), the data
duplicated another variable already included in the analysis (Robinson et al., 2011), or the
study did not provide sufficient data to calculate relevant effect sizes (Bevelander,
Anschu
¨tz, et al., 2013).
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Results
Overall effect of modeling on food intake
As expected, social models had a significant effect on participants’ food intake:
Participants ate more food when the model ate a lot than when the model ate very little.
The overall effect size across all studies was r¼.39 ( p,.001, 95% CI ¼.33 to .44),
indicating that social models had what could be considered a “large” effect on the amount
of food that participants ate (Figure 1).
A comparison of correlational and experimental studies showed that correlational
studies (r¼.56) produced larger effect sizes than did experimental studies (r¼.31), Q
(1) ¼12.94, p,.001 (see Table 2). Note, however, that the effect size for experimental
studies would still be considered moderate-to-large in magnitude.
Figure 1. Forest plot of effect sizes for all studies included in the meta-analysis. For ease of
presentation, an average effect size is provided for each individual study.
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Inhibiting versus augmenting models
We next examined the inhibiting and augmenting effects of a social model by comparing,
separately, the effects of a low-intake confederate versus an eat-alone (no norm) control
condition, and the effects of a high-intake confederate versus an eat-alone (no norm) control
condition. These analyses were conducted separately because some studies included both
low-intake and high-intake conditions along with the control condition, whereas others
included only a low-intake or high-intake condition with the control condition. Thus,
statistical comparison between the two mean effect sizes was not possible. The mean effect
size for low-intake versus control was r¼2.23 ( p,.001, 95% CI ¼2.11 to 2.35),
indicating that low-intake models produce a moderate inhibiting effect on participants’ food
intake (see Figure 2). Consistent with previous theory and research, high-intake models did
augment participants’ food intake, but the mean effect size for high intake versus control
was small (r¼.14, p¼.001, 95% CI ¼.05 to .22; see Figure 3).
Moderators of the modeling effect
There was significant heterogeneity of effect size distribution for the overall modeling
effect, Q(66) ¼253.47, p,.001, with effect sizes ranging from 2.18 to .93. Therefore,
we proceeded to examine the moderators of the overall modeling effect (see Table 2).
Table 2. Moderators of modeling effects.
Moderator N
E
Effect size (r) 95% CI Zp
Study design
Experimental 44 .313 .264, .360 11.91 ,.001
Correlational 23 .557 .439, .656 7.82 ,.001
Participant sex
Female 43 .393 .329, .452 11.19 ,.001
Male 5 .173 2.029, .362 1.68 .09
Mixed 19 .450 .320, .564 6.21 ,.001
Age group
Children 15 .470 .324, .594 5.75 ,.001
Adults 52 .372 .310, .431 10.90 ,.001
Model type
Live model 33 .312 .256, .366 10.30 ,.001
Remote confederate 10 .301 .193, .402 5.28 ,.001
Model body size
Thin 2 .406 .071, .659 2.35 .02
Normal weight 2 .432 .229, .599 3.95 ,.001
Obese 2 .144 2.092, .365 1.20 .23
Familiarity
Familiar 9 .528 .335, .679 4.82 ,.001
Unfamiliar 58 .369 .310, .426 11.40 ,.001
Food type
Meal 6 .455 .375, .528 9.93 ,.001
Snack 61 .381 .318, .440 11.01 ,.001
Task type
Eating task 23 .342 .274, .406 9.38 ,.001
Incidental access 44 .422 .343, .495 9.53 ,.001
Appropriateness concerns
Low 7 .362 .157, .537 3.36 .001
High 7 .507 .258, .693 3.72 ,.001
Note: N
E
, number of effects.
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Characteristics of the participants
Among studies that provided data separately for female participants and for male
participants, the mean effect size was larger among female participants (r¼.39) than
among male participants (r¼.17), Q(1) ¼4.70, p¼.03. The mean effect size for studies
that included both male and female participants (r¼.45) was significantly larger than the
mean effect size for studies with only male participants ( p¼.02) but did not differ from
studies with only female participants ( p¼.42). Studies examining modeling effects
among children (r¼.47) and among adults (r¼.37) did not differ in their mean effect
size, Q(1) ¼1.56, p¼.21. Interestingly, a follow-up meta-regression for studies
involving children showed that children’s mean age was positively associated with the
degree of modeling (slope ¼.06, SE ¼.02, p,.001).
Characteristics of the model
Studies involving a remote confederate produced a mean effect size (r¼.30) that was
virtually identical to that of studies using a live confederate (r¼.31), Q(1) ¼0.03,
Figure 2. Forest plot of effect sizes for low-intake confederate condition versus control condition.
For ease of presentation, an average effect size is provided for each individual study.
Figure 3. Forest plot of effect sizes for control condition versus high-intake confederate condition.
For ease of presentation, an average effect size is provided for each individual study.
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p¼.85. As for the confederate’s body size, thin confederates (r¼.41) and normal-weight
confederates (r¼.43) produced equivalent effect sizes ( p¼.88). Furthermore, although
thin confederates and normal-weight confederates produced larger mean effect sizes than
did obese confederates (r¼.14), these differences were not statistically significant
(ps..05), which is probably due to the small number of studies involved (k¼2 per
group). Finally, the mean effect sizes for eating with a familiar companion (r¼.53) and
an unfamiliar companion (r¼.37) did not differ significantly, Q(1) ¼2.49, p¼.12.
Characteristics of the eating context
There was no significant difference in mean effect size for meals (r¼.46) compared to
snacks (r¼.38), Q(1) ¼2.17, p¼.14. There was also no significant difference in mean
effect size when the task was presented as an eating task (e.g., a taste test; r¼.35) or a
non-eating task (e.g., incidental access to snack foods as part of a separate task; r¼.42), Q
(1) ¼2.04, p¼.15.
Concern with eating appropriately
Conditions that would be expected to elicit higher concern with eating appropriately
(r¼.51) did not differ from conditions that should elicit lower concern with eating
appropriately (r¼.36), Q(1) ¼0.92, p¼.34.
Discussion
Social factors play an important role in dictating how much food people will eat in a given
situation. Our meta-analysis summarized the effect sizes from studies on the modeling of
food intake, and found an overall mean effect size of r¼.39, which corresponds to a large
effect (Lipsey & Wilson, 2001). We also found that inhibiting models tend to have a
greater effect on people’s food intake than do augmenting models. Herman et al. (2003)
argued that people are motivated to maximize their intake of palatable foods without
appearing to eat excessively, and “excess” in social settings is defined as eating more than
other people are eating. When the model eats very little, this sets a relatively low ceiling
for acceptable food intake, leading people to suppress their food intake relative to how
much they would eat if they were alone. In contrast, when the model eats a great deal,
people essentially have the freedom to eat as much as they typically would and may even
have permission to eat somewhat more than they typically would (as indicated by the
relatively small difference between the high-intake conditions and the eat-alone control
condition).
In addition to examining the overall effect size, we also examined a range of potential
moderators of modeling effects. With respect to research design, we found that effect sizes
were larger for correlational studies than for experimental studies. Of course, correlational
studies are limited by the fact that there is no clear “model” in those studies and thus
nothing can be said about causal effects on eating behavior; indeed, in these contexts, there
is the potential for mutual influence by both members of the dyad. It is also the case,
however, that correlational studies more closely approximate “natural” eating contexts
because one rarely (if ever) eats with a confederate who has been instructed to eat a
particular amount. Furthermore, correlational studies have the advantage of increasing
variability in food intake because intake is not constrained by the experimenters’
instructions, allowing researchers to examine the relationship between co-eaters’ food
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intake (Salvy, Jarrin, Paluch, Irfan, & Pliner, 2007). It may be that the increased variability
in participants’ intake in correlational studies, as well as the reciprocal influence that each
member of the dyad can have on the other person’s food intake, accounts for the larger
effect sizes observed in those studies compared to experimental studies. Capitalizing on
the unique features of correlational designs, it would be interesting for future research to
examine which factors determine who will lead and who will follow in naturally occurring
social eating situations.
Very few other factors that we examined were significant moderators of the modeling
effect. In terms of characteristics of the participant, we found that women showed larger
effects than did men. Although this sex difference is consistent with research suggesting
that women might be more concerned with how they are viewed by others while they are
eating (Vartanian et al., 2007), other work suggests that men are more influenced by
external eating cues, including portion size (Zlatevska, Dubelaar, & Holden, 2014), and
that men show stronger social facilitation effects (Bellisle, Dalix, & de Castro, 1999). Thus,
whether the observed sex difference in modeling effects reflects a genuine difference in the
extent to which women’s (relative to men’s) food intake is influenced by social cues, or
reflects something more mundane such as differences in how undergraduate male
participants respond to free food, remains an open question. We also found that, among
children, participants’ mean age was positively correlated with the magnitude of the
modeling effect. This finding is consistent with other research showing, for example, that
older children are more likely to be influenced by portion size than are younger children
(Rolls, Engell, & Birch, 2000), and suggests that a tendency to rely on external cues (and
perhaps a tendency to want to eat “appropriately”) is learned over time. Determining at
what age children begin relying less on internal signals and more on normative signals with
respect to their food intake would be an important direction for future research.
With respect to characteristics of the model, leaner models produced somewhat larger
effects than did heavier models but this difference was not statistically significant,
probably because of the small number of studies represented. It may be that obese models
elicit less modeling because they are not seen as being relevant guides to appropriate
behavior or that people somehow adjust for assumed baseline differences in how much
obese people eat. We might also expect that participants’ own weight status would interact
with the confederate’s weight status in predicting the degree of modeling. Future research
is needed to clarify the effect of participants’ and models’ weight status on food intake, as
well as the mechanisms underlying those effects.
An important finding from our meta-analysis is the fact that we observed identical
effect sizes for studies that used a live model versus a remote confederate. How can
modeling effects occur when the “model” is not actually present? Vartanian et al. (2013)
found that both live models and remote confederates create and convey a norm of
appropriate food intake, which in turn affects how much participants eat. However, it may
be that some of the mechanisms underlying modeling effects differ between live models
and remote confederates. For example, Robinson et al. (2011) and Robinson, Benwell, and
Higgs (2013) have argued that empathy plays an important role in modeling the food
intake of another person, but not in modeling of remote confederates. It might also be that
some components of modeling (e.g., behavioral mimicry; Hermans, Lichtwarck-Aschoff,
et al., 2012) are observed only with live models. Future research examining the different
processes underlying modeling effects in live and remote confederate designs would help
elucidate the mechanisms underlying the modeling of food intake.
Although we were able to examine a number of moderators of effect sizes, there are
other factors that need to be considered in future research that would broaden our
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understanding of the scope and limits of modeling effects. For example, little is known
about the modeling of healthy foods, such as fruits and vegetables. Most modeling studies
employ highly palatable but unhealthy foods, such as pizza or cookies. Only one study has
examined modeling of healthy snacks (Hermans, Larsen, et al., 2009) and, although
modeling of vegetable intake was observed, it is not known how consistent that effect is,
what the mechanisms are, or whether there are any moderators of the effect. Given that
very few people meet their target recommendations for fruit and vegetable consumption
(e.g., Guenther, Dodd, Reedy, & Krebs-Smith, 2006), research examining the modeling of
healthy food intake would be an important direction for future research.
Why do people model other people’s food intake? Herman et al. (2003) argued that the
appropriate amount to eat is often ambiguous and that people therefore rely on the example
of others to determine how much they themselves should eat. Furthermore, Vartanian et al.
(2013) showed that perceived norms of appropriate intake mediated the link between a
model’s behavior and participants’ own food intake. Surprisingly, our meta-analysis found
no difference in the mean effect size for modeling between conditions that should elicit
relatively high or relatively low concern with eating appropriately. Indeed, even
conditions that are thought to “minimize” modeling effects (e.g., low appropriateness-
concern conditions) still produce sizable effects (average r¼.36). It should be noted,
however, that those studies used experimental manipulations or self-report measures that
only indirectly tapped into people’s concern with eating appropriately (e.g., trait empathy
or eating with an unsociable confederate). It may be that some of the methods used to
either manipulate or measure appropriateness concerns do not adequately capture that
construct. It is also possible that most of the participants in these studies—even many of
those who were categorized as low in concern for appropriateness on the basis of a median
split on some measure—were relatively high in concern for appropriateness. A more
compelling test of the possible role of concern for appropriateness awaits direct
manipulation of people’s level of concern with eating appropriately, the identification of
measures that adequately capture concern with behaving appropriately, and/or the
identification of samples that are demonstrably high and low in this attribute.
Situating the findings of the current meta-analysis in the context of other external
influences on food intake, a recent meta-analysis of the effect of portion size on people’s
food intake found that the mean effect size was r¼.22 (a moderate effect; Zlatevska et al.,
2014). Although direct comparisons between the two literatures are difficult due to various
methodological differences, it is notable that the average modeling effect in our meta-
analysis (r¼.39) is considerably larger than the average portion-size effect reported by
Zlatevska et al. (2014). The effect of larger portions has justly received a great deal of
attention in the research literature and in the popular media, but the findings of the present
meta-analysis suggest that modeling effects deserve just as much attention.
Conclusion
Social models provide a powerful influence on people’s food intake, with implications for
their ability to appropriately regulate food intake. First, eating with a low-intake model can
lead people to restrict their food intake. Although in some cases restriction (or reduced
“overconsumption”) might be seen as healthy and desirable, in other cases it might
exacerbate unhealthy restrictive eating patterns, particularly among individuals at risk for
disordered eating. Second, eating with a high-intake model can lead to overindulgence and
excess energy intake. The potential for overindulgence in social situations might be
heightened by the contemporary food environment, characterized by widespread
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availability of high-calorie foods and oversized portions (Brownell & Horgen, 2004).
Efforts to help people eat a healthy diet might potentially include using social models to
promote the consumption of healthy foods, as well as helping people discern when a
model’s food intake is an appropriate guide to behavior and when it is not.
Acknowledgements
We wish to thank Suzanna Azevedo, Alana Eyles, and Robyn Winwood-Smith for their assistance
retrieving and coding the articles. We also wish to thank the authors who generously responded to
our request for additional data.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This research was supported under Australian Research Council’s Discovery Projects funding
scheme (project number DP110101124).
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