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. Author manuscript; available in PMC: 2022 Jan 7.
Published in final edited form as: Clin Psychol Sci. 2021 May 24;9(6):1115–1127. doi: 10.1177/21677026211004582

Extending Expectancy Theory to Food Intake: Effect of a Simulated Fast-Food Restaurant on Highly and Minimally Processed Food Expectancies

Jenna R Cummings 1,a, Lindzey V Hoover 1, Meredith I Turner 1, Kalei Glozier 1, Jessica Zhao 1, Ashley N Gearhardt 1
PMCID: PMC8740953  NIHMSID: NIHMS1680419  PMID: 35003906

Abstract

Unhealthy diets are widespread and linked to a number of detrimental clinical outcomes. The current preregistered experiment extended Expectancy Theory into the study of food intake; specifically, we tested whether a fast-food restaurant affects food expectancies, or the emotions one expects to feel while eating highly (e.g., pizza) and minimally (e.g., carrots) processed foods. Participants (N = 200, Mage = 18.79) entered a simulated fast-food restaurant or a neutral space, completed questionnaires, and engaged in a ‘bogus’ taste test. The simulated fast-food restaurant increased positive highly-processed food expectancies (d = 0.29). Palatable eating coping motives scores did not moderate the effect; however, this clinically-relevant pattern of eating behavior was associated with greater positive highly-processed food expectancies. In addition, there was an indirect effect of the fast-food restaurant on ad libitum food intake through positive highly-processed food expectancies. Reducing positive highly-processed food expectancies may improve diet, which may broadly impact health.

Keywords: eating behavior, emotions, expectancies, fast-food restaurant, highly processed food

Unhealthy diets are widespread. The average person in the U.S. gets the majority of their energy intake from highly processed foods, or processed foods that are high in refined carbohydrates and fats (Martinez Steele et al., 2016). A diet that largely consists of highly processed foods at the expense of minimally processed foods (e.g., fruits, vegetables) is associated with a number of clinical outcomes including all-cause mortality, overall cardiovascular diseases, coronary heart diseases, cerebrovascular diseases, hypertension, metabolic syndrome, depression, irritable bowel syndrome, overall cancer, and postmenopausal breast cancer (Chen et al., 2020). Moreover, an estimated 15% of the U.S. population experiences an addictive-like response to highly processed foods including subjective experiences of impaired control, cravings, tolerance, and withdrawal (Schulte & Gearhardt, 2018). It is therefore imperative to understand the mechanisms guiding food intake, particularly of highly processed options. Extending theory from psychological science into the study of food intake may provide novel insight.

Expectancy Theory is a long-standing psychological theory explaining behavior (James, 1890; Tolman, 1932). It proposes that people learn from personal or vicarious experiences what outcomes a behavior can lead to, including positive and negative emotions. These “expectancies” influence the likelihood of engaging in the behavior (Bandura, 1977). For instance, a person may learn from personal experiences or other people that alcohol can make people feel happier; positive alcohol expectancies encourage a person to drink more frequently and heavily (Goldman, Del Boca, & Darkes, 1999). Psychologists have predominately applied Expectancy Theory to explain substance use behaviors including alcohol, cigarette, and marijuana use (Buckner & Schmidt, 2008; Cohen, McCarthy, Brown, & Myers, 2002; Morean, Corbin, & Treat, 2012), and disordered eating including symptoms of anorexia and bulimia (Culbert, Racine, & Klump, 2015; Pearson, Riley, Davis, & Smith, 2014). More recently, Cummings, Joyner, and Gearhardt (2020) developed a measure of food expectancies, or the positive and negative emotions one anticipates feeling while eating highly (e.g., pizza) and minimally (e.g., carrots) processed foods. Food expectancies—in particular, positive highly-processed food expectancies (e.g., anticipating one will feel happy while eating pizza)—were associated with less healthy patterns of food intake among adults.

Expectancy Theory posits that expectancies form early in development, dynamically change during adolescence, and become relatively stable in young adulthood (Smith & Goldman, 1994). However, the situational-specificity hypothesis from Expectancy Theory suggests that environments can temporarily distort expectancies about environment-relevant behavior (Wall, McKee, & Hinson, 2000). For example, when college students were in a bar as opposed to a neutral space they were more likely to anticipate feeling “happy” and “outgoing” from drinking alcohol (Wall et al., 2000). In addition to increasing positive alcohol expectancies, one study found that being in a bar had a non-uniform effect on negative alcohol expectancies: when college students were in a bar as opposed to a neutral space they were less likely to anticipate feeling “clumsy” and “slow” but were more likely to anticipate they would “act aggressively” and “be loud, boisterous, or noisy” from drinking alcohol (Wall, Hinson, McKee, & Goldstein, 2001). However, two other studies found that being in a bar only increased positive alcohol expectancies (Monk & Heim, 2013; Wall et al., 2000). Overall, empirical support for the situational-specificity hypothesis supports the notion that environments acutely increase, at least, positive expectancies about environment-relevant behavior.

No study (to our knowledge) has tested whether an environment can temporarily distort food expectancies. One major environment that may affect food expectancies is a fast-food restaurant, or a limited-service restaurant that quickly prepares food for individuals to eat at the restaurant or to bring elsewhere (James, Arcaya, Parker, Tucker-Seeley, & Subramanian, 2014). An estimated 78% of U.S. ZIP codes have at least one fast-food restaurant (Powell, Chaloupka, & Bao, 2007). In particular, fast-food restaurants almost exclusively offer highly processed foods with a very limited number of minimally processed foods (Kirkpatrick et al., 2014). Menus from fast-food restaurants often describe highly processed foods with positive emotion words (e.g., McDonalds “Happy” Meal; Wendy’s “S’Awesome” Bacon Cheeseburger). Advertisements for fast-food restaurants make up about one third of food advertisements on general market television in the U.S. (Henderson & Kelly, 2005), and marketers construct food advertisements so viewers associate emotions with the food advertised (Page & Brewster, 2009). Also, fast-food restaurants include pleasant stimuli like music, brightly-colored menus, and brightly-colored furniture as well as food cues like food smells and sight of other patrons’ food, which patrons may associate with the highly processed foods offered there—albeit other types of restaurants include these pleasant stimuli and food cues too. A fast-food restaurant is therefore a candidate environment for acutely changing food expectancies (at least increasing positive highly-processed food expectancies) because of the types of food it offers and because it houses food descriptions, an array of stimuli, and food cues.

It is also possible that certain individuals may be more susceptible to environmental effects on expectancies, yet this has not been tested in prior work on the situational-specificity hypothesis (Monk & Heim, 2013; Wall et al., 2000; Wall et al., 2001). In the context of fast-food restaurants, which create positive emotional experiences around eating highly processed foods, food expectancies may especially be affected among individuals who typically eat highly processed foods for emotional reasons. This notion is supported by prior work showing that individuals who eat for emotional reasons are more reactive to laboratory mood inductions (van Strien, Herman, Anschutz, Engels, & de Weerth, 2012; van Strien et al., 2013)—albeit, different laboratory groups have found inconsistent effects (see Bongers & Jansen, 2016 for a review). Focusing on individual differences in emotional eating when investigating food expectancies nonetheless holds clinical relevance because emotional eating is associated with clinical outcomes such as metabolic syndrome, diabetes, and depression (Epel et al., 2004; Ouwens et al., 2009; Tsenkova, Boylan, & Ryff, 2013). Among college students, in particular, a 1-point change in palatable eating coping motives scores (i.e., a specific pattern of eating highly processed foods to cope with negativity) predicted an average weight gain of 10.5 lbs. over two years (Boggiano et al., 2015).

Furthermore, if expectancies have a causal influence on behavior, then temporary distortions in expectancies caused by the environment should subsequently impact environment-relevant behavior (Bandura, 1977). However, prior work investigating the situational-specificity hypothesis has not tested this critical tenet of Expectancy Theory (Monk & Heim, 2013; Wall et al., 2000; Wall et al., 2001). Identifying whether temporarily distorted food expectancies affects food intake is not only theoretically important but also practically important. Indeed, successful substance use and disordered eating interventions have targeted substance and thinness expectancies, respectively, which suggests that food expectancies could be an important target for dietary interventions (Scott-Sheldon, Terry, Carey, Garey, & Carey, 2012; Annus, Smith, & Masters, 2008). Without evidence that food expectancies causally impact food intake, however, there lacks support for the development of such interventions.

To fill the gap in the existing literature, the primary preregistered aim of the current study was to test the effect of a simulated fast-food restaurant on food expectancies. This environment was used in a prior study on the effects of a food cue-rich environment on food intake (Joyner, Kim, & Gearhardt, 2017). In accordance with the situational-specificity hypothesis, the preregistered hypothesis was that exposure to this simulated fast-food restaurant would increase positive highly-processed food expectancies. Given mixed findings regarding the effect of a bar on negative alcohol expectancies (Monk & Heim, 2013; Wall et al., 2000; Wall et al., 2001), there was no preregistered hypothesis about the effect of the simulated fast-food restaurant on negative highly-processed food expectancies (e.g., anticipating one will feel down while eating pizza). Also, since fast-food restaurants predominately offer highly processed foods with fewer minimally processed foods (Kirkpatrick et al., 2014), there were no preregistered hypotheses about the effect of the simulated fast-food restaurant on positive minimally-processed food expectancies (e.g., anticipating one will feel happy while eating carrots) or negative minimally-processed food expectancies (e.g., anticipating one will feel down while eating carrots).

To understand whether there may be individual differences in environmental effects on food expectancies, a secondary preregistered aim of the current study was to examine whether palatable eating coping motives scores moderated the effect of the simulated fast-food restaurant on food expectancies (Burgess, Turan, Lokken, Morse, & Boggiano, 2014). Lastly, to test the causal influence of food expectancies on food intake, another secondary preregistered aim was to test for an indirect effect of the simulated fast-food restaurant on ad libitum food intake through food expectancies. Ad libitum food intake was observed through a ‘bogus’ taste test—a valid method for measuring actual food intake without the limitations of self-report methods (Robinson et al., 2017).

Methods

Design

The study design was a 2-level (fast-food restaurant versus neutral space) randomized between-subjects experiment. We preregistered methods on the Open Science Framework at https://osf.io/ty9u6, with an amendment at https://osf.io/uzewm.

Participants

Prior research observed a medium effect (η2 = .06) of the simulated fast-food restaurant on food “wanting” (Joyner et al., 2017). A priori power analysis in G*Power Version 3.1.7 (Faul, Erdfelder, Lang, & Buchner, 2007) indicated a sample size of 144 for the current study based on the following parameters: One-Way Analysis of Variance (ANOVA), η2 = .06, α = .05, .85 power, and two groups. We aimed to recruit at least 144 participants from the University of Michigan’s Department of Psychology SONA pool; however, to further increase power, to mitigate concern of participants who would be excluded, and because resources were available to continue supporting the study, we continued recruiting participants through the end of the academic semester for Fall 2019. In total, we recruited 203 participants. Following the preregistration plan, we excluded three participants because of researcher error [i.e., research assistant did not administer the Anticipated Effects of Food Scale (n = 1)] or participant ineligibility [i.e., participant requested data be destroyed (n = 1), participant verbally admitted to intoxication during study (n = 1)]. The final sample thus comprised 200 participants. Table 1 presents demographic and behavioral characteristics of the sample overall and by experimental condition.

Table 1.

Demographic and Behavioral Characteristics of the Sample Overall and by Experimental Condition

Total
N = 200		 Fast-Food Restaurant
n = 94		 Neutral Space
n = 106		 F or X2 p η2 or Φ
Age (M, SD) 18.78(1.13) 18.81(1.36) 18.76(0.88) 0.08 .782 .00
Sex (n, %) 0.01 .927 .01
 Male 73(36.5%) 34(36.2%) 39(36.8%)
 Female 127(63.5%) 60(63.8%) 67(63.2%)
Race/ethnicity (n, %) 6.52 .259 .18
 Native Hawaiian/Pacific Islander 1(0.5%) 0(0.0%) 1(1.0%)
 Asian 54(27.3%) 28(30.1%) 26(24.8%)
 Black or African American 7(3.5%) 4(4.3%) 3(2.9%)
 White 111(56.1%) 48(51.6%) 63(60.0%)
 Hispanic/Latinx 6(3.0%) 1(1.1%) 5(4.8%)
 Bi- or multi-racial or other 19(9.6%) 12(12.9%) 7(6.7%)
Parental education (n, %) 10.60 .060 .23
 Less than high school 5(2.5%) 0(0.0%) 5(4.7%)
 High school graduate 14(7.0%) 8(8.5%) 6(5.7%)
 Some college 19(9.5%) 12(12.8%) 7(6.6%)
 Associates degree 4(2.0%) 0(0.0%) 4(3.8%)
 Bachelors degree 52(26.0%) 25(26.6%) 27(25.5%)
 Advanced degree 106(53.0%) 49(52.1%) 57(53.8%)
BMI (M, SD) 23.21(3.76) 22.90(3.25) 23.48(4.16) 1.19 .277 .01
Hunger (M, SD) 40.91(23.01) 42.48(21.31) 39.52(24.43) 0.82 .365 .00
PEMS Coping Motives (M, SD) 1.99(0.89) 1.94(0.86) 2.03(0.92) 0.47 .492 .00
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Notes: BMI = Body Mass Index, PEMS = Palatable Eating Motives Scale

Procedure

Participants were recruited to participate in a study in which they would report on their beliefs about food, alcohol, and other drugs. The University Institutional Review Board approved the research procedure in accordance with the provisions of the World Medical Association Declaration of Helsinki. Sessions were scheduled between 1PM and 5:30PM on Mondays through Fridays.

After providing written informed consent, participants were randomly assigned to enter the simulated fast-food restaurant or a neutral space (control). The simulated fast-food restaurant included the following characteristics: brightly colored booths, dining tables and chairs, and visible industrial-style food storage and preparation appliances through a kitchen window (Joyner et al., 2017). Prior to participants arriving to the lab, research assistants cooked French fries in the kitchen to create food smells that would last for participants’ visits. For photo images of the simulated fast-food restaurant, see Figure S1 in Supplemental Materials available online. We selected an office space for the neutral space because people occasionally eat at the office but it is not a food cue-rich environment (Oh, Erinosho, Dunton, Perna, & Berrigan, 2014). The neutral space included the following characteristics: office chairs, office desks, filing cabinets, a printer, and a desktop computer. The walls, carpet, and furniture had neutral colors.

Participants seated themselves in the environment they were randomly assigned to and then completed an initial battery of questionnaires in randomized order including the Anticipated Effects of Food Scale. Hunger, taste ratings of foods, and ad libitum food intake were next measured during a ‘bogus’ taste test paradigm (Robinson et al., 2017). Participants were instructed to taste and rate one mini-Oreo cookie (from ~25), Lay’s plain potato chip (from ~20), baby carrot (from ~20), and grape (from ~25) in their preferred order. Then, participants were left alone for five minutes to help themselves to the remaining mini-Oreo cookies, Lay’s plain potato chips, baby carrots, and grapes while research assistants set up for the next task. Participants were also given a bottle of water (8 fl. oz.).

Following the ‘bogus’ taste test paradigm, participants completed a second battery of questionnaires in randomized order including the Palatable Eating Motives Scale and “liking” of food items. Participants then reported on when they last ate before the study and their demographics. Research assistants measured participants’ heights with a stadiometer and weights using an InBody 570 (Cerritos, CA). Lastly, participants were debriefed and compensated with course credit. Research assistants rated the participants’ levels of suspicion of the study’s true purpose as an average 1.71 (SD = 0.79) out of 5 (1 = No suspicion, 5 = Very suspicious).

Measures1

Anticipated Effects of Food Scale (AEFS).

The AEFS (Cummings et al., 2020) is a 62-item questionnaire that measures the anticipated positive and negative emotional outcomes of eating highly (labeled as “junk” food to improve readability) and minimally (labeled as “healthy” food to improve readability) processed foods. To assess highly processed food expectancies, the instructions are as follows, “Imagine that you are eating JUNK food (e.g., sweets, salty snacks, fast foods, sugary drinks)…how much do you expect to feel the following feelings while eating JUNK food?” The provided examples of highly processed foods are adopted from the Yale Food Addiction Scale (Gearhardt, Corbin, & Brownell, 2009). Participants were asked how much they expected to feel 31 positive (e.g., relaxed, cheerful, glad) and negative (e.g., disgusting, worried, frustrated) emotions. Participants rated items on a 6-point Likert scale from 1 (Definitely Not) to 6 (Definitely).

To assess minimally process food expectancies, the instructions are as follows “Imagine that you are eating HEALTHY food (e.g., fruits, vegetables)…how much do you expect to feel the following feelings while eating HEALTHY food?” Participants were asked how much they expected to feel the same 31 positive and negative emotions as they did for the highly processed food on the same 6-point Likert scale. Overall, the scale yields four food expectancy subscales [positive highly-processed food expectancies (α = .88), negative highly-processed food expectancies (α = .91), positive minimally-processed food expectancies (α = .91), and negative minimally-processed food expectancies (α = .87)], which all demonstrated high internal consistency. These subscales were calculated by taking the average across the positive emotion items for highly processed food, the negative emotion items for highly processed food, the positive emotion items for minimally processed food, and the negative emotion items for minimally processed food.

Hunger, taste ratings of foods, and ad libitum food intake.

At the beginning of the ‘bogus’ taste test paradigm, participants rated their level of hunger on a visual analog scale from 0 (Not hungry at all) to 100 (It’s all I can think about). After trying one of each food item, participants rated how much they liked the taste of the food on a visual analog scale from −100 (Extremely dislike) to 100 (Extremely like). Before and after the ‘bogus’ taste test paradigm, research assistants weighed the mini-Oreo cookies, Lay’s plain potato chips, baby carrots, and grapes in grams. We chose restaurant-inconsistent foods because prior work demonstrated the simulated fast-food restaurant increased intake of restaurant-consistent (i.e., cheeseburgers, fries) foods (Joyner et al., 2017). Choosing restaurant-inconsistent foods therefore allowed us to increase detection of an indirect effect (whether food intake differed between-groups because of food expectancies) in addition to the direct effect of the simulated fast-food restaurant on food intake. Choosing restaurant-inconsistent foods also allowed us to test whether the direct effect of the simulated fast-food restaurant would generalize to restaurant-inconsistent foods.

Nutritional information for each food item is provided in Table S1 in Supplemental Materials available online. To index ad libitum food intake, we first subtracted the post-weight of each food item from the pre-weight, and we multiplied the results by the number of calories per gram in the food item (provided on the labeled nutrition facts). We next categorized the number of calories eaten from the available highly (i.e., mini-Oreo cookies and Lay’s plain potato chips) and minimally (i.e., baby carrots and grapes) processed foods. Lastly, we divided the number of highly-processed food calories eaten by the number of minimally-processed food calories eaten to create a ratio. A higher ratio indicated that a participant ate a greater number of calories from highly processed foods at the expense of calories from minimally processed foods. We chose to index ad libitum food intake this way based on trends in epidemiological research on diet and health (Kennedy, Ohls, Ma, & Fleming, 1995; Monteiro et al., 2018). These trends recognize that indexes simultaneously accounting for multiple types of food intake may be more clinically meaningful than indexes capturing only one type of food intake.

Palatable Eating Motives Scale (PEMS).

The PEMS (Burgess et al., 2014) measures different reasons for eating highly processed foods; for the current study, we focused on the PEMS Coping Motives subscale (i.e., a pattern of eating highly processed foods to cope with negativity). Participants were given examples of highly processed foods adopted from the Yale Food Addiction Scale (Gearhardt et al., 2009), and then asked to report how often they ate these foods for each reason included in the 4-item subscale. Sample items from the PEMS Coping Motives subscale include “to forget your worries” and “because it helps you when you feel depressed or nervous.” Participants rated items on a 5-point Likert scale from 1 (Almost Never/Never) to 5 (Almost Always/Always). The PEMS Coping Motives subscale demonstrated good internal consistency (α = .88).

“Liking” of foods, when participants last ate, and demographics.

Participants reported how much they generally like mini-Oreo cookies, Lay’s plain potato chips, baby carrots, and grapes on a visual analog scale from −100 (Extremely dislike) to 100 (Extremely like). Participants reported when they last ate (“0–1 hours ago,” “1–3 hours ago,” “3–5 hours ago,” “5–7 hours ago,” or “7+ hours ago”), their age, their biological sex assigned at birth, their race/ethnicity, and the highest level of their parents’ education. We calculated body mass index (BMI) from height and weight measurements using the standard formula (kg/m2).

Data Analytic Plan

Data and syntax are publicly available at https://osf.io/xkjyp/. Following the preregistration plan, we assessed all variables were for normality. Age and BMI evidenced skew (>1) and kurtosis (>3) and we thus log-transformed those variables for analysis. We created dummy codes for biological sex (0 = Male, 1 = Female) and race/ethnicity (0 = non-Black, 1 = Black); we dummy coded race/ethnicity this way because the prevalence of obesity is substantially higher in Black compared to White populations in the U.S. (Hales et al., 2018; Ogden, Carroll, Kit, & Flegal, 2012). To identify covariates, we conducted bivariate correlations between food expectancies and levels of suspicion, hunger, taste ratings of foods, “liking” of foods, when participants last ate, and demographics. Positive highly-processed food expectancies were correlated with biological sex (r = −.15, p = .037) such that male participants had greater levels [M(SD) = 3.07(0.65)] compared to female participants [M(SD) = 2.88(0.59)]. Greater positive highly-processed food expectancies were correlated with greater hunger (r = .19, p = .009).

To examine the primary aim of the current study, One-Way ANOVAs were conducted. Food expectancies were the dependent variables. Experimental condition was the between-subjects factor. To examine the secondary aims of the current study, multiple regressions were conducted predicting food expectancies from palatable eating coping motives and their interaction with the experimental condition (environment was dummy coded such that 0 = neutral space, 1 = fast-food restaurant). We used the PROCESS Model 4 macro (Hayes, 2013) to test the indirect effect of the experimental condition on ad libitum food intake through food expectancies. We used 10,000 bootstrap samples to create 95% bias-corrected and accelerated (BCa) confidence intervals to test the significance of indirect effects. Indirect effects are significant at p < .05 if the 95% confidence interval does not include zero. We present unadjusted estimates from analysis conducted without covariates in the Results section below, and we present adjusted estimates from analysis conducted with covariates in Supplemental Materials available online. We conducted all analyses in SPSS Version 25 (IBM Corporation, Armonk, NY).

Results

Table 1 presents estimates showing that there were no differences in demographic and behavioral characteristics by experimental condition, and no differences in hunger by experimental condition.

Primary Aim

Table 2 presents group means and standard deviations for food expectancies, and unadjusted estimates from analysis. Participants in the simulated fast-food restaurant reported greater positive highly-processed food expectancies compared to participants in the neutral space. Participants in each environment did not significantly differ on their reports of negative highly-processed food expectancies, nor did they differ on positive and negative minimally-processed food expectancies. Including hunger and biological sex as covariates in the model did not change the direction, magnitude, or significance of these results, and there was no significant interaction between environment and biological sex on food expectancies (see Table S2 in Supplemental Materials available online for adjusted estimates).

Table 2.

Means and Standard Deviations of Food Expectancies Overall and by Experimental Condition

Total
N = 200		 Fast-Food Restaurant
n = 94		 Neutral Space
n = 106
M(SD) M(SD) M(SD) Levene Statistic p F P η 2 Cohen’s d
+HPF expectancies 2.95(0.62) 3.04(0.57) 2.86(0.66) 0.50 .482 4.11 .044 .02 0.29
−HPF expectancies 2.66(0.77) 2.59(0.71) 2.71(0.82) 1.11 .293 1.16 .284 .01 0.15
+MPF expectancies 3.69(0.72) 3.76(0.70) 3.63(0.74) 0.46 .501 1.53 .218 .01 0.18
−MPF expectancies 1.72(0.45) 1.72(0.47) 1.72(0.43) 0.49 .484 0.00 .989 .00 0.00
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Notes: +HPF = Positive highly-processed food, −HPF = Negative highly-processed food, +MPF = Positive minimally-processed food, −MPF = Negative minimally-processed food

Secondary Aims

Table 3 presents bivariate correlations among food expectancies, palatable eating coping motives, and ad libitum food intake.

Table 3.

Correlations among Food Expectancies, Palatable Eating Coping Motives, and Ad Libitum Food Intake

1. 2. 3. 4. 5. 6. 7. 8.
1. +HPF expectancies −.27*** .05 .14 .22** 23** .09 .27***
2. −HPF expectancies .34*** .39*** .25*** −.10 −.11 −.05
3. +MPF expectancies −.22** .06 −.08 .08 −.16*
4. −MPF expectancies .29*** .08 −.15* .24**
5. PEMS Coping Motives .12 .05 .12
6. Total Number of HPF calories eaten .34*** .52***
7. Total Number of MPF calories eaten −.35***
8. Ratio of HPF to MPF calories eaten
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Notes: +HPF = Positive highly-processed food, −HPF = Negative highly-processed food, +MPF = Positive minimally-processed food, −MPF = Minimally-processed food, PEMS = Palatable Eating Motives Scale, HPF = Highly processed food, MPF = Minimally processed food.

*

p < .05,

**

p < .01,

***

p < .001

Palatable Eating Coping Motives.

Palatable eating coping motives scores did not moderate the effect of the simulated fast-food restaurant on food expectancies (ps > .33). Irrespective of experimental condition, greater palatable eating coping motives scores predicted greater positive highly-processed food expectancies [unadjusted B(SE) = 0.16(0.05), p = .001, 95% CI (0.06, 0.25)], negative highly-processed food expectancies [unadjusted B(SE) = 0.22(0.06), p < .001, 95% CI (0.10, 0.33)], and negative minimally-processed food expectancies [unadjusted B(SE) = 0.15(0.03), p < .001, 95% CI (0.08, 0.21)]. Palatable eating coping motives scores did not predict positive minimally-processed food expectancies [unadjusted B(SE) = 0.06(0.06), p = .33, 95% CI (−0.06, 0.17)]. Including hunger and biological sex as covariates in these models did not change the direction, magnitude, or significance of these results (see Table S3 in Supplemental Materials available online for adjusted estimates).

Ad Libitum Food Intake.

Figure 1 presents the unadjusted mediation model with a, b, and c’ path estimates from PROCESS Model 4. The indirect effect of the simulated fast-food restaurant on ad libitum food intake through positive highly-processed food expectancies was significant, unadjusted B(SE) = 0.14(0.08), 95% BCa CI (0.02, 0.36). Specifically, the simulated fast-food restaurant increased positive highly-processed food expectancies compared to control, which in turn caused participants to eat a greater number of calories from highly processed foods at the expense of calories from minimally processed foods. There were no indirect effects on ad libitum food intake through negative highly-processed, positive minimally-processed, and negative minimally-processed food expectancies. Including hunger and biological sex as covariates in the model did not change the direction, magnitude, or significance of these results (see Figure S2 in Supplemental Materials available online for adjusted estimates).

Figure 1.

Figure 1.

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PROCESS Model-4 path estimates from testing the indirect effect of the simulated fast-food restaurant on ad libitum food intake through food expectancies. Unstandardized coefficients and standard errors are presented. A dummy code was created for environment (0 = Neutral Space, 1 = Fast-Food Restaurant). +HPF = Positive highly-processed food, −HPF = Negative highly-processed food, +MPF = Positive minimally-processed food, −MPF = Negative minimally-processed food. *p < .05, **p < .001

Discussion

This is the first study (to our knowledge) to test the effect of an environment on food expectancies, to test moderation effects, and to test whether acute changes in expectancies due to the environment cause changes in environment-related behavior. In accordance with the preregistered hypothesis, the simulated fast-food restaurant increased positive highly-processed food expectancies (e.g., anticipating one will feel happy while eating pizza). These results are consistent with prior studies showing a bar increased positive alcohol expectancies (Monk & Heim, 2013; Wall et al., 2000; Wall et al., 2001). They also build evidence for the situational-specificity hypothesis from Expectancy Theory, which suggests environments temporarily distort expectancies about environment-relevant behavior (Wall et al., 2000).

Prior work found inconsistent evidence for the effect of a bar on negative alcohol expectancies (Monk & Heim, 2013; Wall et al., 2000; Wall et al., 2001). In the current study, the simulated-fast food restaurant did not affect negative highly-processed food expectancies (e.g., anticipating one will feel down while eating pizza). It is thus possible environments only can distort positive expectancies about environment-relevant behavior. However, fast-food restaurants (like bars) foster positive emotional experiences. Environments fostering negative emotional experiences around eating highly processed foods (e.g., a physician’s office) may have different effects. Likewise, the simulated fast-food restaurant did not impact positive or negative minimally-processed food expectancies (e.g., anticipating one will feel happy or down while eating carrots). Fast-food restaurants predominately offer highly processed foods to the exclusion of minimally processed foods (Kirkpatrick et al., 2014). Environments that predominately offer minimally processed foods (e.g., a farmer’s market) might impact minimally-processed food expectancies. Overall, the selectivity of the results emphasizes the limitations of an environment to influence expectancies about a behavior more generally. Future research might continue exploring the effect of different environments on food expectancies, and the extent to which environments affect expectancies more generally.

Palatable eating coping motives scores did not moderate the effect of the simulated fast-food restaurant on food expectancies. The lack of moderation may underscore the ubiquitous effect of this environment: there was no evidence that it differentially affected food expectancies among individuals based on whether they typically eat highly processed foods to cope with negativity. The lack of moderation also adds to the literature finding inconsistent moderation effects of emotional eating in laboratory mood inductions (Bongers & Jansen, 2016). It is possible that these kinds of moderation effects cannot be detected when self-report methods are used to identify those who eat for emotional reasons (Bongers & Jansen, 2016); however, the Palatable Eating Motives Scale used in this study has been validated against real-time sampling of reasons for eating highly processed foods among college students (Boggiano et al., 2015).

Although palatable eating coping motives scores did not moderate the effect of the simulated fast-food restaurant on food expectancies, greater palatable eating coping motives scores did predict greater positive and negative highly-processed food expectancies, and greater negative (but not positive) minimally-processed food expectancies. Links between palatable eating coping motives scores and food expectancies are important to understand because palatable eating coping motives scores are associated with weight gain among college students (Boggiano et al., 2015). More broadly, individual differences in emotional eating are associated with clinical outcomes such as metabolic syndrome, diabetes, and depression (Epel et al., 2004; Ouwens et al., 2009; Tsenkova, Boylan, & Ryff, 2013). The links observed between palatable eating coping motives scores and positive highly-processed food expectancies may reflect the strong preference and tendency to eat highly not minimally processed foods for emotional reasons—a preference conserved across species (Adam & Epel, 2007). In addition, although negative expectancies may lead to less of a behavior, the links between negative expectancies and psychopathology may be more complex (Mann, Chassin, & Sher, 1987). Those with substance use disorder, for example, have experienced negative consequences of substance use (e.g., quit attempt failures, guilt), which could increase their tonic levels of negative substance expectancies (McMahon, Jones, & O’Donnell, 1994). Individuals scoring higher in palatable eating coping motives may have therefore endorsed greater negative food expectancies because they have similarly experienced negative consequences from eating certain foods (e.g., diet failure; Deluchi, Costa, Friedman, Goncalves, & Bizarro, 2017; Nijs & Franken, 2012)2. Future research might test this directly.

The associations between palatable eating coping motives scores and some types of food expectancies might indicate these constructs are capturing the same latent construct, yet the associations were small in magnitude. Also, the experimental condition did not affect palatable eating coping motives scores (unlike food expectancies). Although expectancies and motives may be related, expectancy measures require individuals to think about the anticipated consequence of a behavior (e.g., “I expect to feel happy while eating highly processed food”) and motives measures require individuals to think about why they drink (e.g., “I eat highly processed foods to forget my worries”), which makes these constructs conceptually distinct (Kuntsche, Wiers, Janssen, & Gmel, 2010). For example, one could expect to feel happy while eating highly processed foods but not eat those foods to forget their worries. In the alcohol use literature, researchers have demonstrated that alcohol expectancies and drinking motives were statistically distinct even when nearly identical items were used to assess each construct (e.g., Expectancy = “How likely is it that you would be sociable if you drink alcohol?” Motive = “How often have you drunk alcohol to be sociable?”; Kuntsche et al., 2010). Moreover, a large twin study indicated that, while drinking motives were genetically heritable, environmental influences shaped alcohol expectancies (Agrawal et al., 2008). Future research in the area of palatable eating motives and food expectancies could empirically test for distinctions in a similar fashion.

In accordance with Expectancy Theory, increases in positive highly-processed food expectancies caused participants to eat a greater number of calories from highly processed foods at the expense of calories from minimally processed foods. These results are theoretically critical because they extend a main tenet of Expectancy Theory into a novel domain of behavior, and they show that the temporary distortions in expectancies caused by the environment affect environment-relevant behavior. They also have important real-world implications. Diets that largely consist of highly processed foods at the expense of minimally processed foods cause weight gain, and predict chronic disease and premature death (Hall et al., 2019; Monteiro et al., 2018; Schnabel et al., 2019). These results suggest that a psychological intervention targeting positive high-processed food expectancies could minimize the impact of a fast-food restaurant on food intake, which may improve diet. Indeed, interventions reducing positive alcohol expectancies consistently decrease how often young adults drink and how much alcohol they consume (Scott-Sheldon et al., 2012) and interventions reducing thinness expectancies have decreased disordered eating (Annus et al., 2008). However, before developing any expectancy-based dietary interventions, interventionists must carefully pilot how to effectively change positive highly-processed food expectancies. For instance, the current study data show that expecting to feel negative emotions while eating highly processed foods was not associated with less ad libitum food intake. It thus may be critical to reduce positive highly-processed food expectancies without simultaneously increasing negative expectancies in their place (Cummings et al., 2020).

These results should be interpreted in light of study limitations. In the current study, there was very limited diversity with regards to race/ethnicity and level of parental education among the sample. In prior work, Black compared to non-Black participants rated food expectancies at higher levels but this was not replicated here perhaps due to the sample’s limitation (Cummings et al., 2020). Greater fast-food restaurant prevalence disproportionally occurs in U.S. neighborhoods with predominately Black residents (James et al., 2014). U.S. Black compared to non-Black populations also report greater daily energy intake from highly processed foods (Baraldi, Martinez Steele, Canella, & Monteiro, 2018), and are more likely to die prematurely from chronic disease (Van Dyke et al., 2018). Future research on the effect of an environment on food expectancies should consider how effects emerge in context of these racial/ethnic disparities and other disparities like those driven by socioeconomic status (Langer et al., 2018).

The current sample also was young and had an average BMI in the “normal” range; this may limit the clinical relevance of the results because those with overweight and obesity are more likely to have experienced clinical outcomes like cardiovascular diseases (Guh et al., 2009). However, body fat doubles from young adult to middle adulthood (Shimokata et al., 1989), and younger compared to older adults report greater intake of highly processed foods (Howarth, Huang, Roberts, Lin, & McCrory, 2007). Young adults may therefore represent a key population to study food expectancies in because of the potential clinical impact of dietary interventions targeting food expectancies in this age group. That is, reducing positive highly processed food expectancies among young adults may prevent the development of clinical outcomes later in adulthood. Successful substance use interventions targeting substance expectancies have likewise been delivered to this age group (Scott-Sheldon et al., 2012). Moreover, it should be noted that a diet largely consisting of highly processed foods at the expense of minimally processed foods has been found to be associated with some clinical outcomes independent of BMI (Chen et al., 2020).

The observed effect size for the effect of the simulated fast-food restaurant on positive highly-processed food expectancies was small, which is consistent with the one prior study that reported an effect size for the effect of a bar on positive alcohol expectancies (Monk & Heim, 2013). This suggests that environments minimally impact expectancies, at least in this age group, and larger sample sizes may be needed to detect similar effects in the future. Future research might investigate other factors that impact food expectancies such as parental, peer, and media influence, which are implicated in alcohol expectancies (Smit et al., 2018), and future research might determine which factors have larger effects on food expectancies. Nevertheless, even the small observed increase in positive highly-processed food expectancies led participants to eat a greater number of highly-processed food calories, and very small increases in daily caloric intake (~100 kcal/day) increase body weight over time (Hill et al., 2003). This suggests that repeatedly experiencing small increases in positive highly-processed food expectancies could have larger net effects on health, which is important to consider when roughly 35% of U.S. adults visit a fast-food restaurant daily (Nguyen & Powell, 2014). Future research should directly test this.

While the environmental condition indirectly explained ad libitum food intake through positive highly-processed food expectancies, it also directly explained the behavior independent of food expectancies. The lack of full mediation may reflect that the available measurement method for food expectancies is an explicit, self-report questionnaire, which may not be sensitive enough to fully capture environmental effects. A meta-analysis on explicit and implicit (e.g., free associates, implicit association test) measures of alcohol expectancies, for instance, indicated that explicit and implicit measures appear to uniquely capture variance in drinking behavior (Reich, Below, & Goldman, 2010). Also, it is likely that food expectancies represent one of multiple psychological mechanisms through which environments impact food intake; for example, in accordance with Incentive-Sensitization Theory, food “wanting” has mediated environmental effects on food intake (Joyner et al., 2017). To fully explain food intake, researchers might consider simultaneously measuring multiple mediators in future research.

In conclusion, translating principles from Expectancy Theory into the study of food intake is an emerging research area. The current study provided evidence for the situational-specificity hypothesis by showing that an environment affects environment-relevant food expectancies, and that this impacts food intake among young adults. In particular, greater positive highly-processed food expectancies caused young adults to eat a greater number of calories from highly processed foods at the expense of calories from minimally processed foods. A diet that largely consists of highly processed foods at the expense of minimally processed foods is associated with a number of detrimental clinical outcomes (Chen et al., 2020). Future psychological science on food expectancies will shed light on overlooked social, cognitive, and emotional mechanisms relevant to this pattern of food intake. This kind of research may support the development of dietary interventions that target food expectancies, which may improve diet quality and benefit public health.

Supplementary Material

1

Acknowledgments

Jenna R. Cummings was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (Award Number T32HD079350, Intramural Research Program). The present research was in part supported by funds allocated for research through T32HD079350. The content of the manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The authors acknowledge Lindsey Parnarouskis, Emma Schiestl, Alexandria Bodfish, Lily Carlson, Aviva Hirsch, Jolie Horne, Benjamin Hsu, Elizabeth Kennedy, Afeefah Khan, Zoe Kolender, Riley Olson, and Rhianna Vergeer for their hard work related to developing the study concept, contributing to the study design, or collecting data.

Footnotes

Declaration(s) of Conflict

The authors declared no conflicts of interest with respect to the authorship or the publication of this article.

Open Practices Statement

Study preregistration is available on the Open Science Framework at https://osf.io/ty9u6, with an amendment at https://osf.io/uzewm. De-identified data along with the data analysis scripts are posted at https://osf.io/xkjyp/; please abide by the posted data use and publication policy. The measures used in this study are widely available.

1

We collected additional measures that are not reported here but are reported in the study’s preregistration at https://osf.io/ty9u6, with an amendment at https://osf.io/uzewm. We collected those measures for potential secondary analyses projects separate from the current study aims (plans for potential secondary analyses projects are described in the preregistration).

2

In response to a reviewer’s suggestion, we expanded the multiple regressions predicting positive and negative highly-processed food expectancies from palatable eating coping motives and their interaction with the experimental condition to include BMI in post hoc analysis. There were no significant three-way interactions among palatable eating coping motives, experimental condition, and BMI in predicting these food expectancies. There was no significant two-way interactions among these variables in predicting positive highly-processed food expectancies but there was a significant two-way interaction between palatable eating coping motives and BMI in predicting negative highly-processed food expectancies [B(SE) = 1.08(0.49), p = .029, 95% CI (0.11, 2.04)]. For individuals with higher compared to lower BMI, the association between palatable eating coping motives and negative highly-processed food expectancies was stronger irrespective of experimental condition.

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