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Abstract Cue reactivity may be determined by the ability of cues to evoke expectations that a reward will be imminently received. To test this possibility, the current study examined the effects of manipulating expectations about the receipt of food (pizza) on self-reported and physiological responses to pizza cues, and attentional bias to pizza pictures. It was predicted that expecting to eat pizza would increase salivation, self-reported measures of motivation and attentional bias to pizza cues relative to conditions where there was no eating expectancy. In a within-subjects counterbalanced design, 42 hungry participants completed two pizza-cue exposures in a single experimental session during which their expectation of consuming the pizza was manipulated (i.e., expectancy of eating imminently vs. no eating expectancy). They also completed a computerised attentional bias task during which the probability of receiving pizza (0%, 50% or 100%) was manipulated on a trial-by-trial basis. Participants showed reliable increases in hunger and salivation in response to the pizza cues, as well as a bias in attentional maintenance on pizza pictures. However, these responses were not influenced by eating expectancy. Contrastingly, expectancy did influence early attentional processing (initial orientation of attention) in that participants directed their first gaze towards pizza pictures more often on 100% and 50% probability trials relative to 0% trials. Overall, our findings indicate that exposure to food cues triggers appetitive responses regardless of explicit expectancy information. Methodological features of the study that may account for these findings are discussed.

Introduction The consumption of food is highly rewarding and therefore supports the development of conditioned responses. Initially, food acts as an unconditioned stimulus (US) that elicits unconditioned responses (URs). Through a Pavlovian conditioning process, the rewarding properties of food become associated with external cues that are present at the time of consumption, such as the sight and smell of food. These external cues become conditioned stimuli (CS) that influence eating initiation and meal size and evoke a number of conditioned responses (CRs) (Weingarten, 1985). Accordingly, exposure to food cues in humans has been shown to reliably elicit CRs such as changes in subjective state (increased hunger, desire to eat and craving), physiological readiness to eat (increased salivation and heart rate) and cognitive changes, such as food-related attentional bias (Fedoroff et al., 1997, Ferriday and Brunstrom, 2011, Nederkoorn et al., 2000, Rogers and Hill, 1989 and Smeets et al., 2009). There is also evidence that food cue exposure increases the amount of food that is subsequently consumed (Fedoroff et al., 1997 and Ferriday and Brunstrom, 2008). Expectancy theory (Bolles, 1972) holds that experience of Pavlovian contingencies gives rise to explicit knowledge of the predictive relationships between environmental stimuli and reward. In this way, the cue (CS) signifies the availability of the reward and elicits an expectation that it will be imminently received. Expectancy may therefore be critical for the initial development of CRs (Field and Cox, 2008, Hogarth and Duka, 2006 and Jedras et al., 2014). This prospect is supported by human conditioning studies with addictive substances which indicate that nicotine CRs, such as salivary responses, attentional bias and subjective craving, depend on participants having explicit knowledge of, and hence an expectation about, the CS-US contingency (Field and Duka, 2001 and Hogarth and Duka, 2006). Further evidence is provided by studies that experimentally manipulated participants’ expectations about substance availability and observed effects on cue reactivity (e.g., Carter & Tiffany, 2001). In one study, the probability that participants would receive beer (100%, 50%, or 0%) was manipulated on a trial-by-trial basis in an attentional bias eyetracking task ( Field et al., 2011). Results indicated that, in light drinkers, attentional bias towards alcohol-related pictures was seen only on 100% probability trials (i.e., when alcohol was expected imminently), whereas heavy drinkers’ attentional bias was insensitive to alcohol expectancy. These findings were replicated using both chocolate and alcohol rewards ( Jones et al., 2012) and, intriguingly, effects were not outcome-specific; that is, the expectation of receiving alcohol increased attentional bias for both alcohol and chocolate cues, and vice versa. This general transfer effect might reflect an increase in arousal when rewards are anticipated, which enhances attentional bias for a variety of motivationally-salient cues ( Jones et al., 2012). There has been much less consideration of the role of expectancy in human eating studies. In the majority of existing food cue reactivity studies, it is not clear whether participants expected to be able to eat the cued foods. This is potentially problematic because, on the basis of the above evidence, eating expectancy would be predicted to strongly influence the magnitude of food cue reactivity. In order to address this important issue, some studies have manipulated expectations about whether the cued foods will be available for subsequent consumption. In a study with dietary restrained women, Higgs (2007) manipulated information about the post-task availability of a cued food (chocolate cake) and examined concurrent performance on a reaction time task. It was predicted that reaction time performance would be impaired when participants expected to subsequently eat the chocolate cake because consumption of a forbidden food would trigger diet-related anxieties. However, there was no evidence for an effect of post-task food availability on reaction time. More recently, Werthmann, Roefs, Nederkoorn, and Jansen (2013) found that the perceived availability of chocolate did not affect chocolate-related attentional bias, craving or chocolate intake in healthy weight female participants. These null findings stand in marked contrast to results from the addiction studies but it is possible that they are explained by methodological differences. For example, in the Werthmann et al. (2013) study, the time delay between giving participants the availability information and the actual opportunity to consume the food (approximately 15–20 min) may have resulted in the availability information losing its motivational impact. This is supported by a study by Field and Duka (2004) in which participants who expected to smoke had to wait around 20 min before being able do so and there were no effects on smoking cue reactivity (craving and physiological reactivity). On the basis of existing studies, it would appear that the effects of reward anticipation on cue reactivity are most prominent when expectancy is manipulated on an immediate, trial-by-trial basis. This ensures that participants expect to receive the reward (or not receive it) at the exact moment that the response is measured (Jedras et al., 2014). The current study examined the effects of expectancy information about the imminent receipt of an appetizing food (pizza) on reactivity to food cues. In addition to assessing attentional bias and self-reported responses to food cues, we also included a physiological measure of cue reactivity (salivation). Salivary responses are sensitive to food cue exposure (Brunstrom et al., 2004, Ferriday and Brunstrom, 2011, Nederkoorn et al., 2000 and Rogers and Hill, 1989), contextual appetitive conditioning (van den Akker, Jansen, Frentz, & Havermans, 2013) and smoking expectancy (Field & Duka, 2001); however, to our knowledge, salivation has not yet been examined in the context of explicit manipulations of eating expectancy. Our primary hypothesis was that expecting to eat pizza would increase salivation, attentional bias to pizza and self-reported measures of cue reactivity relative to comparable no-eating expectancy conditions. We also anticipated that higher levels of cue reactivity as a result of explicit eating expectancy would, in turn, be expressed behaviourally as increased food intake. In this way, our secondary hypothesis predicted that increased cue reactivity during conditions of eating expectancy (relative to conditions of no-expectancy) would be predictive of subsequent ad libitum intake of pizza. Our attentional bias task was adapted from that used by Field et al. (2011) and Jones et al. (2012) and critically enabled the manipulation of pizza expectancy on an imminent, trial-by-trial basis. The task also included alcohol-related pictures. This was to determine whether expectancy of receiving pizza would increase attentional bias towards other reward-relevant stimuli thus indicating a general transfer effect, as has recently been reported ( Jones et al., 2012). Finally, divergent findings between the drug and food studies might also be explained by differences in the level of substance exposure. In the nicotine studies for example, participants were daily smokers (e.g., smoking at least 15 cigarettes per day in Field and Duka (2001)) whereas, in the eating studies, habitual consumption of the target food was less frequent (e.g., less than once a day in Werthmann et al. (2013)). This greater exposure might lead to stronger associations between drugs and cues, thus affecting both the magnitude of the CRs themselves and responses to expectancy information. In order to address this possibility, we examined whether individual differences in habitual consumption of pizza (i.e., more-frequent vs. less-frequent consumers) would moderate the effects of the expectancy manipulation on food cue reactivity.

Results Participant characteristics Due to technical problems with the eye-tracker, data from two participants were lost. Therefore, we analysed 40 complete data sets. Gender, age, body mass index (BMI), TFEQ-restraint and –disinhibition are shown in Table 1. On average, participants reported eating pizza between “once a month” and “fortnightly” (mean score (SD) on the 0–6 point scale = 4.55 (0.93)). Fifty-five per cent of participants (N = 22) were classified as more frequent consumers (reported eating pizza “fortnightly” or “every week or more” on the 0–6 point scale). The remaining 45% (N = 18) were classified as less frequent consumers (reported eating pizza “every 2–3 months” and “once a month”). Average reported consumption of alcohol was between “2–4 times a month” and “2–3 times a week” (mean score on the 0–5 point scale was 2.25 (0.87)). The average time that participants had abstained from eating any food prior to the test session was 3.97 (1.69) hours. Table 1. Descriptive characteristics of the sample. Values are means with SDs in parentheses, unless otherwise specified. Characteristic Value N 40 (27 F, 13 M) Age (y) 27.83 (8.26) BMI (kg/m2) 23.72 (4.44) TFEQ-Restraint (0–21) 8.98 (5.04) TFEQ-Disinhibition (0–16) 7.75 (3.16) F = female, M = male; TFEQ = Three Factor Eating Questionnaire. Table options Salivation The total amount of saliva produced (g) was analysed using a 2 × 2 × 2 mixed analysis of variance (ANOVA). The within-subjects factors were expectancy condition (1. EE and 2. NEE) and time (1. Pre-exposure and 2. During exposure). The between-subjects factor was condition order (1. EE first, NEE second or 2. NEE first, EE second). There was no main effect of expectancy and no evidence for the critical expectancy by time interaction (all ps ⩾ .31). There was a main effect of time, F(1, 38) = 27.50, p < .001, indicating that, overall, participants salivated more during the pizza exposure relative to pre-exposure (means (SDs) = 0.42 (0.26) and 0.29 (0.18) g, respectively). There was no main effect of condition order and no evidence for an expectancy by time by order interaction (all ps ⩾ .197). Self-reported measures Self-reported hunger (100-mm VAS) was analysed using a 2 × 2 × 2 mixed analysis of variance (ANOVA). The within-subjects factors were expectancy condition (1. EE and 2. NEE) and time (1. Pre-exposure and 2. During exposure). The between-subjects factor was condition order (1. EE first, NEE second or 2. NEE first, EE second). There was no main effect of expectancy and no evidence for the critical expectancy by time interaction (all ps ⩾ .198). There was a main effect of time, F(1, 38) = 18.33, p < .001, indicating that, overall, participants reported higher levels of hunger during the pizza exposure relative to pre-exposure (means (SDs) = 73.19 (18.10) and 67.95 (17.84) mm, respectively). There was no main effect of condition order and no evidence for an expectancy by time by order interaction (all ps ⩾ .15). Ratings of pizza pleasantness, desire to eat the pizza, excitedness, and alertness were analysed using separate mixed ANOVAs. In each analysis, the within-subjects factor was expectancy condition (1. EE and 2. NEE) and the between-subjects factor was condition order (1. EE first, NEE second or 2. NEE first, EE second). There were no main effects of expectancy on pleasantness, desire to eat, or alertness (all ps ⩾ .509). There was, however, a main effect of expectancy on excitedness, F(1, 38) = 5.26, p = .027, whereby participants rated higher levels of excitement when they expected to eat the pizza relative to when they did not expect to eat it ( Fig. 1). There was no main effect of condition order and no expectancy by order interaction for excitedness or alertness. However, there was an expectancy by order interaction for both pleasantness, F(1, 38) = 16.75, p < .001, and desire to eat, F(1, 38) = 7.13, p = .01. These interactions were investigated using paired t-tests. For participants in the condition order 1 group (i.e., EE first, NEE second), pleasantness and desire to eat ratings were higher in the NEE condition relative to the EE condition (means (SDs); pleasantness, 82.45 (11.14) and 76.23 (16.71) mm, respectively, t(19) = −2.62, p = .017; desire to eat, 78.90 (16.25) and 72.98 (17.47) mm, respectively, t(19) = −1.87, p = .08). However, the converse was true for participants in the condition order 2 group (i.e., NEE first, EE second) where pleasantness and desire to eat ratings were higher in the EE condition relative to the NEE condition (means (SDs); pleasantness, 74.80 (16.64) and 66.15 (16.12) mm, respectively, t(19) = 3.15, p = .005; desire to eat, 69.48 (25.83) and 63.65 (20.03) mm, respectively, t(19) = 1.91, p = .07). These results thus indicate a general effect for pleasantness and desire to eat ratings to be higher during the second cue exposure, regardless of expectancy condition. Mean 100-mm visual analogue scale (VAS) ratings during pizza cue exposure in the ... Fig. 1. Mean 100-mm visual analogue scale (VAS) ratings during pizza cue exposure in the eating expectancy (EE) condition and the no eating expectancy (NEE) condition. Values are mean ± SEM. *Significant difference between EE and NEE conditions, p = .027. Figure options Attentional bias Gaze dwell time (ms) was analysed using a 3 (probability: 100%, 50% and 0%) × 2 (picture pair: pizza/neutral and alcohol/neutral) × 2 (picture type: pizza/alcohol and neutral) repeated measures ANOVA. There was no main effect of probability and no indication of significant interactions between probability, picture pair and picture type (all ps > .35). There was a main effect of picture pair, F(1, 39) = 30.21, p < .001, which indicated that, overall, participants maintained their gaze on pizza/neutral pictures for longer than alcohol/neutral pictures (Means (SDs) = 286.29 (77.14) and 257.62 (76.44) ms, respectively). There was also evidence of a picture pair by picture type interaction, F(1, 39) = 3.79, p = .059. This interaction is shown in Fig. 2. Paired samples t-tests indicated that participants maintained their gaze on pizza pictures for longer than on neutral pictures, t (39) = 2.41, p = .021. There was no difference between alcohol and neutral pictures, t(39) = 0.75, p = .46 ( Fig. 2). This indicates an overall attentional bias for pizza pictures (relative to neutral pictures), but not for alcohol pictures. However, this bias was not moderated by the expectancy of receiving pizza points. Mean dwell time (ms) on pizza and alcohol pictures relative to neutral pictures ... Fig. 2. Mean dwell time (ms) on pizza and alcohol pictures relative to neutral pictures averaged across probability trials. Values are mean ± SEM. *Significantly different from respective neutral picture, p = .02. Figure options Gaze direction bias was analysed using a 3 (probability: 100%, 50% and 0%) × 2 (picture pair: pizza/neutral and alcohol/neutral) repeated measures ANOVA. There was a main effect of probability, F(2, 78) = 4.41, p = .015 ( Fig. 3). However, there was no evidence for an interaction between probability and picture pair, indicating that probability had similar effects on responses to both pizza and alcohol pictures (i.e., a general effect). As shown in Fig. 3, there was a greater direction bias towards the pizza and alcohol pictures on 100% probability trials relative to 0% probability trials, t(39) = 2.81, p = .008, and on 50% trials relative to 0% trials, t(39) = 2.40, p = .02, but there was no difference between the 100% and 50% trials (p = .36). One-sample t tests indicated that none of the gaze direction bias scores were significantly different from the criterion level of 50% (ps > .08) (which would indicate a higher proportion of first fixations directed towards the pizza or alcohol pictures relative to the neutral pictures). Thus there was little evidence for an overall attentional bias for reward pictures relative to neutral pictures on gaze direction. Mean gaze direction bias averaged across pizza and alcohol pictures as a ... Fig. 3. Mean gaze direction bias averaged across pizza and alcohol pictures as a function of perceived probability of receiving pizza. Values are mean ± SEM. *Significantly different from 0% trials, p ⩽ .02. Figure options Moderating factors To examine potential moderating effects of habitual pizza consumption, the above analyses were re-run with pizza frequency group (i.e., more frequent consumers vs. less frequent consumers) entered as a between-subjects factor. We found no evidence for an interaction between expectancy and pizza frequency group for salivation, attentional bias (dwell time and direction bias), self-reported hunger, desire-to-eat or alertness (ps > .1). However, for both self-reported pizza pleasantness and excitedness, there was an interaction between expectancy and pizza frequency group (F(1, 38) = 3.96, p = .05, and F(1, 38) = 4.98, p = .03, respectively). Further exploration of the data indicated higher levels of pizza pleasantness and excitedness in the EE condition compared to the NE condition only in less frequent consumers (t(17) = 2.08, p = .05, and t(17) = 3.07, p = .007, respectively). There was no effect of eating expectancy in more frequent consumers (t(21) = −.84, p = .41, and t(21) = .31, p = .76, respectively). Post hoc, we also examined whether sensitivity to the expectancy manipulation was moderated by individual differences in BMI and overall desire for pizza. Median splits were conducted on BMI (high BMI group ⩾ 22.57 kg/m2) and desire to eat the pizza (averaged across the EE and NE conditions; high desire group ⩾ 77.25 mm). The above analyses were re-run first with BMI group (i.e., high vs. low) and then with desire group (high vs. low) as a between-subjects factor. Across all the dependent variables, there was little evidence for interactions between expectancy and BMI (ps > .07) or between expectancy and desire (ps > .06). Associations with pizza intake In order to determine whether increased responses in the expectancy conditions relative to the no-expectancy conditions predicted pizza intake (our secondary hypothesis), we computed difference scores for all outcome measures. For salivation and the self-report measures, difference scores were computed by subtracting the value recorded during the NEE condition from that recorded during the EE condition. Difference scores were also computed for the attentional measures. For pizza gaze dwell time, we first calculated bias scores by subtracting attention to the neutral pictures from attention to the respective pizza pictures within probability trials (a positive score indicates an attentional bias toward pizza pictures). We then computed a difference score by subtracting the bias score on 0% trials from the bias score on 100% trials. Similarly, for pizza direction bias, the difference score was computed by subtracting the mean values recorded during the 0% trials from those recorded during the 100% trials. In all cases, a positive difference score indicated an increased response in the EE condition (or 100% trials) relative to the NEE condition (or 0% trials). Each difference score was then correlated with pizza intake (kcal). There was no evidence that change in any of the variables was associated with pizza intake (all ps ⩾ .08). Given the general lack of difference between expectancy conditions, we also examined correlation coefficients between pizza intake and absolute responses for all outcome measures. Absolute values were computed by averaging responses across EE and NE conditions for salivation and the self-report measures, and across 100%, 50% and 0% trials for the attentional measures. As shown in Table 2, pizza intake correlated positively with absolute levels of hunger and desire to eat pizza. Salivation did not correlate with any of the other variables. Pizza dwell time bias correlated positively with pizza direction bias but neither measure correlated significantly with any other variables. Table 2. Pearson correlation coefficients between absolute values and ad libitum pizza intake. Absolute values were computed by averaging responses across EE and NE conditions (or across 100%, 50% and 0% trials for attentional measures). Variable 1. 2. 3. 4. 5. 6. 7. 8. 9. 1. Pizza intake (kcal) 750.07 (360.11) 2. Salivation (g) −.09 .42(.26) 3. Hunger (mm) .44⁎ .00 73.19 (18.10) 4. Pleasantness (mm) .08 .11 .45⁎ 74.91 (14.73) 5. Desire to eat (mm) .39⁎ .05 .82⁎ .57⁎ 71.25 (19.35) 6. Excitedness (mm) .29 .13 .43⁎ .47⁎ .46⁎ 54.24 (24.57) 7. Alertness (mm) .01 −.05 .15 .23 .20 .43⁎ 65.75 (17.12) 8. Pizza dwell time bias (ms) .02 −.13 .11 .25 .22 .20 .17 23.86 (62.63) 9. Pizza direction bias (%) −.22 −.03 .00 −.09 −.07 −.02 −.19 .41⁎ 51.06 (8.40) Note. Off-diagonal shows correlation coefficients (r); Diagonal shows means (standard deviations in parentheses). ⁎ Significant correlations (p < .05).