Food labels promote healthy choices by a decision bias in the amygdala

NeuroImage 74 (2013) 152–163

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Food labels promote healthy choices by a decision bias in the amygdala Fabian Grabenhorst a, b,⁎, Frank P. Schulte b, c, Stefan Maderwald b, Matthias Brand b, c a b c

Department of Physiology, Development and Neuroscience, University of Cambridge, UK Erwin L. Hahn Institute for Magnetic Resonance Imaging, Essen, Germany General Psychology, Cognition, University Duisburg-Essen, Germany

a r t i c l e

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Article history: Accepted 10 February 2013 Available online 18 February 2013 Keywords: Amygdala Food Reward Decision Emotion Obesity

a b s t r a c t Food labeling is the major health policy strategy to counter rising obesity rates. Based on traditional economic theory, such strategies assume that detailed nutritional information will necessarily help individuals make better, healthier choices. However, in contrast to the well-known utility of labels in food marketing, evidence for the efficacy of nutritional labeling is mixed. Psychological and behavioral economic theories suggest that successful marketing strategies activate automatic decision biases and emotions, which involve implicit emotional brain systems. Accordingly, simple, intuitive food labels that engage these neural systems could represent a promising approach for promoting healthier choices. Here we used functional MRI to investigate this possibility. Healthy, mildly hungry subjects performed a food evaluation task and a food choice task. The main experimental manipulation was to pair identical foods with simple labels that emphasized either taste benefits or health-related food properties. We found that such labels biased food evaluations in the amygdala, a core emotional brain system. When labels biased the amygdala's evaluations towards health-related food properties, the strength of this bias predicted behavioral shifts towards healthier choices. At the time of decision-making, amygdala activity encoded key decision variables, potentially reflecting active amygdala participation in food choice. Our findings underscore the potential utility of food labeling in health policy and indicate a principal role for emotional brain systems when labels guide food choices. © 2013 Elsevier Inc. All rights reserved.

Introduction Human food choice is unique among primates as it is guided not only by motivational states and the sensory features of foods but also by abstract information conveyed through language. Such information frequently impinges on us in the form of food labels. For example, marketing uses food labels to direct consumers' preferences towards specific brands (Harris et al., 2009; Zimmerman, 2011). Food labeling is also the major health policy strategy to counter rising obesity rates and associated costs to health care systems (Downs et al., 2009; Just and Payne, 2009; Kiesel et al., 2011). A prime example is the recently enacted national calorie labeling law in the United States. Despite this prevalence of food labels in our daily lives, their underlying neural and psychological mechanisms are not well understood. Psychological and linguistic theories closely link language-based information processing to explicit thought (Berwick and Chomsky, 2011; Jackendoff, 2002). This could suggest that food labels provide input to deliberate, conscious decision-making. Indeed, label-based health policy strategies tacitly adopt this view. Based on traditional economic theory, these strategies assume that by providing detailed nutritional information, food labeling will necessarily help individuals ⁎ Corresponding author at: University of Cambridge, Department of Physiology, Development and Neuroscience, Downing Street, Cambridge, CB2 3DY, England. E-mail address: [email protected] (F. Grabenhorst). 1053-8119/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neuroimage.2013.02.012

make better, healthier choices (Just and Payne, 2009). Yet, although widely implemented, evidence for the efficacy of nutritional labeling is mixed (Downs et al., 2009; Kiesel et al., 2011). By contrast, the utility of simple, intuitive labels in food marketing is well known (Harris et al., 2009). Psychological and behavioral economic theories suggest that marketing actions activate automatic biases and emotions, which form integral parts of the human decision-making faculty (Harris et al., 2009; Kahneman and Tversky, 1984; Sharot, 2011), and which involve implicit emotional brain systems including the amygdala (Davis and Whalen, 2001; Dolan, 2007; Pessoa and Adolphs, 2010; Phelps and LeDoux, 2005; Sharot et al., 2007). Thus, from a health policy perspective, the design of labels that capitalize on these biases and engage the same neural systems could offer a promising approach for promoting healthier choices. Rather than instilling explicit nutritional knowledge, such labels could implicitly guide consumers towards healthier choices, in line with “libertarian paternalism” as guiding principle for policy interventions (Loewenstein et al., 2007; Thaler and Sunstein, 2008). The design of food labels in health policy could benefit from a principled, mechanistic understanding of the brain systems for food valuation and choice, and of the physiology of the specific biases according to which these systems operate. In a pioneering study (McClure et al., 2004), the influence of brand identity cues on drinks preferences was related to activation of the hippocampus, potentially reflecting the recall of cultural, brand-specific memories. Other studies showed that basic

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responses to taste, flavor, olfactory and visual food stimuli in the amygdala and other reward structures can be influenced by positive descriptions and attentional instructions (Bender et al., 2009; de Araujo et al., 2005; Grabenhorst and Rolls, 2008; Grabenhorst et al., 2008a; Linder et al., 2010; Ng et al., 2011; Plassmann et al., 2008). Extending these findings, a recent study showed that explicit instructions to consider healthiness influenced prefrontal cortex activity and increased healthy food choices (Hare et al., 2011), similar to the effects of deliberate self control (Hare et al., 2009). Despite these advances, the neural mechanisms mediating the influence of incidental labels on food valuation and choice in the absence of explicit instructions remain unclear, even though these constitute the main category of labels in marketing and health policy (Bollinger et al., 2011; Harris et al., 2009; Kiesel et al., 2011). Here, we used functional MRI to investigate how simple, incidental labels influence behavior and neural activity in two separate experimental paradigms, a food valuation task and a food choice task. Existing evidence suggested that brain reward structures adapt their responsiveness to contextual parameters (Bermudez and Schultz, 2010; Grabenhorst and Rolls, 2011; Padoa-Schioppa, 2011; Schultz, 2011). Therefore, we hypothesized that food labels might similarly modulate the representation of valuation and decision variables in these brain systems. Materials and methods Experimental design Subjects performed 120 trials of a food valuation task and 120 trials of a food choice task (Fig. 1A). Food pictures were paired with different labels that either described the hedonic taste benefits of the food or the potential health costs associated with the food, as indicated by their nutritional properties. Taste labels consisted of brief descriptions in German that were typical of the specific food presented on a given trial and of the type commonly used in food marketing (for example “sweet and juicy” for strawberries). Health labels conveyed information typically found in health policy information sources (e.g. brochures, websites) and were designed to match the simple intuitive labels used in food marketing. The information provided by health labels depended on the specific foods and could either emphasize positive or negative health-related properties of the foods (e.g. “high in calories” or “low fat content”). Because we conceptualized decision processes in this task as a cost-benefit choice, we use the term “health costs” to describe the content of the health labels. Labels were designed to provide a simple, brief description of either hedonic benefits or health costs, similar to labels used in food marketing, but markedly different from detailed nutritional labels found on food packaging. In two further experimental conditions, the same food pictures were shown either without any label or with both types of labels shown simultaneously. Labels were presented either above or below the food picture on the monitor, as determined by random permutation. All label conditions were run in randomly permuted manner. Across the experiment, valuation and choice trials were run in random order for all subjects. This ensured that performance in one task would not systematically influence behavior in the other task. In our design of the semantic labels, we followed previous fMRI studies that investigated the effect of labels on reward processing (de Araujo et al., 2005; Grabenhorst et al., 2008a; Hare et al., 2011; McCabe et al., 2008). Labels in the present study were comparable between conditions in that they consisted of two to four words, typically constructed using one noun and one adjective or two adjectives and one conjunction.

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very healthy/very unattractive). Our use of a healthiness rating scale on which positive values reflect subjective unhealthiness was motivated by our conceptualization of the food choice task in terms of decisions that trade off taste benefits vs. health costs. Subjects were pre-trained in using a button box to provide ratings on the scales so that they were confident to provide the ratings within the allowed time on each trial. This experimental protocol of obtaining ratings of different stimulus properties has been used successfully in previous fMRI studies, and ratings of taste pleasantness have been shown to reflect subjective hedonic state in studies when pleasantness was experimentally manipulated using ranges of stimuli (de Araujo et al., 2003; Grabenhorst and Rolls, 2009; Grabenhorst et al., 2007, 2010a; Kringelbach et al., 2003). Rating scales were followed by a variable inter-trial interval (ITI) with jittered duration of 2–6 s. During the ITI an instruction was presented on the monitor to inform subjects about whether the next trial would be a valuation or choice trial. Choice task Subjects also performed a food choice task, which involved subjective preference decisions between two successively presented food items. Subjects were instructed that choices were consequential, in that one choice would be randomly selected and implemented after the experiment. This experimental strategy has been used previously in fMRI studies of reward-based decision-making (Grabenhorst et al., 2008b; Hare et al., 2009). Both valuation and choice task trials were run in the same sessions in randomly permuted manner. At the start of each choice trial, a picture of a food item was shown for 4 s, followed by a blank screen of 3 s. The second food item was then shown, followed by a blank screen of 4 s. Subjects were then prompted to report their choice by showing the options “First” and “Second” on the monitor. Subjects reported their choices by selecting the appropriate option shown at the top or bottom of the monitor using a button box. The arrangement of both options on the monitor was determined by random permutation and options were presented for 2 s. Subjects were pre-trained so that they were confident to report their choice in the allowed time. After the choice response period, subjects reported a rating of decision confidence using a visual analog scale, ranging from +2 (high confidence) to −2 (low confidence) which was presented for 3 s. The rating scale was followed by a variable ITI with jittered duration of 2–6 s. Subjects Thirteen subjects participated in the experiments (7 females, mean age=24.2 years, age range=22–27 years). (For similar sample sizes see (Christopoulos et al., 2009; Kable and Glimcher, 2007; Sharot et al., 2010; Small et al., 2008)). All subjects were healthy, normal-weighted (body mass index between 22 and 25), right-handed and did not suffer from neurological or psychiatric diseases. Subjects were not dieting and did not have a history of or were currently suffering from eating disorders. Potential current symptoms of eating disorders were screened for with the Eating Disorder Examination Questionnaire (Hilbert and Tuschen-Caffier, 2006). Written informed consent was obtained before the start of the experiment. The experiment was approved by the local ethics committee. Subjects were asked not to eat for at least three hours before the experiment, so that they were mildly hungry and motivated to eat. Following procedures used in previous studies, the experiments were conducted three hours after typical meal times either in the late morning or mid afternoon, due to fixed scanning slots. Food pictures

Valuation task At the start of a valuation trial, a food picture was shown for 4 s, followed by a blank screen for 3 s. Subjects then rated the expected taste pleasantness, unhealthiness, and attractiveness on three separate visual analog scales, each presented for three seconds ranging from +2 (very pleasant/very unhealthy/very attractive) to −2 (very unpleasant/

Pictures of ten common food stimuli were obtained from an online photo database. Stimuli were selected based on pilot experiments to range from neutral to highly pleasant and attractive as well as from healthy to unhealthy, and to reflect a range of different combinations of pleasantness and healthiness (e.g. pizza, snack bars, French

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Fig. 1. Experimental tasks and behavioral effects of label manipulation. (A) In the valuation task, subjects rated expected taste pleasantness, attractiveness and health costs of common foods. In the choice task, subjects made preference choices between two successively presented foods, and rated decision confidence. (B) Effects of label manipulation on behavior in the valuation task. Mean ratings in both label conditions (±standard error of the mean, s.e.m.). Foods were selected based on prior testing so that half of the items would be relatively healthy and the other half relatively unhealthy; collapsing across healthy and unhealthy foods therefore resulted in mean health ratings close to zero. (C–E) Effects of label manipulation on behavior in the choice task. (C) Logistic regression coefficients for relative taste pleasantness (Δtasterel) and relative health costs (Δhealthrel) on choices (a.u.: arbitrary units). (D) Healthy choice index: ratio of choices for relatively pleasant but unhealthy foods to less pleasant but healthy foods (based on ratings in the valuation task). (E) Logistic fits of Δtasterel and Δhealthrel to choices, averaged across subjects. *: Pb 0.05, ns: non significant.

fries, chicken breast, fruits). The pictures showed the simple food, ready for consumption, without any information about brands, packaging, or other information not directly related to the food itself. In

the MRI scanner, pictures were back-projected on a computer screen. Visual stimulus presentation was controlled using PRESENTATION (Neurobehavioral Systems Inc., Albany, CA) software.

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Our motivation for using a relatively limited set of food items was as follows: First, using identical food items across all label conditions in the valuation task ensured that any ratings differences between label conditions could not be explained in terms of simple differences between items. This balanced presentation of foods across label conditions made it necessary to repeat items. A further motivation for stimulus repetition was to minimize the effects of novelty of specific food items on ratings. Presenting each stimulus multiple times made it unlikely that extreme ratings due to novelty effects systematically influenced any comparisons between label conditions. Moreover, because the presentation of specific stimuli was randomized across conditions for all subjects we ruled out that systematic habituation effects could have biased our results. To directly test for habituation effects, we regressed the trial-by-trial ratings on the explanatory variables stimulus identity, label condition, and stimulus repetition, with separate regressions for taste pleasantness and health costs ratings. Significant effects of stimulus repetition on ratings (Pb 0.05) were found in only 2 out of 13 subjects for taste pleasantness; none were found for health cost ratings. This suggested that stimulus habituation effects were minor and did not have a systematic influence on stimulus valuations. Behavioral data analysis Analysis of choice data Choice data were analyzed using logistic regression analysis with subject treated as a random factor. We hypothesized that preference decisions in the choice task would be governed by two key decision variables, the relative difference in expected taste pleasantness between foods (Δtasterel) and the relative difference in health costs (Δhealthrel). This hypothesis was motivated by previous studies which demonstrated that in sequential decision tasks, the relative (i.e. signed) difference in valuations between successive stimuli can be used to model choice behavior (Grabenhorst and Rolls, 2009; Rolls et al., 2010a, 2010c). In the present study, Δtasterel and Δhealthrel were calculated for each trial in each subject with respect to the second stimulus by subtracting the mean taste/health rating for a specific stimulus that was shown as the first food item from the mean taste/health rating given to the second food item. The ratings used for this procedure were the mean ratings for a given food item obtained during the valuation task. For example, if the first food item received a mean pleasantness rating of 1.5 in the valuation task, and the second item received a mean pleasantness rating of 0.5, the decision variable Δtasterel for that trial corresponded to −1.0. (The corresponding value for the variable Δtasteabs, i.e. the absolute, unsigned difference used for the fMRI regression analysis would be scored as +1.0.) Thus, for every trial we defined two separate relative difference terms, one based on taste pleasantness ratings and one based on healthiness ratings. This procedure allowed us to relate Δtasterel and Δhealthrel to the probability of choosing the second food item on each trial using logistic regression analysis with both Δtasterel and Δhealthrel as regressors in the same model. Thus, Δtasterel = tastefood2–tastefood1, where tastefood2 and tastefood1 are the average taste pleasantness ratings given to the second and first food, respectively; and Δhealthrel = healthfood2–healthfood1, where healthfood2 and healthfood1 are the average health cost rating given to the second and first food, respectively. The analyses just described were performed separately for the different label conditions. The relative (i.e. signed) differences in expected pleasantness and healthiness were used for behavioral analysis. The absolute (i.e. unsigned) differences were used as regressors for neural activity, based on previous fMRI and computational modeling investigations as described in the Results section. These absolute differences were defined as Δtasteabs = |Δtasterel| and Δhealthabs = |Δhealthrel|. This overall procedure of using averages for behavioral analysis and trial-by-trial ratings to construct parametric fMRI regressors follows standard approaches used in many previous studies (de Araujo et al., 2005; Grabenhorst et al., 2008a, 2010b; Linder et al., 2010; McCabe and Rolls, 2007; McCabe et al., 2008).

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Index for unhealthy choices To quantify the effect of health labels on decision-making at the level of individual food choices we calculated a choice index for each subject as follows. Based on the decision variables Δtasterel and Δhealthrel we grouped choice trials into trials in which a relatively more pleasant (Δtasterel > 0) but less healthy (Δhealthrel >0) food item was chosen, and trials in which a relatively less pleasant (Δtasterel b 0) but healthier (Δhealthrel b 0) food item was chosen. This grouping was performed separately for taste and health label conditions. The unhealthy choice index was calculated as the ratio of choices for relatively more pleasant but less healthy foods to relatively less pleasant but healthier foods. In other words, based on the participants' individual ratings from the valuation trials, we compared the number of choice trials in which the individual participant made a choice in favor of a food that she/he rated as more pleasant but less healthy than the comparison item that was on offer on the same trial, and those foods which she/he rated as less pleasant but more healthy than the comparison item. Thus, the relative pleasantness and relative healthiness were calculated for each choice with reference to the two relevant choice options that were on offer on a given trial. Intuitively, this index can be interpreted as a preference for liked unhealthy foods over relatively less liked but healthier foods. Functional imaging data acquisition and analysis Images were acquired at the Erwin L. Hahn Institute for Magnetic Resonance Imaging in Essen, Germany. We used a 7.0-T whole-body scanner (Magnetom 7 T, Siemens Healthcare, Erlangen, Germany) equipped with a gradient system capable of 45 mT/m maximum amplitude and a slew rate of 220 mT/m/ms. For signal generation and reception, a custom-built 8-channel transmit/receive (Tx/Rx) head coil (Orzada et al., 2009) was used. For each subject, a sagittal T1-weighted 3D magnetization-prepared rapid gradient echo structural image (MPRAGE) was obtained (TR=2500 ms, TE=1.27 ms, TI=1100 ms, flip angle=7°, 192 slices with a non-interpolated voxel size of 1×1×1 mm3). For the acquisition of functional images, subjects were scanned in six subsequent sessions, each lasting about 15 min to acquire a total of 2710 volumes. Functional images were acquired using a BOLD contrast sensitive EPI sequence optimized for 7.0-T (TR=2000 ms, TE=22 ms, flip angle=76°, in-plane resolution =3×3 mm2; (Poser and Norris, 2009)) using 52 contiguous 2 mm coronal slices. As the custom build head coil array allows parallel imaging, the GRAPPA (Generalized Autocalibrating Partially Parallel Acquisitions) algorithm was used with a reduction factor of R=2 to reconstruct the undersampled k-space (Griswold et al., 2002). Improving the static magnetic field (B0) homogeneity is particularly important at 7.0-T because B0 field distortions are more prominent compared to lower field strengths. Therefore, additional B0 fieldmaps were acquired prior to the EPI-sequence. Imaging data were analyzed using SPM5 (Statistical Parametric Mapping, Wellcome Trust Centre for Neuroimaging, London). Pre-processing of the data used SPM5 realignment, reslicing with sinc interpolation, unwarping, normalization to the MNI coordinate system (Montreal Neurological Institute), and spatial smoothing with a 6 mm full width at half maximum isotropic Gaussian kernel. Time series non-sphericity at each voxel was estimated and corrected for, and a high-pass filter with a cut-off period of 128 s was applied. General linear models (GLMs) assuming first-order autoregression were applied to the time course of activation in which event onsets were modeled as single impulse response functions convolved with the canonical hemodynamic response function. Time derivatives were included in the basis functions set. Following smoothness estimation, linear contrasts of parameter estimates were defined to test specific effects in each individual dataset. Voxel values for each contrast resulted in a statistical parametric map of the corresponding t statistic. In the second (group random-effects) stage, subject-specific linear contrasts of these parameter estimates were entered into one-sample t-tests,

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factorial designs or multiple regression models, as described below, resulting in group-level statistical parametric maps. In our main GLM (GLM1), valuation task trials were modeled with an indicator function for the onset of the food picture which was parametrically modulated by the trial-specific taste pleasantness ratings and healthiness ratings. We also included separate indicator functions for the onset of each rating scale which occurred later in the trial. Choice task trials were modeled with an indicator function for the onset of the first food picture, an indicator function for the onset of the second food picture modulated by trial-specific relative difference variables Δtasteabs and Δhealthabs, and separate indicator functions for the onset of the choice response period and the confidence ratings scale that occurred later in the trial. Trial-specific difference variables Δtasteabs and Δhealthabs were defined separately for trials in the different label conditions. In addition, the GLM included movement parameters resulting from the realignment pre-processing step as covariates of no interest as well as six indicator functions for the different sessions. Our main parametric regressors for fMRI analysis, taste pleasantness and healthiness and related decision variables were largely uncorrelated across subjects and label conditions. The mean within-subject correlation between taste pleasantness and healthiness was −0.05 (±0.08 s.e.m.), between Δtasterel and Δhealthrel was −0.12 (±0.09), and between Δtasteabs and Δhealthabs was 0.12 (±0.06). We nevertheless took a conservative approach and removed the serial Gram–Schmidt orthogonalization procedure from the SPM analysis to ensure that any shared variance between parametric modulators would be excluded and not attributed to any regressor (Andrade et al., 1999; Draper and Smith, 1998). This model was used to generate the statistical maps shown in Figs. 2 and 3A. For the analysis shown in these figures, we regressed neural activity on trial-by-trial ratings of taste pleasantness, health costs and related decision variables, separately for taste and health label conditions, and then contrasted the resulting statistical maps. These contrasts thus test for stronger relationships of neural activity and pleasantness ratings, health

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Psychophysiological interaction analysis We performed psychophysiological interaction (PPI) analyses (Friston et al., 1997; Gitelman et al., 2003) to investigate how activity in pairs of brain regions is modulated as a function of label condition. The PPI models performed at the single subject level included the following main regressors. The first regressor consisted of the timeseries of activity in a seed brain area (the amygdala) identified in a separate analysis as described below. The timeseries was extracted in each subject by drawing a 8 mm sphere around the peak voxel from the group contrast analysis and then finding the individual's peak voxel within that sphere. The second regressor consisted of a task-related contrast (i.e. the taste label condition vs health label condition). The third regressor consisted of the interaction between the first and second regressors. The regressors were constructed using the standard deconvolution procedure as implemented in SPM5 (Gitelman et al., 2003). In addition the model included movement parameters resulting from the realignment pre-processing step as covariates of no interest as well as six session constants. This model was used to generate the statistical map in Fig. 3C.

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cost ratings, or related decision variables between the taste and health label conditions. In the second GLM (GLM2) we included the trialspecific confidence ratings instead of the difference variables for all choice task trials. This model was used to generate the statistical map in Fig. 3B. In the third, supplementary GLM (GLM3), we included a cost-benefit regressor instead of the separate taste-based and health-based difference variables on choice trials. This cost-benefit regressor was calculated for every trial by first subtracting the mean health cost rating given to the first/second food item from the corresponding mean taste pleasantness rating, and then subtracting the cost-benefit term of the first food item from the cost-benefit term of the second food item. Parameter estimates for our main task periods of interest were uniquely specified as indicated by the SPM design matrix outputs.

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Fig. 2. Neural effects of label manipulation in the food valuation task. (A) Taste labels biased neural coding towards pleasantness: amygdala responses to foods correlated more strongly with pleasantness ratings when taste labels were shown compared to health labels (MNI coordinates (x, y, z): [22 −4 −20] P=0.02, small volume-corrected). Parameter estimates are shown to illustrate the pattern of effects and were extracted from all voxels in the sphere used for small volume correction. The sphere was defined based on coordinates from a previous study (see Materials and Methods section). (The negative relationship between neural activity and taste pleasantness ratings during the health label condition suggested by the negative parameter estimate was non-significant. (P>0.2)) (B) Health labels biased neural coding towards health costs: amygdala responses to foods correlated more strongly with health ratings when health labels were shown compared to taste labels ([18 −6 −22] P=0.009, whole-brain corrected at cluster level). (C) Average time courses of the percentage change in the blood oxygen level-dependent (BOLD) signal in the amygdala in the health label condition. Time courses are color-coded according to health cost ratings. Within each subject, time course data were grouped into terciles of health cost ratings. Data were extracted in each subject from an 8 mm sphere centered on coordinates from a previous study. (D) Across subjects, variation in the neural health cost bias in Fig. 2B explained variation in the extent to which subjects based their choices on health cost valuations in the health label condition (indicated by logistic regression betas for relative health costs) ([20 −4 −22] P=0.002, small volume corrected). The scatter plot shows parameter estimates from the neural valuation bias against the logistic regression betas for relative health costs (R2 =0.489, P=0.013, robust regression). The scatter plot is shown to illustrate the pattern of the effect. Our main statistical inference for the across-subjects regression was carried out in the SPM framework. Parameter estimates for this plot were extracted from an 8 mm sphere centered on coordinates from a previous study, ensuring independence from whole-brain analyses.

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Fig. 3. Neural effects of label manipulation in the food choice task. (A) Health labels biased neural coding towards a health-based decision variable: amygdala activity in the choice task was more strongly related to the health-based decision variable Δhealthabs when health labels were shown compared to taste labels ([22 −2 −20] P = 0.028, small volume-corrected). (B) Negative relationship between amygdala activity in the choice task and decision confidence ratings ([18 −4 −14] P = 0.015, small volume-corrected). (C) Label-dependent functional connectivity between amygdala and prefrontal cortex: Stronger negative correlation between amygdala activity (seed area) and DLPFC in the choice task when health labels were shown compared to taste labels ([−20 24 34] P = 0.021, small volume-corrected).

The contrast maps for the PPI analysis were taken from the same first level model as the contrast maps for the other analyses as describes in the Functional Imaging Data Acquisition and Analysis section. The specific contrast used for the PPI analysis was a contrast between the taste label and health label conditions. Accordingly, the PPI analysis tested whether any brain areas were differentially correlated with the amygdala seed area as a function of the label condition, in line with the hypothesis outlined in the Results section, PPI analysis.

Brain areas of interest and criteria for statistical significance We report results for effects that survive whole-brain correction for multiple comparisons (P b 0.05, corrected for family-wise error at the cluster level). The corrected cluster size (spatial extent) threshold for whole-brain corrections was determined using the function corrClusTh.m provided by Thomas Nichols (http://www.sph.umich. edu/~nichols/JohnsGems5.html). The cluster threshold sizes determined this way ranged from 90 to 163 voxels, depending on the specific statistical map. In addition, we had prior hypotheses about key brain areas involved in food valuation and decision-making, including the amygdala (Bechara et al., 2003; de Araujo et al., 2005; De Martino et al., 2006), ventromedial prefrontal cortex (VMPFC) (Basten et al., 2010; Grabenhorst and Rolls, 2011; Hare et al., 2011), dorsolateral prefrontal cortex (DLPFC) (Heekeren et al., 2004, 2006; Rolls et al., 2010b) and ventral striatum (Basten et al., 2010; Sharot et al., 2009). For these areas we defined small volume spheres centered on coordinates from prior studies in which we corrected for multiple comparisons (p b 0.05, family-wise error). For consistency, we used coordinates from a single previous study (Basten et al., 2010) to define these coordinates. The reason for this approach was that this study was conceptually very similar to the present study as it investigated value-based cost-benefit decisions and because it identified effects in all our key areas of interest. We note that our key results in amygdala and DLPFC are robust even if coordinates from other studies were used (e.g. basing our coordinates for amygdala on the studies by De Martino et al. (2006) or de Araujo et al. (2005), or basing our coordinates for DLPFC on the study by Heekeren et al. (2004)). For the amygdala and ventral striatum we used spheres of 8 mm radius; for the larger cortical areas in VMPFC and DLPFC we used spheres of 20 mm radius. The specific coordinates were [18 −2 −20] for amygdala, [−4 60 −6] for ventromedial prefrontal cortex, [−22 18 44] for dorsolateral prefrontal cortex, and [−10 10 −6] for ventral striatum. For display purposes, to show the extent of activations, statistical maps in the figures are shown at Pb 0.005, uncorrected. No brain areas other than those reported in the results showed whole brain-corrected effects. For completeness, we also show in Table 2 all effect that survived a threshold of Pb 0.001, uncorrected, with a cluster threshold 5 contiguous voxels. Following procedures in previous studies, we report these effects as exploratory findings only, and do not base any of our conclusions on them.

Results Behavioral data We first tested for significant differences in pleasantness and health cost ratings between label conditions in the valuation task. We then used logistic regression analysis to test for influences of relative taste pleasantness and relative health costs on food choices in the different label conditions. To assess potential differences in choices for subjectively pleasant but unhealthy foods between label conditions, we defined an appropriate choice index. Effects of label manipulation on pleasantness and healthiness ratings In valuation trials, subjects rated foods as more pleasant (P=0.005, planned t-test after within-subject ANOVA, F(3, 13)=3.14, P=0.025) when exposed to taste labels compared with health labels (Fig. 1B), reflecting the known efficacy of such marketing strategies in enhancing hedonic expectation (Harris et al., 2009). A similar effect was found for attractiveness ratings (P=0.02). Expected pleasantness and attractiveness ratings were strongly correlated in our sample of healthy, nondieting subjects; therefore, we focus our analysis in the present report on expected taste pleasantness. Pleasantness ratings were also higher in the taste label condition compared with a control condition without labels, suggesting that taste labels resulted in enhanced pleasantness evaluations relative to a baseline (Pb 0.03). By contrast, health cost ratings did not differ significantly between label conditions (F(3, 13)=0.68, P=0.56). Despite this non-significant main effect in the ANOVA, we performed a planned paired t-test on healthiness ratings between taste and health label conditions, as this was our main contrast of interest for the fMRI data analysis. No significant difference in health ratings was found between these label conditions. Identical results were obtained if differences in health ratings were assessed separately for healthy food items and unhealthy food items (based on either subject-specific or group average ratings). Thus, taste labels modulated reported hedonic expectations whereas health labels did not influence healthiness evaluations. Effects of label manipulation on choices In choice trials, logistic regressions revealed that decisions were governed by two key variables, the relative difference in expected taste pleasantness between foods (Δtasterel) and the relative difference in health costs (Δhealthrel). In both label conditions, subjects based their choices mostly on Δtasterel (Pb 0.001; t-test for regression coefficient, Fig. 1C), similar to previous findings in healthy, non-dieting adults (Hare et al., 2011). By contrast, Δhealthrel influenced choices only in the health but not taste label condition (P= 0.008; t-test for regression coefficient Fig. 1C, Table 1). This resulted in fewer choices for subjectively pleasant but unhealthy foods when health labels were shown (P= 0.03, paired t-test, Fig. 1D). Logistic regression weights (Fig. 1C) and fits to

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Table 1 Logistic regression of food choice on the taste-based decision variable Δtasterel and on the health-based decision variable Δhealthrel. Beta values (±s.e.m.) for taste-based and health-based regressors are shown for both taste and health label conditions. Chi-square denotes result of omnibus test for significance of model coefficients.

Beta Δtasterel Δhealthrel Constant Summary statistics Chi-square Cox and Snell R2 Nagelkerke R2

Taste label

Health label

1.38 (0.21)⁎⁎⁎ −0.07 (0.07) 0.08 (0.12)

1.49 (0.21)⁎⁎⁎ −0.2 (0.08)⁎⁎ 0.07 (0.12)

98.68⁎⁎⁎ 0.23 0.31

109.41⁎⁎⁎ 0.26 0.35

⁎⁎⁎ Significant at P b 0.001; ⁎⁎ Significant at P b 0.005.

choice data (Fig. 1E) showed that in the health label condition Δtasterel and Δhealthrel had opposite effects on food choice probability. Thus, even though expected taste pleasantness dominated behavior, health labels could bias choices towards healthier foods. Our randomized design precluded that performance of the valuation task systematically influenced performance in the choice task. To confirm this, we performed a supplementary logistic regression analysis in which we tested whether choices between two food items were influenced by the pleasantness or healthiness valuations given on any immediately preceding valuation task trial. For example, if a choice trial was preceded by a valuation trial in which a subjectively pleasant but unhealthy food was presented, this could potentially bias the subsequent choice towards selecting the relatively more pleasant but less healthy food. This supplementary analysis confirmed our main behavioral choice analysis in that differential pleasantness on the current trial was a significant predictor for choice, whereas the pleasantness of the preceding trial showed no significant effect on subsequent choice (P>0.1). Similar results were obtained if the regression model also tested for healthiness on the preceding trial (P > 0.6) or interaction terms between relative pleasantness/healthiness on the present trial and pleasantness/ healthiness of the preceding trial (all P> 0.1). In a further supplementary logistic regression analysis we found that gender or other between-subjects variables did not show significant effects (P> 0.1) when regressing choices on taste- and health-based decision variables. Imaging data To assess the influence of the label manipulation on neural encoding of taste benefits and health costs we first regressed neural activity during food presentation in the valuation task on taste pleasantness and health cost ratings, separately for taste and health label conditions, and then contrasted the resulting regression coefficients. In across-subjects analyses, we related the magnitude of these effects to the behaviorally derived logistic regression betas. We next analyzed the effects of the label manipulation on neural activity during food presentation in the choice task, by regressing activity on taste- and health-based decision variables and confidence ratings. Finally, we assessed label-dependent functional connectivity between pairs of brain areas using PPI analysis. Imaging data: valuation task Effects of label manipulation on taste pleasantness coding To identify the brain mechanisms underlying the observed behavioral effects, we first examined neural activity in the valuation trials. We hypothesized that labels might modulate food evaluations in brain reward structures, similar to modulations induced by other contextual parameters (Bermudez and Schultz, 2010; Grabenhorst and Rolls, 2011; Padoa-Schioppa, 2011; Schultz, 2011). To test this hypothesis, we regressed neural activity on trial-by-trial ratings of expected taste

pleasantness, separately for taste and health label conditions, and contrasted the resulting statistical parametric maps for both label conditions. Consistent with our prediction, in the taste label condition compared to the health label condition we found a stronger effect of expected pleasantness on neural activity in the amygdala (Fig. 2A; MNI coordinates (x, y, z): [22 −4 −20] P=0.02, small volume-corrected), a central component of the brain's emotional and reward systems (Pessoa and Adolphs, 2010; Phelps and LeDoux, 2005; Rolls, 2000; Seymour and Dolan, 2008). No significant effects were found in other brain areas, although the left anterior insula showed a weaker, non-significant effect. This result implicated modulation of neural coding strength in the amygdala as a candidate mechanism by which labels might have influenced pleasantness evaluations. Effects of label manipulation on health cost coding We next reasoned that, despite the absence of any overt effect on health cost ratings, health labels might have induced an implicit (i.e. not behaviorally expressed) bias in neural valuation systems, which might have been related to the behaviorally observed bias towards healthier decisions in the choice task. This conjecture is in line with behavioral evidence that incidental information can implicitly guide behavior, even if it is not reflected in self-reports (Harris et al., 2009; Kahneman and Tversky, 1984). We tested this prediction by regressing neural activity on trial-by-trial health cost ratings, separately for taste and health label conditions, and contrasting the resulting statistical parametric maps. Whole-brain statistical mapping showed a stronger effect of health cost coding in the amygdala when health labels were shown compared to taste labels (Fig. 2B; [18 −6 −22] P = 0.009, whole-brain corrected at cluster level), resembling the effect found for pleasantness coding in the taste label condition. Examination of the time course of this effect suggested that it occurred time-locked to the food picture presentation (Fig. 2C). In addition to the amygdala, and similar to the analysis reported in the previous section, a non-significant effect was present in the left anterior insula (Table 2). However, we did not have strong a priori hypotheses about insula involvement and the effects did not pass our criteria for statistical significance; we therefore consider this finding exploratory and do not base our conclusions on it. Thus, food evaluations in the amygdala were susceptible to both taste and health label manipulations. What is the behavioral significance of the neural health cost bias in the amygdala? Theoretical models of neural decision processes suggest that valuation signals serve as input for decision systems, which translate these valuations into choices. Accordingly, if health labels heightened the amygdala's sensitivity to health costs, this might potentially be reflected in healthier choices. Therefore, we tested in a whole-brain regression analysis whether inter-individual variation in the health cost bias could explain inter-individual variation in healthy choices. Specifically, we entered subject-specific behavioral choice indices (Fig. 1E) as regressors for the first-level (individual subject) contrast maps (Fig. 2B) for the differential healthiness correlation in health and taste label conditions. Across subjects, the magnitude of the health cost bias in the amygdala observed during the valuation task explained variation in behaviorally derived decision weights for health costs in the choice task (Fig. 2D; [20 − 4 − 22] P = 0.002 small volume corrected). The effect was robust even if additional control variables were included as covariates. Control variables included the behaviorally derived logistic regression betas for Δtasterel, the Eating Disorder Examination Questionnaire sum score, an indicator variable for gender, self-reported minutes since the last meal, and body mass index. No correlations between the neural health valuation bias in the amygdala and any of these variables were found. Thus, the label-induced valuation bias in the amygdala, measured in the absence of choice, predicted the extent to which subjects based their decisions on health cost valuations.

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Table 2 Significant results for the fMRI analyses in brain areas with a priori hypotheses (amygdala, ventromedial prefrontal cortex, dorsolateral prefrontal cortex, ventral striatum, see Materials and Methods section), and findings in areas without a priori hypotheses. (Findings in areas without a priori hypotheses are shown for completeness and should be considered as exploratory. None of our conclusions are based on these findings.) Effect Health cost correlation, health label > taste label Taste pleasantness correlation, taste label >health label

Δhealthabs correlation, health label >taste label Δtasteabs correlation, taste label >health label Confidence correlation: negative Cost-benefit correlation

PPI: health label > taste label (seed area: amygdala)

a b c

Brain area a

Amygdala Anterior insulab Amygdalac Hippocampal areab Anterior insulab Anterior insulab Amygdalac Mid-cingulate cortexb Lateral prefrontal cortexc Anterior insulab Amygdalab Ventromedial prefrontal cortexb Ventral striatumc Superior temporal gyrusb Lateral prefrontal cortexc Anterior cingulate cortexb Posterior insulab Parietal operculumb

Laterality

Cluster size (voxels)

x

y

z

Peak z-score

R L R R R L R – L L R L L R L L R R

126 5 22 15 6 17 5 23 118 20 104 7 30 160 91 5 64 18

18 −40 22 12 40 −42 22 0 −32 −28 18 −8 −6 64 −20 −10 36 60

−6 12 −4 −10 10 8 −2 10 22 24 −4 44 18 −4 24 22 −16 −20

−22 12 −20 −22 −6 6 −20 24 34 14 −14 −18 −6 6 34 24 2 28

3.78 3.50 3.56 3.56 3.52 3.35 3.06 3.61 3.17 3.72 3.48 3.09 3.54 3.73 3.50 3.48 3.78 3.30

Whole-brain corrected (P b 0.05, corrected for family-wise error at the cluster level). Exploratory findings: P b 0.001, uncorrected, cluster threshold 5 contiguous voxels. Small volume corrected (P b 0.05, corrected for family-wise error).

Imaging data: choice task Effects of label manipulation on coding of health-based decision variables To examine the neural effects of taste and health labels at the time of decision-making, we regressed activity in the choice task on key decision parameters. During value-based decision-making, neural signals in decision-related brain areas often reflect the absolute value difference between choice options (Grabenhorst and Rolls, 2011; Heekeren et al., 2004, 2006; Hunt et al., 2012; Rolls et al., 2010a, 2010b). This is usually interpreted as the signature of a competitive decision mechanism. Intuitively, the reason for this is that when the evidence in favor of one choice option is strong (i.e. when the absolute value difference between options is large), the population of neurons tuned to represent the evidence for the dominant option will inhibit the neuronal population representing the alternative option more strongly compared to when the evidence is weak (Heekeren et al., 2004; Kim and Shadlen, 1999; Rolls et al., 2010a; Wang, 2002). Results from biophysically realistic computational models and related fMRI investigations suggest that this competitive process at the neuronal level translates into an fMRI signal that scales with the absolute value difference between choice options (Heekeren et al., 2004, 2006; Hunt et al., 2012; Rolls et al., 2010a, 2010b; Ruff et al., 2010; Wang, 2002, 2008). Accordingly, if the amygdala was involved in food decisions in the present study, its activity might encode the relevant decision variable Δhealthabs (i.e. the absolute difference in health costs between foods) more strongly in the health label condition when health evaluations guided choices. We regressed neural activity in the choice task on Δhealthabs separately for taste and health label conditions, and contrasted the resulting statistical parametric maps to test for label-dependent coding of this health-based decision variable. Consistent with our prediction, amygdala activity in the choice task was better related to Δhealthabs when health labels were shown compared to taste labels (Fig. 3A; [22 −2 −20] P = 0.028, small volume-corrected). Across subjects, the strength of this effect explained variation in regression weights for health costs on choices ([16 0 −18] P = 0.030, small volume-corrected), suggesting that the amygdala's coding of the decision variable was behaviorally relevant. A supplementary analysis testing for coding of the relative, i.e. signed, variable Δhealthrel showed no significant effects. Thus, amygdala activity at the time of decision-making encoded a health-based decision variable specifically when health costs guided choices.

Correlates of decision confidence Decision confidence is an important aspect of subjective experience during decision making (Vickers, 1979; Vickers and Packer, 1982). It is well established that subjective decision confidence is related to the discriminability between choice options (Vickers, 1979; Vickers and Packer, 1982), which in our case can be operationalized as Δtasteabs and Δhealthabs. Moreover, in computational modeling it has been shown that the output activity of a biophysically plausible simulated neural choice system is related to the discriminability, i.e. the absolute difference in value, of the choice options, from which a decision confidence signal can be computed (Deco et al., 2007; Insabato et al., 2010; Rolls et al., 2010a). Accordingly, we incorporated a confidence rating scale in the present experiment to assess relationships between neural activity and trial-by-trial changes in subjectively experienced decision confidence. Previous reports of emotional and decision deficits following amygdala lesions indicated amygdala involvement particularly when implicit valuations guide choices (Bechara et al., 1999; Brand et al., 2007). Therefore, to examine relationships between amygdala activity and subjects' experience during decision-making, we regressed activity at the time of choice on the confidence that subjects placed in their decisions. We found that amygdala activity was negatively related to confidence ratings (Fig. 3B; [18 −4 −14] P=0.015, small volume-corrected). Thus, subjects had less confidence in decisions when amygdala activity was high, consistent with existing theories of implicit amygdala involvement in behavior (Adolphs, 2010; Bechara et al., 1999; Dolan, 2007; Pessoa and Adolphs, 2010; Phelps, 2006; Phelps and LeDoux, 2005; Seymour and Dolan, 2008; Sharot et al., 2007). Effects of label manipulation on coding of taste-based decision variables We next tested which brain areas reflected a decision variable based on expected taste pleasantness by regressing neural activity on the taste-based decision variable Δtasteabs (i.e. the absolute difference in expected taste pleasantness between foods), separately for taste and health label conditions, and contrasting the statistical parametric maps. This analysis revealed a significant effect in the DLPFC ([−32 22 34], P = 0.046, small volume corrected). The effect was found in close to the superior frontal sulcus area previously implicated in decision processes in other choice tasks (Basten et al., 2010; Heekeren et al., 2004). However, the effect was partly located in white matter; we are therefore cautious in interpreting this finding.

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Effects of label manipulation on functional connectivity of the amygdala Given that amygdala activity reflected health costs primarily when health labels were shown, the amygdala might also show label-dependent functional connectivity with other brain systems with known roles in decisions. We tested this hypothesis using functional connectivity analysis with the amygdala as seed area. Specifically, we performed a psychophysiological interaction (PPI) analysis in which we tested for stronger correlations between the amygdala and other brain areas in the health label condition relative to the taste label condition. This analysis revealed stronger negative coupling between amygdala and DLPFC when health labels were shown compared to taste labels (Fig. 3C; [−20 24 34] P= 0.021, small volume-corrected). The location of this effect in the superior frontal sulcus region closely matches those from previous studies in which DLPFC encoded a perceptual decision mechanism (Heekeren et al., 2004), as well as the effect reported in the previous paragraph for encoding of a taste-based decision variable. Correlates of a cost-benefit decision variable In addition to the amygdala and DLPFC, the ventromedial prefrontal cortex and striatum are key areas for value-based choice and food valuation and we expected that these areas participate in the present decision task (Basten et al., 2010; Grabenhorst et al., 2008a; Hare et al., 2009, 2011; Linder et al., 2010; Ng et al., 2011; Plassmann et al., 2008). We did not find significant effects in these areas in the analyses reported above. However, as previous studies suggested that the VMPFC might be especially involved in integrating different decision variables (Behrens et al., 2008; Hare et al., 2010), we tested in a supplementary analysis for coding of a cost-benefit decision variable that incorporated both taste pleasantness and health cost evaluations. We found that activity in the striatum in the health label condition showed a significant positive relationship with this cost-benefit variable ([−6 18 −6; Pb 0.05, small volume-corrected]). A similar effect was present in VMPFC (Table 2) but just missed our significance criteria. Discussion We found that simple, incidental labels which promoted the taste benefits of foods similar to marketing strategies, enhanced subjective hedonic expectations and increased the neural encoding of taste pleasantness in the amygdala (Fig. 2A). Similarly, labels which indicated potential health costs of foods in line with health policy aims, increased the neural encoding of health costs in the amygdala (Fig. 2B), even in the absence of changes in reported healthiness judgments. Thus, a novel finding of the present study is that label-based marketing and health policy strategies may influence subjective food evaluations via a common neural mechanism: Depending on the information conveyed by the label such strategies bias the responsiveness of a key component of the brain's valuation system towards either the appetitive, hedonic properties of foods or potential health costs. We suggest that this differential relationship between amygdala activity and taste pleasantness or health costs reflects the biasing of a valuation signal encoded by neuronal populations within the amygdala. Previous neurophysiological studies demonstrated that individual amygdala neurons code the magnitude and economic value of liquid rewards in monkeys (Bermudez and Schultz, 2010; Grabenhorst et al., 2012) and of food rewards in humans (Jenison et al., 2011). Similarly, previous imaging studies reported value-related activity in the amygdala during decision-making (Basten et al., 2010; De Martino et al., 2006) and during the consumption of basic food rewards (Gottfried et al., 2003; Grabenhorst et al., 2010b; Small et al., 2008). These findings suggest a key role for the amygdala in providing value signals which serve as inputs to decision-making. Our present findings suggest that the amygdala's valuation signal can be biased by abstract, linguistic labels to emphasize either the hedonic or health-related aspects of foods. A second novel finding is that the amygdala was involved during decision-making when incidental labels guided food choices. Although

enhanced health cost valuations in the amygdala were not immediately expressed in reported healthiness judgments, the magnitude of this effect predicted behavioral shifts towards healthier choices (Fig. 2D). This may indicate that, rather than passively tracking food evaluations, the amygdala may be actively involved in decision-making about foods. Consistent with this interpretation, at the time of choice, the amygdala encoded a health-based decision variable when health labels were shown (Fig. 3A) and its activity was associated with low confidence in decisions (Fig. 3B). Further, when health labels were shown, amygdala activity showed a negative relationship with the lateral prefrontal cortex (Fig. 3C), an area previously implicated in decision processes. The observed coding of decision variables could indicate amygdala participation in a decision process which translates valuations into choices (Bechara et al., 1999; Brand et al., 2007; Heekeren et al., 2004; Rolls et al., 2010a), although future examination of these signals at better temporal resolution will be important (Hunt et al., 2012). Given the amygdala's function in gating attention towards significant events (Davis and Whalen, 2001; Pessoa and Adolphs, 2010; Phelps, 2006; Phelps and LeDoux, 2005), these effects might also reflect modulation of other brain systems with known roles in decisions, including the prefrontal cortex and striatum (Basten et al., 2010; Hare et al., 2009, 2011; Heekeren et al., 2004; Hunt et al., 2012; Murawski et al., 2012; Sharot et al., 2009, 2010; Wunderlich et al., 2010). In either case, our findings emphasize the amygdala's relevance for human food choice, in addition to its known emotional functions (Adolphs, 2010; Adolphs et al., 1994; Arana et al., 2003; Davis and Whalen, 2001; Dolan, 2007; Pessoa and Adolphs, 2010; Phelps, 2006; Phelps and LeDoux, 2005). Previous studies on food choice used explicit attentional instructions or self-control conditions and reported effects in prefrontal areas rather than the amygdala (Hare et al., 2009, 2011; Siep et al., 2009) (but see (Jenison et al., 2011)). Here we used simple, incidental labels (i.e. without explicit instructions) resembling those used in marketing (Downs et al., 2009; Kiesel et al., 2011) and found that amygdala activity reflected their influence even when reported valuations were unaffected. This fits with evidence from experimental psychology and behavioral economics that incidental cues can implicitly guide behavior (Bargh and Morsella, 2008; Kahneman and Tversky, 1984). Further, the observed negative relationship between amygdala activity and decision confidence is consistent with theories of implicit amygdala involvement in behavior (Bechara et al., 1999; Pessoa and Adolphs, 2010; Phelps and LeDoux, 2005; Seymour and Dolan, 2008). Together with the presently observed inverse relationship between amygdala and prefrontal cortex activity, these observations indicate that amygdala and prefrontal cortex may contribute differently to decision-making, as also suggested by previous imaging and lesion studies (Arana et al., 2003; Bechara et al., 1999). Accordingly, amygdala involvement in food choice may be particularly prominent when implicit information guide valuations and decisions. A recent study found that the amygdala encoded a cost signal during monetary decision-making (Basten et al., 2010). This might suggest that the presently observed health cost coding simply reflected amygdala specialization for negative evaluations in line with traditional views of amygdala involvement in negative emotions. Arguing against this interpretation, we also found that amygdala activity encoded taste benefits when taste labels were shown. Indeed, our findings are consistent with an earlier study by De Martino and colleagues (De Martino et al., 2006). In that study, amygdala activity reflected subjects' monetary decisions to either gamble or play the safe option, depending on the frame that was provided, suggesting amygdala participation beyond cost processing. Other studies demonstrated that individual neurons in the amygdala code both positive and negative value signals (Bermudez and Schultz, 2010; Jenison et al., 2011; Paton et al., 2006), and that the human amygdala responds to both rewarding and aversive food-related stimuli (Gottfried et al., 2003; Grabenhorst et al., 2010b; O'Doherty et al., 2001; Small et al., 2008). Together with these observations, the present findings suggest amygdala involvement in food choice beyond cost

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evaluation per se, potentially emphasizing those characteristics of choice options that are contextually salient (Davis and Whalen, 2001; Pessoa and Adolphs, 2010; Phelps and LeDoux, 2005). Complementary to its well-known role in emotion (Dolan, 2007; Pessoa and Adolphs, 2010; Phelps and LeDoux, 2005; Seymour and Dolan, 2008), the amygdala is part of the neural circuitry involved in food intake (Rolls, 2007; Small, 2009; Volkow et al., 2011; Zald, 2003). In both human and non-human primates, the amygdala encodes sensory details of food, including its taste, odor, flavor, temperature, texture and fat content (de Araujo et al., 2003; Grabenhorst et al., 2010b; Kadohisa et al., 2005; Rolls, 2007; Small et al., 2003, 2005), conditioned associations between food rewards and other stimuli (Gottfried et al., 2003; Rolls, 2000; Seymour and Dolan, 2008), and motivational state (de Araujo et al., 2006; Gottfried et al., 2003; Rolls, 2007; Yan and Scott, 1996). A recent fMRI study revealed a relative specialization of the amygdala for anticipatory chemosensation in the processing of food rewards (Small et al., 2008), suggesting that the amygdala supports affective sensory processes to guide food selection. Our data extend these findings by showing that this evolutionarily ancient brain system, often associated with unconscious information processing (Pessoa and Adolphs, 2010; Phelps and LeDoux, 2005; Seymour and Dolan, 2008), also processes abstract linguistic information about foods, an information source unique to humans and usually linked to explicit thought (Berwick and Chomsky, 2011; Jackendoff, 2002). Together with existing data, our findings indicate that the amygdala processes both basic chemosensory and high-level linguistic information about foods to implicitly guide food intake. Given the amygdala's chemosensory functions (Gottfried et al., 2003; Rolls, 2007; Small et al., 2008; Zald, 2003), it has been suggested that inter-individual differences in amygdala responsiveness to food cues may be important in obesity (Small, 2009). In support of this proposal, we found that inter-individual differences in amygdala responsiveness to health labels predicted behavioral shifts towards healthier foods (Fig. 2C). We acknowledge that the relatively small sample size in the present study may have lead to a higher probability of false positives in the across-subjects analysis. Future studies should therefore test for relationships between amygdala activity and inter-individual differences in reward valuation using larger groups of subjects. We note that our main result in the amygdala survives whole brain correction and matches specific coordinates from prior studies. This indicates that our sample size was adequate to detect effects in our key areas of interest and also that the effects are of considerable strength. Understanding the factors by which labels influence the amygdala's food evaluations across individuals might help to improve the efficacy of food labeling in health policy. What is the nature of the label-induced valuation and decision processes in the amygdala? Amygdala involvement has been demonstrated in such diverse domains as fear memory and recognition (Adolphs et al., 1994; Pessoa and Adolphs, 2010; Phelps and LeDoux, 2005), social interaction (Adolphs, 2010), optimistic expectation (Sharot, 2011; Sharot et al., 2007), and decision-making (De Martino et al., 2006; Roiser et al., 2009). Common to these domains is the modulation of cognition by emotion, a central theme in theories of amygdala function (Bechara et al., 1999; Davis and Whalen, 2001; Pessoa and Adolphs, 2010; Phelps and LeDoux, 2005; Seymour and Dolan, 2008). In the present study, amygdala activity was associated with “cognitive-rational” influences (i.e. guided by healthiness considerations) on reward-dominated decisions. Given the amygdala's primacy for emotion (Davis and Whalen, 2001; Dolan, 2007; Pessoa and Adolphs, 2010; Phelps and LeDoux, 2005; Seymour and Dolan, 2008; Zald, 2003), we speculate that the present effects may well be rooted in affective processes. This interpretation is supported by evidence that human amygdala lesions impair both decision-making and associated somatic-affective states (Bechara et al., 1999; Brand et al., 2007). Further, single neurons in the primate including human amygdala encode different types of valuation signals during economic decision making (Grabenhorst et al., 2012; Jenison et al., 2011). We suggest that the presently observed amygdala coding of taste pleasantness or health costs,

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depending on the label condition, can be interpreted as a food valuation signal that incorporates those characteristics of the foods that are externally cued by the labels. This interpretation is consistent with existing evidence for encoding of framing effects in the amygdala during monetary decisions (De Martino et al., 2006). The detailed mechanism by which economic value representations in the amygdala can be contextually modulated will be an important topic for future investigations. The presently observed effects in the amygdala were particularly prominent in the right hemisphere. Although a potential lateralization of amygdala function is not well understood (Zald, 2003), our findings are consistent with some previous studies in which the right amygdala's response to visual food stimuli was modulated by satiety (Fuhrer et al., 2008), and its taste-related response was attenuated after repeated oro-sensory stimulation with sweet taste (Smeets et al., 2011). We also note that activity in different parts of the amygdala seemed related to different variables. Relationships with subjective decision confidence were observed in relatively more dorsal parts of the amygdala, potentially consistent with a functional specialization for this region in processing of general affective state (Balleine and Killcross, 2006). By contrast, the influence of labels on health cost valuation was expressed in a more ventral region, potentially overlapping with the basolateral complex. This might be consistent with a proposed role for this area in encoding relationships between affective states and the specific sensory features of a stimulus (Balleine and Killcross, 2006). The elucidation of laterality differences and regional functional specializations in the amygdala will be an important topic for further investigation, including studies using high-resolution fMRI specifically targeting the amygdala. A strength of the present study is that the food labels used were directly analogous to those used in food marketing (i.e. emphasizing taste benefits of foods) or in health policy (i.e. emphasizing health benefits or health costs of different foods), and accordingly reflected the opposing aims of marketing and health policy strategies. Critically, our main fMRI data analyses tested for relationships between neural activity and parametric, trial-by-trial variables (ratings and decision variables) and did not test for simple activation differences between taste and health labels conditions. Accordingly, our main fMRI results are influenced by the taste and health labels only in so far as the labels influenced the relationship between the BOLD signal in a given brain structure and the taste or health rating regressors. Thus, our main results cannot be explained by simple differences in activation that resulted from using positive taste labels and both positive and negative health labels. The PPI analysis was not based on parametric ratings or decision variables but instead used trial-by-trial variations in the amygdala BOLD signal; simple activation differences between label conditions may therefore have contributed to the results of this analysis. Our key finding that activity in the amygdala covaried with rated health costs in the health label condition and with rated pleasantness in the taste label condition indicated that our design was sensitive to detect differences in taste/health coding between label conditions. Our findings highlight the potential utility of simple food labels in health policy. Our results indicate that such labels engage a key emotional brain system (Adolphs et al., 1994; Davis and Whalen, 2001; Pessoa and Adolphs, 2010; Phelps and LeDoux, 2005; Seymour and Dolan, 2008; Sharot, 2011) and bias its evaluations towards appetitive or health properties of foods. From a public health perspective, these results lend weight to the efficacy of libertarian-paternalistic policy proposals to guide consumers towards decisions that will ultimately benefit them (Downs et al., 2009; Thaler and Sunstein, 2008). However, they also indicate the need for careful ethical consideration and public disclosure (Thaler and Sunstein, 2008) when using such labels in health policy. Likewise, our findings substantiate demands (Harris et al., 2009) for regulation of specific types of food marketing strategies. Conflict of Interest Statement The authors declare that there is no conflict of interest.

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