Multivariate representation of food preferences in the human brain

Brain and Cognition xxx (2016) xxx–xxx

Contents lists available at ScienceDirect

Brain and Cognition journal homepage: www.elsevier.com/locate/b&c

Multivariate representation of food preferences in the human brain Luca Pogoda a,b,c, Matthias Holzer a,b, Florian Mormann b, Bernd Weber a,b,⇑ a

Center for Economics and Neuroscience, University of Bonn, 53127 Bonn, Germany Department of Epileptology, University Hospital Bonn, 53127 Bonn, Germany c FOM University of Applied Sciences for Economics and Management, 50678 Cologne, Germany b

a r t i c l e

i n f o

Article history: Received 23 March 2015 Revised 2 September 2015 Accepted 31 December 2015 Available online xxxx Keywords: Multi voxel pattern analysis Food preferences Decision making Medial prefrontal cortex Dorsolateral prefrontal cortex

a b s t r a c t One major goal in decision neuroscience is to investigate the neuronal mechanisms being responsible for the computation of product preferences. The aim of the present fMRI study was to investigate whether similar patterns of brain activity, reflecting category dependent and category independent preference signals, can be observed in case of different food product categories (i.e. chocolate bars and salty snacks). To that end we used a multivariate searchlight approach in which a linear support vector machine (l-SVM) was trained to distinguish preferred from non-preferred chocolate bars and subsequently tested its predictive power in case of chocolate bars (within category prediction) and salty snacks (across category prediction). Preferences were measured by a binary forced choice decision paradigm before the fMRI task. In the scanner, subjects saw only one product per trial which they had to rate after presentation. Consistent with previous multi voxel pattern analysis (MVPA) studies, we found category dependent preference signals in the ventral parts of medial prefrontal cortex (mPFC), but also in dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (dlPFC). Category independent preference signals were observed in the dorsal parts of mPFC, dACC, and dlPFC. While the first two results have also been reported in a closely related study, the activation in dlPFC is new in this context. We propose that the dlPFC activity does not reflect the products’ value computation per se, but rather a modulatory signal which is computed in anticipation of the forthcoming product rating after stimulus presentation. Furthermore we postulate that this kind of dlPFC activation emerges only if the anticipated choices fall into the domain of primary rewards, such as foods. Thus, in contrast to previous studies which investigated preference decoding for stimuli from utterly different categories, the present study revealed some food domain specific aspects of preference processing in the human brain. Ó 2016 Published by Elsevier Inc.

1. Introduction In classical economics, preferences are usually determined by a revealed preference approach, e.g. by performing choices between alternatives (Von Neumann & Morgenstern, 1944). Related to this, humans facing a binary choice are often assumed to make their decisions based on the computation of subjective values (SV). SVs are proposed to serve as a common currency that allow the comparison of complex and qualitatively different alternatives on a common scale (Bartra, McGuire, & Kable, 2013; Kahneman & Tversky, 1979; Samuelson, 1937). To be more precise, during decision making a scalar subjective value is assumed to be computed for each alternative first, and the one with the greatest SV will be chosen subsequently. One major goal in decision neuroscience is ⇑ Corresponding author at: Center for Economics and Neuroscience, University of Bonn, Nachtigallenweg 86, 53127 Bonn, Germany. E-mail address: [email protected] (B. Weber).

to investigate the underlying neural computations and processes of subjective values and revealed preferences. Functional MRI studies using a univariate approach have consistently revealed the medial prefrontal cortex (mPFC) to be highly involved in the computation of value related and preference related signals (Bartra et al., 2013; Chib, Rangel, Shimojo, & O’Doherty, 2009; FitzGerald, Seymour, & Dolan, 2009; Kim, Shimojo, & O’Doherty, 2011; Lebreton, Jorge, Michel, Thirion, & Pessiglione, 2009; Levy & Glimcher, 2011; Lin, Adolphs, & Rangel, 2012; Peters & Büchel, 2010; Smith et al., 2010). Furthermore, these studies revealed an overlap of value related activation for different types of stimuli (e.g. consumer goods, monetary rewards, social rewards), especially in the ventral parts of mPFC which has led to the hypothesis that a common value representation for objects from different categories might be computed in the ventro medial prefrontal cortex (vmPFC). Multi voxel pattern analysis approaches (MVPA) were used to provide further evidence for this hypothesis (Pereira, Mitchell, & Botvinick, 2009). The multivariate

http://dx.doi.org/10.1016/j.bandc.2015.12.008 0278-2626/Ó 2016 Published by Elsevier Inc.

Please cite this article in press as: Pogoda, L., et al. Multivariate representation of food preferences in the human brain. Brain and Cognition (2016), http:// dx.doi.org/10.1016/j.bandc.2015.12.008

2

L. Pogoda et al. / Brain and Cognition xxx (2016) xxx–xxx

approach enabled to investigate whether preferences for products/ objects/activities belonging to identical or different categories can be predicted significantly above chance from the coordinated activation of multiple voxels in certain regions of interest. The first MVPA study in this field, conducted by Tusche, Bode, and Haynes (2010), revealed that consumer choices for different types of cars can be predicted from voxel clusters in mPFC (dorsal and ventral parts), occipital areas and the insula. However, this study allowed only inferences about the involvement of certain brain regions in case of a within category prediction because only one product class (i.e. cars) was used. Another MVPA study, conducted by McNamee, Rangel, and O’Doherty (2013), investigated whether a classifier cannot only decode stimulus/category dependent value representations but also across category value patterns by using a monetary valuation/bidding paradigm. To test for the presence of across category value signals, the classifier was trained to decode SVs from samples drawn from one of three stimulus categories (i.e. food, money, noncomestible consumer items) and tested in predicting the SV of the stimuli from the remaining two categories. In order to test for category dependent value codes, the classifier was trained and tested on stimuli of the same category. Data analysis revealed across category value patterns in the inferior parts of vmPFC which is also referred to as medial orbitofrontal cortex (mOFC) by some authors. In contrast, category dependent value codes were found in the superior regions of the vmPFC (i.e. the area above the mOFC). The finding of value related signals in the OFC was also confirmed by a MVPA study conducted by Kahnt, Park, Haynes, and Tobler (2014), which primarily focused on disentangling neural representations of value and salience. The most recent MVPA study in this field, conducted by Gross et al. (2014), dealt with the question whether individual preferences can be predicted across fundamentally different categories (i.e. snack foods, engaging activities) in the absence of monetary evaluation. To that end, subjects were presented with different activities and snack foods in written form during fMRI scanning and instructed to imagine the pleasure they would derive from eating the snack item or engaging in the activity. Data analysis located voxels carrying value signals across categories in the anterior and dorsal parts of mPFC (bilateral) as well as in the anterior cingulate cortex (ACC). Most interestingly, no cluster was found in the ventral parts of the mPFC. The finding that preferences can be predicted for different types of objects/stimuli by functional MRI is very promising for clinical and neuro commercial applications. Especially the domain of predicting food preferences might be very useful from a clinical point of view in the domain of obesity research/prevention and should also be of a high interest in the domain of consumer neuroscience. However, previous MVPA studies did not focus on the prediction of food related value/preference signals per se but rather on more global aspects of value computation and preference prediction across utterly different stimulus categories. Because foods constitute primary rewards being linked to gustatory perception and serve the intake of nutrients and electrolytes, there might be some domain specific characteristics in neuronal processing that might not emerge in case of across category predictions beyond the domain of food products. Thus, in the present study we wanted to investigate whether similar patterns of brain activity can be observed in case of different food products that are of more similar category than the classes of stimuli used in previous studies. To that end we used a multivariate whole brain searchlight approach (Kriegeskorte, Goebel, & Bandettini, 2006): In a first step participants were asked for their preferences regarding 20 different chocolate bars and 20 different salty snacks by means of a computer based pretest. In a second step, a classifier was trained to decode product preferences from fMRI data recorded during the presentation of preferred and non-

preferred chocolate snacks. In a third step, we assessed the classifier’s performance in predicting preferences for products of the same category (within category prediction) as well as for products belonging to a very similar, although different, category (i.e. salty snacks; across category prediction). Voxel clusters having a predictive power above chance level in case of the within category prediction are assumed to reflect category dependent value/ preference signals whereas predictive clusters in case of the across category classification indicate category independent preference signals, as put forward by previous studies (Gross et al., 2014; McNamee et al., 2013). However, we also applied an even stronger criterion for identifying category independent preference signals by selecting only those voxels showing a predictive power significantly above chance in case of the within and the across category prediction (i.e. voxels predicting preferences in case of both conditions significantly above chance no matter which stimulus class the classifier was initially trained on). 2. Methods 2.1. Participants Sixteen healthy subjects (i.e. no neurological, psychiatric or gastrointestinal diseases, no eating disorders) participated in the experiment (6 female; mean age = 23.93; SD = 4.13). The mean body weight across all participants was 70.18 kg (SD = 11.96). The average body height was 1.74 m (SD = 0.09). This resulted in a mean body mass index (BMI) of 22.99 (SD = 2.23). All participants were neither vegans nor did they report any food allergies or dietary restrictions. The experiment was conducted with the understanding and the written consent of each participant. The study conformed with the Declaration of Helsinki and was approved by the local ethics committee of the University of Bonn. 2.2. Experimental paradigm Participants were asked for their preferences for 20 different chocolate bars and 20 different salty snack products in the absence of monetary evaluation. This was done by means of two computer based pretests (one for the chocolate bars and one for the salty snacks) in which our participants had to perform binary choices between all items, presented pairwise in all possible combinations, leading to 190 choices in each of the two categories (Fig. 1A). All stimuli were color images of sweet and salty food products in the original packaging (i.e. as they are sold in the store) presented in front of a white background (see also Fig. 1A for examples). Since we were interested in preference/value decoding, we instructed our participants explicitly to choose the product they liked more and which they considered to be more pleasant (for a similar approach see Gross et al., 2014). In order to enhance the authenticity of the decision making process, subjects were informed about receiving one product of each category after the experiment (these two products were randomly drawn from the participant’s choices). A preference ranking list was calculated separately for each product category based on the results from the corresponding binary choice task. From both ranking lists, the five highest and the five lowest ranked products were selected as stimuli for the subsequent fMRI experiment (Fig. 1B). In the MRI scanner, participants were presented with only one product at a time which lasted for three seconds. After a jittered delay of 2–4.5 s, a response screen was displayed for five seconds in which the participants had to perform a liking rating on a scale from one to four (Fig. 1C). The order of the numbers on the response screen was randomized, to control for preparation effects.

Please cite this article in press as: Pogoda, L., et al. Multivariate representation of food preferences in the human brain. Brain and Cognition (2016), http:// dx.doi.org/10.1016/j.bandc.2015.12.008

L. Pogoda et al. / Brain and Cognition xxx (2016) xxx–xxx

3

Fig. 1. Experimental design. (A) Subjects performed two binary choice tasks, one comprising 20 chocolate bars, and one comprising 20 salty snacks. (B) Preference rankings were calculated from the results of the binary choice tasks. The five highest and the five lowest-ranked products were selected and categorized as preferred and nonpreferred, respectively, for subsequent classification. (C) The products thus selected were presented in the MRI-scanner. After product presentation, the subjects had to rate the product on a randomly ordered scale from one to four. The fMRI paradigm consisted of four runs with chocolate bars and one run with salty snacks. Within each run, products were presented three times.

Importantly, the fMRI data of the liking rating were not used for MVPA classification. The liking rating was only introduced to ensure that the preferences measured previously to the experiment persisted during the scanning session. Between the trials, a fixation cross was displayed for 1.5–4 s. The whole fMRI experiment consisted of four runs with ten different chocolate bars presented three times each and one run with ten different salty snacks presented three times, enabling a within category prediction for the chocolate category as well as an across category prediction for the salty snack condition (i.e. training the classifier on the chocolate category and testing it separately on the products from the chocolate and salty snack category). Each run was separated by a time interval of 35 s. The reason for refraining from a ‘‘full” prediction approach (i.e. training also on the salty condition and testing on the salty as well as on the chocolate condition), was that the additional time needed for fMRI data acquisition would have extended the imaging session from 40 to 80 min, which we considered too long for our subjects to stay sufficiently attentive during the whole experiment. For the same reason we refrained from including additional conditions with non-food products, which might have been useful for comparing food preference decoding with the prediction of preferences across utterly different categories. 2.3. Analysis of behavioral data 2.3.1. Computer based preference ranking The relation between reaction time and difference in rank (DRank) was analyzed on the group level (i.e. by pooling the data from all subjects) separately for each product category using a Spearman rank correlation. 2.3.2. Product ratings in the scanner The difference in rating for preferred and non-preferred products was verified for later classification by applying a paired ttest on the averaged ratings for preferred and non-preferred products of each subject. Furthermore, the difference in reaction time for products rated as ‘‘preferred” and ‘‘non-preferred” during the scanning was analyzed by applying a paired t-test on the averaged reaction times for preferred and non-preferred products of each subject.

case, a Spearman rank correlation was applied to the pooled data from all subjects, but separately for each product category. In the latter case, a Spearman rank correlation was calculated separately for each subject and each product category. Statistical significance was then determined by testing the resulting correlation coefficients individually against the null hypothesis by means of a tstatistic. 2.4. Image acquisition Subjects were placed into a 3 Tesla Siemens Trio Scanner with an 8-channel head coil. fMRI data were acquired using an echo planar imaging (EPI) sequence with a repetition time (TR) of 2.5 s and an echo time (TE) of 30 ms. Each recorded volume comprised 37 ascending measured slices in axial orientation (3 mm isotropic voxels, matrix size 64  64). The onsets of all experimental trials were synchronized with the scanner pulse. 2.5. Preprocessing of fMRI data The fMRI data were realigned and normalized to MNI space using SPM8. No spatial smoothing was applied in order to retain the high spatial resolution. A brain mask was created on group level (using the FSL brain extraction tool; FMRIB Software Library v5.0), based on averaged volumes from all subjects, and used to remove voxels lying outside our subjects’ brains from the data. Next, temporal detrending and z-scoring procedures were applied separately for each run. (No GLM modeling was performed, because we considered preference decoding based on the ‘‘raw data” to be less susceptible for possible ‘‘peek picking” effects induced by statistical modeling; see also: Poline & Brett, 2012). Afterwards, the volumes recorded two TRs after trial onset were selected for preference decoding. To be more precise, only the third volume recorded after stimulus presentation was used for MVPA classification. Thus, the information of the rating was not included in the data used for the classification. Thereby we intended to ensure that the decoding of preference related activation patterns was not influenced by the rating task. Trials in which no rating was entered (on average 4% of all trials per subject) were excluded from subsequent analyses. 2.6. Multi voxel pattern analysis

2.3.3. Correlation between preference ranking and product rating We analyzed the relation between preference ranks and liking ratings on group as well as on single subject level. In the former

A multivariate searchlight approach (Kriegeskorte et al., 2006) was applied to locate clusters of voxels predicting preferences for

Please cite this article in press as: Pogoda, L., et al. Multivariate representation of food preferences in the human brain. Brain and Cognition (2016), http:// dx.doi.org/10.1016/j.bandc.2015.12.008

4

L. Pogoda et al. / Brain and Cognition xxx (2016) xxx–xxx

chocolate bars (within category prediction) and salty snacks (across category prediction) above the chance level of 50% separately for each subject. To that end, a sphere with a radius of four voxels was constructed around each voxel in the subject’s brain. Within each sphere, a linear Support Vector Machine (l-SVM; for further details on l-SVM see for example: Bishop, 2006; Mur, Bandettini, & Kriegeskorte, 2009) was trained to classify the products from the chocolate condition into preferred vs. non-preferred items (based on the results from the computer based pretest). Only three of the four chocolate runs were used for training. The remaining chocolate run was used to test the classifiers performance regarding the within category prediction while the salty snack run was used to assess the classifiers performance in case of across category prediction. This training–testing procedure was embedded into a 4-fold cross-validation in which 3 chocolate runs were used as training data in each cross validation step (‘leave one run out cross-validation’; Etzel, Valchev, & Keysers, 2011). To be more precise, each run consisted of 30 trials (15 trials with preferred products and 15 trials with non-preferred products). In each trial, we used the third volume recorded after stimulus onset for classification. Thus, 90 volumes were used for training and 30 volumes were used for testing in each cross-validation step. Because our subjects failed to rate some of products we removed those non-responding trails from the data in a balanced fashion (i.e. if one trial corresponding to a preferred product was removed, we also removed one trial of a non-preferred product in order to keep the balance and avoid classification biases). Classification was done using the Python based PyMVPA and Sklearn toolboxes (Hanke et al., 2009; Pedregosa & Varoquaux, 2011). The single subject accuracies resulting from the searchlight analysis were tested at the group level (voxelwise and separately for both conditions) against the statistical chance level of 50% by using a one-sided one sample t-test (no smoothing of the accuracy maps was applied). Only voxels with a mean accuracy above 50% (at group level) were tested for statistical significance, because accuracies below chance level are neither of interest nor interpretable. As a consequence, only a minority of the voxels in the accuracy maps were tested for statistical significance (i.e. 40,375 of 70,359 voxels in case of the within condition and 26,271 of 70,359 voxels in case of the across condition), resulting in a smaller number of independent statistical tests compared to conventional univariate fMRI analyses. To correct for multiple comparisons a cluster-sizedthresholding approach was used instead of voxelwise FWER or FDR procedures (see e.g.: Forman et al., 1995; Huettel, Song, & McCarthy, 2004; Xiong, Gao, Lancaster, & Fox, 1995). Since each voxel in the accuracy map represents the multivariate activation of 257 neighboring voxels, we considered a correction for multiple comparisons by means of cluster-sized-thresholding as most appropriate. To that end the voxel level threshold was set to aV = 0.05 (uncorrected) meaning that only voxels with p 6 0.05 were considered. Next, the cluster-sized-threshold was set to 5 voxels resulting in an alpha value for each cluster of aC = 0.055 = 3.31 ⁄ 10 7 meaning that only clusters of 5 voxels with pCluster 6 3.31 ⁄ 10 7 were considered as statistical significant. By applying this cluster threshold of 5 voxels, the expected number of false positive clusters in the data (ffp) equals 0.0016 in case of the within condition and 0.0001 in case of the across condition and can be calculated by the following formula: ffp = aC ⁄ nC where nC is the number of clusters in the data. The probability of having none false positive results in the data (pfp) can be calculated by the following formula: pfp = (1 aC)nc. In case of the within condition pfp equals 0.9983 whereas in case of the across condition pfp equals 0.9998. Thus, although a cluster level threshold of 5 voxels might appear quite liberal at first sight, the information stated above clearly indicate that a cluster level threshold of 5 voxels is

sufficiently strict to correct for multiple comparisons in the present study. 2.7. Identifying brain regions involved in within or across category decoding In order to identify functional brain regions involved in the prediction of preferences within or across category, the thresholded datasets from the searchlight analysis were segmented into Brodmann areas using the ‘‘Brodmann Areas Template” from MRIcro. Brodmann areas with less than 15 active voxels were considered to be not sufficiently involved in the decoding of preference related information. This identification of functional brain regions was done separately for the thresholded dataset from the within category condition and for the thresholded dataset from the across category prediction. For brain regions revealing more than 15 active voxels in case of both decoding conditions, we performed a voxelwise conjunction analysis (Nichols, Brett, Andersson, Wager, & Poline, 2005) in order to test whether different or identical voxels were involved in the within and across category prediction. By means of the conjunction analysis, a certain voxel was selected only if this voxel had a predictive power significantly above chance in case of the within and the across category condition (logical ‘AND’). Consequently, a voxel was disregarded by the conjunction analysis if activation was observed only in one of both decoding conditions. 3. Results 3.1. Behavioral data 3.1.1. Computer based preference ranking For both product categories we found a high negative correlation between reaction time and difference in rank (chocolate/ snacks: q = 0.96/ 0.90, p = 1.71 ⁄ 10 11/9.96 ⁄ 10 8). Thus, choices between products with similar preference ranks required longer reaction times, than choices between products which were more distinct in their ranking. 3.1.2. Product ratings in the scanner As expected, there was a significant difference between the averaged ratings of preferred products (chocolate/snacks: M = 1.3/1.3, SD = 0.31/0.14) and non-preferred products (chocolate/snacks: M = 3.5/3.4, SD = 0.3/0.2) in case of both product categories (chocolate/snacks: t = 24.62/ 23.79, p = 1.53 ⁄ 10 13/ 2.52 ⁄ 10 13). In contrast, the difference between the averaged reaction times of preferred (M = 1082.98, SD = 173.16) and nonpreferred products (M = 1056.88, SD = 168.32) was not significant (t = 1.61, p = 0.12). 3.1.3. Correlation between preference ranking and product rating As expected, we found a high positive correlation between the computer based preference ranking and the liking rating during the fMRI scanning session in case of both product categories (single subject level chocolate/snacks: all q P 0.73/0.75, all p 6 0.01/0.01; group level chocolate/snacks: q = 0.86/0.86, p = 7.79 ⁄ 10 49/ 3.1 ⁄ 10 48). 3.2. fMRI data 3.2.1. Within category prediction With regard to prefrontal areas, the biggest clusters were found bilateral in the dorsolateral prefrontal cortex (dlPFC). In the left dlPFC a cluster of 94 voxel was found in BA9 (MNI 21, 44, 37; Fig. 2A) while a cluster of 61 voxel was found in BA46 (MNI 27,

Please cite this article in press as: Pogoda, L., et al. Multivariate representation of food preferences in the human brain. Brain and Cognition (2016), http:// dx.doi.org/10.1016/j.bandc.2015.12.008

L. Pogoda et al. / Brain and Cognition xxx (2016) xxx–xxx

5

Fig. 2. Results of the within-category prediction. A multivariate searchlight approach with a sphere of 4 voxels was used to locate clusters of voxels predicting preferences for chocolate bars. The classifier was initially trained on products belonging to the same category (i.e. chocolate bars). Voxels of the resulting searchlight maps were tested at the group level against the statistical chance level of 50% by means of a one-sample t-test and corrected for multiple comparisons by means of cluster-sized thresholding. Brain regions (i.e. Brodmann Areas) with at least 15 active voxels were considered to be involved in the computation of preference-related signals. Those voxels are depicted in the figure, while different colors were used in order to indicate different Brodmann Areas. Although parts A–H highlight the observed activation in the prefrontal regions, they also show a large activation in posterior brain regions, which we assume to reflect visual information related to stimulus processing. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

41, 28; Fig. 2B). In right dlPFC a cluster comprising 88 voxels was located in BA9 (MNI 33, 38, 43; Fig. 2C) and 30 voxels showed activation in BA46 (MNI 39, 44, 34; Fig. 2D). In the left ventromedial prefrontal cortex (vmPFC) a voxel cluster was found in the upper anterior parts of BA11 (MNI 3, 62, 11; Fig. 2E) extending into the neighboring ventromedial regions of BA10 (MNI 9, 65, 1; Fig. 2F). Furthermore, this cluster was not solely limited to the medial parts of both Brodmann areas but extended to the more lateral regions of BA10 (MNI 15, 62, 7) and BA11 (MNI 18, 62, 2). In total, the cluster in BA 10 comprised 40 voxels while 21 active voxels were found in BA11. With regard to right vmPFC a cluster of 19 voxels was found in the medial anterior parts of BA11 (MNI 3, 50, 20; Fig. 2G). Another cluster of 21 voxels was found in the right dorsal anterior cingulate cortex (dACC, BA32, MNI 6, 44, 19; Fig. 2H). Beyond the prefrontal regions, activation was also found in somatosensory and motor areas as well as in regions associated with higher level visual processing (see Table 1 and Fig. 2A–H).

3.2.2. Across category prediction Predictive clusters were found almost exclusively in prefrontal regions, except for some minor activations in regions involved in somatosensory, motor and visual processing (see Table 1). Activation was found bilaterally in the dACC (BA32). On the left side a

cluster comprising 32 voxels was located in more inferior regions of BA32 (MNI 6, 50, 22, Fig. 3A) compared to the activation found in the right dACC (MNI 6, 23, 43; Fig. 3B) which was located in more superiorcaudal areas of BA32 comprising 18 voxels. A second cluster was located bilaterally in dorsal parts of the mPFC (BA8). The cluster on the left side comprised 30 voxels in the medial parts of BA 8 (MNI 6, 32, 46; Fig. 3C). The activation of 17 voxels found on the right side within the medial parts of BA 8 (MNI 6, 32, 46; Fig. 3D) bordered on the cluster found in the right dACC. A third cluster was found in left dlPFC. To be more precise, 24 voxels were active in BA9 (MNI 21, 38, 43; Fig. 3E) while 15 voxels were located in BA46 (MNI 36, 20, 43; Fig. 3F).

3.2.3. Conjunction analysis The results from the within and the across category prediction revealed that the left dlPFC and the right dACC were the only prefrontal regions showing activation under both decoding conditions. In order to test whether different or identical voxels were involved in the within and across category prediction we performed a voxelwise conjunction analysis within these regions. With regard to left dlPFC the conjunction analysis revealed 13 voxels to be active under both conditions: 4 voxels were located in left middle frontal gyrus within BA 46 (MNI 30, 41, 28; Fig. 4A) while the remaining 9 voxels were located in left superior frontal gyrus within BA 9

Please cite this article in press as: Pogoda, L., et al. Multivariate representation of food preferences in the human brain. Brain and Cognition (2016), http:// dx.doi.org/10.1016/j.bandc.2015.12.008

6

L. Pogoda et al. / Brain and Cognition xxx (2016) xxx–xxx

Table 1 Brain regions with more than 15 voxels predicting preferences within or across food categories significantly above chance. Brain regions

BA

Number of voxels Within category

Across

L

R

L

41

23 210

category Somatosensory areas Motor areas Visual areas

Parietal–Temporal–Occipital association areas (PTO) Ventral mPFC Dorsal ACC Dorsal mPFC dlPFC Ventral PCC Other

3 7 4 6 17 18 19 20 37 39 40 10 11 32 8 9 46 23 36 48

464 929 373 16 59 32 40 21

94 61 32

20 31

42 271 942 750 22 274 130 176 19 21 88 30 16 17

R

16 39

32 30 24 15

18 17

26

(MNI 21, 38, 40; Fig. 4B). For the right dACC we found no overlapping activation. Thus, different voxels were involved in the within and the across category decoding in the right dACC. 4. Discussion With the present MVPA study we wanted to investigate whether similar patterns of brain activity can be observed while subjects evaluate food products from different categories. To that end we used a multivariate searchlight approach in which a linear support vector machine (l-SVM) was trained to distinguish preferred from non-preferred chocolate bars and subsequently tested its predictive power in case of chocolate bars (within category prediction) and salty snacks (across category prediction). The within category prediction revealed areas in the vmPFC (bilateral), right dACC and dlPFC (bilateral) to be involved in the processing of preference related information in a category dependent manner. The finding of category dependent activation in the orbitofrontal parts of vmPFC (i.e. BA11) is consistent with the results from the MVPA study by McNamee et al. (2013) in which the mOFC was identified to encode category dependent values for food and trinkets. However, McNamee et al. (2013) reported more posterior regions of the mOFC to exhibit food dependent value coding whereas in our case activation was found in more anterior regions of the mOFC. Similar category dependent activations to those found in the left vmPFC at the border of BA10 and BA11 were reported by the MVPA study from Tusche et al. (2010) in which consumer choices were predicted for different types of cars. Although, Tusche et al. (2010) located the center of activation in the medial regions of BA10, slightly more superior compared to our results. The finding of within category activation in right dACC has not been reported in previous studies in humans. However, single-cell recordings in monkeys revealing value signals in the ACC seem to support the view that the dACC might be involved in the computation of category dependent value signals (Cai & Padoa-Schioppa, 2012; Kennerley, Behrens, & Wallis, 2011; Wallis & Kennerley, 2010). Similar to related searchlight studies (Tusche et al., 2010; Van der Laan, De Ridder, Viergever, & Smeets, 2012), we found a large

activation in visual areas, the PTO and neighboring areas of posterior parietal cortex (esp. BA 7). Since this large pattern of activation did not emerge in case of the across category decoding, we assume, that these activations mainly reflect category dependent information which can be used for preference decoding. In this context we explicitly use the term ‘‘preference decoding” and not ‘‘value decoding” in order to emphasize that the prediction of preferences in these areas is probably not based on value signals computed within these regions but rather on effects of adaptive coding/processing of visual stimuli guided by top down modulation from prefrontal areas (similar to Duncan, 2001, 2010, 2013). To be more precise, we assume that different aspects of visual stimuli are processed in each brain region (i.e. physical properties of stimuli in early areas; object binding, identification and related aspects in higher perceptual regions) and that the modulatory influences on these processes may contain some value related information which were initially computed in the prefrontal regions. In our study, the classifier was capable to extract this information about stimulus value transmitted by the modulatory signals from the prefrontal cortex. Several MVPA studies investigating stimulus processing in the brain support this assumption: First, Li, Ostwald, Giese, and Kourtzi (2007) revealed that the human brain uses a flexible code across all stages of sensory processing in order to establish an adaptive categorization of visual stimuli which is most probably orchestrated by the prefrontal cortex. To be more precise, they found that activation patterns in lower visual areas are shaped to reflect physical similarity between stimuli while multi voxel patterns in higher visual areas (esp. lateral occipital complex, LOC and intraparietal sulcus, IPS) are especially shaped by categorizations based on visual form (LOC) and visual motion (IPS). Second, Woolgar, Williams, and Rich (2015) demonstrated that top-down allocated attention enhances the multi voxel representation of visual stimuli in higher and lower perceptual areas. The MVPA patterns in early visual areas seem to reflect the stimulus’ features (and not the stimulus as a coherent object) because these patterns became weaker when the stimuli were degraded in quality (and were thus hard to perceive). In contrast, the MVPA patterns in higher visual areas were not only sensitive to the allocation of attention but also robust to stimulus degradation indicating object recognition. Third, Kahnt et al. (2014) revealed that information about stimulus value and salience could be decoded from multi voxel patterns in the posterior parietal cortex (in humans), which is well known to guide saccadic eye movements associated with reward in primates (esp. the lateral intraparietal area, LIP; Louie & Glimcher, 2010; Platt & Glimcher, 1999; Sugrue, Corrado, & Newsome, 2004; Yang & Shadlen, 2007). Area LIP is highly connected with the frontal eye fields (FEF) in the prefrontal cortex which is known to be highly involved in top-down modulation (see for example: Ibos, Duhamel, & Ben Hamed, 2013). The across category prediction revealed significant activation in more dorsal parts of the mPFC (bilateral), dACC (bilateral) and left dlPFC. The first two findings are highly consistent with the MVPA results from Gross et al. (2014) in which individual preferences were predicted in the absence of monetary evaluation across fundamentally different categories (i.e. snack foods, engaging activities). Also in accordance with the results from Gross et al. (2014), we found no multivariate activation in the ventral parts of the mPFC which has frequently been implicated to be involved in the computation of stimuli independent value signals by univariate fMRI studies (Chib et al., 2009; Levy & Glimcher, 2011). In contrast to our results and those from Gross et al. (2014), the closely related MVPA study from McNamee et al. (2013) found category independent value signals in the vmPFC but not in the dorsal regions of mPFC (dmPFC). This difference might be attributed to the fact that McNamee et al. (2013) used a monetary bidding paradigm for value decoding and classification. In contrast, our study as well

Please cite this article in press as: Pogoda, L., et al. Multivariate representation of food preferences in the human brain. Brain and Cognition (2016), http:// dx.doi.org/10.1016/j.bandc.2015.12.008

L. Pogoda et al. / Brain and Cognition xxx (2016) xxx–xxx

7

Fig. 3. Results of the across-category prediction. Same as Fig. 2, but for across-category predictions. Panels A–F highlight the activation observed in different regions of the prefrontal cortex. In contrast to the within-category prediction (see Fig. 2), the figure reveals activation in prefrontal regions only and not in visual areas – most probably because the information encoded in the visual areas is not relevant for across-category decoding.

as Gross et al. (2014) refrained from using any form of monetary evaluation. Comparing the activations found in dACC and dmPFC in case of the across category decoding with the results from the within category prediction reveals, that only the right dACC was active in case of both conditions. In order to assess whether identical voxels were active in the right dACC during both conditions, we conducted a voxelwise conjunction analysis which denied this assumption. Thus, mutually exclusive voxels in the right dACC encode either within or across category value signals. As already mentioned, we also found activation in the dorsolateral prefrontal cortex (dlPFC) for both decoding conditions. To be more precise, we found a bilateral activation of dlPFC for the within category decoding whereas only the left dlPFC was involved in the across category classification. The conjunction analysis revealed that 13 voxels in left dlPFC were activated in case of both decoding conditions. Thus, the dlPFC revealed (a) mutually exclusive voxel clusters encoding preference related information either within or across category and (b) voxels clusters representing relevant information about preferences under both decoding conditions. From our point of view, voxels showing activation under both decoding conditions might be considered to fulfill the quality of representing category independent preference signals in an even stronger sense than voxels responding solely to across category

classification, because in the former case multi voxel clusters are able to predict preferences significantly above chance no matter on which stimuli class the classifier was initially trained on (which does not hold for voxels responding solely to across category decoding). Although we found voxels encoding within category, across category, and category independent information in dlPFC, we do not think that these signals represent the computation of subjective values per se, which we assume to take place in the ventral and dorsal regions of the mPFC as already discussed in the previous sections. The activation in dlPFC could reflect choice related signals which are computed in anticipation of the forthcoming liking rating (that our subjects had to pass several seconds after stimulus presentation) and which have a modulatory influence on the value computation/representation in mPFC. Several studies investigating the role of dlPFC in decision making support this view: First, neurophysiological recordings from the dlPFC of macaque monkey indicate, that dlPFC neurons encode several kinds of future information including the delivery of rewards (Kobayashi, Lauwereyns, Koizumi, Sakagami, & Hikosaka, 2002; Leon & Shadlen, 1999; McClure et al., 2004; Roesch & Olson, 2003; Tsujimoto & Sawaguchi, 2005; Wallis & Miller, 2003), forthcoming actions (Asaad, Rainer, & Miller, 1998; Hasegawa, Sawaguchi, & Kubota, 1998) and anticipated visual objects (Rainer, Rao, &

Please cite this article in press as: Pogoda, L., et al. Multivariate representation of food preferences in the human brain. Brain and Cognition (2016), http:// dx.doi.org/10.1016/j.bandc.2015.12.008

8

L. Pogoda et al. / Brain and Cognition xxx (2016) xxx–xxx

Fig. 4. Results of the conjunction analysis. For brain regions with more than 15 active voxels in both decoding conditions (i.e. left dlPFC, right dACC) a voxelwise conjunction analysis was applied to test whether identical or different voxels were involved. Voxels active during within and across-category decoding (depicted in red) were found exclusively in the left dlPFC comprising BA46 (A) and BA9 (B), but not in right dACC. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Miller, 1999). Second, studies applying transcranial stimulation methods to the dlPFC revealed that this regions is causally involved in the modulation of goal values during decision making. To be more precise, the application of rTMS (repetitive Transcranial Magnetic Stimulation) to dlPFC was shown to cause a significant decrease in the values assigned to various food stimuli (chips and candy bars) at the time of choice (Camus et al., 2009). In addition, the stimulation of dlPFC with tDCS (transcranial direct current stimulation) modulated food cravings as well as food consumption (Fregni et al., 2008). Third, a univariate fMRI study from Liu, Feng, Wang, and Li (2012) revealed distinct neuronal responses during the stage of subjective valuation and the following point of choice in an intertemporal choice tasks. Activation during the valuation stage was found in mesolimbic projection regions (esp. vmPFC, inferior frontal gyrus, posterior cingulate cortex) whereas activation during the choice process was found in lateral cortical regions including dlPFC and inferior frontal gyrus. In this context, we propose similar to Camus et al. (2009) that the modulatory role of dlPFC in anticipated choice might be to send critical inputs related to stimuli being highly relevant for the organism (such as foods) to the medial regions of PFC where they are used to modify or to refine the value representation. This assumption might explain why previous studies on multivariate preference decoding did not report any activation in dlPFC: First, the studies from McNamee et al. (2013) and Gross et al. (2014) did not use a temporally delayed stimulus evaluation/rating at all. Gross et al. (2014) performed a product rating (foods, engaging activities) after the whole scanning session whereas McNamee et al. (2013) performed the stimulus evaluation (foods, money, noncomestible consumer items) simultaneously with stimulus presentation by means of a bidding paradigm and used the fMRI data recorded 5 s after the bid for MVPA decoding. Thus, in both studies there was no choice signal in anticipation of a forthcoming event. Second, the paradigm in the MVPA study of Tusche et al. (2010) was (in one of two conditions) almost identical to ours

(i.e. product presentation with a subsequent liking rating on a scale from one to four) except for the fact that cars were used as stimuli instead of foods. Since food products constitute primary rewards that are closely linked to physiological parameters such as levels of blood sugar or intestinal hormones it might be possible that the observed activation in dlPFC reflects the processing of this highly relevant, food domain specific aspect. Thus, during food choice the dlPFC might send critical inputs regarding the food properties and physiological demands of the organism to the mPFC, were they modulate the value signal computation. This view is also supported by the two transcranial stimulation studies already mentioned in the previous section revealing that the dlPFC is causally involved in the modulation of food choices/valuation and food consumption (Camus et al., 2009; Fregni et al., 2008). Beyond the prefrontal regions, we found clusters predicting preferences across category especially in areas associated with higher level visual processing. Importantly, these activations were much smaller in size compared to the activation patterns found in these areas in case of the within category classification. Similar to the hypotheses drawn in case of the within category classification, we do not think that the across category decoding in these areas is based on value signals computed within this regions but rather on effects of adaptive coding/processing of visual stimuli guided by top down modulation from the prefrontal areas. The main reason for this assumption is, that the huge body of evidence indicates consistently that (a) the computation of values takes place in the prefrontal regions and that (b) the prefrontal cortex has a considerable modulatory influence on brain regions involved in visual/ perceptual processing. In case of the across category decoding in this regions, aspects or influences of category independent value signals computed within the prefrontal regions seems to be included within the transmitted modulatory PFC signals. We assume that this information was extracted by the classifier enabling preference decoding across category beyond the prefrontal regions.

Please cite this article in press as: Pogoda, L., et al. Multivariate representation of food preferences in the human brain. Brain and Cognition (2016), http:// dx.doi.org/10.1016/j.bandc.2015.12.008

L. Pogoda et al. / Brain and Cognition xxx (2016) xxx–xxx

In conclusion our study revealed that the multivariate within and across category decoding of value/preference signals in the domain of food products is, with regard to the activations in the mPFC, mostly consistent with the results from previous MVPA studies in this field which used utterly different stimulus categories. But in addition, we found the dlPFC to be involved in category dependent as well as category independent preference decoding. We suggest that this dlPFC activation reflects computations related to the processing of forthcoming choices falling into the domain of primary rewards, such as foods. However, further research is needed. Especially a slightly modified version of our study which omits the liking rating after stimulus presentation but includes a within category prediction for salty snacks enabling an across category prediction in both directions would be of high interest in order to reassure whether the dlPFC activation occurs only in the context of a forthcoming choice/rating task. Conflict of interest The authors declare no competing financial interests. Acknowledgments LP is funded by a doctoral scholarship from the Studienstiftung des deutschen Volkes. BW is funded by a Heisenberg Grant from the Deutsche Forschungsgemeinschaft (We 4427/3-2). FM is funded by a Lichtenberg Grant from the Volkswagen Foundation and by a Grant from the Deutsche Forschungsgemeinschaft (Mo 930/4-1). References Asaad, W. F., Rainer, G., & Miller, E. K. (1998). Neural activity in the primate prefrontal cortex during associative learning. Neuron, 21, 1399–1407. http://dx. doi.org/10.1016/S0896-6273(00)80658-3. Bartra, O., McGuire, J. T., & Kable, J. W. (2013). The valuation system: A coordinatebased meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. NeuroImage, 76, 412–427. http://dx.doi.org/10.1016/j. neuroimage.2013.02.063. Bishop, C. (2006). Sparse kernel machines. In Pattern recognition and machine learning (pp. 325–358). New York: Springer. http://dx.doi.org/10.1117/1. 2819119. Cai, X., & Padoa-Schioppa, C. (2012). Neuronal encoding of subjective value in dorsal and ventral anterior cingulate cortex. Journal of Neuroscience, 32, 3791–3808. http://dx.doi.org/10.1523/JNEUROSCI.3864-11.2012. Camus, M., Halelamien, N., Plassmann, H., Shimojo, S., O’Doherty, J., Camerer, C., & Rangel, A. (2009). Repetitive transcranial magnetic stimulation over the right dorsolateral prefrontal cortex decreases valuations during food choices. The European Journal of Neuroscience, 30, 1980–1988. http://dx.doi.org/10.1111/ j.1460-9568.2009.06991.x. Chib, V. S., Rangel, A., Shimojo, S., & O’Doherty, J. P. (2009). Evidence for a common representation of decision values for dissimilar goods in human ventromedial prefrontal cortex. Journal of Neuroscience, 29, 12315–12320. http://dx.doi.org/ 10.1523/JNEUROSCI.2575-09.2009. Duncan, J. (2001). An adaptive coding model of neural function in prefrontal cortex. Nature Reviews Neuroscience, 2, 820–829. http://dx.doi.org/10.1038/35097575. Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14, 172–179. http://dx.doi.org/10.1016/j.tics.2010.01.004. Duncan, J. (2013). The structure of cognition: Attentional episodes in mind and brain. Neuron, 80, 35–50. http://dx.doi.org/10.1016/j.neuron.2013.09.015. Etzel, J. A., Valchev, N., & Keysers, C. (2011). The impact of certain methodological choices on multivariate analysis of fMRI data with support vector machines. NeuroImage, 54, 1159–1167. http://dx.doi.org/10.1016/j.neuroimage.2010.08. 050. FitzGerald, T. H. B., Seymour, B., & Dolan, R. J. (2009). The role of human orbitofrontal cortex in value comparison for incommensurable objects. The Journal of Neuroscience, 29, 8388–8395. http://dx.doi.org/10.1523/JNEUROSCI.0717-09. 2009. Forman, S. D., Cohen, J. D., Fitzgerald, M., Eddy, W. F., Mintun, M. A., & Noll, D. C. (1995). Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): Use of a cluster-size threshold. Magnetic Resonance in Medicine, 33, 636–647. http://dx.doi.org/10.1002/mrm.1910330508. Fregni, F., Orsati, F., Pedrosa, W., Fecteau, S., Tome, F. A. M., Nitsche, M. A., & Boggio, P. S. (2008). Transcranial direct current stimulation of the prefrontal cortex

9

modulates the desire for specific foods. Appetite, 51, 34–41. http://dx.doi.org/ 10.1016/j.appet.2007.09.016. Gross, J., Woelbert, E., Zimmermann, J., Okamoto-Barth, S., Riedl, A., & Goebel, R. (2014). Value signals in the prefrontal cortex predict individual preferences across reward categories. The Journal of Neuroscience, 34, 7580–7586. http://dx. doi.org/10.1523/JNEUROSCI.5082-13.2014. Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V., & Pollmann, S. (2009). PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7, 37–53. http://dx.doi.org/10.1007/s12021-008-9041-y. Hasegawa, R., Sawaguchi, T., & Kubota, K. (1998). Monkey prefrontal neuronal activity coding the forthcoming saccade in an oculomotor delayed matching-tosample task. Journal of Neurophysiology, 79, 322–333. Huettel, S. A., Song, A. W., & McCarthy, G. (2004). Statistical analysis: Basic analyses. In Functional magnetic resonance imaging (pp. 331–375). Sunderland, MA: Sinauer. Ibos, G., Duhamel, J.-R., & Ben Hamed, S. (2013). A functional hierarchy within the parietofrontal network in stimulus selection and attention control. The Journal of Neuroscience, 33, 8359–8369. http://dx.doi.org/10.1523/JNEUROSCI.405812.2013. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291. http://dx.doi.org/10.2307/1914185. Kahnt, T., Park, S. Q., Haynes, J.-D., & Tobler, P. N. (2014). Disentangling neural representations of value and salience in the human brain. Proceedings of the National Academy of Sciences, 111, 5000–5005. http://dx.doi.org/10.1073/ pnas.1320189111. Kennerley, S. W., Behrens, T. E. J., & Wallis, J. D. (2011). Double dissociation of value computations in orbitofrontal and anterior cingulate neurons. Nature Neuroscience, 14, 1581–1589. http://dx.doi.org/10.1038/nn.2961. Kim, H., Shimojo, S., & O’Doherty, J. P. (2011). Overlapping responses for the expectation of juice and money rewards in human ventromedial prefrontal cortex. Cerebral Cortex, 21, 769–776. http://dx.doi.org/10.1093/cercor/bhq145. Kobayashi, S., Lauwereyns, J., Koizumi, M., Sakagami, M., & Hikosaka, O. (2002). Influence of reward expectation on visuospatial processing in macaque lateral prefrontal cortex. Journal of Neurophysiology, 87, 1488–1498. http://dx.doi.org/ 10.1152/jn.00472.2001. Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences, 103, 3863–3868. http://dx.doi.org/10.1073/pnas.0600244103. Lebreton, M., Jorge, S., Michel, V., Thirion, B., & Pessiglione, M. (2009). An automatic valuation system in the human brain: Evidence from functional neuroimaging. Neuron, 64, 431–439. http://dx.doi.org/10.1016/j.neuron.2009.09.040. Leon, M. I., & Shadlen, M. N. (1999). Effect of expected reward magnitude on the response of neurons in the dorsolateral prefrontal cortex of the macaque. Neuron, 24, 415–425. Levy, D. J., & Glimcher, P. W. (2011). Comparing apples and oranges: Using rewardspecific and reward-general subjective value representation in the brain. Journal of Neuroscience, 31, 14693–14707. http://dx.doi.org/10.1523/JNEUROSCI.221811.2011. Li, S., Ostwald, D., Giese, M., & Kourtzi, Z. (2007). Flexible coding for categorical decisions in the human brain. The Journal of Neuroscience, 27, 12321–12330. http://dx.doi.org/10.1523/JNEUROSCI.3795-07.2007. Lin, A., Adolphs, R., & Rangel, A. (2012). Social and monetary reward learning engage overlapping neural substrates. Social Cognitive and Affective Neuroscience, 7, 274–281. http://dx.doi.org/10.1093/scan/nsr006. Liu, L., Feng, T., Wang, J., & Li, H. (2012). The neural dissociation of subjective valuation from choice processes in intertemporal choice. Behavioural Brain Research, 231, 40–47. http://dx.doi.org/10.1016/j.bbr.2012.02.045. Louie, K., & Glimcher, P. W. (2010). Separating value from choice: Delay discounting activity in the lateral intraparietal area. The Journal of Neuroscience, 30, 5498–5507. http://dx.doi.org/10.1523/JNEUROSCI.5742-09.2010. McClure, S. M., Li, J., Tomlin, D., Cypert, K. S., Montague, L. M., & Montague, P. R. (2004). Neural correlates of behavioral preference for culturally familiar drinks. Neuron, 44, 379–387. http://dx.doi.org/10.1016/j.neuron.2004.09.019. McNamee, D., Rangel, A., & O’Doherty, J. P. (2013). Category-dependent and category-independent goal-value codes in human ventromedial prefrontal cortex. Nature Neuroscience, 16, 479–485. http://dx.doi.org/10.1038/nn.3337. Mur, M., Bandettini, P. A., & Kriegeskorte, N. (2009). Revealing representational content with pattern-information fMRI – An introductory guide. Social Cognitive and Affective Neuroscience, 4, 101–109. http://dx.doi.org/10.1093/ scan/nsn044. Nichols, T., Brett, M., Andersson, J., Wager, T., & Poline, J. B. (2005). Valid conjunction inference with the minimum statistic. NeuroImage, 25, 653–660. http://dx.doi. org/10.1016/j.neuroimage.2004.12.005. Pedregosa, F., & Varoquaux, G. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning, 12, 2825–2830. Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45, 199–209. http://dx.doi.org/10.1016/ j.neuroimage.2008.11.007. Peters, J., & Büchel, C. (2010). Neural representations of subjective reward value. Behavioural Brain Research, 213, 135–141. http://dx.doi.org/10.1016/j.bbr.2010. 04.031. Platt, M. L., & Glimcher, P. W. (1999). Neural correlates of decision variables in parietal cortex. Nature, 400, 233–238. http://dx.doi.org/10.1038/22268. Poline, J. B., & Brett, M. (2012). The general linear model and fMRI: Does love last forever? NeuroImage, 62, 871–880. http://dx.doi.org/10.1016/j.neuroimage. 2012.01.133.

Please cite this article in press as: Pogoda, L., et al. Multivariate representation of food preferences in the human brain. Brain and Cognition (2016), http:// dx.doi.org/10.1016/j.bandc.2015.12.008

10

L. Pogoda et al. / Brain and Cognition xxx (2016) xxx–xxx

Rainer, G., Rao, S. C., & Miller, E. K. (1999). Prospective coding for objects in primate prefrontal cortex. The Journal of Neuroscience, 19, 5493–5505. Roesch, M. R., & Olson, C. R. (2003). Impact of expected reward on neuronal activity in prefrontal cortex, frontal and supplementary eye fields and premotor cortex. Journal of Neurophysiology, 90, 1766–1789. http://dx.doi.org/10.1152/ jn.00019.2003. Samuelson, P. (1937). A note on measurement of utility. The Review of Economic Studies, 4, 155–161. Smith, D. V., Hayden, B. Y., Truong, T.-K., Song, A. W., Platt, M. L., & Huettel, S. A. (2010). Distinct value signals in anterior and posterior ventromedial prefrontal cortex. The Journal of Neuroscience, 30, 2490–2495. http://dx.doi.org/10.1523/ JNEUROSCI.3319-09.2010. Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2004). Matching behavior and the representation of value in the parietal cortex. Science, 304, 1782–1787. http:// dx.doi.org/10.1126/science.1094765. Tsujimoto, S., & Sawaguchi, T. (2005). Neuronal activity representing temporal prediction of reward in the primate prefrontal cortex. Journal of Neurophysiology, 93, 3687–3692. http://dx.doi.org/10.1152/jn.01149.2004. Tusche, A., Bode, S., & Haynes, J.-D. (2010). Neural responses to unattended products predict later consumer choices. The Journal of Neuroscience, 30, 8024–8031. http://dx.doi.org/10.1523/JNEUROSCI.0064-10.2010. Van der Laan, L. N., De Ridder, D. T. D., Viergever, M. A., & Smeets, P. A. M. (2012). Appearance matters: Neural correlates of food choice and packaging aesthetics. PloS one, 7, e41738. http://dx.doi.org/10.1371/journal.pone.0041738.

Von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton, NJ: Princeton University Press. http://dx.doi.org/10.1177/ 1468795X06065810. Wallis, J. D., & Kennerley, S. W. (2010). Heterogeneous reward signals in prefrontal cortex. Current Opinion in Neurobiology, 20, 191–198. http://dx.doi.org/10.1016/ j.conb.2010.02.009. Wallis, J. D., & Miller, E. K. (2003). Neuronal activity in primate dorsolateral and orbital prefrontal cortex during performance of a reward preference task. European Journal of Neuroscience, 18, 2069–2081. http://dx.doi.org/10.1046/ j.1460-9568.2003.02922.x. Woolgar, A., Williams, M. A., & Rich, A. N. (2015). Attention enhances multi-voxel representation of novel objects in frontal, parietal and visual cortices. NeuroImage, 109, 429–437. http://dx.doi.org/10.1016/j.neuroimage.2014.12. 083. Xiong, J., Gao, J.-H., Lancaster, J. L., & Fox, P. T. (1995). Clustered pixels analysis for functional MRI activation studies of the human brain. Human Brain Mapping, 3, 287–301. http://dx.doi.org/10.1002/hbm.460030404. Yang, T., & Shadlen, M. N. (2007). Probabilistic reasoning by neurons. Nature, 447, 1075–1080. http://dx.doi.org/10.1038/nature05852.

Please cite this article in press as: Pogoda, L., et al. Multivariate representation of food preferences in the human brain. Brain and Cognition (2016), http:// dx.doi.org/10.1016/j.bandc.2015.12.008