Value Signals in the Prefrontal Cortex Predict Individual Preferences across Reward Categories

Abstract

Humans can choose between fundamentally different options, such as watching a movie or going out for dinner. According to the utility concept, put forward by utilitarian philosophers and widely used in economics, this may be accomplished by mapping the value of different options onto a common scale, independent of specific option characteristics (Fehr and Rangel, 2011; Levy and Glimcher, 2012). If this is the case, value-related activity patterns in the brain should allow predictions of individual preferences across fundamentally different reward categories. We analyze fMRI data of the prefrontal cortex while subjects imagine the pleasure they would derive from items belonging to two distinct reward categories: engaging activities (like going out for drinks, daydreaming, or doing sports) and snack foods. Support vector machines trained on brain patterns related to one category reliably predict individual preferences of the other category and vice versa. Further, we predict preferences across participants. These findings demonstrate that prefrontal cortex value signals follow a common scale representation of value that is even comparable across individuals and could, in principle, be used to predict choice.

Identity-specific coding of future rewards in the human orbitofrontal cortex

“…Here, patterns of fMRI activity coding for the predicted value of a sweet odor could be used to reliably classify the predicted value of a savory odor, and vice versa. This finding echoes and extends recent evidence showing that vmPFC activity patterns generalize across different reward categories (4,5,7), such as food items, merchandizing gimmicks, and leisure activities. In these studies, classifiers were trained on exemplars (i.e., different items) associated with different values in one category and could be used to predict the value of exemplars in another category.…”

Neural mechanisms of cue-approach training

“…The lack of cross-task classification in this study stands in contrast to other studies that have demonstrated strong cross-task classification (Eger et al, 2009; Etzel et al, 2008; Lewis-Peacock and Postle, 2008; Meyer et al, 2010; Shinkareva et al, 2011). The lack of cross-task value-level classification success was surprising given previous work demonstrating cross-domain value-level classification (Gross et al, 2014). The lack of cross-task classification in the current study calls for future studies aimed at better defining the conditions under which cross-task classification is possible.…”

“…Previous research supported this design. Indeed, several studies have demonstrated robust classification of abstract cognitive processes such as intentions (Haynes et al, 2007; Soon et al, 2008), attention (Rosenberg et al, 2015) and valuation (Gross et al, 2014) to name only a few. Further demonstrating the power of MVPA classifiers, the three-class classification discriminating the cognitive processes of memory retrieval, valuation and perceptual processing performed well above chance in a standard two-way cross-validation applied to the localizer task fMRI data.…”

“…Previous studies have demonstrated the power of these techniques not only to classify distributed patterns of fMRI activity elicited by different categories of images while the participant was viewing them (Cox and Savoy, 2003; Haxby et al, 2001), but also to classify intentions (Haynes et al, 2007; Soon et al, 2008), attentional states (Rosenberg et al, 2015) and the contents of memory recall (Polyn et al, 2005) using classifiers trained on different sets of stimuli from those being classified. Furthermore, and most germane to our main question of interest in the current study, Gross et al (2014) trained an SVM classifier to discriminate levels of subjective value of foods and predicted the subjective value of engaging activities and vice versa. This supports the idea of common representation of value and the valuation process across domains.…”

Elucidating the underlying components of food valuation in the human orbitofrontal cortex

“…14,15 However, sensory inputs from the visual, auditory, gustatory, olfactory and somatosensory systems arrive into the OFC primarily in the lateral portions of the orbital surface 22 . Thus, we hypothesized that more lateral parts of the OFC would be especially involved in encoding elemental attributes about a food outcome, in contrast to the medial OFC which we hypothesized would be especially involved in encoding an overall subjective goal-value signal for the foods, as found in many previous reports 1,6,13 .…”