Body mass index-dependent shifts along large-scale gradients in human cortical organization explain dietary regulatory success.

Making healthy dietary choices is essential for keeping weight within a normal range. Yet many people struggle with dietary self-control despite good intentions. What distinguishes neural processing in those who succeed or fail to implement healthy eating goals? Does this vary by weight status? To examine these questions, we utilized an analytical framework of gradients that characterize systematic spatial patterns of large-scale neural activity, which have the advantage of considering the entire suite of processes subserving self-control and potential regulatory tactics at the whole-brain level. Using an established laboratory food task capturing brain responses in natural and regulatory conditions (N = 123), we demonstrate that regulatory changes of dietary brain states in the gradient space predict individual differences in dietary success. Better regulators required smaller shifts in brain states to achieve larger goal-consistent changes in dietary behaviors, pointing toward efficient network organization. This pattern was most pronounced in individuals with lower weight status (low-BMI, body mass index) but absent in high-BMI individuals. Consistent with prior work, regulatory goals increased activity in frontoparietal brain circuits. However, this shift in brain states alone did not predict variance in dietary success. Instead, regulatory success emerged from combined changes along multiple gradients, showcasing the interplay of different large-scale brain networks subserving dietary control and possible regulatory strategies. Our results provide insights into how the brain might solve the problem of dietary control: Dietary success may be easier for people who adopt modes of large-scale brain activation that do not require significant reconfigurations across contexts and goals.

Altruism under Stress: Cortisol Negatively Predicts Charitable Giving and Neural Value Representations Depending on Mentalizing Capacity.

Altruism, defined as costly other-regarding behavior, varies considerably across people and contexts. One prominent context in which people frequently must decide on how to socially act is under stress. How does stress affect altruistic decision-making and through which neurocognitive mechanisms? To address these questions, we assessed neural activity associated with charitable giving under stress. Human participants (males and females) completed a charitable donation task before and after they underwent either a psychosocial stressor or a control manipulation, while their brain activity was measured using functional magnetic resonance imaging. As the ability to infer other people's mental states (i.e., mentalizing) predicts prosocial giving and may be susceptible to stress, we examined whether stress effects on altruism depend on participants' general capacity to mentalize, as assessed in an independent task. Although our stress manipulation per se had no influence on charitable giving, increases in the stress hormone cortisol were associated with reductions in donations in participants with high mentalizing capacity, but not in low mentalizers. Multivariate neural response patterns in the right dorsolateral prefrontal cortex (DLPFC) were less predictive of postmanipulation donations in high mentalizers with increased cortisol, indicating decreased value coding, and this effect mediated the (moderated) association between cortisol increases and reduced donations. Our findings provide novel insights into the modulation of altruistic decision-making by suggesting an impact of the stress hormone cortisol on mentalizing-related neurocognitive processes, which in turn results in decreased altruism. The DLPFC appears to play a key role in mediating this cortisol-related shift in altruism.SIGNIFICANCE STATEMENT Altruism is a fundamental building block of our society. Emerging evidence indicates a major role of acute stress and stress-related neuromodulators in social behavior and decision-making. How and through which mechanisms stress may impact altruism remains elusive. We observed that the stress hormone cortisol was linked to diminished altruistic behavior. This effect was mediated by reduced value representations in the right dorsolateral prefrontal cortex and critically depended on the individual capacity to infer mental states of others. Our findings provide novel insights into the modulation of human altruism linked to stress-hormone dynamics and into the involved sociocognitive and neural mechanisms, with important implications for future developments of more targeted interventions for stress-related decrements in social behavior and social cognition.

Higher subjective socioeconomic status is linked to increased charitable giving and mentalizing-related neural value coding.

Socioeconomic status (SES), a concept related to an individual's economic and social position relative to others, can shape social interactions like altruistic behaviors. However, little is known about the exact neurocognitive mechanisms that link SES with altruism. Our study aimed to provide a comprehensive account of the sociocognitive and neural mechanisms through which SES affects charitable giving - an important variant of human altruism. To this end, participants completed a charitable donation task while their brain activity was measured using functional magnetic resonance imaging (fMRI). We also assessed participants' socio-cognitive ability to infer other people's mental states (i.e., mentalizing) - a driver of prosocial behavior - in an independent social task. Behaviorally, we found that both charitable giving and social cognition were status-dependent, as subjective SES positively predicted donations and mentalizing capacity. Moreover, the link between SES and charitable giving was mediated by individuals' mentalizing capacity. At the neural level, a multivariate pattern analysis of fMRI data revealed that higher subjective SES was associated with stronger value coding in the right temporoparietal junction (rTPJ). The strength of this value representation predicted charitable giving and was linked to mentalizing. Furthermore, we observed an increased negative functional coupling between rTPJ and left putamen with higher SES. Together, increased charitable giving in higher-status individuals could be explained by status-dependent recruitment of mentalizing-related value coding and altered functional connectivity in the brain. Our findings provide insights into the socio- and neurocognitive mechanisms explaining why and when higher SES leads to prosociality, which might ultimately inform targeted interventions to promote prosocial behavior in human societies.

Neuroanatomy of the vmPFC and dlPFC Predicts Individual Differences in Cognitive Regulation During Dietary Self-Control Across Regulation Strategies.

Making healthy food choices is challenging for many people. Individuals differ greatly in their ability to follow health goals in the face of temptation, but it is unclear what underlies such differences. Using voxel-based morphometry, we investigated in healthy humans (i.e., men and women) the links between structural variation in gray matter volume and individuals' level of success in shifting toward healthier food choices. We combined MRI and choice data into a joint dataset by pooling across three independent studies that used a task prompting participants to explicitly focus on the healthiness of food items before making their food choices. Within this dataset, we found that individual differences in gray matter volume in the ventromedial prefrontal cortex (vmPFC) and dorsolateral prefrontal cortex (dlPFC) predicted regulatory success. We extended and confirmed these initial findings by predicting regulatory success out of sample and across tasks in a second dataset requiring participants to apply a different regulation strategy that entailed distancing from cravings for unhealthy, appetitive foods. Our findings suggest that neuroanatomical markers in the vmPFC and dlPFC generalized to different forms of dietary regulation strategies across participant groups. They provide novel evidence that structural differences in neuroanatomy of two key regions for valuation and its control, the vmPFC and dlPFC, predict an individual's ability to exert control in dietary choices.SIGNIFICANCE STATEMENT Dieting involves regulating food choices to eat healthier foods and fewer unhealthy foods. People differ dramatically in their ability to achieve or maintain this regulation, but it is unclear why. Here, we show that individuals with more gray matter volume in the dorsolateral and ventromedial prefrontal cortex are better at exercising dietary self-control. This relationship was observed across four different studies examining two different forms of dietary self-regulation, suggesting that neuroanatomical differences in the ventromedial prefrontal cortex and dorsolateral prefrontal cortex may represent a general marker for self-control abilities. These results identify candidate neuroanatomical markers for dieting success and failure, and suggest potential targets for therapies aimed at preventing or treating obesity and related eating disorders.

Decoding the Charitable Brain: Empathy, Perspective Taking, and Attention Shifts Differentially Predict Altruistic Giving.

UNLABELLED: Altruistic behavior varies considerably across people and decision contexts. The relevant computational and motivational mechanisms that underlie its heterogeneity, however, are poorly understood. Using a charitable giving task together with multivariate decoding techniques, we identified three distinct psychological mechanisms underlying altruistic decision-making (empathy, perspective taking, and attentional reorienting) and linked them to dissociable neural computations. Neural responses in the anterior insula (AI) (but not temporoparietal junction [TPJ]) encoded trial-wise empathy for beneficiaries, whereas the TPJ (but not AI) predicted the degree of perspective taking. Importantly, the relative influence of both socio-cognitive processes differed across individuals: participants whose donation behavior was heavily influenced by affective empathy exhibited higher predictive accuracies for generosity in AI, whereas those who strongly relied on cognitive perspective taking showed improved predictions of generous donations in TPJ. Furthermore, subject-specific contributions of both processes for donations were reflected in participants' empathy and perspective taking responses in a separate fMRI task (EmpaToM), suggesting that process-specific inputs into altruistic choices may reflect participants' general propensity to either empathize or mentalize. Finally, using independent attention task data, we identified shared neural codes for attentional reorienting and generous donations in the posterior superior temporal sulcus, suggesting that domain-general attention shifts also contribute to generous behavior (but not in TPJ or AI). Overall, our findings demonstrate highly specific roles of AI for affective empathy and TPJ for cognitive perspective taking as precursors of prosocial behavior and suggest that these discrete routes of social cognition differentially drive intraindividual and interindividual differences in altruistic behavior. SIGNIFICANCE STATEMENT: Human societies depend on the altruistic behavior of their members, but teasing apart its underlying motivations and neural mechanisms poses a serious challenge. Using multivariate decoding techniques, we delineated three distinct processes for altruistic decision-making (affective empathy, cognitive perspective taking, and domain-general attention shifts), linked them to dissociable neural computations, and identified their relative influence across individuals. Distinguishing process-specific computations both behaviorally and neurally is crucial for developing complete theoretical and neuroscientific accounts of altruistic behavior and more effective means of increasing it. Moreover, information on the relative influence of subprocesses across individuals and its link to people's more general propensity to engage empathy or perspective taking can inform training programs to increase prosociality, considering their "fit" with different individuals.

The Neural Representation of Voluntary Task-Set Selection in Dynamic Environments.

When choosing actions, humans have to balance carefully between different task demands. On the one hand, they should perform tasks repeatedly to avoid frequent and effortful switching between different tasks. On the other hand, subjects have to retain their flexibility to adapt to changes in external task demands such as switching away from an increasingly difficult task. Here, we developed a difficulty-based choice task to investigate how subjects voluntarily select task-sets in predictably changing environments. Subjects were free to choose 1 of the 3 task-sets on a trial-by-trial basis, while the task difficulty changed dynamically over time. Subjects self-sequenced their behavior in this environment while we measured brain responses with functional magnetic resonance imaging (fMRI). Using multivariate decoding, we found that task choices were encoded in the medial prefrontal cortex (dorso-medial prefrontal cortex, dmPFC, and dorsal anterior cingulate cortex, dACC). The same regions were found to encode task difficulty, a major factor influencing choices. Importantly, the present paradigm allowed us to disentangle the neural code for task choices and task difficulty, ensuring that activation patterns in dmPFC/dACC independently encode these 2 factors. This finding provides new evidence for the importance of the dmPFC/dACC for task-selection and motivational functions in highly dynamic environments.

Classifying the wandering mind: revealing the affective content of thoughts during task-free rest periods.

Many powerful human emotional thoughts are generated in the absence of a precipitating event in the environment. Here, we tested whether we can decode the valence of internally driven, self-generated thoughts during task-free rest based on neural similarities with task-related affective mental states. We acquired functional magnetic resonance imaging (fMRI) data while participants generated positive and negative thoughts as part of an attribution task (Session A) and while they reported the occurrence of comparable mental states during task-free rest periods (Session B). With the use of multivariate pattern analyses (MVPA), we identified response patterns in the medial orbitofrontal cortex (mOFC) that encode the affective content of thoughts that are generated in response to an external experimental cue. Importantly, these task driven response patterns reliably predicted the occurrence of affective thoughts generated during unconstrained rest periods recorded one week apart. This demonstrates that at least certain elements of task-cued and task-free affective experiences rely on a common neural code. Furthermore, our findings reveal the role that the mOFC plays in determining the affective tone of unconstrained thoughts. More generally, our results suggest that MVPA is an important methodological tool for attempts to understand unguided subject driven mental states such as mind-wandering and daydreaming based on neural similarities with task-based experiences.

Medial prefrontal and anterior cingulate cortical thickness predicts shared individual differences in self-generated thought and temporal discounting.

When deprived of compelling perceptual input, the mind is often occupied with thoughts unrelated to the immediate environment. Previous behavioral research has shown that this self-generated task-unrelated thought (TUT), especially under non-demanding conditions, relates to cognitive capacities such as creativity, planning, and reduced temporal discounting. Despite the frequency and importance of this type of cognition, little is known about its structural brain basis. Using MRI-based cortical thickness measures in 37 participants, we were able to show that individuals with a higher tendency to engage in TUT under low-demanding conditions (but not under high-demanding conditions) show an increased thickness of medial prefrontal cortex (mPFC) and anterior/midcingulate cortex. Thickness of these regions also related to less temporal discounting (TD) of monetary rewards in an economic task, indicative of more patient decision-making. The findings of a shared structural substrate in mPFC and anterior/midcingulate cortex underlying both TUT and TD suggest an important role of these brain regions in supporting the self-generation of information that is unrelated to the immediate environment and which may be adaptive in nature.

A neurocomputational account of the link between social perception and social action.

People selectively help others based on perceptions of their merit or need. Here, we develop a neurocomputational account of how these social perceptions translate into social choice. Using a novel fMRI social perception task, we show that both merit and need perceptions recruited the brain's social inference network. A behavioral computational model identified two non-exclusive mechanisms underlying variance in social perceptions: a consistent tendency to perceive others as meritorious/needy (bias) and a propensity to sample and integrate normative evidence distinguishing high from low merit/need in other people (sensitivity). Variance in people's merit (but not need) bias and sensitivity independently predicted distinct aspects of altruism in a social choice task completed months later. An individual's merit bias predicted context-independent variance in people's overall other-regard during altruistic choice, biasing people towards prosocial actions. An individual's merit sensitivity predicted context-sensitive discrimination in generosity towards high and low merit recipients by influencing other-regard and self-regard during altruistic decision-making. This context-sensitive perception-action link was associated with activation in the right temporoparietal junction. Together, these findings point towards stable, biologically based individual differences in perceptual processes related to abstract social concepts like merit, and suggest that these differences may have important behavioral implications for an individual's tendency toward favoritism or discrimination in social settings.

Automatic processing of political preferences in the human brain.

Individual political preferences as expressed, for instance, in votes or donations are fundamental to democratic societies. However, the relevance of deliberative processing for political preferences has been highly debated, putting automatic processes in the focus of attention. Based on this notion, the present study tested whether brain responses reflect participants' preferences for politicians and their associated political parties in the absence of explicit deliberation and attention. Participants were instructed to perform a demanding visual fixation task while their brain responses were measured using fMRI. Occasionally, task-irrelevant images of German politicians from two major competing parties were presented in the background while the distraction task was continued. Subsequent to scanning, participants' political preferences for these politicians and their affiliated parties were obtained. Brain responses in distinct brain areas predicted automatic political preferences at the different levels of abstraction: activation in the ventral striatum was positively correlated with preference ranks for unattended politicians, whereas participants' preferences for the affiliated political parties were reflected in activity in the insula and the cingulate cortex. Using an additional donation task, we showed that the automatic preference-related processing in the brain extended to real-world behavior that involved actual financial loss to participants. Together, these findings indicate that brain responses triggered by unattended and task-irrelevant political images reflect individual political preferences at different levels of abstraction.

Neural signatures of social inferences predict the number of real-life social contacts and autism severity.

We regularly infer other people's thoughts and feelings from observing their actions, but how this ability contributes to successful social behavior and interactions remains unknown. We show that neural activation patterns during social inferences obtained in the laboratory predict the number of social contacts in the real world, as measured by the social network index, in three neurotypical samples (total n = 126) and one sample of autistic adults (n = 23). We also show that brain patterns during social inference generalize across individuals in these groups. Cross-validated associations between brain activations and social inference localize selectively to the right posterior superior temporal sulcus and were specific for social, but not nonsocial, inference. Activation within this same brain region also predicts autism-like trait scores from questionnaires and autism symptom severity. Thus, neural activations produced while thinking about other people's mental states predict variance in multiple indices of social functioning in the real world.

Evidence accumulation, not 'self-control', explains dorsolateral prefrontal activation during normative choice.

What role do regions like the dorsolateral prefrontal cortex (dlPFC) play in normative behavior (e.g., generosity, healthy eating)? Some models suggest that dlPFC activation during normative choice reflects controlled inhibition or modulation of default hedonistic preferences. Here, we develop an alternative account, showing that evidence accumulation models predict trial-by-trial variation in dlPFC response across three fMRI paradigms and two self-control contexts (altruistic sacrifice and healthy eating). Using these models to simulate a variety of self-control dilemmas generated a novel prediction: although dlPFC activity might typically increase for norm-consistent choices, deliberate self-regulation focused on normative goals should decrease or even reverse this pattern (i.e., greater dlPFC response for hedonistic, self-interested choices). We confirmed these predictions in both altruistic and dietary choice contexts. Our results suggest that dlPFC response during normative choice may depend more on value-based evidence accumulation than inhibition of our baser instincts.

Neural responses to unattended products predict later consumer choices.

Imagine you are standing at a street with heavy traffic watching someone on the other side of the road. Do you think your brain is implicitly registering your willingness to buy any of the cars passing by outside your focus of attention? To address this question, we measured brain responses to consumer products (cars) in two experimental groups using functional magnetic resonance imaging. Participants in the first group (high attention) were instructed to closely attend to the products and to rate their attractiveness. Participants in the second group (low attention) were distracted from products and their attention was directed elsewhere. After scanning, participants were asked to state their willingness to buy each product. During the acquisition of neural data, participants were not aware that consumer choices regarding these cars would subsequently be required. Multivariate decoding was then applied to assess the choice-related predictive information encoded in the brain during product exposure in both conditions. Distributed activation patterns in the insula and the medial prefrontal cortex were found to reliably encode subsequent choices in both the high and the low attention group. Importantly, consumer choices could be predicted equally well in the low attention as in the high attention group. This suggests that neural evaluation of products and associated choice-related processing does not necessarily depend on attentional processing of available items. Overall, the present findings emphasize the potential of implicit, automatic processes in guiding even important and complex decisions.

Neurocomputational models of altruistic decision-making and social motives: Advances, pitfalls, and future directions.

This article discusses insights from computational models and social neuroscience into motivations, precursors, and mechanisms of altruistic decision-making and other-regard. We introduce theoretical and methodological tools for researchers who wish to adopt a multilevel, computational approach to study behaviors that promote others' welfare. Using examples from recent studies, we outline multiple mental and neural processes relevant to altruism. To this end, we integrate evidence from neuroimaging, psychology, economics, and formalized mathematical models. We introduce basic mechanisms-pertinent to a broad range of value-based decisions-and social emotions and cognitions commonly recruited when our decisions involve other people. Regarding the latter, we discuss how decomposing distinct facets of social processes can advance altruistic models and the development of novel, targeted interventions. We propose that an accelerated synthesis of computational approaches and social neuroscience represents a critical step towards a more comprehensive understanding of altruistic decision-making. We discuss the utility of this approach to study lifespan differences in social preference in late adulthood, a crucial future direction in aging global populations. Finally, we review potential pitfalls and recommendations for researchers interested in applying a computational approach to their research. This article is categorized under: Economics > Interactive Decision-Making Psychology > Emotion and Motivation Neuroscience > Cognition Economics > Individual Decision-Making.

Cognitive regulation alters social and dietary choice by changing attribute representations in domain-general and domain-specific brain circuits.

Are some people generally more successful using cognitive regulation or does it depend on the choice domain? Why? We combined behavioral computational modeling and multivariate decoding of fMRI responses to identify neural loci of regulation-related shifts in value representations across goals and domains (dietary or altruistic choice). Surprisingly, regulatory goals did not alter integrative value representations in the ventromedial prefrontal cortex, which represented all choice-relevant attributes across goals and domains. Instead, the dorsolateral prefrontal cortex (DLPFC) flexibly encoded goal-consistent values and predicted regulatory success for the majority of choice-relevant attributes, using attribute-specific neural codes. We also identified domain-specific exceptions: goal-dependent encoding of prosocial attributes localized to precuneus and temporo-parietal junction (not DLPFC). Our results suggest that cognitive regulation operated by changing specific attribute representations (not integrated values). Evidence of domain-general and domain-specific neural loci reveals important divisions of labor, explaining when and why regulatory success generalizes (or doesn't) across contexts and domains.

Distinct mental trainings differentially affect altruistically motivated, norm motivated, and self-reported prosocial behaviour.

Global challenges such as climate change or the refugee crises emphasize the necessity of altruism and cooperation. In a large-scale 9-month intervention study, we investigated the malleability of prosociality by three distinct mental trainings cultivating attention, socio-affective, or socio-cognitive skills. We assessed numerous established measures of prosociality that capture three core facets: Altruistically motivated behaviours, norm motivated behaviours, and self-reported prosociality. Results of multiple time point confirmatory factor analyses support the validity and temporal stability of this model. Furthermore, linear mixed effects models reveal differential effects of mental trainings on the subcomponents of prosociality: Only training care and compassion effectively boosted altruistically motivated behaviour. No effects were revealed for norm-based behaviour. Self-reported prosociality increased with all training modules; this increase was, however, unrelated to changes in task-based measures of altruistic behaviour. These findings corroborate our motivation-based framework of prosociality, challenge economic views of fixed preferences by showing that socio-affective training boosts altruism, and inform policy makers and society about how to increase global cooperation.

From faces to prosocial behavior: cues, tools, and mechanisms.

In this review we ask how looking at people's faces can influence prosocial behaviors towards them. Components of this process have often been studied by disparate literatures: one focused on perception and judgment of faces, using both psychological and neuroscience approaches; and a second focused on actual social behaviors, as studied in behavioral economics and decision science. Bridging these disciplines requires a more mechanistic account of how processing of particular face attributes or features influences social judgments and behaviors. Here we review these two lines of research, and suggest that combining some of their methodological tools can provide the bridging mechanistic explanations.

Cross-modal representations of first-hand and vicarious pain, disgust and fairness in insular and cingulate cortex.

The anterior insula (AI) and mid-anterior cingulate cortex (mACC) have repeatedly been implicated in first-hand and vicarious experiences of pain, disgust and unfairness. However, it is debated whether these regions process different aversive events through a common modality-independent code, reflecting the shared unpleasantness of the experiences or through independent modality-specific representations. Using functional magnetic resonance imaging, we subjected 19 participants (and 19 confederates) to equally unpleasant painful and disgusting stimulations, as well as unfair monetary treatments. Multivoxel pattern analysis identified modality-independent activation maps in the left AI and mACC, pointing to common coding of affective unpleasantness, but also response patterns specific for the events' sensory properties and the person to whom it was addressed, particularly in the right AI. Our results provide evidence of both functional specialization and integration within AI and mACC, and support a comprehensive role of this network in processing aversive experiences for self and others.

Exploring the use of thermal infrared imaging in human stress research.

High resolution thermal infrared imaging is a pioneering method giving indices of sympathetic activity via the contact-free recording of facial tissues (thermal imprints). Compared to established stress markers, the great advantage of this method is its non-invasiveness. The goal of our study was to pilot the use of thermal infrared imaging in the classical setting of human stress research. Thermal imprints were compared to established stress markers (heart rate, heart rate variability, finger temperature, alpha-amylase and cortisol) in 15 participants undergoing anticipation, stress and recovery phases of two laboratory stress tests, the Cold Pressor Test and the Trier Social Stress Test. The majority of the thermal imprints proved to be change-sensitive in both tests. While correlations between the thermal imprints and established stress markers were mostly non-significant, the thermal imprints (but not the established stress makers) did correlate with stress-induced mood changes. Multivariate pattern analysis revealed that in contrast to the established stress markers the thermal imprints could not disambiguate anticipation, stress and recovery phases of both tests. Overall, these results suggest that thermal infrared imaging is a valuable method for the estimation of sympathetic activity in the stress laboratory setting. The use of this non-invasive method may be particularly beneficial for covert recordings, in the study of special populations showing difficulties in complying with the standard instruments of data collection and in the domain of psychophysiological covariance research. Meanwhile, the established stress markers seem to be superior when it comes to the characterization of complex physiological states during the different phases of the stress cycle.