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.

Elucidating medial temporal and frontal lobe contributions to approach-avoidance conflict decision-making using functional MRI and the hierarchical drift diffusion model.

The prefrontal cortex (PFC) has long been associated with arbitrating between approach and avoidance in the face of conflicting and uncertain motivational information, but recent work has also highlighted medial temporal lobe (MTL) involvement. It remains unclear, however, how the contributions of these regions differ in their resolution of conflict information and uncertainty. We designed an fMRI paradigm in which participants approached or avoided object pairs that differed by motivational conflict and outcome uncertainty (complete certainty vs. complete uncertainty). Behavioral data and decision-making parameters estimated using the hierarchical drift diffusion model revealed that participants' responding was driven by conflict rather than uncertainty. Our neural data suggest that PFC areas contribute to cognitive control during approach-avoidance conflict by potentially adjusting response caution and the strength of evidence generated towards either choice, with differential involvement of anterior cingulate cortex and dorsolateral prefrontal cortex. The MTL, on the other hand, appears to contribute to evidence generation, with the hippocampus linked to evidence accumulation for stimuli. Although findings within perirhinal cortex were comparatively equivocal, some evidence suggests contributions to perceptual representations, particularly under conditions of threat. Our findings provide evidence that MTL and PFC regions may contribute uniquely to arbitrating approach-avoidance conflict.

Deliberative control is more than just reactive: Insights from sequential sampling models.

Activating relevant responses is a key function of automatic processes in De Neys's model; however, what determines the order or magnitude of such activation is ambiguous. Focusing on recently developed sequential sampling models of choice, we argue that proactive control shapes response generation but does not cleanly fit into De Neys's automatic-deliberative distinction, highlighting the need for further model development.

The Games We Play: Prosocial Choices Under Time Pressure Reflect Context-Sensitive Information Priorities.

Time pressure is a powerful experimental manipulation frequently used to arbitrate between competing dual-process models of prosocial decision-making, which typically assume that automatic responses yield to deliberation over time. However, the use of time pressure has led to conflicting conclusions about the psychological dynamics of prosociality. Here, we proposed that flexible, context-sensitive information search, rather than automatic responses, underlies these divergent effects of time pressure on prosociality. We demonstrated in two preregistered studies (N = 304 adults from the United States and Canada; Prolific Academic) that different prosocial contexts (i.e., pure altruism vs. cooperation) have distinct effects on information search, driving people to prioritize information differently, particularly under time pressure. Furthermore, these information priorities subsequently influence prosocial choices, accounting for the different effects of time pressure in altruistic and cooperative contexts. These findings help explain existing inconsistencies in the field by emphasizing the role of dynamic context-sensitive information search during social decision-making, particularly under time pressure.

Time to Pay Attention? Information Search Explains Amplified Framing Effects Under Time Pressure.

Decades of research have established the ubiquity and importance of choice biases, such as the framing effect, yet why these seemingly irrational behaviors occur remains unknown. A prominent dual-system account maintains that alternate framings bias choices because of the unchecked influence of quick, affective processes, and findings that time pressure increases the framing effect have provided compelling support. Here, we present a novel alternative account of magnified framing biases under time pressure that emphasizes shifts in early visual attention and strategic adaptations in the decision-making process. In a preregistered direct replication (N = 40 adult undergraduates), we found that time constraints produced strong shifts in visual attention toward reward-predictive cues that, when combined with truncated information search, amplified the framing effect. Our results suggest that an attention-guided, strategic information-sampling process may be sufficient to explain prior results and raise challenges for using time pressure to support some dual-system accounts.

Reframing rationality: Exogenous constraints on controlled information search.

Bermúdez argues that framing effects are rational because particular frames provide goal-consistent reasons for choice and that people exert some control over the framing of a decision-problem. We propose instead that these observations raise the question of whether frame selection itself is a rational process and highlight how constraints in the choice environment severely limit the rational selection of frames.

Accounting for Taste: A Multi-Attribute Neurocomputational Model Explains the Neural Dynamics of Choices for Self and Others.

How do we make choices for others with different preferences from our own? Although neuroimaging studies implicate similar circuits in representing preferences for oneself and others, some models propose that additional corrective mechanisms come online when choices for others diverge from one's own preferences. Here we used event-related potentials (ERPs) in humans, in combination with computational modeling, to examine how social information is integrated in the time leading up to choices for oneself and others. Hungry male and female participants with unrestricted diets selected foods for themselves, a similar unrestricted eater, and a dissimilar, self-identified healthy eater. Across choices for both oneself and others, ERP value signals emerged within the same time window but differentially reflected taste and health attributes based on the recipient's preferences. Choices for the dissimilar recipient were associated with earlier activity localized to brain regions implicated in social cognition, including temporoparietal junction. Finally, response-locked analysis revealed a late ERP component specific to choices for the similar recipient, localized to the parietal lobe, that appeared to reflect differences in the response threshold based on uncertainty. A multi-attribute computational model supported the link between specific ERP components and distinct model parameters, and was not significantly improved by adding time-dependent dual processes. Model simulations suggested that longer response times previously associated with effortful correction may alternatively arise from higher choice uncertainty. Together, these results provide a parsimonious neurocomputational mechanism for social decision-making, additionally explaining divergent patterns of choice and response time data in decisions for oneself and others.SIGNIFICANCE STATEMENT How do we choose for others, particularly when they have different preferences? Whereas some studies suggest that similar neural circuits underlie decision-making for oneself and others, others argue for additional, slower perspective-taking mechanisms. Combining event-related potentials with computational modeling, we found that integration of others' preferences occurs over the same timescale as for oneself while differentially tracking recipient-relevant attributes. Although choosing for others took longer and produced differences in late-emerging neural responses, computational modeling attributed these patterns to greater response caution rather than egocentric bias correction. Computational simulations also correctly predicted when and why choosing differently for others takes longer, suggesting that a model incorporating value integration and evidence accumulation can parsimoniously account for complex patterns in social decision-making.

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.

Midfrontal theta and pupil dilation parametrically track subjective conflict (but also surprise) during intertemporal choice.

Many everyday choices are based on personal, subjective preferences. When choosing between two options, we often feel conflicted, especially when trading off costs and benefits occurring at different times (e.g., saving for later versus spending now). Although previous work has investigated the neurophysiological basis of conflict during inhibitory control tasks, less is known about subjective conflict resulting from competing subjective preferences. In this pre-registered study, we investigated subjective conflict during intertemporal choice, whereby participants chose between smaller immediate versus larger delayed rewards (e.g., $15 today vs. $22 in 30 days). We used economic modeling to parametrically vary eleven different levels of conflict, and recorded EEG data and pupil dilation. Midfrontal theta power, derived from EEG, correlated with pupil responses, and our results suggest that these signals track different gradations of subjective conflict. Unexpectedly, both signals were also maximally enhanced when decisions were surprisingly easy. Therefore, these signals may track events requiring increased attention and adaptive shifts in behavioral responses, with subjective conflict being only one type of such event. Our results suggest that the neural systems underlying midfrontal theta and pupil responses interact when weighing costs and benefits during intertemporal choice. Thus, understanding these interactions might elucidate how individuals resolve self-control conflicts.

Emotional and Utilitarian Appraisals of Moral Dilemmas Are Encoded in Separate Areas and Integrated in Ventromedial Prefrontal Cortex.

Moral judgment often requires making difficult tradeoffs (e.g., is it appropriate to torture to save the lives of innocents at risk?). Previous research suggests that both emotional appraisals and more deliberative utilitarian appraisals influence such judgments and that these appraisals often conflict. However, it is unclear how these different types of appraisals are represented in the brain, or how they are integrated into an overall moral judgment. We addressed these questions using an fMRI paradigm in which human subjects provide separate emotional and utilitarian appraisals for different potential actions, and then make difficult moral judgments constructed from combinations of these actions. We found that anterior cingulate, insula, and superior temporal gyrus correlated with emotional appraisals, whereas temporoparietal junction and dorsomedial prefrontal cortex correlated with utilitarian appraisals. Overall moral value judgments were represented in an anterior portion of the ventromedial prefrontal cortex. Critically, the pattern of responses and functional interactions between these three sets of regions are consistent with a model in which emotional and utilitarian appraisals are computed independently and in parallel, and passed to the ventromedial prefrontal cortex where they are integrated into an overall moral value judgment. Significance statement: Popular accounts of moral judgment often describe it as a battle for control between two systems, one intuitive and emotional, the other rational and utilitarian, engaged in winner-take-all inhibitory competition. Using a novel fMRI paradigm, we identified distinct neural signatures of emotional and utilitarian appraisals and used them to test different models of how they compete for the control of moral behavior. Importantly, we find little support for competitive inhibition accounts. Instead, moral judgments resembled the architecture of simple economic choices: distinct regions represented emotional and utilitarian appraisals independently and passed this information to the ventromedial prefrontal cortex for integration into an overall moral value signal.

The neural correlates of social connection.

Cultivating social connection has long been a goal of psychology, philosophy, religion, and public policy. Yet the psychological and neural responses that accompany a feeling of connection to others remain unclear. In the present study, we used functional neuroimaging to shed light on the neural correlates of self- and other-focused processes during the successful self-generation of feelings of social connection. To do this, we used a trait judgment task to localize functional activation related to self-focused thought. We then examined brain responses during guided exercises designed both to encourage feeling love and connection from others (i.e., self-focused) and to generate feelings of love and connection toward others (i.e., other-focused). Our results indicated that generating feelings of social connection recruited a portion of the medial prefrontal cortex (MPFC) implicated in thinking about both the self and others. Within this larger area, we observed distinct profiles of activation within different subregions. Although rostral anterior cingulate cortex was more strongly activated by other-focused components of the task, a more dorsal portion of MPFC was comparatively more active during primarily self-focused components of the task. Somewhat surprisingly, stronger feelings of social connection were not associated with greater activation in the anterior cingulate, but rather with less activation in the dorsal region of the MPFC related to self-focused thought. These results are consistent with the possibility that reducing certain kinds of self-focused thought might yield a greater sense of social connection to and care for others.

Dietary self-control is related to the speed with which attributes of healthfulness and tastiness are processed.

We propose that self-control failures, and variation across individuals in self-control abilities, are partly due to differences in the speed with which the decision-making circuitry processes basic attributes, such as tastiness, versus more abstract attributes, such as healthfulness. We tested these hypotheses by combining a dietary-choice task with a novel form of mouse tracking that allowed us to pinpoint when different attributes were being integrated into the choice process with temporal resolution at the millisecond level. We found that, on average, tastiness was processed about 195 ms earlier than healthfulness during the choice process. We also found that 13% to 39% of observed individual differences in self-control ability could be explained by differences in the relative speed with which tastiness and healthfulness were processed.

How bad becomes good: A neurocomputational model of affect-informed choice.

People often draw on their current affective experience to inform their decisions, yet little is known about the underlying mechanisms of this process. Understanding them has important implications for many big questions in both the affective and decision sciences. Do the same neural circuits that generate affect generate value? What differentiates people who have greater contextual flexibility in their reliance on affect? Do affective choices invoke processes that are distinct from less affective choices? To investigate these questions, we developed a neurocomputational model of affect-informed choice, in which people convert subjective affect into context-sensitive decision value through a process of weighted evidence accumulation. We then tested model predictions by recording electroencephalography and facial electromyography during a novel affective choice paradigm in a sample of racially diverse undergraduate participants (data collected in 2018-2019). In addition to validating our model, we found that generation of affective responses occurs earlier than, and is neurally distinct from, valuation of that affect. Moreover, individual differences in contextual flexibility of affective weighting correlated only with later valuation processes, not earlier affect generation processes. Our results have important theoretical implications for emotion, emotion regulation, and decision making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

When expert predictions fail.

We examine the opportunities and challenges of expert judgment in the social sciences, scrutinizing the way social scientists make predictions. While social scientists show above-chance accuracy in predicting laboratory-based phenomena, they often struggle to predict real-world societal changes. We argue that most causal models used in social sciences are oversimplified, confuse levels of analysis to which a model applies, misalign the nature of the model with the nature of the phenomena, and fail to consider factors beyond the scientist's pet theory. Taking cues from physical sciences and meteorology, we advocate an approach that integrates broad foundational models with context-specific time series data. We call for a shift in the social sciences towards more precise, daring predictions and greater intellectual humility.

Cognitive regulation during decision making shifts behavioral control between ventromedial and dorsolateral prefrontal value systems.

Cognitive regulation is often used to influence behavioral outcomes. However, the computational and neurobiological mechanisms by which it affects behavior remain unknown. We studied this issue using an fMRI task in which human participants used cognitive regulation to upregulate and downregulate their cravings for foods at the time of choice. We found that activity in both ventromedial prefrontal cortex (vmPFC) and dorsolateral prefrontal cortex (dlPFC) correlated with value. We also found evidence that two distinct regulatory mechanisms were at work: value modulation, which operates by changing the values assigned to foods in vmPFC and dlPFC at the time of choice, and behavioral control modulation, which operates by changing the relative influence of the vmPFC and dlPFC value signals on the action selection process used to make choices. In particular, during downregulation, activation decreased in the value-sensitive region of dlPFC (indicating value modulation) but not in vmPFC, and the relative contribution of the two value signals to behavior shifted toward the dlPFC (indicating behavioral control modulation). The opposite pattern was observed during upregulation: activation increased in vmPFC but not dlPFC, and the relative contribution to behavior shifted toward the vmPFC. Finally, ventrolateral PFC and posterior parietal cortex were more active during both upregulation and downregulation, and were functionally connected with vmPFC and dlPFC during cognitive regulation, which suggests that they help to implement the changes to the decision-making circuitry generated by cognitive regulation.

Recruitment of dlPFC during dietary self-regulation predicts the transience of regulatory effects.

Recent work on the cognitive regulation of dietary decision-making suggests that regulation can alter both the choices that people make in the moment and longer-lasting preferences. However, it is unclear what mechanisms lead to temporary or lingering changes. To address this question, we used fMRI during a task employing the cognitive regulation of food choice and assessed changes in food preference from baseline to post-regulation. We found evidence that regulation may result in a temporary reconfiguration of the neural drivers of choice, de-emphasizing goal-inconsistent value-related computations in the ventromedial prefrontal cortex and resulting in more goal-consistent changes in value-related computations in the dorsolateral prefrontal cortex (dlPFC). Moreover, we find that the extent to which the dlPFC was recruited to represent different regulatory goals during the moment of choice negatively predicted the extent to which those regulatory goals produced lingering changes in preference. Our results suggest that the recruitment of the dlPFC in the service of regulation may have a downside: it is effective at changing behavior in the moment, but its effects on preferences are transient.

Framing Subjective Emotion Reports as Dynamic Affective Decisions.

Self-reports remain affective science's only direct measure of subjective affective experiences. Yet, little research has sought to understand the psychological process that transforms subjective experience into self-reports. Here, we propose that by framing these self-reports as dynamic affective decisions, affective scientists may leverage the computational tools of decision-making research, sequential sampling models specifically, to better disentangle affective experience from the noisy decision processes that constitute self-report. We further outline how such an approach could help affective scientists better probe the specific mechanisms that underlie important moderators of affective experience (e.g., contextual differences, individual differences, and emotion regulation) and discuss how adopting this decision-making framework could generate insight into affective processes more broadly and facilitate reciprocal collaborations between affective and decision scientists towards a more comprehensive and integrative psychological science.

On the accuracy, media representation, and public perception of psychological scientists' judgments of societal change.

At the onset of the COVID-19 pandemic, psychological scientists frequently made on-the-record predictions in public media about how individuals and society would change. Such predictions were often made outside these scientists' areas of expertise, with justifications based on intuition, heuristics, and analogical reasoning (Study 1; N = 719 statements). How accurate are these kinds of judgments regarding societal change? In Study 2, we obtained predictions from scientists (N = 717) and lay Americans (N = 394) in Spring 2020 regarding the direction of change for a range of social and psychological phenomena. We compared them to objective data obtained at 6 months and 1 year. To further probe how experience impacts such judgments, 6 months later (Study 3), we obtained retrospective judgments of societal change for the same domains (Nscientists = 270; Nlaypeople = 411). Bayesian analysis suggested greater credibility of the null hypothesis that scientists' judgments were at chance on average for both prospective and retrospective judgments. Moreover, neither domain-general expertise (i.e., judgmental accuracy of scientists compared to laypeople) nor self-identified domain-specific expertise improved accuracy. In a follow-up study on meta-accuracy (Study 4), we show that the public nevertheless expects psychological scientists to make more accurate predictions about individual and societal change compared to most other scientific disciplines, politicians, and nonscientists, and they prefer to follow their recommendations. These findings raise questions about the role psychological scientists could and should play in helping the public and policymakers plan for future events. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Decision value computation in DLPFC and VMPFC adjusts to the available decision time.

It is increasingly clear that simple decisions are made by computing decision values for the options under consideration, and then comparing these values to make a choice. Computational models of this process suggest that it involves the accumulation of information over time, but little is known about the temporal course of valuation in the brain. To examine this, we manipulated the available decision time and observed the consequences in the brain and behavioral correlates of choice. Participants were scanned with functional magnetic resonance imaging while they chose to eat or not eat basic food items, in two conditions differing in the amount of time provided for choice. After identifying valuation-related regions with unbiased whole-brain general linear models, we analyzed two regions of interest: ventromedial prefrontal cortex (VMPFC) and dorsolateral prefrontal cortex (DLPFC). Finite impulse response models of the upsampled estimated neural activity from those regions allowed us to examine the onset, duration and termination of decision value signals, and to compare across regions. We found evidence for the immediate onset of value computation in both regions, but an extended duration with longer decision time. However, this was not accompanied by behavioral changes in either the accuracy or determinants of choice. Finally, there was modest evidence that DLPFC computation correlated with, but lagged behind, VMPFC computation, suggesting the sharing of information across these regions. These findings have important implications for models of decision value computation and choice.