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).

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.

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.

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.

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.

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).

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.

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.

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.

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.

Temporal dynamics of decision making: A synthesis of computational and neurophysiological approaches.

As interest in the temporal dynamics of decision-making has grown, researchers have increasingly turned to computational approaches such as the drift diffusion model (DDM) to identify how cognitive processes unfold during choice. At the same time, technological advances in noninvasive neurophysiological methods such as electroencephalography and magnetoencephalography now allow researchers to map the neural time course of decision making with millisecond precision. Combining these approaches can potentially yield important new insights into how choices emerge over time. Here we review recent research on the computational and neurophysiological correlates of perceptual and value-based decision making, from DDM parameters to scalp potentials and oscillatory neural activity. Starting with motor response preparation, the most well-understood aspect of the decision process, we discuss evidence that urgency signals and shifts in baseline activation, rather than shifts in the physiological value of the choice-triggering response threshold, are responsible for adjusting response times under speeded choice scenarios. Research on the neural correlates of starting point bias suggests that prestimulus activity can predict biases in motor choice behavior. Finally, studies examining the time dynamics of evidence construction and evidence accumulation have identified signals at frontocentral and centroparietal electrodes associated respectively with these processes, emerging 300-500 ms after stimulus onset. These findings can inform psychological theories of decision-making, providing empirical support for attribute weighting in value-based choice while suggesting theoretical alternatives to dual-process accounts. Further research combining computational and neurophysiological approaches holds promise for providing greater insight into the moment-by-moment evolution of the decision process. This article is categorized under: Psychology > Reasoning and Decision Making Neuroscience > Cognition Economics > Individual Decision-Making.

Alpha oscillations and event-related potentials reflect distinct dynamics of attribute construction and evidence accumulation in dietary decision making.

How does regulatory focus alter attribute value construction (AVC) and evidence accumulation (EA)? We recorded electroencephalogram during food choices while participants responded naturally or regulated their choices by attending to health attributes or decreasing attention to taste attributes. Using a drift diffusion model, we predicted the time course of neural signals associated with AVC and EA. Results suggested that event-related potentials (ERPs) correlated with the time course of model-predicted taste-attribute signals, with no modulation by regulation. By contrast, suppression of frontal and occipital alpha power correlated with the time course of EA, tracked tastiness according to its goal relevance, and predicted individual variation in successful down-regulation of tastiness. Additionally, an earlier rise in frontal and occipital theta power represented food tastiness more strongly during regulation and predicted a weaker influence of food tastiness on behaviour. Our findings illuminate how regulation modifies the representation of attributes during the process of EA.

Beta oscillations following performance feedback predict subsequent recall of task-relevant information.

Reward delivery in reinforcement learning tasks elicits increased beta power in the human EEG over frontal areas of the scalp but it is unclear whether these 20-30 Hz oscillations directly facilitate reward learning. We previously proposed that frontal beta is not specific to reward processing but rather reflects the role of prefrontal cortex in maintaining and transferring task-related information to other brain areas. To test this proposal, we had subjects perform a reinforcement learning task followed by a memory recall task in which subjects were asked to recall stimuli associated either with reward feedback (Reward Recall condition) or error feedback (Error Recall condition). We trained a classifier on post-feedback beta power in the Reward Recall condition to discriminate trials associated with reward feedback from those associated with error feedback and then tested the classifier on post-feedback beta power in the Error Recall condition. Crucially, the model classified error-related beta in the Error Recall condition as reward-related. The model also predicted stimulus recall from post-feedback beta power irrespective of feedback valence and task condition. These results indicate that post-feedback beta power is not specific to reward processing but rather reflects a more general task-related process.

Attentional priorities drive effects of time pressure on altruistic choice.

Dual-process models of altruistic choice assume that automatic responses give way to deliberation over time, and are a popular way to conceptualize how people make generous choices and why those choices might change under time pressure. However, these models have led to conflicting interpretations of behaviour and underlying psychological dynamics. Here, we propose that flexible, goal-directed deployment of attention towards information priorities provides a more parsimonious account of altruistic choice dynamics. We demonstrate that time pressure tends to produce early gaze-biases towards a person's own outcomes, and that individual differences in this bias explain how individuals' generosity changes under time pressure. Our gaze-informed drift-diffusion model incorporating moment-to-moment eye-gaze further reveals that underlying social preferences both drive attention, and interact with it to shape generosity under time pressure. These findings help explain existing inconsistencies in the field by emphasizing the role of dynamic attention-allocation during altruistic choice.

Affect and Decision Making: Insights and Predictions from Computational Models.

In recent years interest in integrating the affective and decision sciences has skyrocketed. Immense progress has been made, but the complexities of each field, which can multiply when combined, present a significant obstacle. A carefully defined framework for integration is needed. The shift towards computational modeling in decision science provides a powerful basis and a path forward, but one whose synergistic potential will only be fully realized by drawing on the theoretical richness of the affective sciences. Reviewing research using a popular computational model of choice (the drift diffusion model), we discuss how mapping concepts to parameters reduces conceptual ambiguity and reveals novel hypotheses.

Empathy is hard work: People choose to avoid empathy because of its cognitive costs.

Empathy is considered a virtue, yet it fails in many situations, leading to a basic question: When given a choice, do people avoid empathy? And if so, why? Whereas past work has focused on material and emotional costs of empathy, here, we examined whether people experience empathy as cognitively taxing and costly, leading them to avoid it. We developed the empathy selection task, which uses free choices to assess the desire to empathize. Participants make a series of binary choices, selecting situations that lead them to engage in empathy or an alternative course of action. In each of 11 studies (N = 1,204) and a meta-analysis, we found a robust preference to avoid empathy, which was associated with perceptions of empathy as more effortful and aversive and less efficacious. Experimentally increasing empathy efficacy eliminated empathy avoidance, suggesting that cognitive costs directly cause empathy choice. When given the choice to share others' feelings, people act as if it is not worth the effort. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Deconstructing Theory-of-Mind Impairment in High-Functioning Adults with Autism.

Inferring the beliefs, desires, and intentions of other people ("theory of mind," ToM) requires specialized psychological processes that represent the minds of others as distinct from our own [1-3]. ToM is engaged ubiquitously in our everyday social behavior and features a specific developmental trajectory [4] that is notably delayed in children with autism spectrum disorder (ASD) [5, 6]. In healthy individuals, model-based analyses of social learning and decision-making have successfully elucidated specific computational components of ToM processing [7-11]. However, the use of this approach to study ToM impairment in ASD has been extremely limited [10, 12]. To better characterize specific ToM impairment in ASD, we developed a novel learning task and applied model-based analyses in high-functioning adults with ASD and matched healthy controls. After completing a charitable donation task, participants performed a "mentalizer" task in which they observed another person (the agent) complete the same charity task. The mentalizer task probed the participants' ability to acquire and use ToM representations. To accurately predict agent behavior, participants needed to dynamically track the agent's beliefs (true or false) about an experimental context that varied over time and use that information to infer the agent's intentions from their actions. ASD participants were specifically impaired at using their estimates of agent belief to learn agent intentions, though their ability to track agent belief was intact and their reasoning about belief and intentions was rational. Furthermore, model parameters correlated with aspects of social functioning, e.g., ADOS severity scores [13]. Together, these results identify novel, and more specific, targets for future research.

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.