Affect and Decision Making: Insights and Predictions from Computational Models

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.

The effect of auditory valence on subsequent visual semantic processing

Emotion influences many cognitive processes and plays an important role in our daily life. Previous studies focused on the effects of arousal on subsequent cognitive processing, but the effect of valence on subsequent semantic processing is still not clear. The present study examined the effect of auditory valence on subsequent visual semantic processing when controlling for arousal. We used instrumental music clips varying in valence while matching in arousal to induce valence states and asked participants to make natural or man-made judgements on subsequent neutral objects. We found that positive and negative valences similarly impaired subsequent semantic processing compared with neutral valence. The linear ballistic accumulator model analyses showed that the valence effects can be attributed to drift rate differences, suggesting that the effects are likely related to attentional selection. Our findings are consistent with a motivated attention model, indicating comparable attentional capture by both positive and negative valences in modulating subsequent cognitive processes.

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.

Face masks influence emotion judgments of facial expressions: a drift-diffusion model

Face masks slow the spread of SARS-CoV-2, but it has been unknown how masks might reshape social interaction. One important possibility is that masks may influence how individuals communicate emotion through facial expressions. Here, we clarify to what extent-and how-masks influence facial emotion communication, through drift-diffusion modeling (DDM). Over two independent pre-registered studies, conducted three and 6 months into the COVID-19 pandemic, online participants judged expressions of 6 emotions (anger, disgust, fear, happiness, sadness, surprise) with the lower or upper face "masked" or unmasked. Participants in Study 1 (N = 228) correctly identified expressions above chance with lower face masks. However, they were less likely-and slower-to correctly identify these expressions relative to without masks, and they accumulated evidence for emotion more slowly-via decreased drift rate in DDM. This pattern replicated and intensified 3 months later in Study 2 (N = 264). These findings highlight how effectively individuals still communicate with masks, but also explain why they can experience difficulties communicating when masked. By revealing evidence accumulation as the underlying mechanism, this work suggests that time-sensitive situations may risk miscommunication with masks. This research could inform critical interventions to promote continued mask wearing as needed.

Identifying the duration of emotional stimulus presentation for conscious versus subconscious perception via hierarchical drift diffusion models

To investigate subliminal priming effects, different durations for stimulus presentation are applied ranging from 8 to 30 ms. This study aims to select an optimal presentation span whichleads to a subconscious processing. 40 healthy participants rated emotional faces (sad, neutral or happy expression) presented for 8.3 ms, 16.7 ms and 25 ms. Alongside subjective and objectivestimulus awareness, task performance was estimated via hierarchical drift diffusion models. Participants reported stimulus awareness in 65 % of the 25 ms trials,in 36 % of 16.7 ms trials, and in 2.5 % of 8.3 ms trials.Emotion-dependent responses were reflected in decreased performance (drift rates, accuracy)during sad trials. The detection rate (probability of making a correct response) during 8.3 ms was 12.2 % and slightly above chance level (33.333 % for three response options) during 16.7 ms trials (36.8 %). The experiments suggest a presentation time of 16.7 ms as optimal for subconscious priming. An emotion-specific response was detected during 16.7 ms while the performanceindicates a subconscious processing.

Whose emotion is it? Perspective matters to understand brain-body interactions in emotions

Feeling happy, or judging whether someone else is feeling happy are two distinct facets of emotions that nevertheless rely on similar physiological and neural activity. Differentiating between these two states, also called Self/Other distinction, is an essential aspect of empathy, but how exactly is it implemented? In non-emotional cognition, the transient neural response evoked at each heartbeat, or heartbeat evoked response (HER), indexes the self and signals Self/Other distinction. Here, using electroencephalography (n = 32), we probe whether HERs' role in Self/Other distinction extends also to emotion - a domain where brain-body interactions are particularly relevant. We asked participants to rate independently validated affective scenes, reporting either their own emotion (Self) or the emotion expressed by people in the scene (Other). During the visual cue indicating to adopt the Self or Other perspective, before the affective scene, HERs distinguished between the two conditions, in visual cortices as well as in the right frontal operculum. Physiological reactivity (facial electromyogram, skin conductance, heart rate) during affective scene co-varied as expected with valence and arousal ratings, but also with the Self- or Other- perspective adopted. Finally, HERs contributed to the subjective experience of valence in the Self condition, in addition to and independently from physiological reactivity. We thus show that HERs represent a trans-domain marker of Self/Other distinction, here specifically contributing to experienced valence. We propose that HERs represent a form of evidence related to the 'I' part of the judgement 'To which extent do I feel happy'. The 'I' related evidence would be combined with the affective evidence collected during affective scene presentation, accounting at least partly for the difference between feeling an emotion and identifying it in someone else.

ERP and attachment dimensions as predictors of seeking care or food comfort in stressful situations

In this study, we aimed to test the association of the Late Positive Potential (LPP) response and attachment dimensions in the choice of care/food pictures and its reaction time (RT) in threatening versus neutral conditions. Fifty-two participants (38 females, M_age 22.62) responded to the ECR questionnaire and were exposed to adequate visual stimuli, during EEG recording. Results showed that threatening stimuli increase the choice of care, decrease RT, and increase LPP magnitude in centro-parietal areas (Cpz, Pz, P3 and P4). Food choice was lower, with increased RT. Furthermore, larger LPP magnitude in centro-parietal cluster was associated with increased RT in the choice of care. Considering the dimensions of attachment, in threatening conditions, while anxiety was not associated with RT and care/food choice, avoidance was associated with an increase in care choice and RT. In conclusion, the specific association of increased RT in care choice with high LPP magnitude centro-parietal cluster may be explained in terms of a functional interference of these areas in the choice of care, but not of food. Further, we postulate that the increased RT of avoidant individuals may reflect a more articulated choice process.

Self-judgment dissected: A computational modeling analysis of self-referential processing and its relationship to trait mindfulness facets and depression symptoms

Cognitive theories of depression, and mindfulness theories of well-being, converge on the notion that self-judgment plays a critical role in mental health. However, these theories have rarely been tested via tasks and computational modeling analyses that can disentangle the information processes operative in self-judgments. We applied a drift-diffusion computational model to the self-referential encoding task (SRET) collected before and after an 8-week mindfulness intervention (n = 96). A drift-rate regression parameter representing positive-relative to negative-self-referential judgment strength positively related to mindful awareness and inversely related to depression, both at baseline and over time; however, this parameter did not significantly relate to the interaction between mindful awareness and nonjudgmentalness. At the level of individual depression symptoms, at baseline, a spectrum of symptoms (inversely) correlated with the drift-rate regression parameter, suggesting that many distinct depression symptoms relate to valenced self-judgment between subjects. By contrast, over the intervention, changes in only a smaller subset of anhedonia-related depression symptoms showed substantial relationships with this parameter. Both behavioral and model-derived measures showed modest split-half and test-retest correlations. Results support cognitive theories that implicate self-judgment in depression and mindfulness theories, which imply that mindful awareness should lead to more positive self-views.

Cardiac cycle affects risky decision-making

This study investigates whether decision-making under uncertainty is influenced by the cardiac cycle. To test this hypothesis, we examined the influence of the cardiac cycle on an individual's decision-making process in a gambling experiment. Participants were asked to choose one option with a sure payout or uncertain option with varying degrees of winning probability, ambiguity, and monetary amounts. The onset of presentation of the options is timed to coincide with either cardiac ventricular systole or diastole. The results show that, for most participants, the risk aversion score was lower in the systole trial than in the diastole trial. Model-based exploratory analysis revealed that the higher propensity to take risks in the systole trial compared with that in the diastole trial could be captured better by the change in the gambling bias against the utility of the risky options, rather than by a change in risk attitude. The results provide evidence that the natural fluctuation of cardiac afferent signals can affect risky decision-making.

Financial incentives facilitate stronger neural computation of prosocial decisions in lower empathic adult females

Financial incentives are commonly used to motivate behaviors. However, there is also evidence that incentives can impede the behavior they are supposed to foster, for example, documented by a decrease in blood donations if a financial incentive is offered. Based on these findings, previous studies assumed that prosocial motivation is shaped by incentives. However, so far, there is no direct evidence showing an interaction between financial incentives and a specific prosocial motive. Combining drift-diffusion modeling and fMRI, we investigated the effect of financial incentives on empathy, i.e., one of the key motives driving prosocial decisions. In the empathy-alone condition, participants made prosocial decisions based on empathy. In the empathy-bonus condition, they were offered a financial bonus for prosocial decisions, in addition to empathy induction. On average, the bonus enhanced the information accumulation in empathy-based decisions. On the neural level, this enhancement was related to the anterior insula, the same region that also correlated with empathy ratings. Moreover, the effect of the financial incentive on anterior insula activation was stronger the lower a person scored on empathy. These findings show that financial incentives enhance prosocial motivation in the absence of empathy.

Cortico-Brainstem Mechanisms of Biased Perceptual Decision-Making in the Context of Pain

Prior expectations can bias how we perceive pain. Using a drift diffusion model, we recently showed that this influence is primarily based on changes in perceptual decision-making (indexed as shift in starting point). Only during unexpected application of high-intensity noxious stimuli, altered information processing (indexed as increase in drift rate) explained the expectancy effect on pain processing. Here, we employed functional magnetic resonance imaging to investigate the neural basis of both these processes in healthy volunteers. On each trial, visual cues induced the expectation of high- or low-intensity noxious stimulation or signaled equal probability for both intensities. Participants categorized a subsequently applied electrical stimulus as either low- or high-intensity pain. A shift in starting point towards high pain correlated negatively with right dorsolateral prefrontal cortex activity during cue presentation underscoring its proposed role of "keeping pain out of mind". This anticipatory right dorsolateral prefrontal cortex signal increase was positively correlated with periaqueductal gray (PAG) activity when the expected high-intensity stimulation was applied. A drift rate increase during unexpected high-intensity pain was reflected in amygdala engagement and increased functional connectivity between amygdala and PAG. Our findings suggest involvement of the PAG in both decision-making bias and altered information processing to implement expectancy effects on pain. PERSPECTIVE: Modulation of pain through expectations has been linked to changes in perceptual decision-making and altered processing of afferent information. Our results suggest involvement of the dorsolateral prefrontal cortex, amygdala, and periaqueductal gray in these processes.

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.

Neurocomputational mechanisms engaged in moral choices and moral learning

The neural circuitry involved in moral decisions has been studied since the early days of cognitive neuroscience, mainly using moral dilemma. However, the neurocomputational mechanisms describing how the human brain makes moral decisions and learns in various moral contexts are only starting to be established. Here we review recent results from an emerging field using model-based fMRI, which describes moral choices at a mechanistic level. These findings unify the field of moral decision making, extend a conceptual framework previously developed for value-based decision making and characterize how moral processes are computed in the brain. Moral dilemma can be modeled as value-based decisions that weigh self-interests against moral costs/harm to others and different types of prediction errors can be distinguished in different aspects of moral learning. These key computational signals help to describe moral choices and moral learning at an algorithmic level and to reveal how these cognitive operations are implemented in the brain. This researches provide a foundation to account for the neurocomputational mechanisms underlying moral decision making.

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.

OCD Influences Evidence Accumulation During Decision Making in Males but Not Females During Perceptual and Value-Driven Choice

Individuals with obsessive-compulsive disorder (OCD) often have difficulty making decisions. Valuation and value-based judgements are particularly difficult. The mechanisms underlying these impairments are still poorly understood. Previous work has suggested that individuals with OCD require more information prior to making a choice during perceptual discrimination tasks. Little previous work has examined value-guided choice in OCD. Here we examined perceptual and value-based decision making in adults with OCD, using a novel task in which the two types of decision are tested in parallel using the same individually calibrated sets of visual stimuli (Perceptual and Value-based decision-making task, PVDM). Twenty-seven unmedicated participants with OCD (16 female) and thirty-one healthy controls (15 female) were tested. Data were analyzed using hierarchical drift-diffusion modeling (HDDM). Decision formation was altered in OCD, but differentially between genders: males with OCD, but not females, accumulated more information (i.e., were more cautious) and were less effective in evidence accumulation than age- and IQ-matched healthy males. Furthermore, males with OCD, but not females, were less likely than controls to adjust the process of evidence accumulation across decision contexts. These unexpectedly gender-dimorphic effects suggest that more attention should be paid to gender differences in studies of OCD, and of pathophysiology more broadly.

Diffusion model-based understanding of subliminal affective priming in continuous flash suppression

Affective states influence our decisions even when processed unconsciously. Continuous flash suppression (CFS) is a new variant of binocular rivalry that can be used to render the prime subliminal. Nonetheless, how prior information from emotional faces suppressed by CFS influences subsequent decision-making remains unclear. Here, we employed a CFS priming task to examine the effect of the two main types of information conveyed by faces, i.e., facial identity and emotion, on the evaluation of target words as positive or negative. The hierarchical diffusion model was used to investigate the underlying mechanisms. A significant interaction effect on response time was observed following the angry face prime but not the happy or neutral face primes. The results of the diffusion model analyses revealed that the priming effects of facial identity were mapped onto the drift rate and erased the 'positive bias' (the processing advantage of positive over negative stimuli). Meanwhile, the positive emotional faces increased the nondecision time in response to negative target words. The model-based analysis implies that both facial identity and emotion are processed under CFS.

Identifying the neural dynamics of category decisions with computational model-based functional magnetic resonance imaging

Successful categorization requires a careful coordination of attention, representation, and decision making. Comprehensive theories that span levels of analysis are key to understanding the computational and neural dynamics of categorization. Here, we build on recent work linking neural representations of category learning to computational models to investigate how category decision making is driven by neural signals across the brain. We uniquely combine functional magnetic resonance imaging with drift diffusion and exemplar-based categorization models to show that trial-by-trial fluctuations in neural activation from regions of occipital, cingulate, and lateral prefrontal cortices are linked to category decisions. Notably, only lateral prefrontal cortex activation was associated with exemplar-based model predictions of trial-by-trial category evidence. We propose that these brain regions underlie distinct functions that contribute to successful category learning.

A computational reward learning account of social media engagement

Social media has become a modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (likes), portraying the online world as a Skinner Box for the modern human. Yet despite such portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. Here, we apply a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyze over one million posts from over 4000 individuals on multiple social media platforms, using computational models based on reinforcement learning theory. Our results consistently show that human behavior on social media conforms qualitatively and quantitatively to the principles of reward learning. Specifically, social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction. Results further reveal meaningful individual difference profiles in social reward learning on social media. Finally, an online experiment (n = 176), mimicking key aspects of social media, verifies that social rewards causally influence behavior as posited by our computational account. Together, these findings support a reward learning account of social media engagement and offer new insights into this emergent mode of modern human behavior.

Computational modelling of attentional bias towards threat in paediatric anxiety

Computational modelling can be used to precisely characterize the cognitive processes involved in attentional biases towards threat, yet so far has only been applied in the context of adult anxiety. Furthermore, studies investigating attentional biases in childhood anxiety have largely used tasks that conflate automatic and controlled attentional processes. By using a perceptual load paradigm, we separately investigate contributions from automatic and controlled processes to attentional biases towards negative stimuli and their association with paediatric anxiety. We also use computational modelling to investigate these mechanisms in children for the first time. In a sample of 60 children (aged 5‐11 years) we used a perceptual load task specifically adapted for children, in order to investigate attentional biases towards fearful (compared with happy and neutral) faces. Outcome measures were reaction time and percentage accuracy. We applied a drift diffusion model to investigate the precise cognitive mechanisms involved. The load effect was associated with significant differences in response time, accuracy and the diffusion modelling parameters drift rate and extra‐decisional time. Greater anxiety was associated with greater accuracy and the diffusion modelling parameter ‘drift rate’ on the fearful face trials. This was specific to the high load condition. These findings suggest that attentional biases towards fearful faces in childhood anxiety are driven by increased perceptual sensitivity towards fear in automatic attentional systems. Our findings from computational modelling suggest that current attention bias modification treatments should target perceptual encoding directly rather than processes occurring afterwards.