SOMAScience: A Novel Platform for Multidimensional, Longitudinal Pain Assessment.

Chronic pain is one of the most significant health issues in the United States, affecting more than 20% of the population. Despite its contribution to the increasing health crisis, reliable predictors of disease development, progression, or treatment outcomes are lacking. Self-report remains the most effective way to assess pain, but measures are often acquired in sparse settings over short time windows, limiting their predictive ability. In this paper, we present a new mobile health platform called SOMAScience. SOMAScience serves as an easy-to-use research tool for scientists and clinicians, enabling the collection of large-scale pain datasets in single- and multicenter studies by facilitating the acquisition, transfer, and analysis of longitudinal, multidimensional, self-report pain data. Data acquisition for SOMAScience is done through a user-friendly smartphone app, SOMA, that uses experience sampling methodology to capture momentary and daily assessments of pain intensity, unpleasantness, interference, location, mood, activities, and predictions about the next day that provide personal insights into daily pain dynamics. The visualization of data and its trends over time is meant to empower individual users' self-management of their pain. This paper outlines the scientific, clinical, technological, and user considerations involved in the development of SOMAScience and how it can be used in clinical studies or for pain self-management purposes. Our goal is for SOMAScience to provide a much-needed platform for individual users to gain insight into the multidimensional features of their pain while lowering the barrier for researchers and clinicians to obtain the type of pain data that will ultimately lead to improved prevention, diagnosis, and treatment of chronic pain.

Conceptual representations of uncertainty predict risky decision-making.

Decisions made under uncertainty often are considered according to their perceived subjective value. We move beyond this traditional framework to explore the hypothesis that conceptual representations of uncertainty influence risky choice. Results reveal that uncertainty concepts are represented along a dimension that jointly captures probabilistic and valenced features of the conceptual space. These uncertainty representations predict the degree to which an individual engages in risky decision-making. Moreover, we find that most individuals have two largely distinct representations: one for uncertainty and another for certainty. In contrast, a minority of individuals exhibit substantial overlap between their representations of uncertainty and certainty. Together, these findings reveal the relationship between the conceptualization of uncertainty and risky decisions.

A probabilistic map of emotional experiences during competitive social interactions.

Theories of emotion and decision-making argue that negative, high arousing emotions-such as anger-motivate competitive social choice (e.g., punishing and defecting). However, given the long-standing challenge of quantifying emotion and the narrow framework in which emotion is traditionally examined, it remains unclear which emotions are actually associated with motivating these types of choices. To address this gap, we combine machine learning algorithms with a measure of affect that is agnostic to any specific emotion label. The result is a probabilistic map of emotion that is used to classify the specific emotions experienced by participants in a variety of social interactions (Ultimatum Game, Prisoner's Dilemma, and Public Goods Game). Our results reveal that punitive and uncooperative choices are linked to a diverse array of negative, neutrally arousing emotions, such as sadness and disappointment, while only weakly linked to anger. These findings stand in contrast to the commonly held assumption that anger drives decisions to punish, defect, and freeride-thus, offering new insight into the role of emotion in motiving social choice.

A generalizable framework for assessing the role of emotion during choice.

The study of emotion has been plagued by several challenges that have left the field fractionated. To date, there is no dominant method for measuring the nebulous and often ill-defined experience of emotion. Here, we offer a new way forward, one that marries numerically precise measurements of affect with current models of human behavior, to more deeply understand the role of emotion during choice, and in particular, during social decision-making. This tool can be combined with multiple other measures that capture different features and levels of the emotional experience, making it particularly flexible to be used in any number of contexts. By operationalizing the classic circumplex model of affect so that it can deliver fine-grained, continuous measurements as affect evolves overtime, our goal is to provide a generalizable and flexible framework for computing affect to infer emotions so that we can assess their impact on human behavior. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Emotion prediction errors guide socially adaptive behaviour.

People make decisions based on deviations from expected outcomes, known as prediction errors. Past work has focused on reward prediction errors, largely ignoring violations of expected emotional experiences-emotion prediction errors. We leverage a method to measure real-time fluctuations in emotion as people decide to punish or forgive others. Across four studies (N = 1,016), we reveal that emotion and reward prediction errors have distinguishable contributions to choice, such that emotion prediction errors exert the strongest impact during decision-making. We additionally find that a choice to punish or forgive can be decoded in less than a second from an evolving emotional response, suggesting that emotions swiftly influence choice. Finally, individuals reporting significant levels of depression exhibit selective impairments in using emotion-but not reward-prediction errors. Evidence for emotion prediction errors potently guiding social behaviours challenge standard decision-making models that have focused solely on reward.

Emotional responses to prosocial messages increase willingness to self-isolate during the COVID-19 pandemic.

The COVID-19 pandemic may be one of the greatest modern societal challenges that requires widespread collective action and cooperation. While a handful of actions can help reduce pathogen transmission, one critical behavior is to self-isolate. Public health messages often use persuasive language to change attitudes and behaviors, which can evoke a wide range of negative and positive emotional responses. In a U.S. representative sample (N = 955), we presented two messages that leveraged either threatening or prosocial persuasive language, and measured self-reported emotional reactions and willingness to self-isolate. Although emotional responses to the interventions were highly heterogeneous, personality traits known to be linked with distinct emotional experiences (extraversion and neuroticism) explained significant variance in the arousal response. While results show that both types of appeals increased willingness to self-isolate (Cohen's d = 0.41), compared to the threat message, the efficacy of the prosocial message was more dependent on the magnitude of the evoked emotional response on both arousal and valence dimensions. Together, these results imply that prosocial appeals have the potential to be associated with greater compliance if they evoke highly positive emotional responses.

Why we don't always punish: Preferences for non-punitive responses to moral violations.

While decades of research demonstrate that people punish unfair treatment, recent work illustrates that alternative, non-punitive responses may also be preferred. Across five studies (N = 1,010) we examine non-punitive methods for restoring justice. We find that in the wake of a fairness violation, compensation is preferred to punishment, and once maximal compensation is available, punishment is no longer the favored response. Furthermore, compensating the victim-as a method for restoring justice-also generalizes to judgments of more severe crimes: participants allocate more compensation to the victim as perceived severity of the crime increases. Why might someone refrain from punishing a perpetrator? We investigate one possible explanation, finding that punishment acts as a conduit for different moral signals depending on the social context in which it arises. When choosing partners for social exchange, there are stronger preferences for those who previously punished as third-party observers but not those who punished as victims. This is in part because third-parties are perceived as relatively more moral when they punish, while victims are not. Together, these findings demonstrate that non-punitive alternatives can act as effective avenues for restoring justice, while also highlighting that moral reputation hinges on whether punishment is enacted by victims or third-parties.

Norms and the Flexibility of Moral Action.

A complex web of social and moral norms governs many everyday human behaviors, acting as the glue for social harmony. The existence of moral norms helps elucidate the psychological motivations underlying a wide variety of seemingly puzzling behavior, including why humans help or trust total strangers. In this review, we examine four widespread moral norms: Fairness, altruism, trust, and cooperation, and consider how a single social instrument-reciprocity-underpins compliance to these norms. Using a game theoretic framework, we examine how both context and emotions moderate moral standards, and by extension, moral behavior. We additionally discuss how a mechanism of reciprocity facilitates the adherence to, and enforcement of, these moral norms through a core network of brain regions involved in processing reward. In contrast, violating this set of moral norms elicits neural activation in regions involved in resolving decision conflict and exerting cognitive control. Finally, we review how a reinforcement mechanism likely governs learning about morally normative behavior. Together, this review aims to explain how moral norms are deployed in ways that facilitate flexible moral choices.

Rat intersubjective decisions are encoded by frequency-specific oscillatory contexts.

INTRODUCTION: It is unknown how the brain coordinates decisions to withstand personal costs in order to prevent other individuals' distress. Here we test whether local field potential (LFP) oscillations between brain regions create "neural contexts" that select specific brain functions and encode the outcomes of these types of intersubjective decisions. METHODS: Rats participated in an "Intersubjective Avoidance Test" (IAT) that tested rats' willingness to enter an innately aversive chamber to prevent another rat from getting shocked. c-Fos immunoreactivity was used to screen for brain regions involved in IAT performance. Multi-site local field potential (LFP) recordings were collected simultaneously and bilaterally from five brain regions implicated in the c-Fos studies while rats made decisions in the IAT. Local field potential recordings were analyzed using an elastic net penalized regression framework. RESULTS: Rats voluntarily entered an innately aversive chamber to prevent another rat from getting shocked, and c-Fos immunoreactivity in brain regions known to be involved in human empathy-including the anterior cingulate, insula, orbital frontal cortex, and amygdala-correlated with the magnitude of "intersubjective avoidance" each rat displayed. Local field potential recordings revealed that optimal accounts of rats' performance in the task require specific frequencies of LFP oscillations between brain regions in addition to specific frequencies of LFP oscillations within brain regions. Alpha and low gamma coherence between spatially distributed brain regions predicts more intersubjective avoidance, while theta and high gamma coherence between a separate subset of brain regions predicts less intersubjective avoidance. Phase relationship analyses indicated that choice-relevant coherence in the alpha range reflects information passed from the amygdala to cortical structures, while coherence in the theta range reflects information passed in the reverse direction. CONCLUSION: These results indicate that the frequency-specific "neural context" surrounding brain regions involved in social cognition encodes outcomes of decisions that affect others, above and beyond signals from any set of brain regions in isolation.

The MR2: A multi-racial, mega-resolution database of facial stimuli.

Faces impart exhaustive information about their bearers, and are widely used as stimuli in psychological research. Yet many extant facial stimulus sets have substantially less detail than faces encountered in real life. In this paper, we describe a new database of facial stimuli, the Multi-Racial Mega-Resolution database (MR2). The MR2 includes 74 extremely high resolution images of European, African, and East Asian faces. This database provides a high-quality, diverse, naturalistic, and well-controlled facial image set for use in research. The MR2 is available under a Creative Commons license, and may be accessed online.