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The human face plays a central role in emotions and social communication. The emotional and somatic motor networks generate facial behaviors, but whether facial behaviors have representations in the structural anatomy of the human brain is unknown. We coded 16 facial behaviors in 55 healthy older adults who viewed five videos that elicited emotions and examined whether individual differences in facial behavior were related to regional variation in gray matter volume. Voxel-based morphometry analyses revealed that greater emotional facial behavior during the disgust trial (i.e. greater brow furrowing and eye tightening as well as nose wrinkling and upper lip raising) and the amusement trial (i.e. greater smiling and eye tightening) was associated with larger gray matter volume in midcingulate cortex, supplementary motor area, and precentral gyrus, areas spanning both the emotional and somatic motor networks. When measured across trials, however, these facial behaviors (and others) only related to gray matter volume in the precentral gyrus, a somatic motor network hub. These findings suggest that the emotional and somatic motor networks store structural representations of facial behavior and that the midcingulate cortex is critical for generating the predictable movements in the face that arise during emotions.

BackgroundPublic health crises, such as the COVID-19 pandemic, have prompted a need for health agencies to improve their disease preparedness strategies, informing their communities of new information and promoting preventive behaviors to help curb the spread of the virus.MethodsWe ran unsupervised machine learning and emotion analysis, validated with manual coding, on posts of health agencies (N = 1588) and their associated public comments (N = 7813) during a crucial initial period of the COVID-19 pandemic (January 2020 to February 2021) among nine different counties with a higher proportion of vaccine-hesitant communities in Northern California. In addition, we explored differences in concerns and expressed emotions by two key group-level factors, county-level COVID-19 death rate and political party affiliation.ResultsWe consistently find that while health agencies primarily disseminated information about COVID-19 and the vaccine, they failed to address the concerns of their communities as expressed in public comment sections. Topics among public audiences focused on concerns with the COVID-19 vaccine safety and rollout, state mandates, flu vaccination, and frustration with politicians, and they expressed more positive and more negative emotions than health agencies. Further, there were several differences in primary topics and emotions expressed among public audiences by county-level COVID-19 death rate and political party affiliation.ConclusionWhile this research serves as a case study, findings indicate how local health agencies, and their audiences, discuss their perceptions and concerns regarding the COVID-19 pandemic and may inform health communication researchers and practitioners on how to prepare and manage for emerging health crises.

Everyone agrees that feelings and actions are intertwined, but cannot agree how. According to dominant models, actions are directed by estimates of value and these values shape or are shaped by affect. I propose instead that affect is the only form of value that drives actions. Our mind constantly represents potential future states and how they would make us feel. These states collectively form a gradient reflecting feelings we could experience depending on actions we take. Motivated behavior reflects the process of traversing this affective gradient, towards desirable states and away from undesirable ones. This affective gradient hypothesis solves the puzzle of where values and goals come from, and offers a parsimonious account of apparent conflicts between emotion and cognition.

Academic abstractPatience has been of great interest to religious scholars, philosophers, and psychological scientists. Their efforts have produced numerous insights but no cohesive theoretical approach to understanding the broad set of experiences people label as patience. I propose a novel view of patience, one that departs from but ties together existing approaches. Grounded in theories of emotion and emotion regulation, I propose impatience as a discrete emotion triggered by an objectionable delay of some sort, and patience (as a state or process rather than a virtue) as a form of emotion regulation that targets the subjective experience and outward expression of impatience. I propose a number of predictors and consequences of patience and impatience and provide initial evidence for many of the theory's tenets. This theoretical approach, the process model of patience, reveals coherence across varied fields and methodologies and generates novel, testable, and timely questions for future patience scholars.Public abstract"Patience is a virtue" is a familiar exhortation, and patience has been of great interest to religious scholars, philosophers, and psychological scientists. Their efforts have produced numerous insights but no cohesive theoretical approach to understanding the broad set of experiences people label as patience. This paper proposes an entirely novel view of patience, one that departs from but ties together existing approaches. I propose that impatience is an emotion, triggered by a frustrating delay of some sort, and patience captures the various ways people try to deal with their experience of impatience. I also propose that various aspects of the situation and the person combine to determine the intensity of impatience and the effectiveness of patience. Finally, I discuss the implications of a theoretical model, the process model of patience, for both scientific inquiry and issues of social justice, which are often fueled by appropriate experiences of impatience.

This article examines individuals' cognitive, emotional, and behavioral responses to the destabilization of their occupations, how their responses differ, and why. We focus on the context of journalism, an occupation undergoing severe destabilization in the U.S. and seen as deeply meaningful by many of its incumbents. Drawing on two waves of interviews with 72 unemployed or former newspaper journalists, conducted over five months, and additional interviews with 22 others, we identified two sets of responses, each characterized by distinctive cognitive, emotional, and behavioral patterns. Building on these findings, we developed the construct of "meaning fixedness" to capture the extent to which individuals view the meaning of the different components of their work to be fixed within one occupational context or flexible across different occupations. We found that participants held different interpretations of journalism's destabilization and assessments of how portable their work components were to other occupational contexts: flexible-meaning perceivers generally engaged in actions to reinvent their career, while fixed-meaning perceivers engaged in actions to persist in journalism with the hope that their occupation could be restored. Our findings culminate in a model of meaning fixedness and how it shapes individuals' navigation of occupational destabilization. This research uncovers an individual-level perception that has the potential to shape the varied responses to occupational changes observed in prior research, contributing to the literatures on occupations, the meaning of work, and role transitions. [ABSTRACT FROM AUTHOR]

Racial stereotypes are commonly activated by informational cues that are detectable in people's faces. Here, we used a sequential priming task to examine whether and how the salience of emotion (angry/scowling vs. happy/smiling expressions) or apparent race (Black vs. White) information in male face primes shapes racially biased weapon identification (gun vs. tool) decisions. In two experiments (Ntotal = 546) using two different manipulations of facial information salience, racial bias in weapon identification was weaker when the salience of emotion expression versus race was heightened. Using diffusion decision modeling, we tested competing accounts of the cognitive mechanism by which the salience of facial information moderates this behavioral effect. Consistent support emerged for an initial bias account, whereby the decision process began closer to the "gun" response upon seeing faces of Black versus White men, and this racially biased shift in the starting position was weaker when emotion versus race information was salient. We discuss these results vis-à-vis prior empirical and theoretical work on how facial information salience moderates racial bias in decision-making.

University brings many changes and challenges in students inducing a great amount of emotion fluctuation. During these times of emotion fluctuation, students can experience a drop off in academic performance and a rise in stress levels. In these times, effective emotion regulation has demonstrated to be a valuable tool correlated with improving academic performance and lowering stress. Music and light/color have been shown to be effective aspects of a smart environment for emotion regulation. Combining this knowledge along with physiological signal analysis correlating with emotions, we set out to develop a smart environment system set to aid students with emotion regulation. Our goal is to develop a low-cost accessible tool for university students that enhances student performance, emotional self understanding, and positive emotion regulation habits.Faculty Advisor: Professor Dang

Although guilt is often considered the most prevalent emotional outcome of work–family conflict (WFC), most work–family research focuses on family‐related guilt stemming from work interference with family, rather than job‐related guilt stemming from family interference with work (FIW). In addition, there is little understanding of how different employee social groups experience the implications of FIW in their daily lives. To address these research gaps, this study explores the relationship between daily FIW and job‐related guilt, and its subsequent impact on job satisfaction. It also investigates variation in these relationships by (1) gender and (2) the intersection of gender and ethnicity. Bayesian multilevel structural equation modeling using data from 5‐day diary surveys from 210 solicitors in Britain shows daily FIW is associated with higher job‐related guilt and subsequently lower job satisfaction. The relationship is stronger for women than men in general, but is also stronger for South Asian women than white British women (and men), and for South Asian men than white British men. This suggests that studies focusing on single social group characteristics (e.g., gender) are likely to obscure intersectional effects that might produce significant within‐group variation. The findings also highlight the importance of integrating workplace inequality arguments into theorization of WFC. [ABSTRACT FROM AUTHOR]

From advertisers and marketers to salespeople and leaders, everyone wants to hold attention. They want to make ads, pitches, presentations, and content that captivates audiences and keeps them engaged. But not all content has that effect. What makes some content more engaging? A multimethod investigation combines controlled experiments with natural language processing of 600,000 reading sessions from over 35,000 pieces of content to examine what types of language hold attention and why. Results demonstrate that linguistic features associated with processing ease (e.g., concrete or familiar words) and emotion both play an important role. Rather than simply being driven by valence, though, the effects of emotional language are driven by the degree to which different discrete emotions evoke arousal and uncertainty. Consistent with this idea, anxious, exciting, and hopeful language holds attention while sad language discourages it. Experimental evidence underscores emotional language's causal impact and demonstrates the mediating role of uncertainty and arousal. The findings shed light on what holds attention; illustrate how content creators can generate more impactful content; and, as shown in a stylized simulation, have important societal implications for content recommendation algorithms. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Urgency has been defined as the tendency towards rash speech and behavior in the context of emotion. Measures of Urgency have been found to have robust predictive power for psychopathologies and problematic behaviors. In the current study, we question whether emotions are unique drivers of urgency, or if emotions are potent exemplars of contexts that lead to rash speech and behavior. The Emotion Specific model and the Broader Contexts model correspond with these two conceptualizations of urgency, and they frame our pre-registered hypotheses. METHODS: Participants from two well-powered samples (n = 600,n = 588) completed 9 modified items from the Urgency and Positive Urgency scales to assess rash responses in each of four contexts - Upset, Excited, Tired, and Hungry - and a fifth In General set. After data cleaning, we used principal components analysis to construct a unidimensional, 4-item set that was applied to capture impulsive behavior across the five contexts. RESULTS: We found that this research tool, called the Contexts of Impulsive Behaviors (CIBS), replicated in the second dataset, and it had adequate internal reliability in both samples. Although the Emotion Specific model was supported by the fact that the Upset context had a greater mean and greater variance than the Tired and Hungry contexts, most results supported the Broader Contexts model. That is, CIBS contexts were highly intercorrelated, and bivariate correlations with psychopathology were not significantly different across contexts. In partial correlations, effects of the Upset and Excited contexts were partially or fully statistically mediated by the Tired and Hungry contexts. DISCUSSION: These findings suggest that emotions are potent contexts for impulsive behaviors. At the same time, those with high urgency are vulnerable to impulsivity in other contexts, such as fatigue and hunger, that challenge the regulatory functions of the prefrontal cortex. Limitations, future directions, and clinical implications are discussed.

Emotions, including stress and anxiety, strongly influence cognition and learning experiences. This study investigates the impacts of stress on cognitive load during learning, considering baseline anxiety levels and fluctuating stress. With a focus on technology-based learning, a web-based HTML introduction module was used. Using a social stress test, 15 participants underwent a stressful situation during learning, while the control group of 15 were in a neutral condition. Results indicate significantly elevated stress levels in the experimental group throughout the experiment, with a corresponding decrease in learning performance. For high perceived difficulty, the stressed condition demonstrated a significant increase in response time compared to the control condition. In contrast, when experiencing low perceived difficulty, a significant difference in response time across conditions was not found. Findings emphasise the importance of managing stress in educational contexts to optimise learning outcomes in the evolving landscape of technology-based learning.

The framework of event perception suggests that people segment continuous perceptual input into discrete events by forming mental representations of ongoing activity. Prior work extending the segmentation framework to emotion perception shows that a richer emotion vocabulary is associated with segmentation of emotion events in greater agreement with the cultural ingroup. However, little is known about how labeling behaviors themselves shape the segmentation of emotion events. Here, we look at the effect of labeling on emotion segmentation. Participants were randomly assigned to simply segment videos into discrete emotion events or to segment only when an emotion label is available and to label the segmented event. We found that compared to the group that segmented without providing labels, the group that segmented with explicit labeling behaviors were less sensitive at discriminating emotion events from non-emotion events and more conservative to identify an emotion event. The results are discussed with respect to competing theoretical accounts of the impact of labeling on emotion perception and suggest that the conceptual broadening account (where labels invoke idiographic emotion representations) may best account for the findings.

Counterfactual reasoning is at the centre of human daily life and plays a key role in shaping our moral and social judgments. Its effect on moral judgment in adulthood, such as justifying immoral behavior (e.g., “If you had not left your phone on the table, it would not have been stolen.”), has been studied for years. However, we still know very little about when counterfactual reasoning starts to affect humans' moral judgments. To test this, we examined the effect of better and worse counterfactual alternatives on 5-6-year-olds' (N = 91) moral and happiness judgments. We found that children judged social exclusion (e.g., a new kid has to play alone while other children play together) as less morally acceptable after imagining how it could have been better (e.g., the new kid and other children at the school could have played all together), but, contrary to past work with adults, they did not justify it after imagining how it could have been even worse (e.g., the other children could have broken the new kid's toy). However, children's happiness judgments showed the opposite effect: they reported feeling happier about reality after imagining a worse counterfactual alternative compared to children who only thought about what actually happened.Keywords: counterfactuals; moral judgment; children; happiness judgment

Metaphors are often used to intuitively communicate about movement. Here, expert pianists played two melodies while keeping eight different metaphors in mind, contrasting arousal level, valence direction, and metaphor type (action-related and emotion-related metaphors). Measures of keystroke timing and velocity were analyzed to assess the relative contribution of metaphor content and melodic note sequence to motor performance, alongside ratings of semantic similarity between metaphors. Using Bayesian multilevel models, results indicate that the arousal level of the metaphor has the most influence on keystroke force, average tempo, and tempo variability. Additionally, interactions with valence are seen for the timing measures, and for both valence and type in force. No effects of the melody sequence were found. Similarity ratings of metaphor pairs indicate that mental similarities largely mirror performance similarities. These findings show the potential effects of mental imagery on motor performance and have implications for teaching complex movements in practical settings.

The amount of resources someone has can influence their emotional responses to events. Two preregistered experiments investigated whether adults and children consider others' resource quantities when inferring their emotions. Sixty adults (Experiment 1) and 135 8-10-year-olds (Experiment 2) saw stories about people wanting an item but differing in the number of items they have enough money to buy (ranging from 1 to 5). Participants rated how these people felt both when buying the item and when losing it. Both adults and children judged that the fewer resources someone has, the sadder they felt when the item was lost, and the bigger emotional change they experienced (relative to when buying the item). Adults also judged that the impact of resource scarcity on emotion was most significant when the person had depleted all their resources, as opposed to still retaining some to influence the negative outcome, and this pattern is emerging in children. These findings suggest that even when the same negative event occurs, adults and children as young as 8 consider others' available resources when inferring their emotional responses to the event.

To succeed in social situations, we must learn how social cues predict subsequent events. How do we quickly form associations between a variety of social cues, such as individuals signaling their current emotion state, and social outcomes? To address this question, we developed a task in which participants viewed images of individuals conveying different emotions and searched among these images to gain rewards. Rewards were associated with either individuals' identities or emotion cues. Across four experiments (N=720), individuals learned about rewards more efficiently from individual identity cues versus a wide variety of emotion cues. Participants also generalized cue-outcome associations more easily for individuals versus emotions. Learning was worse if participants experienced a change in the association rule, especially when switching from learning individual-based associations to emotion-based associations. Overall, we show that social cue type influences how associations between cues and rewards are learned, with implications for understanding learning in social contexts.

While people may be reluctant to explicitly state social stereotypes, their underlying beliefs may nonetheless leak out in subtler conversational cues, such as surprisal reactions that convey information about expectations. Across 3 experiments with adults and children (ages 4-9), we compare permissive responses ("Sure, you can have that one") that vary the presence of surprisal cues (interjections "oh!" and disfluencies "um"). In Experiment 1 (n = 120), children by 6-to-7 use surprisal reactions to infer that a boy more likely made a counter-stereotypical choice. In Experiment 2, we demonstrate that these cues are sufficient for children (n = 120) and adults (n = 80) to learn a novel expectation about a group of aliens. In Experiment 3, adults (n = 150) use the distribution of surprisal information to infer whether a novel behavior is gender-stereotyped. Across these experiments, we see emerging evidence that conversational feedback may provide a crucial and unappreciated avenue for the transmission of social beliefs.

Children's acquisition of emotion words is a topic of interest across the domains of emotion research, early language acquisition, and social development. Prior research has shown a slow, gradual process of emotion word acquisition during ages 2–5 years and beyond. However, this research has used tasks that are demanding for young children, such as asking them to label or sort facial expressions. Here, in a preregistered study, we used a child-friendly looking-time paradigm---the "looking-while-listening'' task---to assess children's understanding of four emotion words, "happy,'' "sad,'' "angry,'' and "scared.'' We presented 64 two-year-olds (Mean age = 2.51, range: 2.00-2.97) with facial expressions and measured their preferential looking to the target face upon hearing an emotion word. Both younger and older two-year-olds showed above-chance performance when the target and distractor faces differed in valence (e.g., happy vs. sad). When the target and distractor faces were of the same valence (e.g., angry vs. sad), younger two-year-olds' results did not reach significance, but older two-year-olds' results were significantly above chance. These results suggest that within-valence mappings of emotion words to facial expressions emerges at least during the second half of age two. Full paper here: [https://osf.io/preprints/psyarxiv/nsq5t].

Emotion recognition is crucial for enhancing human-computer interaction. Due to considerable individual differences in emotion manifestation, traditional models do not adapt well to new individuals. Moreover, existing algorithms typically focus on identifying a single emotion, overlooking intrinsic connections among multiple emotions. Therefore, we propose a multi-task adversarial domain adaption (MADA) model for EEG-based emotion recognition. First, domain matching is employed to identify the most similar individual from the dataset as the source domain, alleviating individual differences and reducing training time. Subsequently, multi-task learning is utilized to simultaneously classify multiple emotions, capturing their intrinsic connections. Finally, adversarial domain adaption is applied to learn the individual differences between the source and target domains. Cross-subject experiments on the DEAP dataset indicate that our model achieves accuracies of 78.08%, 68.36%, and 69.64% on the valence, arousal, and dominance, respectively, surpassing state-of-the-art methods. This indicates the effectiveness of our model in recognizing multi-dimensional emotions.

Intentional binding (IB) is the experience of temporal interval compression between voluntary actions and subsequent events when the latter are perceived to be caused on purpose by the agent's actions. It can be measured experimentally by comparing the judgments of temporal intervals between either a voluntary act or an external event, and a later sensory consequence. Evidence suggests this might be modulated by the emotional valence of the consequence. However, controversies have arisen over the consistency of the results and the methodology they were obtained with. Here, we aimed to measure this affective modulation using a two-interval forced-choice (2AFC) discrimination task and word stimuli. Three factors were employed: agency (agency and passive), emotional valence (neutral, positive, and negative words), and interval duration ratio determined based on individual values of just noticeable differences (JND). Participants had to judge which of two intervals presented in each trial was shorter. Generalized linear mixed model analysis indicated that there was an effect of IB, but no affective modulation. Dissociation of component mechanisms of SoA are discussed to better understand results and suggest further directions.

Learning with dynamic facial expressions often results in higher face recognition performance than with static images. However, few studies have used both positive and negative facial expressions to investigate the effects of dynamic facial expression information on recognition memory. The present study examined whether the effect of dynamic facial expressions depends on the type of facial expression used during the learning and recognition phases. Participants viewed individuals with smiling or angry expressions in either static or dynamic images in a learning session. Participants then performed a recognition task using static images with neutral, angry, or smiling expressions. The results showed that when tested with the neutral static faces, the advantage of the dynamic expression was observed regardless of the facial expression during learning (Experiment 1). However, when tested with the angry static faces, the dynamic expression advantage was not observed, but the recognition performance was better for the faces learned with the angry static faces (i.e., identical to the faces in the recognition task) (Experiment 2). In the recognition task with the static smiling faces, the advantage of dynamic expression was again observed in addition to the emotion congruency effect (i.e., better performance for the faces learned with the smiling expression) (Experiment 3). These results suggest that the effect of dynamic facial expression information on recognition depends on the type of facial expression during learning and recognition.

While ample work has examined how to increase empathy within situational contexts, little research has focused on how the language used to communicate with others may elicit empathy. Here, we investigate how (Study 1) the degree of a narrator's culpability and (Study 2) narrative framing of personal narratives (focusing on experienced sensations, emotions, or neither) affects feelings of empathy reported by listeners. Across our two studies, 901 participants read narratives describing common life events, rated their empathy towards the narrator, and were given an option to write a response to the narrator. Our findings indicate that people report less empathy towards narrators that caused their misfortune, although their written responses were more focused on the narrator. By contrast, however, highlighting sensory or emotional details in a narrative did not significantly impact the degree of empathy reported by listeners, yet still affected the language of responses produced by listeners.

We compare storytelling in GPT-3.5, a recent large language model, with human storytelling. We hypothesized that GPT differs from humans in the kind of memories it possesses, and thus could perform differently on tasks influenced by memory, such as storytelling. We used an existing dataset of human stories, either recalled or imagined (Sap et al., 2022), and generated GPT stories with prompts designed to align with human instructions. We found that GPT's stories followed a common narrative flow of the story prompt (analogous to semantic memory in humans) more than details occurring in the specific context of the event (analogous to episodic memory in humans). Furthermore, despite lacking episodic details, GPT-generated stories exhibited language with greater word affect (valence, arousal, and dominance). When provided with examples of human stories (through few-shot prompting), GPT was unable to match its stories' narrative flow or affective aspects with human stories.

In social interactions, inferring the interaction partner's hidden mental state is crucial for predicting their actions and optimiz- ing our responses. Effective models for this inference must account for how these mental states evolve due to the interac- tion history and environmental changes. For example, recog- nizing someone's emotional state can help forecast their be- havior. Our study investigates how making these latent states visible influences decision-making in social interactions. Us- ing the repeated trust game paradigm, we show how to use hid- den Markov models (HMM) to formally represent latent state dependent strategies of the players. HMMs fitted to human dyadic play in the trust game are then used to specify adap- tive AI agents that simulate changes in mental dispositions of human players, such as the level of trust in the opponent, dur- ing a repeated interaction. Making these artificial HMM based agents take the role of the investor and interact with real hu- man trustees, we then explore how displaying “emotion” cues to the opponent's latent state affects people's actions. We find that the presence of cues was associated with more cooperative behavior from the human trustees, and that patterns of behav- ior that promote the maintenance of cooperation emerged in the presence of latent state cues and were transferred to set- tings where the cues were subsequently hidden.

People have strong intuitions about which colors do and do not match particular pieces of music — a phenomenon often conceptualized through semantic mediation. We further explored the specific strategies people employ to navigate these color-music associations which offers crucial insights to identifying the cognitive mechanisms that enable such cross-domain associations. We show that while some people rely more on intuition, other people actively seek justification of association by consulting common linguistic descriptors, emotional contents, and environmental cues. We found that more spontaneous strategies lessen the role of semantic mediation, while more evaluative strategies, especially those involving the use of language, amplify it. Notably, the use of evaluative strategies introduced an asymmetrical effect for matching and mismatching colors. Additionally, individuals employing similar strategies associated a given music excerpt with more similar colors, suggesting that strategy alignment enhances the consistency of color-music associations. Interestingly, this pattern of convergence was not observed among individuals who predominantly relied on guessing.

People learn new words in narrative contexts. Little is known about the influence of the emotional valence of the text on word learning. In a pre-registered experiment, we investigated whether emotional narrative context shapes word learning. English adults (N = 76) read 30 novel adjectives embedded in 60 short narratives (20 positive, 20 negative, and 20 neutral valence). Post-tests assessed learning (immediate and 24 hours later) and examined whether the valence of the novel words can be inferred from contextual valence. Compared to the neutral context, emotional contexts (both positive and negative) facilitated word form learning in the immediate post-tests, but only negative emotion words were recognized better 24 hours later. Furthermore, the valence of the context was reflected in the word meanings participants generated for each novel word. These findings are discussed with reference to theories of affective embodiment and its implications for supporting the learning of abstract concepts.

In this pre-registered study, we investigated whether emotional inner speech influences heart rate. Participants were asked to engage in 3-minute sessions of: positive inner speech, negative inner speech, or inner counting while their heart rate was monitored. Participants were lying on a bed and asked to remain still. Motion tracking was applied to control for body movement. Median heart rate across each inner speech session was analyzed and a significant difference was found between emotional inner speech and inner counting. No difference between positive and negative inner speech was observed. Post-hoc analyses investigated the relationship between movement and heart rate and found an effect with a peak lag of approximately 14 seconds. Removing these effects did not change the effect of emotional inner speech. Additional analyses showed that heart rate and respiration rate were linked. Including respiration rate as a covariate did not alter the effect of emotion.

We investigated the emotion-based modulation in the attentional mechanism by presenting angry and happy faces simultaneously in the extrafoveal vision. In a letter discrimination task at the fixation, pairs of task-irrelevant happy and angry faces were displayed peripherally (≥5° away from the fixation) to study the valence-facilitated attentional capture under mutual competition for processing resources. Selective orienting was assessed using eye movement measures such as the probability of first fixation on these emotional face images. Results revealed a higher probability of first fixation for happy faces than angry ones. Processing of affective stimuli in the extrafoveal indicates early occurring covert orienting of attention followed by overt attention in the foveal vision. The attentional capture advantage by happy faces occurred in the absence of differences in arousal levels. We propose that happy faces have a unique capacity to capture attention when competing with angry faces.

Though it is well-understood that beliefs about future emotions (affective forecasting) influence decision-making, less is known about where these forecasts come from. Here, we investigate how intuitive theories of cognition (cognitive forecasting) influence affective forecasts and, consequently, risk preferences. We found that forecasts of cognitive states—expectations, attention, and information-seeking—are linked to affective forecasts and risk preferences (Study 1). There was great diversity in people's intuitive theories of cognition: One subgroup associated attention and information-seeking with positive emotions for optimists but negative emotions for pessimists, and therefore predicted greater risk-seeking in optimists but not pessimists; the other large subgroup consistently perceived forecasted attention and information-seeking as affectively negative (Studies 2a-b). These results connect behavioral economics and cognitive science by exploring how metacognitive intuitions influence our preferences.

This study investigates the cognitive plausibility of a pretrained multimodal model, CLIP, in recognizing emotions evoked by abstract visual art. We employ a dataset comprising images with associated emotion labels and textual rationales of these labels provided by human annotators. We perform linguistic analyses of rationales, zero-shot emotion classification of images and rationales, apply similarity-based prediction of emotion, and investigate color-emotion associations. The relatively low, yet above baseline, accuracy in recognizing emotion for abstract images and rationales suggests that CLIP decodes emotional complexities in a manner not well aligned with human cognitive processes. Furthermore, we explore color-emotion interactions in images and rationales. Expected color-emotion associations, such as red relating to anger, are identified in images and texts annotated with emotion labels by both humans and CLIP, with the latter showing even stronger interactions. Our results highlight the disparity between human processing and machine processing when connecting image features and emotions.

People often experience regret when we consider counterfactuals to our past actions, which can help us improve our future behaviours. However, existing developmental measures of regret typically involve no means of foreseeing the eventual outcome, which means that any reported experiences of regret may not aid children in making better choices in similar future situations. We investigated if 4- to 9-year-olds (N = 144) experienced stronger regret towards a choice where they could have foreseen the eventual outcome. Children selected one box each from two pairs of boxes, with both selected boxes leading to sub-optimal outcomes. Critically, one pair of boxes had windows on the bottom, such that children could have apparently foreseen the sub-optimal outcome of their choice if only they had first looked underneath the boxes. Not until 8 years of age did many children feel worse about the box selection with the foreseeable outcome.

The current study used a real-time interactive “advisor-decider” task, in which advice given by one participant results in an onerous workload for another participant, to show that self-conscious affect based on performance in one domain shapes decisions to engage in prosocial behavior in an unrelated domain: Advisors that performed at or worse than the norm, in terms of giving incorrect advice, made more frequent subsequent charity donations. Intriguingly, when advisors were given social information about their performance relative to the norm, this pattern was reversed, such that advisors that performed worse than the norm made less frequent donations. We interpret this finding as reflecting a shift in the emotion driving the behavior, from guilt to shame. Consistent with this interpretation, trait measures of guilt proneness but not of shame proneness predicted an increase in both the probability and magnitude of donations.

Emotion space models are frameworks that represent emotions in a multidimensional space, providing a structured way to understand and analyze the complex landscape of human emotions. However, the dimensional representation of emotions is still debatable. In this work, we are probing the higher dimensional space constituted by emotion labeling done by participants from India on multimedia stimuli.Our approach formalizes the study of emotion in the investigation of representational state spaces capturing semantic variation in emotion-related response.We have created a high-dimensional space of emotional ratings by participants to represent emotional stimuli. Using t-SNE, we have projected the higher dimensional space into two dimensions. We observed that the structure of emotional categories and clusters formed of these emotional categories is similar to Russell's circumplex model. The transition from the blended complex emotional states to the discrete emotional states is projected out from the centre, and discrete emotional states occur in the periphery.

Emotions regulate social interactions from early in ontogeny, but are difficult to assess in young children. Previous studies used body posture to measure emotion expressions, employing depth-sensor imaging cameras. Advances in artificial intelligence allow researchers to track posture from existing videos. The reported studies explored the feasibility of OpenPose to capture children's emotional expressions. In Study 1, we analysed posture data from previous studies and found that children's expressed valence was positively related to changes in upper-body expansion whereas expressed arousal was related to overall movement. In Study 2, children (n = 64, 5-10 years) recalled emotional episodes of ‘happiness', ‘sadness', ‘pride', and ‘shame'. There were no effects of specific emotion categories on posture changes, but exploratory analyses revealed that recalling positive emotions yielded greater changes in upper-body expansion compared to negative emotions. Together, these results suggest that the valence and arousal of expressed emotions can be captured using OpenPose.

An assertion about a fact can in principle be tested in observations. That is impossible for assertions about what is permissible or obligatory, i.e., deontic assertions based on moral principles, conventions, rules, or laws. Many modal logics concern these matters. But an integrated theory of emotions and reasoning predicts that emotional reactions and strength of belief should be correlated for deontic assertions, but not for factual assertions. You can be convinced that it is wrong to take paperclips from the office, and that it is right for society to provide health care for everyone, and your emotional response to these two assertions is likely to correlate with the strength of your beliefs in them. In contrast, you can be convinced both that fresh snow is white and that fossil fuels are making the world hotter, but have an emotional reaction to only the second of these assertions. Grounds for factual assertions are empirical findings. But assessments of deontic assertions depend in part on the emotions that they elicit. Previous studies have corroborated this prediction for moral claims, matters of convention, prudential rules, and personal recommendations. We report two experiments that yield the same interaction for legal pronouncements from the Italian Civil Code compared with parallel factual assertions. People like propositions they believe, and they believe propositions they like. We discuss several remaining unknowns including the potential role of emotions in reasoning about legal and other deontic propositions.

The experiment reported here investigated the neural correlates of predictive processing of angry and neutral speech. Twenty-six participants listened to recordings of angry and neutral conversation segments, as well as to speech-shaped noise, while their EEG was recorded. Oscillatory power in the gamma band (30–80 Hz) and the N400 component of event-related potentials (ERP) to sentence-final words were analyzed. In comparison to neutral words, negative emotional valence significantly reduced the amplitude of the N400 elicited by sentence-final words. Furthermore, there was larger gamma power during exposure to angry speech in comparison to neutral speech. The results generally suggest increased prediction and facilitated semantic integration in negative as compared to neutral speech. To date, the predictability effects on gamma power have been reported with relation to the semantic-lexical content of words. The present findings demonstrate that gamma power is also modulated by the emotional content of speech.

A plethora of research has identified that undergraduate students experience higher levels of general anxiety disorder (GAD) with questionnaires primarily being used diagnose students. However, the potential for linguistic analysis and the emotional Stroop test as measurements of GAD remains unexplored, especially if both were to be used together. The present study aimed to produce a novel measure of GAD using both linguistic measures and the emotional Stroop task. This research study employed a quantitative approach and an experimental research design. A volunteer sample of 17 undergraduate students completed an online questionnaire, a written task, and an emotional Stroop task distributed via social media and the University of Birmingham Research Participation Scheme. This study produced a novel questionnaire for GAD and was utilised as a baseline measure. This study used two independent T-tests to measure the overall sentiment of written responses of participants and the frequency of first-person singular pronouns in written response. Additionally, two independent T-tests were used to measure reaction times and accuracy on the emotional Stroop Task. The findings highlighted no statistical differences between higher and lower levels of GAD and linguistic responses and reaction times and accuracy on the emotional Stroop test, suggesting that the measures utilised in the present study may not be able to predict GAD. As such, the findings of this study underscore the complexity of GAD and extend our understanding of GAD measurements. By illuminating the emotional, behavioural and cognitive factors of GAD, this study advocates for more awareness in university settings and proactive support for undergraduate students.

Modern intelligent tutoring systems, exploiting technological advances in augmented and virtual reality and large language models, offer fluent natural language interaction between a virtual character and a student complemented with a multimodal interface, including recognition and synthesis of affects and intentions expressed in speech tonality, facial expression, gaze, and body language. Being concerned with consumer satisfaction, developers of such systems often miss the educational needs. Here we present a Virtual Tutor that, using the above technologies, helps students to self-regulate during learning. This is made possible based on the self-regulated learning theory integrated into an emotional cognitive architecture. Virtual Tutor uses its emotional intelligence to model, guide, and motivate students to engage in self-regulation. It does it in parallel with performing the basic tutoring functions. Results of our preliminary study provide some evidence of support for Virtual Tutor. This work was supported by the Russian Science Foundation Grant #22-11-00213, https://rscf.ru/en/project/22-11-00213/.

Code tracing involves simulating at a high level the steps a computer takes when it executes a computer program. This is a fundamental skill needed for programming activities, but one that novices find challenging. Thus, work is needed on how to support novice programmers in this activity. We conducted an experimental study with university students (N = 56) learning to code trace in two conditions, namely peer tutoring and solo code tracing. Our primary outcome variable was learning. However, since how students feel is also an important factor in educational settings, we also measured student emotion. Contrary to prior work in other domains, there was no significant benefit of peer tutoring and self-reported levels of emotion were similar in the two conditions; Bayesian statistics provided evidence for the null model in the majority of cases.

Emotions can be insensitive to certain attributes of a situation. A large body of evidence shows that information on probabilities, large numerical counts, or intentions is frequently disregarded in the elicitation and regulation of emotions. To date, no existing theory comprehensively accounts for the features that tend to be overlooked by emotion. In this paper I call attention to the common denominator of such features: they cannot be perceived nor contribute to the iconic representation of events. For instance, the exceedingly low probability of a plane crash does not affect its imagistic representation (i.e., the iconic representation of the event is silent about the event's probability). I introduce the Iconic Prioritization Hypothesis, positing that the prioritization of the iconic format in emotion can explain the neglect of information that is representationally silent in this format. Emotion may favour iconicity as it is the format of immediate, first-hand evidence about our surroundings.

In decision making scenarios, reasoning can be viewed as an agent executing an algorithm p ‚àà P that selects an action a ‚àà A, aiming to optimize some outcome. Metareasoning extends this by selecting p itself through a meta-algorithm p^{meta}. Previous approaches to study metareasoning in humans have required that the transition/reward distributions are known by the agent, but the value function isn't. We extend these efforts to study metareasoning for agents acting in unknown environments by formalizing the meta problem as a meta Bayes adaptive Markov decision problem (meta-BAMDP). We formally investigate the theoretical consequences of this framework within the context of two-armed Bernoulli bandit (TABB) tasks. Not only do we make theoretical progress in making the (usually intractable) metareasoning problem tractable, but we also generate predictions for a resource rational account of human exploration in TABB tasks.

Do verbally reported feelings follow reinforcement learning principles? Prediction errors—differences between expectations and outcomes—are key in models of learning across humans, animals, and machines. Historically, the emphasis has been on outcomes in the environment (e.g., money or food), focusing relatively less on the fact that humans can also report correspondingly expected and experienced affect (i.e., feelings). Recent research suggests that expected and experienced affect, including prediction errors, can explain behavior beyond outcomes in the environment alone. However, the properties of affective prediction errors underlying this explanatory power are unknown. We address this gap across two studies. We show that affective prediction errors can decrease over time, but that the decrease depends on introspection (Study 1). We then replicate this finding while additionally documenting transfer effects across tasks (Study 2). Crucially, decreases in affective prediction errors generally occurred independent of changes in behavior.

In our study, we address conflicts between individuals and groups, such as cyberbully on social media, as a challenge related to the distinction between the self and others. To address this issue using technology, we propose the concept of introducing facial synchronization in the virtual realm as a means to manipulate the boundary between oneself and others.We designed an experiment using Cyberball that simulates an ostracism environment, effectively partitioning the boundary between the self and others. This task was conducted in Virtual Reality (VR), with the agent's facial expressions synchronized with those of the participant.Our findings indicated a reduction in feelings of alienation within the ostracism environment. This discovery has potential implications for communication media, particularly in enhancing interfaces for individuals who may experience exclusionary behavior on social media.

Much of social cognition involves reasoning about others'minds: predicting their reactions, inferring their feelings, andexplaining their behavior. By representing mental contents likebeliefs, desires, and emotions, Bayesian theory of mind mod-els have made progress in capturing how humans manage thesecognitive feats. But social life is not merely observation: hu-mans must also plan to intervene on these same mental con-tents. The present work models how people choose interven-tions to influence others' emotions. Building on a prior modelof people's intuitive theory of emotions, we model how peopleuse their intuitive theory to evaluate and simulate the effectsof different interventions. We apply our model to data frombehavioral experiments requiring counterfactual and joint in-terventions, and show a close alignment with human choices.Our results provide a step towards a potentially unifying expla-nation for emotion prediction and intervention, suggesting thatthey could arise from the same underlying generative model.