Computational Models of Emotion Inference in Theory of Mind: A Review and Roadmap

The effects of implicit emotion on the use of theory of mind among college students in China

This research aims to study the impact of implicit emotion on the use of theory of mind and enrich the research on emotions and the use of theory of mind, thus allowing adults to apply theory of mind more effectively in the context of social interaction. This study includes 120 college students as participants. A two (level of theory of mind: high vs. low) * three (implicit emotional state: implicit positive emotion, implicit neutral emotion, or implicit negative emotion) * two (private knowledge: endowed vs. unendowed) between-subjects three-factor design was employed. This study obtained the following results: (1) The main effect of different implicit emotional states on college students' use of theory of mind is significant. College students with implicit positive emotions use theory of mind much less than those with implicit neutral and negative emotions. (2) In cases of implicit positive emotions, college students with a low level of theory of mind use theory of mind substantially less than students with a high level of theory of mind. In cases of implicit neutral and negative emotions, college students with the high and low theory of mind do not exhibit substantial differences in their use of theory of mind. This study concludes that different emotional states affect college students' use of theory of mind.

Face and context integration in emotion inference is limited and variable across categories and individuals

The ability to make nuanced inferences about other people's emotional states is central to social functioning. While emotion inferences can be sensitive to both facial movements and the situational context that they occur in, relatively little is understood about when these two sources of information are integrated across emotion categories and individuals. In a series of studies, we use one archival and five empirical datasets to demonstrate that people could be integrating, but that emotion inferences are just as well (and sometimes better) captured by knowledge of the situation alone, while isolated facial cues are insufficient. Further, people integrate facial cues more for categories for which they most frequently encounter facial expressions in everyday life (e.g., happiness). People are also moderately stable over time in their reliance on situational cues and integration of cues and those who reliably utilize situation cues more also have better situated emotion knowledge. These findings underscore the importance of studying variability in reliance on and integration of cues.

Hybrid computational model depicts the contribution of non-significant lobes of human brain during the perception of emotional stimuli

Emotions are synchronizing responses of human brain while executing cognitive tasks. Earlier studies had revealed strong correlation between specific lobes of the brain to different types of emotional valence. In the current study, a comprehensive three-dimensional mapping of human brain for executing emotion specific tasks had been formulated. A hybrid computational machine learning model customized from Custom Weight Allocation Model (CWAM) and defined as Custom Rank Allocation Model (CRAM). This regression-based hybrid computational model computes the allocated tasks to different lobes of the brain during their respective executive stage. Event Related Potentials (ERP) were obtained with significant effect at P1, P2, P3, N170, N2, and N4. These ERPs were configured at Pz, Cz, F3, and T8 regions of the brain with maximal responses; while regions like Cz, C4 and F4 were also found to make effective contributions to elevate the responses of the brain, and thus these regions were configured as augmented source regions of the brain. In another circumstance of frequent -deviant - equal (FDE) presentation of the emotional stimuli, it was observed that the brain channels C3, C4, P3, P4, O1, O2, and Oz were contributing their emotional quotient to the overall response of the brain regions; whereas, the interaction effect was found presentable at O2, Oz, P3, P4, T8 and C3 regions of brain. The proposed computational model had identified the potential neural pathways during the execution of emotional task.

Computational Social Psychology

Social psychologists attempt to explain how we interact by appealing to basic principles of how we think. To make good on this ambition, they are increasingly relying on an interconnected set of formal tools that model inference, attribution, value-guided decision making, and multi-agent interactions. By reviewing progress in each of these areas and highlighting the connections between them, we can better appreciate the structure of social thought and behavior, while also coming to understand when, why, and how formal tools can be useful for social psychologists.

Storytelling as Inverse Inverse Planning

Great storytelling takes us on a journey the way ordinary reality rarely does. But what exactly do we mean by this "journey?" Recently, literary theorist Karin Kukkonen proposed that storytelling is "probability design:" the art of giving an audience pieces of information bit by bit, to craft the journey of their changing beliefs about the fictional world. A good "probability design" choreographs a delicate dance of certainty and surprise in the reader's mind as the story unfolds from beginning to end. In this paper, we computationally model this conception of storytelling. Building on the classic Bayesian inverse planning model of human social cognition, we treat storytelling as inverse inverse planning: the task of choosing actions to manipulate an inverse planner's inferences, and therefore a human audience's beliefs. First, we use an inverse inverse planner to depict social and physical situations, and present behavioral studies indicating that inverse inverse planning produces more expressive behavior than ordinary "naïve planning." Then, through a series of examples, we demonstrate how inverse inverse planning captures many storytelling elements from first principles: character, narrative arcs, plot twists, irony, flashbacks, and deus ex machina are all naturally encoded in the flexible language of probability design.

Infants expect friends, but not rivals, to be happy for each other when they succeed

A friend telling you good news earns them a smile while witnessing a rival win an award may make you wrinkle your nose. Emotions arise not just from people's own circumstances, but also from the experiences of friends and rivals. Across three moderated, online looking time studies, we asked if human infants hold expectations about others' vicarious emotions and if they expect those emotions to be guided by social relationships. Ten- and 11-month-old infants (N = 154) expected an observer to be happy rather than sad when the observer watched a friend successfully jump over a wall; infants looked longer at the sad response compared to the happy response. In contrast, infants did not expect the observer to be happy when the friend failed, nor when a different, rival jumper succeeded; infants' looking times to the two emotion responses in these conditions were not reliably different. These results suggest that infants are able to integrate knowledge across social contexts to guide expectations about vicarious emotional responses. Here infants connected an understanding of agents' goals and their outcomes with knowledge of social relationships to infer an emotion response. Biased concern for friends but not adversaries is not just a descriptive feature of human relationships, but an expectation about the social world present from early in development. Further, the successful integration of these information types welcomes the possibility that infants can jointly reason about goals, emotions, and social relationships under an intuitive theory of psychology. RESEARCH HIGHLIGHTS: 11-month-old infants use knowledge of relationships to make inferences about others' vicarious emotions. In Experiment 1 infants expected an observer to respond happily to a friend's success but not their failure. Experiments 2 and 3 varied the relationship between the observer and actor and found that infants' expectation of vicarious happiness is strongest for positive relationships and absent for negative relationships. The results may reflect an intuitive psychology in which infants expect friends to adopt concern for one another's goals and to thus experience one another's successes as rewarding.

Mood congruency affects physiological synchrony but not empathic accuracy in a naturalistic empathy task

Empathy is a crucial aspect of our daily lives, as it enhances our wellbeing and is a proxy for prosocial behavior. It encompasses two related but partially distinct components: cognitive and affective empathy. Both are susceptible to context, biases and an individual's physiological state. Few studies have explored the effects of a person's mood on these empathy components, and results are mixed. The current study takes advantage of an ecological, naturalistic empathy task - the empathic accuracy (EA) task - in combination with physiological measurements to examine and differentiate between the effects of one's mood on both empathy components. Participants were induced with positive or negative mood and presented videos of targets narrating autobiographical negative stories, selected from a Chinese empathy dataset that we developed (now publicly available). The stories were conveyed in audio-only, visual-only and full-video formats. Participants rated the target's emotional state while watching or listening to their stories, and physiological measures were taken throughout the process. Importantly, similar measures were taken from the targets when they narrated the stories, allowing a comparison between participants' and targets' measures. We found that in audio-only and visual-only conditions, participants whose moods were congruent with the target showed higher physiological synchrony than those with incongruent mood, implying a mood-congruency effect on affective empathy. However, there was no mood effect on empathic accuracy (reflecting cognitive empathy), suggesting a different influence of mood on the two empathy components.

Calculated Feelings: How Children Use Probability to Infer Emotions

Developing the ability to accurately infer others' emotions is crucial for children's cognitive development. Here, we offer a new theoretical perspective on how children develop this ability. We first review recent work showing that with age, children increasingly use probability to infer emotions. We discuss how these findings do not fit with prominent accounts of how children understand emotions, namely the script account and the theory of mind account. We then outline a theory of how probability allows children to infer others' emotions. Specifically, we suggest that probability provides children with information about how much weight to put on alternative outcomes, allowing them to infer emotions by comparing outcomes to counterfactual alternatives.

Children's Reasoning About Empathy and Social Relationships

Across the lifespan, empathic and counter-empathic emotions are shaped by social relationships. Here we test the hypothesis that this connection is encoded in children's intuitive theory of psychology, allowing them to predict when others will feel empathy versus counter-empathy and to use vicarious emotion information to infer relationships. We asked 4- to 7-year-old children (N = 79) to make emotion predictions or relationship inferences in response to stories featuring two characters, an experiencer and an observer, and either a positive or negative outcome for the experiencer. In the context of positive outcomes, we found that children engaged in robust joint reasoning about relationships and vicarious emotions. When given information about the characters' relationship, children predicted empathy from a friendly observer and counter-empathy from a rival observer. When given information about the observer's response to the experiencer, children inferred positive and negative relationships from empathic and counter-empathic responses, respectively. In the context of negative outcomes, children predicted that both friendly and rival observers would feel empathy toward the experiencer, but they still used information about empathic versus counter-empathic responses to infer relationship status. Our results suggest that young children in the US have a blanket expectation of empathic concern in response to negative outcomes, but otherwise expect and infer that vicarious emotions are connected to social relationships. Future research should investigate if children use this understanding to select social partners, evaluate their own relationships, or decide when to express empathy toward others.

Rational inferences about social valuation

The decisions made by other people can contain information about the value they assign to our welfare-for example how much they are willing to sacrifice to make us better off. An emerging body of research suggests that we extract and use this information, responding more favorably to those who sacrifice more even if they provide us with less. The magnitude of their trade-offs governs our social responses to them-including partner choice, giving, and anger. This implies that people have well-designed cognitive mechanisms for estimating the weight someone else assigns to their welfare, even when the amounts at stake vary and the information is noisy or sparse. We tested this hypothesis in two studies (N=200; US samples) by asking participants to observe a partner make two trade-offs, and then predict the partner's decisions in other trials. Their predictions were compared to those of a model that uses statistically optimal procedures, operationalized as a Bayesian ideal observer. As predicted, (i) the estimates people made from sparse evidence matched those of the ideal observer, and (ii) lower welfare trade-offs elicited more anger from participants, even when their total payoffs were held constant. These results support the view that people efficiently update their representations of how much others value them. They also provide the most direct test to date of a key assumption of the recalibrational theory of anger: that anger is triggered by cues of low valuation, not by the infliction of costs.

Emotion prediction as computation over a generative theory of mind

From sparse descriptions of events, observers can make systematic and nuanced predictions of what emotions the people involved will experience. We propose a formal model of emotion prediction in the context of a public high-stakes social dilemma. This model uses inverse planning to infer a person's beliefs and preferences, including social preferences for equity and for maintaining a good reputation. The model then combines these inferred mental contents with the event to compute 'appraisals': whether the situation conformed to the expectations and fulfilled the preferences. We learn functions mapping computed appraisals to emotion labels, allowing the model to match human observers' quantitative predictions of 20 emotions, including joy, relief, guilt and envy. Model comparison indicates that inferred monetary preferences are not sufficient to explain observers' emotion predictions; inferred social preferences are factored into predictions for nearly every emotion. Human observers and the model both use minimal individualizing information to adjust predictions of how different people will respond to the same event. Thus, our framework integrates inverse planning, event appraisals and emotion concepts in a single computational model to reverse-engineer people's intuitive theory of emotions. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.

Inferential Emotion Tracking reveals impaired context-based emotion processing in individuals with high Autism Quotient scores

Emotion perception is essential for successful social interactions and maintaining long-term relationships with friends and family. Individuals with autism spectrum disorder (ASD) experience social communication deficits and have reported difficulties in facial expression recognition. However, emotion recognition depends on more than just processing face expression; context is critically important to correctly infer the emotions of others. Whether context-based emotion processing is impacted in those with Autism remains unclear. Here, we used a recently developed context-based emotion perception task, called Inferential Emotion Tracking (IET), and investigated whether individuals who scored high on the Autism Spectrum Quotient (AQ) had deficits in context-based emotion perception. Using 34 videos (including Hollywood movies, home videos, and documentaries), we tested 102 participants as they continuously tracked the affect (valence and arousal) of a blurred-out, invisible character. We found that individual differences in Autism Quotient scores were more strongly correlated with IET task accuracy than they are with traditional face emotion perception tasks. This correlation remained significant even when controlling for potential covarying factors, general intelligence, and performance on traditional face perception tasks. These findings suggest that individuals with ASD may have impaired perception of contextual information, it reveals the importance of developing ecologically relevant emotion perception tasks in order to better assess and treat ASD, and it provides a new direction for further research on context-based emotion perception deficits in ASD.

Computational models of subjective feelings in psychiatry

Research in computational psychiatry is dominated by models of behavior. Subjective experience during behavioral tasks is not well understood, even though it should be relevant to understanding the symptoms of psychiatric disorders. Here, we bridge this gap and review recent progress in computational models for subjective feelings. For example, happiness reflects not how well people are doing, but whether they are doing better than expected. This dependence on recent reward prediction errors is intact in major depression, although depressive symptoms lower happiness during tasks. Uncertainty predicts subjective feelings of stress in volatile environments. Social prediction errors influence feelings of self-worth more in individuals with low self-esteem despite a reduced willingness to change beliefs due to social feedback. Measuring affective state during behavioral tasks provides a tool for understanding psychiatric symptoms that can be dissociable from behavior. When smartphone tasks are collected longitudinally, subjective feelings provide a potential means to bridge the gap between lab-based behavioral tasks and real-life behavior, emotion, and psychiatric symptoms.

Interaction in acting training and its different manifestations in novice and professional actors

This study aimed to identify the characteristics of interactions during acting training and the underlying intrapersonal changes evoked by a training process that emphasizes paying attention to a partner (the Meisner technique). This was operationalized by conducting a post-hoc analysis and categorizing the utterances made by novice and professional actors during acting training based on video and audio recordings. In Study 1, novice participants tended to change their way of communication as the course progressed, decreasing the number of utterances that simply described the partner's behavior and increasing those that speculated about the partner's inner state. We then used a different focus placed on the interaction, as implied by the different kinds of utterances used, to describe the divergences between novice and professional actors regarding their interaction characteristics. In Study 2, results showed that while professional actors devoted themselves more to the connection with their partner and demonstrated more balanced communication, novice actors relied on general inference to speculate about others' affective states. By comparing the characteristics of the utterances between novice and professional actors as they played different roles or made switches (i.e., changing from passive to active utterance in communication), this study suggests that an important impact of acting training on social abilities relates to its potential to increase the levels of involvement in on-going interactions.

Planning with Theory of Mind

Understanding Theory of Mind should begin with an analysis of the problems it solves. The traditional answer is that Theory of Mind is used for predicting others' thoughts and actions. However, the same Theory of Mind is also used for planning to change others' thoughts and actions. Planning requires that Theory of Mind consists of abstract structured causal representations and supports efficient search and selection from innumerable possible actions. Theory of Mind contrasts with less cognitively demanding alternatives: statistical predictive models of other people's actions, or model-free reinforcement of actions by their effects on other people. Theory of Mind is likely used to plan novel interventions and predict their effects, for example, in pedagogy, emotion regulation, and impression management.

Consistency among social groups in judging emotions across time

We measured judgments about emotions across time. In Study 1 (N = 254) and Study 2 (N = 162), LGBTQ-Latinx, straight-Latinx, LGBTQ-White, and straight-White emerging adults rated how they would feel if a perpetrator acted positively (P) or negatively (N) toward them in single, isolated events. In Study 2, participants also responded to a new emotions across time task where they judged how they would feel interacting with a hypothetical perpetrator across three timepoints: (1) an initial past event, (2) a recent past event, and (3) an uncertain future-oriented event (e.g., seeing the perpetrator again). Participants further predicted their thoughts and decisions in the uncertain future-oriented event. The past emotional events appeared in various sequences (PP, NN, NP, PN). Results indicated that participants judged events as emotionally unambiguous when occurring first in a sequence or in isolation (positive events feel better than negative events). In contrast, initial events shaped emotional reactions to subsequent events: Participants responded more intensely to episodes that were preceded by events of the same valence. In addition to this augmenting effect, initial negative events were especially sticky: Participants rated a positive event following a negative event as feeling less good than when a positive event appeared first or in isolation, but they judged negative events to feel equivalently bad regardless of order. When evaluating future-oriented affective states, participants drew from the prior experiences and prioritized the recent past (more positive emotions, thoughts, and decisions for PP > NP > PN > NN). Effects replicated across all social groups. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Work Emotion Intervention and Guidance Training Method for Enterprise Employees Based on Virtual Reality

Objective

Improper management of employees' emotions has a great impact on work status and work efficiency, and emotional management has increasingly become a very important part of corporate human resource management. How to adjust the various emotions of employees, so that employees can work in a fuller and more passionate attitude, is a problem that enterprise managers need to consider. From the perspective of employees themselves, a good mood will also bring happiness and satisfaction to life and work. Only in this way can the company and the employees be satisfied with each other and continue to maintain the good development momentum of the company.

Method

This paper proposes a new method of using virtual reality scenes to induce human emotions. Based on computer science and psychological cognitive science, a scene library of "virtual reality emotion induction system" is constructed. Emotion induction based on virtual reality scenes has better interactivity and generalization, as well as stronger immersion and user operability. Its emotional arousal is high, the data is reliable, and the interference of environmental factors in the induction process is weakened, and relatively objective emotional data can be obtained. The factors affecting the emotional management of the enterprise are investigated by means of a questionnaire survey, statistics and problems are found, and the objective problems existing in the emotional management work of the company are determined. According to the analysis, the problems are summarized, and it is believed that the company still has certain room for improvement and improvement potential in terms of corporate culture construction, emotional counseling system construction, and human resource career planning. In the research of this paper, through the combination of theory and practice, the enterprise's emotional management work has been comprehensively analyzed, and the practical problems existing in its emotional management work have been found through questionnaires. Results/Discussion. In the field of enterprise human resource management, emotion management is always an eternal topic worthy of our attention. But unfortunately, emotional management has not been given enough attention by managers. Most companies often think that emotions are their own business. Employees should self-regulate, control, and tolerate themselves. They should not bring personal emotions to work and even think that emotional work is unprofessional. This kind of misunderstanding has led to the fact that emotional management has not received due attention and affirmation in many enterprises.

Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review

Theory of Mind (ToM)-the ability of the human mind to attribute mental states to others-is a key component of human cognition. In order to understand other people's mental states or viewpoint and to have successful interactions with others within social and occupational environments, this form of social cognition is essential. The same capability of inferring human mental states is a prerequisite for artificial intelligence (AI) to be integrated into society, for example in healthcare and the motoring industry. Autonomous cars will need to be able to infer the mental states of human drivers and pedestrians to predict their behavior. In the literature, there has been an increasing understanding of ToM, specifically with increasing cognitive science studies in children and in individuals with Autism Spectrum Disorder. Similarly, with neuroimaging studies there is now a better understanding of the neural mechanisms that underlie ToM. In addition, new AI algorithms for inferring human mental states have been proposed with more complex applications and better generalisability. In this review, we synthesize the existing understanding of ToM in cognitive and neurosciences and the AI computational models that have been proposed. We focus on preference learning as an area of particular interest and the most recent neurocognitive and computational ToM models. We also discuss the limitations of existing models and hint at potential approaches to allow ToM models to fully express the complexity of the human mind in all its aspects, including values and preferences.

Infusing Context Into Emotion Perception Impacts Emotion Decoding Accuracy

The accurate decoding of facial emotion expressions lies at the center of many research traditions in psychology. Much of this research, while paying lip service to the importance of context in emotion perception, has used stimuli that were carefully created to be deprived of contextual information. The participants' task is to associate the expression shown in the face with a correct label, essentially changing a social perception task into a cognitive task. In fact, in many cases, the task can be carried out correctly without engaging emotion recognition at all. The present article argues that infusing context in emotion perception does not only add an additional source of information but changes the way that participants approach the task by rendering it a social perception task rather than a cognitive task. Importantly, distinguishing between accuracy (perceiving the intended emotions) and bias (perceiving additional emotions to those intended) leads to a more nuanced understanding of social emotion perception. Results from several studies that use the Assessment of Contextual Emotions demonstrate the significance and social functionality of simultaneously considering emotion decoding accuracy and bias for social interaction in different cultures, their key personality and societal correlates, and their function for close relationships processes.

Cognition Without Neural Representation: Dynamics of a Complex System

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DST) mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science (EECS).

The computational challenge of social learning

The complex reward structure of the social world and the uncertainty endemic to social contexts poses a challenge for modeling. For example, during social interactions, the actions of one person influence the internal states of another. These social dependencies make it difficult to formalize social learning problems in a mathematically tractable way. While it is tempting to dispense with these complexities, they are a defining feature of social life. Because the structure of social interactions challenges the simplifying assumptions often made in models, they make an ideal testbed for computational models of cognition. By adopting a framework that embeds existing social knowledge into the model, we can go beyond explaining behaviors in laboratory tasks to explaining those observed in the wild.

Emotion as Information in Early Social Learning

The majority of research on infants’ and children’s understanding of emotional expressions has focused on their abilities to use emotional expressions to infer how other people feel. However, an emerging body of work suggests that emotional expressions support rich, powerful inferences not just about emotional states but also about other unobserved states, such as hidden events in the physical world and mental states of other people (e.g., beliefs and desires). Here we argue that infants and children harness others’ emotional expressions as a source of information for learning about the physical and social world broadly. This “emotion as information” framework integrates affective, developmental, and computational cognitive sciences, extending the scope of signals that count as “information” in early learning.

Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset

Human emotions unfold over time, and more affective computing research has to prioritize capturing this crucial component of real-world affect. Modeling dynamic emotional stimuli requires solving the twin challenges of time-series modeling and of collecting high-quality time-series datasets. We begin by assessing the state-of-the-art in time-series emotion recognition, and we review contemporary time-series approaches in affective computing, including discriminative and generative models. We then introduce the first version of the Stanford Emotional Narratives Dataset (SENDv1): a set of rich, multimodal videos of self-paced, unscripted emotional narratives, annotated for emotional valence over time. The complex narratives and naturalistic expressions in this dataset provide a challenging test for contemporary time-series emotion recognition models. We demonstrate several baseline and state-of-the-art modeling approaches on the SEND, including a Long Short-Term Memory model and a multimodal Variational Recurrent Neural Network, which perform comparably to the human-benchmark. We end by discussing the implications for future research in time-series affective computing.

Associations between brain and behavioral processing of facial expressions of emotion and sensory reactivity in young children

The ability to decode and accurately identify information from facial emotions may advantage young children socially. This capacity to decode emotional information may likewise be influenced by individual differences in children's temperament. This study investigated whether sensory reactivity and perceptual awareness, two dimensions of temperament, as well as children's ability to accurately label emotions relates to the neural processing of emotional content in faces. Event related potentials (ERPs) of 4 to 6 year-old children (N  = 119) were elicited from static displays of anger, happy, fearful, sad, and neutral emotion faces. Children, as a group, exhibited differential early (N290) and mid-latency (P400) event-related potentials (ERPs) in response to facial expressions of emotion. Individual differences in children's sensory reactivity were associated with enhanced P400 amplitudes to neutral, sad, and fearful faces. In a separate task, children were asked to provide an emotional label for the same images. Interestingly, children less accurately labeled the same neutral, sad, and fearful faces, suggesting that, contrary to previous work showing enhanced attentional processing to threatening cues (i.e., fear), children higher in sensory reactivity may deploy more attentional resources when decoding ambiguous emotional cues.

Applying Probabilistic Programming to Affective Computing

Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which models psychological-grounded theories as generative models of emotion, and implements them as stochastic, executable computer programs. We first review probabilistic approaches that integrate reasoning about emotions with reasoning about other latent mental states (e.g., beliefs, desires) in context. Recently-developed probabilistic programming languages offer several key desidarata over previous approaches, such as: (i) flexibility in representing emotions and emotional processes; (ii) modularity and compositionality; (iii) integration with deep learning libraries that facilitate efficient inference and learning from large, naturalistic data; and (iv) ease of adoption. Furthermore, using a probabilistic programming framework allows a standardized platform for theory-building and experimentation: Competing theories (e.g., of appraisal or other emotional processes) can be easily compared via modular substitution of code followed by model comparison. To jumpstart adoption, we illustrate our points with executable code that researchers can easily modify for their own models. We end with a discussion of applications and future directions of the probabilistic programming approach.

Emotivity in the Voice: Prosodic, Lexical, and Cultural Appraisal of Complaining Speech

Emotive speech is a social act in which a speaker displays emotional signals with a specific intention; in the case of third-party complaints, this intention is to elicit empathy in the listener. The present study assessed how the emotivity of complaints was perceived in various conditions. Participants listened to short statements describing painful or neutral situations, spoken with a complaining or neutral prosody, and evaluated how complaining the speaker sounded. In addition to manipulating features of the message, social-affiliative factors which could influence complaint perception were varied by adopting a cross-cultural design: participants were either Québécois (French Canadian) or French and listened to utterances expressed by both cultural groups. The presence of a complaining tone of voice had the largest effect on participant evaluations, while the nature of statements had a significant, but smaller influence. Marginal effects of culture on explicit evaluation of complaints were found. A multiple mediation analysis suggested that mean fundamental frequency was the main prosodic signal that participants relied on to detect complaints, though most of the prosody effect could not be linearly explained by acoustic parameters. These results highlight a tacit agreement between speaker and listener: what characterizes a complaint is how it is said (i.e., the tone of voice), more than what it is about or who produces it. More generally, the study emphasizes the central importance of prosody in expressive speech acts such as complaints, which are designed to strengthen social bonds and supportive responses in interactive behavior. This intentional and interpersonal aspect in the communication of emotions needs to be further considered in research on affect and communication.

Theoretical, and epistemological challenges in scientific investigations of complex emotional states in animals

This review brings to light critical epistemological and theoretical considerations when studying complex emotional states in animals. We discuss anthropomorphic and Umwelt perspectives of nonhuman animals and the ways in which distinct theories of consciousness and neural processing may restrict the potential for the development of knowledge on the topic. Within the same line of argumentation, we consider influences of the debate between monism and dualism and psychology's behaviorism and cognitive theories. Finally, we contrast the affective consciousness, higher-order emotional consciousness, and constructed emotion theories to further our understanding of complex emotional states in animals.

The neural and computational systems of social learning

Learning the value of stimuli and actions from others - social learning - adaptively contributes to individual survival and plays a key role in cultural evolution. We review research across species targeting the neural and computational systems of social learning in both the aversive and appetitive domains. Social learning generally follows the same principles as self-experienced value-based learning, including computations of prediction errors and is implemented in brain circuits activated across task domains together with regions processing social information. We integrate neural and computational perspectives of social learning with an understanding of behaviour of varying complexity, from basic threat avoidance to complex social learning strategies and cultural phenomena.

Robots Learning to Say “No”

“No” is one of the first ten words used by children and embodies the first form of linguistic negation. Despite its early occurrence, the details of its acquisition remain largely unknown. The circumstance that “no” cannot be construed as a label for perceptible objects or events puts it outside the scope of most modern accounts of language acquisition. Moreover, most symbol grounding architectures will struggle to ground the word due to its non-referential character. The presented work extends symbol grounding to encompass affect and motivation. In a study involving the child-like robot iCub, we attempt to illuminate the acquisition process of negation words. The robot is deployed in speech-wise unconstrained interaction with participants acting as its language teachers. The results corroborate the hypothesis that affect or volition plays a pivotal role in the acquisition process. Negation words are prosodically salient within prohibitive utterances and negative intent interpretations such that they can be easily isolated from the teacher’s speech signal. These words subsequently may be grounded in negative affective states. However, observations of the nature of prohibition and the temporal relationships between its linguistic and extra-linguistic components raise questions over the suitability of Hebbian-type algorithms for certain types of language grounding.

Editors' Introduction: Computational Approaches to Social Cognition

What place should formal or computational methods occupy in social psychology? We consider this question in historical perspective, survey the current state of the field, introduce the several new contributions to this special issue, and reflect on the future.