Caregiver speech predicts the emergence of children's emotion vocabulary.

Learning about emotions is an important part of children's social and communicative development. How does children's emotion-related vocabulary emerge over development? How may emotion-related information in caregiver input support learning of emotion labels and other emotion-related words? This investigation examined language production and input among English-speaking toddlers (16-30 months) using two datasets: Wordbank (N = 5520; 36% female, 38% male, and 26% unknown gender; 1% Asian, 4% Black, 2% Hispanic, 40% White, 2% others, and 50% unknown ethnicity; collected in North America; dates of data collection unknown) and Child Language Data Exchange System (N = 587; 46% female, 44% male, 9% unknown gender, all unknown ethnicity; collected in North America and the UK; data collection dates, were available between 1962 and 2009). First, we show that toddlers develop the vocabulary to express increasingly wide ranges of emotional information during the first 2 years of life. Computational measures of word valence showed that emotion labels are embedded in a rich network of words with related valence. Second, we show that caregivers leverage these semantic connections in ways that may scaffold children's learning of emotion and mental state labels. This research suggests that young children use the dynamics of language input to construct emotion word meanings, and provides new techniques for defining the quality of infant-directed speech.

Simulation does not just inform choice, it changes choice.

Simulation - imagining future events - plays a role in decision-making. In Conviction Narrative Theory, people's emotional responses to their simulations inform their choices. Yet imagining one possible future also increases its plausibility and accessibility relative to other futures. We propose that the act of simulation, in addition to affective evaluation, drives people to choose in accordance with their simulations.

The representational structure of mental states generalizes across target people and stimulus modalities.

Each individual experiences mental states in their own idiosyncratic way, yet perceivers can accurately understand a huge variety of states across unique individuals. How do they accomplish this feat? Do people think about their own anger in the same ways as another person's anger? Is reading about someone's anxiety the same as seeing it? Here, we test the hypothesis that a common conceptual core unites mental state representations across contexts. Across three studies, participants judged the mental states of multiple targets, including a generic other, the self, a socially close other, and a socially distant other. Participants viewed mental state stimuli in multiple modalities, including written scenarios and images. Using representational similarity analysis, we found that brain regions associated with social cognition expressed stable neural representations of mental states across both targets and modalities. Together, these results suggest that people use stable models of mental states across different people and contexts.

Clarifying the link between music and social bonding by measuring prosociality in context.

To corroborate the music and social bonding hypothesis, we propose that future investigations isolate specific components of social bonding and consider the influence of context. We deconstruct and operationalize social bonding through the lens of social psychology and provide examples of specific measures that can be used to assess how the link between music and sociality varies by context.

The Social Brain Automatically Predicts Others' Future Mental States.

Social life requires people to predict the future: people must anticipate others' thoughts, feelings, and actions to interact with them successfully. The theory of predictive coding suggests that the social brain may meet this need by automatically predicting others' social futures. If so, when representing others' current mental state, the brain should already start representing their future states. To test this hypothesis, we used fMRI to measure female and male human participants' neural representations of mental states. Representational similarity analysis revealed that neural patterns associated with mental states currently under consideration resembled patterns of likely future states more so than patterns of unlikely future states. This effect manifested in activity across the social brain network and in medial prefrontal cortex in particular. Repetition suppression analysis also supported the social predictive coding hypothesis: considering mental states presented in predictable sequences reduced activity in the precuneus relative to unpredictable sequences. In addition to demonstrating that the brain makes automatic predictions of others' social futures, the results also demonstrate that the brain leverages a 3D representational space to make these predictions. Proximity between mental states on the psychological dimensions of rationality, social impact, and valence explained much of the association between state-specific neural pattern similarity and state transition likelihood. Together, these findings suggest that the way the brain represents the social present gives people an automatic glimpse of the social future.SIGNIFICANCE STATEMENT When you see a ball in flight, your brain calculates, not just its static visual features such as size and shape, but also predicts its future trajectory. Here, we investigated whether the same might hold true in the social world: when we see someone flying into a rage, does our brain automatically predict their social trajectory? In this study, we scanned participants' brain activity while they judged others' mental states. We found that neural activity associated with a given state resembled activity associated with likely future states. Additionally, unpredictable sequences of states evoked more brain activity than predictable sequences, consistent with monitoring for, and updating from, prediction errors. These results suggest that the social brain automatically predicts others' future mental states.

Mental models accurately predict emotion transitions.

Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

Successful simulation requires bridging levels of abstraction.

Although many simulations draw upon only one level of abstraction, the process for generating rich simulations requires a dynamic interplay between abstract and concrete knowledge. A complete model of simulation must account for a mind and brain that can bridge the perceptual with the conceptual, the episodic with the semantic, and the concrete with the abstract.

Neural evidence that three dimensions organize mental state representation: Rationality, social impact, and valence.

How do people understand the minds of others? Existing psychological theories have suggested a number of dimensions that perceivers could use to make sense of others' internal mental states. However, it remains unclear which of these dimensions, if any, the brain spontaneously uses when we think about others. The present study used multivoxel pattern analysis (MVPA) of neuroimaging data to identify the primary organizing principles of social cognition. We derived four unique dimensions of mental state representation from existing psychological theories and used functional magnetic resonance imaging to test whether these dimensions organize the neural encoding of others' mental states. MVPA revealed that three such dimensions could predict neural patterns within the medial prefrontal and parietal cortices, temporoparietal junction, and anterior temporal lobes during social thought: rationality, social impact, and valence. These results suggest that these dimensions serve as organizing principles for our understanding of other people.

Disclosing information about the self is intrinsically rewarding.

Humans devote 30-40% of speech output solely to informing others of their own subjective experiences. What drives this propensity for disclosure? Here, we test recent theories that individuals place high subjective value on opportunities to communicate their thoughts and feelings to others and that doing so engages neural and cognitive mechanisms associated with reward. Five studies provided support for this hypothesis. Self-disclosure was strongly associated with increased activation in brain regions that form the mesolimbic dopamine system, including the nucleus accumbens and ventral tegmental area. Moreover, individuals were willing to forgo money to disclose about the self. Two additional studies demonstrated that these effects stemmed from the independent value that individuals placed on self-referential thought and on simply sharing information with others. Together, these findings suggest that the human tendency to convey information about personal experience may arise from the intrinsic value associated with self-disclosure.

Neural representations of situations and mental states are composed of sums of representations of the actions they afford.

Human behavior depends on both internal and external factors. Internally, people's mental states motivate and govern their behavior. Externally, one's situation constrains which actions are appropriate or possible. To predict others' behavior, one must understand the influences of mental states and situations on actions. On this basis, we hypothesize that people represent situations and states in terms of associated actions. To test this, we use functional neuroimaging to estimate neural activity patterns associated with situations, mental states, and actions. We compute sums of the action patterns, weighted by how often each action occurs in each situation and state. We find that these summed action patterns reconstructed the corresponding situation and state patterns. These results suggest that neural representations of situations and mental states are composed of sums of their action affordances. Summed action representations thus offer a biological mechanism by which people can predict actions given internal and external factors.

Why do we think? The dynamics of spontaneous thought reveal its functions.

Spontaneous thought-mind wandering, daydreaming, and creative ideation-makes up most of everyday cognition. Is this idle thought, or does it serve an adaptive function? We test two hypotheses about the functions of spontaneous thought: First, spontaneous thought improves memory efficiency. Under this hypothesis, spontaneous thought should prioritize detailed, vivid episodic simulations. Second, spontaneous thought helps us achieve our goals. Under this hypothesis, spontaneous thought should prioritize content relevant to ongoing goal pursuits, or current concerns. We use natural language processing and machine learning to quantify the dynamics of thought in a large sample (N = 3,359) of think aloud data. Results suggest that spontaneous thought both supports memory optimization and keeps us focused on current concerns.

Individual differences in emotion prediction and implications for social success.

The social world requires people to predict others' thoughts, feelings, and actions. People who successfully predict others' emotions experience significant social advantages. What makes a person good at predicting emotions? To predict others' future emotional states, a person must know how one emotion transitions to the next. People learn how emotions transition from at least two sources: (a) internal information, or one's own emotion experiences, and (b) external information, such as the social cues detected in a person's face. Across five studies collected between 2018 and 2020, we find evidence that both sources of information are related to accurate emotion prediction: individuals with atypical personal emotion transitions, difficulty understanding their own emotional experiences, and impaired emotion perception displayed impaired emotion prediction. This ability to predict others' emotions has real-world social implications. Individuals who make accurate emotion predictions have better relationships with their friends and communities and experience less loneliness. In contrast, disruptions in both internal and external information sources explain prediction inaccuracy in individuals with social difficulties, specifically with social communication difficulties common in autism spectrum disorder. These findings provide evidence that successful emotion prediction, which relies on the perception of accurate internal and external data about how emotions transition, may be key to social success. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Neural correlates of anchoring-and-adjustment during mentalizing.

Recent studies have suggested that the medial prefrontal cortex (MPFC) contributes both to understanding the mental states of others and to introspecting about one's own mind. This finding has suggested that perceivers might use their own thoughts and feelings as a starting point for making inferences about others, consistent with "simulation" or "self-projection" views of social cognition. However, perceivers cannot simply assume that others think and feel exactly as they do; social cognition also must include processes that adjust for perceived differences between self and other. Recent cognitive work has suggested that such correction occurs through a process of "anchoring-and-adjustment" by which perceivers serially tune their inferences from an initial starting point based on their own introspections. Here, we used functional MRI to test two predictions derived from this anchoring-and-adjustment view. Participants (n = 64) used a Likert scale to judge the preferences of another person and to indicate their own preferences on the same items, allowing us to calculate the discrepancy between the participant's answers for self and other. Whole-brain parametric analyses identified a region in the MPFC in which activity was related linearly to this self-other discrepancy when inferring the mental states of others. These findings suggest both that the self serves as an important starting point from which to understand others and that perceivers customize such inferences by serially adjusting away from this anchor.

Egocentric Projection is a Rational Strategy for Accurate Emotion Prediction.

People need to accurately understand and predict others' emotions in order to build and maintain meaningful social connections. However, when they encounter new social partners, people often do not have enough information about them to make accurate inferences. Rather, they often resort to an egocentric heuristic, and make predictions about a target by using their own self-knowledge as a proxy. Is this egocentric heuristic a form of cognitive bias, or is it a rational strategy for real-world social prediction? If egocentrism provides a rational and effective solution to the challenging task of social prediction in naturalistic contexts, we should expect that a) egocentric predictions tend to be more accurate, and b) people rely on self-knowledge to a greater extent when it's more likely to be a good proxy. Using an emotion prediction task and personality measures, we assessed similarity and predictive accuracy between first-year college students and their new acquaintance roommate. Results demonstrated that, when people need to predict an unfamiliar target's emotions, self-knowledge can often effectively approximate knowledge about others, and thus support accurate predictions. Moreover, participants that were typical of the sample, whose self-knowledge can better approximate information about the target, relied more on self-knowledge in their predictions, and thus achieved higher accuracy. These findings suggest that people rationally tune their use of egocentrism based on whether it is likely to pay off. Overall, these findings demonstrate a rational side to a cognitive phenomenon usually framed as a cognitive pitfall, namely egocentric projection, when its natural decision context is taken into consideration.

Transition dynamics shape mental state concepts.

People have a unique ability to represent other people's internal thoughts and feelings-their mental states. Mental state knowledge has a rich conceptual structure, organized along key dimensions, such as valence. People use this conceptual structure to guide social interactions. How do people acquire their understanding of this structure? Here we investigate an underexplored contributor to this process: observation of mental state dynamics. Mental states-including both emotions and cognitive states-are not static. Rather, the transitions from one state to another are systematic and predictable. Drawing on prior cognitive science, we hypothesize that these transition dynamics may shape the conceptual structure that people learn to apply to mental states. Across nine behavioral experiments (N = 1,439), we tested whether the transition probabilities between mental states causally shape people's conceptual judgments of those states. In each study, we found that observing frequent transitions between mental states caused people to judge them to be conceptually similar. Computational modeling indicated that people translated mental state dynamics into concepts by embedding the states as points within a geometric space. The closer two states are within this space, the greater the likelihood of transitions between them. In three neural network experiments, we trained artificial neural networks to predict real human mental state dynamics. The networks spontaneously learned the same conceptual dimensions that people use to understand mental states. Together these results indicate that mental state dynamics-and the goal of predicting them-shape the structure of mental state concepts. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Creativity at rest: Exploring functional network connectivity of creative experts.

The neuroscience of creativity seeks to disentangle the complex brain processes that underpin the generation of novel ideas. Neuroimaging studies of functional connectivity, particularly functional magnetic resonance imaging (fMRI), have revealed individual differences in brain network organization associated with creative ability; however, much of the extant research is limited to laboratory-based divergent thinking measures. To overcome these limitations, we compare functional brain connectivity in a cohort of creative experts (n = 27) and controls (n = 26) and examine links with creative behavior. First, we replicate prior findings showing reduced connectivity in visual cortex related to higher creative performance. Second, we examine whether this result is driven by integrated or segregated connectivity. Third, we examine associations between functional connectivity and vivid distal simulation separately in creative experts and controls. In accordance with past work, our results show reduced connectivity to the primary visual cortex in creative experts at rest. Additionally, we observe a negative association between distal simulation vividness and connectivity to the lateral visual cortex in creative experts. Taken together, these results highlight connectivity profiles of highly creative people and suggest that creative thinking may be related to, though not fully redundant with, the ability to vividly imagine the future.

Accurate emotion prediction in dyads and groups and its potential social benefits.

Emotion dynamics vary considerably from individual to individual and from group to group. Successful social interactions require people to track this moving target in order to anticipate the thoughts, feelings, and actions of others. In two studies, we test whether people track others' emotional idiosyncrasies to make accurate, target-specific emotion predictions. In both studies, participants predicted the emotion transitions of a specific target-either a close friend (Study 1) or a first-year college roommate (Study 2)-as well as an average group member. Results demonstrate that people can make highly accurate predictions both for specific individuals and specific groups. Accurate predictions rely on target-specific knowledge; new community members were able to make accurate predictions at zero-acquaintance, but accuracy increased over time as individuals accrued specialized knowledge. Results also suggest that accurate emotion prediction is associated with social success in both individual and communal relationships and that such a relation might emerge over time. Overall, our studies suggest that people accurately make individualized predictions of others' emotion transitions and that doing so fulfills a meaningful function in the social world. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Six dimensions describe action understanding: The ACT-FASTaxonomy.

Humans engage in a wide variety of different actions and activities. These range from simple motor actions like reaching for an object, to complex activities like governing a nation. Navigating everyday life requires people to make sense of this diversity of actions. We suggest that the mind simplifies this complex domain by attending primarily to the most essential features of actions. Using a parsimonious set of action dimensions, the mind can organize action knowledge in a low-dimensional representational space. In seven studies, we derive and validate such an action taxonomy. Study 1 uses large-scale text analyses to generate and test potential action dimensions. Study 2 validates interpretable labels for these dimensions. Studies 3-5 demonstrate that these dimensions can explain human judgments about actions. We perform model selection on data from these studies to arrive at the optimal set of six psychological dimensions, together forming the Abstraction, Creation, Tradition, Food, Animacy, Spiritualism Taxonomy (ACT-FAST). Study 6 demonstrates that ACT-FAST can predict socially relevant qualities of actions, including how, when, where, why, and by whom they are performed. Finally, Study 7 shows that ACT-FAST can explain action-related patterns of brain activity using naturalistic functional MRI (MRI). Together, these studies reveal the dimensional structure the mind applies to organize action concepts. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The promises and pitfalls of functional magnetic resonance imaging hyperscanning for social interaction research.

Social neuroscience combines tools and perspectives from social psychology and neuroscience to understand how people interact with their social world. Here we discuss a relatively new method-hyperscanning-to study real-time, interactive social interactions using functional magnetic resonance imaging (fMRI). We highlight three contributions that fMRI hyperscanning makes to the study of the social mind: (1) Naturalism: it shifts the focus from tightly-controlled stimuli to more naturalistic social interactions; (2) Multi-person Dynamics: it shifts the focus from individuals as the unit of analysis to dyads and groups; and (3) Neural Resolution: fMRI hyperscanning captures high-resolution neural patterns and dynamics across the whole brain, unlike other neuroimaging hyperscanning methods (e.g., electroencephalogram, functional near-infrared spectroscopy). Finally, we describe the practical considerations and challenges that fMRI hyperscanning researchers must navigate. We hope researchers will harness this powerful new paradigm to address pressing questions in today's society.

The 3d Mind Model characterizes how people understand mental states across modern and historical cultures.

Humans rely on social interaction to achieve many important goals. These interactions rely in turn on people's capacity to understand others' mental states: their thoughts and feelings. Do different cultures understand minds in different ways, or do widely shared principles describe how different cultures understand mental states? Extensive data suggest that the mind organizes mental state concepts using the 3d Mind Model, composed of the psychological dimensions: rationality (vs. emotionality), social impact (states which affect others more vs. less), and valence (positive vs. negative states). However, this evidence comes primarily from English-speaking individuals in the United States. Here we investigated mental state representation in 57 contemporary countries, using 163 million English language tweets; in 17 languages, using billions of words of text from internet webpages; and across more than 2000 years of history, using curated texts from four historical societies. We quantified mental state meaning by analyzing the text produced by each culture using word embeddings. We then tested whether the 3d Mind Model could explain which mental states were similar in meaning within each culture. We found that the 3d Mind Model significantly explained mental state meaning in every country, language, and historical society that we examined. These results suggest that rationality, social impact, and valence form a generalizable conceptual backbone for mental state representation.