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People experience instances of social feedback as interdependent with potential implications for their entire self-concept. How do people maintain positivity and coherence across the self-concept while updating self-views from feedback? We present a network model describing how the brain represents the semantic dependency relations among traits and uses this information to avoid an overall loss of positivity and coherence. Both male and female human participants received social feedback during a self-evaluation task while undergoing functional magnetic resonance imaging. We modeled self-belief updating by incorporating a reinforcement learning model within the network structure. Participants learned more rapidly from positive than negative feedback and were less likely to change self-views for traits with more dependencies in the network. Further, participants back propagated feedback across network relations while retrieving prior feedback on the basis of network similarity to inform ongoing self-views. Activation in ventromedial prefrontal cortex (vmPFC) reflected the constrained updating process such that positive feedback led to higher activation and negative feedback to less activation for traits with more dependencies. Additionally, vmPFC was associated with the novelty of a trait relative to previously self-evaluated traits in the network, and angular gyrus was associated with greater certainty for self-beliefs given the relevance of prior feedback. We propose that neural computations that selectively enhance or attenuate social feedback and retrieve past relevant experiences to guide ongoing selfevaluations may support an overall positive and coherent self-concept. [ABSTRACT FROM AUTHOR]

Attributing outcomes to your own actions or to external causes is essential for appropriately learning which actions lead to reward and which actions do not. Our previous work showed that this type of credit assignment is best explained by a Bayesian reinforcement learning model which posits that beliefs about the causal structure of the environment modulate reward prediction errors (RPEs) during action value updating. In this study, we investigated the brain networks underlying reinforcement learning that are influenced by causal beliefs using functional magnetic resonance imaging while human participants (n = 31; 13 males, 18 females) completed a behavioral task that manipulated beliefs about causal structure. We found evidence that RPEs modulated by causal beliefs are represented in dorsal striatum, while standard (unmodulated) RPEs are represented in ventral striatum. Further analyses revealed that beliefs about causal structure are represented in anterior insula and inferior frontal gyrus. Finally, structural equation modeling revealed effective connectivity from anterior insula to dorsal striatum. Together, these results are consistent with a possible neural architecture in which causal beliefs in anterior insula are integrated with prediction error signals in dorsal striatum to update action values. [ABSTRACT FROM AUTHOR]

Power is accompanied by a sense of entitlement, which shapes reactions to self-relevant injustices. We propose that powerful people more strongly expect to be treated fairly and are faster to perceive unjust treatment that violates these expectations. After preliminary data demonstrated that power leads people to expect fair outcomes for themselves, we conducted four experiments. Participants primed with high (vs. low) power were faster to identify violations of distributive justice in which they were victims (Study 1). This effect was specific to self-relevant injustices (Study 2) and generalized to violations of interpersonal justice (Study 3). Finally, participants primed with high power were more likely to take action against unfair treatment (Study 4). These findings suggest a process by which hierarchies may be maintained: Whereas the powerless are comparatively less sensitive to unfair treatment, the powerful may retain their social standing by quickly perceiving and responding to self-relevant injustices. [ABSTRACT FROM AUTHOR]

One of the most robust ways that people protect themselves from social-evaluative threat is by emphasizing the desirability of their personal characteristics, yet the neural underpinnings of this fundamental process are unknown. The current fMRI study addresses this question by examining self-evaluations of desirability (in comparison with other people) as a response to threat. Participants judged how much personality traits described themselves in comparison with their average peer. These judgments were preceded by threatening or nonthreatening social-evaluative feedback. Self-evaluations made in response to threat significantly increased activation in a number of regions including the OFC, medial pFC, lateral pFC, amygdala, and insula. Individual differences in the extent to which threat increased desirability were significantly correlated with medial OFC activity. This is the first study to examine the neural associations of a fundamental self-protection strategy: responding to threat by emphasizing the self's desirability. Although neural research has separately examined self-evaluation processes from the regulation of social-evaluative threat, little is known about the interplay between the two. The findings build on this previous research by showing that regions, often associated with self-evaluation, are modulated by the degree to which people respond to threat by emphasizing their own desirability. [ABSTRACT FROM AUTHOR]

The United States is increasingly politically polarized, fueling intergroup conflict and intensifying partisan biases in cognition and behavior. To date, research on intergroup bias has separately examined biases in how people search for information and how they interpret information. Here, we integrate these two perspectives to elucidate how partisan biases manifest across the information processing stream, beginning with (a) a biased selection of information, leading to (b) skewed samples of information that interact with (c) motivated interpretations to produce evaluative biases. Across three experiments and four internal meta-analyses, participants (N = 2,431) freely sampled information about ingroup and outgroup members or ingroup and outgroup political candidates until they felt confident to evaluate them. Across experiments, we reliably find that most participants begin sampling information from the ingroup, which was associated with individual differences in group-based motives, and that participants sampled overall more information from the ingroup. This sampling behavior, in turn, generates more variability in ingroup (relative to outgroup) experiences. We find that more variability in ingroup experiences predicted when participants decided to stop sampling and was associated with more biased evaluations. We further demonstrate that participants employ different sampling strategies over time when the ingroup is de facto worse—obfuscating Real Group Differences—and that participants selectively integrate their experiences into evaluations based on congeniality. The proposed framework extends classic findings in psychology by demonstrating how biases in sampling behavior interact with motivated interpretations to produce downstream evaluative biases and has implications for intergroup bias interventions. [ABSTRACT FROM AUTHOR]

People learn about themselves from social feedback, but desires for coherence and positivity constrain how feedback is incorporated into the self-concept. We developed a network-based model of the self-concept and embedded it in a reinforcement-learning framework to provide a computational account of how motivations shape self-learning from feedback. Participants (N = 46 adult university students) received feedback while evaluating themselves on traits drawn from a causal network of trait semantics. Network-defined communities were assigned different likelihoods of positive feedback. Participants learned from positive feedback but dismissed negative feedback, as reflected by asymmetries in computational parameters that represent the incorporation of positive versus negative outcomes. Furthermore, participants were constrained in how they incorporated feedback: Self-evaluations changed less for traits that have more implications and are thus more important to the coherence of the network. We provide a computational explanation of how motives for coherence and positivity jointly constrain learning about the self from feedback, an explanation that makes testable predictions for future clinical research. [ABSTRACT FROM AUTHOR]

The visual perception of individuals has received considerable attention (visual person perception), but little social psychological work has examined the processes underlying the visual perception of groups of people (visual people perception). Ensemble-coding is a visual mechanism that automatically extracts summary statistics (e.g., average size) of lower-level sets of stimuli (e.g., geometric figures), and also extends to the visual perception of groups of faces. Here, we consider whether ensemble-coding supports people perception, allowing individuals to form rapid, accurate impressions about groups of people. Across nine studies, we demonstrate that people visually extract high-level properties (e.g., diversity, hierarchy) that are unique to social groups, as opposed to individual persons. Observers rapidly and accurately perceived group diversity and hierarchy, or variance across race, gender, and dominance (Studies 1-3). Further, results persist when observers are given very short display times, backward pattern masks, color- and contrast-controlled stimuli, and absolute versus relative response options (Studies 4a-7b), suggesting robust effects supported specifically by ensemble-coding mechanisms. Together, we show that humans can rapidly and accurately perceive not only individual persons, but also emergent social information unique to groups of people. These people perception findings demonstrate the importance of visual processes for enabling people to perceive social groups and behave effectively in group-based social interactions. [ABSTRACT FROM AUTHOR]

Racial stereotypes exert pernicious effects on decision-making and behavior, yet little is known about how stereotypes disrupt people's ability to learn new associations. The current research interrogates a fundamental question about the boundary conditions of probabilistic learning by examining whether and how learning is influenced by preexisting associations. Across three experiments, participants learned the probabilistic outcomes of different card combinations based on feedback in either a social (e.g., forecasting crime) or nonsocial (e.g., forecasting weather) learning context. During learning, participants were presented with either task-irrelevant social (i.e., Black or White faces) or nonsocial (i.e., darker or lighter clouds) stimuli that were stereotypically congruent or incongruent with the learning context. Participants exhibited learning disruptions in the social compared to nonsocial learning context, despite repeated instructions that the stimuli were unrelated to the outcome (Studies 1 and 2). We also found no differences in learning disruptions when participants learned in the presence of negatively (Black and criminal) or positively valenced stereotypes (Black and athletic; Study 3). Finally, we tested whether learning decrements were due to "first-order" stereotype application or inhibition at the trial level, or due to "second-order" cognitive load disruptions that accumulate across trials due to fears of appearing prejudiced (aggregated analysis). We found no evidence of first-order disruptions and instead found evidence for second-order disruptions: participants who were more internally motivated to respond without prejudice, and thus more likely to self-monitor their responses, learned less accurately over time. We discuss the implications of the influence of stereotypes on learning and memory. [ABSTRACT FROM AUTHOR]

Simulating other people can shift one's self-concept, an effect known as simulation-induced malleability. How does imagining others shift the self? We propose that the activation of self-knowledge is the key factor by which simulation of others alters one's self-concept. We test this possibility across four studies that each manipulate self-knowledge activation indirectly during simulation and measure the impact on subsequent self-ratings. Results demonstrate that increasing activation of self-knowledge during simulation is associated with increased self-concept change. People experienced greater self-concept change when simulating similar others (Studies 1 and 2). People also generalized simulation-induced changes to aspects of the self-concept that were semantically similar to the simulated content (Study 3). Finally, people who are less likely to recruit self-knowledge (i.e., older adults) during simulation were less susceptible to self-concept change (Study 4). These studies highlight self-knowledge activation as an essential component of the effects of simulation on self-rated change. (PsycInfo Database Record (c) 2024 APA, all rights reserved).