Transmission of social bias through observational learning.

People often rely on social learning-learning by observing others' actions and outcomes-to form preferences in advance of their own direct experiences. Although typically adaptive, we investigated whether social learning may also contribute to the formation and spread of prejudice. In six experiments (n = 1550), we demonstrate that by merely observing interactions between a prejudiced actor and social group members, observers acquired the prejudices of the actor. Moreover, observers were unaware of the actors' bias, misattributing their acquired group preferences to the behavior of group members, despite identical behavior between groups. Computational modeling revealed that this effect was due to value shaping, whereby one's preferences are shaped by another's actions toward a target, in addition to the target's reward feedback. These findings identify social learning as a potent mechanism of prejudice formation that operates implicitly and supports the transmission of intergroup bias.

Race effects on impression formation in social interaction: An instrumental learning account.

How does race influence the impressions we form through direct interaction? In two preregistered experiments (N = 239/179), White American participants played a money-sharing game with Black and White players, based on a probabilistic reward reinforcement learning task, in which they chose to interact with players and received feedback on whether a player shared. We found that participants formed stronger reward preferences for White relative to Black players despite equivalent reward feedback between groups-a pattern that was stronger among participants with low internal motivation to respond without prejudice and high explicit prejudice. This race effect in reward learning was evident in participants' behavioral choice preferences, but not in their self-reported perceptions of group members' reward rates. Computational modeling suggested two mechanisms through which race affected instrumental learning: race (a) influenced White participants' initial expectancies (i.e., priors) about Black compared with White players' behavior and (b) led participants to update reward representations of Black and White players according to separate learning rates. These findings demonstrate that race can influence the formation of impressions through direct social interaction and introduce an instrumental learning framework to understand the effects of bias in intergroup interactions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A longitudinal analysis of conspiracy beliefs and Covid-19 health responses.

BACKGROUND: Little is known about how conspiracy beliefs and health responses are interrelated over time during the course of the coronavirus disease 2019 (Covid-19) pandemic. This longitudinal study tested two contrasting, but not mutually exclusive, hypotheses through cross-lagged modeling. First, based on the consequential nature of conspiracy beliefs, we hypothesize that conspiracy beliefs predict an increase in detrimental health responses over time. Second, as people may rationalize their behavior through conspiracy beliefs, we hypothesize that detrimental health responses predict increased conspiracy beliefs over time. METHODS: We measured conspiracy beliefs and several health-related responses (i.e. physical distancing, support for lockdown policy, and the perception of the coronavirus as dangerous) at three phases of the pandemic in the Netherlands (N = 4913): During the first lockdown (Wave 1: April 2020), after the first lockdown (Wave 2: June 2020), and during the second lockdown (Wave 3: December 2020). RESULTS: For physical distancing and perceived danger, the overall cross-lagged effects supported both hypotheses, although the standardized effects were larger for the effects of conspiracy beliefs on these health responses than vice versa. The within-person change results only supported an effect of conspiracy beliefs on these health responses, depending on the phase of the pandemic. Furthermore, an overall cross-lagged effect of conspiracy beliefs on reduced support for lockdown policy emerged from Wave 2 to 3. CONCLUSIONS: The results provide stronger support for the hypothesis that conspiracy beliefs predict health responses over time than for the hypothesis that health responses predict conspiracy beliefs over time.

Neural Basis of Prejudice and Prejudice Reduction.

Social prejudices, based on race, ethnicity, gender, or other identities, pervade how we perceive, think about, and act toward others. Research on the neural basis of prejudice seeks to illuminate its effects by investigating the neurocognitive processes through which prejudice is formed, represented in the mind, expressed in behavior, and potentially reduced. In this article, we review current knowledge about the social neuroscience of prejudice regarding its influence on rapid social perception, representation in memory, emotional expression and relation to empathy, and regulation, and we discuss implications of this work for prejudice reduction interventions.

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning.

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution-individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.

Propagation of societal gender inequality by internet search algorithms.

Humans increasingly rely on artificial intelligence (AI) for efficient and objective decision-making, yet there is increasing concern that algorithms used by modern AI systems produce discriminatory outputs, presumably because they are trained on data in which societal biases are embedded. As a consequence, their use by human decision makers may result in the propagation, rather than reduction, of existing disparities. To assess this hypothesis empirically, we tested the relation between societal gender inequality and algorithmic search output and then examined the effect of this output on human decision-making. First, in two multinational samples (n = 37, 52 countries), we found that greater nation-level gender inequality was associated with more male-dominated Google image search results for the gender-neutral keyword "person" (in a nation's dominant language), revealing a link between societal-level disparities and algorithmic output. Next, in a series of experiments with human participants (n = 395), we demonstrated that the gender disparity associated with high- vs. low-inequality algorithmic outputs guided the formation of gender-biased prototypes and influenced hiring decisions in novel scenarios. These findings support the hypothesis that societal-level gender inequality is recapitulated in internet search algorithms, which in turn can influence human decision makers to act in ways that reinforce these disparities.

Missing context from experimental studies amplifies, rather than negates, racial bias in the real world.

We agree with Cesario's premise but reject his conclusion: Although experimental studies of racial stereotyping, weapons perception, and shoot decisions typically exclude real-world contextual factors and thus have limited relevance to race disparities (e.g., in policing), these excluded factors comprise systemic, institutional, and individual-level biases that are more likely to amplify racial disparities than negate them.

Context-dependent learning in social interaction: Trait impressions support flexible social choices.

[Correction Notice: An Erratum for this article was reported in Vol 123(4) of Journal of Personality and Social Psychology (see record 2023-02979-001). In the error, the Study 2 heading Computational Mode of Learning should instead appear as Computational Model of Learning. All versions of this article have been corrected.] How do humans learn, through social interaction, whom to depend on in different situations? We compared the extent to which inferred trait attributes-as opposed to learned reward associations previously examined as part of feedback-based learning-could adaptively inform cross-context social decision-making. In four experiments, participants completed a novel task in which they chose to "hire" other players to solve math and verbal questions for money. These players varied in their trait-level competence across these contexts and, independently, in the monetary rewards they offered to participants across contexts. Results revealed that participants chose partners primarily based on context-specific traits, as opposed to either global trait impressions or material rewards. When making choices in novel contexts-including determining who to choose for social and emotional support-participants generalized trait knowledge from past contexts that required similar traits. Reward-based learning, by contrast, demonstrated significantly weaker context-sensitivity and generalization. These findings suggest that people form context-dependent trait impressions from interactive feedback and use this knowledge to make flexible social decisions. These results support a novel theoretical account of how interaction-based social learning can support context-specific impression formation and adaptive decision-making. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Effects of high and low power on the visual encoding of faces.

The experience of power is typically associated with social disengagement, yet power has also been shown to facilitate configural visual encoding - a process that supports the initial perception of a human face. To investigate this apparent contradiction, we directly tested whether power influences the visual encoding of faces. Two experiments, using neural and psychophysical assessments, revealed that low power impeded both first-order configural processing (the encoding of a stimulus as a face, assessed by the N170 event-related potential) and second-order configural processing (the encoding of feature distances within configuration, assessed using the face inversion paradigm), relative to high-power and control conditions. Power did not significantly affect facial feature encoding. Results reveal an early and automatic effect of low power on face perception, characterized primarily by diminished face processing. These findings suggest a novel interplay between visual and cognitive processes in power's influence on social behavior.

A computational reward learning account of social media engagement.

Social media has become a modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (likes), portraying the online world as a Skinner Box for the modern human. Yet despite such portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. Here, we apply a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyze over one million posts from over 4000 individuals on multiple social media platforms, using computational models based on reinforcement learning theory. Our results consistently show that human behavior on social media conforms qualitatively and quantitatively to the principles of reward learning. Specifically, social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction. Results further reveal meaningful individual difference profiles in social reward learning on social media. Finally, an online experiment (n = 176), mimicking key aspects of social media, verifies that social rewards causally influence behavior as posited by our computational account. Together, these findings support a reward learning account of social media engagement and offer new insights into this emergent mode of modern human behavior.

The Social Neuroscience of Prejudice.

The social neuroscience approach to prejudice investigates the psychology of intergroup bias by integrating models and methods of neuroscience with the social psychology of prejudice, stereotyping, and discrimination. Here, we review major contemporary lines of inquiry, including current accounts of group-based categorization; formation and updating of prejudice and stereotypes; effects of prejudice on perception, emotion, and decision making; and the self-regulation of prejudice. In each section, we discuss key social neuroscience findings, consider interpretational challenges and connections with the behavioral literature, and highlight how they advance psychological theories of prejudice. We conclude by discussing the next-generation questions that will continue to guide the social neuroscience approach toward addressing major societal issues of prejudice and discrimination.

Model-Based and Model-Free Social Cognition: Investigating the Role of Habit in Social Attitude Formation and Choice.

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions - computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each type of learning was expressed in both advisor choices and post-task self-reported liking of advisors. Specifically, participants preferred advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Although participants relied more heavily on model-based learning overall, they varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

Scarcity disrupts the neural encoding of Black faces: A socioperceptual pathway to discrimination.

When economic resources are scarce, racial minorities are often devalued and disenfranchised. We proposed that this pattern extends to visual processing, such that the encoding of minority group faces is disrupted under scarcity-an effect that may facilitate discrimination and contribute to racial disparities. Specifically, we used EEG and fMRI to test whether scarce economic conditions induce deficits in neural encoding of Black faces, and we examined whether this effect is associated with discriminatory resource allocation in behavior. In Study 1, framing resources as scarce (vs. neutral) selectively impaired the neural encoding of Black (vs. White) faces, as indexed by a delayed face-related N170 ERP response, and the degree of this encoding deficit predicted anti-Black allocation decisions. In Study 2, we replicated and extended this effect using fMRI: Resources framed as scarce (vs. neutral) reduced face-sensitive fusiform activity to Black (vs. White) faces. Furthermore, scarcity-decreased fusiform activity to Black faces corresponded with decreased valuation-related striatum activity to predict anti-Black allocation decisions. These findings suggest that impaired visual processing and devaluation occur selectively for minorities under scarcity-an implicit effect that may promote discrimination and contribute to rising disparities observed during economic stress. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Social robotics and the modulation of social perception and bias.

The field of social robotics offers an unprecedented opportunity to probe the process of impression formation and the effects of identity-based stereotypes (e.g. about gender or race) on social judgements and interactions. We present the concept of fair proxy communication-a form of robot-mediated communication that proceeds in the absence of potentially biasing identity cues-and describe how this application of social robotics may be used to illuminate implicit bias in social cognition and inform novel interventions to reduce bias. We discuss key questions and challenges for the use of robots in research on the social cognition of bias and offer some practical recommendations. We conclude by discussing boundary conditions of this new form of interaction and by raising some ethical concerns about the inclusion of social robots in psychological research and interventions. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.

The Nature and Consequences of Essentialist Beliefs About Race in Early Childhood.

It is widely believed that race divides the world into biologically distinct kinds of people-an essentialist belief inconsistent with reality. Essentialist views of race have been described as early emerging, but this study found that young children (n = 203, Mage  = 5.45) hold only the more limited belief that the physical feature of skin color is inherited and stable. Overall, children rejected the causal essentialist view that behavioral and psychological characteristics are constrained by an inherited racial essence. Although average levels of children's causal essentialist beliefs about race were low, variation in these beliefs was related to children's own group membership, exposure to diversity, as well as children's own social attitudes.

Social Cognition 2.0: An Interactive Memory Systems Account.

For 40 years, research on impression formation and attitudes has relied on dual-process theories that represent knowledge in a single associative network. Although such models explain priming effects and some implicit responses, they are generally silent on other forms of learning and on the interface of social cognition with perception and action. Meanwhile, advances in cognitive neuroscience reveal multiple, interacting forms of learning and memory (e.g., semantic associative memory, Pavlovian conditioning, and instrumental learning), with detailed models of their operations, neural bases, and connections with perceptual and behavioral systems. This memory systems perspective offers a more refined, neurally plausible model of social cognition and attitudes that, I argue, provides a useful and generative account of human social behavior.

Computational neuroscience approaches to social cognition.

How do we form impressions of people and groups and use these representations to guide our actions? From its inception, social neuroscience has sought to illuminate such complex forms of social cognition, and recently these efforts have been invigorated by the use of computational modeling. Computational modeling provides a framework for delineating specific processes underlying social cognition and relating them to neural activity and behavior. We provide a primer on the computational modeling approach and describe how it has been used to elucidate psychological and neural mechanisms of impression formation, social learning, moral decision making, and intergroup bias.