Traditional gendered arrangements—norms, roles, prejudices, and hierarchies—shape every human life. Associated harms are primarily framed as women’s issues due to more severe consequences women face. Yet, gendered arrangements also shape men ’s relationships, career paths, and health. Current work on gender equity overlooks men’s perspectives. Despite benefits they gain from out-ranking women, men’s position paradoxically entraps them in restrictive roles, compelling them to prioritize dominance. An inclusive framework challenges prevailing narratives by considering personal costs borne by men. Identifying with a man’s traditional role is a mixed privilege, as five gendered arrangements show for men who subscribe to them: 1. Masculine norms can restrict men’s choices and are associated with adverse health trajectories; 2. Some men’s disengagement from communal roles denies them positive outcomes associated with caring for others; 3. Hostile sexism fosters antipathy, fueling tension in some men’s interactions with women; 4. Benevolent sexism forces some men into scripted interactions, preventing genuine connections and burdening them with unrealistic breadwinner and protector roles; 5. Societal shifts in gender hierarchies can elicit threat responses in men, depending on intersections with social class and racial identities. Understanding costs to men calls for more empirical research. Gender equity for men, whose circumstances differ from those of women, would enable men to make informed choices and achieve better outcomes for themselves—paralleling the progress women have made in many areas of life. Striving for equity for all genders can ultimately enhance overall human well-being.

Traditional explanations for stereotypes assume that they result from deficits in humans (ingroup-favoring motives, cognitive biases) or their environments (majority advantages, real group differences). An alternative explanation recently proposed that stereotypes can emerge when exploration is costly. Even optimal decision makers in an ideal environment can inadvertently form incorrect impressions from arbitrary encounters. However, all these existing theories essentially describe shortcuts that fail to explain the multidimensionality of stereotypes. Stereotypes of social groups have a canonical multidimensional structure, organized along dimensions of warmth and competence. We show that these dimensions and the associated stereotypes can result from feature-based exploration: When individuals make self-interested decisions based on past experiences in an environment where exploring new options carries an implicit cost and when these options share similar attributes, they are more likely to separate groups along multiple dimensions. We formalize this theory via the contextual multiarmed bandit problem, use the resulting model to generate testable predictions, and evaluate those predictions against human behavior. We evaluate this process in incentivized decisions involving as many as 20 real jobs and successfully recover the classic dimensions of warmth and competence. Further experiments show that intervening on the cost of exploration effectively mitigates bias, further demonstrating that exploration cost per se is the operating variable. Future diversity interventions may consider how to reduce exploration cost, in ways that parallel our manipulations.

Abstract Psychological theories continue to expand our understanding of stereotype content and processes. Stereotype content refers to what people think about social groups’ characteristics. Stereotype processes reflect how people integrate information to navigate social interactions. Advances in artificial intelligence introduce innovative analytical tools to revolutionize how psychologists understand stereotypes. Content research builds on theory-driven surveys (e.g. warmth and competence), to data-driven multidimensional scaling (e.g. belief), to large-scale linguistic analysis (e.g. emotion, appearance) to describe a myriad of dimensions people use for social evaluations. Process research starts from an information-processor metaphor (e.g. decoding, encoding), to the predictive brain (e.g. statistical learning, probabilistic modeling), and now to a feedback loop framework (e.g. reinforcement learning, algorithmic bias), paving the way to understand how and why people evaluate others. Understanding stereotypes is a collective enterprise, as evidenced by the scholarly debate that has helped move the field forward.

Understanding the impact of digital platforms on user behavior presents foundational challenges, including issues related to polarization, misinformation dynamics, and variation in news consumption. Comparative analyses across platforms and over different years can provide critical insights into these phenomena. This study investigates the linguistic characteristics of user comments over 34 y, focusing on their complexity and temporal shifts. Using a dataset of approximately 300 million English comments from eight diverse platforms and topics, we examine user communications’ vocabulary size and linguistic richness and their evolution over time. Our findings reveal consistent patterns of complexity across social media platforms and topics, characterized by a nearly universal reduction in text length, diminished lexical richness, and decreased repetitiveness. Despite these trends, users consistently introduce new words into their comments at a nearly constant rate. This analysis underscores that platforms only partially influence the complexity of user comments but, instead, it reflects a broader pattern of linguistic change driven by social triggers, suggesting intrinsic tendencies in users’ online interactions comparable to historically recognized linguistic hybridization and contamination processes.

Five studies (N = 7972) validated a brief measure and model of four facets of social evaluation (friendliness and morality as horizontal facets; ability and assertiveness as vertical facets). Perceivers expressed their personal impressions or estimated society’s impression of different types of targets (i.e., envisioned or encountered groups or individuals) and numbers of targets (i.e., between six and 100) in the separate, items-within-target mode or the joint, targets-within-item mode. Factor analyses confirmed that a two-items-per-facet measure fit the data well and better than a four-items-per-dimension measure that captured the Big Two model (i.e., no facets, just the horizontal and vertical dimensions). As predicted, the correlation between the two horizontal facets and between the two vertical facets was higher than the correlations between any horizontal facet and any vertical facet. Perceivers’ evaluations of targets on each facet were predictors of unique and relevant behavior intentions. Perceiving a target as more friendly, moral, able, and assertive increased the likelihood of relying on the target’s loyalty, fairness, intellect, and hubris in an economic game, respectively. These results establish the external, internal, convergent, discriminant, and predictive validity of the brief measure and model of four facets of social evaluation.

Interaction and cooperation with humans are overarching aspirations of artificial intelligence (AI) research. Recent studies demonstrate that AI agents trained with deep reinforcement learning are capable of collaborating with humans. These studies primarily evaluate human compatibility through "objective" metrics such as task performance, obscuring potential variation in the levels of trust and subjective preference that different agents garner. To better understand the factors shaping subjective preferences in human-agent cooperation, we train deep reinforcement learning agents in Coins, a two-player social dilemma. We recruit participants for a human-agent cooperation study and measure their impressions of the agents they encounter. Participants' perceptions of warmth and competence predict their stated preferences for different agents, above and beyond objective performance metrics. Drawing inspiration from social science and biology research, we subsequently implement a new "partner choice" framework to elicit revealed preferences: after playing an episode with an agent, participants are asked whether they would like to play the next round with the same agent or to play alone. As with stated preferences, social perception better predicts participants' revealed preferences than does objective performance. Given these results, we recommend human-agent interaction researchers routinely incorporate the measurement of social perception and subjective preferences into their studies.

The animal stereotype approach dissolves 'animals' into diverse images depending on their species. First, we reviewed recent research showing the attributes socially ascribed to different animal species. Next, we discussed how the animal stereotype approach may complement dehumanization by broadening the distinct forms of animalized dehumanization based on 1) intentions (warm, friendly, and harmful), 2) abilities (perceptual and cognitive), 3) physical appearance (size, aesthetic appeal), 4) affective capacities, 5) physiological needs, and 6) domestic-wild nature.

According to ambivalent sexism theory (Glick & Fiske, 1996), the coexistence of gendered power differences and mutual interdependence creates two apparently opposing but complementary sexist ideologies: hostile sexism (HS; viewing women as manipulative competitors who seek to gain power over men) coincides with benevolent sexism (BS; a chivalrous view of women as pure and moral, yet weak and passive, deserving men's protection and admiration, as long as they conform). The research on these ideologies employs the Ambivalent Sexism Inventory, used extensively in psychology and allied disciplines, often to understand the roles sexist attitudes play in reinforcing gender inequality. Following contemporary guidelines, this systematic review utilizes a principled approach to synthesize the multidisciplinary empirical literature on ambivalent sexism. After screening 1,870 potentially relevant articles and fully reviewing 654 eligible articles, five main domains emerge in ambivalent sexism research (social ideologies, violence, workplace, stereotypes, intimate relationships). The accumulating evidence across domains offers bottom-up empirical support for ambivalent sexism as a coordinated system to maintain control over women (and sometimes men). Hostile sexism acts through the direct and diverse paths of envious/resentful prejudices, being more sensitive to power and sexuality cues; Benevolent sexism acts through prejudices related to interdependence (primarily gender-based paternalism and gender-role differentiation), enforcing traditional gender relations and being more sensitive to role-related cues. Discussion points to common methodological limitations, suggests guidelines, and finds future avenues for ambivalent sexism research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

People belong to multiple social groups simultaneously. However, much remains to be learned about the rich semantic perceptions of multiply-categorized targets. Two pretests and three main studies (n = 1,116) compare perceptions of single social categories to perceptions of two intersecting social categories. Unlike previous research focusing on specific social categories (e.g., race and age), our studies involve intersections from a large sample of salient societal groups. Study 1 provides evidence for biased information integration (vs. averaging), such that ratings of intersecting categories were more similar to the constituent with more negative and more extreme (either very positive or very negative) stereotypes. Study 2 indicates that negativity and extremity also bias spontaneous perceptions of intersectional targets, including dimensions beyond Warmth and Competence. Study 3 shows that the prevalence of emergent properties (i.e., traits attributed to intersecting categories but not the constituents) is greater for novel targets and targets with incongruent constituent stereotypes (e.g., one constituent is stereotyped as high Status and the other as low Status). Finally, Study 3 suggests that emergent (vs. present in constituents) perceptions are more negative and tend to be more about Morality and idiosyncratic content and less about Competence or Sociability. Our findings advance understanding about perceptions of multiply-categorized targets, information integration, and the connection between theories of process (e.g., individuation) and content. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Artificial intelligence increasingly suffuses everyday life. However, people are frequently reluctant to interact with A.I. systems. This challenges both the deployment of beneficial A.I. technology and the development of deep learning systems that depend on humans for oversight, direction, and regulation. Nine behavioral studies (N = 3,300) demonstrate that social-cognitive processes guide human interactions across a diverse range of real-world A.I. systems. Across studies, perceived warmth and competence emerge prominently in participants’ impressions of A.I. systems. Judgments of warmth and competence systematically depend on human-A.I. interdependence and autonomy. In particular, participants perceive systems that optimize interests aligned with human interests as warmer and systems that operate independently from human direction as more competent. Finally, a prisoner’s dilemma game shows that warmth and competence judgments predict participants’ willingness to cooperate with a deep-learning system. These results underscore the generality of intent detection to interactions with a broad array of algorithmic actors. Researchers and policymakers should carefully consider the degree and alignment of interdependence between humans and new artificial intelligence systems.

People form impressions about brands as they do about social groups. The Brands as Intentional Agents Framework (BIAF) a decade ago derived from the Stereotype Content Model (SCM) two dimensions of consumers' brand perception: warmth (worthy intentions) and competence (ability). The BIAF dimensions and their predictive validity have replicated the general primacy of warmth (intentions) and developed the congruence principle of fit to context. BIAF domains include various brands, product design, and countries as origins of products and as travel destinations. Brand anthropomorphism plays a role in perceiving brands' morality, personality, and humanity. Consumer–brand relations follow from anthropomorphism: perceived brand-self congruence, brand trust, and brand love. Corporate social (ir)responsibility and human relations, especially warm, worthy intent, interplay with BIAF dimensions, as do service marketing, service recovery, and digital marketing. Case studies describe customer loyalty, especially to warm brands, corresponds to profits, charitable donations, and healthcare usage. As the SCM and BIAF evolve, research potential regards the dimensions and beyond. BIAF has stood the tests of time, targets (brands, products, and services), and alternative theory (brand personality, brand relationships), all being compatible. Understanding how people view corporations as analogous to social groups advances theory and practice in consumer psychology.

Mental representations of human social groups – social stereotypes – are widespread and consequential. Such mental representations are systematic and multidimensional (e.g., stereotypes of immigrant groups are organized by perceived warmth and competence). We show that adaptive exploration alone can create structured societal stereotypes that cascade from historical affordances – such as which group happened to be the first one adequate at a job – without requiring decision-makers to have malicious intentions or cognitive limitations, or social groups to differ in information accessibility or intrinsic quality. Rather than framing social perception as a static, one-shot event, we consider the consequences of sequential decisions in a setting where exploring new options carries an implicit cost (resulting in an “explore-exploit tradeoff”). In this setting, when groups have equal and high potential to succeed in diverse jobs, decision-makers nonetheless settle on a single social group to perform each job and form impressions of that group accordingly. Using stereotypes of immigrant groups based on warmth and competence as an example, we formalize this process as a contextual multi-armed bandit problem, show testable predictions from computational simulations, and demonstrate that human participants act consistently with these predictions in behavioral experiments. Our results show how rich, multidimensional stereotypes can emerge in an absolutely minimal setting.

Ageism manifests in responses that range from explicit and overt bias (regular, reportable attitudes) to implicit, latent bias (subtle, modern, covert incivilities). Social psychology distinguishes among key indicators of both explicit and implicit bias: simple evaluative attitudes (preferences), cognitive stereotypes (beliefs, expectations), emotional prejudices (specific affect, such as pity or resentment), and discriminatory behavior (constraining action).

The spontaneous stereotype content model (SSCM) describes a comprehensive taxonomy, with associated properties and predictive value, of social-group beliefs that perceivers report in open-ended responses. Four studies (N = 1,470) show the utility of spontaneous stereotypes, compared to traditional, prompted, scale-based stereotypes. Using natural language processing text analyses, Study 1 shows the most common spontaneous stereotype dimensions for salient social groups. Our results confirm existing stereotype models' dimensions, while uncovering a significant prevalence of dimensions that these models do not cover, such as Health, Appearance, and Deviance. The SSCM also characterizes the valence, direction, and accessibility of reported dimensions (e.g., Ability stereotypes are mostly positive, but Morality stereotypes are mostly negative; Sociability stereotypes are provided later than Ability stereotypes in a sequence of open-ended responses). Studies 2 and 3 check the robustness of these findings by: using a larger sample of social groups, varying time pressure, and diversifying analytical strategies. Study 3 also establishes the value of spontaneous stereotypes: compared to scales alone, open-ended measures improve predictions of attitudes toward social groups. Improvement in attitude prediction results partially from a more comprehensive taxonomy as well as a construct we refer to as stereotype representativeness: the prevalence of a stereotype dimension in perceivers' spontaneous beliefs about a social group. Finally, Study 4 examines how the taxonomy provides additional insight into stereotypes' influence on decision-making in socially relevant scenarios. Overall, spontaneous content broadens our understanding of stereotyping and intergroup relations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).