The present work evaluated the predictive model for national life satisfaction from the World Happiness Report. Drawing on national data from 165 countries spanning 19 annual surveys (2005-2023), between- and within-nation variation in national life satisfaction and six predictors were examined: log gross domestic product per capita, healthy life expectancy at birth, and social perceptions concerning generosity, support, freedom, and corruption. The pooled regression model employed by the World Happiness Report explained 75% of the variance in national life satisfaction. In contrast, two-level models generated for the present analyses explained 84% of the between-nation variance and either 25% (fixed effects model) or 67% (random effects model) of the within-nation variance. Nation-specific regression models generated for the present analyses explained between 19% and 97% of the variance within individual countries. The statistical significance, magnitude, and sign/direction for each of the six predictors varied widely depending on the analytic model and level of analysis. Few countries were characterized by a pattern of predictive effects that matched the pooled regression model. Thus, rather than suggesting a single set of results that generalizes around the world, the predictive model for national life satisfaction appears to differ by country. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Amid rising political polarization, derogatory rhetoric targeting social groups has become common and highly contested across party lines. These partisan divides are often attributed to disagreement about whether bias has occurred, and theories of social norms, harm-based moral judgment, and cognitive consistency suggest that recognizing bias should track moral condemnation. Yet other theories suggest that recognition and condemnation are psychologically distinct. We propose that, even when partisans similarly recognize explicit bias ("yeah, it's biased"), they may diverge in moral response ("but …")-depending on the target's advantaged or disadvantaged status. Across six prestudies (N = 1,197), eight preregistered experiments (N = 7,145), and 16 supplemental studies, including two preregistered experiments (N = 1,029), we examined this "yeah, but" phenomenon. For speech that partisans equivalently recognized as biased based on race and gender, Democrats condemned bias against disadvantaged targets more than Republicans; Republicans condemned bias against advantaged targets more than Democrats-patterns that extended to endorsing sanctions (Study 1). Partisans invoked the same justifications-invulnerability to harm and honesty-but selectively so, depending on the target (Study 2). These patterns emerged for biases based on socioeconomic status and religion (Studies 3-4), behaviors (Study 5), and partisanship (which lacks a stable status hierarchy; Study 6). However, a status-juxtaposition intervention (judging bias against both advantaged and disadvantaged groups) narrowed Democrats' within-party asymmetry (Studies 7a-b). By disentangling bias recognition from bias condemnation, we show that moral polarization may persist even when Democrats and Republicans both "see" bias and invoke the same moral concerns. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Artificial intelligence (AI) systems increasingly serve as advisors, evaluators, and decision-makers, yet little is known about how people perceive AI as a social entity. Across five primary studies and eight supplementary studies, we show that people assign racial identities to AI systems, overwhelmingly perceiving them as White-even though these systems provide no visual, vocal, or identity cues from which race might ordinarily be inferred. Using forced-choice, open-ended, and implicit reverse-correlation measures, Studies 1 and 2 demonstrate that AI is explicitly and implicitly associated with Whiteness across diverse samples. Study 3 extends these findings beyond the United States, showing that AI is perceived as White in Japan and India. Study 4 examines the implications of AI racialization, showing that perceiving an AI system as more White is associated with greater trust in and persuasiveness of its recommendations. Supplementary studies identified two potential mechanisms: stereotype spillover linking intelligence with Whiteness and ecosystem-based inferences based on beliefs about who creates, trains, and uses AI. Building on this account, Study 5 provides causal evidence by isolating a key ecosystem cue-the racial composition of AI training data. Participants assigned less cognitively demanding tasks to AI systems described as trained on Black and Latino data than to otherwise identical systems trained on White or unspecified data. Together, these findings show that AI is not perceived as socially neutral but instead acquires racial meanings associated with credibility, authority, and capability, demonstrating how social categories shape perceptions of novel technological entities beyond their underlying algorithmic properties. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
The development of narcissism has been theoretically tied to childhood experiences, yet little empirical research has examined the influence of the family environment on adult narcissism using long-term longitudinal data. The present study identified family factors in late childhood and early adolescence that are prospectively associated with narcissism (grandiosity, superiority, exploitativeness, vulnerability) in emerging adulthood using data from a longitudinal study of Mexican-origin youth (N = 674) spanning from age 10 to 26. Using structural equation modeling of multimethod data (child report, parent and spouse report, and coded parent-child interactions), we found that grandiosity and superiority were significantly predicted by positive aspects of the family environment (e.g., parental warmth, parent-child relationship quality, parental relationship quality), whereas exploitativeness and vulnerability were significantly predicted by negative aspects of the family environment (e.g., parental hostility, parent-child conflict). These findings were much weaker and often nonsignificant after controlling for earlier levels of narcissism, suggesting that the family environment exerts its effect on adult narcissism via its concurrent effect on adolescent narcissism. Overall, our study highlights several underexplored family factors in childhood that contribute to the development and maintenance of narcissism in adulthood. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
People frequently encounter minor aversive experiences-such as rude interactions or social slights-that, while not major events, can still affect psychological well-being. In this research, we examine how people experience and perceive social support from artificial intelligence (AI) following such experiences. Drawing on mind perception theory, we propose that initial expectations about AI's experiential capacity relative to humans shape how people experience and perceive AI support. A pilot study demonstrates that people have different initial expectations of AI's versus humans' experiential capacity. Across six studies (five preregistered; total N = 3,341), we found that these initial expectations produce two distinct effects: People report lower fear of negative evaluation after receiving support from AI versus a human, which in turn improves well-being outcomes (Studies 1-3), and people perceive AI as exceeding their expectations for empathy, which in turn drives perceived support effectiveness (Studies 4-6). These effects emerged across controlled simulations and real interactions with actual human and AI support givers. Together, these findings reveal that initial psychological expectations about AI's experiential capacity play a central role in shaping the experience and perception of AI support. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
