Psychological bulletin最新文献

Experiencing success in life has been a principal human endeavor for millennia. Despite broad interest in the subject, no consensual taxonomy exists for domains of life success. In response, we conceptualize and define life success as a multidimensional construct domain, and we then validate a provisional rational taxonomy of 14 life success categories. We subsequently use this taxonomy to quantitatively review and synthesize meta-analyses reporting relations of the Big Five personality traits to variables associated with life success. Altogether, we identify 111 meta-analyses reporting relations to 206 variables, representing N > 2.25 million participants from k > 3,300 studies. First, we examine the extent to which Big Five traits predict life success variables. Next, we distinguish the extent to which prediction is due to personality's elevation (profile level effects) or its configuration (profile pattern effects) using meta-analytic criterion profile analysis (Wiernik et al., 2021). Finding that configurations are vital, we then use clustering algorithms to synthesize similarities among personality configurations across variables and uncover 10 clusters that are hierarchically nested in three metaclusters: contentment (gratification, balance, deference), agentic engagement (accomplishment, high performance, citizenship, ingenuity, leadership), and self-transcendence (support, inclusion). We suggest that these 10 life success clusters represent archetypal life success goals and their associated personality profiles reflect archetypal means for realizing these diverse ends. In summary, we provide a theoretically integrative map of domains of life success. We conclude by discussing implications and future research directions, as well as contributions and limitations of our findings. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Trust in other people and institutions is associated with people's well-being across the lifespan. Yet, the strength of these associations varies considerably across studies, and it remains unclear which factors account for this variation. Moreover, some longitudinal studies indicate that trust not only predicts well-being but that subjective well-being also predicts levels of trust. This preregistered meta-analysis provides a comprehensive synthesis of this vast field of research, establishing both the direction and strength of the association between trust and subjective well-being. Based on 991 effect sizes, including a total of 2,518,769 participants, the results of the multilevel meta-analysis revealed a positive overall association between trust and subjective well-being (r = .21). The strength of this association varied depending on the type of trust and component of subjective well-being examined, the age of the participants, and the national-level generalized trust of the country in which the study took place. Despite these variations, the association between trust and subjective well-being was found across all sample and study characteristics examined, underlining the robustness of the association. In addition, results of the longitudinal meta-analyses showed that trust predicts subsequent well-being (k = 55) and that well-being predicts subsequent trust (k = 49). Together, these results suggest that trust and well-being mutually reinforce each other in a cyclical fashion. Since trust needs to be built, these findings underscore the importance of being genuinely trustworthy, whether as an individual or as an institution. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Artificial intelligence (AI) is transforming human life. While some studies find that people prefer humans over AI (AI aversion), others find the opposite (AI appreciation). To reconcile these conflicting findings, we introduce the Capability-Personalization Framework. This theoretical framework posits that when deciding between AI and humans in a context, individuals focus on two dimensions: (a) perceived capability of AI and (b) perceived necessity for personalization. We propose that AI appreciation occurs when (a) AI is perceived as more capable than humans and (b) personalization is perceived as unnecessary in a given decision context, whereas AI aversion occurs when these conditions are not met. Our Capability-Personalization Framework is substantiated by a meta-analysis of 442 effect sizes from 163 studies (N = 82,078): AI appreciation occurs (d = 0.27, 95% CI [0.17, 0.37]) when AI is perceived as more capable than humans and personalization is perceived as unnecessary in a given decision context; otherwise, AI aversion occurs (d = -0.50, 95% CI [-0.63, -0.37]). Moderation analyses suggest that AI appreciation is more pronounced for tangible robots (vs. intangible algorithms), for attitudinal (vs. behavioral) outcomes, in between-subjects (vs. within-subjects) study designs, and in low unemployment countries, while AI aversion is more pronounced in countries with high levels of education and internet use. Overall, our integrative framework and meta-analysis advance knowledge about AI-human preferences and offer valuable implications for AI developers and users. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Electronic screens are everywhere and are easily accessible to children. Parents report fears that screens cause socioemotional problems. But most research has been cross-sectional, making it difficult to establish causality. We reviewed the longitudinal evidence to answer two fundamental questions: Does screen use lead to socioemotional problems, and do socioemotional problems lead children to use screens more often? A total of 132 longitudinal studies met the inclusion criteria and were included in the systematic review. From these, 117 studies (292,739 children; 2,284 effects) were meta-analyzed. Small significant associations were found in both directions: Screen use led to socioemotional problems, b = 0.06, 95% confidence interval (CI) [0.02, 0.11], p ≤ 0.05, n = 200,018, K = 117, and socioemotional problems led to greater screen use (b = 0.06, 95% CI [0.01, 0.12], p = .01, n = 200,018, K = 117). Moderation analyses showed stronger effects in both directions when screens were used for gaming than for other purposes: Socioemotional problems led to more gaming behavior (b = 0.44, 95% CI [0.29, 0.60], n = 80,809, K = 31), and playing games led to later socioemotional problems (b = 0.32, 95% CI [0.23, 0.42], n = 80,809, K = 31). The reciprocal relationship between socioemotional problems and screen use was moderated by children's age, total screen time at baseline, and type of socioemotional problem (i.e., externalizing and internalizing behavior). Compared with prior cross-sectional studies, our temporal evidence reinforces the benefits of screen time guidelines but suggests a change in focus. Instead of merely emphasizing the reduction of screen time, guidelines should prioritize improving the quality of screen content and enhancing social interactions during screen use. Additionally, screen time guidelines should discourage high levels of the most high-risk behaviors like gaming. (PsycInfo Database Record (c) 2025 APA, all rights reserved).