Psychological bulletin最新文献

The extent to which procedural skills involving motor components decay is an issue that has ramifications for skill theory, organizational effectiveness, and individual performance. Prior reviews concluded that longer intervals of nonuse are associated with greater skill decay, but these reviews were based on a modest number of studies, most with short retention intervals. In previous reviews, retention interval has not been investigated as a continuous variable. We report a meta-analysis of skill retention that aims to address these gaps. A search led to the inclusion of 1,344 effect sizes from 457 reports. Random-effects metaregression models were computed with retention interval as a predictor of effect sizes representing changes in performance between skill acquisition and retention. Results indicated that mean differences in SD units grew stronger by 0.08/month for accuracy-based performance measures and 0.06/month for speed-based and mixed performance measures. With lack of use, half of initial skill acquisition performance gains were lost after approximately 6.5 months for accuracy, 13 months for speed, and 11 months for mixed performance. Task type, task complexity, intermittent performance opportunities, and task instructions were identified as possible moderators of skill decay rates. Findings provide researchers with estimates of how much skill decay can be expected if skills are not frequently used and, therefore, when refresher training should be considered. Important methodological considerations for skill retention research were also identified, including the need to isolate retention performance from relearning effects and the need to account for speed-accuracy trade-off functions when interpreting changes in performance over time. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

There is a long-standing debate about the extent to which vowel hyperarticulation, the production of acoustically exaggerated vowels, occurs in infant-directed speech (IDS). This exaggeration has been argued to result in clearer speech sounds that are easier for infants to process and might be positively related to infants' linguistic outcomes. However, previous findings regarding the presence of vowel hyperarticulation in IDS are seemingly inconsistent and contradictory, making it hard to advance our understanding of the role of hyperarticulation in language development and, consequently, the potential functions/roles of IDS in language acquisition. Thus, we adopted a systematic review and meta-analytic approach to investigate the robustness of vowel hyperarticulation in IDS and identify sources of heterogeneity in the literature. We employed four complementary meta-analytic approaches and evaluated the robustness of results across the different choices. We performed both traditional (Frequentist) and Bayesian meta-analyses first on methodologically consistent studies (20 studies, 42 effect sizes) and then on all studies of vowel hyperarticulation in IDS irrespective of the method (35 studies, 80 effect sizes). Findings indicate the presence of vowel hyperarticulation in IDS compared to adult-directed speech (effect sizes ranging from 0.41 to 0.69), as well as systematic and unsystematic variability due to, for example, cross-linguistic variability and methods employed, making it difficult to identify specific factors associated with stronger vowel hyperarticulation. The quantitative results combined with a systematic review of the literature also enable important methodological insights, which we summarize into recommended practices such as enlarging sample sizes and explicitly incorporating sources of heterogeneity in analyses. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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).