Recent research in personality science has highlighted influences of life experiences on personality trait development. Yet, the line of inquiry on how work experiences shape personality development has generally yielded mixed findings. Furthermore, prior work has yet to more systematically examine the role of time in shaping the influences of work experiences, to more appropriately tease apart within- and between-person effects, and to simultaneously model both the selection effect of personality and the socialization effect of work experiences at the within-person level. We conducted a preregistered continuous time meta-analysis of longitudinal studies (k = 11 large-scale archival studies, N ≈ 134,515) to investigate how income-one crucial form of work experience reflecting how people successfully fulfill work responsibilities to achieve high social status-influences employees' development of the Big Five personality traits at the within-person level. We simultaneously examined the selection effect of personality. We found, on average, positive lagged influences of income on subsequent changes of emotional stability and conscientiousness, and the influence on extraversion was generally negative. The lagged influences of conscientiousness and emotional stability on subsequent income changes were generally positive, and the influence was negative for extraversion, suggesting reciprocal relationships. The magnitude of the influences varied over time in an inverted U-shaped fashion with certain levels of between-study heterogeneity. As a more stringent test of the social investment and corresponsive principles, the findings provide more support to the Neo-Socioanalytic Model than the Five Factor Model. Our research showcases the importance of greater cross-fertilization among different areas of psychology for advancing research on personality development in the workplace. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Cognitive impairment is a common, yet underrecognized, feature of bipolar disorder (BD). While cognition has emerged as a contributing factor to poor functioning in BD, previous reviews did not examine the role of cognitive domains (i.e., social cognition, nonsocial cognition) or the role of key potential moderators, including age and sex. A systematic literature search identified studies that included assessments of cognition and functioning in BD (103 reports, 2,200 effect sizes, 9,323 BD participants). Multilevel models tested the association between cognition and functioning, as well as moderation by cognitive domain, functional domain, biological factors, and clinical factors. Better overall cognitive performance was associated with better overall functioning (r = .20, p < .001). In contrast to the schizophrenia literature, nonsocial cognition was more strongly related to functioning (r = .21, p < .001) than social cognition (r = .16, p < .001). The magnitude of the relationship differed across cognitive and functional domains and was moderated by age, sex, clinical factors, and risk of bias. Social and nonsocial cognition make small, but statistically significant, contributions (i.e., 4% variance) to daily functioning in people with BD. This finding supports cognition as a viable treatment target to improve functioning in BD, with appropriate consideration of key moderators. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Research indicates that perfectionism is on the rise among college students. This study updates and expands on this work in several ways. First, we investigate whether self-oriented and socially prescribed perfectionism continue to increase in tandem with personal standards, concerns about mistakes, and doubts about actions. Second, we examine generational differences in higher order dimensions of perfectionism (perfectionistic strivings and concerns). Third, we investigate whether changes in gross domestic product per capita and inequality account for temporal differences. Fourth, we test whether relationships between perfectionism and psychopathology are changing over time. Cross-temporal meta-analyses of 307 samples encompassing 82,939 American, Canadian, and British college students revealed that self-oriented perfectionism, concerns over mistakes, and doubts about actions increased linearly. Socially prescribed perfectionism followed a quadratic trajectory, with a notable acceleration starting in the early 2000s. At the higher order level, perfectionistic strivings increased linearly, whereas perfectionistic concerns followed a quadratic trajectory. Declining gross domestic product per capita was associated with higher perfectionistic strivings, while rising inequality was associated with steeper increases in perfectionistic concerns. Furthermore, correlations between perfectionism and psychopathology remained stable over time. Overall, results reveal that college students increasingly perceive others as excessively demanding while becoming more demanding of themselves, accompanied by growing indecisiveness, uncertainty, and sensitivity about making mistakes. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Using a metascience framework for improving meta-analyses, Jansen et al. (2025) tested the accuracy and efficiency of data extraction from primary studies used in meta-analyses with a range of large language models. Efficiency was impressive: Across thousands of studies and hundreds of variables, eight large language models took less than an hour combined to extract hundreds of thousands of data points-work estimated to take a human coder >6,500 hr. Nevertheless, accuracy was inconsistent, ranging from high to low depending on the variable. From these results, Jansen et al. recommended (a) a research agenda for investigating the use of artificial intelligence (AI) for data extraction and (b) methods for using AI as a partner for data extraction when conducting systematic reviews. This commentary expands on recommendations for the research agenda, such as investigating AI-induced bias, the illusion of exploratory depth, and using AI to extract study quality data. This commentary also offers further considerations regarding using AI as a meta-analysis partner, such as how iterative prompts might reduce coding independence. Finally, the commentary discusses speed-accuracy tradeoffs in meta-analyses. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
