Communications Psychology最新文献

Young infants can change their behaviour and learn through interactions with novel environments. This ability has been demonstrated through group-averaged analyses. However, it remains unclear whether averaged behavioural changes accurately capture the diverse changes occurring at the individual level. To address this, we measured limb movement alterations in 185 infants aged 2 to 3 months before and after their arm was tethered to an overhead mobile and analysed individual differences in addition to conventional group analyses. While the group-averaged data showed a gradual increase in arm movements, individual learning curves rarely exhibited such simple gradual increases and instead displayed more complex patterns. To disentangle the complex movement patterns, we applied time-series clustering and dynamical systems modelling to our large-scale dataset. As a result, the infants were divided into distinct clusters with significant differences in spontaneous movements before learning, rather than after. A dynamical systems model further demonstrated that only differences in spontaneous movements could explain the diversity of overall behavioural changes. These findings indicate that the varying degrees of behavioural change reflect infants' unique learning processes rather than their learning capabilities. Furthermore, learning, as a process that reduces individual difference, suggest that infants harness their unique spontaneous movements to acquire instrumental behaviours.

Metabolic and depressive disorders are major chronic global health concerns, often co-occurring and mutually reinforcing each other. Thus, understanding risk and protective factors underlying their development is crucial for identifying effective preventive strategies. Participants included N = 10,446 participants (31,418 observations) from the Adolescent Brain Cognitive Development Study aged 10-15 years. Primary outcomes were internalizing problem scores, and random slopes quantifying the within-person coupling between waist-to-height ratio and internalizing problems. Predictors included early-life adversity measures and potentially protective environments measured at the family, community, peer, and school level. Early-life adversity and protective environment scores were examined as moderators of the coupling between body composition and internalizing problems. Early-life adversity was significantly associated with the magnitude of within-person coupling (random slope); individuals with higher early-life adversity exhibited a stronger coupling between waist-to-height ratio and internalizing problems (r²=4.6%, t = 26.6, p < 10^-¹⁰). The adversity-related amplification of waist-to-height ratio and internalizing coupling was mitigated by the protective environment score (t = -5.3, p < 10^-6), with family and community components showing the strongest effects. Early-life adversity intensifies the coupling between waist-to-height ratio and internalizing problems, but protective environments may mitigate these effects. These findings motivate research into interventions that reduce early adversity and strengthen protective environments to improve youth mental and physical health.

Spontaneous thoughts are a window into one's mind, as they offer rich information about ongoing psychological processes and value systems. We accessed the contents of these thoughts using a free association paradigm combined with natural language processing techniques to examine how happiness is associated with what people think about and prioritize in daily life. Our analyses revealed that participants (n = 210 from the US/UK) with higher subjective well-being, particularly those with more frequent positive affect, generated thoughts semantically more similar to 'friend,' but not to 'money.' A similar pattern was also found in an independent sample (n = 350 from South Korea), showing consistency of the findings across different cultural contexts. Notably, the semantic similarity of participants' generated thoughts to 'friend' predicted the extent to which participants prioritized social relationships over monetary gains in a realistic dilemma task. By exploring individuals' minds with a computational approach, our work elucidates how the value of social relationships is manifested in spontaneous thought contents and everyday decisions, providing insights into the sources of happiness.

Following the U.S. Supreme Court ruling to eliminate the federal right to abortion (the "Dobbs decision"), a new landscape of highly variable abortion policies emerged across the U.S. Given that individuals' attitudes toward those who have abortions wield significant power in shaping abortion policies, it is critical to understand the factors which underly these attitudes toward those who have abortions. The current work investigated whether White Americans' attitudes toward abortion may be related to their mental representations of those who have abortions, with implications for restrictive abortion policy support. Across three pre-registered online study sets (N = 2414) and one nationally representative sample (N = 452), the findings suggest that White Americans' mental representations of those who have abortions are suffused with racial and gender bias, particularly when imagining those who have abortions for non-medical reasons, and these visualizations impact abortion policy attitudes.

The aperiodic (1/f-like) component of electrophysiological data, whereby power systematically decreases with increasing frequency, as quantified by the aperiodic exponent, has been shown to differentiate sleep stages. Previous studies typically measured this exponent over narrow frequency ranges and averaged across sleep stages. A systematic review following PRISMA 2020 guidelines, which identified 16 eligible studies examining aperiodic neural activity during sleep, revealed heterogeneous frequency ranges and methodological approaches across studies. Building on these insights, the present study expands the analysis to include wider frequency ranges and alternative models, such as detecting 'knees' in the aperiodic component, which reflect bends in the power spectrum indicating changes in the exponent. Additionally, we applied time-resolved analyses to examine the dynamic patterns of aperiodic activity during sleep. We analyzed data from two sources: intracranial EEG (iEEG) from 106 epilepsy patients and high-density EEG from 17 healthy individuals and compared different frequency ranges and model forms of aperiodic activity. Results showed that broadband aperiodic models and the inclusion of a 'knee' feature effectively captured sleep stage-dependent differences in aperiodic activity. The knee parameter exhibited stage-specific variations, indicating different processing timescales across sleep stages. Time-resolved analysis of the aperiodic exponent tracked sleep stage transitions and responses to external stimuli, highlighting rapidly varying temporal dynamics during sleep. These findings offer valuable insights into brain dynamics during sleep and reveal novel insights and interpretations for understanding aperiodic neural activity during sleep.

Hypothetical scenarios provide an extremely useful alternative to field experiments for scholars interested in nudging behavior change, comprising a substantial proportion of the literature. Yet the extent to which hypotheticals accurately estimate real-world treatment effects is not well understood. To investigate, we identified five recent field studies of real-world nudges in distinct domains and designed four styles of hypothetical scenarios to approximate each one. This setup allows for clear comparison of old field data with new hypothetical data. Across our 20 experiments (N = 16,114), hypothetical scenarios nearly always estimated the correct direction of treatment effects. However, they varied widely in estimating magnitudes, making them unreliable inputs to real-world policy applications such as cost-benefit analyses. Our findings underscore the promising value of hypotheticals, but also the need for greater investigation into strategies to calibrate their estimates.

Whether individuals feel confident about their own actions, choices, or statements being correct, and how these confidence levels differ between individuals are two key primitives for countless behavioral theories and phenomena. In cognitive tasks, individual confidence is typically measured as the average of reports about choice accuracy, but how reliable is the resulting characterization of within- and between-individual confidence remains surprisingly undocumented. Here, we perform a large-scale resampling exercise in the Confidence Database (103 studies, 6000 participants) to investigate the reliability of individual confidence estimates, and of comparisons across individuals' confidence levels. Our results show that confidence estimates are more stable than their choice-accuracy counterpart, reaching a reliability plateau after roughly 50 trials, regardless of a number of task design characteristics. While constituting a reliability upper-bound for task-based confidence measures, and thereby leaving open the question of the reliability of the construct itself, these results characterize the robustness of past and future task designs.

People often feel less empathy towards outgroup compared to ingroup targets. Overcoming this intergroup empathy bias is important for fostering positive intergroup relations. In five pre-registered and high-powered online studies (n = 4776 (745/745/1056/1236/994)), we attempted to replicate and generalize motivated empathy interventions that previously have made people more empathetic and prosocial towards outgroup targets. Using both between- and within-subject designs, self-reported empathy measures and factual monetary donations, we examined the effects of several brief interventions. The interventions targeted avoidance motivations based on beliefs about the un/limited nature of empathy or approach motivations based on beliefs about empathy's malleability or normatively desirability. Across studies, we tested the interventions in several in- and intergroup contexts, using both novel and preexisting stimuli. In general, interventions failed to increase empathy or prosocial behaviour. Instead, inducing beliefs about the limited nature of empathy often reduced participants' empathy. Motivating people to withhold empathy may be easier than motivating them to increase it.

Humans and other animals can generalise from local to global relationships in a transitive manner. Recent research has shown that asymmetrically biased learning, where beliefs about only the winners (or losers) of local comparisons are updated, is well-suited for inferring relational structures from sparse feedback. However, less is known about how belief-updating biases intersect with humans' capacity to adapt to changes in relational structure, where re-valuing an item may have downstream implications for inferential knowledge pertaining to unchanged items. We designed a transitive inference paradigm involving one of two possible changepoints for which an asymmetric (winner- or loser-biased) learning rule was more or less optimal. Participants (N = 83) exhibited differential sensitivity to changes in relational structure: whereas participants readily learned that a hitherto low-ranking item increased its rank ('up' condition), moving a high-ranking item down the hierarchy impaired downstream inferential knowledge ('down' condition). Behaviour was best captured by a reinforcement learning model which exhibited an initially winner-biased learning strategy that was nonetheless adaptable - that is, while this winner bias predominantly limited participants' flexibility in the 'down' condition, well-performing participants were able to reduce or even reverse their winner bias in order to appropriately accommodate the relational change. Our results indicate that asymmetric learning not only accounts for efficient inference of latent relational structures but also for differences in the ease with which learners accommodate structural changes.

Transparent and comprehensive statistical reporting is critical for ensuring the credibility, reproducibility, and interpretability of psychological research. This paper offers a structured set of guidelines for reporting statistical analyses in quantitative psychology, emphasizing clarity at both the planning and results stages. Drawing on established recommendations and emerging best practices, we outline key decisions related to hypothesis formulation, sample size justification, preregistration, outlier and missing data handling, statistical model specification, and the interpretation of inferential outcomes. We address considerations across frequentist and Bayesian frameworks and fixed as well as sequential research designs, including guidance on effect size reporting, equivalence testing, and the appropriate treatment of null results. To facilitate implementation of these recommendations, we provide the Transparent Statistical Reporting in Psychology (TSRP) Checklist that researchers can use to systematically evaluate and improve their statistical reporting practices ( https://osf.io/t2zpq/ ). In addition, we provide a curated list of freely available tools, packages, and functions that researchers can use to implement transparent reporting practices in their own analyses to bridge the gap between theory and practice. To illustrate the practical application of these principles, we provide a side-by-side comparison of insufficient versus best-practice reporting using a hypothetical cognitive psychology study. By adopting transparent reporting standards, researchers can improve the robustness of individual studies and facilitate cumulative scientific progress through more reliable meta-analyses and research syntheses.