Communications Psychology最新文献

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.

Climate change is one of the biggest challenges facing humanity. To limit its damaging impacts, billions of people must take pro-environmental actions. However, these often require effort and people avoid effort. It is vital to identify psychological interventions that increase willingness to exert effort. 3055 people from six diverse countries completed an effort-based decision-making task (Pro-Environmental Effort Task; Bulgaria: n = 404, Greece: n = 85, Nigeria: n = 660, Sweden: n = 1090, UK: n = 482, USA: n = 334). Participants chose whether to exert physical effort (50-95% of their maximum) to reduce carbon emissions, after experiencing one of 11 expert crowd-sourced interventions or no intervention. We applied computational modelling to precisely quantify motivation to help the climate, compared to a closely matched non-environmental cause. We found two interventions, which reduced the psychological distance to climate change impacts or promoted climate action as patriotic and protecting participants' way of life, had consistent positive effects on increasing effortful pro-environmental behaviours, across measures and control analyses. At the individual level, motivation to benefit the climate was associated with belief in climate change and support for pro-environmental policies. In contrast, trait apathy and effort aversion were linked with reduced motivation to benefit both the climate and food cause. Together, our results have crucial implications for promoting effortful actions that help mitigate climate change.

Complementary colour afterimages have driven our understanding of human colour perception since the foundations of modern colour science. Despite their fundamental importance, decades of research have failed to establish the precise nature of colour afterimages and the neural mechanisms of adaptation at their origin. To date, it is unclear whether afterimage formation is caused by adaptation in the cone photoreceptors, of colour-opponent neurons in the subcortical pathway, or requires the assumption of yet unknown cortical mechanisms. To establish the neural mechanisms underlying afterimage formation, this study exploited the fact that different candidate mechanisms make fundamentally different predictions about the hue and saturation of afterimages. Using tailormade experimental paradigms, the exact colours perceived in afterimages were measured for a large range of inducers to test those predictions. Three experiments tested predictions of afterimage hue and saturation with varying inducer hues, and changes of afterimage hues depending on inducer saturation (Exp. 1.a: 8 colours, tested across 31 participants; Exp. 1.b: 24 colours, tested across 52 participants; Exp.2.a: 72 colours, replicated with 10 participants; Exp. 2b: 72 colours, replicated with 2 participants; Exp. 3: 48-216 colours, replicated with 5 participants). Results across all three experiments very consistently demonstrated that afterimage colours are not colour-opponent, as widely assumed, but closely follow a quantitative model of adaptation in the cone photoreceptors. These findings unequivocally establish the origin of afterimages along the hierarchy of neural processing, hence resolving all prevailing misconceptions and contradictions. By linking the perceptual nature and the neural origin of afterimages, the present paradigm also provides a technique for probing those neural mechanisms behaviourally and in first-person experience.