Communications Psychology最新文献

Loneliness is associated with negative social behaviors, impairing social relationships. However, the underlying mechanisms are poorly understood. Here, we investigated the relationship between paranoid thoughts and lonely individuals' willingness to rely on expectations of partner reciprocity in an investment game with individuals with and without psychosis (54 participants). We found that loneliness and paranoia were strongly correlated with each other and with more distrustful behavior after breaches of trust. Sensitivity to changes in partner reciprocity was higher in lonelier and more paranoid individuals. Lonelier individuals also trusted highly reciprocating partners less. Computational modeling revealed that lonelier and more paranoid individuals were less willing to rely on expectations of partner reciprocity. Importantly, these effects were observed in both patients and controls, indicating the important role of loneliness and paranoia in both clinical and general populations. These findings demonstrate how loneliness relates to social behaviors and expectations, pointing to important downstream implications for lonely individuals' relationships.

Why do children learn some words earlier than others? Can children's speech patterns reveal how their evolving models of language determine what they learn? This study presents a systemic analysis of children's speech using low-dimensional embeddings to examine how the contextual knowledge reflected in their utterances reorganizes as linguistic experience increases. We analyzed age-stratified samples from the CHILDES database (18-36 months: n = 1,693,641 tokens; 3-6 years: n = 1,750,007; 5-12 years: n = 1,721,828) and adult speech from the SUBS2VEC subtitle corpus (n = 1,742,885). Our results suggest that the order and position of words in sequences produced by children from different age groups reflect changes in the way they represent categories of words. Rather than being ungrammatical, children's utterances appear to be structured by temporary grammars that optimize the distribution of information in sequences. The results point to shifts in how words are organized in semantic space, reflecting the gradual alignment of lexical categories during learning; this restructuring appears to draw on functionally ambiguous (multipurpose) categories in English. These findings are somewhat counterintuitive, as they suggest that not knowing the exact meaning of words can facilitate both learning and communication.

Human values inform behaviors, attitudes, and beliefs, but what shapes the human value system? Here, we employ Pareto analysis on the European Social Survey data (N = 411,904). Pareto analysis relies on an optimization framework to extract the drivers that shape people's individual variations. We found that individual differences in values are linked to balancing trade-offs between three adaptive tasks: Self-enhancement, Growth, and Conservation. Notably, value combinations that represent non-adaptive trade-off solutions, though considered theoretically possible, are absent from the data. These adaptive tasks are robust across two global samples, multiple countries, different religiosity levels, and age groups. Beyond identifying adaptive tasks, Pareto analysis provided a framework for assessing trade-off shifts across development and religiosity levels. This work paves the way for investigating the tasks' etiology and their underlying mechanisms. More broadly, Pareto analysis offers a principled approach to understanding individual differences in humans, revealing the adaptive tasks and trade-offs that drive complex psychological systems.

Accents are ubiquitous in spoken communication, and while listeners can rapidly adapt to accented speech, the neural mechanisms supporting this flexibility remain poorly understood. Successful adaptation requires developing new sound representations without compromising the stability of long-term speech norms. This delicate balance between plasticity and stability illustrates a fundamental challenge faced by all cognitive systems. To investigate how the brain manages this trade-off, we recorded electroencephalographic activity from 23 native English speakers as they categorized words produced in either canonical American English or an unfamiliar accent. We contrasted two potential mechanisms: one in which listeners fully restructure their sound-to-category mappings to reflect accent-specific pronunciations, and another in which they downweight the functional relevance of sounds that deviate from long-term expectations. Listeners relied on short-term speech regularities to reduce perceptual weighting of acoustic dimensions that did not conform to the canonical norm. Consistent with this perceptual shift, we observed less robust neural encoding of sound differences along the downweighted dimensions. Notably, these adaptive neural adjustments emerged as early as 100 milliseconds, at latencies associated with subphonemic auditory processing, and persisted through later stages linked to phonological and post-phonological processing. These findings indicate that rapid adaptation to unfamiliar accents involves downweighting the functional relevance of sound cues based on short-term input statistics, rather than fully restructuring native sound-to-category mappings. This mechanism enables flexible adjustment to novel speech inputs while preserving long-term linguistic representations, illustrating how the auditory system negotiates the trade-off between plasticity and representational stability.

The origins of human prosociality, in particular between strangers, are multifaceted. While laboratory studies support a cost-benefit account of helping, real-life scenarios involve additional socio-emotional motives grounded in subjective intuitions. How the cost-benefit model generalizes to everyday helping behavior remains unclear. In this study, we comprehensively assessed how motivations jointly shape helping across 100 naturalistic helping scenarios: an online sample (N₁ = 215) rated willingness to help after reading brief vignettes, and a subset (N₂ = 140) rated the strengths of candidate motivations elicited by each scenario. Two key factors-benefit to both helper and helpee, and cost to the helper-were identified through a factor analysis of the motivation ratings. We then successfully predicted helping decisions as a linear weighted sum of the two motivational factors, along with a dispositional helping bias. While a higher helping bias was associated with greater trait agreeableness and dispositional empathy, whereas individuals who prioritized cost over benefit exhibited higher levels of punishment sensitivity. Finally, we characterized the helping scenarios in three associated spaces: a decision space (willingness to help levels), a motivation space (two key motivational factors), and a semantic space (14 semantic types). Combining computational modeling with naturalistic helping contexts, this approach provides an integrated account of prosocial motivation and clarifies how individual differences in personality map onto real-world helping behaviors.

Self-report questionnaires are widely used across psychology and related disciplines, yet the cognitive and neural processes underlying how individuals generate responses to such items remain poorly understood. Here, we investigated whether items from the same psychological scale evoke similar neural activation patterns in the medial prefrontal cortex (mPFC), a region consistently implicated in self-referential processing. While undergoing functional magnetic resonance imaging (fMRI), 32 participants completed a self-reference task in which they judged how well 72 personality-related questionnaire items (e.g., from the Big Five, emotion regulation, and well-being scales) described themselves. Using representational similarity analysis (RSA), we found that items from the same scale elicited more similar multivoxel activation patterns in the mPFC compared to items from different scales. This effect was specific to the self-reference task and was not observed during a semantic judgment control task using the same items. Furthermore, the mPFC encoded not only categorical scale membership but also a small but consistent graded component of psychological similarity among scales, as reflected in inter-scale behavioral correlations. Importantly, these effects remained significant even after controlling for sentence-level semantic similarity using multiple regression RSA, indicating that the observed neural structure reflects psychological rather than linguistic similarity. These findings suggest that the mPFC integrates internally constructed evidence in a construct-sensitive manner during self-report. They also provide insight into how psychological assessment corresponds to neural representation.

Light exposure can modulate cognitive function, yet its effects outside of controlled laboratory settings remain insufficiently explored. To examine the relationship between real-world light exposure and cognitive performance, we assessed personal light exposure and measured subjective sleepiness, vigilance, working memory, and visual search performance over 7 days of daily life, in a convenience sample of UK adults (n = 58) without significant circadian challenge (shiftwork or jet-lag). A subset of participants (n = 41) attended an in-lab session comprising a battery of pupillometric and psychophysical tests aimed to quantify melanopsin-driven visual responses. We find significant associations between recent light exposure and subjective sleepiness. Recent light exposure was also associated with reaction times for both psychomotor vigilance and working memory tasks. In addition, higher daytime light exposure and an exposure pattern with reduced fragmentation were linked to improved cognitive performance across visual search, psychomotor vigilance, and working memory tasks. Higher daytime light exposure and earlier estimated bedtimes were associated with stronger relationships between recent light exposure and subjective sleepiness. These results provide real world support for the notion that intra- and inter-individual differences in light exposure meaningfully influence aspects of cognition, with beneficial effects of short-term bright light and of habitual light exposure patterns characterized by brighter daytimes, earlier rest phase, and greater intra- and inter-daily stability.

The pleasurable urge to move to music, termed "groove," is thought to arise from the tension between top-down metric expectations or predictions and rhythmic complexity. Specifically, groove ratings are highest for moderately complex rhythms, balancing expectation and surprise. To test this, meter and rhythmic complexity need to be manipulated independently to assess their impact on groove. Thus, we compared Western listeners' ratings for musical clips of varying rhythmic complexity composed in either the most common Western meter (4/4) or less common meters (e.g., 7/8). In several behavioral studies (Experiment 1, N = 143; Experiment 2, N = 120; Experiment 3, N = 120), we used Bayesian regression to show that groove is greatest for moderately complex rhythms, but only in 4/4. In non-4/4 meters, simpler rhythms elicited the greatest groove. This provides support for the theory that bottom-up rhythmic features interact with meter in a way that shapes the pleasurable urge to move to music.

Understanding how individuals perceive and recall information in their natural environments is critical to understanding potential failures in perception (e.g., sensory loss) and memory (e.g., dementia). Event segmentation, the process of identifying distinct events within dynamic environments, is central to how we perceive, encode, and recall experiences. This cognitive process not only influences moment-to-moment comprehension but also shapes event specific memory. Despite the importance of event segmentation and event memory, current research methodologies rely heavily on human judgements for assessing segmentation patterns and recall ability, which are subjective and time-consuming. A few approaches have been introduced to automate event segmentation and recall scoring, but validity with human responses and ease of implementation require further advancements. To address these concerns, we leverage Large Language Models (LLMs) to automate event segmentation and assess recall of written narratives, employing chat completion and text-embedding models, respectively. We validated these models against human annotations and determined that LLMs can accurately identify event boundaries, and that human event segmentation is more consistent with LLMs than among humans themselves. Using this framework, we advanced an automated approach for recall assessments which revealed semantic similarity between segmented narrative events and participant recall can estimate recall performance. Our findings demonstrate that LLMs can effectively simulate human segmentation patterns and provide recall evaluations that are a scalable alternative to manual scoring. This research opens avenues for studying the intersection between perception, memory, and cognitive impairment using methodologies driven by artificial intelligence.

Sensorimotor processing in the human brain is largely lateralized, with the corpus callosum integrating these processes into a unified experience. Following complete callosotomy, this integration breaks down, resulting in disconnection syndromes. We asked how much of the corpus callosum is sufficient to support functional unity-the absence of disconnection syndrome-by comparing three complete callosotomy patients with one retaining only the splenium. Using lateralized tasks across visual, tactile, visuospatial, and language domains, we predicted domain-specific deficits in the splenium-only patient based on established anatomical models of callosal topography. Strikingly, while complete callosotomy patients exhibited disconnection syndromes, the splenium patient demonstrated functional unity across all domains-as if his entire corpus callosum were intact. Our findings highlight the brain's remarkable capacity to maintain behavioral integration through minimal preserved pathways, highlighting how the structure-dependent reorganizational capacity of the human brain may allow to preserve functional unity.