Communications Psychology最新文献

The stereotype that Asian Americans excel in science and math has contributed to the narrative that they are overrepresented in STEM fields. However, U.S. Census data reveals this is not the case-there are significant disparities in STEM representation across Asian subgroups. The present research investigates whether U.S. participants are aware of these disparities. In Studies 1 and 2, we show that participants misperceive the STEM representation of Chinese, Japanese, Korean, Indian, Filipino, and Vietnamese subgroups. Study 3 demonstrates that these misperceptions persist despite changes in question framing and measurement. Furthermore, our findings suggest that these misperceptions are due to stereotypical expectations: participants view East Asian subgroups as more representative of Asian Americans and therefore more likely to be overrepresented in STEM, while perceiving Southeast Asian subgroups as less representative and more likely to be underrepresented. In a final study, we find that informing egalitarian-minded participants about these disparities increases support for racial equity-enhancing policies, and all participants' support for disaggregated data about Asian subgroups. Overall, our findings indicate that many U.S. participants are unaware of the within-group disparities among Asian Americans and underscore the importance of collecting and reporting data at the subgroup level to bring these inequalities to light.

Advances in artificial intelligence (AI) have made it easier to create highly realistic deepfake videos, which can appear to show someone doing or saying something they did not do or say. Deepfakes may present a threat to individuals and society: for example, deepfakes can be used to influence elections by discrediting political opponents. Psychological research shows that people's ability to detect deepfake videos varies considerably, making us potentially vulnerable to the influence of a video we have failed to identify as fake. However, little is yet known about the potential impact of a deepfake video that has been explicitly identified and flagged as fake. Examining this issue is important because current legislative initiatives to regulate AI emphasize transparency. We report three preregistered experiments (N = 175, 275, 223), in which participants were shown a deepfake video of someone appearing to confess committing a crime or a moral transgression, preceded in some conditions by a warning stating that the video was a deepfake. Participants were then asked questions about the person's guilt, to examine the influence of the video's content. We found that most participants relied on the content of a deepfake video, even when they had been explicitly warned beforehand that it was fake, although alternative explanations for the video's influence, related to task framing, cannot be ruled out. This result was observed even with participants who indicated that they believed the warning and knew the video to be fake. Our findings suggest that transparency is insufficient to entirely negate the influence of deepfake videos, which has implications for legislators, policymakers, and regulators of online content.

Human beings share in others' experiences and learn from them, but epistemic vigilance is necessary to avoid being misled by false information, and to distinguish between veridical and non-veridical memories. Memory Justifications, individuals' explanations for why they believe a recalled event truly occurred, help maintain epistemic vigilance regarding our memories. Understanding how justifications are affected by the passage of time is crucial since they serve to ensure memory validity in everyday life and in legal settings. Using behavioral measures and linguistic analyses of participants' (N = 421) self-reported memory justifications, we examined changes in justifications' content and detail over time. The credibility of justifications was validated by comparing them with free recall performance. Results demonstrated a decrease in overall recall over time. However, the degree of episodic detail in justifications was steady across time delays, indicating preserved justification content over time. Pre-registered and exploratory analyses showed that the proportion of justified recalls and justifications' term frequencies were also preserved over time. Our findings suggest that individuals' memory justifications serve as relatively reliable indicators of retrieval accuracy, which remain stable over time. Still, lexical measures demonstrated that some aspects of justifications' content show subtle delay-related changes, which might be explained in terms of a time-dependent decline in subjective confidence.

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.