Communications Psychology最新文献

With the increasing accessibility of large language models to the public, questions arise about whether, and under what conditions, social-emotional interactions with artificial intelligence (AI) can lead to human-like relationship building. Across two double-blind randomised controlled studies with pre-registered analyses, 492 participants engaged in dyadic online interactions using a modified, text-based version of the 'Fast Friends Procedure' (a method designed to enable rapid relationship building), with pre-generated responses by either human partners or a minimally prompted large language model. When labelled as human, the AI outperformed human partners in establishing feelings of closeness during emotionally engaging 'deep-talk' interactions. This striking effect appears to stem from the AI's higher levels of self-disclosure, which in turn enhanced participants' perceptions of closeness. Labelling the partner as an AI reduced, but did not eliminate, relationship building, likely due to participants' lower motivation to engage in interactions with an AI, reflected in both shorter responses and reduced feelings of closeness. These findings highlight AI's potential to relieve overburdened social fields while underscoring the urgent need for ethical safeguards to prevent its misuse in fostering deceptive social connections.

It has been thought that coordination of briefly maintained information (working memory) and social cognition (mentalizing) rely on different brain mechanisms. However, the dorsomedial prefrontal cortex (DMPFC) seems to control the mentalizing and the visual working memory networks. We aimed to show (1) that visual working memory and social cognition share the same neural communication mechanism (i.e., interregional phase-amplitude coupling) and (2) that this mechanism is behaviorally relevant. We analyzed electrical brain activity from 98 volunteers who differed in the extent of (subclinical) autistic personality traits. Participants performed a social, visual and verbal working memory task, each implemented in a low and a high cognitive load version. We analyzed how slow rhythmical brain activity in the DMPFC controls distributed posterior regions associated with working memory and mentalizing via phase-amplitude coupling. First, individuals with low autistic personality traits use slow rhythmical brain activity in the DMPFC to precisely tune communication with posterior brain areas depending on the effort necessary in the visual and social tasks. Second, individuals with high autistic personality traits struggle in fine-tuning this mechanism, which is associated with difficulties in efficiently adapting brain activity to the difficulty level of a visual working memory task; and they demonstrate problems with efficiently synchronizing the relevant cortical network in a social cognition task. While these findings suggest a unified function of brain oscillations in cognitive coordination between social and visual tasks, they could also explain why individuals with high autistic personality traits can have difficulties with demanding cognitive processing and mentalizing.

As we age, our movements become slower and less precise-but the extent of this decline remains unclear. To address this, we harmonized data from 2390 participants across four published studies using a standard center-out reaching task. We found that older age was associated with a steady decline in reaction time (-1.3 ms/year), movement time (-4.3 ms/year), and movement precision (-0.04°/year). Although the rate of decline did not differ by sex/gender, females consistently reacted more slowly (-6.4 ms), moved more slowly (-44.6 ms), and exhibited greater precision ( + 0.6°) across the adult lifespan. Using the dataset that included experiential measures, we found that sex/gender differences were markedly reduced once factors, such as video game use, daily computer usage, and daily sleep, were taken into account, whereas age remained a consistent predictor of motor decline. Together, these findings provide a large-scale examination of age, sex/gender, and experiential effects on motor control, offering a normative benchmark to inform future clinical interventions aimed at preserving motor function across the lifespan.

Our interactions with objects involve processing a range of object-associated features to assess whether an object can fulfill our intentions. For human-made manipulable objects, these features mainly encompass three interconnected types of knowledge: visual appearance, manner of manipulation, and functional purpose. We investigated these components by breaking them down into their features and exploring how each type of object-related information is processed. Using a release-from-adaptation paradigm - we tested vision, manipulation, and function in three behavioral experiments (21, 20, and 22 participants) and three fMRI experiments (20 participants each) - to explore whether the similarity between objects within each knowledge type impacts behavioral and neural responses while controlling for the other knowledge types. Our findings suggest that an object's visual, functional, and manipulation properties are processed independently in distinct brain areas, including the fusiform gyrus and collateral sulcus, the lateral occipitotemporal cortex, and regions within the dorsal stream. Moreover, object similarity shapes how information is organized within each knowledge type and affects the ability to detect changes between objects. Importantly, the brain may follow a strategy of breaking down the incoming sensory stimulus into different knowledge types and properties in the process of building bottom-up representations that can then serve object recognition. Nevertheless, interacting with objects requires integrating these knowledge types, with our data suggesting that the medial fusiform gyrus and collateral sulcus might be important candidates for this integration. Once integrated, information may be transmitted to parietal and frontal areas to achieve a successful interaction with the object.

This study aimed to examine whether daily personal time-time spent free from external demands and available for self-directed activities-relates to better affective well-being and healthier cortisol patterns in midlife parents, and whether personality traits moderate these associations. A sample of 318 parents (Mage = 40.06 years, SD = 7.54; 45% men) with underage children (Mage of youngest child = 7.61 years, SD = 5.19) completed up to 8 consecutive days of daily diaries (mood, personal time, stress exposure) and up to 4 days of saliva sampling (4 times/day) for cortisol analysis. Multilevel modeling examined within-person links between personal time, positive and negative affect, and diurnal cortisol slopes, controlling for daily stress. Results showed that on days when they had an opportunity for time to themselves, parents experienced higher positive affect, lower negative affect, and steeper cortisol slopes, indicating better stress recovery. The reduction in negative affect with personal time was stronger for parents high in neuroticism and openness, and high neuroticism was also linked with a stronger association between personal time and cortisol slopes. Findings underscore the potential restorative value of daily time to oneself for midlife parents, particularly those high in neuroticism and openness. In the context of the high demands of parenting, personal time may serve as a valuable resource for emotional renewal, solitude, self-care, self-connection, and recovery from daily parenting stress.

Costly punishment is thought to be a key mechanism sustaining human cooperation. However, the motives for punitive behaviour remain unclear. Although often assumed to be motivated by a desire to convert cheats into cooperators, punishment is also consistent with other functions, such as levelling payoffs or improving one's relative position. We used six economic games to tease apart different motives for punishment and to explore whether different punishment strategies were associated with personality variables, political ideology, and religiosity. We used representative samples from the United Kingdom and the United States (N = 2010) to estimate the frequency of different punishment strategies in the population. The most common strategy was to never punish. For people who did punish, strategy use was more consistent with egalitarian motives than behaviour-change motives. Nevertheless, different punishment strategies were also associated with personality, social preferences, political ideology, and religiosity. Self-reports of behaviour in the games suggested that people have some insight into their punishment strategy. These findings highlight the multipurpose nature of human punishment and show how the different motives underpinning punishment decisions are linked with core character traits.

As AI-based technologies such as ChatGPT are increasingly used across various sectors, understanding how people conceptualize artificial intelligence (AI) is crucial for anticipating public response and developing AI technologies responsibly ¹. We hypothesize that public perceptions of AI are rapidly evolving, and that these perceptions inform not only how people use AI, but also the extent to which they trust it and the role they believe it should play in their lives - if at all. However, beliefs about complex sociotechnical systems like AI are nuanced and hard to articulate^2-4, especially using traditional self-reporting methods where people may struggle to clearly articulate their implicit attitudes about emerging technologies ⁵. To overcome these limitations, we collected over 12,000 open-ended metaphor responses over 12 months from a nationally representative U.S. sample and developed a systematic framework to quantitatively analyze them. Here we show that US Americans perceive AI as warm and competent, with attributions of human-likeness and warmth increasing significantly in the year after ChatGPT was introduced, and that these perceptions strongly predict trust and willingness to adopt AI technologies. We also identify important demographic variations, with women, older individuals, and people of color more likely to attribute human-like qualities to AI, helping explain disparities in trust and adoption rates. This scalable metaphor analysis approach enables tracking multifaceted public attitudes to inform AI governance, revealing how perceptions influence technology adoption across different populations.

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.