Communications Psychology最新文献

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.

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.

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.

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.