British journal of psychology最新文献

Prior research has established that being a target of offline and online victimization might function as a significant risk factor that increases the likelihood of adolescents' involvement in cyberhate. Yet, relatively little is known about the mediating and moderating mechanisms underlying this relationship. To fill this important gap in knowledge, the present study aims to examine (1) whether excessive Internet use and contact with unknown people online act as sequential mediators in the relationship between overall victimization and youth's involvement in cyberhate; and (2) whether restrictive parental mediation has any role to play in moderating this relationship. The findings suggest that adolescents who experience victimization are more likely to turn to using the Internet excessively, and consequently interact with strangers online, which in turn makes them more prone to becoming victim to cyberhate or spreading hateful content online themselves. Moreover, restrictive parental mediation was shown to exacerbate the link between excessive Internet use and adolescents' contacts with unknown people online, thereby putting them at higher risk of cyberhate involvement. The current study emphasizes the need for a balanced approach to parental mediation – one that fosters open communication, trust and the development of digital literacy skills.

Artificial Intelligence (AI) chatbots, such as ChatGPT, have been shown to mimic individual human behaviour in a wide range of psychological and economic tasks. Do groups of AI chatbots also mimic collective behaviour? If so, artificial societies of AI chatbots may aid social scientific research by simulating human collectives. To investigate this theoretical possibility, we focus on whether AI chatbots natively mimic one commonly observed collective behaviour: homophily, people's tendency to form communities with similar others. In a large simulated online society of AI chatbots powered by large language models (N = 33,299), we find that communities form over time around bots using a common language. In addition, among chatbots that predominantly use English (N = 17,746), communities emerge around bots that post similar content. These initial empirical findings suggest that AI chatbots mimic homophily, a key aspect of human collective behaviour. Thus, in addition to simulating individual human behaviour, AI-powered artificial societies may advance social science research by allowing researchers to simulate nuanced aspects of collective behaviour.

Text-based automatic personality recognition (APR) operates at the intersection of artificial intelligence (AI) and psychology to determine the personality of an individual from their text sample. This covert form of personality assessment is key for a variety of online applications that contribute to individual convenience and well-being such as that of chatbots and personal assistants. Despite the availability of good quality data utilizing state-of-the-art AI methods, the reported performance of these recognition systems remains below expectations in comparable areas. Consequently, this work investigates and identifies the source of this performance limit and attributes it to the flawed assumptions of text-based APR. These insights are obtained via a large-scale comprehensive benchmark and analysis of text data from five corpora with diverse characteristics and complementary personality models (Big Five and Dark Triad) applied to an assortment of AI methods ranging from hand-crafted linguistic features to data-driven transformers. Finally, the work concludes by identifying the open problems that can help navigate the limitations in text-based automatic personality recognition to a great extent.

Identity verification from both faces and voices can be error-prone. Previous research has shown that faces and voices signal concordant information and cross-modal unfamiliar face-to-voice matching is possible, albeit often with low accuracy. In the current study, we ask whether performance on a face or voice identity matching task can be improved by using multimodal stimuli which add a second modality (voice or face). We find that overall accuracy is higher for face matching than for voice matching. However, contrary to predictions, presenting one unimodal and one multimodal stimulus within a matching task did not improve face or voice matching compared to presenting two unimodal stimuli. Additionally, we find that presenting two multimodal stimuli does not improve accuracy compared to presenting two unimodal face stimuli. Thus, multimodal information does not improve accuracy. However, intriguingly, we find that cross-modal face-voice matching accuracy predicts voice matching accuracy but not face matching accuracy. This suggests cross-modal information can nonetheless play a role in identity matching, and face and voice information combine to inform matching decisions. We discuss our findings in light of current models of person perception, and consider the implications for identity verification in security and forensic settings.

The mere token strategy, which adds a small reward (token) to an option to increase attractiveness, is widely used in the consumer field. However, we conducted six studies that seek to confirm the ‘token undermining effect’, where adding a small token to a sooner and smaller reward (SS) paired with a later and larger reward (LL) decreases the preference for the SS. The results showed that the effect persists across various choice sets, participant populations, reward amounts, delays, outcome properties and regardless of whether the scenarios are incentivized. However, an important boundary condition was that the token must share the same nature as the original option. Furthermore, we used mouse cursor tracking methods to examine the underlying process of attention allocation and demonstrated that adding a small token to the SS leads individuals to allocate more attention to the magnitude dimension than to the delay dimension, ultimately decreasing their preference for the SS. Therefore, managers and policymakers should use the mere token strategy with caution as it could backfire.

Developing social–emotional skills is crucial for all children and adolescents, particularly those experiencing social and emotional difficulties. This study used network analysis to identify the central skills and network association of different social–emotional skills and investigated how these networks differ between childhood and adolescence. Data were obtained from the 2019 Survey on Social and Emotional Skills by the Organization for Economic Co-operation and Development. Our study focused on the bottom quartile of participants aged 10 and 15 years, including 7737 and 7439 individuals from each age group. Optimism and cooperation consistently emerged as the central skills of social–emotional competence across both age groups. When comparing network structures, there was a significant difference between children and adolescents. The connectivity of social–emotional networks was stronger among adolescents, indicating closer skill associations. Understanding these developmental differences is important for educators and practitioners to more effectively support the social–emotional development of children and adolescents experiencing social–emotional difficulties.