British journal of psychology最新文献

Artificial intelligence (AI) can enhance human communication, for example, by improving the quality of our writing, voice or appearance. However, AI mediated communication also has risks-it may increase deception, compromise authenticity or yield widespread mistrust. As a result, both policymakers and technology firms are developing approaches to prevent and reduce potentially unacceptable uses of AI communication technologies. However, we do not yet know what people believe is acceptable or what their expectations are regarding usage. Drawing on normative psychology theories, we examine people's judgements of the acceptability of open and secret AI use, as well as people's expectations of their own and others' use. In two studies with representative samples (Study 1: N = 477; Study 2: N = 765), we find that people are less accepting of secret than open AI use in communication, but only when directly compared. Our results also suggest that people believe others will use AI communication tools more than they would themselves and that people do not expect others' use to align with their expectations of what is acceptable. While much attention has been focused on transparency measures, our results suggest that self-other differences are a central factor for understanding people's attitudes and expectations for AI-mediated communication.

We collected data from two sources - social media and online questionnaires - to investigate the emotional consequences of social sharing during the COVID-19 pandemic. Study 1 tracked and analysed sentiment of tweets posted over the course of a month in the crisis period and found that users who tweeted more frequently about COVID-19 expressed decreasing negative sentiment and increasing positive sentiment over time. Granger causality tests confirmed that this association was better interpreted in the forward direction (sharing levels predicting sentiment) than in the reverse direction (sentiment predicting sharing levels). Study 2 focused on immediate emotional consequences of sharing COVID-19-related events and found that participants reported improved overall affect about an event after sharing it, especially when that event was a personal experience rather than a news story. Reported positive feelings about both kinds of events were also significantly higher after sharing. Taken together, both studies suggested that social sharing is linked with emotional relief and may therefore help people to deal with their negative experiences during a persistent collective crisis.

CONFLICT OF INTEREST STATEMENT

The author declares no conflict of interest.

Our current perception and decision-making are shaped by recent experiences, a phenomenon known as serial dependence. While serial dependence is well-documented in visual perception and has been recently explored in time perception, their functional similarities across non-temporal and temporal domains remain elusive, particularly in relation to task relevance and working memory load. To address this, we designed a unified experimental paradigm using coherent motion stimuli to test both direction and time reproduction. The direction and time tasks were randomly mixed across trials. Additionally, we introduced pre-cue versus post-cue settings in separate experiments to manipulate working memory load during the encoding phase. We found attractive biases in time reproduction but repulsive biases in direction estimation. Notably, the temporal attraction was more pronounced when the preceding task was also time-related. In contrast, the direction repulsion remained unaffected by the nature of the preceding task. Additionally, both attractive and repulsive biases were enhanced by the post-cue compared to the pre-cue. Our findings suggest that opposing sequential effects in non-temporal and temporal domains may originate from different processing stages linked to sensory adaptation and post-perceptual processes involving working memory.

This longitudinal study examined the reciprocal relationship between the Big Five personality traits and sense of purpose over a 13-year period using a nationally representative sample of American adults (N = 11,010). The random intercept cross-lagged panel model revealed unidirectional effects: increases in sense of purpose predicted subsequent increases in openness, agreeableness, conscientiousness, and extraversion, as well as decreases in neuroticism. Conversely, changes in personality traits did not predict future changes in sense of purpose. One potential mechanism underlying this effect might involve the link between sense of purpose and optimal self-regulatory processes and outcomes, such as successful planning, goal-directed behaviour, and self-control, which promote gradual positive changes in personality traits. Another plausible mechanism may be the association between purpose and improved subjective well-being and reduced psychological distress, which have been found to predict positive changes in personality traits. These findings challenge previous research that has interpreted cross-sectional associations as evidence that personality traits are predictive of purpose, rather than the other way around. The findings that intentionally cultivating a strong sense of purpose may facilitate positive personality change in adulthood.

Recent years have witnessed some rapid and tremendous progress in natural language processing (NLP) techniques that are used to analyse text data. This study endeavours to offer an up-to-date review of NLP applications by examining their use in counselling and psychotherapy from 1990 to 2021. The purpose of this scoping review is to identify trends, advancements, challenges and limitations of these applications. Among the 41 papers included in this review, 4 primary study purposes were identified: (1) developing automated coding; (2) predicting outcomes; (3) monitoring counselling sessions; and (4) investigating language patterns. Our findings showed a growing trend in the number of papers utilizing advanced machine learning methods, particularly neural networks. Unfortunately, only a third of the articles addressed the issues of bias and generalizability. Our findings provided a timely systematic update, shedding light on concerns related to bias, generalizability and validity in the context of NLP applications in counselling and psychotherapy.