British journal of psychology最新文献

Prior beliefs are central to Bayesian accounts of cognition, but many of these accounts do not directly measure priors. More specifically, initial states of belief heavily influence how new information is assumed to be utilized when updating a particular model. Despite this, prior and posterior beliefs are either inferred from sequential participant actions or elicited through impoverished means. We had participants to play a version of the game ‘Plinko’, to first elicit individual participant priors in a theoretically agnostic manner. Subsequent learning and updating of participant beliefs was then directly measured. We show that participants hold various priors that cluster around prototypical probability distributions that in turn influence learning. In follow-up studies, we show that participant priors are stable over time and that the ability to update beliefs is influenced by a simple environmental manipulation (i.e., a short break). These data reveal the importance of directly measuring participant beliefs rather than assuming or inferring them as has been widely done in the literature to date. The Plinko game provides a flexible and fecund means for examining statistical learning and mental model updating.

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.

Collaboration has an essential role in memory, and how to appropriately use it to affect individual memory positively is a matter of concern. The meta-analysis generally assessed the effect of collaboration on subsequent individual retrieval, registered on the PROSPERO platform and adhering to the PRISMA guidelines, using the Web of Science, Science Direct, CNKI and WanFang databases with post-collaborative memory as the main subject, screened studies published up to December 31, 2023, a total of 64 studies with 101 effect sizes, including 13,398 participants from 11 countries. Heterogeneity test, sensitivity analysis, subgroup analysis and meta-regression analysis were performed on the included studies, while publication bias was assessed. The results found that collaboration improves subsequent individual retrieval memory more than individuals, and collaboration has a moderate facilitating effect on subsequent individual retrieval. Group size, material category, category size, collaboration phase, collaboration approach, task process and test method were among the moderating variables. The study emphasizes the role of collaboration in cognition and demonstrates the post-collaborative benefits. The conclusions are of value for developing methods to improve individual memory.

Creativity is defined by three key factors: novelty, feasibility and value. While many creativity tests focus primarily on novelty, they often neglect feasibility and value, thereby limiting their reflection of real-world creativity. In this study, we employ GPT-4, a large language model, to assess these three dimensions in a Japanese-language Alternative Uses Test (AUT). Using a crowdsourced evaluation method, we acquire ground truth data for 30 question items and test various GPT prompt designs. Our findings show that asking for multiple responses in a single prompt, using an 'explain first, rate later' design, is both cost-effective and accurate (r = .62, .59 and .33 for novelty, feasibility and value, respectively). Moreover, our method offers comparable accuracy to existing methods in assessing novelty, without the need for training data. We also evaluate additional models such as GPT-4 Turbo, GPT-4 Omni and Claude 3.5 Sonnet. Comparable performance across these models demonstrates the universal applicability of our prompt design. Our results contribute a straightforward platform for instant AUT evaluation and provide valuable ground truth data for future methodological research.

Psychological research has acknowledged that the commonly accepted definitions of ‘transgender’, ‘sex’ and ‘gender’ within psychological research have resulted in limitations in accounting for the lived realities of transgender individuals. Such limitations include, but are not limited to, the continued pathologization of transgender experiences through idealizing sex and gender congruence and incapacity to account for non-normative and non-binary transition pathways. This paper provides a review of these limitations to first demonstrate how the incongruence definition of ‘transgender’ is reliant on the idea of a ‘true’ gender, and next suggest that problematising the idea of a ‘true’ gender allows new conceptions of transgender experiences to be advanced. To undertake this problematization, the work of Judith Butler and Sara Ahmed is used to consider how gender could be conceptualized otherwise in psychology and then applied to transgender experiences. In all, this paper theorizes transgender experiences without a reliance on the assertion of a true gender, to suggest instead a focus on contextualized transgender experiences. Last, the limitations and implications of this definition of transgender are briefly discussed. Overall, transgender experiences are conceptualized as those experiences that run counter to the dominant (re)production of binary sexed gender.

A glance is enough to assign psychological attributes to others. Attractiveness is associated with positive attributes (‘beauty-is-good’ stereotype). Here, we raise the question of a similar but negative bias. Are people with facial anomalies associated with negative personal characteristics? We hypothesized that biases against faces with anomalies arise because of negative stereotypes (less warmth and competence) and forms of dehumanization (animalistic and mechanistic). We enrolled 1493 mTurk participants (N = 1306 after exclusion) to assess 31 traits of photographed people using 60 pairs of photographs of the same person before and after plastic surgery. Half anomalous faces had a scar and the other half had a palsy. To calculate warmth and competence, we conducted a principal components analysis of the 31 attributes. Animalistic dehumanization was assessed by averaging reverse-scored ratings corresponding to moral sensibility and rationality/logic, and mechanistic dehumanization by averaging across reverse-scored ratings corresponding to emotional responsiveness and interpersonal warmth. We found that both kinds of anomalous faces were seen as less warm, competent and were dehumanized. Our findings suggest that an ‘anomalous-is-bad’ stereotype generalizes regardless of the aetiology of the anomaly. This effect may be related to a reverse halo effect, that is, the horn effect.

The latent state-trait theory posits that a psychological construct may reflect stable influences specific to a person (i.e., trait), ephemeral influences from situations (i.e., state), and interactions between them (i.e., state-trait interactions). Researchers conventionally apply mixture modelling to explore heterogeneity in variables by identifying homogenous classes with respect to the measured variable, yet rarely distinguishing between person- and situation-specific classes. The current study introduces novel categorical latent state-trait models to identify subgroups in states and traits, quantifying the effects of person-specific classes, situation-specific classes, and person-situation interactions. The proposed models are applied to an empirical dataset. We discuss statistical inference, effect size measures, and model visualization for the proposed models. Based on realistic parameter values from the empirical dataset, preliminary simulation studies were conducted to investigate models' performances. Bayesian estimation in the proposed models allows flexible testing of a wide range of hypotheses related to state, trait, and interaction effects. We discuss limitations and future directions.

First impressions formed from facial appearance predict important social outcomes. Existing models of these impressions indicate they are underpinned by dimensions of Valence and Dominance, and are typically derived by applying data reduction methods to explicit ratings of faces for a range of traits. However, this approach is potentially problematic because the trait ratings may not fully capture the dimensions on which people spontaneously assess faces. Here, we used natural language processing to extract 'topics' directly from participants' free-text descriptions (i.e., their first impressions) of 2222 face images. Two topics emerged, reflecting first impressions related to positive emotional valence and warmth (Topic 1) and negative emotional valence and potential threat (Topic 2). Next, we investigated how these topics were related to Valence and Dominance components derived from explicit trait ratings. Collectively, these components explained only ~44% of the variance in the topics extracted from free-text descriptions and suggested that first impressions are underpinned by correlated valence dimensions that subsume the content of existing trait-rating-based models. Natural language offers a promising new avenue for understanding social cognition, and future work can examine the predictive utility of natural language and traditional data-driven models for impressions in varying social contexts.

Explainable AI (XAI) methods provide explanations of AI models, but our understanding of how they compare with human explanations remains limited. Here, we examined human participants' attention strategies when classifying images and when explaining how they classified the images through eye-tracking and compared their attention strategies with saliency-based explanations from current XAI methods. We found that humans adopted more explorative attention strategies for the explanation task than the classification task itself. Two representative explanation strategies were identified through clustering: One involved focused visual scanning on foreground objects with more conceptual explanations, which contained more specific information for inferring class labels, whereas the other involved explorative scanning with more visual explanations, which were rated higher in effectiveness for early category learning. Interestingly, XAI saliency map explanations had the highest similarity to the explorative attention strategy in humans, and explanations highlighting discriminative features from invoking observable causality through perturbation had higher similarity to human strategies than those highlighting internal features associated with higher class score. Thus, humans use both visual and conceptual information during explanation, which serve different purposes, and XAI methods that highlight features informing observable causality match better with human explanations, potentially more accessible to users.

Throughout the day, humans show natural fluctuations in arousal that impact cognitive function. To study the behavioural dynamics of cognitive control during high and low arousal states, healthy participants performed an auditory conflict task during high-intensity physical exercise (N = 39) or drowsiness (N = 33). In line with the pre-registered hypotheses, conflict and conflict adaptation effects were preserved during both altered arousal states. Overall task performance was markedly poorer during low arousal, but not for high arousal. Modelling behavioural dynamics with drift diffusion analysis revealed evidence accumulation and non-decision time decelerated, and decisional boundaries became wider during low arousal, whereas high arousal was unexpectedly associated with a decrease in the interference of task-irrelevant information processing. These findings show how arousal differentially modulates cognitive control at both sides of normal alertness, and further validate drowsiness and physical exercise as key experimental models to disentangle the interaction between physiological fluctuations on cognitive dynamics.