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Comparing latent profiles of psychopathy in the general population

The extent to which latent profiles of psychopathy manifest across the full spectrum of psychopathy (i.e., general population) is unknown. The present study sought to address this gap by subtyping adults based on psychopathic features and exploring whether these profiles differ based on external correlates: motivational tendencies, impulsivity, triarchic psychopathy, anxiety/depression and personality traits. A demographically representative sample of US adults (N = 446; M age = 46.10; 51% female; 78.0% White) completed measures of four-factor psychopathy, motivational tendencies, impulsivity, anxiety/depression and personality traits. We identified four latent profiles of psychopathy that were conceptually similar to profiles reported in male offender samples. The impulsive-antisocial profile (i.e., high antisocial behaviour, erratic lifestyle; comparatively lower interpersonal manipulation, callous affect) was characterized by lack of premeditation, sensation seeking, low behavioural inhibition and reduced reward responsiveness when compared to the non-antisocial psychopathic profile (i.e., high interpersonal manipulation, callous affect, erratic lifestyle; comparatively lower scores on antisocial behaviour). Overall, we found evidence in favour of the suitability of self-reported psychopathy to profile individuals in the general population and its ability to distinguish between these subtypes on theoretically relevant external variables.

Importance of transgender nuances in research and advocacy: Reply to Morgenroth (2025) and Tate (2025)

In their responses to our paper ‘Conceptualizing transgender experiences in psychology: Do we have a “true” gender?’ (The British Journal of Psychology, 2024, 115, 723), Tate (2025) and Morgenroth (2025) provide reflections on the importance of nuance when researching gender and in transgender advocacy. In this reply, I note where this paper is situated in the literature and engage in a discussion of the role of definitions in transgender advocacy. Over-reliance on an individual's true gender when evaluating transgender people's legitimacy may exclude individuals whose gender is not understandable as ‘true’ to a cisgender majority. I suggest that a broad definition of transgender can allow legitimization to arise from transgender identification and gender self-categorization alongside broader experiences of body and gender.

Language about gender/sex should be used intentionally and flexibly

In their paper, ‘Conceptualizing transgender experiences in psychology: Do we have a ‘true’ gender?’ Jackson and Bussey (British Journal of Psychology, 115, 723) critique the idea of having a ‘true’ gender and propose that the term ‘transgender experience’ may be more appropriate than ‘transgender identity’. In this commentary, I reflect on the usefulness of the terms transgender identity and transgender experience and argue that both hold value and can contribute to a more nuanced discussion of gender/sex. I use the discussion of these two terms as a springboard to make a broader point: As researchers, we should use language about gender/sex flexibly and intentionally. As psychologists, we are trained to use clear operationalizations for the constructs we study, yet it is often unclear whether authors refer to biological sex, gender identity, social perception, or socialization when they speak of ‘women’ and ‘men’. I encourage researchers to be more mindful in their language use and to engage with the nuanced terms that gender scholars (including Jackson and Bussey) have put forward when discussing gender/sex – both in the context of transgender identities and experiences and when discussing cisgender identities and experiences.

Nuance and specificity always matter—Especially for all the meanings of ‘gender’: A reply to Jackson and Bussey (2024)

Jackson and Bussey (The British Journal of Psychology, 2024, 115, 723) present the provocative argument that the concept of a “true gender” is not a useful conceptual object for psychology. While the article does make 2 valid and worthwhile points, the remainder of that article lacks necessary nuance in developing its general conclusion.

Issue Information

Personality psychology: A new perspective By Janek Musek, Switzerland: Springer Nature AG. 2024. Ebook £96.29. ISBN 978‐3‐031‐55308‐0

Undoing Whiteness in Disability Studies: The Special Education System and British South Asian Mothers By Sana Rizvi, Palgrave Macmillan. 2021. VII+224 pp; €44.99 (eBook). ISBN: 9783030795733; DOI:

The reliability of UFO witness testimony By V.‐J. Ballester Olmos & Richard W. Heiden (Eds.), Turin: Upiar Press. 2023. pp. 711

Fostering sustainability in higher education: Leveraging human behavior in organizations By Elise L. Amel, Christie M. Manning, Catherine S. Daus, Makayla Quinn, Cham, Switzerland: Springer. 2023. £109.99, ISBN 9783031505546

Psychology and gender: An advanced reader By Sadhana Avinash Natu (Ed.), London: Routledge India. 2024. 222 pp. ₹1295. ISBN: 9781032518732

Identifying subtypes in persons, situations and person–situation interactions: Categorical latent state–trait modelling approaches

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.

Sounds of the future and past

We report evidence of sound symbolism for the abstract concept of time across seven experiments (total N = 825). Participants associated the future and past with distinct phonemes (Experiment 1). In particular, using nearly 8000 pseudowords, we found associations between the future and high front vowels and voiced fricatives/affricatives, and between the past and /θ/ and voiced stops (Experiment 2). This association was present not only among English speakers but also by speakers of a closely related language (German) and those of a more distantly related language (Hungarian; Experiment 3). This time-sound symbolism does not appear to be due to embodied articulation (Experiment 4). In sum, these studies identify a robust time sound symbolism effect, along with tests of underlying mechanisms.

A challenge to identity: Identity processing style and moral injury

Moral injury is a potentially deleterious mental health outcome that can result from exposure to morally challenging events. Treatment of moral injury is currently hindered by incomplete understanding of its underlying mechanisms. Theories of adaptation posit that maintaining a coherent sense of self while realigning one's sense of self with reality aids in adaptation following a disruptive life event. Differences in identity processing style are thought to impact the extent to which an individual engages with the challenges of maintaining a coherent sense of self following identity-related challenges. However, little is known about how identity processing style relates to moral injury event-related distress. This study sought to investigate a hypothesized relationship between identity processing style and event-related distress as well as alternative outcomes including traumatic stress, depression and anxiety. Adults (N = 167) who had been exposed to a potentially morally injurious event were recruited online and completed validated measures of event-related distress, traumatic stress, depression, anxiety and identity processing style. There were significant positive associations between diffuse-avoidant processing and all mental health outcomes, no significant associations between informational processing and any mental health outcomes, and significant negative associations between normative processing and event-related distress and depression.

Leading by example: Experimental evidence that therapist lived experience disclosures can model the path to recovery for clients

A common guideline for self-disclosure is that therapists should only share recovered personal experiences with clients (i.e., no longer distressing). However, theoretical rationale and empirical support for this claim is limited. Drawing on identity leadership theorizing, we investigated whether recovery disclosures are beneficial to the extent that they signal a therapist's aspirational prototypicality (i.e., embodiment of an aspirational identity for clients). Across two experimental studies (N = 545), we recruited clients, therapists and general population adults. Participants read a group therapy for depression vignette in which the therapist disclosed: nothing, professional experience with depression, current depression, recovered depression or recovered anxiety. Participants rated the prototypicality of the therapist, the extent to which they perceived the therapist positively, the therapist's expertness and the expected prognosis for therapy. Contrary to our hypotheses, the type of disclosure did not significantly affect positive perceptions, expertness or expected prognosis ratings. However, the therapist disclosing a recovered and relevant condition (recovered depression) was rated as significantly more aspirationally prototypical than the other therapists. Given prior evidence that group therapists are more effective when viewed as aspirationally prototypical, our findings suggest that recovery disclosures may represent one way therapists can signal their prototypicality and enhance their effectiveness.

The token undermining effect: When and why adding a small reward to a dated outcome makes it less preferred

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.

Individual differences in the evolution of causal illusions

In this research, we investigated individual differences in the formation and persistence of causal illusions. In a re-analysis of existing data, we identified two clusters of participants – persistent and adjusting – based on their trajectories in learning from repeated exposure to null contingencies. The persistent cluster maintained stable causal illusions, while the adjusting cluster demonstrated a reduction over time. This re-analysis provided a nuanced understanding of individual differences in causal learning, emphasizing the differential role of probability estimations in predicting causal judgements. These findings were replicated in a subsequent study, highlighting the robustness of the identified effects. In a pre-registered study, we extended the paradigm to include a second phase (active phase) to assess how individual differences in causal illusion trajectories in the passive phase would manifest when participants had agency in the information gathering process. The results were consistent with those of the two previous studies and confirmed our primary hypothesis that the adjusting cluster would exhibit a lower tendency to introduce the candidate cause on learning trials, and would, therefore, observe a higher frequency of cause–absent trials. Together, these studies provide comprehensive insights into the underpinnings of causal illusion development and persistence, potentially informing de-biasing interventions.

Face and voice identity matching accuracy is not improved by multimodal identity information

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.

Beyond the screen: Dissecting the nexus of victimization and cyberhate among adolescents through excessive internet use, online interactions with strangers and parental restrictions

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.

New evidence finds young people in Mainland China are now bicultural

This study reports new evidence that young people in Mainland China are now bicultural. We followed the established method of testing biculturalism by priming participants with images from two different cultures and measuring whether those images activate different thought styles. First, we replicated findings from 25 years ago that college students in Hong Kong are bicultural (Study 1). Next, we found that priming Mainland Chinese college students with Chinese culture increased external attributions (which are more common in China), whereas priming American culture increased internal attributions (which are more common in the US; Study 2). Next, we tested a “negative control” group that we expected should not respond to bicultural primes. Older adults who were born before China's Reform and Opening policy in 1978 showed no evidence of biculturalism (Study 3). This new evidence extends biculturalism to Mainland China, and it provides a crucial negative control test for biculturalism research.

The global prevalence of emotional eating in overweight and obese populations: A systematic review and meta‐analysis

This systematic review aims to investigate the current prevalence of emotional eating and its associated factors in overweight and obese populations. We included studies that (1) reported prevalence of emotional eating; (2) were in the context of weight gain or overweight and obesity; (3) used a validated psychometric tool to assess emotional eating; (4) were published as an internationally referred journal article and (5) were reported in the English language. Articles were searched on eight electronic databases (CINAHL, EMBASE, PsychINFO, ProQuest, PubMed, Scopus, The Cochrane Library and Web of Science) from the journals' inception to 11 April 2024. A total of 18 studies, representing a total of 21,237 people, were included in the review. Our study suggested that emotional eating is significantly prevalent at 44.9%. High heterogeneity observed (I 2: 98.7%) can be attributed to differences in measurement tools for emotional eating, but not differences in geographical regions. By providing insight to the current prevalence of emotional eating and its relevant factors, this study outlines the steps to take in future research and practice to tackle emotional eating and related health issues like obesity. There is a need to develop standardized measurement tools for emotional eating, and further investigate sociodemographic factors.

Learning in the face of failure: The benefit of autistic traits

This study aims to explore how learning performance differs for non-diagnosed adults with high and low levels of autistic traits (ATs) for positive versus negative feedback delivered via social and nonsocial stimuli. College student participants were tested on their ability to learn novel words (i.e., Korean characters) in a simple memory experiment incorporating either positive or negative feedback. A 2 (positive feedback vs. negative feedback) × 2 (ATs: high vs. Low) between-subject design was adopted in both Experiments 1 and 2. Social feedback stimuli were used in Experiment 1 and nonsocial feedback stimuli were used in Experiment 2. The results revealed that individuals with both high and low levels of ATs showed learning for both types of feedback (success and failure) using social and nonsocial stimuli (Experiments 1 and 2, respectively). However, individuals with low levels of ATs learned less from failure than from success, whereas individuals with high levels of ATs showed equivalent learning for failure and success, for both social and nonsocial stimuli. These results suggest that for college students, a benefit of having high levels of ATs is increased resilience and an ability to continue to learn in the face of failure.

Expressed emotion of caregivers to children at familial high risk of schizophrenia or bipolar disorder: The Danish High Risk and Resilience Study – VIA 11

A high level of parental expressed emotion is thought to play an important role in the development and course of mental disorders in offspring. This study investigates expressed emotion among primary caregivers to 11-year-old children at familial high risk of schizophrenia or bipolar disorder and population-based controls, and whether potential differences in expressed emotion are related to child psychopathology. Expressed emotion was assessed with the Five-Minute Speech Sample and a total of 440 audio files from primary caregivers were collected. Child psychopathology was assessed with the Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children. Primary caregivers from families with a parental diagnosis of schizophrenia spectrum disorder or bipolar disorder were classified as high on expressed emotion significantly more often than population-based controls. Between-group differences remained significant after adjusting for any current axis I child diagnosis indicating that high expressed emotion is not solely attributable to the presence of child psychopathology. These findings underline the importance of assessing the emotional climate in families with parental schizophrenia and bipolar disorder.

Attraction in every sense: How looks, voice, movement and scent draw us to future lovers and friends

What makes someone attractive has been examined for faces, in terms of common preferences, and for opposite-sex interactions. We expanded on this by considering also other non-verbal modalities, personal preferences and same-sex interactions. We presented the face, body motion, voice and body odour from 61 non-verbal agents (34 women) as stimuli in an attractiveness rating to 71 perceivers (37 women). Our results showed that the modalities were differently attractive and that some correlated more than others. Specifically, body odours were least and audio–video stimuli most attractive. Voice/looks as well as body odour/movement showed fairly robust positive associations. Both common and personal preferences accounted for variance in the data. Most effects compared between opposite- and same-sex ratings, with only a few exceptions, including that only same-sex ratings showed a clear dominance of personal over common preferences. We conclude that the different non-verbal modalities are equally relevant for attraction but differ in absolute attractiveness and redundancy, likely due to their different suitability for communicating stable (e.g. genetic) versus variable (e.g. hormonal) person characteristics. Beauty excites agreement and disagreement; it matters not only in encounters with the other sex but in social interactions more broadly.

Development of a theory of mind assessment for children using multidimensional Rasch modelling

Existing theory of mind (ToM) measures for children meet challenges from three perspectives. Developmentally, they lack items covering the entire spectrum of ToM abilities, namely, the early, basic and advanced levels. Dimensionally, most measures are unidimensional, not distinguishing between cognitive and affective ToM. Practically, most measures rely heavily on verbal abilities or lack engaging formats. This study aimed to address these critical issues by developing a Theory of Mind Assessment (ToMA). The items were generated based on classical scenarios spanning all developmental levels. The responses from 574 neurotypical children aged 37 to 194 months were analysed using the multidimensional Rasch model. Ten items showed satisfactory model fits when classified into cognitive (4 items) and affective (6 items) domains, with 16 misfit items excluded. Two items seemed easier for girls and two for boys, and the item difficulties were adjusted. The mean Rasch person reliabilities were 0.78 and 0.81. The scores exhibited small and high correlations with subjective and objective criteria. The newly developed measure may offer reliable, valid and sex-unbiased assessments while satisfying developmental, multidimensional and practical requirements. It seems promising for application in clinical and research settings and is worthy of future refinement and validation to provide high-quality ToM assessment.

Emotion ensemble judgement: Cognitive training for a positive perspective

Emotion ensemble judgement refers to the cognitive process by which individuals extract the general emotional tone of a busy visual scene. This study investigated whether emotion ensemble judgement can be changed through cognitive training. Two groups of participants underwent interpretation and visual attention training towards positivity, respectively, while the third group served as the control (total n = 102). All three groups participated in an emotion ensemble-rating task where they rated the overall emotion of a set of facial expressions three times (pre-training, immediately post-training and 7-days post-training). The results demonstrated the malleability of perceptual judgement of an emotion ensemble. The interpretation-training group exhibited a shift towards positivity, particularly for fearful ensembles. Similarly, the attention-training group also showed a positive shift, along with increased eye movements towards happy stimuli immediately after training. These findings help shed light on the formation and correction of biases in emotion perception and judgement.

Deliberate memory display can enhance conveyed value

Letting someone else know that you value their presence, characteristics, effort or activities is central to building and maintaining human relationships. We investigated whether deliberate memory display is an effective means to convey such value. We examined these questions in the context of a simulated job interview (Experiments 1, 2 and 3, total N = 404) and a simulated ‘ice breaker’ exercise between new acquaintances (Experiment 4, total N = 156). Across experiments, results consistently indicated that memory display was not only an effective method of conveying value, but that memory display made other efforts to convey value more effective. Moreover, without external prompting, participants underutilized memory display despite its efficacy. These findings document the efficacy of memory display in the deliberate communication of value and suggest that deliberate memory display might be an underutilized strategic asset in the management of human relationships.

Close encounters: Interpersonal proximity amplifies social appraisals

Social appraisals reflect the rapid integration of available perceptual information with broader contextual factors (e.g., intentions). While interpersonal distance affects both information availability and social context, how it changes trait impressions remains unknown. Over four experiments, we used a novel paradigm to address this question. In Experiment 1, we assessed participants' attributions of attractiveness, competence, dominance and trustworthiness of life size full body images of people when they appeared at near (1 m) and far (4 m) distances. Proximity amplified the relative magnitude of both positive and negative socio-evaluative impressions. However, this effect of proximity leading to more extreme positive or negative ratings was selectively weaker for aesthetic (attractiveness) judgements. In Experiment 2 (size) and Experiment 3 (spatial frequency), we held distance constant while manipulating visual cues relating to implied distance, revealing broadly similar results to Experiment 1. In Experiment 4, we used the interpersonal comfort distance paradigm to confirm that our life-sized projected images elicited similar comfort distance to interacting with a real person, helping to validate our general approach. These findings demonstrate the crucial role of interpersonal distance in impression judgements.

Why moral judgements change across variations of trolley‐like problems

In the standard ‘trolley problem’, respondents must decide whether to save a condemned group of individuals by sacrificing a safe bystander. Although respondents often are willing to sacrifice the bystander in some circumstances (e.g., by pulling a lever), they are loath to sacrifice the bystander in others (e.g., by pushing the bystander off a footbridge). This difference in responding has been explained via a Dual Process theory of moral judgements (DPT). DPT, however, is a classic boxes-and-arrows model that only makes directional predictions. Meehl (1967, Philosophy of Science, 34, 103) cautioned against theories that only make directional predictions, explaining that they are notoriously difficult to falsify. Meehl (1967, Philosophy of Science, 34, 103) argued that researchers should follow the lead of Physics and develop computational models that make functional and point predictions. Here, we use a value-based, computational cognitive model of decision-making (Psychological Value Theory) to predict precisely both the speed and kind of response in trolley-like problems in three experiments. We show that this model accounts for the changes in choices across variations of the trolley problem with a response bias parameter.

Working memory capacity and self‐cues: Consistent benefits in children and adults

From attentional prioritization to enhanced memory, self-cues trigger a variety of effects within human cognition. Recent work suggests that self-reference may also enhance working memory, possibly via attentional prioritization. However, there is no direct evidence that self-cues enhance working memory capacity, or that such boosts covary with individuals' attentional function. Here, we provide the first direct evidence of enhanced working memory capacity for self-referential cues, independent of attentional processing. We adapted a verbal working memory complex span to create a ‘Self’ condition (featuring the participant's own name), ‘Other’ condition (featuring a non-self-name), and Control condition (with no name), in 7–9-year-old children (Exp.1, N = 71) and adults (Exp.2, N = 52). In both experiments, the Self condition elicited significantly higher spans than the other conditions (Exp 1: p < .001, η p 2 = .32; Exp 2: p < .001, η p 2 = .25), but this increase in capacity was unrelated to measures of attentional processing or backward digit span. Moreover, equivalent boosts were observed in children and adults, despite adults' significantly higher underlying capacity. We propose a chunking interpretation based on enhanced binding of self-associated items, directly benefiting individual's working memory capacity regardless of their current attentional competence or ‘baseline’ capacity.

Sex differences in reading and math test scores of children: A heterodoxical model By Hoben Thomas, Monographs in the psychology of education, Springer, 2023; ISBN: 978‐3‐031‐41271‐4 (print), 978‐3‐031‐41272‐1 (eBook)

Quantitative psychology: The 88th Annual Meeting of the Psychometric Society By Marie Wiberg, Jee‐Seon Kim, Heungsun Hwang, Hao Wu, Tracy Sweet (eds.), Springer proceedings in Mathematics & Statistics, 2024, 452nd volume; ISBN: 978‐3‐031‐55547‐3 (Print), 978‐3‐031‐55548‐0 (eBook)

The alone team: How an alone mindset affects group processes

Being alone is a basic and prevalent human experience, yet little is known about its effects on social identity. When alone, social identity may weaken because individuals feel their groups are less valuable or less salient. Conversely, it may strengthen because craving for bonding increases in-group perceived value. We tested these ideas with five experiments (N = 1312). Study 1, using existing groups, and Study 2, using minimal groups, showed that aloneness (vs. being with others) reduced in-group identity through its effect on in-group esteem. In Study 3, this effect was equivalent to the effect observed following social exclusion. Study 4 showed that individuals alone were indifferent in allocating money between in-group and out-group members. Last, Study 5 indicated that these effects do not stem from reduced salience of in-group members when alone. In conclusion, an alone mindset affects social identity by decreasing in-group esteem, signalling broad social implications for this basic social condition.

Keep bright in the dark: Multimodal emotional effects on donation‐based crowdfunding performance and their empathic mechanisms

How to raise donations effectively, especially in the E-era, has puzzled fundraisers and scientists across various disciplines. Our research focuses on donation-based crowdfunding projects and investigates how the emotional valence expressed verbally (in textual descriptions) and visually (in facial images) in project descriptions affects project performance. Study 1 uses field data (N = 3817), grabs project information and descriptions from a top donation-based crowdfunding platform, computes visual and verbal emotional valence using a deep-learning-based affective computing method and analyses how multimodal emotional valence influences donation outcomes. Study 2 conducts experiments in GPT-4 (Study 2a, N = 400) and humans (Study 2b, N = 240), manipulates the project's visual and verbal emotional valence through AI-generated stimuli and then assesses donation decisions (both GPT-4 and humans) and corresponding state empathy (humans). The results indicate a multimodal positivity superiority effect: both visual and verbal emotional valence promote initial whether-to-donate decisions, whereas only verbal emotional valence further promotes the how-much-to-donate decisions. Notably, such multimodal emotional effects can be explained through different mediating paths of empathic concern and empathic hopefulness. The current study theoretically facilitates our understanding of the emotional motivations underlying human prosociality and provides insights into crafting impactful advertisements for online donations.

Family‐focused treatment for child and adolescent mental health: A new paradigm By Paul Sunseri, New York, NY: Routledge. 2024. 256. £28.79. ISBN 978‐1‐003‐39736‐6

Mathematical psychology: The world of thoughts as a quantum space‐time with a gravitational core By Norbert Schwarzer, Jenny Stanford Publishing. 2024. 511 pp. ISBN: 9789815129274 (Hardcover), 9781003582038 (eBook)

Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methodsby Donna M. Mertens, SAGE Publications. 2024. 6th edition, 528 pp. ISBN: 978‐1‐0718‐5380‐1

Predictors of online and offline activism in hybrid regime society – Serbian study

In Serbia, categorized as a hybrid or semi-authoritarian system by Freedom House, exploring activism is crucial. Two cross-sectional studies were conducted to examine predictors of online and offline civic activism in a society favouring punitive measures and limiting open dialogue. The first study involved 1107 participants (65.1% female, 33.2% male and 1.6% in other categories; Mage = 28.15 years), while the second included 2306 individuals (67.5% female, 30.2% male and 2.3% in other categories; Mage = 28.1 years). We modelled the relationship between predictor variables and a dependent variable at different quantiles of the distribution (25th, 50th, 75th and quantile marked with theoretical middle points of online and activism scales) using quantile regression. Results highlighted perceived policy control, social generativity and conscientiousness as key predictors across different levels of both online and offline activism. Personality traits like extroversion, neuroticism and honesty have different effects on online versus offline activism, suggesting different motivations. Additionally, future-oriented people participate more offline. The quantile regression, analysing coefficients at different points in the distribution, offered valuable insights into manifestations of activism. These findings are discussed highlighting the complex dynamic of different factors in shaping civic engagement in Serbia's challenging sociopolitical landscape.

Artificial intelligence chatbots mimic human collective behaviour

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.

Bridging minds and machines: Unmasking the limits in text‐based automatic personality recognition for enhanced psychology–AI synergy

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.

Computers and chess masters: The role of AI in transforming elite human performance

Advances in Artificial Intelligence (AI) have made significant strides in recent years, often supplementing rather than replacing human performance. The extent of their assistance at the highest levels of human performance remains unclear. We analyse over 11.6 million decisions of elite chess players, a domain commonly used as a testbed for AI and psychology due to its complexity and objective assessment. We investigated the impact of two AI chess revolutions: the first in the late 1990s with the rise of powerful PCs and internet access and the second in the late 2010s with deep learning-powered chess engines. The rate of human improvement mirrored AI advancements, but contrary to expectations, the quality of decisions mostly improved steadily over four decades, irrespective of age, with no distinct periods of rapid improvement. Only the youngest top players saw marked gains in the late 1990s, likely due to better access to knowledge and computers. Surprisingly, the recent wave of neural network-powered engines has not significantly impacted the best players – at least, not yet. Our research highlights AI's potential to enhance human capability in complex tasks, given the right conditions, even among the most elite performers.

Automated face recognition assists with low‐prevalence face identity mismatches but can bias users

We present three experiments to study the effects of giving information about the decision of an automated face recognition (AFR) system to participants attempting to decide whether two face images show the same person. We make three contributions designed to make our results applicable to real-word use: participants are given the true response of a highly accurate AFR system; the face set reflects the mixed ethnicity of the city of London from where participants are drawn; and there are only 10% of mismatches. Participants were equally accurate when given the similarity score of the AFR system or just the binary decision but shifted their bias towards match and were over-confident on difficult pairs when given only binary information. No participants achieved the 100% accuracy of the AFR system, and they had only weak insight about their own performance.

The differences in essential facial areas for impressions between humans and deep learning models: An eye‐tracking and explainable AI approach

This study explored the facial impressions of attractiveness, dominance and sexual dimorphism using experimental and computational methods. In Study 1, we generated face images with manipulated morphological features using geometric morphometrics. In Study 2, we conducted eye tracking and impression evaluation experiments using these images to examine how facial features influence impression evaluations and explored differences based on the sex of the face images and participants. In Study 3, we employed deep learning methods, specifically using gradient-weighted class activation mapping (Grad-CAM), an explainable artificial intelligence (AI) technique, to extract important features for each impression using the face images and impression evaluation results from Studies 1 and 2. The findings revealed that eye-tracking and deep learning use different features as cues. In the eye-tracking experiments, attention was focused on features such as the eyes, nose and mouth, whereas the deep learning analysis highlighted broader features, including eyebrows and superciliary arches. The computational approach using explainable AI suggests that the determinants of facial impressions can be extracted independently of visual attention.

Explainability increases trust resilience in intelligent agents

Even though artificial intelligence (AI)-based systems typically outperform human decision-makers, they are not immune to errors, leading users to lose trust in them and be less likely to use them again—a phenomenon known as algorithm aversion. The purpose of the present research was to investigate whether explainable AI (XAI) could function as a viable strategy to counter algorithm aversion. We conducted two experiments to examine how XAI influences users' willingness to continue using AI-based systems when these systems exhibit errors. The results showed that, following the observation of algorithms erring, the inclination of users to delegate decisions to or follow advice from intelligent agents significantly decreased compared to the period before the errors were revealed. However, the explainability effectively mitigated this decline, with users in the XAI condition being more likely to continue utilizing intelligent agents for subsequent tasks after seeing algorithms erring than those in the non-XAI condition. We further found that the explainability could reduce users' decision regret, and the decrease in decision regret mediated the relationship between the explainability and re-use behaviour. These findings underscore the adaptive function of XAI in alleviating negative user experiences and maintaining user trust in the context of imperfect AI.

Generative neural networks for experimental manipulation: Examining dominance‐trustworthiness face impressions with data‐efficient models

An important development in the study of face impressions was the introduction of dominance and trustworthiness as the primary and potentially orthogonal traits judged from faces. We test competing predictions of recent accounts that address evidence against the independence of these judgements. To this end we develop a version of recent ‘deep models of face impressions’ better suited for data-efficient experimental manipulation. In Study 1 (N = 128) we build impression models using 15 times less ratings per dimension than previously assumed necessary. In Study 2 (N = 234) we show how our method can precisely manipulate dominance and trustworthiness impressions of face photographs and observe how the effects' pattern of the cues of one trait on impressions of the other differs from previous accounts. We propose an altered account that stresses how a successful execution of the two judgements' functional roles requires impressions of trustworthiness and dominance to be based on cues of both traits. Finally we show our manipulation resulted in larger effect sizes using a broader array of features than previous methods. Our approach lets researchers manipulate face stimuli for various face perception studies and investigate new dimensions with minimal data collection.

Editorial: Introducing the British Psychological Society journals' landmark special issue on the United Nation's Sustainability Goals

People have different expectations for their own versus others' use of AI‐mediated communication tools

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.

Scoping review on natural language processing applications in counselling and psychotherapy

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.

Assessing novelty, feasibility and value of creative ideas with an unsupervised approach using GPT‐4

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.

Decoding the language of first impressions: Comparing models of first impressions of faces derived from free‐text descriptions and trait ratings

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.

Explanation strategies in humans versus current explainable artificial intelligence: Insights from image classification

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.

Unveiling the factors of aesthetic preferences with explainable AI

The allure of aesthetic appeal in images captivates our senses, yet the underlying intricacies of aesthetic preferences remain elusive. In this study, we pioneer a novel perspective by utilizing several different machine learning (ML) models that focus on aesthetic attributes known to influence preferences. Our models process these attributes as inputs to predict the aesthetic scores of images. Moreover, to delve deeper and obtain interpretable explanations regarding the factors driving aesthetic preferences, we utilize the popular Explainable AI (XAI) technique known as SHapley Additive exPlanations (SHAP). Our methodology compares the performance of various ML models, including Random Forest, XGBoost, Support Vector Regression, and Multilayer Perceptron, in accurately predicting aesthetic scores, and consistently observing results in conjunction with SHAP. We conduct experiments on three image aesthetic benchmarks, namely Aesthetics with Attributes Database (AADB), Explainable Visual Aesthetics (EVA), and Personalized image Aesthetics database with Rich Attributes (PARA), providing insights into the roles of attributes and their interactions. Finally, our study presents ML models for aesthetics research, alongside the introduction of XAI. Our aim is to shed light on the complex nature of aesthetic preferences in images through ML and to provide a deeper understanding of the attributes that influence aesthetic judgements.

Understanding anxiety through uncertainty quantification

Uncertainty has been a central concept in psychological theories of anxiety. However, this concept has been plagued by divergent connotations and operationalizations. The lack of consensus hinders the current search for cognitive and biological mechanisms of anxiety, jeopardizes theory creation and comparison, and restrains translation of basic research into improved diagnoses and interventions. Drawing upon uncertainty decomposition in Bayesian Decision Theory, we propose a well-defined conceptual structure of uncertainty in cognitive and clinical sciences, with a focus on anxiety. We discuss how this conceptual structure provides clarity and can be naturally applied to existing frameworks of psychopathology research. Furthermore, it allows formal quantification of various types of uncertainty that can benefit both research and clinical practice in the era of computational psychiatry.