This editorial gives a brief introduction to the articles included in the thematic section of Europe's Journal of Psychology, which is devoted to selected recent advances and applications of the theory of planned behavior (TPB). The five contributions address two thematic streams: (1) adjustments and extensions of the original theory and (2) applications of the TPB in public health and the political sciences.
Despite a surge in robotics research dedicated to inferring and understanding human intent, a universally accepted definition remains elusive since existing works often equate human intention with specific task-related goals. This article seeks to address this gap by examining the multifaceted nature of intention. Drawing on insights from psychology, it attempts to consolidate a definition of intention into a comprehensible framework for a broader audience. The article classifies different types of intention based on psychological and communication studies, offering guidance to researchers shifting from pure technical enhancements to a more human-centric perspective in robotics. It then demonstrates how various robotics studies can be aligned with these intention categories. Finally, through in-depth analyses of collaborative search and object transport use cases, the article underscores the significance of considering the diverse facets of human intention.
Rukma Talwadker, Surajit Chakrabarty, Aditya Pareek
et al.
Games are one of the safest source of realizing self-esteem and relaxation at the same time. An online gaming platform typically has massive data coming in, e.g., in-game actions, player moves, clickstreams, transactions etc. It is rather interesting, as something as simple as data on gaming moves can help create a psychological imprint of the user at that moment, based on her impulsive reactions and response to a situation in the game. Mining this knowledge can: (a) immediately help better explain observed and predicted player behavior; and (b) consequently propel deeper understanding towards players' experience, growth and protection. To this effect, we focus on discovery of the "game behaviours" as micro-patterns formed by continuous sequence of games and the persistent "play styles" of the players' as a sequence of such sequences on an online skill gaming platform for Rummy. We propose a two stage deep neural network, CognitionNet. The first stage focuses on mining game behaviours as cluster representations in a latent space while the second aggregates over these micro patterns to discover play styles via a supervised classification objective around player engagement. The dual objective allows CognitionNet to reveal several player psychology inspired decision making and tactics. To our knowledge, this is the first and one-of-its-kind research to fully automate the discovery of: (i) player psychology and game tactics from telemetry data; and (ii) relevant diagnostic explanations to players' engagement predictions. The collaborative training of the two networks with differential input dimensions is enabled using a novel formulation of "bridge loss". The network plays pivotal role in obtaining homogeneous and consistent play style definitions and significantly outperforms the SOTA baselines wherever applicable.
Music serves as a powerful reflection of individual identity, often aligning with deeper psychological traits. Prior research has established correlations between musical preferences and personality, while separate studies have demonstrated that personality is detectable through linguistic analysis. Our study bridges these two research domains by investigating whether individuals' musical preferences leave traces in their spontaneous language through the lens of the Big Five personality traits (Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism). Using a carefully curated dataset of over 500,000 text samples from nearly 5,000 authors with reliably identified musical preferences, we build advanced models to assess personality characteristics. Our results reveal significant personality differences across fans of five musical genres. We release resources for future research at the intersection of computational linguistics, music psychology and personality analysis.
In this article we explore the application of Large Language Models (LLMs) in assessing the content validity of psychometric instruments, focusing on the Big Five Questionnaire (BFQ) and Big Five Inventory (BFI). Content validity, a cornerstone of test construction, ensures that psychological measures adequately cover their intended constructs. Using both human expert evaluations and advanced LLMs, we compared the accuracy of semantic item-construct alignment. Graduate psychology students employed the Content Validity Ratio (CVR) to rate test items, forming the human baseline. In parallel, state-of-the-art LLMs, including multilingual and fine-tuned models, analyzed item embeddings to predict construct mappings. The results reveal distinct strengths and limitations of human and AI approaches. Human validators excelled in aligning the behaviorally rich BFQ items, while LLMs performed better with the linguistically concise BFI items. Training strategies significantly influenced LLM performance, with models tailored for lexical relationships outperforming general-purpose LLMs. Here we highlights the complementary potential of hybrid validation systems that integrate human expertise and AI precision. The findings underscore the transformative role of LLMs in psychological assessment, paving the way for scalable, objective, and robust test development methodologies.
Affective Recommender Systems are an emerging class of intelligent systems that aim to enhance personalization by aligning recommendations with users' affective states. Reflecting a growing interest, a number of surveys have been published in this area, however they lack an organizing taxonomy grounded in psychology and they often study only specific types of affective states or application domains. This survey addresses these limitations by providing a comprehensive, systematic review of affective recommender systems across diverse domains. Drawing from Scherer's typology of affective states, we introduce a classification scheme that organizes systems into four main categories: attitude aware, emotion aware, mood aware, and hybrid. We further document affective signal extraction techniques, system architectures, and application areas, highlighting key trends, limitations, and open challenges. As future research directions, we emphasize hybrid models that leverage multiple types of affective states across different modalities, the development of large-scale affect-aware datasets, and the need to replace the folk vocabulary of affective states with a more precise terminology grounded in cognitive and social psychology. Through its systematic review of existing research and challenges, this survey aims to serve as a comprehensive reference and a useful guide for advancing academic research and industry applications in affect-driven personalization.
Abstract Background The transition from Child and Adolescent Mental Health Services (CAMHS) to Adult Mental Health Services (AMHS) presents significant challenges for young people with eating disorders and their families. These transitions often occur during critical periods of neurological, social-emotional development, and major life changes, all of which can influence broader psychosocial and treatment outcomes. This study represents the initial phase of a broader co-production project aimed at developing a new intervention model, Transition for Eating Disorder Youth intervention (TEDYi), and explored the lived experiences of young people, carers, and mental health professionals during transitions. Methods Fifteen semi-structured interviews were conducted with young people (n = 6) and carers (n = 9), alongside two focus groups involving 12 mental health professionals. These took place across six NHS sites in England, including two adult and four adolescent specialist eating disorder services. Results The data were analysed with Reflexive Thematic Analysis (RTA) which revealed four key themes: navigating the complexity of transitions, we need carers involved, the shadow of separation, and suggestions for the TEDYi intervention related to coping strategies and self-management. Conclusions These findings have significant clinical implications for transitional care, emphasising the need for a more standardised and supportive approach to the transition from CAMHS to AMHS. The forthcoming intervention model seeks to address these challenges, with this study helping to prioritise key areas identified by TEDYi, which has been endorsed as a preparatory resource for enhancing clinical practice.
Artificial intelligence (AI) ethics has emerged as a global discourse within both academic and policy spheres. However, translating these principles into concrete, real-world applications for AI development remains a pressing need and a significant challenge. This study aims to bridge the gap between principles and practice from a regulatory government perspective and promote best practices in AI governance. To this end, we developed the Ethical Regulatory Framework for AI (ERF-AI) to guide regulatory bodies in constructing mechanisms, including role setups, procedural configurations, and strategy design. The framework was developed through a systematic review, thematic analysis, and the integration of interdisciplinary concepts. A comprehensive search was conducted across four electronic databases (PubMed, IEEE Xplore, Web of Science, and Scopus) and four additional sources containing AI standards and guidelines from various countries and international organizations, focusing on studies published from 2014 to 2024. Thematic analysis identified and refined key themes from the included literature and integrated concepts from process control theory, computer science, organizational management, information technology, and behavioral psychology. This study adhered to the PRISMA guidelines and employed NVivo for thematic analysis. The resulting framework encompasses 23 themes, particularly emphasizing three feedback-loop processes: the ethical review process, the incentive and penalty process, and the mechanism improvement process, offering theoretical guidance for the construction of ethical regulatory mechanisms. Based on this framework, a seven-step process and case examples for mechanism design are presented, enhancing the practicality of ERF-AI in developing ethical regulatory mechanisms. Future research is expected to explore customization of the framework to remain responsive to emerging AI trends and challenges, supported by empirical studies and rigorous testing for further refinement and expansion.
Jamie D. Feusner, Alicja Nowacka, Ronald Ly
et al.
Abstract Anorexia nervosa is an often-severe psychiatric illness characterized by significantly low body weight, fear of gaining weight, and distorted body image. Multiple neuroimaging studies have shown abnormalities in cortical morphology, mostly associated with the starvation state. Investigations of white matter, while more limited in number, have suggested global and regional volume reductions, as well as abnormal diffusivity in multiple regions including the corpus callosum. Yet, no study has specifically examined thickness of the corpus callosum, a large white matter tract instrumental in the inter-hemispheric integration of sensory, motor, and cognitive information. We analyzed MRI data from 48 adolescents and adults with anorexia nervosa and 50 healthy controls, all girls/women, to compare corpus callosum thickness and examined relationships with body mass index (BMI), illness duration, and eating disorder symptoms (controlling for BMI). There were no significant group differences in corpus callosum thickness. In the anorexia nervosa group, severity of body shape concerns was significantly, positively correlated with callosal thickness in the rostrum, genu, rostral body, isthmus, and splenium. In addition, there were significant positive correlations between eating disorder-related obsessions and compulsions and thickness of the anterior midbody, rostral body, and splenium. There were no significant associations between callosal thickness and BMI or illness duration. In sum, those with AN with worse concerns about bodily appearance and worse eating disorder-related obsessive thought patterns and compulsive behaviours have regionally thicker corpus callosum, independent of current weight status. These findings provide important neurobiological links to key, specific eating disorder behavioural phenotypes.
Aaron T. Seaman, Julia H. Rowland, Samantha J. Werts
et al.
Introduction: Cancer rates increase with age, and older cancer survivors have unique medical care needs, making assessment of health status and identification of appropriate supportive resources key to delivery of optimal cancer care. Comprehensive geriatric assessments (CGAs) help determine an older person’s functional capabilities as cancer care providers plan treatment and follow-up care. Despite its proven utility, research on implementation of CGA is lacking.Methods: Guided by a qualitative description approach and through interviews with primary care providers and oncologists, our goal was to better understand barriers and facilitators of CGA use and identify training and support needs for implementation. Participants were identified through Cancer Prevention and Control Research Network partner listservs and a national cancer and aging organization. Potential interviewees, contacted via email, were provided with a description of the study purpose. Eight semi-structured interviews were conducted via Zoom, recorded, and transcribed verbatim by a professional transcription service. The interview guide explored providers’ knowledge and use of CGAs. For codebook development, three representative transcripts were independently reviewed and coded by four team members. The interpretive process involved reflecting, transcribing, coding, and searching for and identifying themes.Results: Providers shared that, while it would be ideal to administer CGAs with all new patients, they were not always able to do this. Instead, they used brief screening tools or portions of CGAs, or both. There was variability in how CGA domains were assessed; however, all considered CGAs useful and they communicated with patients about their benefits. Identified facilitators of implementation included having clinic champions, an interdisciplinary care team to assist with implementation and referrals for intervention, and institutional resources and buy-in. Barriers noted included limited staff capacity and competing demands on time, provider inexperience, and misaligned institutional priorities.Discussion: Findings can guide solutions for improving the broader and more systematic use of CGAs in the care of older cancer patients. Uptake of processes like CGA to better identify those at risk of poor outcomes and intervening early to modify treatments are critical to maximize the health of the growing population of older cancer survivors living through and beyond their disease.
Dragutin T. Mihailovic, Darko Kapor, Sinisa Crvenkovic
et al.
From the perspective of the physics of complex systems (1) we deal with the current state of modern physics including the crisis in physics demonstrated through its epistemological, psychological, economical as well as the social context; (2) considering the strength of the Goedel Incompleteness Theorems we point out the following open questions in physics: (i) the limits of the precision of certainty, (ii) the limits of making decisions on information and (iii) the limitations of reasoning that sometimes affect progress, (3) since further advances will necessarily require synergy between physics and seemingly distinct fields - mathematics, information science, chemistry, biology, medicine, psychology, and art. We illustrate this relation by providing examples based on our research. Point (1) is discussed in Ch1 while Ch2-Ch5 encompasses the point (2) and point (3) is covered in Ch6-Ch10.
Çalışmanın amacı, ilahiyat/İslami ilimler fakültelerinde öğrenim gören öğrencilerin din-bilim ilişkisine yönelik yaklaşımlarının belirlenmesi için hazırlanan bir din-bilim ilişkisi tutum ölçeği geliştirmektir. Din-bilim ilişkisi din, bilim ve felsefesinin en önemli kesişim noktalarından birini oluşturmaktadır. Modern dönemde bu ilişkinin sorgulanmasına dair tartışmalar farklı unsurların etkisiyle oldukça ileri boyutlara ulaşmıştır. Son yıllarda ülkemizde bu durumun yoğunlaştığı çevrelerden biri de ilahiyat/İslami ilimler fakülteleridir. Özellikle Bu fakültelerin öğrencileri açısından meselenin farklı boyutlarıyla ele alınması önem arz etmektedir. Ancak çalışmalar ağırlıklı olarak teorik boyutta yürütülmekte olup, nicel boyutların eksik kaldığı söylenebilir. Bu açıdan makale alanda yapılan ilk örnek konumundadır. Ölçek sorularının oluşturulmasında, din-bilim ilişkisine yönelik ortaya konan en kapsamlı çalışmalardan biri olan Ian G. Barbour’un dörtlü tipolojisi esas alınmıştır. Özellikle bu tipolojideki çatışma, ayrışma ve uzlaşma yaklaşımları belirleyici olmuştur. Üç farklı çalışma grubu oluşturulmuş ve Atatürk Üniversitesi İlahiyat fakültesinde öğrenim görmekte olan toplam 594 öğrenci ile çalışılmıştır. Ölçeğin geliştirilmesinde karma yöntem araştırmalarından keşfedici sıralı desen kullanılmış olup, dört adım bulunmaktadır. İlk adımda araştırmacılar tarafından maddeler yazılmış ve alan uzmanlarının görüşleri alınarak düzeltmeler yapılmıştır. İkinci adımda madde analizi için uygulama yapılmış, üçüncü adımda açımlayıcı faktör analizi (AFA) için uygulama yapılmış, dördüncü ve son adımda doğrulayıcı faktör analizi (DFA) yapılmıştır. Çalışmadan elde edilen bulgulara göre ölçeğin çatışma/ayrışma ve uzlaşma diye isimlendirilen iki alt boyuttan oluşan bir ölçek geliştirilmiştir. Ölçeğinin güvenirliği iç tutarlılık (cronbach alpha) yöntemi ile analiz edilmiş ve her bir alt boyutu için cronbach alpha değerleri çatışma alt boyutu için .90, uzlaşma alt boyu için de .77 olarak hesaplanmıştır. Bu sonuçlar ölçeğin her bir alt boyutunun güvenilir olduğuna işaret etmektedir.