Cognitive styles diversity and group creativity: Evidence from fNIRS hyperscanning.

Factors for analytical and intuitive cognition in strategy consultants: A multivariate analysis.

Revisiting object contextual cueing: A replication study on implicit learning.

Abstract We surveyed 60 German investors—including business angels and investors from venture capital, private equity, and corporate venture capital—to understand how they make investment decisions and interact with their portfolio companies. The extant literature explains the investment process of venture capital firms in a variety of countries—most notably the USA—but to date, no study has specifically examined different types of German investors and their decision-making heuristics. This paper is original in its focus on Germany and its emphasis on investors’ personality characteristics. This study extends existing research by adding questions on investors’ personality traits, cognitive styles, and social perception, and allows a descriptive and comparative overview of investors’ (i) pre-investment activities, (ii) investment decision-making processes, and (iii) post-investment interactions. The study’s empirical findings suggest that investors’ cognitive styles have a strong impact on their overall evaluation of investment opportunities as well as on their offered valuations, with analytically oriented investors placing greater importance on business models and valuations. However, and contrary to expectations, personality traits measured with a modified version of the Big Five personality scale do not explain variance in investment behavior. This research highlights distinct cognitive patterns among investor types, particularly emphasizing business angels’ reliance on intuition, thus offering practical insights for entrepreneurs seeking tailored engagement strategies with different investor profiles.

L2 vocabulary learning of late bilinguals is characterized by the mediation of their L1, which may lead to differences in access efficiency and activation pathway among learners with different representational preferences. In the experiment, we used statistical methods to compare the importance of representational preferences with the well-investigated factors, i.e., L2 proficiency and cognitive style, on late bilinguals’ L2 lexical access. The results showed that representational preference was a more effective variable for subject classification. Furthermore, participants with different representational preferences were compared in response time to word translation judgment tasks. The results showed that participants with different representational preferences showed differences in lexical access efficiency, and those with imagistic preference also implied shifts in the access pathway between unfamiliar and familiar words.

ABSTRACT This study examined the relationship between paranormal beliefs and right-wing ideology in a German quota sample (N = 1,139). We also explored whether individual differences in intuitive versus analytical cognitive styles explain these relationships. Results indicated that higher scores on right-wing authoritarianism (RWA) and social dominance orientation (SDO), but not right-wing identity, were positively associated with paranormal beliefs. However, both right-wing ideology and intuitive versus analytical thinking independently contributed to paranormal belief endorsement. The results suggest that right-wing ideology plays an important role in understanding paranormal beliefs. While cognitive styles are also relevant, they do not explain the affinity of right-wing individuals for paranormal phenomena.

When weak imagery is worse than none: Core aphantasia and hypophantasia relate differently to mental health, mediated by subjective interoception.

This empirical study examines the relationship between individual psychological differences and English language learning outcomes among university students. A sample of 80 students (mean age = 19.8 years, SD = 1.2) from a large urban university participated in this research. The study utilized standardized instruments including the Eysenck Personality Questionnaire (EPQ) to assess temperament, the Riding Cognitive Style Analysis to measure cognitive styles (verbal-imagery and wholist-analytic dimensions), and Cambridge English Placement Test to evaluate language proficiency. Results indicate significant correlations between extraversion and speaking performance (r = 0.42, p < 0.01), between imagery cognitive style and vocabulary acquisition (r = 0.38, p < 0.01), and between analytic style and grammar comprehension (r = 0.44, p < 0.01). Multiple regression analysis revealed that cognitive style dimensions predicted 31% of variance in overall English proficiency (R² = 0.31, p < 0.001). Based on these findings, we propose a framework for personalized language instruction that considers learner temperament and cognitive preferences. The study demonstrates that individual differences significantly impact language learning processes and outcomes, suggesting the need for differentiated pedagogical approaches in EFL contexts.

Abstract Background Housing property is a major part of family assets, and its value changes affect financial conditions and mental health. In recent years, falling urban housing prices have become common, leaving many families facing asset shrinkage and financial pressure. Studies show that major property loss greatly increases depression risk, yet most research focuses on acute crises like unemployment or bankruptcy. Little work examines depressive emotions triggered by falling housing prices as a chronic stressor. From a psychological perspective, property loss influences self-worth, security, and expectations. Cognitive appraisal theory holds that subjective evaluation of stressors shapes emotional reactions. This study examines how falling housing prices affect depressive emotions among family financial managers and explores the roles of cognitive appraisal, coping styles, and social support. Methods The study uses a cross-sectional and longitudinal design. Participants are primary financial decision-makers from 800 families who purchased homes in a first-tier city from 2021 to 2023 and experienced at least a 15% price drop. Participants are 25–55 years old and are recruited through the housing transaction center and community committees. Baseline assessment uses a self-developed questionnaire that collects purchase time, price drop level, and loan pressure. The Beck Depression Inventory II (BDI-II) assesses depressive symptoms. A cognitive appraisal scale measures threat appraisal and controllability appraisal. A brief coping questionnaire measures positive and negative coping. A social support scale assesses objective support, subjective support, and support utilization. Confounders include income, marital status, and psychiatric history. Follow-up assessments occur at 6 and 12 months to track depressive symptom trajectories. Data analysis uses multiple regression, structural equation modeling, and mediation tests. Results Baseline results showed that 28.7% of financial managers experiencing falling housing prices had mild to moderate depressive symptoms (BDI-II ≥ 14), higher than the 12.3% in the general population (p<.001). Housing price decline correlated with depression scores (r = 0.42, p<.001). After controlling for age, gender, and income, housing price decline, mortgage-to-income ratio, and purchase time remained significant predictors (β = 0.35, 0.28, 0.19, p<.01). Mediation analysis showed that falling prices increased perceived financial threat (β = 0.51, p<.001), which raised depressive emotions. Positive coping reduced the impact (β = −0.23, p=.006), whereas negative coping intensified it (β = 0.31, p=.002). Longitudinal follow-up found that 56.3% of high-risk individuals developed clinically significant depression (BDI-II ≥ 20) at 12 months without intervention. Discussion The study confirmed that falling housing prices were a major risk factor for depressive emotions among family financial managers. Cognitive appraisal linked financial loss with emotional reactions. When individuals saw falling prices as an uncontrollable threat, they were more likely to feel helpless and depressed. These findings supported cognitive behavioral theory and pointed toward cognitive restructuring as a key intervention. Coping styles and social support moderated this process, with stronger psychological resources helping maintain better mental health under stress. The study offered a framework for identifying high-risk groups during housing price fluctuations and highlighted targets such as cognitive reframing, coping training, and social support enhancement. Future research should track long-term effects of housing price changes, design targeted interventions, examine demographic differences, and explore asymmetry between the impacts of rising versus falling prices.

ABSTRACT Immersive Virtual Reality (IVR) creates a highly immersive learning environment for learners. Exploring learning behavior patterns and effectiveness across various information processing modes in IVR experiments helps understand how IVR affects learning effectiveness. This paper focuses on VR experiments in the field of mechanical engineering from the perspective of cognitive style. By comparing the performance of learners with different cognitive styles in both immersive and non‐immersive VR experiments, covariance analysis, moderation effect analysis, and lagged sequence analysis are used to analyze learners' knowledge retention and skill transfer abilities, learning behavior patterns, and explore the moderating effect of cognitive style. The study found that: (1) Compared to non‐immersive virtual experiments, IVR experiments are more effective in enhancing the abilities of knowledge retention and skill transfer. (2) Cognitive styles moderate the impact of IVR experiments on learning effectiveness. (3) Field‐independent learners exhibited more operational behaviors, enhancing their visual experience and information processing in the IVR environment, which significantly improved their learning outcomes. In contrast, field‐dependent learners displayed more auxiliary behaviors, which affected their sense of presence, suppressed their positive emotions, and consequently inhibited the improvement of their learning effectiveness. These findings highlight the moderating effect of cognitive style on learning outcomes in IVR experiments, and provide insights for educators to design learner‐centered activities and establish personalized learning paths to meet diverse needs.

Background: Autistic adults experience disproportionately high rates of depression, yet Diagnostic and Statistical Manual of Mental Disorders , 5th ed., text rev. (DSM-5-TR ) diagnostic criterion descriptions may not fully reflect their lived experiences, impacting opportunities for identification and support. Autism-related differences, such as alexithymia, interoception, cognitive styles, and sensory processing, may influence how autistic adults perceive, experience, and describe depression. This study examines whether autistic adults’ descriptions of depressive symptoms align with, or diverge from, DSM-5-TR criteria descriptions, to inform more accurate diagnostic practices. Methods: We conducted an exploratory qualitative study with 109 autistic adults and 13 caregivers, who reported on the autistic adult they support. Participants described depressive symptoms relative to their typical autistic baseline via an online survey ( n = 112) or semi-structured interview ( n = 10). Using direct content analysis, we identified symptoms consistent with DSM-5-TR symptom descriptions, and through inductive analysis, we identified additional features outside DSM-5-TR criteria. Results: Participants described depressive symptoms that both aligned with and diverged from DSM-5-TR descriptors. Participants often expressed depressed mood as anger or irritability, or as emotional numbness. Anhedonia involved a loss of enjoyment in deep interests ( autistic anhedonia ), a source of emotional regulation. They described fatigue as a pervasive physical heaviness and reported depression attacks , characterized by sudden, overwhelming suicidal ideation. Participants described depression as influencing autistic characteristics, including variations in sensory sensitivities, stronger insistence on sameness and routine, changes in hyperfocus within deep interests, and self-injurious behavior. They also reported that emotional awareness and expression difficulties, alongside heightened sensitivity and dysregulation, intensify during depression. Conclusion: Autistic adults described depressive symptoms, which we coded against DSM-5-TR criteria, and while most symptom descriptions aligned with DSM-5-TR criteria, some differed in form or expression. For some, reports suggested a bidirectional relationship between autistic characteristics and depression. Further understanding of autistic adults’ depressive symptoms is critical to improving diagnostic accuracy and guiding the design of autism-informed assessment tools.

Image-generation tools have been widely adopted in product conceptual design, showing promise in supporting cognitive activities and fostering creativity. However, existing studies have primarily focused on the aggregate-level impact, overlooking individual differences in designers’ cognitive styles, making it difficult for AI to understand and adapt to the needs of different groups. To fill this gap, we aim to explore whether and how image-generation tools affect designers’ cognitive styles and to analyze the relationship between cognitive style and design performance. Through a within-subject experiment, we invited 28 designers to complete conceptual design tasks under SK-condition (traditional sketching) and AI-condition (image-generation tools). We found that AI-condition significantly influenced designers’ cognitive styles, with approximately 50% of designers exhibiting cognitive style shifts. Although changes in cognitive style did not have a significant impact on design performance, there was a significant association between cognitive style and design performance under AI-condition. Designers identified as Treasure Hunters performed best in novelty, variety, and fluency, while those exhibiting Focused Prober style, traditionally associated with experts, performed worse under AI-condition. These empirical findings provide valuable insights for customising tools and educational approaches that align with the needs of different cognitive styles.

This study explores the relationship between cognitive preferences and scientific achievement through both theoretical frameworks and empirical investigation. Drawing on models of cognitive styles such as analytic versus holistic processing, field dependence-independence, and divergent-convergent thinking the research examines how individual differences in preferred modes of information processing influence the pursuit and attainment of scientific knowledge. The theoretical analysis situates cognitive preferences within broader psychological and educational paradigms, highlighting their potential role in shaping creativity, problem-solving strategies, and disciplinary specialization. Empirical findings, based on surveys and performance measures of scientists and students across diverse fields, reveal significant correlations between specific cognitive styles and indicators of scientific productivity, including publication output, innovation, and recognition. The study concludes that cognitive preferences are not deterministic but act as enabling or constraining factors that interact with environmental, motivational, and social variables. Implications are discussed for science education, talent development, and fostering interdisciplinary collaboration. In light of these findings, it is recommended that educational systems cultivate awareness of diverse cognitive styles, or cognitive preferences, and integrate flexible pedagogical approaches that nurture both analytic and holistic thinking, encourage environments that balance individual strengths with collaborative opportunities, and design talent development programs that recognize cognitive diversity as a resource for innovation. Furthermore, fostering interdisciplinary platforms that bridge different cognitive orientations can enhance creativity and resilience in scientific practice, ensuring that cognitive preferences are leveraged as assets rather than limitations in the pursuit of knowledge.

ABSTRACT Growing concerns about student wellbeing in architectural design education have underscored the need to understand how cultural and pedagogical factors shape psychological stress during studio work. This study investigates stage-specific perceived stress (SSPS) across a ten-stage design process within a standardized Korea – China joint architecture program. Survey data were collected during the 2024–2025 academic year using an identical instrument: Fall 2024 in Korea (n = 180) and Summer 2025 in China (n = 212) validated SSPS survey. Quantitative analyses – including t-tests, ANOVA, two-way ANOVA, and multiple regression – revealed distinct stress patterns: Korean students reported elevated stress during concept development and schematic design, with a second peak at final presentation, whereas Chinese students peaked at the final review. A nationality-by-year interaction showed heightened precedent-work stress among Chinese second-year students – dissipating by the fourth year – while senior-year differences at the final review persisted. Interpreted through a tripartite framework of process segmentation, cultural mediation, and educational control – and informed by Hofstede’s cultural dimensions, Nisbett’s cognitive styles, and Self-Determination Theory – these findings position SSPS as a function of instructional ecology rather than static cultural traits. Practical recommendations include stage-targeted interventions such as divergence – convergence cycles, rubric-guided milestones, emotion-regulation routines, and mixed-nationality juries to foster culturally adaptive, resilient studio environments.

Understanding the psychological determinants of academic performance is essential for developing evidence-based educational strategies. Applying the Analytic Hierarchy Process (AHP), a well-established decision-analytic method, provides a systematic means to quantify the relative influence of multiple psychological factors on students' academic outcomes. Data were collected from 200 university students (150 undergraduates and 50 postgraduates) at Yan'an University, China, using a purposive-convenience sampling approach. Participants evaluated six key psychological variables: Motivation, Anxiety, Self-Efficacy, Emotional Well-Being, Cognitive Styles, and Self-Regulation-through structured pairwise comparisons following a preparatory orientation session. The AHP results identified Motivation (0.439) as the most dominant factor influencing academic performance, followed by Anxiety (0.218) and Self-Efficacy (0.148). Emotional Well-Being (0.097), Cognitive Styles (0.056), and Self-Regulation (0.042) demonstrated comparatively lower yet meaningful contributions. The model's Consistency Ratio (CR = 0.042) confirmed high reliability of participant judgments. Findings highlight the central role of motivational and affective dimensions in shaping academic success. Educational interventions that strengthen motivation, foster self-efficacy, and mitigate detrimental anxiety can enhance both performance and well-being, underscoring the importance of integrating psychological principles into instructional design and student support systems.

Digital systems are becoming increasingly feature-rich and interaction-intensive, while users vary widely in their goals and expertise, cognitive styles, accessibility requirements, and device contexts. This diversity makes one-size-fits-all interfaces inefficient and, at times, frustrating, often increasing error rates, cognitive load, and user dissatisfaction. Existing personalization approaches, such as themes, fixed preferences, and rule-based customizations, offer limited flexibility and fail to adapt to evolving user behavior and contextual changes. Although AI-driven adaptive interfaces have shown improvements, most approaches remain system-centric and insufficiently address human-centered considerations. This often results in disruptive interface changes, a perceived loss of control, and diminished user trust. This paper proposes a Human-Centered Deep Reinforcement Learning (HC-DRL) framework for generating personalized user interfaces, in which UI adaptation is modeled as a constrained sequential decision-making process. The framework combines continuous user modeling with a structured representation of the user interface based on a design system. A DRL agent predicts viable adaptation policies using a UX-sensitive reward function that explicitly maximizes task success and efficiency while accounting for user satisfaction, cognitive load, trust, perceived control, and disruption penalties. Safety guardrails are incorporated to enforce accessibility and usability constraints and to enable rollback to stable interface states when risks or performance degradation are detected. An end-to-end implementation and evaluation pipeline, including comparisons with static and heuristic baselines, ablation studies to quantify component contributions, and user studies, was employed to validate the proposed approach. The results demonstrate that HC-DRL provides a practical and robust foundation for adaptive interfaces that enhance functionality without compromising stability, accessibility, or user confidence.

ABSTRACT This systematic review examines the functional role of neurodivergence, specifically autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), in the context of violent extremism, radicalisation, and mass violence. While there is no evidence of a direct causal link in the general population, certain neurodivergent traits and experiences may contextualise vulnerability, resilience, and disengagement in unique ways within extremist populations. By synthesising fragmented knowledge across disciplines, this review contributes to a more nuanced understanding of neurodivergence in violent extremism contexts. This review identified 93 publications. Nine key themes emerged, including social and relationship difficulties, hyperfixation and restricted interests, cognitive styles, sensory issues, vivid ideation, emotional difficulties, and the presence of complex needs. These traits and experiences, especially when combined, may shape individual pathways to risk or resilience. These findings highlight considerations for practice and research with neurodivergent individuals within the extremism context. This review identifies significant gaps in the literature, particularly the scarcity of empirical studies and overreliance on open-source case reports. Key priorities for future research include conducting high-quality empirical studies, analysis of functional links based on detailed closed-source records, and the development of a more nuanced understanding of risk and protective factors in neurodivergent individuals involved in extremism.

Deep learning technologies have revolutionised pedagogical techniques in recent years by enabling individualised, adaptive learning environments in English for Specific Purposes (ESP) training. The efficacy of these AI-driven systems depends on how well they align with students' cognitive learning styles, including visual, introspective, and kinesthetic styles, which influence how they process and interact with information. This study examines the impact of cognitive learning styles on student performance and perceptions in deep learning-based ESP classes. Through stratified random sampling, 240 undergraduate students from Universitas Muhammadiyah Gresik participated in the study, which used a mixed-methods explanatory sequential design. Validated tools to evaluate cognitive styles and ESP performance were used to collect quantitative data, while semi-structured interviews with a purposive subsample provided qualitative data. Visual learners performed significantly better than their reflective and kinesthetic peers, as indicated by structural equation modeling (β = 0.42, p < 0.001). The results of a qualitative study showed that visual learners preferred graphical input, reflective learners needed depth and timing, and kinesthetic learners expressed disengagement from static interfaces. Emotional responses, including anxiety and a decline in self-efficacy, emerged as a recurrent pattern among non-visual learners. The study concludes that cognitive congruence has a critical role in determining affective participation and academic success in AI-mediated ESP situations. By emphasising the need for inclusive instructional design that considers a range of cognitive profiles, these discoveries contribute to the discussion of customised learning in digitally enhanced language training.

The impact of mental images on reasoning: A study on aphantasia.

Risk assessments are often mandated within the juvenile justice system (JJS). Yet, it is unclear whether these protocols reflect equitable clinical tools, and little is known about the community's perspectives on commonly assessed risk domains. In response, we introduced, and subsequently tested, a multifaceted definition for fairness in risk assessment. An embedded mixed-method study was conducted, such that Study 1 informed Study 2's methods, and the studies were subsequently integrated. In Study 1, caregivers (N = 22) and adolescents (N = 21; Mage = 14.28; 42.9% identified as Black, 42.6% White; 66.7% Male) involved with a JJS-diversion or probation program completed qualitative interviews on risk domains for offending behavior. Next, we examined the statistical fairness of salient risk domains from Study 1 in a sample of JJS-involved adolescents (N = 1,354; Mage = 16.04; 41.4% Black, 33.5% Hispanic, 20.2% White; 86.4% as male). An evidence-based medicine analytic approach, which was compared to artificial neural networks, tested subpopulation differences across performance indices. Overall, temperament, peer relations, cognitive styles, and school functioning emerged as salient risk domain themes across identities and informants. Subsequently, moral disengagement and delinquent peers emerged as equitable predictors of prospective violent and nonviolent rule-breaking behavior. A model comprised of these predictors was acceptable (i.e., areas under the curves ≥ 0.70; diagnostic likelihood ratios ≥ 2.0) and equitable. Artificial neural network models did not improve prediction. Risk assessments focused on moral disengagement and peer delinquency may lead to community-aligned and statistically fair assessment processes. These findings can lead to more equitable and engaging JJS risk-management approach. (PsycInfo Database Record (c) 2026 APA, all rights reserved).