Acta Psychologica最新文献

Background and aims: Physical activity is part of the treatment for several mental diseases, including anxiety disorders. However, it is not known whether individuals with higher levels of physical fitness experience lower levels of anxiety or stressful emotions. It is also unknown whether those individuals can better control their emotions in stressful situations. The aim of this study was to examine whether high cardiorespiratory fitness is associated with lower trait levels of anger and anxiety, and whether individuals with different fitness levels show distinct emotional responses to unpleasant visual stimuli.

Methods: On separate days, 40 healthy young participants completed two sessions. Trait anxiety and anger were assessed during the first session to characterize the participants. The participants self-reported their exercise practice to predict cardiorespiratory fitness (CRF). The participants' anger/anxiety levels were assessed before and after exposure to a 69-picture set of unpleasant or neutral pictures for 30 min.

Results: Multiple regression analysis showed that maximal oxygen uptake (V̇O₂max) (β = -0.241, t = -3.173, 95% confidence interval CI [-0.395; -0.087], p = 0.003) and anger-out (β = 0.333, t = 2.124, 95%CI [0.015; 0.651], p = 0.040) were predictors of changes in anger-state in the unpleasant picture presentation. Individuals with a V̇O₂max below average (BA) had a 775% greater risk for changing their classification from intermediate to high anxiety levels compared to above average (AA) individuals (OR = 8.754, 95%CI [1.202; 63.759]). V̇O₂max was a predictor of trait anxiety (β = -0.456, t = -3.485, 95%CI [-0.721; -0.191]; p = 0.001).

Conclusion: CRF is associated with lower levels of trait anxiety and higher resilience in situations of emotional stress.

Orthorexia nervosa refers to a pathological obsession with healthy eating, yet its classification, causes, and clinical relevance remain contested. This qualitative meta-synthesis explores the lived experiences of individuals with orthorexic behaviors to better understand the psychological, social, and cultural dynamics that sustain this condition. Drawing on thematic synthesis of 16 peer-reviewed, qualitative studies, three major themes emerged: (1) Origins and Motivations for Orthorexic Behavior, highlighting the role of perfectionism, identity, and sociocultural ideals; (2) The Orthorexic Experience and Its Consequences, illustrating the emotional, physical, and social costs of rigid dietary practices; and (3) Individual's Perspectives on Orthorexia as a Disorder, revealing ambivalence toward diagnosis, conceptual confusion, and overlaps with other eating disorders. Findings suggest that orthorexia serves both as a personal coping strategy and a moralized performance of health shaped by contemporary wellness culture. The synthesis reveals how orthorexic behaviors are often normalized, socially reinforced, and deeply tied to individual identity-rendering recognition and recovery particularly challenging. Rather than advocating for diagnostic categorization, this study highlights how orthorexic practices are embedded in broader cultural narratives around health, control, and morality.

In an era where Financial technology (FinTech) firms are increasingly dependent on blockchain technology, FinTech employees are confronted with information overload that can affect organizational performance. Grounded in socio-technical systems theory (STS), this study explores the role of blockchain financial communication adequacy in driving employee-perceived economic outcomes with occupational well-being and employee's workplace resilience as two mediating variables while employee perceived chatbot assistance in blockchain finance plays the moderating role. Data from 436 employees from a total of 3 FinTech firms with a blockchain native to Saudi Arabia were analyzed using SmartPLS. Results show that communication adequacy directly increases perceived economic outcomes and works through improved well-being and resilience. Moreover, every mediation channel is further enhanced with AI-chatbot support. These insights shed light on the synergies between humans and machines that are required to bring ledger transparency to financial success and provide a blueprint for practitioners looking to optimize socio-technical interfaces in digital finance.

Artificial intelligence is reshaping higher education, yet little is known about how students' AI related beliefs connect to their career expectations in emerging economy systems. This study examines links between AI literacy, perceived usefulness of AI, attitudes toward AI, AI self-efficacy, and career expectations in AI-driven industries among Vietnamese undergraduates. A cross-sectional survey of 850 students across five campuses was analysed using Partial Least Squares Structural Equation Modelling within an integrated Technology Acceptance Model and Social Cognitive Career Theory framework. AI literacy significantly influenced perceived usefulness, attitudes, and AI self-efficacy, while perceived usefulness was the strongest impact on both attitudes and self-efficacy. Together, literacy, usefulness, and attitudes explained 47.1% of the variance in AI self-efficacy, which in turn was the strongest effect on career expectations in AI-driven industries, accounting for 37.4% of their variance. These findings show that students' confidence in using AI is a key psychological mechanism linking AI literacy and perceived usefulness to career expectations for working in AI-intensive environments. The study provides large-scale evidence from a non-Western context and suggests that higher education institutions should embed structured AI literacy pathways, authentic AI-supported learning tasks, and explicit attention to responsible AI practices in order to strengthen both academic confidence and students' career expectations in AI-driven industries.

This paper examines how economic valuation - an economic process - is influenced by individual differences in goals - a psychological process. Specifically, we demonstrate how to estimate economic value in the context of health behaviors by incorporating psychological variations in goal priorities. The study is motivated by the need to better understand how healthcare interventions shape individual behaviors, particularly in the management of chronic conditions such as Cystic Fibrosis (CF). The primary contribution of this paper is conceptual, introducing a model that integrates economic and psychological processes. Empirical findings, while specific to the context studied, serve as a secondary contribution that supports and extends this conceptual framework. Using a stated-choice experiment and a best-worst experiment embedded within a survey of adults with CF, we test these interrelationships through a flexible factor-analytic choice model. The results indicate that a model linking economic and psychological processes provides a better fit than one that treats these processes independently. These findings reinforce the premise that the most accurate estimates of economic value emerge when psychological differences among individuals are appropriately represented. This approach offers a deeper understanding of how healthcare users navigate competing goals and behaviors, providing valuable insights for designing more effective interventions. Furthermore, it contributes to the broader theoretical discourse on goal setting and decision-making in healthcare contexts.

As artificial intelligence (AI) becomes embedded in higher education, it is significant to test whether students form parasocial, friendship-like relationships with AI and how such ties shape emotional engagement and learning motivation. However, few studies have compared the emotional and motivational outcomes of student-AI versus human-peer friendships in educational contexts via experimental methods. Within the context of Chinese college students' spoken English learning, this study, drawing on self-determination theory, conducted a five-week controlled experiment in which participants were randomly assigned to either an AI-peer group (n = 44) or a human-peer group (n = 44). Data were collected through a three-wave questionnaire, yielding 238 observations. Group-wise Multilevel Modeling (MLM) revealed that friendship significantly affects enjoyment and shame in both groups. Enjoyment consistently mediated the effect of friendship on Willingness to Communicate (WTC) in English in both groups (indirect effect: AI-peer = 0.088; human-peer = 0.154) with no significant difference between groups (Δ = 0.066, 95% CI [-0.047, 0.180]). Contrary to expectations, shame did not mediate the relationship between friendship and WTC in both groups. Additionally, heterogeneity analyses revealed that the effects of gender were not significant in the AI-peer group, indicating that human-AI friendships were less sensitive to gender differences. However, AI-peer friendship significantly reduced shame among students from rural backgrounds, highlighting AI's potential to promote emotional equity in spoken English learning. These findings offer new empirical insights into affective AI design, suggesting that AI can serve as a meaningful companion that supports students' WTC.

Multiple representation theories of lexical semantics assume meaning is retrieved through simulation. However, simulation mechanisms are not well understood. Mental state inferencing - the capacity to understand mental states - has been proposed as a simulation mechanism, particularly for cognitive concepts (concepts referring to cognitive events, states, and products) (Kiefer et al., 2022). In a pre-registered study, we tested this proposal using an individual differences approach. Adult participants (n = 297) completed three measures of mentalizing skills. They also completed two syntactic classification tasks (is this word a verb; is this word a noun?) to measure semantic processing and test for the cognition effect (more cognitive words processed more efficiently than less cognitive words). We replicated the cognition effect, with high-cognition words eliciting faster and more accurate responses than low-cognition words. Additionally, we found significant interactions between mentalizing and the cognition effect: participants with stronger mentalizing skills showed larger cognition effects. This relationship suggests that mentalizing may serve as a simulation mechanism when accessing word meaning.

This study investigates the psychological mechanisms driving consumer responsibility for sustainable consumption and its influence on sustainable purchasing decisions. Building on the premise that consumer responsibility predicts sustainable consumption behaviors, we propose a framework that examines how emotional intelligence and flourishing affect willingness to pay more for ecological products, with the mediating roles of personal norms, frugality, and pro-ecological behavior. Using structural equation modeling (PLS-SEM), on data from 430 surveyed participants, the findings reveal that emotional intelligence positively influences flourishing, while personal norms and frugality mediate the relationship between flourishing and consumer responsibility for sustainable consumption. Additionally, pro-ecological behavior mediates the effect of consumer responsibility on willingness to pay more for ecological products. By uncovering the emotional and normative antecedents of sustainable consumption, this study refines existing theories and provides actionable insights for policies, educational programs, and marketing strategies that promote sustainability and address environmental challenges, offering valuable contributions towards a more sustainable future.

This research investigated whether students' AI-supported higher-order thinking (critical thinking, creativity, metacognition, synthesis) relates to EFL academic writing quality (AWQ) and through what mechanisms. Using blind-rated essays and survey measures from 492 undergraduates at urban Chinese universities, we estimated a comparative structural model with writing self-efficacy (L2WSE) as a mediator and trust in AI feedback as a moderator. The model explained 58% of the variance in AWQ. All four predictors showed statistically significant but modest direct effects (e.g., β_metacognition≈0.22; β_creativity≈0.12), and L2WSE was positively associated with AWQ. Indirect effects via L2WSE were small yet significant, consistent with a motivation-linked pathway. Interaction terms for trust were positive but small (β ≈ 0.06-0.09). Robustness checks compared a second-order specification of AI-supported higher-order thinking against the four first-order predictors and examined possible nonlinearity in trust. Findings should be interpreted cautiously: the design is cross-sectional, effects are modest, and most predictors are self-reported. Even so, the results suggest that when AI tools are used to support metacognitive planning/monitoring, critical evaluation, synthesis, and creative exploration, students' confidence and writing performance may improve. We discuss implications for instruction that emphasize metacognitive routines and calibrated trust in AI feedback, while outlining directions for longitudinal and experimental research.