Acta Psychologica最新文献

The mechanisms that make the unique sociality of humans possible have long been in the focus of cognitive science. In this paper, we review recent advancements in the study of three fundamental social cognitive functions (social categorization, theory of mind and social learning) in order to outline a theory that emphasizes the need to consider their interconnectedness. We highlight that while the literature on theory of mind mostly focuses on computations that rely on episodic information to infer the current mental state of a social partner, a large part of mentalization processes depends on a shortcut whereby the mentalizer attributes a certain body of knowledge to the partner by default. These default attributions may depend on salient characteristics of the target person, such as their social category membership. Thus, we propose that the ability to represent the social world along categories and the capacity to read others' minds are used in an integrated way to efficiently assess the epistemic states of fellow humans by creating a shared representational space. Moreover, we emphasize that the emergence of this shared representational space is both the result of and a prerequisite to efficiently learn about both the physical and the social environment.

Ethical consumer behaviour has been receiving much attention in recent times, due to heightened social issue on sustainability, corporate social responsibility and ethical consumption from the community. Integrated with the Value-Belief-Norm (VBN) Theory, this study investigates the influences of altruistic, biospheric, and hedonic values on ethical buying behaviour under the mediating mechanism of behavioural beliefs, personal norms, and subjective norms. Moreover, the present research also explores the moderating effects of perceived product quality and social stigma in the relationshipbetween subjective norms, personal norms and ethical buying behaviour. A cross-sectional survey design approach was employed where data were gathered from students studying in India. The findings indicate that altruistic, biospheric, and hedonic values have an indirect impact on ethical buying behaviour via related behavioural beliefs, personal norms, and subjective norms. The moderating roles of quality and stigma show some interesting counteracting influences. Product quality was found to enhance the subjective norms and ethical buying behaviour relationship while undermining the relationship between personal norms and ethical buying behaviour. By contrast, stigma reduced the influence of subjective norms but enhanced the effect of personal norms on ethical buying behaviour. The work adds to the ethical consumption literature by combining theoretical perspectives and emphasising the complexity of how these moderating variables function in ethical decision-making. The findings provide both theoretical implications from the perspective of the psychological mechanism of ethical behaviourand managerial implications for firms looking to motivate ethical consumption.

Purpose: The aim of this study was to examine the neural and behavioral correlates of visual statistical learning in adults with and without developmental dyslexia (DD), using a predictor-target paradigm. Reaction times (RTs) and event-related potentials (ERPs) were used to assess participants' sensitivity to predictor-target contingencies that varied throughout the task.

Methods: English-speaking neurotypical adults (N = 36) and those with DD (N = 16) were engaged in a visual statistical learning task while RTs and ERPs were measured. In Phase 1, participants' sensitivity to the contingencies was assessed, in which one stimulus was highly predictive of the target (90 % transitional probability) and another was less predictive of the target (10 % transitional probability). In Phase 2, these contingencies switched to equal probability (50/50), to assess participants' ability to adapt to the change in stimulus predictability.

Results: In Phase 1, both groups showed behavioral evidence of learning the contingencies but only the neurotypical group showed ERP effects indicative of learning. In Phase 2, both groups showed similar ERPs and RTs, indicating that sensitivity to the contingencies from Phase 1 persisted into Phase 2 across groups despite the contingencies being changed.

Conclusions: Similar to previous work on children with DD (Singh, Walk, & Conway, 2018), adults with DD showed atypical neural responses but intact motor learning of the visual statistical patterns. These findings suggest that adults with DD may rely on different prediction-based learning mechanisms to acquire visual statistical contingencies, consistent with theories positing differences in visual-attention and basal ganglia-based learning in DD.

The Body Image Concern Inventory (BICI) is a brief self-report measure of dysmorphic concern, which is characterized by intense preoccupation with perceived flaw(s) in one's appearance. Previous investigations of the factor structure of the BICI have demonstrated mixed findings, and further investigation within Western populations is particularly needed among men. While sexual minority men are known to exhibit heightened body image disturbance compared to their heterosexual counterparts, the structural validity of the BICI has not been evaluated within this population. As such, we investigated the factor structure of the BICI within a sample of sexual minority men in an iterative fashion according to models tested or called for in prior research. First, we examined a one-, two-, and three-factor first order structure of the BICI using confirmatory factor analysis. None of these structures provided an excellent fit to the current sample across multiple indices (e.g., comparative fit index [CFI] < 0.90, root mean square error of approximation [RMSEA] > 0.08). Next, we examined higher-order models of two- and three-factor structures of the BICI, which also did not adequately fit the data (e.g., CFI < 0.90, RMSEA >0.08). Finally, we employed exploratory factor analysis, which revealed a three-factor model that slightly differed from previously supported three-factor models. Factor loadings suggested subpar item functioning of four of nineteen items on the BICI (i.e., items 3, 4, 12, and 13), implications of which are discussed.

The growing dependence on mobile phones for communication has raised concerns regarding the neurological impact of radio-frequency electromagnetic fields, especially in audio and video calls. This research investigates the impact of WhatsApp audio and video calls on cognitive load and mental fatigue using non-invasive electroencephalography (EEG) signals. EEG signals were recorded from 28 healthy participants during baseline, 3-minute, and 4-minute call sessions, with participants equally distributed between audio and video call groups. The signals were preprocessed using bandpass filtering, continuous wavelet transform, and independent component analysis to isolate theta and alpha frequency bands. Cognitive load was assessed using the theta-alpha ratio (TAR), and mental fatigue was measured using Mahalanobis distance-based analysis of theta and alpha rhythms. The findings reveal that video calls impose significantly more cognitive load (e.g., TAR mean: 0.78 (SD: 0.36) for 3-minute video call and mean: 0.76 (SD: 0.37) for 3-minute audio call, p<0.05) and mental fatigue (e.g., 42.11 microvolts for 4-minute video call and 38.84 microvolts for 4-minute audio call) than audio calls, and both effects become stronger for prolonged durations. Machine learning (ML) classification also demonstrated high separability, with receiver operating characteristic analysis exhibiting area under the curve values above 0.90 for distinguishing call conditions. These results demonstrate that video communication places higher cognitive and fatigue demands as compared to audio communication. By integrating EEG indices, self-report measures, and ML classification, this study exhibits convergent evidence for the cognitive impact of mobile phone calls and illustrates the necessity to consider communication modality when assessing workload and fatigue in real-world contexts.

This study examines how parasocial interaction with social media influencers triggers authentic cognitive and affective processing in the absence of direct experience. Drawing on attachment theory, mental-imagery research, and flow theory, we propose and empirically test a dual-pathway model of anticipatory motivation in the context of prospective nature-based experiences. Deep involvement with influencer-generated content about natural environments was hypothesised to elicit (a) vicarious flow states characterized by focused attentional absorption, temporal dissociation, and intrinsic enjoyment, and (b) favorable cognitive-affective mental representations of a target natural setting. Using partial least squares structural equation modeling (PLS-SEM) on data from 293 participants, results confirmed that influencer involvement exerts a significant direct effect on behavioral intention and strong indirect effects via flow experience (partial mediation) and cognitive-affective destination image (partial mediation). Notably, destination image significantly enhanced flow experience, demonstrating that mental imagery acts as an affective priming mechanism for immersive processing. These findings provide the first empirical evidence that mediated symbolic stimuli can induce genuine pre-experiential emotional attachment and immersive cognitive absorption, thereby extending attachment and flow theories to purely digital, anticipatory contexts. The results highlight the interplay of affective bonding, mental simulation, and attentional immersion as core psychological mechanisms underlying anticipatory motivation.

This study examined the interpretation of p-values and confidence intervals (CIs) among Saudi linguists and social scientists. Using both performance-based tasks and self-reports with statistical concepts, the study revealed widespread conceptual misunderstandings regarding the meaning of p-value and the proper interpretation of CIs. Among the most challenging p-value statements were those reflecting inverse probability fallacies, with 70 % of participants incorrectly interpreting p-values as proof of hypothesis truth or as probabilities of hypothesis accuracy. CIs were even more problematic, with 78 % of participants endorsing a Bayesian misinterpretation that assigned a 95 % probability to the parameter falling within a single observed interval. The findings highlight the prevalence of persistent statistical misconceptions among academics, despite formal training and research experience. These results highlight the need for statistical education that emphasizes interpretation and conceptual clarity over calculation. It also shows the existing dissociation between statistical practice and statistical understanding in these fields. Consequently, improving statistical literacy in this population is essential for enhancing research quality, peer review, and evidence-based scholarly communication.

Insufficient Effort Responding (IER) undermines survey validity but its detection remains methodologically challenging due to its multidimensional nature. Traditional univariate IER detecting methods often overlook interactions among response behaviors. We integrated seven complementary indicators-response time, maximum longstring, response entropy, mahalanobis distance, psychometric synonym-antonym and even-odd consistency-and used latent profile analysis to classify 126 online respondents. Four profiles emerged: (1) Attentive/Engaged, reflecting conscientious participation; (2) Impulsive/Erratic, characterized by random and inconsistent answers; (3) Repetitive/Monotonic, marked by mechanical repetition; and (4) Distracted/Disengaged, exhibiting extremely prolonged response times. We then compared the four groups on executive-function tasks (Numerical Stroop, Tower of London, Card Sorting Task). Exploratory analyses revealed distinct patterns: the Repetitive/Monotonic group showed lower performance on tasks requiring cognitive flexibility and attentional control, suggesting their IER might be associated with cognitive limitations. In contrast, both Distracted/Disengaged and Impulsive/Erratic groups showed adequate or even superior cognitive performance despite exhibiting IER, suggesting motivational disengagement rather than cognitive deficits. These findings suggest IER arises from dual mechanisms-cognitive constraints versus motivational factors-and underscore the importance of multidimensional approaches in IER detection. Understanding these distinct pathways may inform differentiated intervention strategies to enhance survey design and data quality.

Understanding the relationship between psychological states and financial decision-making is crucial for a comprehensive view of investor behavior. This study examines how mood, depression, and trading performance influence forecast bias in stock market predictions. Drawing on fixed-effects regression models applied to panel data collected over three waves spanning nine months, the findings reveal that mood fluctuations and depressive symptoms systematically affect investors' return expectations. Specifically, the results show that investors experiencing a negative mood exhibit less optimistic bias in their forecasts of market returns over both the short term and the long term. Depressive symptoms, however, increase positive forecast bias for the long-term period, possibly reflecting compensatory optimism or altered risk perception during depressive episodes. Additionally, investors with stronger trading performance tend to express more optimistic forecasts, indicating a self-reinforcing bias linked to prior success. These results underscore the impact of emotional and cognitive factors, such as framing effects, overconfidence, and self-serving biases, on financial decision-making.