Social Sciences

Abstract: During dyadic interaction, another person’s unfolding action may proceed successfully as expected, succeed after correction, or ultimately fail, with different implications for how the observer should respond. It remains unclear whether the brain differentiates these action courses while the interaction is still unfolding and whether extensive dyadic motor experience sharpens this differentiation. Here, we recorded EEG from expert table tennis players and novices while they observed three-stroke interactions containing uninterrupted success, corrected success, or unsuccessful action. Experts showed a stronger ERP distinction between corrected success and unsuccessful action than novices. Time-frequency and multivariate analyses further revealed temporally evolving and decodable information about action course, particularly in experts, although expertise-related enhancement was not consistent across measures. Experts were more accurate overall in predicting landing location, but this behavioral advantage did not vary across action courses. These findings show that unfolding action courses can be neurally differentiated during dyadic observation and that extensive motor experience selectively sharpens some of these distinctions. More broadly, neural monitoring during dyadic interaction appears sensitive to how another person’s action develops and to its relevance for how the interaction is likely to continue.

Abstract: Large language models (LLMs) are increasingly used for qualitative coding, yet it remains unclear how closely their coding outputs align with frameworks developed by human experts. Existing studies tend to report overall similarity but rarely distinguish thematic granularity from dimension-level semantic alignment or systematically identify which constructs are stably recovered. This study therefore compares human expert coding with three zero-shot LLM-generated qualitative analyses using a structured framework-level mapping method. The method maps human-coded dimensions to LLM-generated qualitative frameworks and classifies each relationship as direct, partial or merged, or not recovered. To quantify alignment, three evaluation metrics are used: Direct Coverage (DC), which measures strict boundary recovery; Broad Coverage (BC), which includes partial or merged mappings; and Frequency-Weighted Mapping (FWM), which weights each dimension by its human coding frequency to reflect construct prominence. The method is demonstrated using a human-derived emotional-intelligence framework developed from 34 Chinese behavioral-event interviews and three zero-shot frameworks generated from the same corpus by DeepSeek V4.0 PRO, Qwen 3.6, and GPT 5.6 under an identical prompt. Two senior experts jointly evaluated 36 dimension-level relationships. The three LLM outputs achieved direct coverage ranging from 75.0% to 91.7%, broad coverage from 83.3% to 100.0%, and frequency-weighted mapping from 80.3% to 94.9%. Eight of the 12 human-derived dimensions were directly recovered across all three LLM frameworks, whereas guidance and motivation, big-picture awareness, care and support orientation, and teamwork showed variable alignment. The framework-level comparison distinguishes thematic organization from construct-boundary recovery and supports the use of LLMs as complementary analytical assistants while retaining human responsibility for contextual interpretation and construct definition.

Abstract: Understanding how visual exploration evolves over time during complex decision making remains challenging, as the dynamics of underlying cognitive processes are difficult to observe. Hidden Markov models are widely used to model eye movement sequences, but their assumption of stationary transition probabilities limits their ability to identify successive phases. This study investigated whether Switching Hidden Markov Models (SHMMs), trained only on fixation positions, could segment visual exploration during an obstacle-avoidance task requiring a left/right decision. Individual SHMMs with two or three high-level (HI) states were trained without using reaction times. HI states were characterized through transition-matrix variability, saliency maps, cumulative dwell times in regions of interest, and their association with reaction times. Both models produced temporal segmentations consistent with decision making. Early HI states showed greater interindividual variability and exploration focused on obstacles, whereas later states were more homogeneous and reflected decision confirmation followed by reorientation toward the center of the screen. The transition to the final HI state of the three-state model best predicted reaction time. Furthermore, cumulative log-likelihood significantly improved this prediction, indicating that SHMM confidence provided complementary behavioral information. These findings show that SHMMs trained only on fixation positions can identify phases consistent with the organization of the decision-making process, including early evidence accumulation and late decision confirmation.

Abstract: People often recognize spoken words more accurately and more quickly than written words. From the perspective of active inference under the free-energy principle, the present study develops a formal account of this speaking-over-writing advantage in production-modality retrieval. We modeled retrieval under spoken and written production-modality states, together with the selection of an early or late response policy according to its expected sensory consequences. University students spoke or wrote words presented on a computer screen and later judged the modality in which each word had been produced. Responses were followed by explicit visual feedback indicating whether each judgment was correct or incorrect, thereby providing an observable sensory consequence of the recognition response. Response times were implemented as early and late policies within a partially observable Markov decision process (POMDP). The model included modality-specific likelihood parameters linking response timing to feedback outcomes. Spoken words were identified more quickly and accurately than written words, and early responding was more strongly associated with correct feedback under the speaking state than under the writing state. Bayesian model selection favored the POMDP over a simpler linear model estimated using Variational Laplace, indicating that the POMDP provided a better balance of fit and complexity. These findings show how production modality, response timing, and expected sensory consequences can be represented within a common generative framework. The framework provides a basis for future extensions that model uncertainty about production modality, preserve continuous response timing, incorporate learning across trials, and compare active-inference accounts with alternative models of recognition memory.

Abstract: Physical exercise improves working memory (WM) across the lifespan, yet the neural mechanisms underlying this benefit remain incompletely understood, and it is unclear whether intervention-induced neuroplastic changes and long-term exercise-related differences converge on common neural circuits. We systematically searched PubMed, Web of Science, PsycINFO, and CNKI through June 2026 and included 11 task-based fMRI studies (6 longitudinal interventions and 5 cross-sectional comparisons; 403 par-ticipants) reporting whole-brain activation coordinates. Activation likelihood estima-tion (ALE) meta-analyses were performed separately for activation increases and de-creases within each design. Longitudinal studies revealed exercise-induced activation increases in the bilateral cerebellum (posterior lobe, cerebellar tonsil) and decreases in the right thalamus. Cross-sectional studies revealed greater activation in the left middle temporal gyrus (BA 21) and reduced activation in the right cingulate gyrus (BA 24) and left caudate body among long-term exercisers relative to controls. Critically, the two designs yielded spatially non-overlapping patterns. These findings support a du-al-mechanism model in which exercise strengthens task-positive network engagement while optimizing the suppression of task-irrelevant processing, and they suggest that exercise shapes working memory circuitry across distinct, timescale-dependent neural circuits.

Abstract: The hypothesis defended here is as follows. Before the emergence of predication, language consisted only of pre-words used exclusively to request something or call someone over, with only one pre-word occurring in each message and always with the same intonation. How did these meanings become detached from their invariant function and intonation and thereby turn into genuine words? The most plausible possibility is that a recipient was puzzled upon hearing a request for something that was no longer available or a call addressed to someone who was not in the area. The recipient would then repeat the pre-word, but without its conative function and intonation, using it instead as a thema, and would add another former pre-word that now functioned as a rhema or predicate. The transformation this entailed soon required a further innovation: vocal signifiers had to split into two mutually detachable planes, the articulatory-phonetic and the intonational. In this way, each signifier could be perceived as exactly the same both when it constituted a message used to call someone over or to ask for something and when it was integrated with one or more other signifiers into a single intonational pattern.

Abstract: Teacher creativity plays a central role in fostering creative learning environments. However, most evaluations of creativity training focus on performance outcomes and self-report measures rather than changes in underlying cognitive organisation. We investigated whether an eight-month certified teacher-training course based on the Scientific Creativity in Practice (SCIP) framework was associated with changes in Austrian secondary teachers’ creative performance, semantic organisation, and professional mindsets. Seventeen teachers completed pre- and post-training assessments, including the Alternative Uses Task, Verbal Fluency, Word-Sentence-Construction, Behavioural Forma Mentis associations with valence ratings, and written reflections. We assessed originality in the AUT with two independent AI-based systems. Semantic and affective changes were modelled through cognitive networks, mindset streams, and measures of emotional framing. At POST, teachers had higher AUT originality scores and generated largely different concepts than at PRE. They produced broader conceptual repertoires and larger semantic networks, although other structural changes differed across tasks. Affective changes were more modest. Positive valence predominated at both measurement points and became slightly more common, while negative valence declined. Separate PRE and POST mindset streams showed substantial pathway turnover but remained predominantly positive. These findings suggest that creativity training may support changes in how teachers organise and connect professional knowledge, and does not only impact how creatively they perform. Furthermore, our findings show that cognitive network analyses can complement traditional creativity assessments by revealing changes in semantic organisation and professional mindsets that remain invisible in performance scores alone.

Abstract: The “creative” outputs of artificial intelligence systems—from GPT composing poetry to AlphaFold predicting protein structures—raise a philosophical question: where does creativity come from? Is it simple reproduction of training data, or a chance product of stochastic algorithms? This paper proposes “The Creativity Conjecture”: the creative output of an AI system is neither pure reproduction of training data nor pure random generation, but an emergent phenomenon arising from “experience training” and “random perturbation” under a “multi-level data fusion” mechanism. The conjecture comprises three conditions: (1) empirical density—the system has internalized sufficient pattern structure from training data; (2) random perturbation—the system introduces randomness of appropriate intensity during generation; (3) fusion depth—the system possesses the capacity for cross-level, cross-modal data fusion. All three are necessary: randomness without experience is noise; experience without randomness is copying; experience plus randomness without fusion is collage. Only when all three fuse does “structured surprise”—i.e., creativity—emerge. This paper argues for the conjecture on three levels: structural analysis (the mechanism of each element), fusion mechanism (how the three couple to produce creative emergence), and experimental verifi- cation (the effects of randomness intensity and fusion depth on creative output). We further propose the concept of “weak creativity” to delineate the epistemological status of machine creativity—it transcends random generation and data reproduction, yet does not reach the level of human phenomenal creativity. This framework provides a structural foundation for understanding AI creativity and offers a new path for the philosophy of creativity, moving from “genius theory” to “structural theory.”

Abstract: This integrative literature review examines how generative artificial intelligence is shaping human thinking, cognitive processes and writing practices, with a sustained focus on education across schools, universities, adult learning and professional settings. It considers how AI-supported composing alters the ways writers plan, generate, organise, revise and evaluate text, and how these changes reshape cognition, agency, authorship and learning. Rather than treating generative AI simply as a tool for productivity, the review approaches it as a relational and quasi-agentic technology that participates in meaning-making, decision-making and textual production. Research is synthesised across education, cognitive psychology, writing studies, digital literacy and human-computer interaction. Albert Bandura's social cognitive theory provides the analytical lens, particularly the concepts of triadic reciprocal causation, self-efficacy, observational learning, self-regulation and human agency. The review asks how generative AI mediates the relationship between person, behaviour and environment, and how it reshapes both confidence and dependence in writing. The discussion develops a critical account that recognises clear benefits while warning against cognitive offloading, uncritical trust, homogenised expression and weakened metacognitive control. The article concludes by proposing guidelines for educators across sectors to support critical AI awareness, reflective writing practice and agentic human-AI engagement.

Abstract: Skilled readers are thought to rely on bottom-up processing to activate word meanings in long-term memory from their form (i.e., spelling or sound) in a process described as ‘context-free’ lexical processing. However other research suggests skilled readers recognize words faster when they appear in predictable and/or plausible contexts, despite efficient bottom-up processing [1]. Research on individual differences in skilled comprehension suggests skilled readers only use context to support word recognition when bottom-up processing is slow [2]. Therefore, highly skilled readers may not rely on context, but less-skilled readers may continue to rely on context during word recognition due to poor bottom-up processing. The current study investigated whether individual differences in lexical quality would influence contextual processing during word recognition. Participants eye movements were tracked as they read sentences containing either a high frequency (e.g., baby) or low frequency (e.g., elbow) target word preceded by either a related prime 20 word (mother – baby) or an unrelated control word (teeth – elbow). Results show relatedness had a significant effect on readers across the range of lexical knowledge, however the specific effect depended on both vocabulary and target word frequency. The results suggest that context supports word recognition and comprehension when readers have adequate vocabulary.

Abstract: This literature review synthesizes the growing body of scholarship on cognitive hacking, a form of cyberattack that targets human perception and decision-making rather than technical infrastructure. Cognitive hacking sits squarely within Libicki’s framework of semantic attack, and countermeasures against such attacks are expected to constitute an important area of research in the science of intelligence and security informatics (Thompson, 2004). Drawing on foundational works originating from the Semantic Hacking Project, contemporary studies in behavioral cybersecurity, and recent empirical scholarship on artificial intelligence (AI)–driven disinformation, this review identifies four overarching themes: the definition and taxonomy of cognitive attacks; the psychological mechanisms underlying cognitive exploitation; the technological tools that amplify such attacks; and the countermeasures available to researchers and practitioners. Recent experimental evidence indicates that generative AI systems now produce disinformation that is more compelling than human-written content (Spitale et al., 2023) and can out-persuade human interlocutors when supplied with minimal personal information about their targets (Salvi et al., 2025). With generative AI enabling faster, cheaper, and more convincing tailored disinformation, scholars and policymakers are urgently seeking coordinated ways to regulate and mitigate the impact of deepfakes (Romanishyn et al., 2025). At the same time, meta-analytic and experimental work on psychological inoculation demonstrates that resilience to manipulation can be cultivated at scale (Roozenbeek et al., 2022; van der Linden, 2022). The review identifies significant research gaps, particularly at the intersection of generative AI and cognitive resilience, and concludes that a multidisciplinary approach integrating cognitive psychology, security informatics, and public policy is essential for developing robust defenses.

Abstract: Two people can view the same information but can take different actions. Most importantly, while thinking about moral, political issues, such as abortion, depending on the variation in the psychological systems, a perception of threat can arise. To exploit this aspect of the psychological systems, state and non-state actors make use of propaganda techniques to influence any target audience. Although the spread of disinformation is extensively studied in the information eco-system, an understanding of the effects of propaganda that result from interactions between the schemata of the individuals and schemata of the perceptual stimuli is not accounted for in systemic analysis of the disinformation threat vector. Moreover, while aiming to undermine the norms, values, and institutions in free societies, propaganda does not target the material objects or networks; it targets the psychological systems. Therefore, to improve the systemic understanding of disinformation propaganda, this research scrutinized the possibility of using the moral foundation theory (MFT) to include the psychological systems in a target audience analysis. The moral sentiments of an information operation were analyzed and the MFT dictionary was used to determine the moral scheme of the information streams. The words and hashtags were clustered by using the MFT dictionary; then, the moral cluster and word frequency analyses in the context of disinformation propaganda were conducted. The moral cluster frequency approach can augment the target audience analysis; it can ameliorate the counter information operation endeavors and threat vector analyses. To further study this, a quantum open system model of interaction between an information stream and the target audience cognitive system was built. The effects of non-selective measurement were studied via modeling and simulating a quantum open system based cognitive model.

Abstract: The article examines the transition from traditional to digital experiences by analyzing the relationships between attitudes toward digital and non-digital environments in four domains: navigation, work, public transportation, and shopping. In four studies, participants’ attitudes were examined toward a parallel digital experience (a navigation app, remote work, the use of digital means in public transportation, and online shopping) compared to the traditional experience. In three contexts (navigation, remote work, and online shopping), a negative correlation between attitudes was found, indicating that the technology is perceived as a substitute for the traditional environment. In contrast, in the context of public transportation, a positive relationship was found, as digital means are perceived as a complementary component to the physical experience. The findings are interpreted using the exemplar approach to memory, according to which digital and traditional experiences are stored in separate or overlapping sets of exemplars. The article proposes an extension to classical technology acceptance models and highlights practical implications for designing implementation processes that emphasize experiential continuity between the traditional and digital environments.

Abstract: Ultrasound mid-air haptics (UMAH) can create touch sensations without physical contact, but it remains unclear whether they evoke steady-state somatosensory evoked potentials (SSSEPs) that could serve as objective markers of user experience. This study tested whether 20 Hz UMAH delivered with a commercial device at maximum available intensity elicited SSSEPs comparable to those produced by vibrotactile stimulation (VTS). Electroencephalography was recorded from 26 participants during three stimulation conditions: full-intensity VTS, subjectively matched-intensity VTS, and full-intensity UMAH. Signal-to-noise ratio (SNR) and power spectral density (PSD) at 20 Hz were analyzed over contralateral and ipsilateral somatosensory regions using linear mixed-effects models, with baseline estimates derived from no-stimulation intervals. Full-intensity VTS produced clear contralateral SSSEPs, and subjectively matched-intensity VTS yielded weaker but significant responses. In the present setup, UMAH did not yield a detectable 20 Hz SSSEP relative to baseline in either SNR or PSD. These findings support SSSEPs as sensitive markers of contact vibrotactile stimulation, but suggest that, with the present apparatus and analysis approach, they are not yet a robust objective measure for evaluating UMAH experience.

Abstract: Background: The Analytic Thinking Score (ATS) has been interpreted as a linguistic marker of organized thought. Written language typically shows higher ATS than spoken language. From the perspective of active inference under the free energy principle, we proposed a neurocomputational model of the mechanism underlying this writing-over-speaking advantage. We propose that ATS reflects precision-weighted inference over latent conceptual-organization (CO) states during language production. We hypothesize that written production supports higher ATS when it increases posterior confidence in high-CO states. Methods: University students described Thematic Apperception Test images in spoken and written modalities. ATS values were obtained from the resulting language samples. Participants were modeled as active-inference agents using a two-timestep Markov decision process (MDP) in which observed speaking and writing cues updated beliefs about latent CO states. Belief updating was formalized through variational message passing and interpreted in terms of prediction-error signaling and precision-weighted neuronal synaptic gain. An attention-related parameter (AP) controlled the precision and directionality of the mapping between latent CO states and observed production cues. Bayesian model selection was used to assess the model’s construct validity. Results: Written responses showed higher ATS than spoken responses. The group-level AP estimate indicated that writing cues supported posterior inference toward high-CO states stronger than speaking cues. Bayesian model selection favored the active-inference MDP over a Variational Laplace linear model. Conclusions: Writing may increase ATS by providing production cues that support precision-weighted inference toward high-CO states. ATS is therefore interpreted as a downstream linguistic trace of latent CO inferred during language production.

Abstract: Social anxiety (SA) negatively impacts myriad aspects of an individual’s life. Although research with adults and children highlights an important link between SA and social-cognitive abilities (e.g., reasoning about others’ thoughts and emotions), findings are mixed. We hypothesized that these mixed findings stem from the various combinations of social-cognitive components of SA under investigation and the different types of measures used. Understanding these relationships in middle to late childhood is especially important, given that it is a period of substantial social-cognitive development and a common onset age for SA. Seventy-eight children (Mage=8.15 years, SD=1.61) and their parents completed measures capturing different components of anxiety (i.e., social worry, fear of negative evaluation, and social avoidance) and social cognition (i.e. emotion recognition, mental state understanding, and social perspective taking). Contrary to our expectations, measures of social cognition were only weakly correlated. Consistent with our expectations, associations between social cognition and social anxiety were measure-dependent. Self-reported fear of negative evaluation emerged as a positive predictor of accuracy in a behavioral measure of mental state understanding but a negative predictor of parent-reported mental state understanding. In addition, social avoidance accounted for additional variance only when predicting lower self-reported perspective-taking. Together, our findings underscore the multifaceted nature of social cognition and SA and highlight the need for distinguishing these facets in future work.

Abstract: Despite the dramatic advances made in artificial intelligence (AI) and other fields of computer science towards implementing “intelligent” systems expert in specific tasks, the goal of devising algorithms and machines able to interact with human beings just as naturally as other humans do is still elusive. As this naturalness is arguably a consequence of the similarity of the underlying ‘hardware’ (the human brain), it is reasonable to claim that only artificial systems closely inspired by the actual functioning of the human brain and mind have the potential to render this possible. More specifically, the aim of this paper is to propose a new, biologically inspired computational model able to mimic, in a more accurate way than existing ones, the set of functionalities know as Theory of Mind. This is a set of mental processes that allow an individual to attribute mental states to others. In human social interactions this mechanism is crucial, as it allows one to explain the observed behaviour of others, to guess their intentions and to effectively predict their future conduct. This happens by modelling and selecting the most likely (unobservable) mental states of the considered person, which are the primary causes of everyone’s observed actions. The proposed model combines a number of concepts, including those of hierarchical structure, hypotheses pre-activation, and the notion of agent class or ‘stereotype’. It rests on one of the main psychological approaches to Theory of Mind, termed Simulation Theory (ST), and is supported by significant neuroscientific evidence. Crucially, unlike previous efforts in AI, the proposed model puts the learning element at the forefront, in the belief that simulations of other intelligent being’s reasoning processes need to be learned from experience. In this perspective, a possible implementation of the model in terms of deep, reconfigurable neural networks, trained in a reinforcement learning setting, is outlined.

Abstract: The increasing adoption of generative artificial intelligence (AI) in higher education has created new opportunities to enhance Learning Management Systems (LMS) with personalized feedback, adaptive assessment, and learning analytics. Despite these advances, many LMS platforms remain primarily focused on content delivery and grade management, with limited support for metacognitive assessment and intelligent feedback. This study presents CONF.i, a confidence-informed assessment and AI feedback framework integrated with Canvas LMS using Google Apps Script and Google Gemini AI. Developed through a design-based research approach, the framework combines traditional assessment scores with student self-reported confidence levels to support personalized formative feedback and diagnostic learning insights. The proposed system integrates Canvas LTI standards, a Google Apps Script backend, and Gemini AI services to automate scoring, confidence tracking, and AI-generated educational feedback within existing institutional infrastructure. A prototype implementation was evaluated using simulated learner profiles representing different combinations of performance and confidence patterns. The framework identified four illustrative assessment profiles: aligned mastery, underconfident competence, overconfident struggle, and aligned struggle. These patterns demonstrate how confidence-informed assessment can reveal metacognitive dimensions of learning that are not visible through conventional grading alone. Preliminary usability observations indicated positive perceptions regarding the integration within the familiar Canvas environment and the relevance of AI-generated feedback, while also identifying limitations related to response latency and feedback specificity. The findings suggest that integrating confidence-informed assessment with generative AI may support more personalized and reflective learning experiences without requiring major institutional infrastructure changes or commercial licensing costs. This study contributes an exploratory prototype framework for AI-enhanced formative assessment in higher education and provides a practical model for institutions seeking to extend existing LMS platforms with confidence-aware analytics and personalized feedback capabilities.

Abstract: Digital Transformation (DT) increasingly relies on project-based organizing to develop and deploy new capabilities, yet corporate innovation projects frequently stall not for lack of ideas but because of recurring governance and resource-commitment bottlenecks. This study presents a micro-longitudinal, AI-enabled, and human-reviewed analysis of 711 episodes drawn from 28 weekly project governance meetings across two corporate startup initiatives participating in the same internal incubation program, conducted between November 2024 and April 2025. Employing a six-stage analytical pipeline that combines episode-level segmentation, linguistic tension markers, and a large language model (LLM) classifier, we identify 28 decision-relevant governance tensions, which are then abductively grouped into 13 project governance dilemmas and mapped onto Teece's dynamic capabilities framework (sensing, seizing, reconfiguring). The key finding is that 62% of dilemmas are structural in nature—reflecting persistent governance design tensions between autonomy and control, compliance and agility, and centralization and decentralization—and that 69% concentrate at the seizing stage, corresponding to resource-commitment and execution decisions. This pattern indicates a governance choke point in corporate DT projects that is structural and decisional rather than ideational. By shifting attention from lagging indicators (overruns) to governance-tension leading indicators, the approach supports earlier interventions to reduce decision latency and protect project delivery performance. We further synthesize two incubation-specific meso-level governance dilemmas—stakeholder engagement and compliance vs. agility—that serve as transmission mechanisms between macro structural constraints and micro-level decision bottlenecks. The AI-enabled pipeline is proposed as a replicable early-warning system for project governance tensions in organizations pursuing digital transformation.

Abstract: Artificial intelligence is increasingly embedded in decision-making across organizational and societal contexts, yet it remains unclear whether individuals remain cognitively aligned with decisions generated under algorithmic conditions. Existing research has emphasized trust, fairness, and transparency, but provides limited insight into the cognitive mechanisms that sustain coherent human judgment during system-mediated decision processes.Here we introduce perceptual integrity as a measurable construct capturing the extent to which individuals maintain interpretive coherence and decision authorship in human–AI interaction. We test this framework in a controlled experiment (N = 602) comparing algorithmic imposition with interpretive autonomy. Algorithmic imposition significantly reduced perceptual integrity relative to interpretive autonomy (t(600) = 4.21, p < 0.001, Cohen’s d = 0.38). Perceptual integrity was a significant predictor of trust in AI-assisted decisions (β = 0.36, p < 0.001) and partially mediated the relationship between decision condition and trust (indirect effect = 0.17, 95% CI [0.09, 0.27]).These findings identify perceptual integrity as a cognitive mechanism linking decision structure to trust under system-mediated conditions. More broadly, they suggest that effective integration of algorithmic systems depends not only on performance accuracy but on preserving cognitive alignment during decision formation. This work provides a generalizable framework for understanding how humans remain engaged with decisions in increasingly automated environments.