Toward embodied, ecological cognition with ambulatory virtual reality.

The human visual system adapts to statistical regularities in the environment to facilitate visual processing. While laboratory-based tasks make clear distinctions between how task-relevant and task-irrelevant visual information can guide this adaptation, such discretization is rarely available in the real world. As such, it remains unclear exactly what information the visual system tracks to flexibly adapt to a given task. The current study used a massive visual search dataset from the mobile game Airport Scanner. Effects of exposure over a range of more task-relevant (e.g., target presence) to less task-relevant (e.g., background context) features were analyzed in an omnibus model to predict response times in both target-present and target-absent trials. As in previous work (Kramer et al., Journal of Experimental Psychology: General,151 (8), 1854, 2022), increased exposure to target-present trials significantly sped up the detection of targets and slowed the rejection of target-absent trials. Exposure to salient distractors reduced response times for target-present trials, potentially as a result of learned distractor suppression (Gaspelin & Luck, Trends in cognitive sciences,22 (1), 79-92, 2018) or increased familiarity (Mruczek & Sheinberg, Perception & psychophysics,67 (6), 1016-1031, 2005), but had no effect on target-absent trials. Exposure to background information decreased response times in both target-present and target-absent trials, with notable interactions between target and background exposure. Specifically, the effect of background information was more pronounced when target exposure was low, suggesting that less task-relevant context information is more likely to be tracked in the absence of more task-relevant information, namely, the presentation of targets. The findings highlight the importance of considering multiple sources of exposure in visual search tasks and demonstrate the value of large datasets in quantifying their complex interactions.

Cognitive psychologists have long been interested in the intersection of emotion and memory, given that the emotions associated with a stimulus affect its memorability. Theoretical perspectives within cognitive science have guided research on how affective dimensions, such as valence and arousal, affect aspects of memory, such as accuracy, subjective vividness, consolidation, and retrieval. Here we argue that well-established theories of emotion from affective science represent a fruitful source of ideas whose implications for episodic memory have not yet been thoroughly investigated. In the current article, we propose a model of emotional memory, inspired by psychological-constructionist theories of emotion, that builds upon existing perspectives in this area while generating several novel hypotheses and avenues of investigation. Following psychological constructionism, we conceive of emotions as emergent phenomena constructed when perceivers use conceptual knowledge to make sense of affective sensations in context. The constructionist model of emotional memory (CMEM) highlights new directions for future emotional-memory research, such as investigating the mnemonic consequences of conceptual emotion knowledge and considering the effects of variability in emotion construction at the situational, individual, and cultural levels.

The main aim of this study was to investigate whether there are gender differences in children’s reading ability at the end of their second year of primary school in Iceland ( N = 545; 260 girls, 285 boys, mean age: 7.7 years, SD = 0.39). A second aim was to compare reading outcomes between two groups of students: 47 children in Vestmannaeyjar who participated in the Ignition project using the READ approach (the experimental group), and 498 children from other schools across Iceland who followed the standard curriculum (the control group). All children were assessed in May 2023 using the LÆS test, which measures both reading accuracy and reading comprehension. The results showed no significant differences between boys and girls. However, there was a large and statistically significant difference between the two groups. In Vestmannaeyjar, 83% of students were able to read and understand the text, compared with only 52% of students in the control group. These findings suggest that introducing letter–sound correspondences early in the first year of school, as done in the READ approach, can provide a strong foundation for decoding and later reading development. The READ method combines a structured phonics programme with principles from cognitive science, including deliberate practice and challenges in relation to skills.

Aim . The work aimed to substantiate the concept of applying international experience in legislative establishment of lottery revenue distribution standards in Russian lottery legislation. Objectives. The work seeks to substantiate approaches to analyzing lottery legislation in various jurisdictions in terms of establishment of lottery expense standards; to analyze international experience in legal regulation of lottery expenses based on the proposed approaches; and to identify the legal basis for the formation and distribution of lottery revenue as a set of factors determining lottery revenue distribution standards. Methods . Approaches to a comparative legal analysis of lottery legislation in a representative number of jurisdictions in continental Europe, the United Kingdom, North and South America, and Australia are substantiated in various aspects using general scientific cognition methods. Results . Applying the proposed substantiated approaches to a comparative legal analysis of lottery legislation in various jurisdictions enabled to refine the subject of analysis, namely, to select the laws of states most closely related to Russian lottery legislation, i.e., the laws of federal states belonging to the Romano-Germanic legal family, which are identical to similar Russian legislation in their legislative foundations (conceptual framework, objectives), the established procedure governing the generation and distribution of lottery revenues; and, most importantly, the legal mechanism for establishing lottery revenue distribution standards. Conclusions . The identified factors determining the establishment of lottery revenue distribution standards led to the following concept: the quantitative certainty (normativity) of the lottery revenue distribution structure cannot be reduced to establishing a numerical ratio between its main components, imposed by the dominant objective of financial authorities in a given period. Standards are determined by a system of objectively acting factors and, when established by law, must express in legal form the will of the state, aimed at ensuring that the mechanism for distributing lottery revenues corresponds to the purpose of lottery activities, the type of government structure, the social focus of lotteries, the accountability and auditability of the activities of all participants empowered to organize and conduct lotteries, the stability and predictability of lottery revenue receipts to the budget, and the transparency and legitimacy of state actions at all stages of lottery conducting. The concept presented, as the main idea behind the study of international legislation regulating the distribution of lottery revenues, can serve as an appropriate methodological basis for selecting successful international practices applicable to the Russian Federation.

This article advances the view that cognition is to be found not in the world of abstract mental content but in the intricate interplay between an embodied mind and its world. Accordingly, the guiding theme of this article is that having a mind is about achieving a kind of optimal attunement or grip with the world of perception through embodied means. The article thus begins by situating the concept of grip within its proper philosophical context—the phenomenological tradition—with a special emphasis on the contributions of Husserl and Merleau-Ponty. It then surveys contemporary theoretical and empirical developments within the cognitive and human sciences that extend and further refine the concept of optimal gripping, including enactivist, ecological, and humanistic frameworks. It concludes by reviewing emerging research on the relationship between personality traits and styles of grip in the context of applied personality assessment, demonstrating the significance of optimal gripping—and therefore of phenomenology and embodiment—for psychological theory and praxis.

: <i>Life and Mind: Theoretical and Applied Issues in Contemporary Philosophy of Biology and Cognitive Sciences</i>

The homogenizing effect of large language models on human expression and thought.

Prediction error minimization and embodied cognition theorists posit that abstract self-representations are predicated on models of the self as an embodied agent. While the view of continuity between conceptual and bodily self is common to several frameworks in psychology and cognitive science, empirical tests of this relationship are scant. However, a recent study by Krol et al., (see record 2020-26839-011) found that people low in self-concept clarity (SCC) were more vulnerable to the rubber-hand illusion-in particular, in the asynchronous stroking condition, where the illusion is unwarranted. This study provides preliminary evidence for an association between self-concept strength and vulnerability to illusions regarding the bodily self. Here we sought to replicate this finding in an existing study that assessed SCC and the rubber-hand illusion. Using linear mixed-effects modelling, we found that lower SCC was again associated with greater embodiment of the rubber hand in the asynchronous condition; moreover, we also observed this effect in the synchronous stroking condition, providing additional evidence for the role of SCC in vulnerability to bodily illusions. We discuss the implications of this finding for theories in social cognitive neuroscience. Finally, as the study we drew upon to test the replication effect involved the administration of intranasal oxytocin, we also took this opportunity to replicate a previously observed effect of oxytocin on embodiment of the rubber hand; this effect, however, did not replicate, although methodological difference may have played a role. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Sports and exercise require constant on-the-fly decision-making from professional athletes and recreational exercisers alike. In the final minutes of a game, a team may for instance face the choice of sticking with their current strategy, or surprising the opponents with a new approach. Such decisions can be framed in terms of an exploration-exploitation trade-off: the need to balance exploiting a certain outcome with the potentials and risks that come with trying out something new. The explore-exploit framework has been instrumental in advancing our understanding of decision-making across fields such as cognitive science, behavioral economics, and clinical psychology. However, its potential remains largely untapped in sports and exercise psychology, where it could provide valuable insights into decision-making processes, because in sports and exercise, decisions naturally occur in dynamic, uncertain environments with fluctuating rewards and inherent costs. We propose that integrating standardized computational decision-making paradigms to formally investigate the mechanisms underlying exploration-exploitation decisions in sport contexts is a promising approach for our field. Applying these paradigms can provide novel insights into how athletes and exercisers navigate high-stakes, dynamic environments, identifying the factors that shape exploration-exploitation strategies in athletic performance. These insights might enhance training methodologies, optimize decision-making under pressure, and deepen our understanding of human adaptability in complex environments. Furthermore, studying this trade-off in elite sports presents unique opportunities for basic research, offering a setting for examining the limits of human decision-making and the generalizability of cognitive models to peak performance settings.

CBT for Public Good: Why We Need to Be More Comfortable Using Someone Else's Toothbrush.

In pure coordination games, players aim to give the same response without communication. Cognitive science research has focused on the reasoning and common knowledge necessary as the background conditions for coordination, with less attention paid to the challenge of intuiting responses on which coordination might be possible. Most studies have examined coordination within university samples from a single country, and so the extent of the challenge of coordinating between heterogeneous groups of people may have been underestimated. We conducted three empirical studies (two preregistered) with participants from the United Kingdom, South Africa, and Chile, plus a globally distributed sample (total N = 520). Without communicating, participants were asked to coordinate on answers to simple questions such as "name a city." All groups coordinated at rates far above chance but often coordinated on different responses. Study 1 showed that participants from one group could nevertheless anticipate the responses of another group, while Studies 2 and 3 showed that participants could coordinate with a partner from a different group. Crucially, between-group partners most often coordinated on new responses that were rarely considered for within-group coordination, providing the strongest evidence to date to support Schelling's claim that coordination requires distinctive reasoning, beyond primary and secondary salience. These findings provide evidence that coordination decisions are variable and flexible, resulting in accurate adaptations to achieve coordination. Where previous work has focused predominantly on the forms of reasoning that support coordination, the present findings suggest that it is equally important to examine the content of coordination solutions. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Enabling machines to solve mathematical problems is a vital endeavor in developing intelligence that emulates human-like thinking and reasoning. However, most existing approaches focus on reconstructing human comprehension of problems, which are still far from enough since they neglect the fundamental human ability to learn knowledge from experiences. In this article, we focus on empowering models with the cognitive capacity to autonomously learn knowledge from mathematical problem-solving. We first propose a Cognitive Solver (CogSolver) that contains an intelligent BRAIN-ARM framework as the cognitive structure and operates the knowledge learning process in Store-Apply-Update steps inspired by two cognitive science theories. The BRAIN system stores three basic types of mathematical knowledge, and the ARM system applies them organically in answer reasoning process. After solving problems, the BRAIN updates its stored knowledge based on the ARM's feedback, with knowledge filters to eliminate redundancies and foster a more rational knowledge base. Our CogSolver carries out the above three steps iteratively, emulating a more human-like behavior. Furthermore, in order to overcome knowledge forgetting during the learning process, we extend CogSolver to CogSolver+ by incorporating an essential knowledge Recall mechanism, which is inspired by another prominent cognitive theory. We first discuss and fuse three crucial factors in simulating human memory replay. Then, we propose a influenced-based method with a theoretical guarantee of efficiency to consolidate the updated knowledge. Experiments on three math word problem benchmarks demonstrate the improvements of our CogSolver and CogSolver+ in answer reasoning and clearly illustrate how they acquire knowledge, leading to superior interpretability.

People often favour information aligned with their ideological motives. Can our tendency for directional motivated reasoning be overcome with cognitive control? It remains contested whether cognitive control processes, such as cognitive reflection and inhibitory control, are linked to a greater tendency to engage in politically motivated reasoning, as proposed by the "motivated reflection" hypothesis, or can help people overcome it, as suggested by cognitive science research. In this pre-registered study (N=504 UK participants rating n=4963 news messages), we first provide evidence for motivated reasoning on multiple political and non-political topics. We then investigated the associations of the two cognitive control variables cognitive reflection and inhibitory control with motivated reasoning. We find that associations between cognitive control processes and motivated reasoning are likely small. On political topics specifically, we find that a negative association with cognitive reflection is more likely than a positive association. This finding is contrary to predictions from the popular motivated reflection hypothesis. Results for inhibitory control are inconclusive. We discuss how these findings relate to interdisciplinary literature from cognitive and political psychology.

Abstract Model-driven engineering (MDE) seeks to abstract and automate software development through the systematic use of models and transformations. Despite its conceptual rigor, its tools often impose cognitive burdens that hinder adoption and fluency. This paper explores how modeling tools function as cognitive artifacts that mediate, extend, and sometimes constrain reasoning. Drawing on Heidegger’s phenomenology of tool use and Piaget’s theory of cognitive schema adaptation, we articulate a theoretical framework explaining how transparency and accommodation shape the modeling experience. We argue that achieving cognitive harmony in MDE tools requires aligning architectural mediation with human cognition, a relational condition we conceptualize as cognitive resonance . The paper concludes with principles for the design of cognitively informed modeling tools, connecting philosophy, cognitive science, and model-driven engineering.

The concept of representations is widely used across the cognitive sciences, but its meaning is highly contested. Representations are often thought of as “vehicles” with “content” – that is, internal physical patterns that are correlated with some state of affairs and that usefully convey that state of affairs to the rest of the neural system or the cognitive economy at large. This raises a number of problems: how does an internal pattern come to be correlated with something else? How does the rest of the system know what the pattern means? How does the system know what to do with that information? I suggest that thinking of representations as vehicles with content presents a stumbling block to answering these questions. Instead, thinking of them simply as meaningful patterns offers a more naturalistic framework for understanding their roles in perception, behavioural control, and cognition. Meaning is not contained within a vehicle – it is relational, contextual, and interpretive. Here, I explore the origins of meaningful patterns in simple behavioural control policies and the subsequent evolution of internalised representations, decoupled from obligate action. This transition from pragmatic to semantic meaning demystifies the notion of “content”, situates talk of grounding in referential, consequential, and relational terms, and highlights the dual roles of current activity and stored context.

The rise of artificial intelligence (AI) raises the question of whether we should introduce a new category of representations, next to mental and scientific representations. We argue that AI ‘representations’, in particular of deep neural networks, differ significantly from the mental and scientific representations central to the philosophy of (cognitive) science. These systems lack essential aspects, such as semantic content, the ability to misrepresent, and a clear use condition guiding behavior; it is often unclear why and what they represent. They also lack the capacity to form or identify misrepresentations which makes it impossible to assess their accuracy. Furthermore, AI systems do not satisfy a use condition in the same way as mental and scientific representations. We conclude that, while AI systems can, under certain conditions, be useful tools for scientific discovery, their internal states should not be mistaken for mental and scientific representations.

Generative artificial intelligence increasingly mediates how individuals interpret information, perform cognitive tasks, and participate in economic and political life. While such systems promise efficiency and expanded access to knowledge, their societal effects are unevenly distributed. This article develops the concept of societal bifurcation to explain an emerging structural divergence between a cognitively resilient minority, capable of integrating AI reflectively, and a cognitively dependent majority, whose reliance on automated interpretation reduces interpretative autonomy. Drawing on contemporary empirical evidence from cognitive science, labour research, and human–AI interaction studies, the article shows how unstructured AI use diminishes metacognitive monitoring and inflates confidence, while labour-market restructuring amplifies differences in adaptability and resilience. These cognitive and economic dynamics interact with an increasingly fragile democratic information environment shaped by synthetic communication and declining epistemic trust. The article argues that these processes form a self-reinforcing sociotechnical mechanism through which cognitive dependency, economic inequality, and democratic vulnerability become mutually constitutive. By conceptualising societal bifurcation as a distinct analytical framework, the article contributes to sociological and science and technology studies debates on inequality, agency, and governance in AI-mediated societies, while highlighting the importance of sustaining interpretative autonomy in the age of generative AI.

Blue Eyes Technology represents an advanced paradigm in emotion-aware computing systems that enable machines to perceive, interpret, and respond to human emotional and attentional states through multimodal sensory inputs. Unlike conventional human–computer interaction (HCI) systems that depend primarily on explicit commands such as keyboard, mouse, or touch inputs, Blue Eyes systems incorporate implicit behavioral and physiological cues to achieve context-aware adaptive interaction. By integrating visual signals (facial expressions, gaze tracking, head movement), auditory features (speech tone, pitch variation, prosody), physiological indicators (heart rate, skin conductance, EEG patterns), and behavioral dynamics (gesture patterns, response latency), these systems aim to approximate human-like perceptual intelligence. This paper presents a comprehensive analytical study of Blue Eyes Technology by examining its theoretical foundations in affective computing, cognitive science, and machine learning. It explores sensing modalities, signal acquisition techniques, feature extraction methods, multimodal fusion strategies, and emotion modeling frameworks using both classical machine learning and deep learning architectures. The study further reviews publicly available datasets, benchmarking standards, and experimental validation protocols to ensure reproducibility and generalization across diverse populations.

The sense of agency (SoA) - the experience of controlling one's actions and, through them, events in the external world - is a cornerstone of cognitive science, psychology, and philosophy, underpinning autonomy and responsibility. Yet research on SoA has overwhelmingly focused on Outcome-level Agency (control over external effects) and, to a lesser extent, Action-level Agency (control over bodily movements). A third, upstream dimension - Decision-level Agency, defined as the experience of originating and committing to one's own decisions or intentions even in the absence of overt action - has remained comparatively neglected and rarely operationalized as a distinct target of measurement. Drawing on philosophical analysis and converging neuroscientific evidence, this paper argues that deciding and intending constitute mental actions in their own right, as the brain actively selects, commits to, revises, or withholds intentions. I propose a three-level framework - Decision, Action, and Outcome - that explicitly incorporates Decision-level Agency as a distinct yet hierarchically integrated component of SoA. This reconceptualization is not only theoretically informative but ethically urgent in the era of generative artificial intelligence, where external systems can increasingly shape human autonomy upstream at the level of decision formation rather than action execution. By outlining testable predictions and experimental paradigms, this work establishes Decision-level Agency as an empirically tractable dimension of human volition and provides a framework for understanding and safeguarding autonomy in AI-mediated environments.