Social Sciences

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.

Abstract: Biological cognition depends on learning structured representations in ambiguous environments. Computational models of structure learning typically frame this as an inference problem, but often overlook the temporally extended dynamics that shape learning trajectories under ambiguity. In this paper, we reframe structure learning as an emergent consequence of constraint-based dynamics. Informed by a literature on the role of constraints in complex biological systems, we develop a constraint-based approach to computational cognitive modelling and provide a proof-of-concept model. The model consists of an ensemble of components, each comprising an individual learning process, whose internal updates are locally constrained by both external observations and system-level relational constraints. This is formalised using Bayesian probability as a description of constraint satisfaction rather than epistemic inference. Representational structure is not encoded directly in the model equations but emerges over time through the interaction, stabilisation, and elimination of components under these constraints. Through a series of simulations in environments with varying degrees of ambiguity, we demonstrate that the model reliably differentiates the observation space into stable representational categories. We further analyse how global parameters controlling internal constraint and initial component precision shape learning trajectories and long-term behavioural alignment with the environment. We discuss the formal relationship between the present approach and Bayesian inference accounts, and argue that a constraint-based approach offers a conceptually distinct foundation for relating computational models to biological systems.

Abstract: Large language models (LLMs) have made visible a long‑standing philosophical tension: sophisticated symbolic cognition can arise from large‑scale pattern extraction even in the absence of consciousness. This observation motivates a minimalist conceptual framework grounded in an ontological distinction between conscious regulation and symbolic structures. Language is treated as a crystallized form of human cognition—an externalized, culturally accumulated substrate created by conscious agents over millennia—while the human brain is understood as a biological system that evolved to operate over this symbolic layer. Within this view, consciousness and symbolic cognition are not different degrees of the same process but distinct kinds of cognitive organization: consciousness generates, grounds, and regulates symbols, whereas symbolic cognition manipulates them.LLMs illuminate this asymmetry by reproducing symbolic reasoning without conscious access, motivation, or subjective experience. Their performance therefore raises epistemological questions about the nature of meaning, grounding, and cognitive stability. The proposed framework situates these questions within a broader account of human cognitive evolution shaped by gene–culture coevolution and the emergence of culturally scaffolded symbolic systems. Finally, the article introduces an information‑theoretic constraint (the AI Theorem) suggesting that purely computational systems inevitably accumulate drift in the absence of a regulatory layer, offering a philosophical explanation for why artificial cognition may remain structurally distinct from biological minds.

Abstract: The digital age has fundamentally dissociated the creation of fundamental intellectual frameworks, such as novel theories, methodologies, and paradigms, from their widespread application and economic value realization. The fundamental reason why the creators of such meta-intellectual labor often receive disproportionate returns to the enormous long-term social and commercial value created by their work is that we cannot accurately measure, attribute, and automatically trade the value contained therein. In this paper, we propose a new integrated framework for automated valuation and liquidation of knowledge contribution based on the principle of fusion intelligence. This problem is formalized as a Knowledge Contribution Valuation and Liquidation (KCVS) system, with the dual formalization mechanism as its operational core, and the nine steps of intellectual integration as the maturity model of value creation. It shows how AI systems themselves, especially large language models, can be repositioned as impartial measuring instruments, automated traders, and transparent governance within this framework. Through the analysis of real cases of DeepSeek and Qianwen in scientific research and commercial applications, it is clarified that their underlying architectures have instantiated dual formal mechanisms, thus providing empirical support for the theoretical basis of the system proposed in this paper. This is followed by a blueprint consisting of three pillars: (1) an AI-driven knowledge contribution index for dynamic, multi-dimensional impact measurement; (2) a decentralized micropayment and clearing layer based on smart contracts; and (3) a transparent governance protocol for auditability using distributed ledgers. A simulated economic model is used to assess the feasibility of the framework and demonstrate its potential in building a sustainable, equitable, and self-optimizing ecosystem for foundational intellectual labor. This paper provides a theoretical and practical roadmap for aligning the incentives of knowledge creators with the structure of AI-driven economies, ensuring that future innovation is both dynamic and fair.

Abstract: Radial Analysis (RA) is a methodological framework that transforms radial category theory from static structural mapping into dynamic trajectory modeling. Building on the Trace & Trajectory Framework's (TTF) non-representationalist architecture, RA provides researchers with practical tools for analyzing indexicality, identity navigation, and meaning dynamics in discourse. This paper presents RA as an applied methodology rather than a foundational theory. The framework employs hexagonal geometry (the SpiderWeb architecture—a board game model based on hexagonal tessellation) to formalize navigational patterns: how speakers move through identity space, what these movements cost informationally, and how trajectorial patterns reveal underlying dynamics invisible to categorical approaches. Core innovations include: (1) the three-level terminology (Hexid/Hex/Hxp) for precise analytical description; (2) formally grounded metrics (hexagonal distance, trajectory cost, Temporal Dissipation Rate) enabling principled relational comparison; (3) the λ/ς/σ parameter system distinguishing structural granularity, semiotic depth, and epistemic access; (4) the depth parameter (ς) governing semiotic visibility through shading mechanics; (5) semiotic coherence (SC) as the constitutive principle underlying positional significance; (6) stratified epistemic barriers (Hxₙ) and hex bands (Hx⁽ⁿ⁾) structuring radial distance into qualitatively distinct reference domains with characteristic cost profiles; and (7) direct application to epistemic appropriation dynamics including flattening, internalization, and trajectorial refraction. RA addresses phenomena that categorical frameworks handle only through ad hoc mechanisms: simultaneous multi-level positioning, asymmetric intersubjective dynamics, and the geometric constraints that institutional power imposes on identity navigation. Applications span personal deixis, temporal reference, identity navigation dynamics, and—through integration with recent work on epistemic appropriation—the formal analysis of internalized oppression in clinical and educational contexts.

Abstract: Consciousness remains one of the most persistent problems in philosophy and cognitive science. Despite substantial advances in neuroscience, no consensus exists regarding how physical processes give rise to subjective experience. This paper proposes an evolutionary hypothesis according to which consciousness emerges from the interaction of three fundamental dimensions: neural integration of sensory information, the continuous influence of internal bodily states, and the capacity to simulate and anticipate possible scenarios. Within this framework, conscious experience is interpreted not as a mere byproduct of neural processing but as the phenomenological manifestation of a biological system capable of integrating multiple streams of information to construct a model of the organism situated in its environment. The paper develops the thesis that human consciousness can be understood as the result of an evolutionary simulation system that integrates external perception and internal bodily states within a phenomenal field structured around a bodily located self. Once established, this system may have exceeded its original adaptive functions, supporting complex forms of self-reflection, symbolic language, and cumulative culture. The proposal is also intended as an analysis of how contemporary cognitive science constructs explanatory models of consciousness, connecting neural mechanisms, embodied processes, and evolutionary function.

Abstract: Existing adult-report survey measures provide crucial information about children’s executive function (EF) development across contexts, but lack cultural relevance and ecological validity. To address these limitations, we introduce the Executive Function From Observation and Reflection Tool (EFFORT), a publicly available, open-source item bank designed for cross-cultural adaptation that includes 32 parallel items for caregivers and teachers across six EF domains: sustained attention, response inhibition, interference suppression, working memory, cognitive flexibility, and planning/organization. EFFORT additionally includes 10 assessor-report items intended for use following a structured, standardized assessment session. This study presents the first multinational validation of the tool across seven countries (Argentina, Australia, Bangladesh, Haiti, South Africa, Sri Lanka, United States) leveraging caregiver, teacher, and assessor observations of 1,738 children (aged 3–11 years). Findings revealed acceptable fit for a six-factor structure for caregiver and teacher reports that were not empirically distinct, but yielded highly reliable composites. We further validated a 12-item short form for caregiver and teacher that demonstrated strong unidimensionality, gender invariance, and age-related increases. We demonstrated significant convergence of a short-form caregiver and teachers composite with the assessor-reported measures, as well as convergence of all three adult reports with direct assessments of children’s EF skills. This new tool holds promise to advance the science of how children develop and apply EFs to accomplish everyday goals across different cultural settings and in understudied populations.

Abstract: The proliferation of complex conceptual systems developed in interaction with artificial intelligence agents poses an epistemological problem not anticipated by classical theories of falsification: in such systems, the external validation agent is simultaneously a structural generator of narrative coherence, inducing a functional collapse between the roles of creation and assessment. This collapse is not reducible to Popperian immunization or to the adjustment of auxiliary hypotheses in the Lakatosian sense, since it does not arise from deliberate defensive strategies but from an architectural asymmetry between the way such systems produce coherence and the way their human creators interpret it. This paper proposes the concept of epistemic delusion to designate the methodological state in which the operational conditions of falsification disappear as the cumulative effect of conceptual drift mechanisms, and argues that in AI-mediated self-referential systems this process exhibits a specific vector — systemic narrative induction — not yet systematized in the literature. The paper examines the mechanisms of conceptual drift, the modes of epistemic closure, and a set of methodological safeguards whose normative foundation is derived from the distinction between internally generated coherence and empirically independent corroboration.

Abstract: Chaotic itinerancy—irregular switching among metastable collective states—provides a dynamical substrate for flexible social coordination, yet its mechanistic origin in multi-agent systems remains unclear. We present a multi-agent Active Inference model in which chaotic itinerancy emerges from Expected Free Energy minimization without outcome-level social priors. Agents select actions to minimize Expected Free Energy while updating preferences through a precision-gated learning mechanism modulated by interpersonal trust. Hill-function nonlinearity in state transitions creates bistable “affordance landscapes” that gate behavioral mode switching. Simulations with small number of agents on an Erdos–Rényi trust network reveal spontaneous alternation among multiple metastable behavioral clusters, heavy-tailed dwell-time distributions, and sign-changing finite-time Lyapunov exponents—three hallmarks of chaotic itinerancy. Crucially, replacing Hill-function dynamics with linear transitions reduces the chaotic-itinerancy detection rate from 80% to 20%, demonstrating that nonlinear affordance structure is necessary for generating metastable switching. We further show that agents with simplified internal models of the world sustain richer itinerant dynamics as a group than “perfect-foresight” agents, suggesting that bounded rationality may be functionally advantageous for maintaining behavioral flexibility. These results establish active inference as a principled framework for modeling chaotic itinerancy in social systems and offer a computational account of trust-mediated collective transitions observed in theatre workshops and group dynamics.

Abstract: Background: Motivation research has generated many constructs, yet many theories remain structurally under-specified, relying on flexible verbal accounts or models whose functional form is optimized to data rather than fixed in advance. This limits falsifiability, cross-domain comparison, and principled failure. Theory: Lagun’s Law proposes a fixed six-variable structural equation of volitional drive specifying ignition gating, nonlinear amplification, divisive resistance, and an explicit variability term. The law is defined by its functional architecture rather than by any particular semantic interpretation or measurement instantiation. Objective: This study evaluates Lagun’s Law using straight structural validation: assessing whether a pre-specified equation exhibits recurring empirical signatures when applied without reparameterization, optimization, or post hoc modification. The aim is to test structural admissibility. Method: The equation was instantiated using pre-defined proxies across four independent secondary datasets spanning learning analytics, intelligent tutoring systems, naturalistic smartphone sensing, and laboratory neurophysiology. All proxies respected temporal precedence and outcome non-overlap. Where full instantiation was not possible, analyses were treated as reduced-form tests. Results: Recurring structural signatures were observed across all four datasets. Readiness functioned as a prerequisite rather than a graded predictor, divisive resistance effects were observed in three of four datasets, and independent behavioral variability persisted across contexts. Nonlinear amplification was directly testable in two datasets and attenuated or untestable elsewhere due to measurement constraints. Conclusion: These findings provide empirical grounding for Lagun’s Law as a structurally admissible constraint on volitional drive, clarifying its scope conditions and falsification pathways while avoiding claims of causality, universality, or optimal measurement.

Abstract: This paper proposes a neurophilosophical conceptual model of human consciousness structured as two functional brain states: the personal mode and the meta-reflective mode. The personal mode is defined as a motivationally and socially embedded configuration of neural processes oriented toward adaptation, identity maintenance, and ego-relevant concerns. The meta-reflective mode is characterized as a functional state in which cognition turns upon itself, enabling abstraction, self-objectification, and existential evaluation.The model does not posit a metaphysical dualism nor strictly separable neural systems. Rather, both modes may recruit overlapping brain regions, including prefrontal structures, while differing in dominant functional orientation and hierarchical organization. The distinction is therefore not anatomical but configurational.It is argued that tensions between these modes may account for different categories of psychological crises: identity-based crises primarily emerging within the personal mode, and existential crises arising from intensified meta-reflective activation. The framework further suggests that the development of civilization reflects the structural coexistence of adaptive engagement and reflective distancing. While empirical validation remains limited, the model aims to provide a structured bridge between phenomenological analysis and contemporary neurocognitive theory.

Abstract: Leadership scholarship has traditionally emphasized individual leaders and organizational outcomes, offering influential models such as transformational and authentic leadership; however, these perspectives provide limited explanatory power in contexts characterized by prolonged, multi-level transformation involving distributed authority and complex coordination demands. This study examines Saudi Arabia’s Vision 2030 as a natural experiment to explore leadership as collective cognition at national scale. Using a conceptual–analytical approach, the article interprets publicly available policy narratives and transformation dynamics through constructs derived from organizational cognition, sensemaking, and institutional theory. The analysis indicates that Vision 2030 operates through shared interpretive frameworks guiding institutional decision-making, collective historical cognition linking reform to national identity, and identity continuity mechanisms sustaining legitimacy during modernization. In addition, the findings suggest an emergent normative dimension—collective moral consciousness—reflecting shared values and purpose alignment that reinforce engagement and transformation coherence. These insights support the argument that leadership effectiveness in large-scale transformation contexts emerges from the alignment of cognitive and normative infrastructures rather than individual intervention alone. The study concludes that Vision 2030 provides empirical grounding for collective cognition leadership and establishes a foundation for future research examining leadership as a systemic phenomenon in national and societal transformation.

Abstract: Leadership research has largely emphasized individual leaders and organizational-level outcomes, offering influential theories such as transformational and authentic leadership. However, these approaches provide limited explanatory power in contexts of sustained, large-scale institutional transformation, where authority is diffuse, temporal horizons are extended, and coordination challenges exceed the influence capacity of individual actors. This article develops a conceptual framework that reconceptualizes leadership as a form of collective cognition, defined as shared interpretive structures that guide decision-making, sustain coordination, and maintain continuity under prolonged uncertainty. Drawing on leadership theory, organizational sensemaking, and institutional analysis, the framework specifies three core constructs: implicit decision frameworks, collective historical cognition, and identity continuity mechanisms, and explains their dynamic interrelationships. Rather than advancing a context-specific or prescriptive model, the article offers a generalizable theoretical perspective with clearly articulated boundary conditions. By shifting attention from individual leaders to shared cognitive infrastructures, the framework extends leadership theory and provides a foundation for future empirical research on leadership during large-scale transformation.

Abstract: As organizations navigate rapid digital transformation, leaders must reconcile the cognitive demands of technology with the emotional needs of employees. Drawing on Upper Echelons Theory (UET), Self-Determination Theory (SDT), and paradox theory, this study develops and tests a Cognition–Motivation–Paradox Integration Model (CMPIM) explaining how digital leadership fosters frontline service innovation through dual psychological mechanisms—creative self-efficacy (competence-based cognition) and sense of belonging (relatedness-based emotion). Two multi-wave, time-lagged studies in Malaysia’s hospitality sector (Study 1: N = 370; Study 2: N = 302) reveal that digital leadership enhances service-innovative behavior directly and indirectly through both mechanisms. Findings show that mindfulness serves as a paradoxical boundary condition: it strengthens the cognitive pathway from creative self-efficacy to innovation but weakens the emotional pathway from sense of belonging to innovation, demonstrating a Mindfulness–Innovation Paradox. The findings advance UET by recognizing micro-foundational mechanisms connecting leader cognition to employee innovation, improve SDT by presenting the concept of paradoxical need fulfillment between competence and relatedness, and extends Paradox Theory conceptualize mindfulness as a dual-edged psychological state that concurrently empowers and limits innovation. This integrative framework reframes digital leadership as a paradoxical function of balancing cognitive emphasis and emotional association to sustain innovation in digitally dynamic service organizations.

Abstract: Immersive virtual environments are increasingly investigated as tools capable of modulating conscious experience, yet the specific contribution of graded immersion to altered states of consciousness (ASC), time perception, and cognition remains unclear. The present study examined how different levels of immersion during videogame play influence subjective experience and post-experience cognitive performance. Seventy-two participants played an identical 35-minute segment of the videogame Half-Life: Alyx under one of three conditions: desktop PC (low immersion), head-mounted virtual reality (VR; medium immersion), or VR combined with full-body locomotion via an omnidirectional treadmill (high immersion). Following gameplay, participants completed validated measures of presence (IPQ), immersion (IEQ), ASC (5D-ASC), retrospective time estimation, and cognitive flexibility (Stroop task and Alternative Uses Test). Presence was selectively enhanced in VR relative to desktop play, whereas immersion was highest in the VR plus treadmill condition. Specific ASC dimensions related to embodiment and self-experience (disembodiment, depersonalization, derealization, and altered perception of time and space) were significantly elevated in immersive conditions. Retrospective time estimation accuracy was reduced in the highest immersion condition, indicating increased temporal distortion. Cognitive flexibility measures showed no broad modulation by immersion, with only subtle differences in Stroop accuracy. Overall, the findings indicate that increasing immersion during videogame play selectively reshapes specific dimensions of conscious experience, particularly embodiment- and time-related aspects, without globally altering executive function.

Abstract: Contemporary cognitive science increasingly acknowledges the embodied nature of perception, decision-making, and subjective experience. However, dominant models continue to treat bodily signals primarily as secondary correlates of cognitive or emotional processes, rather than as primary regulators of cognitive orientation. This paper introduces the concept of sensory circulation as a foundational regulatory mechanism underlying embodied cognition. Building upon prior works Subjectica: A Lateralized Embodied Model of Cognitive Stance and Subjectica: Sensory Circulation and Pre-Motor Readiness in Embodied Decision-Making, the present article formulates sensory circulation in academic terms as a dynamic, attention-modulated flow of afferent and proprioceptive signals distributed across bodily configurations. Attention is conceptualized not as a purely mental act, but as a functional mechanism that enables, amplifies, or inhibits sensory circulation through specific bodily regions. We argue that variations in bodily configuration—such as tonic distribution, asymmetry, axial organization, and segmental accessibility—directly shape the character of the sensory stream. This stream, in turn, establishes a stable cognitive background from which perception, motivation, and decision-making emerge. Narrative self-reports, conscious intentions, and explicit reasoning are treated as secondary products of this regulatory process rather than its causal origin. By reframing the body as an operational interface of subconscious regulation, this paper positions sensory circulation as a primary determinant of cognitive orientation. This approach offers a non-interpretive, observable framework for analyzing embodied decision-making, bridging phenomenological experience with neurobiological and behavioral dynamics.