Cognition Theory: A Framework for Lawful Recursion Across Intelligent Systems

Contemporary educational environments increasingly feature artificial intelligence (AI) systems that challenge fundamental philosophical assumptions about cognition, agency, and learning. This article introduces Ensemble Cognition (EC) as a philosophical framework that reconceptualises thinking as emerging from dynamic interactions between human and artificial agents rather than residing solely within individual minds. Through conceptual analysis, this study demonstrates how traditional cognitive theories—including 4E cognition (embodied, embedded, enacted, extended), distributed cognition, and actor-network theory—raise specific conceptual questions when applied to AI-mediated educational contexts where artificial systems exercise functional agency without consciousness. EC articulates five key features: distributed agency, dynamic centrality, cognitive orchestration, multi-representational integration, and context-sensitive switching. These features enable understanding of learning environments where cognitive leadership shifts dynamically between human and AI agents depending on task demands. In such environments, AI systems assume pedagogical roles, and metacognitive awareness encompasses collaborative rather than purely individual processes. The framework addresses philosophical concerns about anthropomorphisation by distinguishing functional agency from moral responsibility, while acknowledging concerns about transparency, cultural embeddedness, and human cognitive development. Rather than prescribing technological solutions, EC offers educational philosophy conceptual resources for critically engaging with AI-mediated learning environments while preserving commitment to human flourishing and educational justice.

Artificial intelligence (AI) is reshaping financial decision-making processes by enhancing analytical precision, reducing uncertainty, and enabling data-driven strategies. This research proposes an AI-driven strategic decision-support framework to optimize financial decision models in complex, uncertain environments. The framework integrates predictive analytics and optimization algorithms to help financial managers and policymakers improve investment decisions, risk assessment, and resource allocation. Building on decision-engineering principles, the study conceptualizes a hybrid model combining elements of machine learning, data analytics, and cognitive decision theory to strengthen the robustness and adaptability of strategic financial decisions. The proposed approach is structured to accommodate dynamic financial and market data, allowing for continuous improvement of forecasts and decision outcomes as new information becomes available. Preliminary results suggest that AI-enhanced decision frameworks can improve forecasting reliability and strategic responsiveness, particularly in contexts characterized by volatility and incomplete information. This research contributes to the growing body of work at the intersection of artificial intelligence and financial management, offering both theoretical and practical insights for the design of intelligent financial systems. This conceptual study also lays the groundwork for a forthcoming empirical validation using financial datasets to evaluate the framework’s robustness and adaptability.

This paper presents Fuzzy Cognitive Maps as an approach in modeling the behavior and operation of complex systems. This technique is the fusion of the advances of the fuzzy logic and cognitive maps theories, they are fuzzy weighted directed graphs with feedback that create models that emulate the behavior of complex decision processes using fuzzy causal relations. There are some applications in diverse domains (manage, multiagent systems, etc.) and novel works (dynamical characteristics, learning procedures, etc.) to improve the performance of these systems. First the description and the methodology that this theory suggests is examined, also some ideas for using this approach in the control process area, and then the implementation of a tool based on Fuzzy Cognitive Maps is described. The application of this theory in the field of control and systems might contribute to the progress of more intelligent and independent control systems. Fuzzy Cognitive Maps have been fruitfully used in decision making and simulation of complex situation and analysis.

Moral decision-making is a pivotal human process that requires a harmonious balance between emotion, reasoning, and social context. With recent advances in cognitive neuroscience, it has been revealed that moral judgements are driven by multiple brain systems, including the amygdala, ventromedial prefrontal cortex (vmPFC), and dorsolateral prefrontal cortex (dlPFC). Together, these regions interact to evaluate dilemmas individuals may face and guide one’s behavior. This literature review synthesizes findings from functional imaging, lesion, and studies to address an overarching question: Are moral judgements primarily driven by emotion or rational processes? Evidence suggests that emotional responses, supported by the vmPFC and limbic structures, are more associated with rapid, intuitive moral judgements, whereas cognitive mechanisms in the dlPFC are utilized for more deliberate, or high-conflict decisions (Qu et al., 2022). The interplay between these two forces bolsters the dual-process theory of moral cognition, where emotion and rationality coexist rather than be opposing forces. Understanding this interplay provides insight and vital information regarding ethical behaviors, legal responsibilities, and artificial intelligence systems that are devised to mimic human’s moral capacities.

The rapid diffusion of “(GenAI)” Generative Artificial Intelligence systems has reshaped everyday activities, yet their adoption remains uneven and cognitively demanding for many users. Existing research has largely relied on conventional technology acceptance models, providing limited insight into cognitive burden and GenAI-specific system characteristics. To address this gap, this study develops an integrated framework combining the Technology Acceptance Model, Cognitive Load Theory, and the DeLone and McLean Information Systems Success Model to explain GenAI adoption among ordinary users. Survey data from 1001 active GenAI users were analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that all core technology acceptance relationships are statistically significant (p < 0.001), while mental load negatively affects perceived usefulness and user attitudes. Moreover, GenAI system attributes—output quality, transparency, friction reduction, and system integration—significantly moderate key adoption pathways and strengthen the translation of behavioral intention into actual use. Predictive assessment indicates that the proposed model outperforms the baseline technology acceptance model, with stronger explanatory power and superior out-of-sample predictive performance (Q2predict > 0.35). The findings offer actionable insights for designing cognitively efficient, trustworthy, and sustainable GenAI systems.

This article introduces a novel architectural paradigm for enterprise data analytics: the Federated Data Insights Platform. Operating as an abstraction layer above distributed data ecosystems, this platform facilitates seamless collaboration between domain experts and artificial intelligence systems. By creating a symbiotic relationship between human contextual understanding and machine analytical capabilities, the platform addresses critical challenges in modern data management: governance enforcement, security maintenance, and the democratization of complex data analysis. The theoretical foundation integrates federated data management principles, cognitive augmentation theories, and organizational learning frameworks. The architecture encompasses an intelligent abstraction layer, modular AI components, collaborative interfaces, and embedded governance mechanisms. Implementation considerations span technical integration strategies, organizational adoption factors, and multidimensional evaluation metrics. The platform delivers interconnected benefits that collectively transform how enterprises derive value from distributed data assets.

Chapter 13 - ACTP: a marriage with cognitive psychology

Fictionality and fictional experiences are ubiquitous in people's everyday lives in the forms of movies, novels, video games, pretense and role playing, and digital technology use. Despite this ubiquity, though, the field of cognitive science has traditionally been dominated by a focus on the real world. Based on the limited understanding from previous research on questions regarding fictional information and the cognitive processes for distinguishing reality from fiction, we argue for the need for a comprehensive and systematic account that reflects on related phenomena, such as narrative comprehension or imagination embedded into general theories of cognition. This is important as incorporating cognitive processing of fictional events into memory theory reshapes the conceptual map of human memory. In this paper, we highlight future challenges for the cognitive studies of fictionality on conceptual, neurological, and computational levels. Taking on these challenges requires an interdisciplinary approach between fields like developmental psychology, philosophy, and the study of narrative comprehension. Our aim is to build on such interdisciplinarity and provide conclusions on the ways in which new theoretical frameworks of fiction cognition can aid understanding human behaviors in a wide range of aspects of people's daily lives, media consumption habits, and digital encounters. Our account also has the potential to inform technological innovations related to training intelligent digital systems to distinguish fact and fiction in the source material.

The increasing intricacy of modern microgrids, driven by uncertain consumption patterns, decentralized renewables, and user behavioral dynamics, calls for innovative optimization methodologies. This study introduces a hybrid quantum-classical framework for demand-side energy management, leveraging behavioral modeling to foster resilience and flexibility. By embedding principles from Social Cognitive Theory—such as behavioral imitation, confidence in personal capability, and social reinforcement—into a multi-objective optimization scheme, the model supports distributed decision-making and promotes adaptive prosumer behavior. The proposed approach employs Quantum Annealing in combination with NSGA-III to efficiently navigate the complex solution space, accounting for real-time uncertainties and the stochastic nature of both demand and renewable supply. The framework is tested within a case study of a peer-to-peer microgrid network, showcasing its effectiveness in enhancing energy efficiency, lowering peak demand, and improving operational resilience. Performance comparisons with traditional methods, including Mixed-Integer Programming and conventional metaheuristics, underline the improved scalability and robustness of the quantum-inspired model in handling trade-offs between cost, reliability, and socially-driven demand response. The research highlights the potential of integrating quantum-inspired optimization with behavioral energy modeling to advance intelligent and socially-responsive microgrid control systems.