Two questions are ordinarily treated as belonging to separate fields: how an inherited artefact came to mean what it meant, and how a novice comes to know what to do with an unfamiliar object. This paper argues that, for a certain class of artefacts, these are not independent problems. We introduce the Hard Problem of Cultural Learning (HPCL): the challenge of reconstructing an artefact's action-goal structure once social-demonstrative guidance (e.g., a model, teacher, or manual) is unavailable. Using a four-case typology built around material recoverability and guidance availability, we focus on artefacts that remain materially recoverable despite this absence. The Rubik's Cube serves as our central case. Read through Malafouris' Material Engagement Theory, the Cube shows how design alone can invite exploration, disclose a grammar of action, and delimit a problem-space with internally assessable outcomes, without ever fixing a single intended goal or efficient solution. We contrast this with the Acheulean tools of Porto Maior, whose oversized forms remain underdetermined despite comparable material survival. Extending this analysis to a catalogue of cultural-learning strategies, we show how rupture strips away most social channels while leaving a narrow set, teleological thinking, exploration, intact. Artefacts, we conclude, do not merely survive cultural rupture: some are built to be legible past it, and that legibility reshapes what archaeology, the study of mind, and the science of learning can ask of a single surviving object.

Abstract: In this paper, we examine the implications for psychiatry of the view that "we are narrative beings, fashioning our selves from the stuff of stories, locating our biographies and life projects in discursive webs of shared meaning." We begin with the claim that narrative capacities, skills, practices, and specific content can contribute to the causes, course, and outcomes of psychopathology as well as to processes of coping, resilience, healing, and recovery. One consequence of this claim is that understanding the human capacity to produce, think, and interact with others through narrative must play a central role in psychiatric theory, research and practice. To approach the interactions between cultural discursive formations, narrative practices, and neurobiological mechanisms in psychiatry, we propose an integrative cultural-ecosocial framework mobilizing the paradigms of enactment and embodiment in cognitive science, narrative theories of selfhood and personality, ecological systems theory in social science, and active inference approaches in computational neuroscience.

Patient–clinician interactions are central to diagnosis and treatment in mental health care. They involve inferential processes grounded in patients’ reports of symptoms and clinicians’ interpretations of signs. These processes are liable to microdynamic looping effects that drive diagnostic belief formation. In facing the hermeneutic challenge of reaching a shared ascription of the patient’s condition, such looping effects can lead to misalignment and give rise to epistemic injustice if a credibility deficit exists between the patient and the clinician. In this paper, we use the active inference framework to develop a computational model of looping microdynamics that captures how patients and clinicians reciprocally interpret symptoms, attribute causes, and negotiate diagnostic categories over time. We present a simulation study showing how specific interactional patterns can amplify or mitigate credibility deficits, depending in part on clinicians’ attributional attitudes toward patients’ self-interpretations. Our results suggest that, in contexts of diagnostic disagreement, positive disconfirmation—offering an alternative explanatory framing rather than simply negating the patient’s perspective—may reduce the risk of epistemic injustice. Overall, the model highlights how looping microdynamics structure mutual belief formation in clinical encounters and how attention to these dynamics can support more epistemically responsible clinical communication.

The proliferation of agentic artificial intelligence has outpaced the conceptual tools needed to characterize agency in computational systems. Prevailing definitions mainly rely on autonomy and goal-directedness. Here, we argue for a minimal notion open to principled inspection given three criteria: intentionality as action grounded in beliefs and desires, rationality as normatively coherent action entailed by a world model, and explainability as action causally traceable to internal states; we subsequently instantiate these as a partially observable Markov decision process under a variational framework wherein posterior beliefs, prior preferences, and the minimization of expected free energy jointly constitute an agentic action chain. Using a canonical T-maze paradigm, we evidence how empowerment, formulated as the channel capacity between actions and anticipated observations, serves as an operational metric that distinguishes zero-, intermediate-, and high-agency phenotypes through structural manipulations of the generative model. We conclude by arguing that as agents engage in epistemic foraging to resolve ambiguity, the governance controls that remain effective must shift systematically from external constraints to the internal modulation of prior preferences, offering a principled, variational bridge from computational phenotyping to AI governance strategy

In this paper, we examine the implications for psychiatry of the view that “we are narrative beings, fashioning our selves from the stuff of stories, locating our biographies and life projects in discursive webs of shared meaning.” We begin with the claim that narrative capacities, skills, practices, and specific content can contribute to the causes, course, and outcomes of psychopathology as well as to processes of coping, resilience, healing, and recovery. One consequence of this claim is that understanding the human capacity to produce, think, and interact with others through narrative must play a central role in psychiatric theory, research and practice. To approach the interactions between cultural discursive formations, narrative practices, and neurobiological mechanisms in psychiatry, we propose an integrative cultural-ecosocial framework mobilizing the paradigms of enactment and embodiment in cognitive science, narrative theories of selfhood and personality, ecological systems theory in social science, and active inference approaches in computational neuroscience.

This article applies the thesis of the extended mind to ambient smart environments. These systems are characterised by an environment, such as a home or classroom, infused with multiple, highly networked streams of smart technology working in the background, learning about the user and operating without an explicit interface or any intentional sensorimotor engagement from the user. We analyse these systems in the context of work on the “classical” extended mind, characterised by conditions such as “trust and glue” and phenomenal transparency, and find that these conditions are ill-suited to describing our engagement with ambient smart environments. We then draw from the active inference framework, a theory of brain function which casts cognition as a process of embodied uncertainty minimisation, to develop a version of the extended mind grounded in a process ontology, where the boundaries of mind are understood to be multiple and always shifting. Given this more fluid account of the extended mind, we argue that ambient smart environments should be thought of as extended allostatic control systems, operating more or less invisibly to support an agent’s biological capacity for minimising uncertainty over multiple, interlocking timescales. Thus, we account for the functionality of ambient smart environments as extended systems, and in so doing, utilise a markedly different version of the classical thesis of extended mind.

In this simulation study, we adopt the comprehensive neurocomputational approach of Active Inference (AIF) to illustrate some key concepts of Material Engagement Theory (MET) [[1][1]]. MET posits that craftwork does not require, or rely on, rich internal ‘pre-planning’, i.e., complex and highly detailed representations that occur mainly in the maker’s head. Instead, the maker engages materiality through ’thinging’ , where the human agent (the maker) is guided by and leverages the materiality of the artefact (such as a spinning of clay or a chunk of marble). MET assigns a crucial co-participatory role to materiality, attributing agency to it. We investigate MET’s claims through the widely adopted theory of AIF [[2][2]]. Our first aim is to simulate the plausibility of the (creative) thinging , adopting a simple modelling scenario. Then, we also discuss its applicability to other, more complex cases, which seem to require greater levels of ‘pre-thinking’ (e.g., planning, imagining and conceptualising outcomes). We highlight how these cases, too, align with the general principle of thinging . With our AIF neurocomputational understanding, we explain that even in these situations, the predictive brains involved in the creative process attempt to minimise the complexity of their internal model. The upshot of this is that, always and everywhere, our human minds engage the materiality to make the most of the characteristic dynamics of the world surrounding us - things and processes alike. ### Competing Interest Statement The authors have declared no competing interest. [1]: #ref-1 [2]: #ref-2

Our commentary suggests that different materialities (fragile, enduring, and mixed) may influence cognitive evolution. Building on Stibbard-Hawkes, we propose that predictive brains minimise errors and seek information, actively structuring environments for epistemic benefits. This perspective complements Stibbard-Hawkes' view.

This paper is the contribution of the editorial team for a special issue designed to celebrate the scientific contributions of Karl Friston on his 65th birthday [...]

Artificial intelligence (AI) is increasingly proposed as a solution to environmental sustainability challenges, with applications aimed at optimizing resource utilization and energy consumption. However, AI technologies also have significant negative environmental impacts. This duality underscores the need to critically evaluate AI's role in sustainable practices. One example of AI's application in sustainability is the Occupant Controlled Smart Thermostat (OCST). These systems optimize indoor temperature management by responding to dynamic signals, such as energy price fluctuations, which reflect power grid stress. Accordingly, regulatory frameworks have mandated performance standards for such technologies to ensure effective demand responsiveness. While OCSTs are effective in managing energy demand through predefined norms like price signals, their current designs often fail to accommodate the complex interplay of conflicting priorities, such as user comfort and grid optimization, particularly in uncertain climatic conditions. For instance, extreme weather events can amplify energy demands and user needs, necessitating a more context sensitive approach. This adaptability requires OCSTs to dynamically shift between multiple normative constraints (i.e., norms), such as prioritizing userdefined temperature settings over price-based energy restrictions when contextually appropriate. In this paper, we propose an innovative approach that combines the theory of active inference from theoretical neuroscience and robotics with a rulebook formalism to enhance the decision-making capabilities of autonomous AI agents. Using simulation studies, we demonstrate how these AI agents can resolve conflicts among norms under environmental uncertainty. A minimal use case is presented, where an OCST must decide whether to warm a room based on two conflicting rules: a “price” rule that restricts energy use above a cost threshold and a “need” rule that prioritizes maintaining the user's desired temperature. Our findings illustrate the potential for advanced AI-driven OCST systems to navigate conflicting norms, enabling more resilient and user-centered solutions to sustainable energy challenges.

Advances in automated systems afford new opportunities for intelligent management of energy at household, local area, and utility scales. Home Energy Management Systems (HEMS) can play a role by optimizing the schedule and use of household energy devices and resources. One challenge is that the goals of a household can be complex and conflicting. For example, a household might wish to reduce energy costs and grid-associated greenhouse gas emissions, yet keep room temperatures comfortable. Another challenge is that an intelligent HEMS agent must make decisions under uncertainty. An agent must plan actions into the future, but weather and solar generation forecasts, for example, provide inherently uncertain estimates of future conditions. This paper introduces EcoNet, a Bayesian approach to household and neighborhood energy management that is based on active inference. The aim is to improve energy management and coordination, while accommodating uncertainties and taking into account potentially conditional and conflicting goals and preferences. Simulation results are presented and discussed.

This paper presents a computational account of how legal norms can influence the behavior of artificial intelligence (AI) agents, grounded in the active inference framework (AIF) that is informed by principles of economic legal analysis (ELA). The ensuing model aims to capture the complexity of human decision-making under legal constraints, offering a candidate mechanism for agent governance in AI systems, that is, the (auto)regulation of AI agents themselves rather than human actors in the AI industry. We propose that lawful and norm-sensitive AI behavior can be achieved through regulation by design, where agents are endowed with intentional control systems, or behavioral safety valves, that guide real-time decisions in accordance with normative expectations. To illustrate this, we simulate an autonomous driving scenario in which an AI agent must decide when to yield the right of way by balancing competing legal and pragmatic imperatives. The model formalizes how AIF can implement context-dependent preferences to resolve such conflicts, linking this mechanism to the conception of law as a scaffold for rational decision-making under uncertainty. We conclude by discussing how context-dependent preferences could function as safety mechanisms for autonomous agents, enhancing lawful alignment and risk mitigation in AI governance.

Genetic determinism - the view that all disease processes can be explained as genetic processes - and data fundamentalism - the view that digital traces provide the only relevant, person-level insights into people's mental health - are two forms of reductionism of personal and superpersonal phenomena that have been presented as fundamental limitations of the paradigms of precision medicine and digital phenotyping. These forms of reductionism are particularly worrisome for the application of precision medicine to psychiatry, which deals with disease entities configured at the superpersonal and personal levels. In this paper, I argue that by adopting a personomic approach to precision medicine and digital phenotyping - a digital personomic approach - one can conceive of non-reductive precision and digital psychiatry programs.

Rapid progress in artificial intelligence (AI) capabilities has drawn fresh attention to the prospect of consciousness in AI. There is an urgent need for rigorous methods to assess AI systems for consciousness, but significant uncertainty about relevant issues in consciousness science. We present a method for assessing AI systems for consciousness that involves exploring what follows from existing or future neuroscientific theories of consciousness. Indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious. This method allows us to make meaningful progress because some influential theories of consciousness, notably including computational functionalist theories, have implications for AI that can be investigated empirically.

While the ubiquity and importance of narratives for human adaptation is widely recognized, there is no integrative framework for understanding the roles of narrative in human adaptation. Research has identified several cognitive and social functions of narratives that are conducive to well-being and adaptation as well as to coordinated social practices and enculturation. In this paper, we characterize the cognitive and social functions of narratives in terms of active inference, to support the claim that one of the main adaptive functions of narrative is to generate more useful (i.e., accurate, parsimonious) predictions for the individual, as well as to coordinate group action (over multiple timescales) through shared predictions about collective behavior. Active inference is a theory that depicts the fundamental tendency of living organisms to adapt by proactively inferring the causes of their sensations (including their own actions). We review narrative research on identity, event segmentation, episodic memory, future projections, storytelling practices, enculturation, and master narratives. We show how this research dovetails with the active inference framework and propose an account of the cognitive and social functions of narrative that emphasizes that narratives are for the future—even when they are focused on recollecting or recounting the past. Understanding narratives as cognitive and cultural tools for mutual prediction in social contexts can guide research on narrative in adaptive behavior and psychopathology, based on a parsimonious mechanistic model of some of the basic adaptive functions of narrative.

Legal autonomy - the lawful activity of artificial intelligence agents - can be achieved in one of two ways. It can be achieved either by imposing constraints on AI actors such as developers, deployers and users, and on AI resources such as data, or by imposing constraints on the range and scope of the impact that AI agents can have on the environment. The latter approach involves encoding extant rules concerning AI driven devices into the software of AI agents controlling those devices (e.g., encoding rules about limitations on zones of operations into the agent software of an autonomous drone device). This is a challenge since the effectivity of such an approach requires a method of extracting, loading, transforming and computing legal information that would be both explainable and legally interoperable, and that would enable AI agents to reason about the law. In this paper, we sketch a proof of principle for such a method using large language models (LLMs), expert legal systems known as legal decision paths, and Bayesian networks. We then show how the proposed method could be applied to extant regulation in matters of autonomous cars, such as the California Vehicle Code.

In this paper, we explore the known connection among sustainability, resilience, and well-being within the framework of active inference. Initially, we revisit how the notions of well-being and resilience intersect within active inference before defining sustainability. We adopt a holistic concept of sustainability denoting the enduring capacity to meet needs over time without depleting crucial resources. It extends beyond material wealth to encompass community networks, labor, and knowledge. Using the free energy principle, we can emphasize the role of fostering resource renewal, harmonious system–entity exchanges, and practices that encourage self-organization and resilience as pathways to achieving sustainability both as an agent and as a part of a collective. We start by connecting active inference with well-being, building on existing work. We then attempt to link resilience with sustainability, asserting that resilience alone is insufficient for sustainable outcomes. While crucial for absorbing shocks and stresses, resilience must be intrinsically linked with sustainability to ensure that adaptive capacities do not merely perpetuate existing vulnerabilities. Rather, it should facilitate transformative processes that address the root causes of unsustainability. Sustainability, therefore, must manifest across extended timescales and all system strata, from individual components to the broader system, to uphold ecological integrity, economic stability, and social well-being. We explain how sustainability manifests at the level of an agent and then at the level of collectives and systems. To model and quantify the interdependencies between resources and their impact on overall system sustainability, we introduce the application of network theory and dynamical systems theory. We emphasize the optimization of precision or learning rates through the active inference framework, advocating for an approach that fosters the elastic and plastic resilience necessary for long-term sustainability and abundance.

This article proposes a novel computational approach to embodied approaches in cognitive archaeology called computational cognitive archaeology (CCA). We argue that cognitive archaeology, understood as the study of the human mind based on archaeological findings such as artefacts and material remains excavated and interpreted in the present, can benefit from the integration of novel methods in computational neuroscience interested in modelling the way the brain, the body and the environment are coupled and parameterized to allow for adaptive behaviour. We discuss the kind of tasks that CCA may engage in with a narrative example of how one can model the cumulative cultural evolution of the material and cognitive components of technologies, focusing on the case of knapping technology. This article thus provides a novel theoretical framework to formalize research in cognitive archaeology using recent developments in computational neuroscience.