A Bayesian model of legal syllogistic reasoning.

Bayesian approaches to legal reasoning propose causal models of the relation between evidence, the credibility of evidence, and ultimate hypotheses, or verdicts. They assume that legal reasoning is the process whereby one infers the posterior probability of a verdict based on observed evidence, or facts. In practice, legal reasoning does not operate quite that way. Legal reasoning is also an attempt at inferring applicable rules derived from legal precedents or statutes based on the facts at hand. To make such an inference, legal reasoning follows syllogistic logic and first order transitivity. This paper proposes a Bayesian model of legal syllogistic reasoning that complements existing Bayesian models of legal reasoning using a Bayesian network whose variables correspond to legal precedents, statutes, and facts. We suggest that legal reasoning should be modelled as a process of finding the posterior probability of precedents and statutes based on available facts.

A Variational Synthesis of Evolutionary and Developmental Dynamics.

This paper introduces a variational formulation of natural selection, paying special attention to the nature of 'things' and the way that different 'kinds' of 'things' are individuated from-and influence-each other. We use the Bayesian mechanics of particular partitions to understand how slow phylogenetic processes constrain-and are constrained by-fast, phenotypic processes. The main result is a formulation of adaptive fitness as a path integral of phenotypic fitness. Paths of least action, at the phenotypic and phylogenetic scales, can then be read as inference and learning processes, respectively. In this view, a phenotype actively infers the state of its econiche under a generative model, whose parameters are learned via natural (Bayesian model) selection. The ensuing variational synthesis features some unexpected aspects. Perhaps the most notable is that it is not possible to describe or model a population of conspecifics per se. Rather, it is necessary to consider populations of distinct natural kinds that influence each other. This paper is limited to a description of the mathematical apparatus and accompanying ideas. Subsequent work will use these methods for simulations and numerical analyses-and identify points of contact with related mathematical formulations of evolution.

Applying the Free Energy Principle to Complex Adaptive Systems.

The free energy principle (FEP) is a formulation of the adaptive, belief-driven behaviour of self-organizing systems that gained prominence in the early 2000s as a unified model of the brain [...].

The first prior: From co-embodiment to co-homeostasis in early life.

The idea that our perceptions in the here and now are influenced by prior events and experiences has recently received substantial support and attention from the proponents of the Predictive Processing (PP) and Active Inference framework in philosophy and computational neuroscience. In this paper we look at how perceptual experiences get off the ground from the outset, in utero. One basic yet overlooked aspect of current PP approaches is that human organisms first develop within another human body. Crucially, while not all humans will have the experience of being pregnant or carrying a baby, the experience of being carried and growing within another person's body is universal. Specifically, we focus on the development of minimal selfhood in utero as a process co-embodiment and co-homeostasis, and highlight their close relationship. We conclude with some implications on several critical questions fuelling current debates on the nature of conscious experiences, minimal self and social cognition.

Multiscale integration: beyond internalism and externalism.

We present a multiscale integrationist interpretation of the boundaries of cognitive systems, using the Markov blanket formalism of the variational free energy principle. This interpretation is intended as a corrective for the philosophical debate over internalist and externalist interpretations of cognitive boundaries; we stake out a compromise position. We first survey key principles of new radical (extended, enactive, embodied) views of cognition. We then describe an internalist interpretation premised on the Markov blanket formalism. Having reviewed these accounts, we develop our positive multiscale account. We argue that the statistical seclusion of internal from external states of the system-entailed by the existence of a Markov boundary-can coexist happily with the multiscale integration of the system through its dynamics. Our approach does not privilege any given boundary (whether it be that of the brain, body, or world), nor does it argue that all boundaries are equally prescient. We argue that the relevant boundaries of cognition depend on the level being characterised and the explanatory interests that guide investigation. We approach the issue of how and where to draw the boundaries of cognitive systems through a multiscale ontology of cognitive systems, which offers a multidisciplinary research heuristic for cognitive science.

TTOM in action: Refining the variational approach to cognition and culture.

The target article "Thinking Through Other Minds" (TTOM) offered an account of the distinctively human capacity to acquire cultural knowledge, norms, and practices. To this end, we leveraged recent ideas from theoretical neurobiology to understand the human mind in social and cultural contexts. Our aim was both synthetic - building an integrative model adequate to account for key features of cultural learning and adaptation; and prescriptive - showing how the tools developed to explain brain dynamics can be applied to the emergence of social and cultural ecologies of mind. In this reply to commentators, we address key issues, including: (1) refining the concept of culture to show how TTOM and the free-energy principle (FEP) can capture essential elements of human adaptation and functioning; (2) addressing cognition as an embodied, enactive, affective process involving cultural affordances; (3) clarifying the significance of the FEP formalism related to entropy minimization, Bayesian inference, Markov blankets, and enactivist views; (4) developing empirical tests and applications of the TTOM model; (5) incorporating cultural diversity and context at the level of intra-cultural variation, individual differences, and the transition to digital niches; and (6) considering some implications for psychiatry. The commentators' critiques and suggestions point to useful refinements and applications of the model. In ongoing collaborations, we are exploring how to augment the theory with affective valence, take into account individual differences and historicity, and apply the model to specific domains including epistemic bias.

Tinkering with cognitive gadgets: Cultural evolutionary psychology meets active inference.

Cognitive Gadgets offers a new, convincing perspective on the origins of our distinctive cognitive faculties, coupled with a clear, innovative research program. Although we broadly endorse Heyes' ideas, we raise some concerns about her characterisation of evolutionary psychology and the relationship between biology and culture, before discussing the potential fruits of examining cognitive gadgets through the lens of active inference.

Thinking through other minds: A variational approach to cognition and culture.

The processes underwriting the acquisition of culture remain unclear. How are shared habits, norms, and expectations learned and maintained with precision and reliability across large-scale sociocultural ensembles? Is there a unifying account of the mechanisms involved in the acquisition of culture? Notions such as "shared expectations," the "selective patterning of attention and behaviour," "cultural evolution," "cultural inheritance," and "implicit learning" are the main candidates to underpin a unifying account of cognition and the acquisition of culture; however, their interactions require greater specification and clarification. In this article, we integrate these candidates using the variational (free-energy) approach to human cognition and culture in theoretical neuroscience. We describe the construction by humans of social niches that afford epistemic resources called cultural affordances. We argue that human agents learn the shared habits, norms, and expectations of their culture through immersive participation in patterned cultural practices that selectively pattern attention and behaviour. We call this process "thinking through other minds" (TTOM) - in effect, the process of inferring other agents' expectations about the world and how to behave in social context. We argue that for humans, information from and about other people's expectations constitutes the primary domain of statistical regularities that humans leverage to predict and organize behaviour. The integrative model we offer has implications that can advance theories of cognition, enculturation, adaptation, and psychopathology. Crucially, this formal (variational) treatment seeks to resolve key debates in current cognitive science, such as the distinction between internalist and externalist accounts of theory of mind abilities and the more fundamental distinction between dynamical and representational accounts of enactivism.

Cultivating creativity: predictive brains and the enlightened room problem.

How can one conciliate the claim that humans are uncertainty minimizing systems that seek to navigate predictable and familiar environments with the claim that humans can be creative? We call this the Enlightened Room Problem (ERP). The solution, we suggest, lies not (or not only) in the error-minimizing brain but in the environment itself. Creativity emerges from various degrees of interplay between predictive brains and changing environments: ones that repeatedly move the goalposts for our own error-minimizing machinery. By (co)constructing these challenging worlds, we effectively alter and expand the space within which our own prediction engines operate, and that function as 'exploration bubbles' that enable information seeking, uncertainty minimizing minds to penetrate deeper and deeper into artistic, scientific and engineering space. In what follows, we offer a proof of principle for this kind of environmentally led cognitive expansion. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Narrative as active inference: an integrative account of cognitive and social functions in adaptation.

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.

Sources of richness and ineffability for phenomenally conscious states.

Conscious states-state that there is something it is like to be in-seem both rich or full of detail and ineffable or hard to fully describe or recall. The problem of ineffability, in particular, is a longstanding issue in philosophy that partly motivates the explanatory gap: the belief that consciousness cannot be reduced to underlying physical processes. Here, we provide an information theoretic dynamical systems perspective on the richness and ineffability of consciousness. In our framework, the richness of conscious experience corresponds to the amount of information in a conscious state and ineffability corresponds to the amount of information lost at different stages of processing. We describe how attractor dynamics in working memory would induce impoverished recollections of our original experiences, how the discrete symbolic nature of language is insufficient for describing the rich and high-dimensional structure of experiences, and how similarity in the cognitive function of two individuals relates to improved communicability of their experiences to each other. While our model may not settle all questions relating to the explanatory gap, it makes progress toward a fully physicalist explanation of the richness and ineffability of conscious experience-two important aspects that seem to be part of what makes qualitative character so puzzling.

Federated inference and belief sharing.

This paper concerns the distributed intelligence or federated inference that emerges under belief-sharing among agents who share a common world-and world model. Imagine, for example, several animals keeping a lookout for predators. Their collective surveillance rests upon being able to communicate their beliefs-about what they see-among themselves. But, how is this possible? Here, we show how all the necessary components arise from minimising free energy. We use numerical studies to simulate the generation, acquisition and emergence of language in synthetic agents. Specifically, we consider inference, learning and selection as minimising the variational free energy of posterior (i.e., Bayesian) beliefs about the states, parameters and structure of generative models, respectively. The common theme-that attends these optimisation processes-is the selection of actions that minimise expected free energy, leading to active inference, learning and model selection (a.k.a., structure learning). We first illustrate the role of communication in resolving uncertainty about the latent states of a partially observed world, on which agents have complementary perspectives. We then consider the acquisition of the requisite language-entailed by a likelihood mapping from an agent's beliefs to their overt expression (e.g., speech)-showing that language can be transmitted across generations by active learning. Finally, we show that language is an emergent property of free energy minimisation, when agents operate within the same econiche. We conclude with a discussion of various perspectives on these phenomena; ranging from cultural niche construction, through federated learning, to the emergence of complexity in ensembles of self-organising systems.

Lifeworlds in pain: a principled method for investigation and intervention.

The experience of pain spans biological, psychological and sociocultural realms, both basic and complex, it is by turns necessary and devastating. Despite an extensive knowledge of the constituents of pain, the ability to translate this into effective intervention remains limited. It is suggested that current, multiscale, medical approaches, largely informed by the biopsychosocial (BPS) model, attempt to integrate knowledge but are undermined by an epistemological obligation, one that necessitates a prior isolation of the constituent parts. To overcome this impasse, we propose that an anthropological stance needs to be taken, underpinned by a Bayesian apparatus situated in computational psychiatry. Here, pain is presented within the context of lifeworlds, where attention is shifted away from the constituents of experience (e.g. nociception, reward processing and fear-avoidance), towards the dynamic affiliation that occurs between these processes over time. We argue that one can derive a principled method of investigation and intervention for pain from modelling approaches in computational psychiatry. We suggest that these modelling methods provide the necessary apparatus to navigate multiscale ontology and epistemology of pain. Finally, a unified approach to the experience of pain is presented, where the relational, inter-subjective phenomenology of pain is brought into contact with a principled method of translation; in so doing, revealing the conditions and possibilities of lifeworlds in pain.

Integrating Evolutionary, Cultural, and Computational Psychiatry: A Multilevel Systemic Approach.

This paper proposes an integrative perspective on evolutionary, cultural and computational approaches to psychiatry. These three approaches attempt to frame mental disorders as multiscale entities and offer modes of explanations and modeling strategies that can inform clinical practice. Although each of these perspectives involves systemic thinking, each is limited in its ability to address the complex developmental trajectories and larger social systemic interactions that lead to mental disorders. Inspired by computational modeling in theoretical biology, this paper aims to integrate the modes of explanation offered by evolutionary, cultural and computational psychiatry in a multilevel systemic perspective. We apply the resulting Evolutionary, Cultural and Computational (ECC) model to Major Depressive Disorder (MDD) to illustrate how this integrative approach can guide research and practice in psychiatry.

Extended active inference: Constructing predictive cognition beyond skulls.

Cognitive niche construction is the process whereby organisms create and maintain cause-effect models of their niche as guides for fitness influencing behavior. Extended mind theory claims that cognitive processes extend beyond the brain to include predictable states of the world. Active inference and predictive processing in cognitive science assume that organisms embody predictive (i.e., generative) models of the world optimized by standard cognitive functions (e.g., perception, action, learning). This paper presents an active inference formulation that views cognitive niche construction as a cognitive function aimed at optimizing organisms' generative models. We call that process of optimization extended active inference.

From Generative Models to Generative Passages: A Computational Approach to (Neuro) Phenomenology.

This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as computational phenomenology because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience.

Cognition coming about: Self-organisation and free-energy: Commentary on Wright, J.J. and Bourke, P.D. (2020) "The growth of cognition: Free energy minimization and the embryogenesis of cortical computation".

Wright and Bourke's compelling article rightly points out that existing models of embryogenesis fail to explain the mechanisms and functional significance of the dynamic connections among neurons. We pursue their account of Dynamic Logic by appealing to the Markov blanket formalism that underwrites the Free Energy Principle. We submit that this allows one to model embryogenesis as self-organisation in a dynamical system that minimises free-energy. The ensuing formalism may be extended to also explain the autonomous emergence of cognition, specifically in the brain, as a dynamic self-assembling process.

A Variational Approach to Scripts.

This paper proposes a formal reconstruction of the script construct by leveraging the active inference framework, a behavioral modeling framework that casts action, perception, emotions, and attention as processes of (Bayesian or variational) inference. We propose a first principles account of the script construct that integrates its different uses in the behavioral and social sciences. We begin by reviewing the recent literature that uses the script construct. We then examine the main mathematical and computational features of active inference. Finally, we leverage the resources of active inference to offer a formal model of scripts. Our integrative model accounts for the dual nature of scripts (as internal, psychological schema used by agents to make sense of event types and as constitutive behavioral categories that make up the social order) and also for the stronger and weaker conceptions of the construct (which do and do not relate to explicit action sequences, respectively).

Active Inferants: An Active Inference Framework for Ant Colony Behavior.

In this paper, we introduce an active inference model of ant colony foraging behavior, and implement the model in a series of in silico experiments. Active inference is a multiscale approach to behavioral modeling that is being applied across settings in theoretical biology and ethology. The ant colony is a classic case system in the function of distributed systems in terms of stigmergic decision-making and information sharing. Here we specify and simulate a Markov decision process (MDP) model for ant colony foraging. We investigate a well-known paradigm from laboratory ant colony behavioral experiments, the alternating T-maze paradigm, to illustrate the ability of the model to recover basic colony phenomena such as trail formation after food location discovery. We conclude by outlining how the active inference ant colony foraging behavioral model can be extended and situated within a nested multiscale framework and systems approaches to biology more generally.

Why Depressed Mood is Adaptive: A Numerical Proof of Principle for an Evolutionary Systems Theory of Depression.

We provide a proof of principle for an evolutionary systems theory (EST) of depression. This theory suggests that normative depressive symptoms counter socioenvironmental volatility by increasing interpersonal support via social signalling and that this response depends upon the encoding of uncertainty about social contingencies, which can be targeted by neuromodulatory antidepressants. We simulated agents that committed to a series of decisions in a social two-arm bandit task before and after social adversity, which precipitated depressive symptoms. Responses to social adversity were modelled under various combinations of social support and pharmacotherapy. The normative depressive phenotype responded positively to social support and simulated treatments with antidepressants. Attracting social support and administering antidepressants also alleviated anhedonia and social withdrawal, speaking to improvements in interpersonal relationships. These results support the EST of depression by demonstrating that following adversity, normative depressed mood preserved social inclusion with appropriate interpersonal support or pharmacotherapy.