Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference

Forgetting ourselves in flow: an active inference account of flow states and how we experience ourselves within them

Flow has been described as a state of optimal performance, experienced universally across a broad range of domains: from art to athletics, gaming to writing. However, its phenomenal characteristics can, at first glance, be puzzling. Firstly, individuals in flow supposedly report a loss of self-awareness, even though they perform in a manner which seems to evince their agency and skill. Secondly, flow states are felt to be effortless, despite the prerequisite complexity of the tasks that engender them. In this paper, we unpick these features of flow, as well as others, through the active inference framework, which posits that action and perception are forms of active Bayesian inference directed at sustained self-organisation; i.e., the minimisation of variational free energy. We propose that the phenomenology of flow is rooted in the deployment of high precision weight over (i) the expected sensory consequences of action and (ii) beliefs about how action will sequentially unfold. This computational mechanism thus draws the embodied cognitive system to minimise the ensuing (i.e., expected) free energy through the exploitation of the pragmatic affordances at hand. Furthermore, given the challenging dynamics the flow-inducing situation presents, attention must be wholly focussed on the unfolding task whilst counterfactual planning is restricted, leading to the attested loss of the sense of self-as-object. This involves the inhibition of both the sense of self as a temporally extended object and higher-order, meta-cognitive forms of self-conceptualisation. Nevertheless, we stress that self-awareness is not entirely lost in flow. Rather, it is pre-reflective and bodily. Our approach to bodily-action-centred phenomenology can be applied to similar facets of seemingly agentive experience beyond canonical flow states, providing insights into the mechanisms of so-called selfless experiences, embodied expertise and wellbeing.

When the interoceptive and conceptual clash: The case of oppositional phenomenal self-modelling in Tourette syndrome

Tourette syndrome (TS) has been associated with a rich set of symptoms that are said to be uncomfortable, unwilled, and effortful to manage. Furthermore, tics, the canonical characteristic of TS, are multifaceted, and their onset and maintenance is complex. A formal account that integrates these features of TS symptomatology within a plausible theoretical framework is currently absent from the field. In this paper, we assess the explanatory power of hierarchical generative modelling in accounting for TS symptomatology from the perspective of active inference. We propose a fourfold analysis of sensory, motor, and cognitive phenomena associated with TS. In Section 1, we characterise tics as a form of action aimed at sensory attenuation. In Section 2, we introduce the notion of epistemic ticcing and describe such behaviour as the search for evidence that there is an agent (i.e., self) at the heart of the generative hierarchy. In Section 3, we characterise both epistemic (sensation-free) and nonepistemic (sensational) tics as habitual behaviour. Finally, in Section 4, we propose that ticcing behaviour involves an inevitable conflict between distinguishable aspects of selfhood; namely, between the minimal phenomenal sense of self-which is putatively underwritten by interoceptive inference-and the explicit preferences that constitute the individual's conceptual sense of self. In sum, we aim to provide an empirically informed analysis of TS symptomatology under active inference, revealing a continuity between covert and overt features of the condition.

The police hunch: the Bayesian brain, active inference, and the free energy principle in action

In the realm of law enforcement, the "police hunch" has long been a mysterious but crucial aspect of decision-making. Drawing on the developing framework of Active Inference from cognitive science, this theoretical article examines the genesis, mechanics, and implications of the police hunch. It argues that hunches - often vital in high-stakes situations - should not be described as mere intuitions, but as intricate products of our mind's generative models. These models, shaped by observations of the social world and assimilated and enacted through active inference, seek to reduce surprise and make hunches an indispensable tool for officers, in exactly the same way that hypotheses are indispensable for scientists. However, the predictive validity of hunches is influenced by a range of factors, including experience and bias, thus warranting critical examination of their reliability. This article not only explores the formation of police hunches but also provides practical insights for officers and researchers on how to harness the power of active inference to fully understand policing decisions and subsequently explore new avenues for future research.

Surfing uncertainty with screams: predictive processing, error dynamics and horror films

Despite tremendous efforts in psychology, neuroscience and media and cultural studies, it is still something of a mystery why humans are attracted to fictional content that is horrifying, disgusting or otherwise aversive. While the psychological benefits of horror films, stories, video games, etc. has recently been demonstrated empirically, current theories emphasizing the negative and positive consequences of this engagement often contradict one another. Here, we suggest the predictive processing framework may provide a unifying account of horror content engagement that provides clear and testable hypotheses, and explains why a 'sweet spot' of fear and fun exists in horror entertainment. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Metacognitive Feelings: A Predictive-Processing Perspective

Metacognitive feelings are affective experiences that concern the subject's mental processes and capacities. Paradigmatic examples include the feeling of familiarity, the feeling of confidence, or the tip-of-the-tongue experience. In this article, we advance an account of metacognitive feelings based on the predictive-processing framework. The core tenet of predictive processing is that the brain is a hierarchical hypothesis-testing mechanism, predicting sensory input on the basis of prior experience and updating predictions on the basis of the incoming prediction error. According to the proposed account, metacognitive feelings arise out of a process in which visceral changes serve as cues to predict the error dynamics relating to a particular mental process. The expected rate of prediction-error reduction corresponds to the valence at the core of the emerging metacognitive feeling. Metacognitive feelings use prediction dynamics to model the agent's situation in a way that is both descriptive and directive. Thus, metacognitive feelings are not only an appraisal of ongoing cognitive performance but also a set of action policies. These action policies span predictive trajectories across bodily action, mental action, and interoceptive changes, which together transform the epistemic landscape within which metacognitive feelings unfold.

A model of time-varying music engagement

The current paper offers a model of time-varying music engagement, defined as changes in curiosity, attention and positive valence, as music unfolds over time. First, we present research (including new data) showing that listeners tend to allocate attention to music in a manner that is guided by both features of the music and listeners' individual differences. Next, we review relevant predictive processing literature before using this body of work to inform our model. In brief, we propose that music engagement, over the course of an extended listening episode, may constitute several cycles of curiosity, attention and positive valence that are interspersed with moments of mind-wandering. Further, we suggest that refocusing on music after an episode of mind-wandering can be due to triggers in the music or, conversely, mental action that occurs when the listener realizes they are mind-wandering. Finally, we argue that factors that modulate both overall levels of music engagement and how it changes over time include music complexity, listener background and the listening context. Our paper highlights how music can be used to provide insights into the temporal dynamics of attention and into how curiosity might emerge in everyday contexts. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Explanatory power by vagueness. Challenges to the strong prior hypothesis on hallucinations exemplified by the Charles-Bonnet-Syndrome

Predictive processing models are often ascribed a certain generality in conceptually unifying the relationships between perception, action, and cognition or the potential to posit a 'grand unified theory' of the mind. The limitations of this unification can be seen when these models are applied to specific cognitive phenomena or phenomenal consciousness. Our article discusses these shortcomings for predictive processing models of hallucinations by the example of the Charles-Bonnet-Syndrome. This case study shows that the current predictive processing account omits essential characteristics of stimulus-independent perception in general, which has critical phenomenological implications. We argue that the most popular predictive processing model of hallucinatory conditions - the strong prior hypothesis - fails to fully account for the characteristics of nonveridical perceptual experiences associated with Charles-Bonnet-Syndrome. To fill this explanatory gap, we propose that the strong prior hypothesis needs to include reality monitoring to apply to more than just veridical percepts.

Overcoming the translational crisis of contemporary psychiatry - converging phenomenological and spatiotemporal psychopathology

Despite all neurobiological/neurocomputational progress in psychiatric research, recent authors speak about a 'crisis of contemporary psychiatry'. Some argue that we do not yet know the computational mechanisms underlying the psychopathological symptoms ('crisis of mechanism') while others diagnose a neglect of subjectivity, namely first-person experience ('crisis of subjectivity'). In this perspective, we propose that Phenomenological Psychopathology, due to its focus on first-person experience of space and time, is in an ideal position to address the crisis of subjectivity and, if extended to the brain's spatiotemporal topographic-dynamic structure as key focus of Spatiotemporal Psychopathology, the crisis of mechanism. We demonstrate how the first-person experiences of space and time differ between schizophrenia, mood disorders and anxiety disorders allowing for their differential-diagnosis - this addresses the crisis of subjectivity. Presupposing space and time as shared features of brain, experience, and symptoms as their "common currency", the structure of abnormal space and time experience may also serve as template for the structure of the brain's spatiotemporal neuro-computational mechanisms - this may address the crisis of mechanism. Preliminary scientific evidence in our examples of schizophrenia, bipolar disorder, anxiety disorder, and depression support such clinically relevant spatiotemporal determination of both first-person experience (crisis of subjectivity) and the brain's neuro-computational structure (crisis of mechanism). In conclusion, converging Phenomenological Psychopathology with Spatiotemporal Psychopathology might help to overcome the translational crisis in psychiatry by delineating more fine-grained neuro computational and -phenomenal mechanisms; this offers novel candidate biomarkers for diagnosis and therapy including both pharmacological and non-pharmacological treatment.

An Introduction to Predictive Processing Models of Perception and Decision-Making

The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision-making, and motor control. This article provides an up-to-date introduction to the two most influential theories within this framework: predictive coding and active inference. The first half of the paper (Sections 2-5) reviews the evolution of predictive coding, from early ideas about efficient coding in the visual system to a more general model encompassing perception, cognition, and motor control. The theory is characterized in terms of the claims it makes at Marr's computational, algorithmic, and implementation levels of description, and the conceptual and mathematical connections between predictive coding, Bayesian inference, and variational free energy (a quantity jointly evaluating model accuracy and complexity) are explored. The second half of the paper (Sections 6-8) turns to recent theories of active inference. Like predictive coding, active inference models assume that perceptual and learning processes minimize variational free energy as a means of approximating Bayesian inference in a biologically plausible manner. However, these models focus primarily on planning and decision-making processes that predictive coding models were not developed to address. Under active inference, an agent evaluates potential plans (action sequences) based on their expected free energy (a quantity that combines anticipated reward and information gain). The agent is assumed to represent the world as a partially observable Markov decision process with discrete time and discrete states. Current research applications of active inference models are described, including a range of simulation work, as well as studies fitting models to empirical data. The paper concludes by considering future research directions that will be important for further development of both models.

Relative fluency (unfelt vs felt) in active inference

For a growing number of researchers, it is now accepted that the brain is a predictive organ that predicts the content of the sensorium and crucially the precision of-or confidence in-its own predictions. In order to predict the precision of its predictions, the brain has to infer the reliability of its own beliefs. This means that our brains have to recognise the precision of their predictions or, at least, their accuracy. In this paper, we argue that fluency is product of this recognition process. In short, to recognise fluency is to infer that we have a precise 'grip' on the unfolding processes that generate our sensations. More specifically, we propose that it is changes in fluency - from unfelt to felt - that are both recognised and realised when updating predictions about precision. Unfelt fluency orients attention to unpredicted sensations, while felt fluency supervenes on-and contextualises-unfelt fluency; thereby rendering certain attentional processes, phenomenologically opaque. As such, fluency underwrites the precision we place in our predictions and therefore acts upon our perceptual inferences. Hence, the causes of conscious subjective inference have unconscious perceptual precursors.

Insight and the selection of ideas

Perhaps it is no accident that insight moments accompany some of humanity's most important discoveries in science, medicine, and art. Here we propose that feelings of insight play a central role in (heuristically) selecting an idea from the stream of consciousness by capturing attention and eliciting a sense of intuitive confidence permitting fast action under uncertainty. The mechanisms underlying this Eureka heuristic are explained within an active inference framework. First, implicit restructuring via Bayesian reduction leads to a higher-order prediction error (i.e., the content of insight). Second, dopaminergic precision-weighting of the prediction error accounts for the intuitive confidence, pleasure, and attentional capture (i.e., the feeling of insight). This insight as precision account is consistent with the phenomenology, accuracy, and neural unfolding of insight, as well as its effects on belief and decision-making. We conclude by reflecting on dangers of the Eureka Heuristic, including the arising and entrenchment of false beliefs and the vulnerability of insights under psychoactive substances and misinformation.

Cessations of consciousness in meditation: Advancing a scientific understanding of nirodha samāpatti

Absence of consciousness can occur due to a concussion, anesthetization, intoxication, epileptic seizure, or other fainting/syncope episode caused by lack of blood flow to the brain. However, some meditation practitioners also report that it is possible to undergo a total absence of consciousness during meditation, lasting up to 7 days, and that these "cessations" can be consistently induced. One form of extended cessation (i.e., nirodha samāpatti) is thought to be different from sleep because practitioners are said to be completely impervious to external stimulation. That is, they cannot be 'woken up' from the cessation state as one might be from a dream. Cessations are also associated with the absence of any time experience or tiredness, and are said to involve a stiff rather than a relaxed body. Emergence from meditation-induced cessations is said to have profound effects on subsequent cognition and experience (e.g., resulting in a sudden sense of clarity, openness, and possibly insights). In this paper, we briefly outline the historical context for cessation events, present preliminary data from two labs, set a research agenda for their study, and provide an initial framework for understanding what meditation induced cessation may reveal about the mind and brain. We conclude by integrating these so-called nirodha and nirodha samāpatti experiences-as they are known in classical Buddhism-into current cognitive-neurocomputational and active inference frameworks of meditation.

Integrated world modeling theory expanded: Implications for the future of consciousness

Integrated world modeling theory (IWMT) is a synthetic theory of consciousness that uses the free energy principle and active inference (FEP-AI) framework to combine insights from integrated information theory (IIT) and global neuronal workspace theory (GNWT). Here, I first review philosophical principles and neural systems contributing to IWMT's integrative perspective. I then go on to describe predictive processing models of brains and their connections to machine learning architectures, with particular emphasis on autoencoders (perceptual and active inference), turbo-codes (establishment of shared latent spaces for multi-modal integration and inferential synergy), and graph neural networks (spatial and somatic modeling and control). Future directions for IIT and GNWT are considered by exploring ways in which modules and workspaces may be evaluated as both complexes of integrated information and arenas for iterated Bayesian model selection. Based on these considerations, I suggest novel ways in which integrated information might be estimated using concepts from probabilistic graphical models, flow networks, and game theory. Mechanistic and computational principles are also considered with respect to the ongoing debate between IIT and GNWT regarding the physical substrates of different kinds of conscious and unconscious phenomena. I further explore how these ideas might relate to the "Bayesian blur problem," or how it is that a seemingly discrete experience can be generated from probabilistic modeling, with some consideration of analogies from quantum mechanics as potentially revealing different varieties of inferential dynamics. I go on to describe potential means of addressing critiques of causal structure theories based on network unfolding, and the seeming absurdity of conscious expander graphs (without cybernetic symbol grounding). Finally, I discuss future directions for work centered on attentional selection and the evolutionary origins of consciousness as facilitated "unlimited associative learning." While not quite solving the Hard problem, this article expands on IWMT as a unifying model of consciousness and the potential future evolution of minds.

Oversampled and undersolved: Depressive rumination from an active inference perspective

Rumination is a widely recognized cognitive deviation in depression. Despite the recognition, researchers have struggled to explain why patients cannot disengage from the process, although it depresses their mood and fails to lead to effective problem-solving. We rethink rumination as repetitive but unsuccessful problem-solving attempts. Appealing to an active inference account, we suggest that adaptive problem-solving is based on the generation, evaluation, and performance of candidate policies that increase an organism's knowledge of its environment. We argue that the problem-solving process is distorted during rumination. Specifically, rumination is understood as engaging in excessive yet unsuccessful oversampling of policy candidates that do not resolve uncertainty. Because candidates are sampled from policies that were selected in states resembling one's current state, "bad" starting points (e.g., depressed mood, physical inactivity) make the problem-solving process vulnerable for generating a ruminative "halting problem". This problem leads to high opportunity costs, learned helplessness and diminished overt behavior. Besides reviewing evidence for the conceptual paths of this model, we discuss its neurophysiological correlates and point towards clinical implications.

Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence

What role do affective feelings (feelings/emotions/moods) play in adaptive behaviour? What are the implications of this for understanding and developing artificial general intelligence? Leading theoretical models of brain function are beginning to shed light on these questions. While artificial agents have excelled within narrowly circumscribed and specialised domains, domain-general intelligence has remained an elusive goal in artificial intelligence research. By contrast, humans and nonhuman animals are characterised by a capacity for flexible behaviour and general intelligence. In this article I argue that computational models of mental phenomena in predictive processing theories of the brain are starting to reveal the mechanisms underpinning domain-general intelligence in biological agents, and can inform the understanding and development of artificial general intelligence. I focus particularly on approaches to computational phenomenology in the active inference framework. Specifically, I argue that computational mechanisms of affective feelings in active inference-affective self-modelling-are revealing of how biological agents are able to achieve flexible behavioural repertoires and general intelligence. I argue that (i) affective self-modelling functions to "tune" organisms to the most tractable goals in the environmental context; and (ii) affective and agentic self-modelling is central to the capacity to perform mental actions in goal-directed imagination and creative cognition. I use this account as a basis to argue that general intelligence of the level and kind found in biological agents will likely require machines to be implemented with analogues of affective self-modelling.

A step-by-step tutorial on active inference and its application to empirical data

The active inference framework, and in particular its recent formulation as a partially observable Markov decision process (POMDP), has gained increasing popularity in recent years as a useful approach for modeling neurocognitive processes. This framework is highly general and flexible in its ability to be customized to model any cognitive process, as well as simulate predicted neuronal responses based on its accompanying neural process theory. It also affords both simulation experiments for proof of principle and behavioral modeling for empirical studies. However, there are limited resources that explain how to build and run these models in practice, which limits their widespread use. Most introductions assume a technical background in programming, mathematics, and machine learning. In this paper we offer a step-by-step tutorial on how to build POMDPs, run simulations using standard MATLAB routines, and fit these models to empirical data. We assume a minimal background in programming and mathematics, thoroughly explain all equations, and provide exemplar scripts that can be customized for both theoretical and empirical studies. Our goal is to provide the reader with the requisite background knowledge and practical tools to apply active inference to their own research. We also provide optional technical sections and multiple appendices, which offer the interested reader additional technical details. This tutorial should provide the reader with all the tools necessary to use these models and to follow emerging advances in active inference research.

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.

Past and Future Explanations for Depersonalization and Derealization Disorder: A Role for Predictive Coding

Depersonalization (DP) and derealization (DR) refer to states of dissociation in which one feels a sense of alienation in relation to one's self and environment, respectively. Whilst transient episodes often diminish without treatment, chronic experiences of DP and DR may last for years, with common treatments lacking a strong evidence base for their efficacy. We propose a theoretical explanation of DP and DR based on interoceptive predictive coding, and discuss how transient experiences of DP and DR may be induced in the non-clinical population using virtual reality. Further, we review the use of heartbeat evoked potentials in detecting the neural correlates of DP and DR allowing for an objective measure of these experiences in the non-clinical population. Finally, we discuss how the induction and detection of transient experiences of DP and DR in the non-clinical population could shed light on how the brain constructs one's sense of self and reality.