The graphical brain: Belief propagation and active inference

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.

A sensory-motor theory of the neocortex

Recent neurophysiological and neuroanatomical studies suggest a close interaction between sensory and motor processes across the neocortex. Here, I propose that the neocortex implements active predictive coding (APC): each cortical area estimates both latent sensory states and actions (including potentially abstract actions internal to the cortex), and the cortex as a whole predicts the consequences of actions at multiple hierarchical levels. Feedback from higher areas modulates the dynamics of state and action networks in lower areas. I show how the same APC architecture can explain (1) how we recognize an object and its parts using eye movements, (2) why perception seems stable despite eye movements, (3) how we learn compositional representations, for example, part-whole hierarchies, (4) how complex actions can be planned using simpler actions, and (5) how we form episodic memories of sensory-motor experiences and learn abstract concepts such as a family tree. I postulate a mapping of the APC model to the laminar architecture of the cortex and suggest possible roles for cortico-cortical and cortico-subcortical pathways.

The neural network of sensory attenuation: A neuroimaging meta-analysis

Sensory attenuation refers to the reduction in sensory intensity resulting from self-initiated actions compared to stimuli initiated externally. A classic example is scratching oneself without feeling itchy. This phenomenon extends across various sensory modalities, including visual, auditory, somatosensory, and nociceptive stimuli. The internal forward model proposes that during voluntary actions, an efferent copy of the action command is sent out to predict sensory feedback. This predicted sensory feedback is then compared with the actual sensory feedback, leading to the suppression or reduction of sensory stimuli originating from self-initiated actions. To further elucidate the neural mechanisms underlying sensory attenuation effect, we conducted an extensive meta-analysis of functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies. Utilizing activation likelihood estimation (ALE) analysis, our results revealed significant activations in a prominent cluster encompassing the right superior temporal gyrus (rSTG), right middle temporal gyrus (rMTG), and right insula when comparing external-generated with self-generated conditions. Additionally, significant activation was observed in the right anterior cerebellum when comparing self-generated to external-generated conditions. Further analysis using meta-analytic connectivity modeling (MACM) unveiled distinct brain networks co-activated with the rMTG and right cerebellum, respectively. Based on these findings, we propose that sensory attenuation arises from the suppression of reflexive inputs elicited by self-initiated actions through the internal forward modeling of a cerebellum-centered action prediction network, enabling the "sensory conflict detection" regions to effectively discriminate between inputs resulting from self-induced actions and those originating externally.

Priors and prejudice: hierarchical predictive processing in intergroup perception

Hierarchical predictive processing provides a framework outlining how prior expectations shape perception and cognition. Here, we highlight hierarchical predictive processing as a framework for explaining how social context and group-based social knowledge can directly shape intergroup perception. More specifically, we argue that hierarchical predictive processing confers a uniquely valuable toolset to explain extant findings and generate novel hypotheses for intergroup perception. We first provide an overview of hierarchical predictive processing, specifying its primary theoretical assumptions. We then review evidence showing how prior knowledge influences intergroup perception. Next, we outline how hierarchical predictive processing can account well for findings in the intergroup perception literature. We then underscore the theoretical strengths of hierarchical predictive processing compared to other frameworks in this space. We finish by outlining future directions and laying out hypotheses that test the implications of hierarchical predictive processing for intergroup perception and intergroup cognition more broadly. Taken together, hierarchical predictive processing provides explanatory value and capacity for novel hypothesis generation for intergroup perception.

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.

On Predictive Planning and Counterfactual Learning in Active Inference

Given the rapid advancement of artificial intelligence, understanding the foundations of intelligent behaviour is increasingly important. Active inference, regarded as a general theory of behaviour, offers a principled approach to probing the basis of sophistication in planning and decision-making. This paper examines two decision-making schemes in active inference based on "planning" and "learning from experience". Furthermore, we also introduce a mixed model that navigates the data complexity trade-off between these strategies, leveraging the strengths of both to facilitate balanced decision-making. We evaluate our proposed model in a challenging grid-world scenario that requires adaptability from the agent. Additionally, our model provides the opportunity to analyse the evolution of various parameters, offering valuable insights and contributing to an explainable framework for intelligent decision-making.

Natural language syntax complies with the free-energy principle

Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating syntactic objects. We argue that recently proposed principles of economy in language design-such as "minimal search" criteria from theoretical syntax-adhere to the FEP. This affords a greater degree of explanatory power to the FEP-with respect to higher language functions-and offers linguistics a grounding in first principles with respect to computability. While we mostly focus on building new principled conceptual relations between syntax and the FEP, we also show through a sample of preliminary examples how both tree-geometric depth and a Kolmogorov complexity estimate (recruiting a Lempel-Ziv compression algorithm) can be used to accurately predict legal operations on syntactic workspaces, directly in line with formulations of variational free energy minimization. This is used to motivate a general principle of language design that we term Turing-Chomsky Compression (TCC). We use TCC to align concerns of linguists with the normative account of self-organization furnished by the FEP, by marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference.

An Overview of the Free Energy Principle and Related Research

The free energy principle and its corollary, the active inference framework, serve as theoretical foundations in the domain of neuroscience, explaining the genesis of intelligent behavior. This principle states that the processes of perception, learning, and decision making-within an agent-are all driven by the objective of "minimizing free energy," evincing the following behaviors: learning and employing a generative model of the environment to interpret observations, thereby achieving perception, and selecting actions to maintain a stable preferred state and minimize the uncertainty about the environment, thereby achieving decision making. This fundamental principle can be used to explain how the brain processes perceptual information, learns about the environment, and selects actions. Two pivotal tenets are that the agent employs a generative model for perception and planning and that interaction with the world (and other agents) enhances the performance of the generative model and augments perception. With the evolution of control theory and deep learning tools, agents based on the FEP have been instantiated in various ways across different domains, guiding the design of a multitude of generative models and decision-making algorithms. This letter first introduces the basic concepts of the FEP, followed by its historical development and connections with other theories of intelligence, and then delves into the specific application of the FEP to perception and decision making, encompassing both low-dimensional simple situations and high-dimensional complex situations. It compares the FEP with model-based reinforcement learning to show that the FEP provides a better objective function. We illustrate this using numerical studies of Dreamer3 by adding expected information gain into the standard objective function. In a complementary fashion, existing reinforcement learning, and deep learning algorithms can also help implement the FEP-based agents. Finally, we discuss the various capabilities that agents need to possess in complex environments and state that the FEP can aid agents in acquiring these capabilities.

Manifestations, social impact, and decay of conceptual beliefs: A cultural perspective

INTRODUCTION

Believing comprises multifaceted processes that integrate information from the outside world through meaning-making processes with personal relevance.

METHODS

Qualitative Review of the current literature in social cognitive neuroscience.

RESULTS

Although believing develops rapidly outside an individual's conscious awareness, it results in the formation of beliefs that are stored in memory and play an important role in determining an individual's behavior. Primal beliefs reflect an individual's experience of objects and events, whereas conceptual beliefs are based on narratives that are held in social groups. Conceptual beliefs can be about autobiographical, political, religious, and other aspects of life and may be encouraged by participation in group rituals. We hypothesize that assertions of future gains and rewards that transcend but are inherent in these codices provide incentives to follow the norms and rules of social groups.

CONCLUSION

The power of conceptual beliefs to provide cultural orientation is likely to fade when circumstances and evidence make it clear that what was asserted no longer applies.

The empirical status of predictive coding and active inference

Research on predictive processing models has focused largely on two specific algorithmic theories: Predictive Coding for perception and Active Inference for decision-making. While these interconnected theories possess broad explanatory potential, they have only recently begun to receive direct empirical evaluation. Here, we review recent studies of Predictive Coding and Active Inference with a focus on evaluating the degree to which they are empirically supported. For Predictive Coding, we find that existing empirical evidence offers modest support. However, some positive results can also be explained by alternative feedforward (e.g., feature detection-based) models. For Active Inference, most empirical studies have focused on fitting these models to behavior as a means of identifying and explaining individual or group differences. While Active Inference models tend to explain behavioral data reasonably well, there has not been a focus on testing empirical validity of active inference theory per se, which would require formal comparison to other models (e.g., non-Bayesian or model-free reinforcement learning models). This review suggests that, while promising, a number of specific research directions are still necessary to evaluate the empirical adequacy and explanatory power of these algorithms.

Active inference as a theory of sentient behavior

This review paper offers an overview of the history and future of active inference-a unifying perspective on action and perception. Active inference is based upon the idea that sentient behavior depends upon our brains' implicit use of internal models to predict, infer, and direct action. Our focus is upon the conceptual roots and development of this theory of (basic) sentience and does not follow a rigid chronological narrative. We trace the evolution from Helmholtzian ideas on unconscious inference, through to a contemporary understanding of action and perception. In doing so, we touch upon related perspectives, the neural underpinnings of active inference, and the opportunities for future development. Key steps in this development include the formulation of predictive coding models and related theories of neuronal message passing, the use of sequential models for planning and policy optimization, and the importance of hierarchical (temporally) deep internal (i.e., generative or world) models. Active inference has been used to account for aspects of anatomy and neurophysiology, to offer theories of psychopathology in terms of aberrant precision control, and to unify extant psychological theories. We anticipate further development in all these areas and note the exciting early work applying active inference beyond neuroscience. This suggests a future not just in biology, but in robotics, machine learning, and artificial intelligence.

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'.

From active affordance to active inference: vertical integration of cognition in the cerebral cortex through dual subcortical control systems

In previous papers, we proposed that the dorsal attention system's top-down control is regulated by the dorsal division of the limbic system, providing a feedforward or impulsive form of control generating expectancies during active inference. In contrast, we proposed that the ventral attention system is regulated by the ventral limbic division, regulating feedback constraints and error-correction for active inference within the neocortical hierarchy. Here, we propose that these forms of cognitive control reflect vertical integration of subcortical arousal control systems that evolved for specific forms of behavior control. The feedforward impetus to action is regulated by phasic arousal, mediated by lemnothalamic projections from the reticular activating system of the lower brainstem, and then elaborated by the hippocampus and dorsal limbic division. In contrast, feedback constraint-based on environmental requirements-is regulated by the tonic activation furnished by collothalamic projections from the midbrain arousal control centers, and then sustained and elaborated by the amygdala, basal ganglia, and ventral limbic division. In an evolutionary-developmental analysis, understanding these differing forms of active affordance-for arousal and motor control within the subcortical vertebrate neuraxis-may help explain the evolution of active inference regulating the cognition of expectancy and error-correction within the mammalian 6-layered neocortex.

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.

Nested Selves: Self-Organization and Shared Markov Blankets in Prenatal Development in Humans

The immune system is a central component of organismic function in humans. This paper addresses self-organization of biological systems in relation to-and nested within-other biological systems in pregnancy. Pregnancy constitutes a fundamental state for human embodiment and a key step in the evolution and conservation of our species. While not all humans can be pregnant, our initial state of emerging and growing within another person's body is universal. Hence, the pregnant state does not concern some individuals but all individuals. Indeed, the hierarchical relationship in pregnancy reflects an even earlier autopoietic process in the embryo by which the number of individuals in a single blastoderm is dynamically determined by cell- interactions. The relationship and the interactions between the two self-organizing systems during pregnancy may play a pivotal role in understanding the nature of biological self-organization per se in humans. Specifically, we consider the role of the immune system in biological self-organization in addition to neural/brain systems that furnish us with a sense of self. We examine the complex case of pregnancy, whereby two immune systems need to negotiate the exchange of resources and information in order to maintain viable self-regulation of nested systems. We conclude with a proposal for the mechanisms-that scaffold the complex relationship between two self-organising systems in pregnancy-through the lens of the Active Inference, with a focus on shared Markov blankets.

Path integrals, particular kinds, and strange things

This paper describes a path integral formulation of the free energy principle. The ensuing account expresses the paths or trajectories that a particle takes as it evolves over time. The main results are a method or principle of least action that can be used to emulate the behaviour of particles in open exchange with their external milieu. Particles are defined by a particular partition, in which internal states are individuated from external states by active and sensory blanket states. The variational principle at hand allows one to interpret internal dynamics-of certain kinds of particles-as inferring external states that are hidden behind blanket states. We consider different kinds of particles, and to what extent they can be imbued with an elementary form of inference or sentience. Specifically, we consider the distinction between dissipative and conservative particles, inert and active particles and, finally, ordinary and strange particles. Strange particles can be described as inferring their own actions, endowing them with apparent autonomy or agency. In short-of the kinds of particles afforded by a particular partition-strange kinds may be apt for describing sentient behaviour.

Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception

Mechanistic insight is achieved only when experiments are employed to test formal or computational models. Furthermore, in analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying healthy auditory perception. With a special focus on tinnitus-as the prime example of auditory phantom perception-we review recent work at the intersection of artificial intelligence, psychology and neuroscience. In particular, we discuss why everyone with tinnitus suffers from (at least hidden) hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that intrinsic neural noise is generated and amplified along the auditory pathway as a compensatory mechanism to restore normal hearing based on adaptive stochastic resonance. The neural noise increase can then be misinterpreted as auditory input and perceived as tinnitus. This mechanism can be formalized in the Bayesian brain framework, where the percept (posterior) assimilates a prior prediction (brain's expectations) and likelihood (bottom-up neural signal). A higher mean and lower variance (i.e. enhanced precision) of the likelihood shifts the posterior, evincing a misinterpretation of sensory evidence, which may be further confounded by plastic changes in the brain that underwrite prior predictions. Hence, two fundamental processing principles provide the most explanatory power for the emergence of auditory phantom perceptions: predictive coding as a top-down and adaptive stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles also play a crucial role in healthy auditory perception. Finally, in the context of neuroscience-inspired artificial intelligence, both processing principles may serve to improve contemporary machine learning techniques.

Action-rule-based cognitive control enables efficient execution of stimulus-response conflict tasks: a model validation of Simon task performance

Introduction

The human brain can flexibly modify behavioral rules to optimize task performance (speed and accuracy) by minimizing cognitive load. To show this flexibility, we propose an action-rule-based cognitive control (ARC) model. The ARC model was based on a stochastic framework consistent with an active inference of the free energy principle, combined with schematic brain network systems regulated by the dorsal anterior cingulate cortex (dACC), to develop several hypotheses for demonstrating the validity of the ARC model.

Methods

A step-motion Simon task was developed involving congruence or incongruence between important symbolic information (illustration of a foot labeled "L" or "R," where "L" requests left and "R" requests right foot movement) and irrelevant spatial information (whether the illustration is actually of a left or right foot). We made predictions for behavioral and brain responses to testify to the theoretical predictions.

Results

Task responses combined with event-related deep-brain activity (ER-DBA) measures demonstrated a key contribution of the dACC in this process and provided evidence for the main prediction that the dACC could reduce the Shannon surprise term in the free energy formula by internally reversing the irrelevant rapid anticipatory postural adaptation. We also found sequential effects with modulated dip depths of ER-DBA waveforms that support the prediction that repeated stimuli with the same congruency can promote remodeling of the internal model through the information gain term while counterbalancing the surprise term.

Discussion

Overall, our results were consistent with experimental predictions, which may support the validity of the ARC model. The sequential effect accompanied by dip modulation of ER-DBA waveforms suggests that cognitive cost is saved while maintaining cognitive performance in accordance with the framework of the ARC based on 1-bit congruency-dependent selective control.

Active learning impairments in substance use disorders when resolving the explore-exploit dilemma: A replication and extension of previous computational modeling results

BACKGROUND

Substance use disorders (SUDs) represent a major public health risk. Yet, our understanding of the mechanisms that maintain these disorders remains incomplete. In a recent computational modeling study, we found initial evidence that SUDs are associated with slower learning rates from negative outcomes and less value-sensitive choice (low "action precision"), which could help explain continued substance use despite harmful consequences.

METHODS

Here we aimed to replicate and extend these results in a pre-registered study with a new sample of 168 individuals with SUDs and 99 healthy comparisons (HCs). We performed the same computational modeling and group comparisons as in our prior report (doi: 10.1016/j.drugalcdep.2020.108208) to confirm previously observed effects. After completing all pre-registered replication analyses, we then combined the previous and current datasets (N = 468) to assess whether differences were transdiagnostic or driven by specific disorders.

RESULTS

Replicating prior results, SUDs showed slower learning rates for negative outcomes in both Bayesian and frequentist analyses (partial η2=.02). Previously observed differences in action precision were not confirmed. Learning rates for positive outcomes were also similar between groups. Logistic regressions including all computational parameters as predictors in the combined datasets could differentiate several specific disorders from HCs, but could not differentiate most disorders from each other.

CONCLUSIONS

These results provide robust evidence that individuals with SUDs adjust behavior more slowly in the face of negative outcomes than HCs. They also suggest this effect is common across several different SUDs. Future research should examine its neural basis and whether learning rates could represent a new treatment target or moderator of treatment outcome.

Depth and hierarchies in the predictive brain: From reaction to action

The human brain is a prediction device, a view widely accepted in neuroscience. Prediction is a rational and efficient response that relies on the brain's ability to create and employ generative models to optimize actions over unpredictable time horizons. We argue that extant predictive frameworks while compelling, have not explicitly accounted for the following: (a) The brain's generative models must incorporate predictive depth (i.e., rely on degrees of abstraction to enable predictions over different time horizons); (b) The brain's implementation scheme to account for varying predictive depth relies on dynamic predictive hierarchies formed using the brain's functional networks. We show that these hierarchies incorporate the ascending processes (driven by reaction), and the descending processes (related to prediction), eventually driving action. Because they are dynamically formed, predictive hierarchies allow the brain to address predictive challenges in virtually any domain. By way of application, we explain how this framework can be applied to heretofore poorly understood processes of human behavioral thermoregulation. Although mammalian thermoregulation has been closely tied to deep brain structures engaged in autonomic control such as the hypothalamus, this narrow conception does not translate well to humans. In addition to profound differences in evolutionary history, the human brain is bestowed with substantially increased functional complexity (that itself emerged from evolutionary differences). We argue that behavioral thermoregulation in humans is possible because, (a) ascending signals shaped by homeostatic sub-networks, interject with (b) descending signals related to prediction (implemented in interoceptive and executive sub-networks) and action (implemented in executive sub-networks). These sub-networks cumulatively form a predictive hierarchy for human thermoregulation, potentiating a range of viable responses to known and unknown thermoregulatory challenges. We suggest that our proposed extensions to the predictive framework provide a set of generalizable principles that can further illuminate the many facets of the predictive brain. This article is categorized under: Neuroscience > Behavior Philosophy > Action Psychology > Prediction.

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.

Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes

While the link between brain structure and function remains an ongoing challenge, the prevailing hypothesis is that the structure-function relationship may itself be gradually decoupling from unimodal to transmodal cortex. However, this hypothesis is constrained by the underlying models which may neglect requisite information. Here we relate structural and functional connectivity derived from diffusion and functional MRI through orthogonal eigenmodes governing frequency-specific diffusion patterns. We find that low-frequency eigenmodes contribute little to functional interactions in transmodal cortex, resulting in divergent structure-function relationships. Conversely, high-frequency eigenmodes predominantly support neuronal coactivation patterns in these areas, inducing structure-function convergence along a unimodal-transmodal hierarchy. High-frequency information, although weak and scattered, could enhance the structure-function tethering, especially in transmodal association cortices. Our findings suggest that the structure-function decoupling may not be an intrinsic property of brain organization, but can be narrowed through multiplexed and regionally specialized spatiotemporal propagation regimes.

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.

Active Vision in Binocular Depth Estimation: A Top-Down Perspective

Depth estimation is an ill-posed problem; objects of different shapes or dimensions, even if at different distances, may project to the same image on the retina. Our brain uses several cues for depth estimation, including monocular cues such as motion parallax and binocular cues such as diplopia. However, it remains unclear how the computations required for depth estimation are implemented in biologically plausible ways. State-of-the-art approaches to depth estimation based on deep neural networks implicitly describe the brain as a hierarchical feature detector. Instead, in this paper we propose an alternative approach that casts depth estimation as a problem of active inference. We show that depth can be inferred by inverting a hierarchical generative model that simultaneously predicts the eyes' projections from a 2D belief over an object. Model inversion consists of a series of biologically plausible homogeneous transformations based on Predictive Coding principles. Under the plausible assumption of a nonuniform fovea resolution, depth estimation favors an active vision strategy that fixates the object with the eyes, rendering the depth belief more accurate. This strategy is not realized by first fixating on a target and then estimating the depth; instead, it combines the two processes through action-perception cycles, with a similar mechanism of the saccades during object recognition. The proposed approach requires only local (top-down and bottom-up) message passing, which can be implemented in biologically plausible neural circuits.

Body as First Teacher: The Role of Rhythmic Visceral Dynamics in Early Cognitive Development

Embodied cognition-the idea that mental states and processes should be understood in relation to one's bodily constitution and interactions with the world-remains a controversial topic within cognitive science. Recently, however, increasing interest in predictive processing theories among proponents and critics of embodiment alike has raised hopes of a reconciliation. This article sets out to appraise the unificatory potential of predictive processing, focusing in particular on embodied formulations of active inference. Our analysis suggests that most active-inference accounts invoke weak, potentially trivial conceptions of embodiment; those making stronger claims do so independently of the theoretical commitments of the active-inference framework. We argue that a more compelling version of embodied active inference can be motivated by adopting a diachronic perspective on the way rhythmic physiological activity shapes neural development in utero. According to this visceral afferent training hypothesis, early-emerging physiological processes are essential not only for supporting the biophysical development of neural structures but also for configuring the cognitive architecture those structures entail. Focusing in particular on the cardiovascular system, we propose three candidate mechanisms through which visceral afferent training might operate: (a) activity-dependent neuronal development, (b) periodic signal modeling, and (c) oscillatory network coordination.

Toward a formal theory for computing machines made out of whatever physics offers

Approaching limitations of digital computing technologies have spurred research in neuromorphic and other unconventional approaches to computing. Here we argue that if we want to engineer unconventional computing systems in a systematic way, we need guidance from a formal theory that is different from the classical symbolic-algorithmic Turing machine theory. We propose a general strategy for developing such a theory, and within that general view, a specific approach that we call fluent computing. In contrast to Turing, who modeled computing processes from a top-down perspective as symbolic reasoning, we adopt the scientific paradigm of physics and model physical computing systems bottom-up by formalizing what can ultimately be measured in a physical computing system. This leads to an understanding of computing as the structuring of processes, while classical models of computing systems describe the processing of structures.

Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task

Introduction

Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation.

Methods

We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning.

Results

Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning.

Discussion

The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.

Experimental validation of the free-energy principle with in vitro neural networks

Empirical applications of the free-energy principle are not straightforward because they entail a commitment to a particular process theory, especially at the cellular and synaptic levels. Using a recently established reverse engineering technique, we confirm the quantitative predictions of the free-energy principle using in vitro networks of rat cortical neurons that perform causal inference. Upon receiving electrical stimuli-generated by mixing two hidden sources-neurons self-organised to selectively encode the two sources. Pharmacological up- and downregulation of network excitability disrupted the ensuing inference, consistent with changes in prior beliefs about hidden sources. As predicted, changes in effective synaptic connectivity reduced variational free energy, where the connection strengths encoded parameters of the generative model. In short, we show that variational free energy minimisation can quantitatively predict the self-organisation of neuronal networks, in terms of their responses and plasticity. These results demonstrate the applicability of the free-energy principle to in vitro neural networks and establish its predictive validity in this setting.

A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks

Deploying unmanned aerial vehicles (UAVs) as aerial base stations is an exceptional approach to reinforce terrestrial infrastructure owing to their remarkable flexibility and superior agility. However, it is essential to design their flight trajectory effectively to make the most of UAV-assisted wireless communications. This paper presents a novel method for improving wireless connectivity between UAVs and terrestrial users through effective path planning. This is achieved by developing a goal-directed trajectory planning method using active inference. First, we create a global dictionary using traveling salesman problem with profits (TSPWP) instances executed on various training examples. This dictionary represents the world model and contains letters representing available hotspots, tokens representing local paths, and words depicting complete trajectories and hotspot order. By using this world model, the UAV can understand the TSPWP's decision-making grammar and how to use the available letters to form tokens and words at various levels of abstraction and time scales. With this knowledge, the UAV can assess encountered situations and deduce optimal routes based on the belief encoded in the world model. Our proposed method outperforms traditional Q-learning by providing fast, stable, and reliable solutions with good generalization ability.

Cognitive effort and active inference

This paper aims to integrate some key constructs in the cognitive neuroscience of cognitive control and executive function by formalising the notion of cognitive (or mental) effort in terms of active inference. To do so, we call upon a task used in neuropsychology to assess impulse inhibition-a Stroop task. In this task, participants must suppress the impulse to read a colour word and instead report the colour of the text of the word. The Stroop task is characteristically effortful, and we unpack a theory of mental effort in which, to perform this task accurately, participants must overcome prior beliefs about how they would normally act. However, our interest here is not in overt action, but in covert (mental) action. Mental actions change our beliefs but have no (direct) effect on the outside world-much like deploying covert attention. This account of effort as mental action lets us generate multimodal (choice, reaction time, and electrophysiological) data of the sort we might expect from a human participant engaging in this task. We analyse how parameters determining cognitive effort influence simulated responses and demonstrate that-when provided only with performance data-these parameters can be recovered, provided they are within a certain range.

Really radical?

I enjoyed reading this compelling account of Conviction Narrative Theory (CNT). As a theoretical neurobiologist, I recognised - and applauded - the tenets of CNT. My commentary asks whether its claims could be installed into a Bayesian mechanics of decision-making, in a way that would enable theoreticians to model, reproduce and predict decision-making.

Active learning impairments in substance use disorders when resolving the explore-exploit dilemma: A replication and extension of previous computational modeling results

Background

Substance use disorders (SUDs) represent a major public health risk. Yet, our understanding of the mechanisms that maintain these disorders remains incomplete. In a recent computational modeling study, we found initial evidence that SUDs are associated with slower learning rates from negative outcomes and less value-sensitive choice (low "action precision"), which could help explain continued substance use despite harmful consequences.

Methods

Here we aimed to replicate and extend these results in a pre-registered study with a new sample of 168 individuals with SUDs and 99 healthy comparisons (HCs). We performed the same computational modeling and group comparisons as in our prior report (doi: 10.1016/j.drugalcdep.2020.108208) to confirm previously observed effects. After completing all pre-registered replication analyses, we then combined the previous and current datasets (N = 468) to assess whether differences were transdiagnostic or driven by specific disorders.

Results

Replicating prior results, SUDs showed slower learning rates for negative outcomes in both Bayesian and frequentist analyses (η 2 =.02). Previously observed differences in action precision were not confirmed. Logistic regressions including all computational parameters as predictors in the combined datasets could differentiate several specific disorders from HCs, but could not differentiate most disorders from each other.

Conclusions

These results provide robust evidence that individuals with SUDs have more difficulty adjusting behavior in the face of negative outcomes than HCs. They also suggest this effect is common across several different SUDs. Future research should examine its neural basis and whether learning rates could represent a new treatment target or moderator of treatment outcome.

Active Inference, Epistemic Value, and Uncertainty in Conceptual Disorganization in First-Episode Schizophrenia

BACKGROUND AND HYPOTHESIS

Active inference has become an influential concept in psychopathology. We apply active inference to investigate conceptual disorganization in first-episode schizophrenia. We conceptualize speech production as a decision-making process affected by the latent "conceptual organization"-as a special case of uncertainty about the causes of sensory information. Uncertainty is both minimized via speech production-in which function words index conceptual organization in terms of analytic thinking-and tracked by a domain-general salience network. We hypothesize that analytic thinking depends on conceptual organization. Therefore, conceptual disorganization in schizophrenia would be both indexed by low conceptual organization and reflected in the effective connectivity within the salience network.

STUDY DESIGN

With 1-minute speech samples from a picture description task and resting state fMRI from 30 patients and 30 healthy subjects, we employed dynamic causal and probabilistic graphical models to investigate if the effective connectivity of the salience network underwrites conceptual organization.

STUDY RESULTS

Low analytic thinking scores index low conceptual organization which affects diagnostic status. The influence of the anterior insula on the anterior cingulate cortex and the self-inhibition within the anterior cingulate cortex are elevated given low conceptual organization (ie, conceptual disorganization).

CONCLUSIONS

Conceptual organization, a construct that explains formal thought disorder, can be modeled in an active inference framework and studied in relation to putative neural substrates of disrupted language in schizophrenia. This provides a critical advance to move away from rating-scale scores to deeper constructs in the pursuit of the pathophysiology of formal thought disorder.

A deep hierarchy of predictions enables online meaning extraction in a computational model of human speech comprehension

Understanding speech requires mapping fleeting and often ambiguous soundwaves to meaning. While humans are known to exploit their capacity to contextualize to facilitate this process, how internal knowledge is deployed online remains an open question. Here, we present a model that extracts multiple levels of information from continuous speech online. The model applies linguistic and nonlinguistic knowledge to speech processing, by periodically generating top-down predictions and incorporating bottom-up incoming evidence in a nested temporal hierarchy. We show that a nonlinguistic context level provides semantic predictions informed by sensory inputs, which are crucial for disambiguating among multiple meanings of the same word. The explicit knowledge hierarchy of the model enables a more holistic account of the neurophysiological responses to speech compared to using lexical predictions generated by a neural network language model (GPT-2). We also show that hierarchical predictions reduce peripheral processing via minimizing uncertainty and prediction error. With this proof-of-concept model, we demonstrate that the deployment of hierarchical predictions is a possible strategy for the brain to dynamically utilize structured knowledge and make sense of the speech input.

A Bayesian computational model to investigate expert anticipation of a seemingly unpredictable ball bounce

During dynamic and time-constrained sporting tasks performers rely on both online perceptual information and prior contextual knowledge to make effective anticipatory judgments. It has been suggested that performers may integrate these sources of information in an approximately Bayesian fashion, by weighting available information sources according to their expected precision. In the present work, we extended Bayesian brain approaches to anticipation by using formal computational models to estimate how performers weighted different information sources when anticipating the bounce direction of a rugby ball. Both recreational (novice) and professional (expert) rugby players (n = 58) were asked to predict the bounce height of an oncoming rugby ball in a temporal occlusion paradigm. A computational model, based on a partially observable Markov decision process, was fitted to observed responses to estimate participants' weighting of online sensory cues and prior beliefs about ball bounce height. The results showed that experts were more sensitive to online sensory information, but that neither experts nor novices relied heavily on prior beliefs about ball trajectories in this task. Experts, but not novices, were observed to down-weight priors in their anticipatory decisions as later and more precise visual cues emerged, as predicted by Bayesian and active inference accounts of perception.

Time-consciousness in computational phenomenology: a temporal analysis of active inference

Time plays a significant role in science and everyday life. Despite being experienced as a continuous flow, computational models of consciousness are typically restricted to a sequential temporal structure. This difference poses a serious challenge for computational phenomenology-a novel field combining phenomenology and computational modelling. By analysing the temporal structure of the active inference framework, we show that an integrated continuity of time can be achieved by merging Husserlian temporality with a sequential order of time. We also show that a Markov blanket of the present moment integrates past and future moments of both subjective temporality and objective time in an asynchronous manner. By applying the integrated continuity, it is clear that active inference makes use of both subjective temporality and objective time in an integrated fashion. We conclude that active inference, on a temporal note, qualifies as a computational model for phenomenological investigations.

Computational psychiatry: from synapses to sentience

This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain-from cognitive and computational neuroscience-as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we-or our brains-encode uncertainty or its complement, precision. It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms.

Remembrance of things perceived: Adding thalamocortical function to artificial neural networks

Recent research has illuminated the complexity and importance of the thalamocortical system but it has been difficult to identify what computational functions it performs. Meanwhile, deep-learning artificial neural networks (ANNs) based on bio-inspired models of purely cortical circuits have achieved surprising success solving sophisticated cognitive problems associated historically with human intelligence. Nevertheless, the limitations and shortcomings of artificial intelligence (AI) based on such ANNs are becoming increasingly clear. This review considers how the addition of thalamocortical connectivity and its putative functions related to cortical attention might address some of those shortcomings. Such bio-inspired models are now providing both testable theories of biological cognition and improved AI technology, much of which is happening outside the usual academic venues.

Hierarchical predictive coding in distributed pain circuits

Predictive coding is a computational theory on describing how the brain perceives and acts, which has been widely adopted in sensory processing and motor control. Nociceptive and pain processing involves a large and distributed network of circuits. However, it is still unknown whether this distributed network is completely decentralized or requires networkwide coordination. Multiple lines of evidence from human and animal studies have suggested that the cingulate cortex and insula cortex (cingulate-insula network) are two major hubs in mediating information from sensory afferents and spinothalamic inputs, whereas subregions of cingulate and insula cortices have distinct projections and functional roles. In this mini-review, we propose an updated hierarchical predictive coding framework for pain perception and discuss its related computational, algorithmic, and implementation issues. We suggest active inference as a generalized predictive coding algorithm, and hierarchically organized traveling waves of independent neural oscillations as a plausible brain mechanism to integrate bottom-up and top-down information across distributed pain circuits.

Flexible intentions: An Active Inference theory

We present a normative computational theory of how the brain may support visually-guided goal-directed actions in dynamically changing environments. It extends the Active Inference theory of cortical processing according to which the brain maintains beliefs over the environmental state, and motor control signals try to fulfill the corresponding sensory predictions. We propose that the neural circuitry in the Posterior Parietal Cortex (PPC) compute flexible intentions-or motor plans from a belief over targets-to dynamically generate goal-directed actions, and we develop a computational formalization of this process. A proof-of-concept agent embodying visual and proprioceptive sensors and an actuated upper limb was tested on target-reaching tasks. The agent behaved correctly under various conditions, including static and dynamic targets, different sensory feedbacks, sensory precisions, intention gains, and movement policies; limit conditions were individuated, too. Active Inference driven by dynamic and flexible intentions can thus support goal-directed behavior in constantly changing environments, and the PPC might putatively host its core intention mechanism. More broadly, the study provides a normative computational basis for research on goal-directed behavior in end-to-end settings and further advances mechanistic theories of active biological systems.

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.

Active inference, morphogenesis, and computational psychiatry

Active inference is a leading theory in neuroscience that provides a simple and neuro-biologically plausible account of how action and perception are coupled in producing (Bayes) optimal behavior; and has been recently used to explain a variety of psychopathological conditions. In parallel, morphogenesis has been described as the behavior of a (non-neural) cellular collective intelligence solving problems in anatomical morphospace. In this article, we establish a link between the domains of cell biology and neuroscience, by analyzing disorders of morphogenesis as disorders of (active) inference. The aim of this article is three-fold. We want to: (i) reveal a connection between disorders of morphogenesis and disorders of active inference as apparent in psychopathological conditions; (ii) show how disorders of morphogenesis can be simulated using active inference; (iii) suggest that active inference can shed light on developmental defects or aberrant morphogenetic processes, seen as disorders of information processing, and perhaps suggesting novel intervention and repair strategies. We present four simulations illustrating application of these ideas to cellular behavior during morphogenesis. Three of the simulations show that the same forms of aberrant active inference (e.g., deficits of sensory attenuation and low sensory precision) that have been used to explain psychopathological conditions (e.g., schizophrenia and autism) also produce familiar disorders of development and morphogenesis when implemented at the level of the collective behavior of a group of cells. The fourth simulation involves two cells with too high precision, in which we show that the reduction of concentration signaling and sensitivity to the signals of other cells treats the development defect. Finally, we present the results of an experimental test of one of the model's predictions in early Xenopus laevis embryos: thioridazine (a dopamine antagonist that may reduce sensory precision in biological systems) induced developmental (anatomical) defects as predicted. The use of conceptual and empirical tools from neuroscience to understand the morphogenetic behavior of pre-neural agents offers the possibility of new approaches in regenerative medicine and evolutionary developmental biology.

Structure learning enhances concept formation in synthetic Active Inference agents

Humans display astonishing skill in learning about the environment in which they operate. They assimilate a rich set of affordances and interrelations among different elements in particular contexts, and form flexible abstractions (i.e., concepts) that can be generalised and leveraged with ease. To capture these abilities, we present a deep hierarchical Active Inference model of goal-directed behaviour, and the accompanying belief update schemes implied by maximising model evidence. Using simulations, we elucidate the potential mechanisms that underlie and influence concept learning in a spatial foraging task. We show that the representations formed–as a result of foraging–reflect environmental structure in a way that is enhanced and nuanced by Bayesian model reduction, a special case of structure learning that typifies learning in the absence of new evidence. Synthetic agents learn associations and form concepts about environmental context and configuration as a result of inferential, parametric learning, and structure learning processes–three processes that can produce a diversity of beliefs and belief structures. Furthermore, the ensuing representations reflect symmetries for environments with identical configurations.

Learning on Arbitrary Graph Topologies via Predictive Coding

Training with backpropagation (BP) in standard deep learning consists of two main steps: a forward pass that maps a data point to its prediction, and a backward pass that propagates the error of this prediction back through the network. This process is highly effective when the goal is to minimize a specific objective function. However, it does not allow training on networks with cyclic or backward connections. This is an obstacle to reaching brain-like capabilities, as the highly complex heterarchical structure of the neural connections in the neocortex are potentially fundamental for its effectiveness. In this paper, we show how predictive coding (PC), a theory of information processing in the cortex, can be used to perform inference and learning on arbitrary graph topologies. We experimentally show how this formulation, called PC graphs, can be used to flexibly perform different tasks with the same network by simply stimulating specific neurons. This enables the model to be queried on stimuli with different structures, such as partial images, images with labels, or images without labels. We conclude by investigating how the topology of the graph influences the final performance, and comparing against simple baselines trained with BP.

Generalized Simultaneous Localization and Mapping (G-SLAM) as unification framework for natural and artificial intelligences: towards reverse engineering the hippocampal/entorhinal system and principles of high-level cognition

Simultaneous localization and mapping (SLAM) represents a fundamental problem for autonomous embodied systems, for which the hippocampal/entorhinal system (H/E-S) has been optimized over the course of evolution. We have developed a biologically-inspired SLAM architecture based on latent variable generative modeling within the Free Energy Principle and Active Inference (FEP-AI) framework, which affords flexible navigation and planning in mobile robots. We have primarily focused on attempting to reverse engineer H/E-S "design" properties, but here we consider ways in which SLAM principles from robotics may help us better understand nervous systems and emergent minds. After reviewing LatentSLAM and notable features of this control architecture, we consider how the H/E-S may realize these functional properties not only for physical navigation, but also with respect to high-level cognition understood as generalized simultaneous localization and mapping (G-SLAM). We focus on loop-closure, graph-relaxation, and node duplication as particularly impactful architectural features, suggesting these computational phenomena may contribute to understanding cognitive insight (as proto-causal-inference), accommodation (as integration into existing schemas), and assimilation (as category formation). All these operations can similarly be describable in terms of structure/category learning on multiple levels of abstraction. However, here we adopt an ecological rationality perspective, framing H/E-S functions as orchestrating SLAM processes within both concrete and abstract hypothesis spaces. In this navigation/search process, adaptive cognitive equilibration between assimilation and accommodation involves balancing tradeoffs between exploration and exploitation; this dynamic equilibrium may be near optimally realized in FEP-AI, wherein control systems governed by expected free energy objective functions naturally balance model simplicity and accuracy. With respect to structure learning, such a balance would involve constructing models and categories that are neither too inclusive nor exclusive. We propose these (generalized) SLAM phenomena may represent some of the most impactful sources of variation in cognition both within and between individuals, suggesting that modulators of H/E-S functioning may potentially illuminate their adaptive significances as fundamental cybernetic control parameters. Finally, we discuss how understanding H/E-S contributions to G-SLAM may provide a unifying framework for high-level cognition and its potential realization in artificial intelligences.