Adam Safron

Here we review recent work attempting to combine the first principles formalism of the Free Energy Principle and Active Inference (FEP-AI) framework with a recently proposed integrative model that attempts to ground personality as control variables for goal-seeking systems: Cybernetic Big 5 Theory (CB5T). First we summarize core aspects of this synthesis, then introduce some novel (and speculative) hypotheses, and then finally consider future implications for personality modeling with FEP-AI and CB5T.

Intelligence has been operationalized as both goal-pursuit capacity across a broad range of environments, and also as learning capacity above and beyond a foundational set of core priors. Within the normative framework of AIXI, intelligence may be understood as capacities for compressing (and thereby predicting) data and achieving goals via programs with minimal algorithmic complexity. Within the Free Energy Principle and Active Inference framework, intelligence may be understood as capacity for inference and learning of predictive models for goal-realization, with beliefs favored to the extent they fit novel data with minimal updating of priors. Most recently, consciousness has been proposed to enhance intelligent functioning by allowing for iterative state estimation of the essential variables of a system and its relationships to its environment, conditioned on a causal world model. This paper discusses machine learning architectures and principles by which all these views may be ...

Consciousness is constituted by a structure that includes contents as foreground and the environment as background. This structural relation between the experiential foreground background presupposes a relationship between the brain and the environment, often neglected in theories of consciousness. The Temporo-spatial Theory of Consciousness (TTC) addresses the brain-environment relation by a concept labeled “temporo-spatial alignment”. Briefly, temporo-spatial alignment refers to the brain’s neuronal activity’s inter-action with and adaption to interoceptive bodily and exteroceptive environmental stimuli, including their symmetry as key for consciousness. Combining theory and empirical data, this article attempts to demonstrate the yet unclear neurophenomenal mechanisms of temporo-spatial alignment. First, we suggest three neuronal layers of the brain’s temporo-spatial alignment to the environment. These neuronal layers span across a continuum from longer to shorter timescales. (1) The background layer comprises longer and more powerful timescales mediating topographic-dynamic similarities between different subjects’ brains. (2) The intermediate layer includes a mixture of medium-scaled timescales allowing for stochastic matching between environmental inputs and neuronal activity through the brain’s intrinsic neuronal timescales (INT) and temporal receptive windows (TRW). (3) The foreground layer comprises shorter and less powerful time-scales for neuronal entrainment of stimuli temporal onset through neuronal phase shifting and resetting. Second, we elaborate on how the three neuronal layers of temporo-spatial alignment correspond to their respective phenomenal layers of consciousness. (1) The inter-subjectively shared contextual background of consciousness. (2) An intermediate layer that mediates the relationship between different contents of consciousness. (3) A foreground layer that includes specific fast-changing contents of consciousness. Overall, temporo-spatial alignment may provide a  mechanism whose different neuronal layers modulate corresponding phenomenal layers of consciousness. Temporo-spatial alignment can provide a bridging principle for linking physical-energetic (free energy), dynamic (symmetry), neuronal (three layers of distinct time-space scales), and phenomenal (form featured by background-intermediate-foreground) mechanisms of consciousness.

Here we consider psychedelics with respect to their mechanisms of action, use, and implications for our understandings of brain and mind. This review is somewhat nontraditional in its scope, with discussions of both basic facts as well as theoretical speculations. We chose this approach given the unique historical context we find ourselves in at present. While we have decades of investigations to draw upon with respect to the clinical science and neuropsychopharmacology of psychedelics, we are also witnessing a renaissance (or perhaps a revolution) in which scientific and public interest in these compounds seems to be exploding. We have decided to split this review between the fundamental and theoretical, with hopes that we can give readers familiarity with well-established knowledge, as well as questions to hold in mind in attempting to make sense of a rapidly shifting epistemic landscape. Below we focus on serotonin 2A receptor (5-HT2aR) agonists, or “classic psychedelics,” and di...

Humanity faces multiple existential risks in the coming decades due to technological advances in AI, and the possibility of unintended behaviors emerging from such systems. We believe that better outcomes may be possible by rigorously exploring frameworks for intelligent (goal-oriented) behavior inspired by computational neuroscience. Here, we explore how the Free Energy Principle and Active Inference (FEP-AI) framework may provide solutions for these challenges via affording the realization of control systems operating according to principles of hierarchical Bayesian modeling and prediction-error (i.e., surprisal) minimization. Such FEP-AI agents are equipped with hierarchically-organized world models capable of counterfactual planning, realized by the kinds of reciprocal message passing performed by mammalian nervous systems, so allowing for the flexible construction of representations of self-world dynamics with varying degrees of temporal depth. We will describe how such systems...

Marek et al. analyzed three very large magnetic resonance imaging (MRI) datasets and concluded that thousands of participants are necessary to ensure replicable results in “brain-wide associations studies,” which they defined as “studies of the associations between common inter-individual variability in human brain structure/function and cognition or psychiatric symptomatology.” This conclusion overgeneralizes the implications of their findings and is likely to have an unwarranted chilling effect on neuroimaging research focused on individual differences, preventing good research with samples in the hundreds from being funded and conducted. To fend off these negative consequences, we explain why their conclusion is not fully justified, discuss methods that can yield larger effects, and suggest practical guidelines for sample size, recognizing the potential utility of samples in the hundreds.

In this chapter we review some of the multifarious roles of synchrony in mating psychology. We describe how synchronous dynamics contribute to the coherence of nervous systems; early bonding and development; and sexual and romantic relationships. While it is difficult to do justice to such a deep topic in a single chapter, we have attempted to provide a broad overview of ways in which rhythms have implications for understanding our social and emotional selves, and how our self-boundaries may expand to include others. Given the importance of wisely choosing and skillfully navigating this sort of co-mingling, we describe how varying degrees of intimacy can both gradually and precipitously build through patterns of synchronized rhythmic interactions, which both help to structure and gate the activities through which increasing degrees of closeness are shaped through mutually-rewarding experiences. While here we primarily focus on sexuality from a scientific and humanistic perspective, ...

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). Particular emphasis is placed on the hippocampal/entorhinal system, which may provide a source of high-level reasoning via predictive contrasting and generalized navigation, so affording multiple kinds of conscious access. Future directions for IIT an...

In this theoretical review, we begin by discussing brains and minds from a dynamical systems perspective, and then go on to describe methods for characterizing the flexibility of dynamic networks. We discuss how varying degrees and kinds of flexibility may be adaptive (or maladaptive) in different contexts, specifically focusing on measures related to either more disjoint or cohesive dynamics. While disjointed flexibility may be useful for assessing neural entropy, cohesive flexibility may potentially serve as a proxy for self-organized criticality as a fundamental property enabling adaptive behavior in complex systems. Particular attention is given to recent studies in which flexibility methods have been used to investigate neurological and cognitive maturation, as well as the breakdown of conscious processing under varying levels of anesthesia. We further discuss how these findings and methods might be contextualized within the Free Energy Principle with respect to the fundamental...

Drawing from both enactivist and cognitivist perspectives on mind, I propose that explaining teleological phenomena may require reappraising both “Cartesian theaters” and mental homunculi in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning as predictive-memory systems and cybernetic controllers. Quasi-homuncular ESMs are suggested to constitute a major organizing principle for neural architectures due to their initial and ongoing significance for solutions to inference problems in cognitive (and affective) development. Embodied experiences provide foundational lessons in learning curriculums in which agents explore increasingly challenging problem spaces, so answering an unresolved question in Bayesian cognitive science: what are biologically plausible mechanisms for equipping learners with sufficiently powerful inductive biases to adequately constrain inference spaces? Drawing on models from neurophysiology, psychology, and developm...

Relative to other neuromodulators, serotonin (5-HT) has received far less attention in machine learning and active inference. We will review prior work interpreting 5-HT1a signaling as an uncertainty parameter with opponency to dopamine. We will then discuss how 5-HT2a receptors may promote more exploratory policy selection by enhancing imaginative planning (as sophisticated affective inference). Finally, we will briefly comment on how qualitatively different effects may be observed across low and high levels of 5-HT2a signaling, where the latter may help agents to change self-adversarial policies and break free of maladaptive absorbing states in POMDPs.

What is motivation and how does it work? Where do goals come from and how do they vary within and between species and individuals? Why do we prefer some things over others? MEDO is a theoretical framework for understanding these questions in abstract terms, as well as for generating and evaluating specific hypotheses that seek to explain goal-oriented behavior. MEDO views preferences as selective pressures influencing the likelihood of particular outcomes. With respect to biological organisms, these patterns must compete and cooperate in shaping system evolution. To the extent that shaping processes are themselves altered by experience, this enables feedback relationships where histories of reward and punishment can impact future motivation. In this way, various biases can undergo either amplification or attenuation, resulting in preferences and behavioral orientations of varying degrees of inter-temporal and inter-situational stability. MEDO specifically models all shaping dynamics...

It has been argued that all of cognition can be understood in terms of Bayesian inference. It has also been argued that analogy is the core of cognition. Here I will propose that these perspectives are fully compatible, in that analogical reasoning can be described in terms of Bayesian inference and vice versa, and that both of these positions require a thorough cybernetic grounding in order to fulfill their promise as unifying frameworks for understanding minds. From the Bayesian perspective of the Free Energy Principle and Active Inference framework, thought is constituted by dynamics of cascading belief propagation through the nodes of probabilistic generative models specified by a cortical heterarchy "rooted" in action-perception cycles that ground the mind as an embodied control system for an autonomous agent. From the analogical structure mapping perspective, thought is constituted by the alignment and comparison of heterogeneous structural representations. Here I wi...

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,...

Cybernetics is the study of goal-directed systems that self-regulate via feedback, a category that includes human beings. Cybernetic Big Five Theory (CB5T) attempts to explain personality in cybernetic terms, conceptualizing personality traits as manifestations of variation in parameters of the neural mechanisms that evolved to facilitate cybernetic control. The Free Energy Principle and Active Inference framework (FEP-AI) is an overarching approach for understanding how it is that complex systems manage to persist in a world governed by the second law of thermodynamics—the inevitable tendency toward entropy. Although these two cybernetic theories were developed independently, they overlap in their theoretical foundations and implications and are complementary in their approaches to understanding persons. FEP-AI contributes a potentially valuable formal modeling framework for CB5T, while CB5T provides detail about the science and structure of personality. In this chapter we explore ...

In this brief commentary on The Hidden Spring: A Journey to the Source of Consciousness, I describe ways in which Mark Solms’ account of the origins of subjective experience relates to Integrated World Modeling Theory (IWMT). IWMT is a synthetic theory that brings together different perspectives, with the ultimate goal of solving the enduring problems of consciousness, including the Hard problem. I describe points of compatibility and incompatibility between Solms’ proposal and IWMT, with particular emphasis on how a Bayesian interpretation of Integrated Information Theory and Global (Neuronal) Workspace Theory may help identify the physical and computational substrates of consciousness.

How is it that psychedelics so profoundly impact brain and mind? According to the highly influential model of "Relaxed Beliefs Under Psychedelics" (REBUS) (Carhart-Harris and Friston, 2019), 5-HT2a agonism is thought to help relax prior expectations, so making room for new perspectives and patterns. This model is contextualized within the Free Energy Principle and Active Inference framework, as well as the associated neuronal processes theory of hierarchical predictive processing. More specifically, excessive excitation of deep pyramidal neurons is thought to cause paradoxical desynchronization, so "flattening" (Bayesian) "belief landscapes" by attenuating large-scale complexes of synchronous neural activity, particularly at alpha frequencies. Here, we introduce an alternative (but largely compatible) perspective, in that while such effects may be both real and important, these alterations may primarily correspond to a rare (but potentially pivotal) regime of very high levels of serotonin 2a receptor (5-HT2aR) agonism. We suggest an opposite effect may occur along much of the dose-response curve of 5-HT2aR stimulation, in which synchronous neural activity becomes more powerful, with accompanying "Strengthened Beliefs Under Psychedelics" (SEBUS) effects. We believe that REBUS effects are indeed crucially important aspects of psychedelic experiences, but suggest these exist alongside SEBUS effects in various combinations. As such, we propose a larger integrative perspective for understanding "Altered Beliefs Under Psychedelics” (ALBUS). The ALBUS framework provides a rich account of cognition based on predictive processing, which we believe provides a means of fruitfully integrating across theories of psychedelic action ranging from REBUS, to “thalamic gating” (Preller et al., 2019), to the newly suggested “cortico-striatal thalamo-cortical” model (Doss et al., 2021). Towards this end we demonstrate the utility of ALBUS by providing neurophenomenological models of psychedelics focusing on mechanisms of conscious perceptual synthesis, as well as hippocampally-orchestrated episodic memory and mental simulation. We further discuss cognitive diversity (including psychopathology) through the lens of these models. We consider the potential significances of modifications of the default mode network and alpha rhythms for creativity and various states of consciousness, including with respect to fundamental alterations in sense of self through ego dissolution. Finally, we survey a broad range of psychedelic phenomena and consider potential explanations, implications, and directions for future work.

Is sexual orientation an evolutionary adaptation or social construct? With respect to sexual preferences, to what extent are we “born that way” and to what extent does learning matter? This chapter discusses how nature and nurture may interact to shape sexual motivation by reviewing existing literature on sexual preferences and orientations, as well as by considering sex/gender differences in erotic plasticity, sexual fluidity, and the specificity of sexual arousal. We describe how these phenomena might be accounted for by processes in which mind body feedback loops amplify some sexual responses over others on multiple levels, which we refer to as the Reward Competition Feedback (RCF) model. With respect to sex/gender differences, we describe how these positive feedback processes might be amplified in men compared with women, potentially substantially driven by differences in the constraints and affordances of female and male anatomy. More specifically, we argue that the well-known ...

The Free Energy Principle and Active Inference Framework (FEP-AI) begins with the understanding that thermodynamically-open systems must regulate environmental exchanges and prevent entropic accumulation. In FEP-AI, nervous systems are predictive controllers, where action-driven perception is realized as probabilistic inference over the causes of sensory observations. Integrated Information Theory (IIT) begins with considering the preconditions for a system to intrinsically exist, as well as axioms regarding the nature of consciousness. IIT has produced controversy because of its surprising entailments: quasi-panpsychism; subjectivity without referents or dynamics; and the possibility of fully-intelligent-yet-unconscious brain simulations. Here, I describe ways that these controversies might be resolved by integrating IIT with FEP-AI, where integrated information only entails consciousness for systems with perspectival reference frames capable of generating models with spatial, temp...

Enactivists have criticized traditional cognitive science as hamstrung by naïve Cartesian assumptions that mischaracterize minds as analyzable apart from the context of embedded bodies. Indeed, the starting place for understanding minds must be in terms of their evolution and development as control systems for niche-constructing organisms. Here, I will draw from both enactivist and cognitivist perspectives on mind, proposing that an adequate characterization of teleological phenomena may require a reappraisal of mental homunculi as embodied self models (ESMs), understood as body maps with quasi-agentic properties. Further, these homunculi may attain awareness through inner theaters (i.e., generative models of space and causation), with which they experience and modify representational content. In brief, this manuscript is an attempt at unification in cognitive science, endeavoring to show how a radically embodied cybernetic Bayesian brain may create foundations for intelligence, con...

Orgasm is one of the most intense pleasures attainable to an organism, yet its underlying mechanisms remain poorly understood. On the basis of existing literatures, this article introduces a novel mechanistic model of sexual stimulation and orgasm. In doing so, it characterizes the neurophenomenology of sexual trance and climax, describes parallels in dynamics between orgasms and seizures, speculates on possible evolutionary origins of sex differences in orgasmic responding, and proposes avenues for future experimentation. Here, a model is introduced wherein sexual stimulation induces entrainment of coupling mechanical and neuronal oscillatory systems, thus creating synchronized functional networks within which multiple positive feedback processes intersect synergistically to contribute to sexual experience. These processes generate states of deepening sensory absorption and trance, potentially culminating in climax if critical thresholds are surpassed. The centrality of rhythmic st...

Major depressive disorder (MDD) is a highly prevalent psychiatric condition in which patients often have difficulties regulating their emotions. Prior studies have shown that attention bias towards negative emotion is linked to activation in regions of the default mode network (DMN) in MDD individuals. Furthermore, MDD patients showed increased resting-state functional connectivity (FC) between the medial prefrontal cortex and other DMN structures. Twenty-one MDD patients that currently experiencing depressive episodes and twenty-five healthy control participants performed the current emotional expectancy paradigm in a gradient-echo SENSE-SPIRAL fMRI. Whole brain and psycho-physiological interaction (PPI) analysis were applied to explore the task-related brain activity and FCs. Relative to healthy participants, we found MDD patients had greater activity in dorsal medial prefrontal cortex as a function of positive vs. neutral expectancy conditions. PPI results revealed a significant ...

Localization and mapping has been a long standing area of research, both in neuroscience, to understand how mammals navigate their environment, as well as in robotics, to enable autonomous mobile robots. In this paper, we treat navigation as inferring actions that minimize (expected) variational free energy under a hierarchical generative model. We find that familiar concepts like perception, path integration, localization and mapping naturally emerge from this active inference formulation. Moreover, we show that this model is consistent with models of hippocampal functions, and can be implemented in silico on a real-world robot. Our experiments illustrate that a robot equipped with our hierarchical model is able to generate topologically consistent maps, and correct navigation behaviour is inferred when a goal location is provided to the system.

Across times and cultures, humans constantly and intentionally tried to ‘lose’ or to ‘escape’ their familiar, ordinary self, to ‘self-detach’ and to radically change the ways of perceiving oneself and the world. In this paper we explore the contrast between the feeling of ‘losing’ the sense of familiarity with one’s self and body in Depersonalisation experiences (DP) and psychedelics (with some consideration of meditative experiences). We explore these radical changes in self-experiences through the lens of Active Inference Framework (AIF). AIF is a process theory aiming to capture the capacity of biological organisms (e.g. living human bodies) to survive and thrive in volatile and uncertain environments. In line with previous work on depersonalisation and psychedelic mechanisms, we suggest that such experiences can involve a stance with radically altered prior expectations, so providing opportunities for flexibly modulating self- and world models. Specifically, we suggest that cont...

How is it that psychedelics so profoundly impact brain and mind? According to the highly influential model of "Relaxed Beliefs Under Psychedelics" (REBUS) (Carhart-Harris and Friston, 2019), wherein 5-HT2a agonism is thought to help relax prior expectations, so making room for new perspectives and patterns. This model is contextualized within the Free Energy Principle and Active Inference framework, as well as the associated neuronal processes theory of hierarchical predictive processing. More specifically, excessive excitation of deep pyramidal neurons is thought to cause paradoxical desynchronization, so "flattening" (Bayesian) "belief landscapes" by attenuating large-scale complexes of synchronous neural activity, particularly at alpha frequencies. Here, we introduce an alternative (but largely compatible) perspective, in that while such effects may be both real and important, these alterations may primarily correspond to a rare (but potentially pivotal) regime of very high levels of serotonin 2a receptor (5-HT2aR) agonism. We suggest an opposite effect may occur along much of the dose-response curve of 5-HT2aR stimulation, in which synchronous neural activity becomes more powerful, with accompanying "Strengthened Beliefs Under Psychedelics" (SEBUS) effects. We believe that REBUS effects are indeed crucially important aspects of psychedelic experiences, but suggest these exist alongside SEBUS effects in various combinations. As such, we propose a larger integrative perspective for understanding "Altered Beliefs Under Psychedelics” (ALBUS). The ALBUS framework provides a rich account of cognition based on predictive processing, which we believe provides a means of fruitfully integrating across theories of psychedelic action ranging from REBUS, to “thalamic gating” (Preller et al., 2019), to the newly suggested “cortico-striatal thalamo-cortical” model (Doss et al., 2021). Towards this end we demonstrate the utility of ALBUS by providing neurophenomenological models of psychedelics focusing on mechanisms of conscious perceptual synthesis, as well as hippocampally-orchestrated episodic memory and mental simulation. We further discuss cognitive diversity (including psychopathology) through the lens of these models. We consider the potential significances of modifications of the default mode network and alpha rhythms for creativity and various states of consciousness, including with respect to fundamental alterations in sense of self through ego dissolution. Finally, we survey a broad range of psychedelic phenomena and consider potential explanations, implications, and directions for future work.

Intelligence has been operationalized as both goal-pursuit capacity across a broad range of environments, and also as learning capacity above and beyond a foundational set of core priors. Within the normative framework of AIXI, intelligence may be understood as capacities for compressing (and thereby predicting) data and achieving goals via programs with minimal algorithmic complexity. Within the Free Energy Principle and Active Inference framework, intelligence may be understood as capacity for inference and learning of predictive models for goal-realization, with beliefs favored to the extent they fit novel data with minimal updating of priors. Most recently, consciousness has been proposed to enhance intelligent functioning by allowing for iterative state estimation of the essential variables of a system and its relationships to its environment, conditioned on a causal world model. This paper discusses machine learning architectures and principles by which all these views may be synergistically combined and contextualized with an Integrated World Modeling Theory of consciousness.

Here we consider psychedelics with respect to their mechanisms of action, use, and implications for our understandings of brain and mind. This review is somewhat nontraditional in its scope, with discussions of both basic facts as well as theoretical speculations. We chose this approach given the unique historical context we find ourselves in at present. While we have decades of investigations to draw upon with respect to the clinical science and neuropsychopharmacology of psychedelics, we are also witnessing a renaissance (or perhaps a revolution) in which scientific and public interest in these compounds seems to be exploding. We have decided to split this review between the fundamental and theoretical, with hopes that we can give readers familiarity with well-established knowledge, as well as questions to hold in mind in attempting to make sense of a rapidly shifting epistemic landscape. Below we focus on serotonin 2A receptor (5-HT2aR) agonists, or “classic psychedelics,” and discuss research suggesting their potential roles in clinical and non-clinical contexts. Particular emphasis is placed on studies suggesting potentially surprising degrees of efficacy for conditions such as depression and addictive disorders. We also evaluate potential mediators and moderators of therapeutic outcomes, including factors such as mystical experiences and psychological flexibility. Finally, we consider likely future directions for psychedelics in science and society.

Drawing from both enactivist and cognitivist perspectives on mind, I propose that explaining teleological phenomena may require reappraising both “Cartesian theaters” and mental homunculi in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning as predictive-memory systems and cybernetic controllers. Quasi-homuncular ESMs are suggested to constitute a major organizing principle for neural architectures due to their initial and ongoing significance for solutions to inference problems in cognitive (and affective) development. Embodied experiences provide foundational lessons in learning curriculums in which agents explore increasingly challenging problem spaces, so answering an unresolved question in Bayesian cognitive science: what are biologically plausible mechanisms for equipping learners with sufficiently powerful inductive biases to adequately constrain inference spaces? Drawing on models from neurophysiology, psychology, and developmental robotics, I describe how embodiment provides fundamental sources of empirical priors (as reliably learnable posterior expectations). If ESMs play this kind of foundational role in cognitive development, then bidirectional linkages will be found between all sensory modalities and frontal-parietal control hierarchies, so infusing all senses with somatic-motoric properties, thereby structuring all perception by relevant affordances, so solving frame problems for embodied agents. Drawing upon the Free Energy Principle and Active Inference framework, I describe a particular mechanism for intentional action selection via consciously imagined (and explicitly represented) goal realization, where contrasts between desired and present states influence ongoing policy selection via predictive coding mechanisms and backward-chained imaginings (as self-realizing predictions). This embodied developmental legacy suggests a mechanism by which imaginings can be intentionally shaped by (internalized) partially-expressed motor acts, so providing means of agentic control for attention, working memory, imagination, and behavior. I further describe the nature(s) of mental causation and self-control, and also provide an account of readiness potentials in Libet paradigms wherein conscious intentions shape causal streams leading to enaction. Finally, I provide neurophenomenological handlings of prototypical qualia including pleasure, pain, and desire in terms of self-annihilating free energy gradients via quasi-synesthetic interoceptive active inference. In brief, this manuscript is intended to illustrate how radically embodied minds may create foundations for intelligence (as capacity for learning and inference), consciousness (as somatically-grounded self-world modeling), and will (as deployment of predictive models for enacting valued goals).

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), turbocodes (establishment of shared latent spaces for multi-modal integration and inferential synergy), and graph neural networks (spatial and somatic modeling and control). Particular emphasis is placed on the hippocampal/entorhinal system, which may provide a source of high-level reasoning via predictive contrasting and generalized navigation, so affording multiple kinds of conscious access. 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. Finally, I go on to describe parallels between FEP-AI and theories of universal intelligence, including with respect to implications for the future of artificially intelligent systems. Particular emphasis is given to recurrent computation and its relationships with feedforward processing, including 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). 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.

I propose a potentially novel theory of humor as the feeling of Rapid Anxiety Reduction (RAR). According to RAR, humor can be expressed in a simple formula: -d(A)/dt. RAR has strong correspondences with False Alarm Theory, Benign Violation Theory, and Cognitive Debugging Theory, all of which represent either special cases or partial descriptions at alternative levels of analysis. Some evidence for RAR includes physiological similarities between hyperventilation and laughter and the fact that smiles often indicate negative affect in non-human primates (e.g. fear grimaces where teeth are exposed as a kind of inhibited threat display, as suggested by Michael Graziano). If humor reliably indicates conditions of a) anxiety induction, b) anxiety reduction, and c) the time-course relating these things--so productively constraining inference spaces regarding latent mental states--then we know a great deal about the values and capacities of the persons experiencing humor. By providing this c...

One’s ability to learn a generative model of the world without supervision depends on the extent to which one can construct abstract knowledge representations that generalize across experiences. To this end, capturing an accurate statistical structure from observational data provides useful inductive biases that can be transferred to novel environments. Here, we tackle the problem of learning to control dynamical systems by applying Bayesian nonparametric methods, which is applied to solve visual servoing tasks. This is accomplished by first learning a state space representation, then inferring environmental dynamics and improving the policies through imagined future trajectories. Bayesian nonparametric models provide automatic model adaptation, which not only combats underfitting and overfitting, but also allows the model’s unbounded dimension to be both flexible and computationally tractable. By employing Gaussian processes to discover latent world dynamics, we mitigate common dat...

Relative to other neuromodulators, serotonin (5-HT) has received far less attention in machine learning and active inference. We will review prior work interpreting 5-HT1a signaling as an uncertainty parameter with opponency to dopamine. We will then discuss how 5-HT2a receptors may promote more exploratory policy selection by enhancing imaginative planning (as sophisticated affective inference). Finally, we will briefly comment on how qualitatively different effects may be observed across low and high levels of 5-HT2a signaling, where the latter may help agents to change self-adversarial policies and break free of maladaptive absorbing states in POMDPs.

The Free Energy Principle and Active Inference Framework (FEP-AI) begins with the understanding that persisting systems must regulate environmental exchanges and prevent entropic accumulation. In FEP-AI, minds and brains are predictive controllers for autonomous systems, where action-driven perception is realized as probabilistic inference. Integrated Information Theory (IIT) begins with considering the preconditions for a system to intrinsically exist, as well as axioms regarding the nature of consciousness. IIT has produced controversy because of its surprising entailments: quasi-panpsychism; subjectivity without referents or dynamics; and the possibility of fully-intelligent-yet-unconscious brain simulations. Here, I describe how these controversies might be resolved by integrating IIT with FEP-AI, where integrated information only entails consciousness for systems with perspectival reference frames capable of generating models with spatial, temporal, and causal coherence for self and world. Without that connection with external reality, systems could have arbitrarily high amounts of integrated information, but nonetheless would not entail subjective experience. I further describe how an integration of these frameworks may contribute to their evolution as unified systems theories and models of emergent causation. Then, inspired by both Global Neuronal Workspace Theory (GNWT) and the Harmonic Brain Modes framework, I describe how streams of consciousness may emerge as an evolving generation of sensorimotor predictions, with the precise composition of experiences depending on the integration abilities of synchronous complexes as self-organizing harmonic modes (SOHMs). These integrating dynamics may be particularly likely to occur via richly connected subnetworks affording body-centric sources of phenomenal binding and executive control. Along these connectivity backbones, SOHMs are proposed to implement turbo coding via loopy message-passing over predictive (autoencoding) networks, so generating maximum a posteriori estimates as coherent vectors governing neural evolution, with alpha frequencies generating basic awareness, and cross-frequency phase-coupling within theta frequencies for access consciousness and volitional control. These dynamic cores of integrated information also function as global workspaces, centered on posterior cortices, but capable of being entrained with frontal cortices and interoceptive hierarchies, so affording agentic causation. Integrated World Modeling Theory (IWMT) represents a synthetic approach to understanding minds that reveals compatibility between leading theories of consciousness, so enabling inferential synergy.

New strategies are needed to help people cope with the repercussions of neurodegenerative disorders such as Alzheimer’s disease. Patients and caregivers face different challenges, but here we investigated an intervention tailored for this combined population. The program focused on training skills such as attending to the present moment nonjudgmentally, which may help reduce maladaptive emotional responses. Patients participated together with caregivers in weekly group sessions over 8 weeks. An assessment battery was individually administered before and after the program. Pre–post analyses revealed several benefits, including increased quality-of-life ratings, fewer depressive symptoms, and better subjective sleep quality. In addition, participants indicated that they were grateful for the opportunity to learn to apply mindfulness skills and that they would recommend the program to others. In conclusion, mindfulness training can be beneficial for patients and their caregivers, it ca...

Localization and mapping has been a long standing area of research, both in neuroscience, to understand how mammals navigate their environment, as well as in robotics, to enable autonomous mobile robots. In this paper, we treat navigation as inferring actions that minimize (expected) variational free energy under a hierarchical generative model. We find that familiar concepts like perception, path integration, localization and mapping naturally emerge from this active inference formulation. Moreover, we show that this model is consistent with models of hippocampal functions, and can be implemented in silico on a real-world robot. Our experiments illustrate that a robot equipped with our hierarchical model is able to generate topologically consistent maps, and correct navigation behaviour is inferred when a goal location is provided to the system.

What do we mean when we talk of “free will?” What are the “varieties of free will worth having” (Dennett, 2003), and to what extent can we be said to possess such capabilities? While preferred definitions may vary across individuals and situations, we may perhaps find broad agreement that free will indicates a capacity for conscious intentions to meaningfully cause actions, which could be considered to be synonymous with agency (Safron, 2021). Free will may further be said to involve a certain open-endedness wherein agents can explore alternative possibilities and pivot based on novel information (Hills, 2019). In these ways, the meaning of free will is linguistically transparent as a capacity for both “willing” and “freedom.” Others may want a freedom of will in which conscious intentions may be said to be the sole determinants of action without additional causal factors. While this may appear to entail a self-contradictory metaphysics involving “uncaused causes,” one may even find support for this more “libertarian” free will as well if it is the case that action selection is influenced by forms of causation that solely exists at intermediate levels of organization at which selfhood and agency emerge (Sinnott-Armstrong, 2019; Safron, 2021), or perhaps even in terms of consciously-experienced intentionality being influenced by stochastic (in terms of limited predictability) processes. In what follows, I suggest all these forms of free will constitute real patterns that have been (and continue to be) selected over the course of evolution and development. I go on to describe how these mechanisms may overlap with serotonergic signaling pathways mediating the effects of psychedelic compounds, potentially helping to explain adaptive significances of these biophysical phenomena. Finally, I explore how this perspective on psychedelics (in terms of altering various degrees of freedom) may help to identify some of the most important sources of variation both across and within individuals.

Cybernetics is the study of goal-directed systems that self-regulate via feedback, a category that includes human beings. Cybernetic Big Five Theory (CB5T) attempts to explain personality in cybernetic terms, conceptualizing personality traits as manifestations of variation in parameters of the neural mechanisms that evolved to facilitate cybernetic control. The Free Energy Principle and Active Inference framework (FEP-AI) is an overarching approach for understanding how it is that complex systems manage to persist in a world governed by the second law of thermodynamics—the inevitable tendency toward entropy. Although these two cybernetic theories were developed independently, they overlap in their theoretical foundations and implications and are complementary in their approaches to understanding persons. FEP-AI contributes a potentially valuable formal modeling framework for CB5T, while CB5T provides detail about the science and structure of personality. In this chapter we explore how CB5T and FEP-AI may begin to be integrated into a unified approach to modeling persons.

Integrated World Modeling Theory (IWMT) is a synthetic model that attempts to unify theories of consciousness within the Free Energy Principle and Active Inference framework, with particular emphasis on Integrated Information Theory (IIT) and Global Neuronal Workspace Theory (GNWT). IWMT further suggests predictive processing in sensory hierarchies may be well-modeled as (folded, sparse, partially disentangled) variational autoencoders, with beliefs discretely-updated via the formation of synchronous complexes—as self-organizing harmonic modes (SOHMs)—potentially entailing maximal a posteriori (MAP) estimation via turbo coding. In this account, alpha-synchronized SOHMs across posterior cortices may constitute the kinds of maximal complexes described by IIT, as well as samples (or MAP estimates) from multimodal shared latent space, organized according to egocentric reference frames, entailing phenomenal consciousness as mid-level perceptual inference. When these posterior SOHMs couple with frontal complexes, this may enable various forms of conscious access as a kind of mental act(ive inference), affording higher order cognition/control, including the kinds of attentional/intentional processing and reportability described by GNWT. Across this autoencoding heterarchy, intermediate-level beliefs may be organized into spatiotemporal trajectories by the entorhinal/hippocampal system, so affording episodic memory, counterfactual imaginings, and planning.

Here, I provide clarifications and discuss further issues relating to Safron (2020), “An Integrated World Modeling Theory (IWMT) of consciousness: Combining Integrated Information and Global Workspace Theories with the Free Energy Principle and Active Inference Framework; towards solving the Hard problem and characterizing agentic causation." As a synthesis of major theories of complex systems and consciousness, with IWMT we may be able to address some of the most difficult problems in the sciences. This is a claim deserving of close scrutiny and much skepticism. What would it take to solve the Hard problem? One could answer the question of how it is possible that something like subjectivity could emerge from objective brain functioning (i.e., moving from a third person to a first person ontology), but a truly satisfying account might still require solving all the “easy” and “real” problems of consciousness. In this way, IWMT does not claim to definitively solve the Hard problem, as explaining all the particular ways that things feel across all relevant aspects of experience is likely an impossible task. Nonetheless, IWMT does claim to have made major inroads into our understanding of consciousness, and here I will attempt to justify this position by discussing challenging problems and outstanding questions with respect to philosophy, (neuro)phenomenology, computational principles, practical applications, and implications for existing theories of mind and life.