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This study explains how the leader-follower relationship and turn-taking could develop in a dyadic imitative interaction by conducting robotic simulation experiments based on the free energy principle. Our prior study showed that introducing a parameter during the model training phase can determine leader and follower roles for subsequent imitative interactions. The parameter is defined as w , the so-called meta-prior, and is a weighting factor used to regulate the complexity term versus the accuracy term when minimizing the free energy. This can be read as sensory attenuation, in which the robot's prior beliefs about action are less sensitive to sensory evidence. The current extended study examines the possibility that the leader-follower relationship shifts depending on changes in w during the interaction phase. We identified a phase space structure with three distinct types of behavioral coordination using comprehensive simulation experiments with sweeps of w of both robots during the interaction. Ignoring behavior in which the robots follow their own intention was observed in the region in which both w s were set to large values. One robot leading, followed by the other robot was observed when one w was set larger and the other was set smaller. Spontaneous, random turn-taking between the leader and the follower was observed when both w s were set at smaller or intermediate values. Finally, we examined a case of slowly oscillating w in anti-phase between the two agents during the interaction. The simulation experiment resulted in turn-taking in which the leader-follower relationship switched during determined sequences, accompanied by periodic shifts of w s. An analysis using transfer entropy found that the direction of information flow between the two agents also shifted along with turn-taking. Herein, we discuss qualitative differences between random/spontaneous turn-taking and agreed-upon sequential turn-taking by reviewing both synthetic and empirical studies. [ABSTRACT FROM AUTHOR]

This paper applies the Free Energy Principle (FEP) to propose that the lack of action in response to the global ecological crisis should be considered a maladaptive symptom of human activity that we refer to as biophilia deficiency syndrome. The paper is organised into four parts: the characterisation of the natural world under the Gaia Hypothesis, the employment of the FEP as a description of the behavior of self-organising systems, the application of the FEP to Gaia to understand coupling dynamics between living systems and purportedly non-living planetary processes, and the offering of positive interventions for addressing the current state of ecological crisis under this framework. For the latter, we emphasize the importance of perturbing stuck states for healthy development, and the necessary appreciation of life existing as nested systems at multiple levels in a hierarchy. We propose the development of human biophilia virtue in accordance with the FEP as a practical intervention for treating biophilia deficiency syndrome and helping to safeguard the balance of planetary processes and the integrity of living systems that depend on them, offering some examples of what this might look like in practice. Overall, this paper provides novel insights into how to catalyse meaningful ecological change, proposing a deliberate and disruptive approach to addressing the dysfunctional relationship between humans and the rest of the natural world.

Tensor networks (TNs) have seen an increase in applications in recent years. While they were originally developed to model many-body quantum systems, their usage has expanded into the field of machine learning. This work adds to the growing range of applications by focusing on planning by combining the generative modeling capabilities of matrix product states and the action selection algorithm provided by active inference. Their ability to deal with the curse of dimensionality, to represent probability distributions, and to dynamically discover hidden variables make matrix product states specifically an interesting choice to use as the generative model in active inference, which relies on ‘beliefs’ about hidden states within an environment. We evaluate our method on the T-maze and Frozen Lake environments, and show that the TN-based agent acts Bayes optimally as expected under active inference.

Robust evidence suggests that humans explore their environment using a combination of topological landmarks and coarse-grained path integration. This approach relies on identifiable environmental features (topological landmarks) in tandem with estimations of distance and direction (coarse-grained path integration) to construct cognitive maps of the surroundings. This cognitive map is believed to exhibit a hierarchical structure, allowing efficient planning when solving complex navigation tasks. Inspired by human behaviour, this paper presents a scalable hierarchical active inference model for autonomous navigation, exploration, and goal-oriented behaviour. The model uses visual observation and motion perception to combine curiosity-driven exploration with goal-oriented behaviour. Motion is planned using different levels of reasoning, i.e., from context to place to motion. This allows for efficient navigation in new spaces and rapid progress toward a target. By incorporating these human navigational strategies and their hierarchical representation of the environment, this model proposes a new solution for autonomous navigation and exploration. The approach is validated through simulations in a mini-grid environment.

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.

Therapeutic affective touch has been recognized as essential for survival, nurturing supportive interpersonal interactions, accelerating recovery—including reducing hospitalisations, and promoting overall health and building robust therapeutic alliances. Through the lens of active inference, we present an integrative model, combining therapeutic touch and communication, to achieve biobehavioural synchrony. This model speaks to how the brain develops a generative model required for recovery, developing successful therapeutic alliances, and regulating allostasis within paediatric manual therapy. We apply active inference to explain the neurophysiological and behavioural mechanisms that underwrite the development and maintenance of synchronous relationships through touch. This paper foregrounds the crucial role of therapeutic touch in developing a solid therapeutic alliance, the clinical effectiveness of paediatric care, and triadic synchrony between health care practitioner, caregiver, and infant in a variety of clinical situations. We start by providing a brief overview of the significance and clinical role of touch in the development of social interactions in infants; facilitating a positive therapeutic alliance and restoring homeostasis through touch to allow a more efficient process of allostatic regulation. Moreover, we explain the role of CT tactile afferents in achieving positive clinical outcomes and updating prior beliefs. We then discuss how touch is implemented in treatment sessions to promote cooperative interactions in the clinic and facilitate theory of mind. This underwrites biobehavioural synchrony, epistemic trust, empathy, and the resolution of uncertainty. The ensuing framework is underpinned by a critical application of the active inference framework to the fields of pediatrics and neonatology.

The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience that can produce human-like behavior through reward-based learning. In this study, we test the ability for the AIF to capture the role of anticipation in the visual guidance of action in humans through the systematic investigation of a visual-motor task that has been well-explored—that of intercepting a target moving over a ground plane. Previous research demonstrated that humans performing this task resorted to anticipatory changes in speed intended to compensate for semi-predictable changes in target speed later in the approach. To capture this behavior, our proposed “neural” AIF agent uses artificial neural networks to select actions on the basis of a very short term prediction of the information about the task environment that these actions would reveal along with a long-term estimate of the resulting cumulative expected free energy. Systematic variation revealed that anticipatory behavior emerged only when required by limitations on the agent's movement capabilities, and only when the agent was able to estimate accumulated free energy over sufficiently long durations into the future. In addition, we present a novel formulation of the prior mapping function that maps a multi-dimensional world-state to a uni-dimensional distribution of free-energy/reward. Together, these results demonstrate the use of AIF as a plausible model of anticipatory visually guided behavior in humans.

Stereotypes are often described as being generally inaccurate and irrational. However, for years, a minority of social psychologists has been proclaiming that stereotype accuracy is among the most robust findings in the field. This same minority also opposes the majority by questioning the power of self-fulfilling prophecies and thereby the construction of social reality. The present paper examines this long-standing debate from the perspective of predictive processing, an increasingly influential cognitive science theory. In this theory, stereotype accuracy and self-fulfilling prophecies are two sides of the same coin, namely prediction error minimisation, pointing to a new middle course between the two existing views. On the one hand, predictive processing indicates that depicting stereotypes as generally inaccurate runs counter to their actual purpose of making the social world predictable, which supports the minority view. On the other hand, predictive processing supposes that expectations, including stereotypes, permanently affect perception and behaviour and thereby co-construct social reality, which supports the majority view. Therefore, from a predictive processing perspective, stereotypes are largely rational and not per se inaccurate, and self-fulfilling prophecies are omnipresent and greatly affect social reality. This new middle course appears to fit the empirical data better than the two existing views. [ABSTRACT FROM AUTHOR]

Although the increase in the use of dynamical modeling in the literature on cultural evolution makes current models more mathematically sophisticated, these models have yet to be tested or validated. This paper provides a testable deep active inference formulation of social behavior and accompanying simulations of cumulative culture in two steps: First, we cast cultural transmission as a bi-directional process of communication that induces a generalized synchrony (operationalized as a particular convergence) between the belief states of interlocutors. Second, we cast social or cultural exchange as a process of active inference by equipping agents with the choice of who to engage in communication with. This induces trade-offs between confirmation of current beliefs and exploration of the social environment. We find that cumulative culture emerges from belief updating (i.e., active inference and learning) in the form of a joint minimization of uncertainty. The emergent cultural equilibria are characterized by a segregation into groups, whose belief systems are actively sustained by selective, uncertainty minimizing, dyadic exchanges. The nature of these equilibria depends sensitively on the precision afforded by various probabilistic mappings in each individual's generative model of their encultured niche. [ABSTRACT FROM AUTHOR]

Our paper takes as its starting point the recent proposal, at the core of this special issue, to use the active inference framework (AIF) to computationally model what it is for a person to live a meaningful life. In broad brushstrokes, the AIF takes experiences of human flourishing to be the result of predictions and uncertainty estimations along many dimensions at multiple levels of neurobiological organization. Our aim in this paper is to explain how AIF models predict that uncertainty can sometimes, under the right conditions, be conducive to the experiences of flourishing. Our focus is on playfulness, because playful individuals have learned a high-level prior that in certain safe contexts, uncertainty and error should be tolerated and explored. They have expanded the phenotypic bound on the amount of surprise they are prepared to tolerate in their lives. The positive embracing of uncertainty has a number of positive knock-on effects for the kind of lives playful individuals are able to lead. First, a playful individual attends to the world in a way that is open and expansive, a mode of attending that is effortless and therefore conducive to being in the present. This openness to the present moment allows for deep engagement and participation in experience that can furnish a renewed appreciation for life. Second, playful individuals will actively seek out spaces at the edge of their own abilities and will therefore be more likely to grow and develop in their skills and relationships in ways that contribute to their living a good life. Finally, playful agents seek out situations in which they can monitor, observe, and learn from their own affective responses to uncertainty. Thus, uncertainty becomes something familiar to them that they not only learn to tolerate but also enjoy positively exploring, in ways that provide them opportunities to grow. For these three reasons, we will argue that playfulness and openness to experiences of uncertainty and the unknown may be important ingredients in human flourishing. [ABSTRACT FROM AUTHOR]

A significant number of persons engage in paradoxical behaviors, such as extreme food restriction (up to starvation) and non-suicidal self-injuries, especially during periods of rapid changes, such as adolescence. Here, we contextualize these and related paradoxical behavior within an active inference view of brain functions, which assumes that the brain forms predictive models of bodily variables, emotional experiences, and the embodied self and continuously strives to reduce the uncertainty of such models. We propose that not only in conditions of excessive or prolonged uncertainty, such as in clinical conditions, but also during pivotal periods of developmental transition, paradoxical behaviors might emerge as maladaptive strategies to reduce uncertainty—by "acting on the body"— soliciting salient perceptual and interoceptive sensations, such as pain or excessive levels of hunger. Although such strategies are maladaptive and run against our basic homeostatic imperatives, they might be functional not only to provide some short-term reward (e.g. relief from emotional distress)—as previously proposed—but also to reduce uncertainty and possibly to restore a coherent model of one's bodily experience and the self, affording greater confidence in who we are and what course of actions we should pursue. [ABSTRACT FROM AUTHOR]

What is it about our current digital technologies that seemingly makes it difficult for users to attend to what matters to them? According to the dominant narrative in the literature on the "attention economy," a user's lack of attention is due to the large amounts of information available in their everyday environments. I will argue that information-abundance fails to account for some of the central manifestations of distraction, such as sudden urges to check a particular information-source in the absence of perceptual information. I will use active inference, and in particular models of action selection based on the minimization of expected free energy, to develop an alternative answer to the question about what makes it difficult to attend. Besides obvious adversarial forms of inference, in which algorithms build up models of users in order to keep them scrolling, I will show that active inference provides the tools to identify a number of problematic structural features of current digital technologies: they contain limitless sources of novelty, they can be navigated by very simple and effortless motor movements, and they offer their action possibilities everywhere and anytime independent of place or context. Moreover, recent models of motivated control show an intricate interplay between motivation and control that can explain sudden transitions in motivational state and the consequent alteration of the salience of actions. I conclude, therefore, that the challenges users encounter when engaging with digital technologies are less about information overload or inviting content, but more about the continuous availability of easily available possibilities for action. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this article, we aim to conceptualize and formalize the construct of resilience using the tools of active inference, a new physics-based modeling approach apt for the description and analysis of complex adaptive systems. We intend this as a first step toward a computational model of resilient systems. We begin by offering a conceptual analysis of resilience, to clarify its meaning, as established in the literature. We examine an orthogonal, threefold distinction between meanings of the word "resilience": (i) inertia, or the ability to resist change (ii) elasticity, or the ability to bounce back from a perturbation, and (iii) plasticity, or the ability to flexibly expand the repertoire of adaptive states. We then situate all three senses of resilience within active inference. We map resilience as inertia onto high precision beliefs, resilience as elasticity onto relaxation back to characteristic (i.e., attracting) states, and resilience as plasticity onto functional redundancy and structural degeneracy. [ABSTRACT FROM AUTHOR]

This narrative review presents key concepts from neurophysiology, phenomenology, psychology, and narrative medicine which underpin a developing enactive-ecological framework for osteopathic practice. This framework aims to provide a coherent theoretical basis for understanding healthcare processes and outcomes, based on the neuroscience principles of active inference and enactivism. It offers insights into factors that influence patients' pain perception and behaviour and foster or inhibit the development of effective therapeutic relationships. Although this approach offers promising opportunities to increase the scope of care by harnessing potential in the unique embodied ecological niches created between patients and osteopaths, it raises challenges to traditional treatment agendas. Healthcare which frames the patient-as-a person, and acknowledges the multidimensional nature of the self, requires practitioners to be collaborative and self-aware, and be able to elicit patients' lived experiences and body stories. Phenomenological and psychological studies into enactivism emphasise the complex, dynamic nature of therapeutic relationships and the need to understand each person's unique lifeworld context. The new framework represents an important step forward, but further research is now needed to explore ways of integrating active and enactive inference into practice, of developing psychological or mindful self- and body-awareness, and narrative communication skills for shared sense-making. [ABSTRACT FROM AUTHOR]

World models is a construct that is used to represent internal models of the world. It is an important construct for human-artificial intelligence systems, because both natural and artificial agents can have world models. The term, natural agents, encompasses individual people and human organizations. Many human organizations apply artificial agents that include machine learning. In this paper, it is explained how human survival first principles of interactions between energy and entropy influence organization's world models, and hence their implementations of machine learning. First, the world models construct is related to human organizations. This is done in terms of the construct's origins in psychology theory-building during the 1930s through its applications in systems science during the 1970s to its recent applications in computational neuroscience. Second, it is explained how human survival first principles of interactions between energy and entropy influence organizational world models. Third, a practical example is provided of how survival first principles lead to opposing organizational world models. Fourth, it is explained how opposing organizational world models can constrain applications of machine learning. Overall, the paper highlights the influence of interactions between energy and entropy on organizations' applications of machine learning. In doing so, profound challenges are revealed for human-artificial intelligence systems. [ABSTRACT FROM AUTHOR]

This paper aims to provide a theoretical framework for explaining the subjective character of pain experience in terms of what we will call 'embodied predictive processing'. The predictive processing (PP) theory is a family of views that take perception, action, emotion and cognition to all work together in the service of prediction error minimisation. In this paper we propose an embodied perspective on the PP theory we call the 'embodied predictive processing (EPP) theory. The EPP theory proposes to explain pain in terms of processes distributed across the whole body. The prediction error minimising system that generates pain experience comprises the immune system, the endocrine system, and the autonomic system in continuous causal interaction with pathways spread across the whole neural axis. We will argue that these systems function in a coordinated and coherent manner as a single complex adaptive system to maintain homeostasis. This system, which we refer to as the neural-endocrine-immune (NEI) system, maintains homeostasis through the process of prediction error minimisation. We go on to propose a view of the NEI ensemble as a multiscale nesting of Markov blankets that integrates the smallest scale of the cell to the largest scale of the embodied person in pain. We set out to show how the EPP theory can make sense of how pain experience could be neurobiologically constituted. We take it to be a constraint on the adequacy of a scientific explanation of subjectivity of pain experience that it makes it intelligible how pain can simultaneously be a local sensing of the body, and, at the same time, a more global, all-encompassing attitude towards the environment. Our aim in what follows is to show how the EPP theory can meet this constraint. [ABSTRACT FROM AUTHOR]

Recently, the mechanistic framework of active inference has been put forward as a principled foundation to develop an overarching theory of consciousness which would help address conceptual disparities in the field (Wiese 2018; Hohwy and Seth 2020). For that promise to bear out, we argue that current proposals resting on the active inference scheme need refinement to become a process theory of consciousness. One way of improving a theory in mechanistic terms is to use formalisms such as computational models that implement, attune and validate the conceptual notions put forward. Here, we examine how computational modelling approaches have been used to refine the theoretical proposals linking active inference and consciousness, with a focus on the extent and success to which they have been developed to accommodate different facets of consciousness and experimental paradigms, as well as how simulations and empirical data have been used to test and improve these computational models. While current attempts using this approach have shown promising results, we argue they remain preliminary in nature. To refine their predictive and structural validity, testing those models against empirical data is needed i.e., new and unobserved neural data. A remaining challenge for active inference to become a theory of consciousness is to generalize the model to accommodate the broad range of consciousness explananda; and in particular to account for the phenomenological aspects of experience. Notwithstanding these gaps, this approach has proven to be a valuable avenue for theory advancement and holds great potential for future research. [ABSTRACT FROM AUTHOR]

Self-evidencing describes the purported predictive processing of all self-organising systems, whether conscious or not. Self-evidencing in itself is therefore not sufficient for consciousness. Different systems may however be capable of self-evidencing in different, specific and distinct ways. Some of these ways of self-evidencing can be matched up with, and explain, several properties of consciousness. This carves out a distinction in nature between those systems that are conscious, as described by these properties, and those that are not. This approach throws new light on phenomenology, and suggests that some self-evidencing may be characteristic of consciousness. [ABSTRACT FROM AUTHOR]

Living systems at all scales are compartmentalized into interacting subsystems. This paper reviews a mechanism that drives compartmentalization in generic systems at any scale. It first discusses three symmetries of generic physical interactions in a quantum-theoretic description. It then shows that if one of these, a permutation symmetry on the inter-system boundary, is spontaneously broken, the symmetry breaking is amplified by the Free Energy Principle (FEP). It thus shows how compartmentalization generically results from permutation symmetry breaking under the FEP. It finally notes that the FEP asymptotically restores the broken symmetry, showing that the FEP can be regarded as a theory of fluctuations away from a permutation-symmetric boundary, and hence from an entangled joint state of the interacting systems. [ABSTRACT FROM AUTHOR]

In this article, we introduce a framework for interpreting Complex Posttraumatic Stress Disorder (C-PTSD) through predictive processing, a neuroscience concept explaining the brain's interpretation and prediction of sensory information. While closely related to PTSD, C-PTSD encompasses additional symptom clusters marked by disturbances in self-organization (DSO), such as negative self-concept, affect dysregulation, and relational difficulties, typically resulting from prolonged traumatic stressors. Our model leverages advances in computational psychiatry and neuroscience, offering a mechanistic explanation for these symptoms by illustrating how prolonged trauma disrupts the brain's predictive processing. Specifically, altered predictive mechanisms contribute to C-PTSD's symptomatology, focusing on DSO: (1) Negative self-concept emerges from maladaptive priors that bias perception towards self-criticism, misaligning expected and actual interoceptive states; (2) Misalignment between predicted and actual interoceptive signals leads to affect dysregulation, with sensitivity to bodily cues; and (3) Relationship challenges arise from skewed social prediction errors, fostering mistrust and withdrawal. This precision-focused approach sheds light on the dynamics underpinning C-PTSD and highlights potential intervention targets aimed at recalibrating the predictive processing system. • Predictive processing framework offers new insights into C-PTSD symptomatology. • Predictive processing explains disturbances in self-organization in C-PTSD. • Interoceptive dysfunction key to affect dysregulation and negative self-concept in C-PTSD. • Maladaptive hyperpriors shape social cognition and relationship difficulties in C-PTSD. [ABSTRACT FROM AUTHOR]

This paper proposes a new framework for investigating neural signals sufficient for a conscious sensation of movement and their role in motor control. We focus on signals sufficient for proprioceptive awareness, particularly from muscle spindle activation and from primary motor cortex (M1). Our review of muscle vibration studies reveals that afferent signals alone can induce conscious sensations of movement. Similarly, studies employing peripheral nerve blocks suggest that efferent signals from M1 are sufficient for sensations of movement. On this basis, we show that competing theories of motor control assign different roles to sensation of movement. According to motor command theories, sensation of movement corresponds to an estimation of the current state based on afferent signals, efferent signals, and predictions. In contrast, within active inference architectures, sensations correspond to proprioceptive predictions driven by efferent signals from M1. The focus on sensation of movement provides a way to critically compare and evaluate the two theories. Our analysis offers new insights into the functional roles of movement sensations in motor control and consciousness. [Display omitted] • New framework for studying the conscious sensation of movement. • Proprioceptive afferent signals are sufficient for conscious sensation of movement. • Efferent signals are sufficient for conscious sensation of movement. • Motor command theories explain results better than an active inference theory. [ABSTRACT FROM AUTHOR]

The theory of Enactive Inference was proposed by Karl Friston and his colleagues to explain how the brain infers knowledge about the world through the subject's interactive experiences. Sensorimotor states induce perturbations in neural activity, and the brain infers hypothetical causes in the world that may explain these perturbations. This article aims to reconcile this neuroscience theory with computer science and artificial-intelligence theories wherein artificial agents receive input data derived from the environment's state and infer internal data structures used to guide decisions. Two critical challenges arise in both the agent's active role and the inference algorithm's scalability as the environment's complexity increases. To address these challenges, we formalize artificial enactive inference through a new Spatial Enactive Markov Decision Process (SEMDP) model. This model rests on low-level control loops enacted in a three-dimensional Euclidean space containing objects. Based on the SEMDP, we present a proof-of-concept cognitive architecture and an experiment to demonstrate the transcription of the theory of enactive inference into the domain of artificial intelligence and robotics. [ABSTRACT FROM AUTHOR]

Depression is characterized by different distortions in interpersonal experience and behavior, ranging from social withdrawal to overt hostility. However, clinical psychological research has largely neglected the need for an integrative framework to operationalize these different phenomena and their dynamic change more accurately in depression. In this article, we draw on active inference theory, a comprehensive theory of perception, action, and learning, to provide a formal model explaining how variations in patients' internal belief-systems lead to differences in social experience and behavior. In this context, we assume that individuals cannot directly grasp the characteristics of their social environment. Instead, they must infer them indirectly from ambiguous social observations, which they themselves generate and alter through their actions. Differences in interpersonal experience and behavior arise from the interplay of patients' prior expectations, their propensity to infer particular social states from certain observations, and their beliefs in their ability to influence these situations through specific actions. We then use concrete examples to demonstrate how future research can take our approach to identify systematic differences in interpersonal experiences and behaviors among depressed patients (or patient subgroups) and to investigate their changes in response to new social experiences. We also discuss potential applications of our approach in diagnosing and treating depression. This work is a move towards understanding the interpersonal aspects of depression in more detail, recognizing their importance in etiology, diagnosis, and treatment. • Individuals with depression exhibit variations in their interpersonal experiences and behaviors. • We offer a formal model that allows researchers to describe these differences with greater precision. • It also enables researchers to examine how patients' interpersonal experiences change in response to their environment. • To accomplish this, we utilize active inference theory, which functionally connects perception, action, and learning. • We suggest that the differences mentioned are due to specific biases in patients' internal belief-systems. • These biases lead to biased conclusions about the actual nature of the social environment. [ABSTRACT FROM AUTHOR]

This study investigated how a physical robot can adapt goal-directed actions in dynamically changing environments, in real-time, using an active inference-based approach with incremental learning from human tutoring examples. Using our active inference-based model, while good generalization can be achieved with appropriate parameters, when faced with sudden, large changes in the environment, a human may have to intervene to correct actions of the robot in order to reach the goal, as a caregiver might guide the hands of a child performing an unfamiliar task. In order for the robot to learn from the human tutor, we propose a new scheme to accomplish incremental learning from these proprioceptive–exteroceptive experiences combined with mental rehearsal of past experiences. Our experimental results demonstrate that using only a few tutoring examples, the robot using our model was able to significantly improve its performance on new tasks without catastrophic forgetting of previously learned tasks.

Consciousness has been described as acting as a global workspace that integrates perception, imagination, emotion and action programming for adaptive decision making. The mechanisms of this workspace and their relationships to the phenomenology of consciousness need to be further specified. Much research in this area has focused on the neural correlates of consciousness, but, arguably, computational modeling can better be used toward this aim. According to the Projective Consciousness Model (PCM), consciousness is structured as a viewpoint-organized, internal space, relying on 3D projective geometry and governed by the action of the Projective Group as part of a process of active inference. The geometry induces a group-structured subjective perspective on an encoded world model, enabling adaptive perspective taking in agents. Here, we review and discuss the PCM. We emphasize the role of projective mechanisms in perception and the appraisal of affective and epistemic values as tied to the motivation of action, under an optimization process of Free Energy minimization, or more generally stochastic optimal control. We discuss how these mechanisms enable us to model and simulate group-structured drives in the context of social cognition and to understand the mechanisms underpinning empathy, emotion expression and regulation, and approach–avoidance behaviors. We review previous results, drawing on applications in robotics and virtual humans. We briefly discuss future axes of research relating to applications of the model to simulation- and model-based behavioral science, geometrically structured artificial neural networks, the relevance of the approach for explainable AI and human–machine interactions, and the study of the neural correlates of consciousness.

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.

Resilience is often characterized as the outcome of well-being maintenance despite threats to that well-being. We suggest that resilience can also be characterized as an emotional-intelligence-related ability to obtain this outcome. We formulate an allostatic active inference model that outlines the primary tools of this resilience ability as monitoring well-being, maintaining stable well-being beliefs while updating situational beliefs and flexibly prioritizing actions that are expected to lead to well-being maintenance or gathering the information needed to discern what those actions could be. This model helps to explain the role of positive emotions in resilience as well as how people high in resilience ability use regulatory flexibility in the service of maintaining well-being and provides a starting point for assessing resilience as an ability.

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.

Classical imitation learning methods suffer substantially from the learning hierarchical policies when the imitative agent faces an unobserved state by the expert agent. To address these drawbacks, we propose an online active learning through active inference approach that encodes the expert’s demonstrations based on observation-action to improve the learner’s future motion prediction. For this purpose, we provide a switching Dynamic Bayesian Network based on the dynamic interaction between the expert agent and another object in its surrounding as a reference model, which we exploit to initialize an incremental probabilistic learning model. This learning model grows and matures based on the free-energy formulation and message passing of active inference dynamically at discrete and continuous levels in an online active learning phase. In this scheme, generalized states of the learning world are represented as distance-vector, where it is the learner’s observation concerning its interaction with a moving object. Considering the distance vector entail intentions, it enables action prediction evaluation in a prospective sense. We illustrate these points using simulations of driving intelligent agents. The learning agent is trained by using long-term predictions from the generative learning model to reproduce the expert’s motion while learning how to select a suitable action through new experiences. Our results affirm that a Dynamic Bayesian optimal approach provides a principled framework and outperforms conventional reinforcement learning methods. Furthermore, it endorses the general formulation of action prediction as active inference.

Although the increase in the use of dynamical modeling in the literature on cultural evolution makes current models more mathematically sophisticated, these models have yet to be tested or validated. This paper provides a testable deep active inference formulation of social behavior and accompanying simulations of cumulative culture in two steps: First, we cast cultural transmission as a bi-directional process of communication that induces a generalized synchrony (operationalized as a particular convergence) between the belief states of interlocutors. Second, we cast social or cultural exchange as a process of active inference by equipping agents with the choice of who to engage in communication with. This induces trade-offs between confirmation of current beliefs and exploration of the social environment. We find that cumulative culture emerges from belief updating (i.e., active inference and learning) in the form of a joint minimization of uncertainty. The emergent cultural equilibria are characterized by a segregation into groups, whose belief systems are actively sustained by selective, uncertainty minimizing, dyadic exchanges. The nature of these equilibria depends sensitively on the precision afforded by various probabilistic mappings in each individual's generative model of their encultured niche.

Motivated by the close relation of predictive coding and activeinference to cognition, we introduce a dynamic artificial neu-ral network-based (ANN) adaptation process, which we termREPRISE: REtrospective and PRospective Inference SchEme.REPRISE first executes a retrospective inference process, in-ferring the unobservable contextual state that best explains itsrecently encountered sensorimotor experiences. It then exe-cutes a prospective inference process, inferring upcoming mo-tor activities in the light of the inferred contextual state anda given goal state. First, the ANN – a recurrent neural net-work – is trained to learn one sensorimotor temporal forwardmodel, that is, the sensorimotor contingencies generated by thebehavior of three moving or flying vehicles. During training,additional three bits are provided as input, indicating whichmode currently applies. After training, goal-directed controland system state inference are activated: Given a goal state,the system imagines a motor command sequence optimizing itwith the prospective objective to minimize the distance to thegoal. Meanwhile, the system evaluates the encountered sen-sorimotor contingencies retrospectively, adapting its vehicleestimation activities and, in order to maintain coherence, theneural hidden states accordingly. This ANN’s ’mind’ is thuscontinuously imagining the future and reflecting on the past –showing superior performance on the posed control problems.The architecture effectively demonstrates that neural error sig-nals and neural activities can be projected into the past and intothe future, respectively, optimizing both neural context codesthat approximately generate the recent past and upcoming be-havior in the light of desired goal states.

Distributed cognition and decision making has been a topic ofintense research in the recent years. In this paper, a computa-tional model of distributed decision making using a commu-nity of predictive coding agents is developed. The agents areembodied multimodal entities and situated in a shared envi-ronment. They have different visibility of the environment dueto unique sensory and generative models. We show that com-munication between agents helps each of them reach a shareddecision in a way that cannot be reached by brain processes ina single agent. Using a simulated environment, we show thatsensory limitations may lead to incorrect or delayed causal in-ferences giving rise to conflicts in the mind of a predictive cod-ing agent, and communication helps to resolve such conflictsand overcome the limitations.

In this article, we aim to conceptualize and formalize the construct of resilience using the tools of active inference, a new physics-based modeling approach apt for the description and analysis of complex adaptive systems. We intend this as a first step toward a computational model of resilient systems. We begin by offering a conceptual analysis of resilience, to clarify its meaning, as established in the literature. We examine an orthogonal, threefold distinction between meanings of the word “resilience”: (i) inertia, or the ability to resist change (ii) elasticity, or the ability to bounce back from a perturbation, and (iii) plasticity, or the ability to flexibly expand the repertoire of adaptive states. We then situate all three senses of resilience within active inference. We map resilience as inertia onto high precision beliefs, resilience as elasticity onto relaxation back to characteristic (i.e., attracting) states, and resilience as plasticity onto functional redundancy and structural degeneracy.

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.

People actively adjust how they acquire sensory information, such as tactile cues, based on how their bodily functions alter their senses.

Precisely timed behavior and accurate time perception plays a critical role in our everyday lives, as our wellbeing and even survival can depend on well-timed decisions. Although the temporal structure of the world around us is essential for human decision making, we know surprisingly little about how representation of temporal structure of our everyday environment impacts decision making. How does the representation of temporal structure affect our ability to generate well-timed decisions? Here we address this question by using a well-established dynamic probabilistic learning task. Using computational modeling, we found that human subjects’ beliefs about temporal structure are reflected in their choices to either exploit their current knowledge or to explore novel options. The model-based analysis illustrates a large within-group and within-subject heterogeneity. To explain these results, we propose a normative model for how temporal structure is used in decision making, based on the semi-Markov formalism in the active inference framework. We discuss potential key applications of the presented approach to the fields of cognitive phenotyping and computational psychiatry. [ABSTRACT FROM AUTHOR]

Borderline personality disorder (BPD) is a severe mental disorder, comprised of heterogeneous psychological and neurobiological pathologies. Here, we propose a predictive processing (PP) account of BPD to integrate these seemingly unrelated pathologies. In particular, we argue that the experience of childhood maltreatment, which is highly prevalent in BPD, leaves a developmental legacy with two facets: first, a coarse-grained, alexithymic model of self and others – leading to a rigidity and inflexibility concerning beliefs about self and others. Second, this developmental legacy leads to a loss of confidence or precision afforded beliefs about the consequences of social behavior. This results in an over reliance on sensory evidence and social feedback, with concomitant lability, impulsivity and hypersensitivity. In terms of PP, people with BPD show a distorted belief updating in response to new information with two opposing manifestations: rapid changes in beliefs and a lack of belief updating despite disconfirmatory evidence. This account of distorted information processing has the potential to explain both the instability (of affect, self-image, and interpersonal relationships) and the rigidity (of beliefs about self and others) which is typical of BPD. At the neurobiological level, we propose that enhanced levels of dopamine are associated with the increased integration of negative social feedback, and we also discuss the hypothesis of an impaired inhibitory control of the prefrontal cortex in the processing of negative social information. Our account may provide a new understanding not only of the clinical aspects of BPD, but also a unifying theory of the corresponding neurobiological pathologies. We conclude by outlining some directions for future research on the behavioral, neurobiological, and computational underpinnings of this model, and point to some clinical implications of it. [ABSTRACT FROM AUTHOR]