Predictions not commands: active inference in the motor system

Sensorimotor learning during synchronous speech is modulated by the acoustics of the other voice

This study tested the hypothesis that speaking with other voices can influence sensorimotor predictions of one's own voice. Real-time manipulations of auditory feedback were used to drive sensorimotor adaptation in speech, while participants spoke sentences in synchrony with another voice, a task known to induce implicit imitation (phonetic convergence). The acoustic-phonetic properties of the other voice were manipulated between groups, such that convergence with it would either oppose (incongruent group, n = 15) or align with (congruent group, n = 16) speech motor adaptation. As predicted, significantly greater adaptation was seen in the congruent compared to the incongruent group. This suggests the use of shared sensory targets in speech for predicting the sensory outcomes of both the actions of others (speech perception) and the actions of the self (speech production). This finding has important implications for wider theories of shared predictive mechanisms across perception and action, such as active inference.

A sensory-motor theory of the neocortex

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

Cortical Mechanisms Underlying Effects of Repetitive Peripheral Magnetic Stimulation on Dynamic and Static Postural Control in Patients with Chronic Non-Specific Low Back Pain: A Double-Blind Randomized Clinical Trial

INTRODUCTION

Patients with chronic non-specific low back pain (CNLBP) often experience impaired postural control, contributing to pain recurrence. Although repetitive peripheral magnetic stimulation (rPMS) combined with core muscle training (CMT) could improve postural control, its neural mechanism remains unclear. This study aims to investigate the postural control-related cortical mechanism of the effect of rPMS on patients with CNLBP.

METHODS

This unicentric, prospective, randomized, double-blind, controlled trial was conducted in a public hospital from May to December 2023. A total of 40 patients (27 females and 13 males, mean age 29.38 ± 7.72) with CNLBP were randomly assigned to either the rPMS group (real rPMS with CMT) or the sham-rPMS group (sham-rPMS with CMT) for 12 sessions over 4 weeks. The rPMS was applied to the lumbar paravertebral multifidus muscle on the painful side. Pain and disability were quantified using the visual analog scale (VAS) and Oswestry dysfunction index (ODI) pre- and post-intervention. Furthermore, the sway area and velocity of the center of pressure (COP) were measured using a force platform. The cortical activities in 6 regions of interest during 4 tasks (standing with eyes open/closed on a stable/unstable plane) were recorded by functional near-infrared spectroscopy (fNIRS) pre- and post-intervention. The repeated measure ANOVA was applied for statistical analysis. Spearman's correlation was used to determine the relationships between variables.

RESULTS

After the intervention, the rPMS group showed decreased pain intensity (p = 0.001) and sway area (unstable eyes-closed task) (p = 0.046) compared to the sham-rPMS group. Additionally, the rPMS group exhibited increased activation in left primary motor cortex (M1) (p = 0.042) and reduced in left supplementary motor area (SMA) (p = 0.045), whereas the sham-rPMS group showed no significant changes. The increased activation of left M1 was negatively correlated to the reduction of pain intensity (r = - 0.537, p = 0.018) and sway area (r = - 0.500, p = 0.029) under the static balancing task. Furthermore, there was a positive correlation between sway velocity and VAS (r = 0.451, p = 0.046) post-rPMS intervention.

CONCLUSION

Repetitive peripheral magnetic stimulation combined with core muscle training demonstrated better analgesic effects and postural control improvements, compared to sham-stimulation. This may be attributed to the increased activation of the left primary motor cortex.

CLINICAL TRIAL REGISTRATION

The trial was registered on ClinicalTrials.gov (ChiCTR2300070943).

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

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

Disentangling pain and fatigue in chronic fatigue syndrome: a resting state connectivity study before and after cognitive behavioral therapy

BACKGROUND

Fatigue is a central feature of myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS), but many ME/CFS patients also report comorbid pain symptoms. It remains unclear whether these symptoms are related to similar or dissociable brain networks. This study used resting-state fMRI to disentangle networks associated with fatigue and pain symptoms in ME/CFS patients, and to link changes in those networks to clinical improvements following cognitive behavioral therapy (CBT).

METHODS

Relationships between pain and fatigue symptoms and cortico-cortical connectivity were assessed within ME/CFS patients at baseline (N = 72) and after CBT (N = 33) and waiting list (WL, N = 18) and compared to healthy controls (HC, N = 29). The analyses focused on four networks previously associated with pain and/or fatigue, i.e. the fronto-parietal network (FPN), premotor network (PMN), somatomotor network (SMN), and default mode network (DMN).

RESULTS

At baseline, variation in pain and fatigue symptoms related to partially dissociable brain networks. Fatigue was associated with higher SMN-PMN connectivity and lower SMN-DMN connectivity. Pain was associated with lower PMN-DMN connectivity. CBT improved SMN-DMN connectivity, compared to WL. Larger clinical improvements were associated with larger increases in frontal SMN-DMN connectivity. No CBT effects were observed for PMN-DMN or SMN-PMN connectivity.

CONCLUSIONS

These results provide insight into the dissociable neural mechanisms underlying fatigue and pain symptoms in ME/CFS and how they are affected by CBT in successfully treated patients. Further investigation of how and in whom behavioral and biomedical treatments affect these networks is warranted to improve and individualize existing or new treatments for ME/CFS.

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

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

Mentalization-based approach for schizophrenia spectrum disorders: a psychotherapeutic proposal for evolved schizophrenic trajectories and serious mental disorders

There is a growing interest in psychotherapeutic approaches to pre-psychotic high-risk states or first-episode psychosis, where mentalization-based treatment has shown its utility. This article presents a mentalization-based approach for the treatment of those individuals diagnosed with an evolved schizophrenia spectrum disorder, whose characteristics make them especially inaccessible to reflective psychotherapeutic treatment. A synthesis of the conceptual frameworks that justify the needs for technical modification of the mentalization-based treatment foundational techniques is carried out, followed by the proposal of adaptations, with a focus in self-agency and patient-therapist dyad. Therapeutic interventions are outlined, including illustrative examples. The mentalizing approach presented here holds promise for future research and treatment opportunities for patients with evolved schizophrenia and other serious mental disorders.

Active Inference With Empathy Mechanism for Socially Behaved Artificial Agents in Diverse Situations

This article proposes a method for an artificial agent to behave in a social manner. Although defining proper social behavior is difficult because it differs from situation to situation, the agent following the proposed method adaptively behaves appropriately in each situation by empathizing with the surrounding others. The proposed method is achieved by incorporating empathy into active inference. We evaluated the proposed method regarding control of autonomous mobile robots in diverse situations. From the evaluation results, an agent controlled by the proposed method could behave more adaptively socially than an agent controlled by the standard active inference in the diverse situations. In the case of two agents, the agent controlled with the proposed method behaved in a social way that reduced the other agent's travel distance by 13.7% and increased the margin between the agents by 25.8%, even though it increased the agent's travel distance by 8.2%. Also, the agent controlled with the proposed method behaved more socially when it was surrounded by altruistic others but less socially when it was surrounded by selfish others.

Collective behavior from surprise minimization

Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from directed movement to milling to disordered swarming. Typically, such macroscopic patterns arise from decentralized, local interactions among constituent components (e.g., individual fish in a school). Preeminent models of this process describe individuals as self-propelled particles, subject to self-generated motion and "social forces" such as short-range repulsion and long-range attraction or alignment. However, organisms are not particles; they are probabilistic decision-makers. Here, we introduce an approach to modeling collective behavior based on active inference. This cognitive framework casts behavior as the consequence of a single imperative: to minimize surprise. We demonstrate that many empirically observed collective phenomena, including cohesion, milling, and directed motion, emerge naturally when considering behavior as driven by active Bayesian inference-without explicitly building behavioral rules or goals into individual agents. Furthermore, we show that active inference can recover and generalize the classical notion of social forces as agents attempt to suppress prediction errors that conflict with their expectations. By exploring the parameter space of the belief-based model, we reveal nontrivial relationships between the individual beliefs and group properties like polarization and the tendency to visit different collective states. We also explore how individual beliefs about uncertainty determine collective decision-making accuracy. Finally, we show how agents can update their generative model over time, resulting in groups that are collectively more sensitive to external fluctuations and encode information more robustly.

An Overview of the Free Energy Principle and Related Research

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

Randomness impacts the building of specific priors, visual exploration, and perception in object recognition

Recognising objects is a vital skill on which humans heavily rely to respond quickly and adaptively to their environment. Yet, we lack a full understanding of the role visual information sampling plays in this process, and its relation to the individual's priors. To bridge this gap, the eye-movements of 18 adult participants were recorded during a free-viewing object-recognition task using Dots stimuli1. Participants viewed the stimuli in one of three orders: from most visible to least (Descending), least visible to most (Ascending), or in a randomised order (Random). This dictated the strength of their priors along the experiment. Visibility order influenced the participants' recognition performance and visual exploration. In addition, we found that while orders allowing for stronger priors generally led participants to visually sample more informative locations, this was not the case of Random participants. Indeed, they appeared to behave naïvely, and their use of specific object-related priors was fully impaired, while they maintained the ability to use general, task-related priors to guide their exploration. These findings have important implications for our understanding of perception, which appears to be influenced by complex cognitive processes, even at the basic level of visual sampling during object recognition.

A computationally informed distinction of interoception and exteroception

While interoception is of major neuroscientific interest, its precise definition and delineation from exteroception continue to be debated. Here, we propose a functional distinction between interoception and exteroception based on computational concepts of sensor-effector loops. Under this view, the classification of sensory inputs as serving interoception or exteroception depends on the sensor-effector loop they feed into, for the control of either bodily (physiological and biochemical) or environmental states. We explain the utility of this perspective by examining the perception of skin temperature, one of the most challenging cases for distinguishing between interoception and exteroception. Specifically, we propose conceptualising thermoception as inference about the thermal state of the body (including the skin), which is directly coupled to thermoregulatory processes. This functional view emphasises the coupling to regulation (control) as a defining property of perception (inference) and connects the definition of interoception to contemporary computational theories of brain-body interactions.

Comparing episodic memory outcomes from walking augmented reality and stationary virtual reality encoding experiences

Episodic Memory (EM) is the neurocognitive capacity to consciously recollect personally experienced events in specific spatio-temporal contexts. Although the relevance of spatial and temporal information is widely acknowledged in the EM literature, it remains unclear whether and how EM performance and organisation is modulated by self-motion, and by motor- and visually- salient environmental features (EFs) of the encoding environment. This study examines whether and how EM is modulated by locomotion and the EFs encountered in a controlled lifelike learning route within a large-scale building. Twenty-eight healthy participants took part in a museum-tour encoding task implemented in walking Augmented Reality (AR) and stationary Virtual Reality (VR) conditions. EM performance and organisation were assessed immediately and 48-hours after trials using a Remember/Familiar recognition paradigm. Results showed a significant positive modulation effect of locomotion on distinctive EM aspects. Findings highlighted a significant performance enhancement effect of stairway-adjacent locations compared to dead-end and mid-route stimuli-presentation locations. The results of this study may serve as design criteria to facilitate neurocognitive rehabilitative interventions of EM. The underlying technological framework developed for this study represents a novel and ecologically sound method for evaluating EM processes in lifelike situations, allowing researchers a naturalistic perspective into the complex nature of EM.

Somatosensory omissions reveal action-related predictive processing

The intricate relation between action and somatosensory perception has been studied extensively in the past decades. Generally, a forward model is thought to predict the somatosensory consequences of an action. These models propose that when an action is reliably coupled to a tactile stimulus, unexpected absence of the stimulus should elicit prediction error. Although such omission responses have been demonstrated in the auditory modality, it remains unknown whether this mechanism generalizes across modalities. This study therefore aimed to record action-induced somatosensory omission responses using EEG in humans. Self-paced button presses were coupled to somatosensory stimuli in 88% of trials, allowing a prediction, or in 50% of trials, not allowing a prediction. In the 88% condition, stimulus omission resulted in a neural response consisting of multiple components, as revealed by temporal principal component analysis. The oN1 response suggests similar sensory sources as stimulus-evoked activity, but an origin outside primary cortex. Subsequent oN2 and oP3 responses, as previously observed in the auditory domain, likely reflect modality-unspecific higher order processes. Together, findings straightforwardly demonstrate somatosensory predictions during action and provide evidence for a partially amodal mechanism of prediction error generation.

Vague sensations. About the background and consequences of discordance between actual and perceived physiological changes

Empirical evidence consistently shows that discordance, also called dissociation or discrepancy, between actual physiological (mainly visceral) events and their perceived counterparts is substantial. On the one hand, we typically do not perceive actual visceral events occurring in our bodies; on the other hand, sometimes we do perceive bodily changes that do not really take place. This narrative review presents the available empirical findings on the discordance, and summarizes possible explanations that approach the phenomenon from the viewpoint of evolution, cognitive development, and predictive processing. Also, the role of top-down factors, such as expectations and experiences is discussed. Finally, practically relevant consequences of the discordance are presented using the examples of mind-body practices, the placebo and nocebo phenomenon, and medically unexplained symptoms. It is concluded that the discordance between actual and perceived body changes can have a negative impact on health, mainly through issues with adherence and other behavioral factors. The existence of actual-perceived discordance should be taught and demonstrated in the elementary and high school, as well as in many areas of higher education.

Dissecting muscle synergies in the task space

The muscle synergy is a guiding concept in motor control research that relies on the general notion of muscles 'working together' towards task performance. However, although the synergy concept has provided valuable insights into motor coordination, muscle interactions have not been fully characterised with respect to task performance. Here, we address this research gap by proposing a novel perspective to the muscle synergy that assigns specific functional roles to muscle couplings by characterising their task-relevance. Our novel perspective provides nuance to the muscle synergy concept, demonstrating how muscular interactions can 'work together' in different ways: (1) irrespective of the task at hand but also (2) redundantly or (3) complementarily towards common task-goals. To establish this perspective, we leverage information- and network-theory and dimensionality reduction methods to include discrete and continuous task parameters directly during muscle synergy extraction. Specifically, we introduce co-information as a measure of the task-relevance of muscle interactions and use it to categorise such interactions as task-irrelevant (present across tasks), redundant (shared task information), or synergistic (different task information). To demonstrate these types of interactions in real data, we firstly apply the framework in a simple way, revealing its added functional and physiological relevance with respect to current approaches. We then apply the framework to large-scale datasets and extract generalizable and scale-invariant representations consisting of subnetworks of synchronised muscle couplings and distinct temporal patterns. The representations effectively capture the functional interplay between task end-goals and biomechanical affordances and the concurrent processing of functionally similar and complementary task information. The proposed framework unifies the capabilities of current approaches in capturing distinct motor features while providing novel insights and research opportunities through a nuanced perspective to the muscle synergy.

The empirical status of predictive coding and active inference

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

Active inference as a theory of sentient behavior

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

Aesthetics and predictive processing: grounds and prospects of a fruitful encounter

In the last few years, a remarkable convergence of interests and results has emerged between scholars interested in the arts and aesthetics from a variety of perspectives and cognitive scientists studying the mind and brain within the predictive processing (PP) framework. This convergence has so far proven fruitful for both sides: while PP is increasingly adopted as a framework for understanding aesthetic phenomena, the arts and aesthetics, examined under the lens of PP, are starting to be seen as important windows into our mental functioning. The result is a vast and fast-growing research programme that promises to deliver important insights into our aesthetic encounters as well as a wide range of psychological phenomena of general interest. Here, we present this developing research programme, describing its grounds and highlighting its prospects. We start by clarifying how the study of the arts and aesthetics encounters the PP picture of mental functioning (§1). We then go on to outline the prospects of this encounter for the fields involved: philosophy and history of art (§2), psychology of aesthetics and neuroaesthetics (§3) and psychology and neuroscience more generally (§4). The upshot is an ambitious but well-defined framework within which aesthetics and cognitive science can partner up to illuminate crucial aspects of the human mind. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

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

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

Computational components of visual predictive coding circuitry

If a full visual percept can be said to be a 'hypothesis', so too can a neural 'prediction' - although the latter addresses one particular component of image content (such as 3-dimensional organisation, the interplay between lighting and surface colour, the future trajectory of moving objects, and so on). And, because processing is hierarchical, predictions generated at one level are conveyed in a backward direction to a lower level, seeking to predict, in fact, the neural activity at that prior stage of processing, and learning from errors signalled in the opposite direction. This is the essence of 'predictive coding', at once an algorithm for information processing and a theoretical basis for the nature of operations performed by the cerebral cortex. Neural models for the implementation of predictive coding invoke specific functional classes of neuron for generating, transmitting and receiving predictions, and for producing reciprocal error signals. Also a third general class, 'precision' neurons, tasked with regulating the magnitude of error signals contingent upon the confidence placed upon the prediction, i.e., the reliability and behavioural utility of the sensory data that it predicts. So, what is the ultimate source of a 'prediction'? The answer is multifactorial: knowledge of the current environmental context and the immediate past, allied to memory and lifetime experience of the way of the world, doubtless fine-tuned by evolutionary history too. There are, in consequence, numerous potential avenues for experimenters seeking to manipulate subjects' expectation, and examine the neural signals elicited by surprising, and less surprising visual stimuli. This review focuses upon the predictive physiology of mouse and monkey visual cortex, summarising and commenting on evidence to date, and placing it in the context of the broader field. It is concluded that predictive coding has a firm grounding in basic neuroscience and that, unsurprisingly, there remains much to learn.

I am not the cause of this pain: An experimental study of the cognitive processes underlying causal attribution in the unpredictable situation whether negative outcomes

OBJECTIVES

Pain causal attribution is the attribution of pain causes to self or others, which may depend on one's choice of actions. The study aimed to examine how the cognitive processes of pain causal attribution as one aspect of the sense of agency change in healthy individuals based on free or forced choice, using a temporal binding (TB) experimental task.

METHODS

Participants pressed keys (action) in a combined TB task, with one key having a high probability of delivering pain (with tone). In fact, only the bias between the free-choice and the forced choice conditions was manipulated. Participants estimated the time between their action and tone, with shorter intervals indicating internal attribution.

RESULTS

Interval estimation was significantly longer in the forced-choice condition than in the free-choice condition when a pain stimulus was delivered.

CONCLUSIONS

Explicit complaints of pain being caused by others may be represented in implicit cognitive processes.

Augmented action observation: Theory and practical applications in sensorimotor rehabilitation

Sensory feedback is a fundamental aspect of effective motor learning in sport and clinical contexts. One way to provide this is through sensory augmentation, where extrinsic sensory information are associated with, and modulated by, movement. Traditionally, sensory augmentation has been used as an online strategy, where feedback is provided during physical execution of an action. In this article, we argue that action observation can be an additional effective channel to provide augmented feedback, which would be complementary to other, more traditional, motor learning and sensory augmentation strategies. Given these similarities between observing and executing an action, action observation could be used when physical training is difficult or not feasible, for example during immobilization or during the initial stages of a rehabilitation protocol when peripheral fatigue is a common issue. We review the benefits of observational learning and preliminary evidence for the effectiveness of using augmented action observation to improve learning. We also highlight current knowledge gaps which make the transition from laboratory to practical contexts difficult. Finally, we highlight the key areas of focus for future research.

Not a general, symptom-unspecific, transdiagnostic marker for functional symptoms: sensorimotor processing of head control is intact in chronic pain

Introduction

Functional disorders are prevalent in all medical fields and pose a tremendous public health problem, with pain being one of the most common functional symptoms. Understanding the underlying, potentially unifying mechanism in functional (pain) disorders is instrumental in facilitating timely diagnosis, stigma reduction, and adequate treatment options. Neuroscientific models of perception suggest that functional symptoms arise due to dysregulated sensorimotor processing in the central nervous system, with brain-based predictions dominating the eventual percept. Experimental evidence for this transdiagnostic mechanism has been established in various functional symptoms. The goal of the current study was to investigate whether erroneous sensorimotor processing is an underlying transdiagnostic mechanism in chronic (functional) pain.

Method

A total of 13 patients with chronic (functional) pain [three patients with chronic (functional) pain disorder, F45.40, ICD-10; 10 patients with chronic pain disorder with somatic and psychological factors, F45.41, ICD-10]; and 15 healthy controls performed large combined eye-head gaze shifts toward visual targets, naturally and with increased head moment of inertia. We simultaneously measured participants' eye and head movements to assess head oscillations at the end of the gaze shift, which are an established indicator of (transdiagnostic) sensorimotor processing deficits of head control.

Results

Using a Bayesian analysis protocol, we found that patients with chronic (functional) pain and control participants stabilized their heads equally well (Bayes Factor 01 = 3.7, Bayes Factor exclusion = 5.23; corresponding to substantial evidence) during all sessions of the experiment.

Conclusion

Our results suggest that patients with chronic (functional) pain do not show measurable symptom-unspecific sensorimotor processing deficits. We discuss outcome parameter choice, organ system specificity, and selection of patient diagnoses as possible reasons for this result and recommend future avenues for research.

Deep kinematic inference affords efficient and scalable control of bodily movements

Performing goal-directed movements requires mapping goals from extrinsic (workspace-relative) to intrinsic (body-relative) coordinates and then to motor signals. Mainstream approaches based on optimal control realize the mappings by minimizing cost functions, which is computationally demanding. Instead, active inference uses generative models to produce sensory predictions, which allows a cheaper inversion to the motor signals. However, devising generative models to control complex kinematic chains like the human body is challenging. We introduce an active inference architecture that affords a simple but effective mapping from extrinsic to intrinsic coordinates via inference and easily scales up to drive complex kinematic chains. Rich goals can be specified in both intrinsic and extrinsic coordinates using attractive or repulsive forces. The proposed model reproduces sophisticated bodily movements and paves the way for computationally efficient and biologically plausible control of actuated systems.

A thermodynamical model of non-deterministic computation in cortical neural networks

Neuronal populations in the cerebral cortex engage in probabilistic coding, effectively encoding the state of the surrounding environment with high accuracy and extraordinary energy efficiency. A new approach models the inherently probabilistic nature of cortical neuron signaling outcomes as a thermodynamic process of non-deterministic computation. A mean field approach is used, with the trial Hamiltonian maximizing available free energy and minimizing the net quantity of entropy, compared with a reference Hamiltonian. Thermodynamic quantities are always conserved during the computation; free energy must be expended to produce information, and free energy is released during information compression, as correlations are identified between the encoding system and its surrounding environment. Due to the relationship between the Gibbs free energy equation and the Nernst equation, any increase in free energy is paired with a local decrease in membrane potential. As a result, this process of thermodynamic computation adjusts the likelihood of each neuron firing an action potential. This model shows that non-deterministic signaling outcomes can be achieved by noisy cortical neurons, through an energy-efficient computational process that involves optimally redistributing a Hamiltonian over some time evolution. Calculations demonstrate that the energy efficiency of the human brain is consistent with this model of non-deterministic computation, with net entropy production far too low to retain the assumptions of a classical system.

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

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

Active production and passive observation of hand movements shift visual hand location

Which factors influence the perception of our hand location is a matter of current debate. Here, we test if sensorimotor processing contributes to the perception of hand location. We developed a novel visuomotor adaptation procedure to measure whether actively performing hand movements or passively observing them, influences visual perception of hand location. Participants had to point with a handheld controller to a briefly presented visual target. When they reached the remembered position of the target, the controller presented a tactile buzz. In adaptation trials, the tactile buzz was presented when the hand had not yet reached the target. Over the course of trials, participants adapted to the manipulation and pointed to a location between the visual target and the tactile buzz. We measured the perceived location of the hand by flashing a virtual pair of left and right hands before and after adaptation. Participants had to judge which hand they perceived closer to their body on the fronto-parallel plane. After adaptation, they judged the right hand, that corresponded to the hand used during adaptation, to be located further away from the body. We conclude that sensorimotor prediction of the consequences of hand movements shape sensory processing of hand location.

Hierarchical network structure as the source of hierarchical dynamics (power-law frequency spectra) in living and non-living systems: How state-trait continua (body plans, personalities) emerge from first principles in biophysics

Living systems are hierarchical control systems that display a small world network structure. In such structures, many smaller clusters are nested within fewer larger ones, producing a fractal-like structure with a 'power-law' cluster size distribution (a mereology). Just like their structure, the dynamics of living systems shows fractal-like qualities: the timeseries of inner message passing and overt behavior contain high frequencies or 'states' (treble) that are nested within lower frequencies or 'traits' (bass), producing a power-law frequency spectrum that is known as a 'state-trait continuum' in the behavioral sciences. Here, we argue that the power-law dynamics of living systems results from their power-law network structure: organisms 'vertically encode' the deep spatiotemporal structure of their (anticipated) environments, to the effect that many small clusters near the base of the hierarchy produce high frequency signal changes and fewer larger clusters at its top produce ultra-low frequencies. Such ultra-low frequencies exert a tonic regulatory pressure that produces morphological as well as behavioral traits (i.e., body plans and personalities). Nested-modular structure causes higher frequencies to be embedded within lower frequencies, producing a power-law state-trait continuum. At the heart of such dynamics lies the need for efficient energy dissipation through networks of coupled oscillators, which also governs the dynamics of non-living systems (e.q., earthquakes, stock market fluctuations). Since hierarchical structure produces hierarchical dynamics, the development and collapse of hierarchical structure (e.g., during maturation and disease) should leave specific traces in system dynamics (shifts in lower frequencies, i.e. morphological and behavioral traits) that may serve as early warning signs to system failure. The applications of this idea range from (bio)physics and phylogenesis to ontogenesis and clinical medicine.

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

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

Action-effect knowledge transfers to similar effect stimuli

The ability to anticipate the sensory consequences of our actions (i.e., action-effects) is known to be important for intentional action initiation and control. Learned action-effects can select the responses that previously have been associated with them. What has been largely unexplored is how learned action-effect associations can aid action selection for effects that have not previously associated with an action but are similar to learned effects. In two studies, we aimed to show that when presented new, unknown action-effects, participants select the responses that have previously been associated with similar action-effects. In the first study (n = 27), action-effect similarity was operationalized via stimuli belonging to the same or different categories as the previously learned action-effects. In the second study (n = 31), action-effect similarity was realized via stimuli that require comparable motor responses in real life. Participants first learned that specific responses are followed by specific visual effect stimuli. In the test phase, learned effect stimuli, new but similar effect stimuli and new but dissimilar effect stimuli were presented ahead of the response. The findings revealed that both learned effect stimuli and new similar effect stimuli affected response times, whereas new dissimilar effects did not. When a learned or a new similar effect was followed by a learned response, compared to an unlearned response, the responses were faster. We interpret these findings in terms of action-effect learning. The action-effect once bound to an action is used to select an action if a similar effect for which no action has been learned yet is presented. However, it is noteworthy that, due to our design, other explanations for the found transfer are conceivable. We address these limitations in the General Discussion.

Emotion regulation from an action-control perspective

Despite increasing interest in emotional processes in cognitive science, theories on emotion regulation have remained rather isolated, predominantly focused on cognitive regulation strategies such as reappraisal. However, recent neurocognitive evidence suggests that early emotion regulation may involve sensorimotor control in addition to other emotion-regulation processes. We propose an action-oriented view of emotion regulation, in which feedforward predictions develop from action-selection mechanisms. Those can account for acute emotional-action control as well as more abstract instances of emotion regulation such as cognitive reappraisal. We argue the latter occurs in absence of overt motor output, yet in the presence of full-blown autonomic, visceral, and subjective changes. This provides an integrated framework with testable neuro-computational predictions and concrete starting points for intervention to improve emotion control in affective disorders.

Relative fluency (unfelt vs felt) in active inference

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

Multisensory decisions from self to world

Classic Bayesian models of perceptual inference describe how an ideal observer would integrate 'unisensory' measurements (multisensory integration) and attribute sensory signals to their origin(s) (causal inference). However, in the brain, sensory signals are always received in the context of a multisensory bodily state-namely, in combination with other senses. Moreover, sensory signals from both interoceptive sensing of one's own body and exteroceptive sensing of the world are highly interdependent and never occur in isolation. Thus, the observer must fundamentally determine whether each sensory observation is from an external (versus internal, self-generated) source to even be considered for integration. Critically, solving this primary causal inference problem requires knowledge of multisensory and sensorimotor dependencies. Thus, multisensory processing is needed to separate sensory signals. These multisensory processes enable us to simultaneously form a sense of self and form distinct perceptual decisions about the external world. In this opinion paper, we review and discuss the similarities and distinctions between multisensory decisions underlying the sense of self and those directed at acquiring information about the world. We call attention to the fact that heterogeneous multisensory processes take place all along the neural hierarchy (even in forming 'unisensory' observations) and argue that more integration of these aspects, in theory and experiment, is required to obtain a more comprehensive understanding of multisensory brain function. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

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

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

Testing predictive coding theories of autism spectrum disorder using models of active inference

Several competing neuro-computational theories of autism have emerged from predictive coding models of the brain. To disentangle their subtly different predictions about the nature of atypicalities in autistic perception, we performed computational modelling of two sensorimotor tasks: the predictive use of manual gripping forces during object lifting and anticipatory eye movements during a naturalistic interception task. In contrast to some accounts, we found no evidence of chronic atypicalities in the use of priors or weighting of sensory information during object lifting. Differences in prior beliefs, rates of belief updating, and the precision weighting of prediction errors were, however, observed for anticipatory eye movements. Most notably, we observed autism-related difficulties in flexibly adapting learning rates in response to environmental change (i.e., volatility). These findings suggest that atypical encoding of precision and context-sensitive adjustments provide a better explanation of autistic perception than generic attenuation of priors or persistently high precision prediction errors. Our results did not, however, support previous suggestions that autistic people perceive their environment to be persistently volatile.

Relationship Between Interoception and Autistic Traits: A Resting-State Functional Connectivity Study

Interoception, the sense of the physiological condition of our body, is impaired in individuals with autism spectrum disorders. Evidence suggests that subclinical autistic traits are mild manifestations of autistic symptoms, present in the general population. We examined the resting-state functional connectivity (rsFC) associating with interoception and autistic traits in 62 healthy young adults. Autistic traits correlated negatively with the rsFC between the lateral ventral anterior insula and anterior cingulate cortex. Interoceptive accuracy and sensibility correlated positively with the rsFC between interoceptive brain networks and the cerebellum, supplementary motor area, and visual regions. The results suggest that a negative relationship between interoception and autistic traits is largely accounted for by both self-report measures and decreased rsFC amongst the interoceptive brain network.

Suicide prevention and ketamine: insights from computational modeling

Suicide is a pressing public health issue, with over 700,000 individuals dying each year. Ketamine has emerged as a promising treatment for suicidal thoughts and behaviors (STBs), yet the complex mechanisms underlying ketamine's anti-suicidal effect are not fully understood. Computational psychiatry provides a promising framework for exploring the dynamic interactions underlying suicidality and ketamine's therapeutic action, offering insight into potential biomarkers, treatment targets, and the underlying mechanisms of both. This paper provides an overview of current computational theories of suicidality and ketamine's mechanism of action, and discusses various computational modeling approaches that attempt to explain ketamine's anti-suicidal effect. More specifically, the therapeutic potential of ketamine is explored in the context of the mismatch negativity and the predictive coding framework, by considering neurocircuits involved in learning and decision-making, and investigating altered connectivity strengths and receptor densities targeted by ketamine. Theory-driven computational models offer a promising approach to integrate existing knowledge of suicidality and ketamine, and for the extraction of model-derived mechanistic parameters that can be used to identify patient subgroups and personalized treatment approaches. Future computational studies on ketamine's mechanism of action should optimize task design and modeling approaches to ensure parameter reliability, and external factors such as set and setting, as well as psychedelic-assisted therapy should be evaluated for their additional therapeutic value.

Toward a Bio-Organon: A model of interdependence between energy, information and knowledge in living systems

What is an organism? In the absence of a fundamental biological definition, what constitutes a living organism, whether it is a unicellular microbe, a multicellular being or a multi-organismal society, remains an open question. New models of living systems are needed to address the scale of this question, with implications for the relationship between humanity and planetary ecology. Here we develop a generic model of an organism that can be applied across multiple scales and through major evolutionary transitions to form a toolkit, or bio-organon, for theoretical studies of planetary-wide physiology. The tool identifies the following core organismic principles that cut across spatial scale: (1) evolvability through self-knowledge, (2) entanglement between energy and information, and (3) extrasomatic "technology" to scaffold increases in spatial scale. Living systems are generally defined by their ability to self-sustain against entropic forces of degradation. Life "knows" how to survive from the inside, not from its genetic code alone, but by utilizing this code through dynamically embodied and functionally specialized flows of information and energy. That is, entangled metabolic and communication networks bring encoded knowledge to life in order to sustain life. However, knowledge is itself evolved and is evolving. The functional coupling between knowledge, energy and information has ancient origins, enabling the original, cellular "biotechnology," and cumulative evolutionary creativity in biochemical products and forms. Cellular biotechnology also enabled the nesting of specialized cells into multicellular organisms. This nested organismal hierarchy can be extended further, suggesting that an organism of organisms, or a human "superorganism," is not only possible, but in keeping with evolutionary trends.

Free energy and inference in living systems

Organisms are non-equilibrium, stationary systems self-organized via spontaneous symmetry breaking and undergoing metabolic cycles with broken detailed balance in the environment. The thermodynamic free-energy (FE) principle describes an organism's homeostasis as the regulation of biochemical work constrained by the physical FE cost. By contrast, recent research in neuroscience and theoretical biology explains a higher organism's homeostasis and allostasis as Bayesian inference facilitated by the informational FE. As an integrated approach to living systems, this study presents an FE minimization theory overarching the essential features of both the thermodynamic and neuroscientific FE principles. Our results reveal that the perception and action of animals result from active inference entailed by FE minimization in the brain, and the brain operates as a Schrödinger's machine conducting the neural mechanics of minimizing sensory uncertainty. A parsimonious model suggests that the Bayesian brain develops the optimal trajectories in neural manifolds and induces a dynamic bifurcation between neural attractors in the process of active inference.

Dual-stream cortical pathways mediate sensory prediction

Predictions are constantly generated from diverse sources to optimize cognitive functions in the ever-changing environment. However, the neural origin and generation process of top-down induced prediction remain elusive. We hypothesized that motor-based and memory-based predictions are mediated by distinct descending networks from motor and memory systems to the sensory cortices. Using functional magnetic resonance imaging (fMRI) and a dual imagery paradigm, we found that motor and memory upstream systems activated the auditory cortex in a content-specific manner. Moreover, the inferior and posterior parts of the parietal lobe differentially relayed predictive signals in motor-to-sensory and memory-to-sensory networks. Dynamic causal modeling of directed connectivity revealed selective enabling and modulation of connections that mediate top-down sensory prediction and ground the distinctive neurocognitive basis of predictive processing.

The integration of action-oriented multisensory information from target and limb within the movement planning and execution

The planning and execution of a grasping or reaching movement toward targets we sense with the other hand requires integrating multiple sources of sensory information about the limb performing the movement and the target of the action. In the last two decades, several sensory and motor control theories have thoroughly described how this multisensory-motor integration process occurs. However, even though these theories were very influential in their respective field, they lack a clear, unified vision of how target-related and movement-related multisensory information integrates within the action planning and execution phases. This brief review aims to summarize the most influential theories in multisensory integration and sensory-motor control by underscoring their critical points and hidden connections, providing new ideas on the multisensory-motor integration process. Throughout the review, I wll propose an alternative view of how the multisensory integration process unfolds along the action planning and execution and I will make several connections with the existent multisensory-motor control theories.

The evidence against somatotopic organization of function in the primate corticospinal tract

We review the spatial organization of corticospinal outputs from different cortical areas and how this reflects the varied functions mediated by the corticospinal tract. A long-standing question is whether the primate corticospinal tract shows somatotopical organization. Although this has been clearly demonstrated for corticofugal outputs passing through the internal capsule and cerebral peduncle, there is accumulating evidence against somatotopy in the pyramidal tract in the lower brainstem and in the spinal course of the corticospinal tract. Answering the question on somatotopy has important consequences for understanding the effects of incomplete spinal cord injury. Our recent study in the macaque monkey, using high-resolution dextran tracers, demonstrated a great deal of intermingling of fibres originating from primary motor cortex arm/hand, shoulder and leg areas. We quantified the distribution of fibres belonging to these different projections and found no significant difference in their distribution across different subsectors of the pyramidal tract or lateral corticospinal tract, arguing against somatotopy. We further demonstrated intermingling with corticospinal outputs derived from premotor and supplementary motor arm areas. We present new evidence against somatotopy for corticospinal projections from rostral and caudal cingulate motor areas and from somatosensory areas of the parietal cortex. In the pyramidal tract and lateral corticospinal tract, fibres from the cingulate motor areas overlap with each other. Fibres from the primary somatosensory cortex arm area completely overlap those from the leg area. There is also substantial overlap of both these outputs with those from posterior parietal sensorimotor areas. We argue that the extensive intermingling of corticospinal outputs from so many different cortical regions must represent an organizational principle, closely related to its mediation of many different functions and its large range of fibre diameters. The motor sequelae of incomplete spinal injury, such as central cord syndrome and 'cruciate paralysis', include much greater deficits in upper than in lower limb movement. Current teaching and text book explanations of these symptoms are still based on a supposed corticospinal somatotopy or 'lamination', with greater vulnerability of arm and hand versus leg fibres. We suggest that such explanations should now be finally abandoned. Instead, the clinical and neurobiological implications of the complex organization of the corticospinal tract need now to be taken into consideration. This leads us to consider the evidence for a greater relative influence of the corticospinal tract on upper versus lower limb movements, the former best characterized by skilled hand and digit movements.

Intertwining the social and the cognitive loops: socially enactive cognition for human-compatible interactive systems

It is increasingly important for technical systems to be able to interact flexibly, robustly and fluently with humans in real-world scenarios. However, while current AI systems excel at narrow task competencies, they lack crucial interaction abilities for the adaptive and co-constructed social interactions that humans engage in. We argue that a possible avenue to tackle the corresponding computational modelling challenges is to embrace interactive theories of social understanding in humans. We propose the notion of socially enactive cognitive systems that do not rely solely on abstract and (quasi-)complete internal models for separate social perception, reasoning and action. By contrast, socially enactive cognitive agents are supposed to enable a close interlinking of the enactive socio-cognitive processing loops within each agent, and the social-communicative loop between them. We discuss theoretical foundations of this view, identify principles and requirements for according computational approaches, and highlight three examples of our own research that showcase the interaction abilities achievable in this way. This article is part of a discussion meeting issue 'Face2face: advancing the science of social interaction'.

[Mother brain: Bayesian theory of maternal interoception during pregnancy and postpartum]

The perinatal period, including pregnancy and postpartum, causes major morphological, endocrinal, and thermal transitions in women. As the fetus grows, abdominal muscle fibers stretch, internal organs such as the bladder or colon move, and the uterine anatomy changes. Many of these changes involve interoception, the perception of internal body signals such as muscle and visceral sensations. Despite the importance of these interoceptive signals, few studies have explored perinatal interoception. We propose an innovative theory of maternal interoception based on recent findings in neuroscience. We show that interoceptive signals processing during pregnancy is crucial for understanding perinatal phenomenology and psychopathology, such as maternal perception of fetal movements, maternal-infant bonding, denial of pregnancy, phantom fetal movements after childbirth, pseudocyesis or even puerperal delusion. Knowing the importance of these interoceptive mechanisms, clinicians in obstetrics, gynecology and mental health should be particularly vigilant to maternal interoception during the perinatal period.

Circadian rhythms in auditory hallucinations and psychosis

Circadian rhythms are imprinted in all organisms and influence virtually all aspects of physiology and behavior in adaptation to the 24-h day-night cycle. This recognition of a circadian timekeeping system permeating essentially all healthy functioning of body and mind quickly leads to the realization that, in turn, human ailments should be probed for the degree to which they are rooted in or marked by disruptions and dysregulations of circadian clock functions in the human body. In this review, we will focus on psychosis as a key mental illness and foremost one of its cardinal symptoms: auditory hallucinations. We will discuss recent empirical evidence and conceptual advances probing the potential role of circadian disruption in auditory hallucinations. Moreover, a dysbalance in excitation and inhibition within cortical networks, which in turn drive a disinhibition of dopaminergic signaling, will be highlighted as central physiological mechanism. Finally, we will propose two avenues for experimentally intervening on the circadian influences to potentially alleviate hallucinations in psychotic disorders.

How the conception of control influences our understanding of actions

Wilful movement requires neural control. Commonly, neural computations are thought to generate motor commands that bring the musculoskeletal system - that is, the plant - from its current physical state into a desired physical state. The current state can be estimated from past motor commands and from sensory information. Modelling movement on the basis of this concept of plant control strives to explain behaviour by identifying the computational principles for control signals that can reproduce the observed features of movements. From an alternative perspective, movements emerge in a dynamically coupled agent-environment system from the pursuit of subjective perceptual goals. Modelling movement on the basis of this concept of perceptual control aims to identify the controlled percepts and their coupling rules that can give rise to the observed characteristics of behaviour. In this Perspective, we discuss a broad spectrum of approaches to modelling human motor control and their notions of control signals, internal models, handling of sensory feedback delays and learning. We focus on the influence that the plant control and the perceptual control perspective may have on decisions when modelling empirical data, which may in turn shape our understanding of actions.

Relating different Dimensions of Bodily Experiences: Review and proposition of an integrative model relying on phenomenology, predictive brain and neuroscience of the self

How we mentally experience our body has been studied in a variety research domains. Each of these domains focuses in its own ways on different aspects of the body, namely the neurophysiological, perceptual, affective or social components, and proposes different conceptual taxonomies. It is therefore difficult to find one's way through this vast literature and to grasp the relationships between the different dimensions of bodily experiences. In this narrative review, we summarize the existing research directions and present their limits. We propose an integrative framework, grounded in studies on phenomenal consciousness, self-consciousness and bodily self-consciousness, that can provide a common basis for evaluating findings on different dimensions of bodily experiences. We review the putative mechanisms, relying on predictive processes, and neural substrates that support this model. We discuss how this model enables a conceptual assessment of the interrelationships between multiple dimensions of bodily experiences and potentiate interdisciplinary approaches.

From fear of falling to choking under pressure: A predictive processing perspective of disrupted motor control under anxiety

Under the Predictive Processing Framework, perception is guided by internal models that map the probabilistic relationship between sensory states and their causes. Predictive processing has contributed to a new understanding of both emotional states and motor control but is yet to be fully applied to their interaction during the breakdown of motor movements under heightened anxiety or threat. We bring together literature on anxiety and motor control to propose that predictive processing provides a unifying principle for understanding motor breakdowns as a disruption to the neuromodulatory control mechanisms that regulate the interactions of top-down predictions and bottom-up sensory signals. We illustrate this account using examples from disrupted balance and gait in populations who are anxious/fearful of falling, as well as 'choking' in elite sport. This approach can explain both rigid and inflexible movement strategies, as well as highly variable and imprecise action and conscious movement processing, and may also unite the apparently opposing self-focus and distraction approaches to choking. We generate predictions to guide future work and propose practical recommendations.

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

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