Active inference, communication and hermeneutics

Selective preservation of prediction-related signals in human sleep

Imagine listening to a familiar song on the radio. As the melody unfolds, you often anticipate the following note or beat before it plays. This ability reflects the brain's capacity to extract statistical regularities from sensory input and predict future sensory events. It is considered automatic, requiring little to no conscious effort or attention.1,2,3,4,5 But to what extent is this predictive ability maintained when cognitive resources are minimized, such as during sleep? Experimental findings from animal and human studies reveal a complex picture of predictive processing during sleep.6,7,8,9,10,11,12,13 Although some forms of predictions persist-evidenced by differential brain responses to unexpected stimuli and rhythmic music8,9,11,12-neural markers of feedback processing linked to predictions are notably disrupted.7,14 Here, we use multivariate pattern analysis (MVPA) to capture different facets of prediction-related signals, determining whether the brain preactivates the low-level features of expected stimuli and tracks statistical associations between stimuli. Using predictable and random tone sequences in a passive-listening paradigm, we recorded brain activity via electroencephalography (EEG) and magnetoencephalography (MEG) during wakefulness and sleep. We first show that subtle changes in tone features (e.g., tone frequency) elicit feature-specific responses in N1 and N2 sleep, though weaker and less sustained than in wakefulness. Critically, even during sleep, the brain preactivates feature-specific representations, but higher-order tracking of statistical associations between tones remains restricted to wakefulness. Altogether, our results suggest that feature-specific auditory processing is retained despite the fading of consciousness, while only some aspects of anticipatory predictive processing are preserved.

Neural substrates of choking under pressure: A 7T-fMRI study

Performance decrement under excessive psychological pressure is known as "choking," yet its mechanisms and neural foundations remain underexplored. Hypothesizing that changes in the internal model could induce choking, we conducted a 7 T functional MRI introducing excessive pressure through a rare Jackpot condition that offers high rewards for successful performance. Twenty-nine volunteers underwent a visual reaching task. We monitored practice and main sessions to map the task's internal model through learning. Participants were pre-informed of four potential reward conditions upon success at the beginning of the main session task. The success rates in the Jackpot condition were significantly lower than in other conditions, indicative of choking. During the preparation phase, activations in the cerebellum and the middle temporal visual area (hMT+) were associated with Jackpot-specific failures. The cluster in the cerebellar hemisphere overlapped with the internal model regions identified by a learning-related decrease in activation during the practice session. We observed task-specific functional connectivity between the cerebellum and hMT+. These findings suggest a lack of sensory attenuation when an internal model predicting the outcome of one's actions is preloaded during motor preparation. Within the active inference framework of motor control, choking stems from the cerebellum's internal model modulation by psychological pressure, manifested through improper sensory attenuation.

Exploring the neural mechanisms underlying cooperation and competition behavior: Insights from stereo-electroencephalography hyperscanning

Cooperation and competition are essential social behaviors in human society. This study utilized hyperscanning and stereo-electroencephalography (SEEG) to investigate intra- and inter-brain neural dynamics underlying these behaviors within the insula and inferior frontal gyrus (IFG), regions critical for executive function and mentalizing. We found distinct high-gamma responses and connectivity patterns, with a stronger influence from IFG to insula during competition and more balanced interactions during cooperation. Inter-brain synchronization shows significantly higher insula gamma synchronization during competition and higher IFG gamma synchronization during cooperation. Cross-frequency coupling suggests that these gamma synchronizations result from intra- and inter-brain interactions. Competition stems from intra-brain alpha-gamma coupling from IFG to insula and inter-brain IFG alpha synchronization, while cooperation is driven by intra-brain beta-gamma coupling from insula to IFG and inter-brain insula beta synchronization. Our findings provide insights into the neural basis of cooperation and competition, highlighting the roles of both insula and IFG.

As One and Many: Relating Individual and Emergent Group-Level Generative Models in Active Inference

Active inference under the Free Energy Principle has been proposed as an across-scales compatible framework for understanding and modelling behaviour and self-maintenance. Crucially, a collective of active inference agents can, if they maintain a group-level Markov blanket, constitute a larger group-level active inference agent with a generative model of its own. This potential for computational scale-free structures speaks to the application of active inference to self-organizing systems across spatiotemporal scales, from cells to human collectives. Due to the difficulty of reconstructing the generative model that explains the behaviour of emergent group-level agents, there has been little research on this kind of multi-scale active inference. Here, we propose a data-driven methodology for characterising the relation between the generative model of a group-level agent and the dynamics of its constituent individual agents. We apply methods from computational cognitive modelling and computational psychiatry, applicable for active inference as well as other types of modelling approaches. Using a simple Multi-Armed Bandit task as an example, we employ the new ActiveInference.jl library for Julia to simulate a collective of agents who are equipped with a Markov blanket. We use sampling-based parameter estimation to make inferences about the generative model of the group-level agent, and we show that there is a non-trivial relationship between the generative models of individual agents and the group-level agent they constitute, even in this simple setting. Finally, we point to a number of ways in which this methodology might be applied to better understand the relations between nested active inference agents across scales.

Frequency-specific cortico-subcortical interaction in continuous speaking and listening

Speech production and perception involve complex neural dynamics in the human brain. Using magnetoencephalography, our study explores the interaction between cortico-cortical and cortico-subcortical connectivities during these processes. Our connectivity findings during speaking revealed a significant connection from the right cerebellum to the left temporal areas in low frequencies, which displayed an opposite trend in high frequencies. Notably, high-frequency connectivity was absent during the listening condition. These findings underscore the vital roles of cortico-cortical and cortico-subcortical connections within the speech production and perception network. The results of our new study enhance our understanding of the complex dynamics of brain connectivity during speech processes, emphasizing the distinct frequency-based interactions between various brain regions.

Generative models for sequential dynamics in active inference

A central theme of theoretical neurobiology is that most of our cognitive operations require processing of discrete sequences of items. This processing in turn emerges from continuous neuronal dynamics. Notable examples are sequences of words during linguistic communication or sequences of locations during navigation. In this perspective, we address the problem of sequential brain processing from the perspective of active inference, which inherits from a Helmholtzian view of the predictive (Bayesian) brain. Underneath the active inference lies a generative model; namely, a probabilistic description of how (observable) consequences are generated by (unobservable) causes. We show that one can account for many aspects of sequential brain processing by assuming the brain entails a generative model of the sensed world that comprises central pattern generators, narratives, or well-defined sequences. We provide examples in the domains of motor control (e.g., handwriting), perception (e.g., birdsong recognition) through to planning and understanding (e.g., language). The solutions to these problems include the use of sequences of attracting points to direct complex movements-and the move from continuous representations of auditory speech signals to the discrete words that generate those signals.

Nature heals: An informational entropy account of self-organization and change in field psychotherapy

This paper reviews biophysical models of psychotherapeutic change based on synergetics and the free energy principle. These models suggest that introducing sensory surprise into the patient-therapist system can lead to self-organization and the formation of new attractor states, disrupting entrenched patterns of thoughts, emotions, and behaviours. We propose that the therapist can facilitate this process by cultivating epistemic trust and modulating embodied attention to allow surprising affective states to enter shared awareness. Transient increases in free energy enable the update of generative models, expanding the range of experiences available within the patient-therapist phenomenal field. We hypothesize that patterns of disorganization at behavioural and physiological levels, indexed by increased entropy, complexity, and lower determinism, are key markers and predictors of psychotherapeutic gains. Future research should investigate how the therapist's openness to novelty shapes therapeutic outcomes.

Can similarity of autistic traits promote neural synchronization?

People with similar levels of autistic traits are reported to exhibit better interactions than those with larger differences in autistic traits. However, whether this "similarity effect" exists at the neural level remains unclear. To address this gap, the present study employed functional near-infrared spectroscopy (fNIRS) hyperscanning technology to assess inter-brain synchronization (IBS) during naturalistic conversations among dyads with three types of autistic trait combinations (20 high-high, 22 high-low, and 18 low-low dyads). The results revealed that the high-high dyads exhibited significantly lower IBS in the right temporoparietal junction (rTPJ) region compared to the low-low dyads, with no significant differences observed between the high-low group and the other two groups. Moreover, though dyadic differences in conversation satisfaction were positively correlated with dyadic autistic trait differences, IBS only showed a significant negative correlation with the dyadic average autistic trait scores and no significant correlation with the dyadic difference scores of autistic traits. These findings suggest that dyads with high autistic traits may have shared feelings about conversations, but cannot produce IBS through successful mutual prediction and understanding.

An Active-Inference Approach to Second-Person Neuroscience

Social neuroscience has often been criticized for approaching the investigation of the neural processes that enable social interaction and cognition from a passive, detached, third-person perspective, without involving any real-time social interaction. With the emergence of second-person neuroscience, investigators have uncovered the unique complexity of neural-activation patterns in actual, real-time interaction. Social cognition that occurs during social interaction is fundamentally different from that unfolding during social observation. However, it remains unclear how the neural correlates of social interaction are to be interpreted. Here, we leverage the active-inference framework to shed light on the mechanisms at play during social interaction in second-person neuroscience studies. Specifically, we show how counterfactually rich mutual predictions, real-time bodily adaptation, and policy selection explain activation in components of the default mode, salience, and frontoparietal networks of the brain, as well as in the basal ganglia. We further argue that these processes constitute the crucial neural processes that underwrite bona fide social interaction. By placing the experimental approach of second-person neuroscience on the theoretical foundation of the active-inference framework, we inform the field of social neuroscience about the mechanisms of real-life interactions. We thereby contribute to the theoretical foundations of empirical second-person neuroscience.

Reduced resting-state periodic beta power in adults who stutter is related to sensorimotor control of speech execution

OBJECTIVE

The primary aim of the current study was to determine whether adults who stutter (AWS) present with anomalous periodic beta (β) rhythms when compared to typically fluent adults in the eyes-open resting state. A second aim was to determine whether lower β power in the RS is related to a measure of β event-related desynchronization (ERD) during syllable sequence execution.

METHODS

EEG data was collected from 128 channels in a 5 min, eyes-open resting state condition and from a syllable sequence repetition task. Temporal independent component analysis (ICA) was used to separate volume conducted EEG sources and to find a set of component weights common to the RS and syllable repetition task. Both traditional measures of power spectral density (PSD) and parameterized spectra were computed for components showing peaks in the β band (13-30 Hz). Parameterization was used to evaluate separable components adjusted for the 1/f part of the spectrum.

RESULTS

ICA revealed frontal-parietal midline and lateral sensorimotor (μ) components common to the RS and a syllable repetition task with peaks in the β band. The entire spectrum for each component was modeled using the FOOOF algorithm. Independent samples t-tests revealed significantly lower periodic β in midline central-parietal and lateral sensorimotor components in AWS. Regression analysis suggested a significant relationship between left periodic sensorimotor β power in the RS and ERD during syllable sequence execution.

CONCLUSIONS

Findings suggest that periodic β peaks in the spectrum are related to hypothesized underlying pathophysiological differences in stuttering, including midline rhythms associated the default mode network (DMN) and lateral sensorimotor rhythms associated with the control of movement.

Exploring the role of therapeutic alliance and biobehavioural synchrony in musculoskeletal care: Insights from a qualitative study

BACKGROUND

Person-centred care underscores the therapeutic alliance (TA) as fundamental, fostering positive treatment outcomes through collaborative patient-clinician interactions. Biobehavioural synchrony within the TA, essential for effective care, reflects an adaptive process where organisms align responses during interactions. Enactivism and active inference provide profound insights into human perception, reshaping musculoskeletal care understanding. Touch and verbal communication, integral to the TA, foster synchrony and alignment of personal beliefs.

AIM

This study aimed to identify the tools used by manual therapists in musculoskeletal care to establish a TA with patients. Furthermore, it endeavours to evaluate the alignment of these strategies with current literature and their correlation with biobehavioural synchrony, enactivism, and the role of touch in active inference.

METHODS

The methodology followed rigorous qualitative research principles, particularly Grounded Theory and interpretative-constructivist principles, conducting eleven semi-structured interviews with open-ended questions.

RESULTS

The core category identified in the study is elucidated as follows: "Interwoven Connection: The Fabric of Therapeutic Synchrony." The interviews unveiled three main categories, each comprising sub-categories: (1) Creating a meaningful dialogue; (2) Promoting active patient participation; (3) Synchronisation.

CONCLUSION

Fostering meaningful dialogue, patient involvement, and therapeutic synchrony is crucial for a robust therapeutic alliance in musculoskeletal care. This underscores the importance of establishing a deep connection between clinicians and patients, central to effective person-centred care. Clinicians must prioritise two-way communication, empathy, and patient collaboration in defining personalised goals. Emphasizing touch and seeking patient feedback are also pivotal. Further research is needed to explore these elements and their impact.

Prior Context and Individual Alpha Frequency Influence Predictive Processing during Language Comprehension

The extent to which the brain predicts upcoming information during language processing remains controversial. To shed light on this debate, the present study reanalyzed Nieuwland and colleagues' (2018) [Nieuwland, M. S., Politzer-Ahles, S., Heyselaar, E., Segaert, K., Darley, E., Kazanina, N., et al. Large-scale replication study reveals a limit on probabilistic prediction in language comprehension. eLife, 7, e33468, 2018] replication of DeLong and colleagues (2015) [DeLong, K. A., Urbach, T. P., & Kutas, M. Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8, 1117-1121, 2005]. Participants (n = 356) viewed sentences containing articles and nouns of varying predictability, while their EEG was recorded. We measured ERPs preceding the critical words (namely, the semantic prediction potential), in conjunction with postword N400 patterns and individual neural metrics. ERP activity was compared with two measures of word predictability: cloze probability and lexical surprisal. In contrast to prior literature, semantic prediction potential amplitudes did not increase as cloze probability increased, suggesting that the component may not reflect prediction during natural language processing. Initial N400 results at the article provided evidence against phonological prediction in language, in line with Nieuwland and colleagues' findings. Strikingly, however, when the surprisal of the prior words in the sentence was included in the analysis, increases in article surprisal were associated with increased N400 amplitudes, consistent with prediction accounts. This relationship between surprisal and N400 amplitude was not observed when the surprisal of the two prior words was low, suggesting that expectation violations at the article may be overlooked under highly predictable conditions. Individual alpha frequency also modulated the relationship between article surprisal and the N400, emphasizing the importance of individual neural factors for prediction. The present study extends upon existing neurocognitive models of language and prediction more generally, by illuminating the flexible and subject-specific nature of predictive processing.

A Broken Duet: Multistable Dynamics in Dyadic Interactions

Misunderstandings in dyadic interactions often persist despite our best efforts, particularly between native and non-native speakers, resembling a broken duet that refuses to harmonise. This paper delves into the computational mechanisms underpinning these misunderstandings through the lens of the broken Lorenz system-a continuous dynamical model. By manipulating a specific parameter regime, we induce bistability within the Lorenz equations, thereby confining trajectories to distinct attractors based on initial conditions. This mirrors the persistence of divergent interpretations that often result in misunderstandings. Our simulations reveal that differing prior beliefs between interlocutors result in misaligned generative models, leading to stable yet divergent states of understanding when exposed to the same percept. Specifically, native speakers equipped with precise (i.e., overconfident) priors expect inputs to align closely with their internal models, thus struggling with unexpected variations. Conversely, non-native speakers with imprecise (i.e., less confident) priors exhibit a greater capacity to adjust and accommodate unforeseen inputs. Our results underscore the important role of generative models in facilitating mutual understanding (i.e., establishing a shared narrative) and highlight the necessity of accounting for multistable dynamics in dyadic interactions.

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.

Dynamic mutual predictions during social learning: A computational and interbrain model

During social interactions, we constantly learn about the thoughts, feelings, and personality traits of our interaction partners. Learning in social interactions is critical for bond formation and acquiring knowledge. Importantly, this type of learning is typically bi-directional, as both partners learn about each other simultaneously. Here we review the literature on social learning and propose a new computational and neural model characterizing mutual predictions that take place within and between interactions. According to our model, each partner in the interaction attempts to minimize the prediction error of the self and the interaction partner. In most cases, these inferential models become similar over time, thus enabling mutual understanding to develop. At the neural level, this type of social learning may be supported by interbrain plasticity, defined as a change in interbrain coupling over time in neural networks associated with social learning, among them the mentalizing network, the observation-execution system, and the hippocampus. The mutual prediction model constitutes a promising means of providing empirically verifiable accounts of how relationships develop over time.

Brain activity supporting alternating speech for semantic words: simultaneous magnetoencephalographic recording

Communication, especially conversation, is essential for human social life. Many previous studies have examined the neuroscientific underpinnings of conversation, i.e. language comprehension and speech production. However, conversation inherently involves two or more people, and unless two people actually interact with one another, the nature of the conversation cannot be truly revealed. Therefore, in this study, we used two magnetoencephalographs that were connected together, and simultaneously recorded brain activity while two people took turns speaking in a word association/alphabet completion task. We compared the amplitude modulation of the alpha- and beta-band rhythms within each of the 62 brain regions under semantic (word association; less predictable) and non-semantic (alphabet completion; more predictable) conditions. We found that the amplitudes of the rhythms were significantly different between conditions in a wide range of brain regions. Additionally, significant differences were observed in nearly the same group of brain regions after versus before each utterance, indicating that a wide range of brain areas is involved in predicting a conversation partner's next utterance. This result supports the idea that mentalizing, e.g. predicting another person's speech, plays an important role in conversation, and suggests that the neural network implicated in mentalizing extends over a wide range of brain regions.

Federated inference and belief sharing

This paper concerns the distributed intelligence or federated inference that emerges under belief-sharing among agents who share a common world-and world model. Imagine, for example, several animals keeping a lookout for predators. Their collective surveillance rests upon being able to communicate their beliefs-about what they see-among themselves. But, how is this possible? Here, we show how all the necessary components arise from minimising free energy. We use numerical studies to simulate the generation, acquisition and emergence of language in synthetic agents. Specifically, we consider inference, learning and selection as minimising the variational free energy of posterior (i.e., Bayesian) beliefs about the states, parameters and structure of generative models, respectively. The common theme-that attends these optimisation processes-is the selection of actions that minimise expected free energy, leading to active inference, learning and model selection (a.k.a., structure learning). We first illustrate the role of communication in resolving uncertainty about the latent states of a partially observed world, on which agents have complementary perspectives. We then consider the acquisition of the requisite language-entailed by a likelihood mapping from an agent's beliefs to their overt expression (e.g., speech)-showing that language can be transmitted across generations by active learning. Finally, we show that language is an emergent property of free energy minimisation, when agents operate within the same econiche. We conclude with a discussion of various perspectives on these phenomena; ranging from cultural niche construction, through federated learning, to the emergence of complexity in ensembles of self-organising systems.

The utilitarian brain: Moving beyond the Free Energy Principle

The Free Energy Principle (FEP) is a normative computational framework for iterative reduction of prediction error and uncertainty through perception-intervention cycles that has been presented as a potential unifying theory of all brain functions (Friston, 2006). Any theory hoping to unify the brain sciences must be able to explain the mechanisms of decision-making, an important cognitive faculty, without the addition of independent, irreducible notions. This challenge has been accepted by several proponents of the FEP (Friston, 2010; Gershman, 2019). We evaluate attempts to reduce decision-making to the FEP, using Lucas' (2005) meta-theory of the brain's contextual constraints as a guidepost. We find reductive variants of the FEP for decision-making unable to explain behavior in certain types of diagnostic, predictive, and multi-armed bandit tasks. We trace the shortcomings to the core theory's lack of an adequate notion of subjective preference or "utility", a concept central to decision-making and grounded in the brain's biological reality. We argue that any attempts to fully reduce utility to the FEP would require unrealistic assumptions, making the principle an unlikely candidate for unifying brain science. We suggest that researchers instead attempt to identify contexts in which either informational or independent reward constraints predominate, delimiting the FEP's area of applicability. To encourage this type of research, we propose a two-factor formal framework that can subsume any FEP model and allows experimenters to compare the contributions of informational versus reward constraints to behavior.

A conversation analytic approach to schizophrenic interaction: methodological reflections on disruptions of the common-sense world

Certain schools of phenomenological psychiatry conceive of schizophrenia as a pathology of common-sense. Ethnomethodological enquiry, with its roots in Schutzian social phenomenology, takes as its domain, topic, and substance of study the ongoing achievement of a common-sense world between social members. Yet, dialogue between psychiatry and ethnomethodological approaches is thin. In this article, we discuss a conversation analytic approach to schizophrenic interaction which has generated and utilized a model of a five-world manifold to frame analyses of talk-in-interaction. 'Worlds' are conceived, after Schutz, as finite domains of meaning, and the model operates as a breach of natural attitude assumptions to examine mechanisms of the constitution of the one-world-in-common of common-sense. It is suggested that certain aspects of schizophrenic talk might receive account in terms of a loss of integration between these five domains of meaning. Conversation Analytic methods were applied to transcripts of audio recordings of psychiatric interviews but encountered hurdles that motivated the broadening of methodological scope. Such hurdles included a weakening of the next turn proof procedure, implicit reification of the schizophrenia construct, and problems of translation presented by the analyst's normative membership encountering non-normative life-worlds of schizophrenic experience. Strategic responses to these hurdles included exploring linkages between phenomenological psychiatry and ethnomethodological approaches, as well as an engagement of ethnomethodological self-reflection and conceptual clarification of the schizophrenia construct in line with Garfinkel's unique adequacy requirement. The manifold model is glossed, and interaction between two of its worlds - a world of concrete, situational immediacies and another of abstract organizations - is explored in more detail via analysis of conversational data. It is suggested that the five-world model, along with further micro-analysis of talk-in-interaction, might have implications in psychiatry for topics such as autism, double bookkeeping, concretism, theories of disturbed indexicality, and insight attribution. We conclude that the consideration of atypical interaction obliges the interaction analyst to take account of their own implicit normative world-frames and that the use of domain-specific top-down models in conjunction with the inductive approach of Conversation Analysis may extend the reach of CA to facilitate productive dialogue with other disciplines.

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

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

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.

Predictive coordination of breathing during intra-personal speaking and listening

It has long been known that human breathing is altered during listening and speaking compared to rest: during speaking, inhalation depth is adjusted to the air volume required for the upcoming utterance. During listening, inhalation is temporally aligned to inhalation of the speaker. While evidence for the former is relatively strong, it is virtually absent for the latter. We address both phenomena using recordings of speech envelope and respiration in 30 participants during 14 min of speaking and listening to one's own speech. First, we show that inhalation depth is positively correlated with the total power of the speech envelope in the following utterance. Second, we provide evidence that inhalation during listening to one's own speech is significantly more likely at time points of inhalation during speaking. These findings are compatible with models that postulate alignment of internal forward models of interlocutors with the aim to facilitate communication.

Spatiotemporal dynamics characterise spectral connectivity profiles of continuous speaking and listening

Speech production and perception are fundamental processes of human cognition that both rely on intricate processing mechanisms that are still poorly understood. Here, we study these processes by using magnetoencephalography (MEG) to comprehensively map connectivity of regional brain activity within the brain and to the speech envelope during continuous speaking and listening. Our results reveal not only a partly shared neural substrate for both processes but also a dissociation in space, delay, and frequency. Neural activity in motor and frontal areas is coupled to succeeding speech in delta band (1 to 3 Hz), whereas coupling in the theta range follows speech in temporal areas during speaking. Neural connectivity results showed a separation of bottom-up and top-down signalling in distinct frequency bands during speaking. Here, we show that frequency-specific connectivity channels for bottom-up and top-down signalling support continuous speaking and listening. These findings further shed light on the complex interplay between different brain regions involved in speech production and perception.

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.

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

BACKGROUND AND HYPOTHESIS

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

STUDY DESIGN

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

STUDY RESULTS

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

CONCLUSIONS

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

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.

Therapeutic touch and therapeutic alliance in pediatric care and neonatology: An active inference framework

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.

Interpersonal attunement in social interactions: from collective psychophysiology to inter-personalized psychiatry and beyond

In this article, we analyse social interactions, drawing on diverse points of views, ranging from dialectics, second-person neuroscience and enactivism to dynamical systems, active inference and machine learning. To this end, we define interpersonal attunement as a set of multi-scale processes of building up and materializing social expectations-put simply, anticipating and interacting with others and ourselves. While cultivating and negotiating common ground, via communication and culture-building activities, are indispensable for the survival of the individual, the relevant multi-scale mechanisms have been largely considered in isolation. Here, collective psychophysiology, we argue, can lend itself to the fine-tuned analysis of social interactions, without neglecting the individual. On the other hand, an interpersonal mismatch of expectations can lead to a breakdown of communication and social isolation known to negatively affect mental health. In this regard, we review psychopathology in terms of interpersonal misattunement, conceptualizing psychiatric disorders as disorders of social interaction, to describe how individual mental health is inextricably linked to social interaction. By doing so, we foresee avenues for an inter-personalized psychiatry, which moves from a static spectrum of disorders to a dynamic relational space, focusing on how the multi-faceted processes of social interaction can help to promote mental health. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

Digitally embodied lifespan neurocognitive development and Tactile Internet: Transdisciplinary challenges and opportunities

Mechanisms underlying perceptual processing and inference undergo substantial changes across the lifespan. If utilized properly, technologies could support and buffer the relatively more limited neurocognitive functions in the still developing or aging brains. Over the past decade, a new type of digital communication infrastructure, known as the "Tactile Internet (TI)," is emerging in the fields of telecommunication, sensor and actuator technologies and machine learning. A key aim of the TI is to enable humans to experience and interact with remote and virtual environments through digitalized multimodal sensory signals that also include the haptic (tactile and kinesthetic) sense. Besides their applied focus, such technologies may offer new opportunities for the research tapping into mechanisms of digitally embodied perception and cognition as well as how they may differ across age cohorts. However, there are challenges in translating empirical findings and theories about neurocognitive mechanisms of perception and lifespan development into the day-to-day practices of engineering research and technological development. On the one hand, the capacity and efficiency of digital communication are affected by signal transmission noise according to Shannon's (1949) Information Theory. On the other hand, neurotransmitters, which have been postulated as means that regulate the signal-to-noise ratio of neural information processing (e.g., Servan-Schreiber et al., 1990), decline substantially during aging. Thus, here we highlight neuronal gain control of perceptual processing and perceptual inference to illustrate potential interfaces for developing age-adjusted technologies to enable plausible multisensory digital embodiments for perceptual and cognitive interactions in remote or virtual environments.

Speaking and listening to inter-brain relationships

Studies of inter-brain relationships thrive, and yet many reservations regarding their scope and interpretation of these phenomena have been raised by the scientific community. It is thus essential to establish common ground on methodological and conceptual definitions related to this topic and to open debate about any remaining points of uncertainty. We here offer insights to improve the conceptual clarity and empirical standards offered by social neuroscience studies of inter-personal interaction using hyperscanning with a particular focus on verbal communication.

Neural underpinning of Japanese particle processing in non-native speakers

Grammar acquisition by non-native learners (L2) is typically less successful and may produce fundamentally different grammatical systems than that by native speakers (L1). The neural representation of grammatical processing between L1 and L2 speakers remains controversial. We hypothesized that working memory is the primary source of L1/L2 differences, by considering working memory within the predictive coding account, which models grammatical processes as higher-level neuronal representations of cortical hierarchies, generating predictions (forward model) of lower-level representations. A functional MRI study was conducted with L1 Japanese speakers and highly proficient Japanese learners requiring oral production of grammatically correct Japanese particles. We assumed selecting proper particles requires forward model-dependent processes of working memory as their functions are highly context-dependent. As a control, participants read out a visually designated mora indicated by underlining. Particle selection by L1/L2 groups commonly activated the bilateral inferior frontal gyrus/insula, pre-supplementary motor area, left caudate, middle temporal gyrus, and right cerebellum, which constituted the core linguistic production system. In contrast, the left inferior frontal sulcus, known as the neural substrate of verbal working memory, showed more prominent activation in L2 than in L1. Thus, the working memory process causes L1/L2 differences even in highly proficient L2 learners.

Motivation from Agency and Reward in Typical Development and Autism: Narrative Review of Behavioral and Neural Evidence

Our ability to perform voluntary actions and make choices is shaped by the motivation from having control over the resulting effects (agency) and positive outcomes (reward). We offer an overview of distinct and common behavioral and neural signatures of agency and reward. We discuss their typical and atypical developmental trajectories, focusing on autism spectrum disorder (ASD), which is characterized by neurodiverse processes underlying action selection. We propose that reduced sensitivity to agency and reward in ASD may be related to atypical multisensory processes and motor planning, with potential for understanding restricted and repetitive behaviors. We emphasize the limitations of the existing literature, and prospects for future research. Understanding the neurocognitive processes that shape the way people with ASD select actions and perceive outcomes is essential to support not only learning, but also volition and self-determination.

Creating a shared musical interpretation: Changes in coordination dynamics while learning unfamiliar music together

The ability to coordinate with others is fundamental for humans to achieve shared goals. Often, harmonious interpersonal coordination requires learning, such as ensemble musicians rehearing together to synchronize their low-level timing and high-level aesthetic musical expressions. We investigated how the coordination dynamics of a professional string quartet changed as they learned unfamiliar pieces together across eight trials. During all trials, we recorded each musician's body sway motion data, and quantified the group's body sway similarity (cross-correlation) and information flow (Granger causality) on each trial. In line with our hypothesis, group similarity increased, while group information flow decreased significantly across trials. In addition, there was a trend such that group similarity, but not information flow, was related to the quality of the performances. As the ensemble converged on a joint interpretation through rehearsing, their body sways reflected the change from interpersonal information flow for coordinative mutual adaptations and corrections, to synchronous musical coordination made possible by the musicians learning a common internally based expressive interpretation.

Interpersonal neural synchrony when predicting others' actions during a game of rock-paper-scissors

As members of a social species, we spend most of our time interacting with others. In interactions, we tend to mutually align our behavior and brain responses to communicate more effectively. In a semi-computerized version of the Rock-Paper-Scissors game, we investigated whether people show enhanced interpersonal neural synchronization when making explicit predictions about others' actions. Across four experimental conditions, we measured the dynamic brain activity using the functional near-infrared spectroscopy (fNIRS) hyperscanning method. Results showed that interpersonal neural synchrony was enhanced when participants played the game together as they would do in real life in comparison to when they played the game on their own. We found no evidence of increased neural synchrony when participants made explicit predictions about others' actions. Hence, neural synchrony may depend on mutual natural interaction rather than an explicit prediction strategy. This study is important, as it examines one of the presumed functions of neural synchronization namely facilitating predictions.

Beauty and Uncertainty as Transformative Factors: A Free Energy Principle Account of Aesthetic Diagnosis and Intervention in Gestalt Psychotherapy

Drawing from field theory, Gestalt therapy conceives psychological suffering and psychotherapy as two intentional field phenomena, where unprocessed and chaotic experiences seek the opportunity to emerge and be assimilated through the contact between the patient and the therapist (i.e., the intentionality of contacting). This therapeutic approach is based on the therapist’s aesthetic experience of his/her embodied presence in the flow of the healing process because (1) the perception of beauty can provide the therapist with feedback on the assimilation of unprocessed experiences; (2) the therapist’s attentional focus on intrinsic aesthetic diagnostic criteria can facilitate the modification of rigid psychopathological fields by supporting the openness to novel experiences. The aim of the present manuscript is to review recent evidence from psychophysiology, neuroaesthetic research, and neurocomputational models of cognition, such as the free energy principle (FEP), which support the notion of the therapeutic potential of aesthetic sensibility in Gestalt psychotherapy. Drawing from neuroimaging data, psychophysiology and recent neurocognitive accounts of aesthetic perception, we propose a novel interpretation of the sense of beauty as a self-generated reward motivating us to assimilate an ever-greater spectrum of sensory and affective states in our predictive representation of ourselves and the world and supporting the intentionality of contact. Expecting beauty, in the psychotherapeutic encounter, can help therapists tolerate uncertainty avoiding impulsive behaviours and to stay tuned to the process of change.

Timing of brain entrainment to the speech envelope during speaking, listening and self-listening

⁠This study investigates the dynamics of speech envelope tracking during speech production, listening and self-listening. We use a paradigm in which participants listen to natural speech (Listening), produce natural speech (Speech Production), and listen to the playback of their own speech (Self-Listening), all while their neural activity is recorded with EEG. After time-locking EEG data collection and auditory recording and playback, we used a Gaussian copula mutual information measure to estimate the relationship between information content in the EEG and auditory signals. In the 2-10 Hz frequency range, we identified different latencies for maximal speech envelope tracking during speech production and speech perception. Maximal speech tracking takes place approximately 110 ms after auditory presentation during perception and 25 ms before vocalisation during speech production. These results describe a specific timeline for speech tracking in speakers and listeners in line with the idea of a speech chain and hence, delays in communication.

Therapeutic Alliance as Active Inference: The Role of Therapeutic Touch and Biobehavioural Synchrony in Musculoskeletal Care

Touch is recognised as crucial for survival, fostering cooperative communication, accelerating recovery, reducing hospital stays, and promoting overall wellness and the therapeutic alliance. In this hypothesis and theory paper, we present an entwined model that combines touch for alignment and active inference to explain how the brain develops "priors" necessary for the health care provider to engage with the patient effectively. We appeal to active inference to explain the empirically integrative neurophysiological and behavioural mechanisms that underwrite synchronous relationships through touch. Specifically, we offer a formal framework for understanding - and explaining - the role of therapeutic touch and hands-on care in developing a therapeutic alliance and synchrony between health care providers and their patients in musculoskeletal care. We first review the crucial importance of therapeutic touch and its clinical role in facilitating the formation of a solid therapeutic alliance and in regulating allostasis. We then consider how touch is used clinically - to promote cooperative communication, demonstrate empathy, overcome uncertainty, and infer the mental states of others - through the lens of active inference. We conclude that touch plays a crucial role in achieving successful clinical outcomes and adapting previous priors to create intertwined beliefs. The ensuing framework may help healthcare providers in the field of musculoskeletal care to use hands-on care to strengthen the therapeutic alliance, minimise prediction errors (a.k.a., free energy), and thereby promote recovery from physical and psychological impairments.

Spatiotemporal Signatures of Surprise Captured by Magnetoencephalography

Surprise and social influence are linked through several neuropsychological mechanisms. By garnering attention, causing arousal, and motivating engagement, surprise provides a context for effective or durable social influence. Attention to a surprising event motivates the formation of an explanation or updating of models, while high arousal experiences due to surprise promote memory formation. They both encourage engagement with the surprising event through efforts aimed at understanding the situation. By affecting the behavior of the individual or a social group via setting an attractive engagement context, surprise plays an important role in shaping personal and social change. Surprise is an outcome of the brain’s function in constantly anticipating the future of sensory inputs based on past experiences. When new sensory data is different from the brain’s predictions shaped by recent trends, distinct neural signals are generated to report this surprise. As a quantitative approach to modeling the generation of brain surprise, input stimuli containing surprising elements are employed in experiments such as oddball tasks during which brain activity is recorded. Although surprise has been well characterized in many studies, an information-theoretical model to describe and predict the surprise level of an external stimulus in the recorded MEG data has not been reported to date, and setting forth such a model is the main objective of this paper. Through mining trial-by-trial MEG data in an oddball task according to theoretical definitions of surprise, the proposed surprise decoding model employs the entire epoch of the brain response to a stimulus to measure surprise and assesses which collection of temporal/spatial components in the recorded data can provide optimal power for describing the brain’s surprise. We considered three different theoretical formulations for surprise assuming the brain acts as an ideal observer that calculates transition probabilities to estimate the generative distribution of the input. We found that middle temporal components and the right and left fronto-central regions offer the strongest power for decoding surprise. Our findings provide a practical and rigorous method for measuring the brain’s surprise, which can be employed in conjunction with behavioral data to evaluate the interactive and social effects of surprising events.

A free energy principle for generic quantum systems

The Free Energy Principle (FEP) states that under suitable conditions of weak coupling, random dynamical systems with sufficient degrees of freedom will behave so as to minimize an upper bound, formalized as a variational free energy, on surprisal (a.k.a., self-information). This upper bound can be read as a Bayesian prediction error. Equivalently, its negative is a lower bound on Bayesian model evidence (a.k.a., marginal likelihood). In short, certain random dynamical systems evince a kind of self-evidencing. Here, we reformulate the FEP in the formal setting of spacetime-background free, scale-free quantum information theory. We show how generic quantum systems can be regarded as observers, which with the standard freedom of choice assumption become agents capable of assigning semantics to observational outcomes. We show how such agents minimize Bayesian prediction error in environments characterized by uncertainty, insufficient learning, and quantum contextuality. We show that in its quantum-theoretic formulation, the FEP is asymptotically equivalent to the Principle of Unitarity. Based on these results, we suggest that biological systems employ quantum coherence as a computational resource and - implicitly - as a communication resource. We summarize a number of problems for future research, particularly involving the resources required for classical communication and for detecting and responding to quantum context switches.

Directed coupling in multi-brain networks underlies generalized synchrony during social exchange

Advances in social neuroscience have made neural signatures of social exchange measurable simultaneously across people. This has identified brain regions differentially active during social interaction between human dyads, but the underlying systems-level mechanisms are incompletely understood. This paper introduces dynamic causal modeling and Bayesian model comparison to assess the causal and directed connectivity between two brains in the context of hyperscanning (h-DCM). In this setting, correlated neuronal responses become the data features that have to be explained by models with and without between-brain (effective) connections. Connections between brains can be understood in the context of generalized synchrony, which explains how dynamical systems become synchronized when they are coupled to each another. Under generalized synchrony, each brain state can be predicted by the other brain or a mixture of both. Our results show that effective connectivity between brains is not a feature within dyads per se but emerges selectively during social exchange. We demonstrate a causal impact of the sender's brain activity on the receiver of information, which explains previous reports of two-brain synchrony. We discuss the implications of this work; in particular, how characterizing generalized synchrony enables the discovery of between-brain connections in any social contact, and the advantage of h-DCM in studying brain function on the subject level, dyadic level, and group level within a directed model of (between) brain function.

I overthink-Therefore I am not: An active inference account of altered sense of self and agency in depersonalisation disorder

This paper considers the phenomenology of depersonalisation disorder, in relation to predictive processing and its associated pathophysiology. To do this, we first establish a few mechanistic tenets of predictive processing that are necessary to talk about phenomenal transparency, mental action, and self as subject. We briefly review the important role of 'predicting precision' and how this affords mental action and the loss of phenomenal transparency. We then turn to sensory attenuation and the phenomenal consequences of (pathophysiological) failures to attenuate or modulate sensory precision. We then consider this failure in the context of depersonalisation disorder. The key idea here is that depersonalisation disorder reflects the remarkable capacity to explain perceptual engagement with the world via the hypothesis that "I am an embodied perceiver, but I am not in control of my perception". We suggest that individuals with depersonalisation may believe that 'another agent' is controlling their thoughts, perceptions or actions, while maintaining full insight that the 'other agent' is 'me' (the self). Finally, we rehearse the predictions of this formal analysis, with a special focus on the psychophysical and physiological abnormalities that may underwrite the phenomenology of depersonalisation.

Integrating Evolutionary, Cultural, and Computational Psychiatry: A Multilevel Systemic Approach

This paper proposes an integrative perspective on evolutionary, cultural and computational approaches to psychiatry. These three approaches attempt to frame mental disorders as multiscale entities and offer modes of explanations and modeling strategies that can inform clinical practice. Although each of these perspectives involves systemic thinking, each is limited in its ability to address the complex developmental trajectories and larger social systemic interactions that lead to mental disorders. Inspired by computational modeling in theoretical biology, this paper aims to integrate the modes of explanation offered by evolutionary, cultural and computational psychiatry in a multilevel systemic perspective. We apply the resulting Evolutionary, Cultural and Computational (ECC) model to Major Depressive Disorder (MDD) to illustrate how this integrative approach can guide research and practice in psychiatry.

Music in the brain

Music is ubiquitous across human cultures - as a source of affective and pleasurable experience, moving us both physically and emotionally - and learning to play music shapes both brain structure and brain function. Music processing in the brain - namely, the perception of melody, harmony and rhythm - has traditionally been studied as an auditory phenomenon using passive listening paradigms. However, when listening to music, we actively generate predictions about what is likely to happen next. This enactive aspect has led to a more comprehensive understanding of music processing involving brain structures implicated in action, emotion and learning. Here we review the cognitive neuroscience literature of music perception. We show that music perception, action, emotion and learning all rest on the human brain's fundamental capacity for prediction - as formulated by the predictive coding of music model. This Review elucidates how this formulation of music perception and expertise in individuals can be extended to account for the dynamics and underlying brain mechanisms of collective music making. This in turn has important implications for human creativity as evinced by music improvisation. These recent advances shed new light on what makes music meaningful from a neuroscientific perspective.

Gamma‐band neural coupling during conceptual alignment

Conceptual alignment is a prerequisite for mutual understanding. However, little is known about the neurophysiological brain‐to‐brain underpinning during conceptual alignment for mutual understanding. Here, we recorded multi‐channel electroencephalogram (EEG) simultaneously from two participants in Experiment 1 and adopted the dual‐tACS techniques in Experiment 2 to investigate the underlying brain‐to‐brain EEG coupling during conceptual alignment and the possible enhancement effect. Our results showed that 1) higher phase‐locking value (PLV), a sensitive measure for quantifying neural coupling strength between EEG signals, at the gamma frequency band (28–40 Hz), was observed in the left temporoparietal site (left TP) area between successful versus unsuccessful conceptual alignment. The left TP gamma coupling strength correlated with the accuracy of conceptual alignment and differentiated whether subjects belonged to the SUCCESS or FAILURE groups in our study. 2) In‐phase gamma‐band transcranial alternating current stimulation (tACS) over the left TP area increased the accuracy of subjects in the SUCCESS group but not the FAILURE group. 3) The effect of perspective‐taking on the accuracy was mediated by the gamma coupling strength within the left TP area. Our results support the role of gamma‐band coupling between brains for interpersonal conceptual alignment. We provide dynamic interpersonal neurophysiological insights into the formation of successful communication.

Model Reduction Through Progressive Latent Space Pruning in Deep Active Inference

Although still not fully understood, sleep is known to play an important role in learning and in pruning synaptic connections. From the active inference perspective, this can be cast as learning parameters of a generative model and Bayesian model reduction, respectively. In this article, we show how to reduce dimensionality of the latent space of such a generative model, and hence model complexity, in deep active inference during training through a similar process. While deep active inference uses deep neural networks for state space construction, an issue remains in that the dimensionality of the latent space must be specified beforehand. We investigate two methods that are able to prune the latent space of deep active inference models. The first approach functions similar to sleep and performs model reduction post hoc. The second approach is a novel method which is more similar to reflection, operates during training and displays “aha” moments when the model is able to reduce latent space dimensionality. We show for two well-known simulated environments that model performance is retained in the first approach and only diminishes slightly in the second approach. We also show that reconstructions from a real world example are indistinguishable before and after reduction. We conclude that the most important difference constitutes a trade-off between training time and model performance in terms of accuracy and the ability to generalize, via minimization of model complexity.

Osteopathic Care as (En)active Inference: A Theoretical Framework for Developing an Integrative Hypothesis in Osteopathy

Osteopathy is a person-centred healthcare discipline that emphasizes the body's structure-function interrelationship-and its self-regulatory mechanisms-to inform a whole-person approach to health and wellbeing. This paper aims to provide a theoretical framework for developing an integrative hypothesis in osteopathy, which is based on the enactivist and active inference accounts. We propose that osteopathic care can be reconceptualised under (En)active inference as a unifying framework. Active inference suggests that action-perception cycles operate to minimize uncertainty and optimize an individual's internal model of the lived world and, crucially, the consequences of their behaviour. We argue that (En)active inference offers an integrative framework for osteopathy, which can evince the mechanisms underlying dyadic and triadic (e.g., in paediatric care) exchanges and osteopathic care outcomes. We propose that this theoretical framework can underpin osteopathic care across the lifespan, from preterm infants to the elderly and those with persistent pain and other physical symptoms. In situations of chronicity, as an ecological niche, the patient-practitioner dyad provides the osteopath and the patient with a set of affordances, i.e., possibilities for action provided by the environment, that through shared intentionally, can promote adaptations and restoration of productive agency. Through a dyadic therapeutic relationship, as they engage with their ecological niche's affordances-a structured set of affordances shared by agents-osteopath and patient actively construct a shared sense-making narrative and realise a shared generative model of their relation to the niche. In general, touch plays a critical role in developing a robust therapeutic alliance, mental state alignment, and biobehavioural synchrony between patient and practitioner. However, its role is particularly crucial in the fields of neonatology and paediatrics, where it becomes central in regulating allostasis and restoring homeostasis. We argue that from an active inference standpoint, the dyadic shared ecological niche underwrites a robust therapeutic alliance, which is crucial to the effectiveness of osteopathic care. Considerations and implications of this model-to clinical practice and research, both within- and outside osteopathy-are critically discussed.

Therapeutic Alliance as Active Inference: The Role of Therapeutic Touch and Synchrony

Recognizing and aligning individuals' unique adaptive beliefs or "priors" through cooperative communication is critical to establishing a therapeutic relationship and alliance. Using active inference, we present an empirical integrative account of the biobehavioral mechanisms that underwrite therapeutic relationships. A significant mode of establishing cooperative alliances-and potential synchrony relationships-is through ostensive cues generated by repetitive coupling during dynamic touch. Established models speak to the unique role of affectionate touch in developing communication, interpersonal interactions, and a wide variety of therapeutic benefits for patients of all ages; both neurophysiologically and behaviorally. The purpose of this article is to argue for the importance of therapeutic touch in establishing a therapeutic alliance and, ultimately, synchrony between practitioner and patient. We briefly overview the importance and role of therapeutic alliance in prosocial and clinical interactions. We then discuss how cooperative communication and mental state alignment-in intentional communication-are accomplished using active inference. We argue that alignment through active inference facilitates synchrony and communication. The ensuing account is extended to include the role of (C-) tactile afferents in realizing the beneficial effect of therapeutic synchrony. We conclude by proposing a method for synchronizing the effects of touch using the concept of active inference.