Bayesian Brain Research Articles

Dancing Together Apart

At a time when the Choreographic takes on a renewed sense of possibility as we adapt to the rhythms of an epidemiological model of society, dances of remediation, that collapse the distinction between interpersonal and infrastructural modes of sensing the world, offer, we argue, possibilities for perceiving and making sense of our changing lifeworlds (Bratton 2022). In this writing, we reflect upon an artistic research project that composed choreographic relations between corporeal, conceptual and informational modes, bringing attention to the expressive possibilities of attuning to, and regulating each other, even at a distance.  Mental Dance involved multiple agents shaping  states of distributed attention across physical and digital networks. In choreographing the work we drew upon current neuroscientific research into the Bayesian Brain and Predictive Coding.       Mental Dance explored the intersection of dance, interactive sound design and psychiatry through a unique performance system that blended these diverse ‘mental spaces’. Hagendoorn (2019) describes ‘mental spaces’ as ‘temporary thought assemblies that are constructed as one thinks and speaks’ (756).  In our project diverse languages of movement and music were remediated through AI and the flux of somatic attention expressed by two live performers. Realtime data generated by the dancers’ movements and tracked by AI pose recognition software shaped a choreosonic environment for an online audience (Brown 2021b).  Our artistic research method drew upon dancers’ intuitive kinesthetic and haptic sensory awareness and AI’s capacity to learn, together with an appropriation of theories of the Bayesian Brain.  Staging provocations from neuroscientific research and psychiatry, we mimicked the idea of the clinic as a site for soma-sci experimentation.  In generating an empathic performance system that addressed relational intimacy and neural resonance at a distance, we navigated the uncertainty and unpredictability of repeated lockdowns in Melbourne, Australia, countering despair and ennui with the excitement and challenge of connecting through remote co-presence.  In Dancing Together Apart, we probe the wiring of relations and the igniting of ideas that sparked this collaborative performance and propose choreosonic improvisation as a method to bring current neuroscientific research into the virtual studio where it can inform and influence the direction of new work and cultivate an altered state of kinetic intensities held between agencies, human and non-human.  

Placebo and Nocebo Effects as Bayesian-Brain Phenomena: The Overlooked Role of Likelihood and Attention.

The Bayesian-brain framework applied to placebo responses and other mind-body interactions suggests that the effects on the body result from the interaction between priors, such as expectations and learning, and likelihood, such as somatosensorial information. Significant research in this area focuses on the role of the priors, but the relevance of the likelihood has been surprisingly overlooked. One way of manipulating the relevance of the likelihood is by paying attention to sensorial information. We suggest that attention can influence both precision and position (i.e., the relative distance from the priors) of the likelihood by focusing on specific components of the somatosensorial information. Two forms of attention seem particularly relevant in this framework: mindful attention and selective attention. Attention has the potential to be considered a "major player" in placebo/nocebo research, together with expectations and learning. In terms of application, relying on attentional strategies as "amplifiers" or "silencers" of sensorial information could lead to an active involvement of individuals in shaping their care process and health. In this contribution, we discuss the theoretical implications of these intuitions with the aim to provide a comprehensive framework that includes Bayesian brain, placebo/nocebo effects, and the role of attention in mind-body interactions.

Stereotypes and self-fulfilling prophecies in the Bayesian brain

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

The Active Inference Model of Coherence Therapy.

Coherence Therapy is an empirically derived experiential psychotherapy based on Psychological Constructivism. Symptoms are viewed as necessary output from an implicit model of the world. The therapist curates experiences and directs attention toward discovering the model. Rendered explicit, the model is juxtaposed with contradictory knowledge driving memory re-consolidation with resolution of the symptom. The Bayesian Brain views perception and action as inferential processes. Prior beliefs are combined in a generative model to explain the hidden causes of sensations through a process of Active Inference. Prior beliefs that are poor fits to the real world are suboptimal. Suboptimal priors with optimal inference produce Bayes Optimal Pathology with behavioral symptoms. The Active Inference Model of Coherence Therapy posits that Coherence Therapy is a dyadic act of therapist guided Active Inference that renders the (probable) hidden causes of a client's behavior conscious. The therapist's sustained attention on the goal of inference helps to overcome memory control bias against retrieval of the affectively charged suboptimal prior. Serial experiences cue memory retrieval and re-instantiation of the physiological/affective state that necessitates production of the symptom in a particular context. As this process continues there is a break in modularity with assimilation into broader networks of experience. Typically, the symptom produced by optimal inference with the suboptimal prior is experienced as unnecessary/inappropriate when taken out of the particular context. The implicit construct has been re-represented and rendered consciously accessible, by a more complex but more accurate model in which the symptom is necessary in some contexts but not others. There is an experience of agency and control in symptom creation, accompanied by the spontaneous production of context appropriate behavior. The capacity for inference has been restored. The Active Inference Model of Coherence Therapy provides a framework for Coherence Therapy as a computational process which can serve as the basis for new therapeutic interventions and experimental designs integrating biological, cognitive, behavioral, and environmental factors.

The Active Bayesian Brain and the Rorschach Task

Abstract. The Rorschach offers a unique and interesting paradigm from the perspective of the (Bayesian) brain. This contribution to the cross-disciplinary special issue considers the Rorschach from the perspective of perceptual inference in the brain and how it might inform subject-specific differences in perceptual synthesis. Before doing so, we provide a broad overview of active inference in its various manifestations. In brief, active inference supposes that our perceptions are the best hypothesis to explain sensory impressions. On a Bayesian account, the requisite belief updating rests sensitively upon the precision or confidence ascribed to sensory input, relative to prior beliefs about the causes of sensations. This focus – on the balance between sensory and prior precision – has been a useful construct in both cognitive science (e.g., as a formal explanation for attention) and neuropsychology (e.g., as a formal explanation for aberrant or false inference in hallucinations). In this setting, false inference is generally understood as abnormally high precision afforded to high-level hypotheses or explanations for visual input, which may compensate for a failure to attenuate sensory precision. On this view, the Rorschach offers an interesting paradigm because the amount of precise information about the causes of visual input is deliberately minimized – and rendered ambiguous – thereby placing greater emphasis on prior beliefs entertained by the respondent. We close by exploring this issue and several other areas of intersection between Rorschach responding and active inference.

Psychoanalytic psychotherapies and the free energy principle.

In this paper I propose a model of the fundamental components of psychoanalytic psychotherapies that I try to explicate with contemporary theories of the Bayesian brain and the Free Energy Principle (FEP). I first show that psychoanalytic therapies require a setting (made up of several envelopes), a particular psychic state and specific processes (transference, free association, dreaming, play, reflexivity and narrativity) in order to induce psychic transformations. I then analyze how these processes of transformations operate and how they can be enlightened by the FEP. I first underline the fact that psychoanalytic therapies imply non-linear processes taking time to unfold and require a setting containing high entropy processes. More precisely, these processes are characterized by an interplay between extension and reduction of free energy. This interplay also favors the emergence of new orders of subjective experience, which occur following states of disorder, according to a certain energetic threshold allowing the modification and improvement of mental functioning. These high entropy states are also characterized by random functioning and psychic malleability which favors the exploration of subjective experience in an original manner. Overall, the approach proposed in this paper support the dialogue between psychoanalysis and other fields of research while underlining how psychoanalytical theoretical and conceptual constructs can also be useful to other disciplines, in particular the neurosciences of subjectivity.

Pertinence of Predictive Models as Regards the Behavior of Observed Biological and Artificial Phenomena

In this assessment, we have made an effort of synthesis on the role of theoretical and observational investigations in the analysis of the concepts and functioning of different natural biological and artificial phenomena. In this context, we pursued the objective of examining published works relating to the behavioral prediction of phenomena associated with its observation. We have examined examples from the literature concerning phenomena with known behaviors that associated to knowledge uncertainty as well as cases concerning phenomena with unknown and changing random behaviors linked to random uncertainty. The concerned cases are relative to brain functioning in neuroscience, modern smart industrial devices, and health care predictive endemic protocols. As predictive modeling is very concerned by the problematics relative to uncertainties that depend on the degree of matching in the link prediction-observation, we investigated first how to improve the model to match better the observation. Thus, we considered the case when the observed behavior and its model are contrasting, that implies the development of revised or amended models. Then we studied the case concerning the practice of modeling for the prediction of future behaviors of a phenomenon that is well known, and owning identified behavior. For such case, we illustrated the situation of prediction matched to observation operated in two cases. These are the Bayesian Brain theory in neuroscience and the Digital Twins industrial concept. The last investigated circumstance concerns the use of modeling for the prediction of future behaviors of a phenomenon that is not well known, or owning behavior varying arbitrary. For this situation, we studied contagion infections with an unknown mutant virus where the prediction task is very complicated and would be constrained only to adjust the principal clinical observation protocol. Keywords: prediction, observation, Bayesian, neuroscience, brain functioning, mutant virus

Separating Uncertainty from Surprise in Auditory Processing with Neurocomputational Models: Implications for Music Perception.

Advances in neuroscience have led to the emergence of two complementary theories of neural information processing. First, the “Bayesian brain hypothesis” proposes that the brain actively predicts and represents incoming sensory information as probabilities that are updated in a near-optimal

The clear and not so clear signatures of perceptual reality in the Bayesian brain

In a Bayesian brain, every perceptual decision will take into account internal priors as well as new incoming evidence. A reality monitoring system—eventually providing the agent us with a subjective sense of reality avoids us them being confused about whether our experience is perceptual or imagined. Yet not all confusions we experience mean that we wonder wonder whether we may be imagining: some confused experiences feel clearly perceptual but still feel not right. What happens in such confused perceptions, and can the Bayesian brain explain this kind of confusion? In this paper, we offer a characterisation of perceptual confusion and argue that it requires our subjective sense of reality to be a composite of several subjective markers, including a categorical one that can clearly identify an experience as perceptual and connecting us to reality. Our composite account makes new predictions regarding the robustness, the non-linear development and the possible breakdowns of the sense of reality in perception.

Mental States: A Key Point in Scam Compliance and Warning Compliance in Real Life.

The internet’s convenience and anonymity have facilitated different types of covert fraud, resulting in economic, mental, and social harm to victims. Understanding why people are deceived and implementing appropriate interventions is critical for fraud reduction. Based on the Bayesian brain theory, individuals’ mental states may be a key point in scam compliance and warning compliance. Fraud victims with different mental states may construct various hypotheses and explanations about the fraud they are exposed to, causing different cognition and behavior patterns. Therefore, we first conducted a semi-structured in-depth interview with online fraud victims to investigate the individual and social factors that affect victims’ mental states. Grounded theory analysis showed five core factors influencing scam compliance: psychological traits, empirical factors, motivation, cognitive biases, and emotional imbalance. Based on our findings of psychological processes and deception’s influential factors, we then designed warnings to inform victims of fraud, particularly for those involving novel types of scams. Tested on a real-life setting, our designed warnings effectively enhanced warning compliance, allowing more fraud victims to avoid financial losses.

Resourceful Event-Predictive Inference: The Nature of Cognitive Effort.

Pursuing a precise, focused train of thought requires cognitive effort. Even more effort is necessary when more alternatives need to be considered or when the imagined situation becomes more complex. Cognitive resources available to us limit the cognitive effort we can spend. In line with previous work, an information-theoretic, Bayesian brain approach to cognitive effort is pursued: to solve tasks in our environment, our brain needs to invest information, that is, negative entropy, to impose structure, or focus, away from a uniform structure or other task-incompatible, latent structures. To get a more complete formalization of cognitive effort, a resourceful event-predictive inference model (REPI) is introduced, which offers computational and algorithmic explanations about the latent structure of our generative models, the active inference dynamics that unfold within, and the cognitive effort required to steer the dynamics—to, for example, purposefully process sensory signals, decide on responses, and invoke their execution. REPI suggests that we invest cognitive resources to infer preparatory priors, activate responses, and anticipate action consequences. Due to our limited resources, though, the inference dynamics are prone to task-irrelevant distractions. For example, the task-irrelevant side of the imperative stimulus causes the Simon effect and, due to similar reasons, we fail to optimally switch between tasks. An actual model implementation simulates such task interactions and offers first estimates of the involved cognitive effort. The approach may be further studied and promises to offer deeper explanations about why we get quickly exhausted from multitasking, how we are influenced by irrelevant stimulus modalities, why we exhibit magnitude interference, and, during social interactions, why we often fail to take the perspective of others into account.

Could Computational Approaches Challenge How we Understand and Tackle Mental Disorders Stigma?

Most psychiatric disorders (between 50% and 90%) remain untreated although effective treatments exist (Kohn et al., 2004). Stigma is frequent toward patients and mental health professionals (Rüsch et al., 2005). It impairs access to care especially during the early signs, amplifying the treatment gap, mainly due to a shortage of resources. How the brain makes prior internal models of the world could explain why we all stigmatize. The Bayesian brain hypothesis describes the optimal combination of priors, coming from our evolution, our memory and our perceptions, to make decisions (Parr et al., 2018). This can be intuitively understood by visual illusions, but also medical categorical diagnostic reasoning (Medow, 2011). These priors allow to simplify the world and categorize people (Sherman et al., 1998). Beyond cognitive modelling, Computational approaches also led to the development of several electronic devices, from apps to social robots, (Gargot et al., 2021). Patients reported that it was easier to self-disclose toward an avatar, that cannot judge, than toward a therapist (Hang and Gratch, 2010). These technologies could improve early, scalable as well as efficient access to care (Andersson et al., 2019). Empathy is the best strategy to tackle stigma. What is it exactly? Good therapist-patient synchronization of brains (Czeszumski et al., 2020) and bodies (Ramseyer et Tschacher, 2011) could foster better communication and thus empathy. There is an urgent need to promote research in empathy (Belzung, 2017), non-specific factors in psychotherapy mechanisms (Miller et Moyers, 2021) and psychotherapy online training resources (Gargot et al., 2020).DisclosureThomas Gargot was paid by the French ministry of research (Doctoral school), French ministry of health (médaille d’argent, CCA-AHU) for a PhD and CCA position. During his PhD, he prepared several scientific presentations with Dr Asselborn. This work led

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.

Gender Is a Complex Number and the Case for Trans Phantoms

Gender is not only a spectrum; it is a complex number. Each individual has their own unique multidimensional number of gender. Gender as a complex number has developed from the gender algorithm. This conceptualization of gender is seated in theorizing gender from a Bayesian brain foundation, specifically the Free Energy Principle. The phenomenon of phantoms in transgender, transsexual, and nonbinary (TGNB) people has only recently been a subject of inquiry. This article builds on ideas developed through clinical practice and harvested from multidisciplinary theory and research. It applies my theories of interoceptive gender and the gender algorithm to formulations of gender. It will demonstrate how these relate to the theory of trans phantoms, which has important clinical implications and applications for TGNB subjectivity and embodiment.

Bayesian Brains and the Rényi Divergence.

Under the Bayesian brain hypothesis, behavioral variations can be attributed to different priors over generative model parameters. This provides a formal explanation for why individuals exhibit inconsistent behavioral preferences when confronted with similar choices. For example, greedy preferences are a consequence of confident (or precise) beliefs over certain outcomes. Here, we offer an alternative account of behavioral variability using Rényi divergences and their associated variational bounds. Rényi bounds are analogous to the variational free energy (or evidence lower bound) and can be derived under the same assumptions. Importantly, these bounds provide a formal way to establish behavioral differences through an α parameter, given fixed priors. This rests on changes in α that alter the bound (on a continuous scale), inducing different posterior estimates and consequent variations in behavior. Thus, it looks as if individuals have different priors and have reached different conclusions. More specifically, α→0+ optimization constrains the variational posterior to be positive whenever the true posterior is positive. This leads to mass-covering variational estimates and increased variability in choice behavior. Furthermore, α→+∞ optimization constrains the variational posterior to be zero whenever the true posterior is zero. This leads to mass-seeking variational posteriors and greedy preferences. We exemplify this formulation through simulations of the multiarmed bandit task. We note that these α parameterizations may be especially relevant (i.e., shape preferences) when the true posterior is not in the same family of distributions as the assumed (simpler) approximate density, which may be the case in many real-world scenarios. The ensuing departure from vanilla variational inference provides a potentially useful explanation for differences in behavioral preferences of biological (or artificial) agents under the assumption that the brain performs variational Bayesian inference.

Unraveling Negative Expectations and Nocebo-Related Effects in Musculoskeletal Pain.

This Perspective adapts the ViolEx Model, a framework validated in several clinical conditions, to better understand the role of expectations in the recovery and/or maintenance of musculoskeletal (MSK) pain. Here, particular attention is given to the condition in which dysfunctional expectations are maintained despite no longer being supported by confirmatory evidence (i.e., belief—lifting the arm leads to permanent tendon damage; evidence—after the patient lifts the arm no tendon damage occurs). While the ViolEx Model suggests that cognitive immunization strategies are responsible for the maintenance of dysfunctional expectations, we suggest that such phenomenon can also be understood from a Bayesian Brain perspective, according to which the level of precision of the priors (i.e., expectations) is the determinant factor accounting for the extent of priors’ updating (i.e., we merge the two frameworks, suggesting that highly precise prior can lead to cognitive immunization responses). Importantly, this Perspective translates the theory behind these two frameworks into clinical suggestions. Precisely, it is argued that different strategies should be implemented when treating MSK pain patients, depending on the nature of their expectations (i.e., positive or negative and the level of their precision).

How Psychedelic-Assisted Treatment Works in the Bayesian Brain.

Psychedelics are experiencing a renaissance in clinical research. In recent years, an increasing number of studies on psychedelic-assisted treatment have been conducted. So far, the results are promising, suggesting that this new (or rather, rediscovered) form of therapy has great potential. One particular reason for that appears to be the synergistic combination of the pharmacological and psychotherapeutic interventions in psychedelic-assisted treatment. But how exactly do these two interventions complement each other? This paper provides the first account of the interaction between pharmacological and psychological effects in psychedelic-assisted treatment. Building on the relaxed beliefs under psychedelics (REBUS) hypothesis of Carhart-Harris and Friston and the contextual model of Wampold, it argues that psychedelics amplify the common factors and thereby the remedial effects of psychotherapy. More precisely, psychedelics are assumed to attenuate the precision of high-level predictions, making them more revisable by bottom-up input. Psychotherapy constitutes an important source of such input. At best, it signalizes a safe and supportive environment (cf. setting) and induces remedial expectations (cf. set). During treatment, these signals should become incorporated when high-level predictions are revised: a process that is hypothesized to occur as a matter of course in psychotherapy but to get reinforced and accelerated under psychedelics. Ultimately, these revisions should lead to a relief of symptoms.

Correction: The Bayesian brain and cooperative communication in schizophrenia.

In the editorial published on Feb. 8, 2022,[1][1] Figure 1 contained an error. The vertical box on the left should have read “ Hierarchical generation of predictions.” This has been corrected at jpn.ca 1. [↵][2]1. Palaniyappan L, 2. Venkatasubramanian G . The Bayesian brain

L’esprit predictif : introduction à la théorie du cerveau bayésien

The question of how the mind works is at the heart of cognitive science. It aims to understand and explain the complex processes underlying perception, decision-making and learning, three fundamental areas of cognition. Bayesian Brain Theory, a computational approach derived from the principles of Predictive Processing (PP), offers a mechanistic and mathematical formulation of these cognitive processes. This theory assumes that the brain encodes beliefs (probabilistic states) to generate predictions about sensory input, then uses prediction errors to update its beliefs. In this paper, we present an introduction to the fundamentals of Bayesian Brain Theory. We show how this innovative theory hybridizes concepts inherited from the philosophy of mind and experimental data from neuroscience, and how it translates complex cognitive processes such as perception, action, emotion, or belief, or even the psychiatric symptomatology.

Towards the Idea of Molecular Brains.

How can single cells without nervous systems perform complex behaviours such as habituation, associative learning and decision making, which are considered the hallmark of animals with a brain? Are there molecular systems that underlie cognitive properties equivalent to those of the brain? This review follows the development of the idea of molecular brains from Darwin’s “root brain hypothesis”, through bacterial chemotaxis, to the recent discovery of neuron-like r-protein networks in the ribosome. By combining a structural biology view with a Bayesian brain approach, this review explores the evolutionary labyrinth of information processing systems across scales. Ribosomal protein networks open a window into what were probably the earliest signalling systems to emerge before the radiation of the three kingdoms. While ribosomal networks are characterised by long-lasting interactions between their protein nodes, cell signalling networks are essentially based on transient interactions. As a corollary, while signals propagated in persistent networks may be ephemeral, networks whose interactions are transient constrain signals diffusing into the cytoplasm to be durable in time, such as post-translational modifications of proteins or second messenger synthesis. The duration and nature of the signals, in turn, implies different mechanisms for the integration of multiple signals and decision making. Evolution then reinvented networks with persistent interactions with the development of nervous systems in metazoans. Ribosomal protein networks and simple nervous systems display architectural and functional analogies whose comparison could suggest scale invariance in information processing. At the molecular level, the significant complexification of eukaryotic ribosomal protein networks is associated with a burst in the acquisition of new conserved aromatic amino acids. Knowing that aromatic residues play a critical role in allosteric receptors and channels, this observation suggests a general role of π systems and their interactions with charged amino acids in multiple signal integration and information processing. We think that these findings may provide the molecular basis for designing future computers with organic processors.