Bayesian Brain Research Articles

How to grow a self: development of self-representation in the Bayesian brain.

The last two decades saw multiple attempts to explain how the self is represented in the brain within the framework of the Bayesian brain. However, these attempts largely focused on describing a developed, adult self-representation. The current paper argues that looking at the developmental trajectory is crucial for understanding the structure of self-representation. It argues that emergence of self-representations should be understood as an instance of the process of acquisition of new internal models of hidden causes of sensory input. The paper proposes how such models emerge and develop over the course of life by looking at different stages of development of bodily and extra-bodily self-representations. It argues that the self arises gradually in a series of discrete steps: from first-person multisensory representations of one’s body to third-person multisensory body representation, and from basic forms of the extended and social selves to progressively more complex forms of abstract self-representation. It discusses how each of them might emerge based on domain-general learning mechanisms, while also taking into account the potential role of innate representations. Finally, it discusses how predictions of the proposed model might be experimentally tested.

Thalamocortical dysrhythmia and reward deficiency syndrome as uncertainty disorders

A common anatomical core has been described for psychiatric disorders, consisting of the dorsal anterior cingulate cortex (dACC) and anterior insula, processing uncertainty. A common neurophysiological core has been described for other brain related disorders, called thalamocortical dysrhythmia (TCD), consisting of persistent cross-frequency coupling between low and high frequencies. And a common genetic core has been described for yet another set of hypodopaminergic pathologies called reward deficiency syndromes (RDS). Considering that some RDS have the neurophysiological features of TCD, it can be hypothesized that TCD and RDS have a common anatomical core, yet a differentiating associated neurophysiological mechanism. The EEGs of 683 subjects are analysed in source space for both differences and conjunction between TCD and healthy controls, RDS and healthy controls, and between TCD and RDS. A balance between current densities of the pregenual anterior cingulate cortex (pgACC) extending into the ventromedial prefrontal cortex (vmPFC) and dACC is calculated as well. TCD and RDS share a common anatomical and neurophysiological core, consisting of beta activity in the dACC and theta activity in dACC extending into precuneus and dorsolateral prefrontal cortex. TCD and RDS differ in pgACC/vmPFC activity and demonstrate an opposite balance between pgACC/vmPFC and dACC. Based on the Bayesian brain model TCD and RDS can be defined as uncertainty disorders in which the pgACC/vmPFC and dACC have an opposite balance, possibly explained by an inverted-U curve profile of both pgACC/vmPFC and dACC.

Dyspnea, dysfunctional breathing disorders, and the Bayesian brain hypothesis.

Clinicians frequently evaluate patients who present with dyspnea. This term describes uncomfortable breathing during physical activity, and the intensity or degree of dyspnea can vary in an individual depending on circumstances and between individuals. In some cases, the level of dyspnea appears out of proportion to other information relevant to the cardiorespiratory system, and this situation has been described as dysfunctional breathing. The Bayesian brain hypothesis helps clinicians understand this symptom in these patients. This hypothesis suggests that prior experiences with dyspnea during physical activity or a respiratory disorder provide the background that is used to interpret current symptoms. This review outlines problems associated with the use of the term "dyspnea" and briefly describes how the Bayesian brain hypothesis might help clinicians understand this symptom better.

Pain perception as hierarchical Bayesian inference: A test case for the theory of constructed emotion.

An intriguing perspective about human emotion, the theory of constructed emotion considers emotions as generative models according to the Bayesian brain hypothesis. This theory brings fresh insight to existing findings, but its complexity renders it challenging to test experimentally. We argue that laboratory studies of pain could support the theory because although some may not consider pain to be a genuine emotion, the theory must at minimum be able to explain pain perception and its dysfunction in pathology. We review emerging evidence that bear on this question. We cover behavioral and neural laboratory findings, computational models, placebo hyperalgesia, and chronic pain. We conclude that there is substantial evidence for a predictive processing account of painful experience, paving the way for a better understanding of neuronal and computational mechanisms of other emotions.

The police hunch: the Bayesian brain, active inference, and the free energy principle in action.

In the realm of law enforcement, the "police hunch" has long been a mysterious but crucial aspect of decision-making. Drawing on the developing framework of Active Inference from cognitive science, this theoretical article examines the genesis, mechanics, and implications of the police hunch. It argues that hunches - often vital in high-stakes situations - should not be described as mere intuitions, but as intricate products of our mind's generative models. These models, shaped by observations of the social world and assimilated and enacted through active inference, seek to reduce surprise and make hunches an indispensable tool for officers, in exactly the same way that hypotheses are indispensable for scientists. However, the predictive validity of hunches is influenced by a range of factors, including experience and bias, thus warranting critical examination of their reliability. This article not only explores the formation of police hunches but also provides practical insights for officers and researchers on how to harness the power of active inference to fully understand policing decisions and subsequently explore new avenues for future research.

The autocorrelated Bayesian sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times.

Normative models of decision-making that optimally transform noisy (sensory) information into categorical decisions qualitatively mismatch human behavior. Indeed, leading computational models have only achieved high empirical corroboration by adding task-specific assumptions that deviate from normative principles. In response, we offer a Bayesian approach that implicitly produces a posterior distribution of possible answers (hypotheses) in response to sensory information. But we assume that the brain has no direct access to this posterior, but can only sample hypotheses according to their posterior probabilities. Accordingly, we argue that the primary problem of normative concern in decision-making is integrating stochastic hypotheses, rather than stochastic sensory information, to make categorical decisions. This implies that human response variability arises mainly from posterior sampling rather than sensory noise. Because human hypothesis generation is serially correlated, hypothesis samples will be autocorrelated. Guided by this new problem formulation, we develop a new process, the Autocorrelated Bayesian Sampler (ABS), which grounds autocorrelated hypothesis generation in a sophisticated sampling algorithm. The ABS provides a single mechanism that qualitatively explains many empirical effects of probability judgments, estimates, confidence intervals, choice, confidence judgments, response times, and their relationships. Our analysis demonstrates the unifying power of a perspective shift in the exploration of normative models. It also exemplifies the proposal that the "Bayesian brain" operates using samples not probabilities, and that variability in human behavior may primarily reflect computational rather than sensory noise. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Decoding of auditory surprise in adult magnetoencephalography data using Bayesian models

BackgroundThe Bayesian brain framework has been proposed to explain how the brain processes and interprets sensory information. Magnetoencephalography (MEG) and electroencephalography (EEG) are two neuroimaging techniques commonly used with decoding models to study neural responses to auditory, visual and somatosensory stimuli. Our study aims to investigate neural responses to auditory stimuli using MEG data and to determine which temporal components in MEG data are sufficient for decoding surprise based on Bayesian models. MethodMEG data acquired from 18 subjects during an auditory binary oddball task was used. The data were pre-processed, and features were selected from different time windows. Five Bayesian learning models were applied to the experimental task stimuli, and each single trial's surprise value was calculated. The relationship between the extracted features in MEG data and the surprise regressors was investigated using linear regression and 5-fold cross-validation. ResultsThe results showed that the middle and late components of the MEG evoked potentials were significantly more informative than the early components. The results indicated that the Dirichlet-Categorical model outperformed the other model's decoding performance as demonstrated by higher R-squared values and lower MSE and BIC values. ConclusionsThe findings of this study provide evidence for the existence of a neural network that generates surprise in the human brain and highlight the importance of the middle and late components of the MEG evoked potentials for decoding the surprise value of auditory stimuli.

Bayesian brain computing and the free-energy principle: an interview with Karl Friston.

The free-energy principle entails the Bayesian brain hypothesis that can be implemented by many schemes considered in this field. The combination of multimodal brain imaging and free-energy minimization has shown promise in unraveling complex brain dynamics and understanding the interactions among distinct brain regions. The Bayesian mechanics of brain computing gives a unique route to understanding authentic (neuromimetic) intelligence and, more importantly, points towards the development of brain-inspired intelligence. NSR spoke to a leading theoretical neuroscientist and authority on brain imaging-Karl Friston, the inventor of statistical parametric mapping, voxel-based morphometry and dynamic causal modeling. Friston is also known for his contributions to theoretical biology in the form of the free-energy principle and applications such as active inference. Friston is currently the Scientific Director of the Wellcome Trust Centre for Neuroimaging, Professor of Neuroscience at Queen Square Institute of Neurology, University College London and Honorary Consultant at The National Hospital for Neurology and Neurosurgery, UK.

Animal affect, welfare and the Bayesian brain.

According to the Bayesian brain hypothesis, the brain can be viewed as a predictive machine, such that predictions (or expectations) affect how sensory inputs are integrated. This means that in many cases, affective responses may depend more on the subject's perception of the experience (driven by expectations built on past experiences) rather than on the situation itself. Little research to date has applied this concept to affective states in animals. The aim of this paper is to explore how the Bayesian brain hypothesis can be used to understand the affective experiences of animals and to develop a basis for novel predictions regarding animal welfare. Drawing from the literature illustrating how predictive processes are important to human well-being, and are often impaired in affective disorders, we explore whether the Bayesian brain theories may help understanding animals' affective responses and whether deficits in predictive processes may lead to previously unconsidered welfare consequences. We conclude that considering animals as predictive entities can improve our understanding of their affective responses, with implications for basic research and for how to provide animals a better life.

Bayesian encoding and decoding as distinct perspectives on neural coding.

The Bayesian brain hypothesis is one of the most influential ideas in neuroscience. However, unstated differences in how Bayesian ideas are operationalized make it difficult to draw general conclusions about how Bayesian computations map onto neural circuits. Here, we identify one such unstated difference: some theories ask how neural circuits could recover information about the world from sensory neural activity (Bayesian decoding), whereas others ask how neural circuits could implement inference in an internal model (Bayesian encoding). These two approaches require profoundly different assumptions and lead to different interpretations of empirical data. We contrast them in terms of motivations, empirical support and relationship to neural data. We also use a simple model to argue that encoding and decoding models are complementary rather than competing. Appreciating the distinction between Bayesian encoding and Bayesian decoding will help to organize future work and enable stronger empirical tests about the nature of inference in the brain.

Puzzled by dysfunctional breathing disorder(s)? Consider the Bayesian brain hypothesis!

There is currently growing clinical concern regarding dysfunctional breathing disorder(s) (DBD), an umbrella term for a set of multidimensional clinical conditions that are characterized by altered breathing pattern associated with a variety of intermittent or chronic symptoms, notably dyspnea, in the absence or in excess of, organic disease. However, several aspects of DBD remain poorly understood and/or open to debate, especially the inconsistent relationship between the array of experienced symptoms and their supposedly underlying mechanisms. This may be partly due to a more general problem, i.e., the prevailing way we conceptualize symptoms. In the present article, after a brief review of the different aspects of DBD from the current perspective, I submit a call for considering DBD under the innovating perspective of the Bayesian brain hypothesis, i.e., a potent and novel model that fundamentally changes our views on symptom perception.

The self and the Bayesian brain: Testing probabilistic models of body ownership through a self-localization task

Simple multisensory manipulations can induce the illusory misattribution of external objects to one's own body, allowing to experimentally investigate body ownership. In this context, body ownership has been conceptualized as the result of the online Bayesian optimal estimation of the probability that one object belongs to the body from the congruence of multisensory inputs. This idea has been highly influential, as it provided a quantitative basis to bottom-up accounts of self-consciousness. However, empirical evidence fully supporting this view is scarce, as the optimality of the putative inference process has not been assessed rigorously. This pre-registered study aimed at filling this gap by testing a Bayesian model of hand ownership based on spatial and temporal visuo-proprioceptive congruences. Model predictions were compared to data from a virtual-reality reaching task, whereby reaching errors induced by a spatio-temporally mismatching virtual hand have been used as an implicit proxy of hand ownership. To rigorously test optimality, we compared the Bayesian model versus alternative non-Bayesian models of multisensory integration, and independently assess unisensory components and compare them to model estimates. We found that individually measured values of proprioceptive precision correlated with those fitted from our reaching task, providing compelling evidence that the underlying visuo-proprioceptive integration process approximates Bayesian optimality. Furthermore, reaching errors correlated with explicit ownership ratings at the single individual and trial level. Taken together, these results provide novel evidence that body ownership, a key component of self-consciousness, can be truly described as the bottom-up, behaviourally optimal processing of multisensory inputs.

A predictive coding approach to psychedelic virtual-induced hallucinations and creative cognition in aging.

Recent research has investigated the potential of psychedelic substances in treating various neurological and psychiatric disorders. In particular, there has been a growth in studies concerning the intersection of psychedelics, Virtual Reality (VR), and Cognitive Flexibility (CF). Indeed, the use of immersive technology allows the simulation of the perceptual and cognitive effects of psychedelic substances without the potential risks associated with them. CF is strongly associated with creative cognition, a complex cognitive mechanism involved in creative thinking and associated with the prefrontal cortex and the neural networks supporting executive functions, memory, attention, and spontaneous modes of thought. The Bayesian brain approach, which is rooted in predictive coding, has emerged as a promising framework for understanding the effects of psychedelic hallucinations on cognitive functioning. Psychedelic substances may enhance creativity by inducing a state of CF, allowing for a wider range of associations and possibilities to be explored and increasing openness to experience. A decline in cognitive abilities, including creative processing and divergent thinking, is observed during the aging process. In particular, studies on Mild Cognitive Impairment (MCI) show poorer performance in executive functions, including CF. The present paper suggests that psychedelic hallucinations induced by VR may help optimize the balance between top-down expectations and bottom-up sensory information. Therefore, enhanced CF and creativity may be crucial during the aging process for maintaining cognitive functions and preventing pathological conditions.

Microgravity induces overconfidence in perceptual decision-making

Does gravity affect decision-making? This question comes into sharp focus as plans for interplanetary human space missions solidify. In the framework of Bayesian brain theories, gravity encapsulates a strong prior, anchoring agents to a reference frame via the vestibular system, informing their decisions and possibly their integration of uncertainty. What happens when such a strong prior is altered? We address this question using a self-motion estimation task in a space analog environment under conditions of altered gravity. Two participants were cast as remote drone operators orbiting Mars in a virtual reality environment on board a parabolic flight, where both hyper- and microgravity conditions were induced. From a first-person perspective, participants viewed a drone exiting a cave and had to first predict a collision and then provide a confidence estimate of their response. We evoked uncertainty in the task by manipulating the motion’s trajectory angle. Post-decision subjective confidence reports were negatively predicted by stimulus uncertainty, as expected. Uncertainty alone did not impact overt behavioral responses (performance, choice) differentially across gravity conditions. However microgravity predicted higher subjective confidence, especially in interaction with stimulus uncertainty. These results suggest that variables relating to uncertainty affect decision-making distinctly in microgravity, highlighting the possible need for automatized, compensatory mechanisms when considering human factors in space research.

Correlations between within-subject variability of pain intensity reports and rubber hand illusion proprioceptive drift

IntroductionConsistent with the Bayesian brain hypothesis, the within-subject variability of pain intensity reports as captured with the Focused Analgesia Selection Test (FAST) might be a surrogate measure of the certainty in ascending noxious signals. The outcomes of a non-pain-related task, the rubber hand illusion, were hypothesized to reflect the same construct. This study aimed to explore whether within-subject differences in variability of pain intensity reports and the outcomes of the rubber hand illusion might be related. MethodsNonclinical participants underwent the classic rubber hand illusion under synchronous (experimental) and asynchronous (control) conditions. Two outcomes were assessed: proprioceptive drift and feeling of ownership. Thereafter, participants underwent the FAST to assess the within-subject variability of pain reports in response to heat stimuli. Intraclass correlation (ICC) and the correlation coefficient (R2) were the main outcomes. Spearman’s correlations were used to assess associations between the outcomes of the 2 tasks. ResultsThirty-six volunteers completed the study. Both FAST outcomes—ICC (Spearman’s r = 0.355, p = 0.033) and R2 (Spearman’s r = 0.349, p = 0.037)—were positively correlated with proprioceptive drift in the synchronous but not asynchronous conditions (p > 0.05). The subjective feeling of ownership and FAST outcomes did not correlate (p > 0.05). ConclusionsThe associations between the 2 tasks’ outcomes imply that both tasks at least partly assess similar constructs. Current knowledge suggests that this construct represents the person’s certainty in perceiving ascending sensory signals, or, in Bayesian terminology, the certainty of the likelihood.

Longing to act: Bayesian inference as a framework for craving in behavioral addiction

Traditionally, craving is considered a defining feature of drug addiction. Accumulating evidence suggests that craving can also exist in behavioral addictions (e.g., gambling disorder) without drug-induced effects. However, the degree to which mechanisms of craving overlap between classic substance use disorders and behavioral addictions remains unclear. There is, therefore, an urgent need to develop an overarching theory of craving that conceptually integrates findings across behavioral and drug addictions. In this review, we will first synthesize existing theories and empirical findings related to craving in both drug-dependent and -independent addictive disorders. Building on the Bayesian brain hypothesis and previous work on interoceptive inference, we will then propose a computational theory for craving in behavioral addiction, where the target of craving is execution of an action (e.g., gambling) rather than a drug. Specifically, we conceptualize craving in behavioral addiction as a subjective belief about physiological states of the body associated with action completion and is updated based on both a prior belief (“I need to act to feel good”) and sensory evidence (“I cannot act”). We conclude by briefly discussing the therapeutic implications of this framework. In summary, this unified Bayesian computational framework for craving generalizes across addictive disorders, provides explanatory power for ostensibly conflicting empirical findings, and generates strong hypotheses for future empirical studies. The disambiguation of the computational components underlying domain-general craving using this framework will lead to a deeper understanding of, and effective treatment targets for, behavioral and drug addictions.

A parahippocampal-sensory Bayesian vicious circle generates pain or tinnitus: a source-localized EEG study.

Pain and tinnitus share common pathophysiological mechanisms, clinical features, and treatment approaches. A source-localized resting-state EEG study was conducted in 150 participants: 50 healthy controls, 50 pain, and 50 tinnitus patients. Resting-state activity as well as functional and effective connectivity was computed in source space. Pain and tinnitus were characterized by increased theta activity in the pregenual anterior cingulate cortex, extending to the lateral prefrontal cortex and medial anterior temporal lobe. Gamma-band activity was increased in both auditory and somatosensory cortex, irrespective of the pathology, and extended to the dorsal anterior cingulate cortex and parahippocampus. Functional and effective connectivity were largely similar in pain and tinnitus, except for a parahippocampal-sensory loop that distinguished pain from tinnitus. In tinnitus, the effective connectivity between parahippocampus and auditory cortex is bidirectional, whereas the effective connectivity between parahippocampus and somatosensory cortex is unidirectional. In pain, the parahippocampal-somatosensory cortex is bidirectional, but parahippocampal auditory cortex unidirectional. These modality-specific loops exhibited theta-gamma nesting. Applying a Bayesian brain model of brain functioning, these findings suggest that the phenomenological difference between auditory and somatosensory phantom percepts result from a vicious circle of belief updating in the context of missing sensory information. This finding may further our understanding of multisensory integration and speaks to a universal treatment for pain and tinnitus-by selectively disrupting parahippocampal-somatosensory and parahippocampal-auditory theta-gamma activity and connectivity.

Free energy and inference in living systems.

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

Synapses, predictions, and prediction errors: A neocortical computational study of MDD using the temporal memory algorithm of HTM.

Synapses and spines play a significant role in major depressive disorder (MDD) pathophysiology, recently highlighted by the rapid antidepressant effect of ketamine and psilocybin. According to the Bayesian brain and interoception perspectives, MDD is formalized as being stuck in affective states constantly predicting negative energy balance. To understand how spines and synapses relate to the predictive function of the neocortex and thus to symptoms, we used the temporal memory (TM), an unsupervised machine-learning algorithm. TM models a single neocortical layer, learns in real-time, and extracts and predicts temporal sequences. TM exhibits neocortical biological features such as sparse firing and continuous online learning using local Hebbian-learning rules. We trained a TM model on random sequences of upper-case alphabetical letters, representing sequences of affective states. To model depression, we progressively destroyed synapses in the TM model and examined how that affected the predictive capacity of the network. We found that the number of predictions decreased non-linearly. Destroying 50% of the synapses slightly reduced the number of predictions, followed by a marked drop with further destruction. However, reducing the synapses by 25% distinctly dropped the confidence in the predictions. Therefore, even though the network was making accurate predictions, the network was no longer confident about these predictions. These findings explain how interoceptive cortices could be stuck in limited affective states with high prediction error. Connecting ketamine and psilocybin's proposed mechanism of action to depression pathophysiology, the growth of new synapses would allow representing more futuristic predictions with higher confidence. To our knowledge, this is the first study to use the TM model to connect changes happening at synaptic levels to the Bayesian formulation of psychiatric symptomatology. Linking neurobiological abnormalities to symptoms will allow us to understand the mechanisms of treatments and possibly, develop new ones.

Can an algorithm become delusional? Evaluating ontological commitments and methodology of computational psychiatry

AbstractThe computational approach to psychiatric disorders, including delusions, promises explanation and treatment. Here, we argue that an information processing approach might be misleading to understand psychopathology and requires further refinement. We explore the claim of computational psychiatry being a bridge between phenomenology and physiology while focussing on the ontological commitments and corresponding methodology computational psychiatry is based on. Interconnecting ontological claims and methodological practices, the paper illustrates the structure of theory-building and testing in computational psychiatry.First, we will explain the ontological commitments computational psychiatry is grounded in, theBayesian Brain hypothesis(BBH) of unconscious inference, paired with normative deontic approaches applied to gauge psychopathology. We then turn to the steps taken in empirical paradigms, from definitions, which are used as starting points, to the operationalisation and isolation of cognitive processes and hypothesis testing based on algorithmic models, to consecutive interpretations regarding the aetiology of psychiatric disorders. We outline how experimental paradigms in computational psychiatry are specifically designed to confirm aberrations in assumed inferential processes, which are thought of as being the underlyingcore invariant features.We will illustrate a gap between the ontological commitments of computational psychiatry and the operationalisation and testing of the cognition assumed to be relevant for psychopathology. This conceptual gap is of utmost importance when designing computational paradigms and may impede a crisp understanding of the approach. Lastly, in evaluating the conceptual gap, it becomes apparent that the information processing formalism used in computational psychiatry is still grounded in rational cognitive psychology.