scholarly journals Precision and the Bayesian brain

2021 ◽  
Vol 31 (17) ◽  
pp. R1026-R1032
Author(s):  
Daniel Yon ◽  
Chris D. Frith
Keyword(s):  
Bayesian Brain

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

2019 ◽  
Author(s):  
Mateusz Wozniak
Keyword(s):  
Conceptual Development ◽  
Human Life ◽  
Predictive Coding ◽  
Body Representation ◽  
Developmental Trajectory ◽  
The Self ◽  
Learning Mechanisms ◽  
Bayesian Brain ◽  
Third Person ◽  
Complex Forms

The last two decades have brought several attempts to explain the self as a part of the Bayesian brain, typically within the framework of predictive coding. However, none of these attempts have looked comprehensively at the developmental aspect of self-representation. The goal of this paper is to argue that looking at the developmental trajectory is crucial for understanding the structure of an adult self-representation. The paper argues that the emergence of the self should be understood as an instance of conceptual development, which in the context of a Bayesian brain can be understood as a process of acquisition of new internal models of hidden causes of sensory input. The paper proposes how such models might emerge and develop over the course of human 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 suggests how the conceptual structure of self-representation might inform the debate about the structure of self-consciousness.

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

2021 ◽  
Vol 8 (3) ◽  
pp. 189-200
Author(s):  
Adel Razek
Keyword(s):  
Link Prediction ◽  
Clinical Observation ◽  
Mutant Virus ◽  
Brain Functioning ◽  
Bayesian Brain ◽  
Digital Twins ◽  
Brain Theory ◽  
Observation Protocol ◽  
Knowledge Uncertainty

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

Extending predictive processing to the body: Emotion as interoceptive inference

2013 ◽  
Vol 36 (3) ◽  
pp. 227-228 ◽  
Author(s):  
Anil K. Seth ◽  
Hugo D. Critchley
Keyword(s):  
Psychiatric Disorders ◽  
Physiological Condition ◽  
Predictive Coding ◽  
The Body ◽  
Predictive Processing ◽  
Bayesian Brain ◽  
New View

AbstractThe Bayesian brain hypothesis provides an attractive unifying framework for perception, cognition, and action. We argue that the framework can also usefully integrate interoception, the sense of the internal physiological condition of the body. Our model of “interoceptive predictive coding” entails a new view of emotion as interoceptive inference and may account for a range of psychiatric disorders of selfhood.

scholarly journals Biased belief in the Bayesian brain: A deeper look at the evidence

2019 ◽  
Author(s):  
Ben M Tappin ◽  
Stephen Gadsby
Keyword(s):  
Bayesian Inference ◽  
Empirical Evidence ◽  
Belief Formation ◽  
Hierarchical Bayesian Models ◽  
Common Occurrence ◽  
Substantial Evidence ◽  
Belief Updating ◽  
Bayesian Brain ◽  
Key Points ◽  
Study Designs

A recent critique of hierarchical Bayesian models of delusion argues that, contrary to a key assumption of these models, belief formation in the healthy (i.e., neurotypical) mind is manifestly non-Bayesian. Here we provide a deeper examination of the empirical evidence underlying this critique. We argue that this evidence does not convincingly refute the assumption that belief formation in the neurotypical mind approximates Bayesian inference. Our argument rests on two key points. First, evidence that purports to reveal the most damning violation of Bayesian updating in human belief formation is counterweighted by substantial evidence that indicates such violations are the rare exception—not a common occurrence. Second, the remaining evidence does not demonstrate convincing violations of Bayesian inference in human belief updating; primarily because this evidence derives from study designs that produce results that are not obviously inconsistent with Bayesian principles.

scholarly journals Symptom perception, placebo effects, and the Bayesian brain

Pain ◽  
2019 ◽  
Vol 160 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Giulio Ongaro ◽  
Ted J. Kaptchuk
Keyword(s):  
Symptom Perception ◽  
Placebo Effects ◽  
Bayesian Brain

scholarly journals What is mood? A computational perspective

2018 ◽  
Vol 48 (14) ◽  
pp. 2277-2284 ◽  
Author(s):  
James E Clark ◽  
Stuart Watson ◽  
Karl J Friston
Keyword(s):  
Mood Disorders ◽  
Active Inference ◽  
Simple Formulation ◽  
Energy Principle ◽  
Free Energy Principle ◽  
New Approach ◽  
Functional Brain ◽  
Bayesian Brain ◽  
Multiple Levels ◽  
Computational Pathology

AbstractThe neurobiological understanding of mood, and by extension mood disorders, remains elusive despite decades of research implicating several neuromodulator systems. This review considers a new approach based on existing theories of functional brain organisation. The free energy principle (a.k.a. active inference), and its instantiation in the Bayesian brain, offers a complete and simple formulation of mood. It has been proposed that emotions reflect the precision of – or certainty about – the predicted sensorimotor/interoceptive consequences of action. By extending this reasoning, in a hierarchical setting, we suggest mood states act as (hyper) priors over uncertainty (i.e. emotions). Here, we consider the same computational pathology in the proprioceptive and interoceptive (behavioural and autonomic) domain in order to furnish an explanation for mood disorders. This formulation reconciles several strands of research at multiple levels of enquiry.

scholarly journals Stochasticity from function — Why the Bayesian brain may need no noise

2019 ◽  
Vol 119 ◽  
pp. 200-213 ◽  
Author(s):  
Dominik Dold ◽  
Ilja Bytschok ◽  
Akos F. Kungl ◽  
Andreas Baumbach ◽  
Oliver Breitwieser ◽  
...  
Keyword(s):  
Bayesian Brain

Placebos in chronic pain: evidence, theory, ethics, and use in clinical practice

BMJ ◽  
2020 ◽  
pp. m1668 ◽  
Author(s):  
Ted J Kaptchuk ◽  
Christopher C Hemond ◽  
Franklin G Miller
Keyword(s):  
Chronic Pain ◽  
Clinical Practice ◽  
Predictive Coding ◽  
Evidence Theory ◽  
Placebo Treatment ◽  
Placebo Effects ◽  
Unified Framework ◽  
Open Label ◽  
Double Blind ◽  
Bayesian Brain

ABSTRACTDespite their ubiquitous presence, placebos and placebo effects retain an ambiguous and unsettling presence in biomedicine. Specifically focused on chronic pain, this review examines the effect of placebo treatment under three distinct frameworks: double blind, deception, and open label honestly prescribed. These specific conditions do not necessarily differentially modify placebo outcomes. Psychological, clinical, and neurological theories of placebo effects are scrutinized. In chronic pain, conscious expectation does not reliably predict placebo effects. A supportive patient-physician relationship may enhance placebo effects. This review highlights “predictive coding” and “bayesian brain” as emerging models derived from computational neurobiology that offer a unified framework to explain the heterogeneous evidence on placebos. These models invert the dogma of the brain as a stimulus driven organ to one in which perception relies heavily on learnt, top down, cortical predictions to infer the source of incoming sensory data. In predictive coding/bayesian brain, both chronic pain (significantly modulated by central sensitization) and its alleviation with placebo treatment are explicated as centrally encoded, mostly non-conscious, bayesian biases. The review then evaluates seven ways in which placebos are used in clinical practice and research and their bioethical implications. In this way, it shows that placebo effects are evidence based, clinically relevant, and potentially ethical tools for relieving chronic pain.

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