Impact Factor 3.209

The 1st most cited journal in Psychology

This article is part of the Research Topic

10 Years of Impactful, Open Neuroscience

Review ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Hum. Neurosci. | doi: 10.3389/fnhum.2018.00061

Computational neuropsychology and Bayesian inference

  • 1University College London, United Kingdom

Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine ‘prior’ beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology – optimal inference with suboptimal priors – and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterised by the set of prior beliefs that would make a patient’s behaviour appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasise the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.

Keywords: active inference, predictive coding, Computational phenotyping, Precision, Neuropsychology

Received: 22 Nov 2017; Accepted: 05 Feb 2018.

Edited by:

Hauke R. Heekeren, Freie Universität Berlin, Germany

Reviewed by:

Francisco Barceló, University of the Balearic Islands, Spain
Gianfranco Spalletta, Fondazione Santa Lucia (IRCCS), Italy  

Copyright: © 2018 Parr, Rees and Friston. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Thomas Parr, University College London, London, United Kingdom, thomas.parr.12@ucl.ac.uk