↓ Skip to main content

Computational Neuropsychology and Bayesian Inference

Overview of attention for article published in Frontiers in Human Neuroscience, February 2018
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
1 news outlet
blogs
6 blogs
twitter
94 X users
facebook
1 Facebook page
wikipedia
1 Wikipedia page
reddit
1 Redditor

Citations

dimensions_citation
115 Dimensions

Readers on

mendeley
376 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Computational Neuropsychology and Bayesian Inference
Published in
Frontiers in Human Neuroscience, February 2018
DOI 10.3389/fnhum.2018.00061
Pubmed ID
Authors

Thomas Parr, Geraint Rees, Karl J. Friston

Abstract

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 characterized by the set of prior beliefs that would make a patient's behavior 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 emphasize 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 94 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 376 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 376 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 66 18%
Student > Master 57 15%
Researcher 49 13%
Student > Bachelor 41 11%
Student > Postgraduate 17 5%
Other 61 16%
Unknown 85 23%
Readers by discipline Count As %
Psychology 83 22%
Neuroscience 78 21%
Engineering 19 5%
Agricultural and Biological Sciences 14 4%
Computer Science 12 3%
Other 56 15%
Unknown 114 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 99. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 October 2023.
All research outputs
#430,084
of 25,492,047 outputs
Outputs from Frontiers in Human Neuroscience
#182
of 7,712 outputs
Outputs of similar age
#9,870
of 344,110 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#5
of 147 outputs
Altmetric has tracked 25,492,047 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,712 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 344,110 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 147 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.