Projects per year
Abstract
Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference i.e. how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: i) how proposed theories differ in accounts of ASD vs. schizophrenia and ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information. This was accomplished through a visual statistical learning paradigm designed to quantitatively assess variations in individuals' likelihoods and priors. The acquisition of the priors was found to be intact along both traits spectra. However, autistic traits were associated with more veridical perception and weaker influence of expectations. Bayesian modeling revealed that this was due, not to weaker prior expectations, but to more precise sensory representations.
Original language | English |
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Number of pages | 44 |
Journal | eLIFE |
DOIs | |
Publication status | Published - 14 May 2018 |
Keywords / Materials (for Non-textual outputs)
- computational psychiatry
- autism
- schizophrenia
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Dive into the research topics of 'Autistic traits, but not schizotypy, predict increased weighting of sensory information in Bayesian visual integration'. Together they form a unique fingerprint.Projects
- 1 Finished
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Probabilistic inference deficits in schizophrenia: computational approaches
Series, P. (Principal Investigator)
14/07/13 → 14/07/17
Project: Research
Profiles
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Peggy Series
- School of Informatics - Personal Chair of Computational Psychiatry
- Institute for Adaptive and Neural Computation
- Edinburgh Neuroscience
- Data Science and Artificial Intelligence
Person: Academic: Research Active