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No increased circular inference in adults with high levels of autistic traits or autism. | LitMetric

No increased circular inference in adults with high levels of autistic traits or autism.

PLoS Comput Biol

Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.

Published: September 2021

AI Article Synopsis

  • This study investigates the relationship between autism spectrum disorders (ASD) and a concept called circular inference, which refers to the imbalance of excitation and inhibition in the brain that can affect decision-making processes.
  • Researchers conducted an online probabilistic decision-making task with 21 participants diagnosed with ASD and 155 with varying autistic traits, using Bayesian models to examine any possible links to circular inference.
  • Results indicated no correlation between circular inference parameters and autistic traits, suggesting that signal reverberation associated with prior beliefs does not occur in autism, highlighting a difference in the computational mechanisms of autism compared to schizophrenia.

Article Abstract

Autism spectrum disorders have been proposed to arise from impairments in the probabilistic integration of prior knowledge with sensory inputs. Circular inference is one such possible impairment, in which excitation-to-inhibition imbalances in the cerebral cortex cause the reverberation and amplification of prior beliefs and sensory information. Recent empirical work has associated circular inference with the clinical dimensions of schizophrenia. Inhibition impairments have also been observed in autism, suggesting that signal reverberation might be present in that condition as well. In this study, we collected data from 21 participants with self-reported diagnoses of autism spectrum disorders and 155 participants with a broad range of autistic traits in an online probabilistic decision-making task (the fisher task). We used previously established Bayesian models to investigate possible associations between autistic traits or autism and circular inference. There was no correlation between prior or likelihood reverberation and autistic traits across the whole sample. Similarly, no differences in any of the circular inference model parameters were found between autistic participants and those with no diagnosis. Furthermore, participants incorporated information from both priors and likelihoods in their decisions, with no relationship between their weights and psychiatric traits, contrary to what common theories for both autism and schizophrenia would suggest. These findings suggest that there is no increased signal reverberation in autism, despite the known presence of excitation-to-inhibition imbalances. They can be used to further contrast and refine the Bayesian theories of schizophrenia and autism, revealing a divergence in the computational mechanisms underlying the two conditions.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494311PMC
http://dx.doi.org/10.1371/journal.pcbi.1009006DOI Listing

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