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Explaining hyper-sensitivity and hypo-responsivity in autism with a common predictive coding-based mechanism.

Sander Van de Cruys1, Kelsey Perrykkad2, Jakob Hohwy2

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|March 22, 2019
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Summary
This summary is machine-generated.

Signal detection theory explains some sensory differences, but not meaning inference. Bayesian predictive coding suggests autism may involve impaired inference, leading to hyporesponsivity alongside hypersensitivity.

Keywords:
Autismhierarchical bayesian inferencehypersensitivitypredictive coding

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Area of Science:

  • Cognitive Neuroscience
  • Sensory Processing
  • Computational Psychiatry

Background:

  • Signal detection theory (SDT) offers a framework for understanding interindividual differences in sensory sensitivity.
  • However, SDT may not fully capture the complexities of how sensory information is interpreted and utilized.

Purpose of the Study:

  • To critically evaluate the limitations of SDT in explaining sensory processing differences.
  • To propose an alternative framework based on Bayesian predictive coding for understanding meaning inference and its implications for behavior.

Main Methods:

  • Theoretical analysis comparing SDT and Bayesian predictive coding frameworks.
  • Conceptual integration of hierarchical inference and precision estimation within Bayesian models.

Main Results:

  • SDT overlooks the crucial role of inferring the meaning of sensory inputs from their underlying causes.
  • Bayesian predictive coding accounts for meaning inference as central to generating context-appropriate behavior.
  • Impaired hierarchical inference, potentially linked to deficient precision estimation in autism, can result in hyporesponsivity, complementing previously noted hypersensitivity.

Conclusions:

  • Bayesian predictive coding provides a more comprehensive account of sensory processing and behavioral responses than SDT.
  • Deficits in inferring the causes of sensory input may underlie certain behavioral characteristics observed in autism spectrum disorder.