A Computational Model of Implicit Memory Captures Dyslexics' Perceptual Deficits

Overview

Journal J Neurosci

Specialty Neurology

Date 2015 Sep 5

PMID 26338323

Citations 21

Authors

Sagi Jaffe-Dax

Ofri Raviv

Nori Jacoby

Yonatan Loewenstein

Merav Ahissar

Affiliations

Soon will be listed here.

Abstract

Significance Statement: This study presents the first attempt to specify the mechanisms underlying dyslexics' perceptual difficulties computationally by applying a specific model, inspired by the Bayesian framework. This model dissociates between the contribution of sensory noise and that of the prior statistics in an auditory perceptual decision task. We show that dyslexics cannot compensate for their perceptual noise by incorporating prior information. By contrast, adequately reading controls' usage of previous information is often close to optimal. We used ERP measurements to assess the neuronal stage of this deficit. We found that unlike their peers, dyslexics' ERP responses are not sensitive to the relations between the current observation and the prior observation, indicating that they cannot establish a reliable prior.

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