A computational model of implicit memory captures dyslexics’ perceptual deficits

Sagi Jaffe-Dax, Ofri Raviv, Nori Jacoby, Yonatan Loewenstein, Merav Ahissar

Research output: Contribution to journalArticlepeer-review

Abstract

Dyslexics are diagnosed for their poor reading skills, yet they characteristically also suffer from poor verbal memory and often from poor auditory skills. To date, this combined profile has been accounted for in broad cognitive terms. Here we hypothesize that the perceptual deficits associated with dyslexia can be understood computationally as a deficit in integrating prior information with noisy observations. To test this hypothesis we analyzed the performance of human participants in an auditory discrimination task using a two-parameter computational model. One parameter captures the internal noise in representing the current event, and the other captures the impact of recently acquired prior information. Our findings show that dyslexics’ perceptual deficit can be accounted for by inadequate adjustment of these components; namely, low weighting of their implicit memory of past trials relative to their internal noise. Underweighting the stimulus statistics decreased dyslexics’ ability to compensate for noisy observations. ERP measurements (P2 component) while participants watched a silent movie indicated that dyslexics’ perceptual deficiency may stem from poor automatic integration of stimulus statistics. This study provides the first description of a specific computational deficit associated with dyslexia.

Original languageEnglish
Pages (from-to)12116-12126
Number of pages11
JournalJournal of Neuroscience
Volume35
Issue number35
DOIs
StatePublished - 2 Sep 2015
Externally publishedYes

Keywords

  • Anchoring deficit
  • Computational neuroscience
  • Event-related potentials
  • Perception
  • Predictive coding
  • Statistical learning

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