September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
A diffusion model decomposition of motion processing performance in children with dyslexia and related neural dynamics
Author Affiliations & Notes
  • Catherine Manning
    University of Oxford
  • Cameron D Hassall
    University of Oxford
  • Laurence T Hunt
    University of Oxford
  • Anthony M Norcia
    Stanford University
  • Eric-Jan Wagenmakers
    University of Amsterdam
  • Margaret J Snowling
    University of Oxford
  • Gaia Scerif
    University of Oxford
  • Nathan J Evans
    University of Queensland
  • Footnotes
    Acknowledgements  This project was funded by a Sir Henry Wellcome Postdoctoral Fellowship awarded to CM (grant number 204685/Z/16/Z), and a James S. McDonnell Foundation Understanding Human Cognition Scholar Award to GS.
Journal of Vision September 2021, Vol.21, 2921. doi:
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      Catherine Manning, Cameron D Hassall, Laurence T Hunt, Anthony M Norcia, Eric-Jan Wagenmakers, Margaret J Snowling, Gaia Scerif, Nathan J Evans; A diffusion model decomposition of motion processing performance in children with dyslexia and related neural dynamics. Journal of Vision 2021;21(9):2921.

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      © ARVO (1962-2015); The Authors (2016-present)

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Children with dyslexia have elevated psychophysical thresholds in global motion tasks. However, threshold estimates conflate multiple processes so it is unclear which processing stages are altered in dyslexia. The drift-diffusion framework offers the possibility to model accuracy and response time distributions to decompose performance into separate processing components, which can then be linked to neural measures. Within this framework, the decision-making process is modelled as an accumulation of noisy sensory information towards one of two decision bounds. The main parameters are drift-rate (reflecting the rate of evidence accumulation), boundary separation (reflecting response conservativeness), and non-decision time (reflecting sensory encoding and response generation). Here, 50 children with a dyslexia diagnosis and 50 typically developing children aged 6 to 14 years judged the direction of coherent motion and Gaussian motion stimuli as quickly and accurately as possible. High-density EEG data were collected for most participants. Dyslexic children were slightly slower to respond for both stimulus types and were less accurate for Gaussian motion stimuli. In our pre-registered analyses, we fitted hierarchical Bayesian diffusion models to the data, both with and without controlling for differences in age. When controlling for differences in age, there was evidence for a reduced drift-rate in dyslexic children compared to typical children for both stimulus types (coherent motion: BF10=4.57; Gaussian motion: BF10=4.28). The evidence for differences in other parameters was inconclusive. We also identified a response-locked EEG component which was maximal over centro-parietal electrodes, which had lower amplitudes in dyslexic children compared to typically developing children. The results suggest that dyslexic children are slower to extract sensory evidence from motion stimuli.


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