Computational modeling of interventions for developmental disorders.

Journal: Psychological review
PMID:

Abstract

We evaluate the potential of connectionist models of developmental disorders to offer insights into the efficacy of interventions. Based on a range of computational simulation results, we assess factors that influence the effectiveness of interventions for reading, language, and other cognitive developmental disorders. The analysis provides a level of mechanistic detail that is generally lacking in behavioral approaches to intervention. We review an extended program of modeling work in four sections. In the first, we consider long-term outcomes and the possibility of compensated outcomes and resolution of early delays. In the second section, we address methods to remediate atypical development in a single network. In the third section, we address interventions to encourage compensation via alternative pathways. In the final section, we consider the key issue of individual differences in response to intervention. Together with advances in understanding the neural basis of developmental disorders and neural responses to training, formal computational approaches can spur theoretical progress to narrow the gap between the theory and practice of intervention. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Authors

  • Michael S C Thomas
    Developmental Neurocognition Lab, Birkbeck, University of London.
  • Anna Fedor
    MTA-ELTE Theoretical Biology and Evolutionary Ecology Research Group.
  • Rachael Davis
    Developmental Neurocognition Lab.
  • Juan Yang
    Department of Neurology, Shanghai Pudong New Area People's Hospital, Shanghai, China.
  • Hala Alireza
    Developmental Neurocognition Lab.
  • Tony Charman
    Institute of Psychiatry, Psychology, and Neuroscience.
  • Jackie Masterson
    Department of Psychology and Human Development.
  • Wendy Best
    Division of Psychology & Language Sciences.