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    A neurocomputational model of developmental trajectories of gifted children under a polygenic model: When are gifted children held back by poor environments?

    Thomas, Michael S.C. (2018) A neurocomputational model of developmental trajectories of gifted children under a polygenic model: When are gifted children held back by poor environments? Intelligence 69 , pp. 200-212. ISSN 0160-2896.

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    Abstract

    From the genetic side, giftedness in cognitive development is the result of contribution of many common genetic variants of small effect size, so called polygenicity (Spain et al., 2016). From the environmental side, educa- tionalists have argued for the importance of the environment for sustaining early potential in children, showing that bright poor children are held back in their subsequent development (Feinstein, 2003a). Such correlational data need to be complemented by mechanistic models showing how gifted development results from the re- spective genetic and environmental influences. A neurocomputational model of cognitive development is pre- sented, using artificial neural networks to simulate the development of a population of children. Variability was produced by many small differences in neurocomputational parameters each influenced by multiple artificial genes, instantiating a polygenic model, and by variations in the level of stimulation from the environment. The simulations captured several key empirical phenomena, including the non-linearity of developmental trajec- tories, asymmetries in the characteristics of the upper and lower tails of the population distribution, and the potential of poor environments to hold back bright children. At a computational level, ‘gifted’ networks tended to have higher capacity, higher plasticity, less noisy neural processing, a lower impact of regressive events, and a richer environment. However, individual instances presented heterogeneous contributions of these neuro- computational factors, suggesting giftedness has diverse causes.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Giftedness, Computational modelling, Artificial neural networks, Cognitive development, Socio-economic status, Behavioural genetics
    School: Birkbeck Schools and Departments > School of Science > Psychological Sciences
    Research Centre: Educational Neuroscience, Centre for
    Depositing User: Michael Thomas
    Date Deposited: 23 Jul 2018 14:19
    Last Modified: 23 Jul 2018 14:19
    URI: http://eprints.bbk.ac.uk/id/eprint/23118

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