Thomas, Michael S.C. and Forrester, N.A. and Ronald, Angelica (2016) Multi-scale modeling of gene-behavior associations in an artificial neural network model of cognitive development. Cognitive Science 40 (1), pp. 51-99. ISSN 1551-6709.
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Abstract
In the multi-disciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction with a variable environment. Given that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multi-scale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description, four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity (to different behaviors, to function versus structure), to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene-environment interactions. We argue that gene-behavior associations can inform cognitive theory with respect to effect size, specificity, and timing. The model demonstrates a means by which researchers can undertake modeling multi-scale modeling with respect to cognition, and develop highly specific and complex hypotheses across multiple levels of description.
Metadata
| Item Type: | Article |
|---|---|
| Additional Information: | This is the peer reviewed version of the article, which has been published in final form at http://dx.doi.org/10.1111/cogs.12230. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving. |
| Keyword(s) / Subject(s): | Multi-scale models, artificial neural networks, population modeling, gene- behavior associations, gene-environment interactions, missing heritability, socio- economic status, development, individual differences |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Psychological Sciences |
| Research Centres and Institutes: | Educational Neuroscience, Centre for, Birkbeck Knowledge Lab, Brain and Cognitive Development, Centre for (CBCD) |
| Depositing User: | Michael Thomas |
| Date Deposited: | 13 Apr 2015 13:58 |
| Last Modified: | 02 Aug 2023 17:15 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/11535 |
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