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Evolving Connectionist Models to Capture Population Variability across Language Development: Modeling Children's Past Tense Formation

Kohli, M; Magoulas, GD; Thomas, MSC; (2020) Evolving Connectionist Models to Capture Population Variability across Language Development: Modeling Children's Past Tense Formation. Artificial Life , 26 (2) pp. 217-241. 10.1162/artl_a_00316. Green open access

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Abstract

Children's acquisition of the English past tense has been widely studied as a testing ground for theories of language development, mostly because it comprises a set of quasi-regular mappings. English verbs are of two types: regular verbs, which form their past tense based on a productive rule, and irregular verbs, which form their past tenses through exceptions to that rule. Although many connectionist models exist for capturing language development, few consider individual differences. In this article, we explore the use of populations of artificial neural networks (ANNs) that evolve according to behavioral genetics principles in order to create computational models capable of capturing the population variability exhibited by children in acquiring English past tense verbs. Literature in the field of behavioral genetics views variability in children's learning in terms of genetic and environmental influences. In our model, the effects of genetic influences are simulated through variations in parameters controlling computational properties of ANNs, and the effects of environmental influences are simulated via a filter applied to the training set. This filter alters the quality of information available to the artificial learning system and creates a unique subsample of the training set for each simulated individual. Our approach uses a population of twins to disentangle genetic and environmental influences on past tense performance and to capture the wide range of variability exhibited by children as they learn English past tenses. We use a novel technique to create the population of ANN twins based on the biological processes of meiosis and fertilization. This approach allows modeling of both individual differences and development (within the lifespan of an individual) in a single framework. Finally, our approach permits the application of selection on developmental performance on the quasi-regular task across generations. Setting individual differences within an evolutionary framework is an important and novel contribution of our work. We present an experimental evaluation of this model, focusing on individual differences in performance. The experiments led to several novel findings, including: divergence of population attributes during selection to favor regular verbs, irregular verbs, or both; evidence of canalization, analogous to Waddington's developmental epigenetic landscape, once selection starts targeting a particular aspect of the task domain; and the limiting effect on the power of selection in the face of stochastic selection (roulette wheel), sexual reproduction, and a variable learning environment for each individual. Most notably, the heritability of traits showed an inverse relationship to optimization. Selected traits show lower heritability as the genetic variation of the population reduces. The simulations demonstrate the viability of linking concepts such as heritability of individual differences, cognitive development, and selection over generations within a single computational framework.

Type: Article
Title: Evolving Connectionist Models to Capture Population Variability across Language Development: Modeling Children's Past Tense Formation
Open access status: An open access version is available from UCL Discovery
DOI: 10.1162/artl_a_00316
Publisher version: http://dx.doi.org/10.1162/artl_a_00316
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Neural network based modeling, behavioral genetics, class imbalance, quasi-regular mappings, English past tense, evolution, individual differences, development, genetic computing
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10122520
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