Modeling Mechanisms of Persisting and Resolving Delay in Language Development

Abstract

PURPOSE In this study, the authors used neural network modeling to investigate the possible mechanistic basis of developmental language delay and to test the viability of the hypothesis that persisting delay and resolving delay lie on a mechanistic continuum with normal development. METHOD The authors used a population modeling approach to study individual rates of development in 1,000 simulated individuals acquiring a notional language domain (in this study, represented by English past tense). Variation was caused by differences in internal neurocomputational learning parameters as well as the richness of the language environment. An early language delay group was diagnosed, and individual trajectories were then traced. RESULTS Quantitative variations in learning mechanisms were sufficient to produce persisting delay and resolving delay subgroups in similar proportions to empirical observations. In the model, persisting language delay was caused by limitations in processing capacity, whereas resolving delay was caused by low plasticity. Richness of the language environment did not predict the emergence of persisting delay but did predict the final ability levels of individuals with resolving delay. CONCLUSION Mechanistically, it is viable that persisting delay and resolving delay are only quantitatively different. There may be an interaction between environmental factors and outcome groups, with individuals who have resolving delay being influenced more by the richness of the language environment.