Simulating Intervention to Support Compensatory Strategies in an Artificial Neural Network Model of Atypical Language Development

Artificial neural networks have been used to model developmental deficits in cognitive and language development, most often by including sub-optimal inputoutput representations or computational parameters in these learning systems. The next step is to simulate intervention to alleviate developmental impairments, to inform the mechanistic basis of remediation. Here we used a sample model of atypical language development (in the well-explored domain of past tense acquisition) to investigate the extent to which alternative training regimes may induce short-term or long-term compensatory changes in underlying function, and the extent to which this depends on the timing of intervention. We present a new method to derive ‘intervention’ training sets as a simulation of behavioral interventions, and assess its adequacy in our sample model.