Simulating behavioural interventions for developmental deficits: When improving strengths produces better outcomes than remediating weaknesses

Computational models of cognitive development have been frequently used to model impairments found in developmental disorders but relatively rarely to simulate behavioural interventions to remediate these impairments. One area of controversy in practices of intervention is whether it is better to attempt to remediate an area of weakness or to build on the child’s strengths. We present an artificial neural network model of productive vocabulary development simulating children with word-finding difficulties. We contrast an intervention to remediate weakness (additional practice on naming) with interventions to improve strengths (improving phonological and semantic knowledge). Remediating weakness served to propel the system more quickly along the same atypical trajectory, while improving strengths produced long-term increases in final vocabulary size. A combination yielded the best outcome. The model represents the first mechanistic demonstration of how interventions targeting strengths may serve to improve behavioural outcomes in developmental disorders. The observed effects in the model are in line with those observed empirically for children with word-finding difficulties.