Post-lesion lateralisation shifts in a computational model of single-word reading

The mechanisms underlying lateralisation of language are incompletely understood. Existing data is inconclusive, for example, in determining which underlying asymmetries in hemispheric anatomy/physiology lead to lateralisation, the precise role of interhemispheric connections in this process, and exactly how and why lateralisation can shift following focal brain damage. Although these issues will ultimately be settled by experimentation, it is likely that computational modelling can be used to suggest, focus, and even interpret such empirical work. We have recently studied the emergence of lateralisation in an artificial neural network model having paired cerebral hemispheric regions, as the model learned to generate the correct pronunciation for simple words. In this paper we extend this previous work by examining the immediate and longer-term changes in lateralisation that occur following simulated acute hemispheric lesions. Among other things, the results demonstrate that the extent to which the non-lesioned model hemispheric region contributes to recovery is a function of lesion size, prelesion lateralisation, and assumptions about the excitatory/inhibitory influences of the corpus callosum. The relevance of these results to the currently controversial suggestion that language lateralisation shifts following focal damage to language areas, and that the unlesioned hemisphere contributes to recovery from stroke-induced aphasia in adults, is discussed.