Active versus latent representations: a neural network model of perseveration, dissociation, and decalage

Children of different ages often perseverate, repeating previous behaviors when they are no longer appropriate, despite appearing to know what they should be doing. Using neural network models, we explore an account of these phenomena based on a distinction between active memory (subserved by the prefrontal cortex) and latent memory (subserved by posterior cortex). The models demonstrate how (a) perseveration occurs when an active memory of currently relevant knowledge is insufficiently strong to overcome a latent bias established by previous experience, (b) apparent dissociations between children's knowledge and action may reflect differences in the amount of conflict between active and latent memories that children need to resolve in the tasks, and (c) differences in when children master formally similar tasks (decalage) may result from differences in the strength of children's initial biases. The models help to clarify how prefrontal development may lead to advances in flexible thinking.