I will present a neurocomputational model that we developed to simulate and explain, at cortical level, word learning and language processes as they are believed to occur in motor and sensory primary, secondary and higher association areas of the (inferior) frontal and (superior) temporal lobes of the human brain. Mechanisms and connectivity of the model aim to reflect, as much as possible, functional and structural features of the corresponding cortices, including well-documented (Hebbian) associative learning mechanisms of synaptic plasticity. The model was able to explain and reconcile seemingly incongruous results on neurophysiological patterns of brain responses to well-learned, familiar sensory input (words) and new, unfamiliar linguistic material (pseudowords), and made novel predictions about the complex interactions between language and attention processes in the human brain. To test the validity of these predictions we carried out a new MEG study in which we presented subjects with familiar words and matched unfamiliar pseudowords during attention demanding tasks and under distraction. The experimental results indicated strong modulatory effects of attention on the brain responses to pseudowords, but not on those to words, fully confirming the model's predictions.

In the second part I will illustrate how the same six-area network architecture, implementing the same functional features, can be used to model and explain also cortical mechanisms underlying working memory processes, in the language as well as in the visual domain. In particular, I will present new simulation results that provide a mechanistic answer to the question of why "memory cells" (neurons exhibiting persistent activity in working memory tasks that require stimulus information to be kept in mind in view of future action) are found more frequently in prefrontal cortex and higher sensory areas than in primary cortices, i.e. far away from the  sensorimotor activations that bring about their formation (a phenomenon that we refer to as "disembodiment" of memory). The results point to the intrinsic connectivity of the sensorimotor cortical structures within which the correlation learning mechanisms operate as to the main factor determining the observed topography of memory cells.