Computational models applied to cognitive neuroscience

Abstract

Science deals with models all the time, as well as cognitive neuroscience, which uses models for studying mental functions as memory and attention. However, creating models of each mental function is just one of the strategies of study. Computational modeling is a strategy of building models of the processes involved in such mental functions; these models are then structured by developing algorithms capable of performing such procedures. The signal detection theory can be used in the generation of computational models that can be applied in the study of memory and attention in order to generate the behaviors involved, without the need for the separations present on traditional models. Thus, the view advocated here is that there is great similarity between these cognitive functions, so that would be advantageous to understanding these phenomena adopt the same general model that makes the translation of the neurophysiological processes for behavior.