Expérience comportementale et modélisation par réseau neuronal des différences entre les processus de catégorisation par règles logiques et par ressemblance familiale
Our present research was twofold. First, we conducted a study of the differences in the categorization processes between explicit (verbalizable) and implicit (nonverbalizable) tasks. The differentiation was done in term of difficulty and time necessary to learn the rule of the category for family resemblance, conjunctive and disjunctive rules. We then used a neural network to model the categorization and had it complete the same tasks. The comparison between the two gave us insight into how (and if) the network can be used as a model of human categorization. The empirical data confirmed an effect of familiarity and typicality, as supported by previous studies, and we confirmed that the disjunctive task was the hardest to learn for humans while our results point toward the family resemblance task as being the easiest. The modelization by the neural network was partially successful at best but we present options that could permit a next generation neural network to model the categorization process truthfully
implicit categorization explicit categorization neural networks modelization learning transfer