Incorporating forgetting in a category learning model

We present a computational model of human category learning that learns the essential structures of the categories by forgetting information that is not useful for the given task. The model shifts attention to salient information and learns associations between items and categories. Attention and association strengths are adjusted according to the degree of prediction errors the model makes. The attention and association weights are interpreted as memory strengths in the model and decay over time, allowing the model to focus on the salient structures. Using memory decay mechanisms, our model simultaneously explained human recognition and classification performances that previous models could not.