Abstract
We investigated the effect of category learning in visual pattern recognition by psychological experiment and neural network model simulation. In a psychological experiment, we used tachistoscopic presentation task, and we found that pattern discriminability was enhanced by category knowledge. We constructed a neural network model with three layers and reciprocal connections. We used Wake-Sleep algorithm for network learning and the network made its internal representation by the interaction of bottom-up and top-down processes. The network model can simulate the profit of having category knowledge observed in psychological experiment. Furthermore we considered the computational explanation of the profit of having category knowledge from the viewpoint of MDL (Minimum Description Length). Category knowledge helps the network to construct efficient (shorter description length) representation of patterns. In conclusion, category knowledge has a functional profit not only in visual object identification but also in efficient processing of pattern recognition.
