  Supervised deep convolutional neural networks (DCNNs) are currently one of
the best computational models that can explain how the primate ventral visual
stream solves object recognition. However, embodied cognition has not been
considered in the existing visual processing models. From the ecological
standpoint, humans learn to recognize objects by interacting with them,
allowing better classification, specialization, and generalization. Here, we
ask if computational models under the embodied learning framework can explain
mechanisms underlying object recognition in the primate visual system better
than the existing supervised models? To address this question, we use
reinforcement learning to train neural network models to play a 3D computer
game and we find that these reinforcement learning models achieve neural
response prediction accuracy scores in the early visual areas (e.g., V1 and V2)
in the levels that are comparable to those accomplished by the supervised
neural network model. In contrast, the supervised neural network models yield
better neural response predictions in the higher visual areas, compared to the
reinforcement learning models. Our preliminary results suggest the future
direction of visual neuroscience in which deep reinforcement learning should be
included to fill the missing embodiment concept.
