How Decisions Are Made in Brains: Unpack 'Black Box' of CNN with Ms. Pac-Man Video Game

Abstract

The convolutional neural network (CNN) is widely used in various computer vision problems such as image recognition and image classification because of its powerful ability to process image data. However, it is an end-to-end model that remains a 'block box' for users. The internal logic of CNN is not explicitly known. Interpreting CNN can help us better understand neural networks and the various ways they benefit us as users. In this paper, we explain the contributions of the convolutional layer of CNN with a neuroscience experiment paradigm: the Ms. Pac-Man video game. Ms. Pac-Man is a popular game that provides a complex yet natural decision-making task rather than a laboratory artifact. An analysis of the game can thus intuitively reveal the complicated decision-making process in animal brains. We sought to (1) elucidate the role of the CNN convolutional layer and (2) analyze the low-level strategies in animal brains based on high-level decisions. We use recorded videos of monkeys playing the Ms. Pac-Man game to empirically demonstrate that our network is able to predict the moving direction of the Pac-Man at every time step. We further find that the decision-making process at work during gameplay is high-reward-driven. A heatmap of the weighted feature map at each convolutional layer shows that CNN makes predictions based on the most important input pattern, which in this case is the high reward entities in the game.