A modular attention hypothesis for modeling visuomotor behaviors

In this dissertation, we explore the hypothesis that complex intelligent behaviors, in vivo, can be decomposed into modules, which are organized in hierarchies and executed in parallel. This organization is similar to a multiprocessing architecture in silico. Biological attention can be viewed as a "process manager" that manages information processing and multiple computations. In this work, we seek to understand and model this modular attention mechanism for humans in a range of behavioral settings. We explain this approach to understanding modular attention at three levels based on David Marr’s paradigm: the computation theory level, the representation and algorithm level, and the hardware implementation level. At the computation theory level, we propose that simple visuomotor behaviors can be broken down into modules that require attention for their execution. At the representation and algorithm level, we model human eye movements and actions in a variety of visuomotor tasks. We collect and publish a large-scale, high-quality dataset of eye movements and actions of humans playing Atari video games. We study the active vision problem by jointly modeling human eye movements and actions, and compare how humans and artificial learning agents play these video games differently. We then propose a modular reinforcement learning model for modeling human subjects’ navigation behaviors in a virtual-reality environment with multiple goals. We further develop a modular inverse reinforcement learning algorithm to efficiently estimate the subjective reward and discount factors associated with each behavioral goal. At the implementation level, we propose a theoretical neuronal communication model named gamma spike multiplexing that allows the cortex to perform multiple computations simultaneously without crosstalk. The model explains how the modular attention hypothesis might be implemented in the biological brain. The end goals of this work are to (1) build models to explain and predict observed human visuomotor behaviors and attention; (2) use these biologically inspired models to develop algorithms for better artificial learning systems.Computer Science