Reinforcement learning has been shown to be a prime candidate as a general mechanism in animals and humans to learn how to progressively choose behaviorally better options. An important problem is how the brain finds representations of relevant sensory input to use for such learning. Extensive empirical data have shown that such representations are also learned throughout development. Thus, learning sensory representations for tasks and learning of task solutions occur simultaneously. Here we propose a novel framework for efficient coding and task learning in the full perception and action cycle and apply it to the learning of disparity representation for vergence eye movements. Our approach integrates learning of a generative model of sensory signals and learning of a behavio...[ Read more ]

Reinforcement learning has been shown to be a prime candidate as a general mechanism in animals and humans to learn how to progressively choose behaviorally better options. An important problem is how the brain finds representations of relevant sensory input to use for such learning. Extensive empirical data have shown that such representations are also learned throughout development. Thus, learning sensory representations for tasks and learning of task solutions occur simultaneously. Here we propose a novel framework for efficient coding and task learning in the full perception and action cycle and apply it to the learning of disparity representation for vergence eye movements. Our approach integrates learning of a generative model of sensory signals and learning of a behavior policy with the identical objective of making the generative model work as effectively as possible. We show that this naturally leads to a self-calibrating system learning to represent binocular disparity and produce accurate vergence eye movements. Our framework is very general and could be useful in explaining the development of various sensorimotor behaviors and their underlying representations.

Keywords – Binocular vision, vergence control, reinforcement learning, sparse coding, neural development