Modeling the value of strategic actions in the superior colliculus

Abstract

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In learning models of strategic game play, an agent constructs a valuation (action value) over possible future choices as a function of past actions and rewards. Choices are then stochastic functions of these action values. Our goal is to uncover a neural signal that correlates with the action value posited by behavioral learning models. We measured activity from neurons in the superior colliculus (SC), a midbrain region involved in planning saccadic eye movements, in monkeys while they performed two saccade tasks. In the strategic task, monkeys competed against a computer in a saccade version of the mixed-strategy game “matching-pennies”. In the instructed task, stochastic saccades were elicited through explicit instruction rather than free choices. In both tasks, neuronal activity and behavior were shaped by past actions and rewards with more recent events exerting a larger influence. Further, SC activity predicted upcoming choices during the strategic task and upcoming reaction times during the instructed task. Finally, we found that neuronal activity in both tasks correlated with an established learning model, the Experience Weighted Attraction model of action valuation (Ho, Camerer, and Chong, 2007). Collectively, our results provide evidence that action values hypothesized by learning models are represented in the motor planning regions of the brain in a manner that could be used to select strategic actions.

Keywords

• Game theory
• monkey
• reinforcement learning
• saccade
• decision
• motor intention