Inferring control objectives in a virtual balancing task in humans and monkeys

Abstract

Natural behaviors have redundancy, which implies that humans and animals can achieve their goals with different control strategies. Given only observations of behavior, is it possible to infer the control strategy that the subject is employing? This challenge is particularly acute in animal behavior because we cannot ask or instruct the subject to use a particular control strategy. This study presents a three-pronged approach to infer an animals control strategy from behavior. First, both humans and monkeys performed a virtual balancing task for which different control strategies could be utilized. Under matched experimental conditions, corresponding behaviors were observed in humans and monkeys. Second, a generative model was developed that identified two main control strategies to achieve the task goal. Model simulations were used to identify aspects of behavior that could distinguish which control strategy was being used. Third, these behavioral signatures allowed us to infer the control strategy used by human subjects who had been instructed to use one control strategy or the other. Based on this validation, we could then infer strategies from animal subjects. Being able to positively identify a subjects control strategy from behavior can provide a powerful tool to neurophysiologists as they seek the neural mechanisms of sensorimotor coordination.