Kinematic data of humans performing the critical stability task

Natural behaviors have redundancy, which implies that humans and animals
can achieve their goals with different control objectives. 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 animal’s control strategy from behavior. First, both humans
and monkeys performed a virtual balancing task for which different control
objectives could be utilized. Under matched experimental conditions,
corresponding behaviors were observed in humans and monkeys. Second, a
generative model was developed that represented two main control
strategies to achieve the task goal. Model simulations were used to
identify aspects of behavior that could distinguish which control
objective was being used. Third, these behavioral signatures allowed us to
infer the control objective used by human subjects who had been instructed
to use one control objective or the other. Based on this validation, we
could then infer strategies from animal subjects. Being able to positively
identify a subject’s control objective from behavior can provide a
powerful tool to neurophysiologists as they seek the neural mechanisms of
sensorimotor coordination.