A key idea in motor learning is that internal models of environmental dynamics are internally represented as functions of spatial variables including position, velocity, and acceleration of body motion. We refer to such a representation as motion dependent. The evidence for a motion-dependent representation is, however, primarily based on examination of the adaptation to motion-dependent dynamic environments. To more rigorously test this idea, we examined the adaptive response to perturbations that cannot be well approximated by motion-state: force-impulses--brief, high-amplitude pulses of force. The induced adaptation characterizes the impulse response of the system--a widely used technique for probing system dynamics in engineering systems identification. Here we examined the adaptive responses to two different force-impulse perturbations during human voluntary reaching movements. We found that although neither could be well approximated by motion-state (R(2) < 0.18 in both cases), both perturbations induced single-trial adaptive responses that were (R(2) > 0.87). Moreover, these responses were similar in shape to those induced by low-fidelity motion-based approximations of the force-impulses (r > 0.88). Remarkably, we found that the motion dependence of the adaptive responses to force-impulses persisted, even after prolonged exposure (R(2) > 0.95). During a 300-trial training period, trial-to-trial fluctuations in the position, velocity, and acceleration of motion accurately predicted trial-to-trial fluctuations in the adaptive response, and the adaptation gradually became more specific to the perturbation, but only via reorganization of the structure of the motion-dependent representation. These results indicate that internal models of environmental dynamics represent these dynamics in a motion-dependent manner, regardless of the nature of the dynamics encountered.

As you read this text, your eyes make saccades that guide your fovea from one word to the next. Accuracy of these movements require the brain to monitor and learn from visual errors. A current model suggests that learning is supported by two different adaptive processes, one fast (high error sensitivity, low retention), and the other slow (low error sensitivity, high retention). Here, we searched for signatures of these hypothesized processes and found that following experience of a visual error, there was an adaptive change in the motor commands of the subsequent saccade. Surprisingly, this adaptation was not uniformly expressed throughout the movement. Rather, after experience of a single error, the adaptive response in the subsequent trial was limited to the deceleration period. After repeated exposure to the same error, the acceleration period commands also adapted, and exhibited resistance to forgetting during set-breaks. In contrast, the deceleration period commands adapted more rapidly, but suffered from poor retention during these same breaks. State-space models suggested that acceleration and deceleration periods were supported by a shared adaptive state which re-aimed the saccade, as well as two separate processes which resembled a two-state model: one that learned slowly and contributed primarily via acceleration period commands, and another that learned rapidly but contributed primarily via deceleration period commands.

In numerous paradigms, from fear conditioning to motor adaptation, memory exhibits a remarkable property: acquisition of a novel behavior followed by its extinction results in spontaneous recovery of the original behavior. A current model suggests that spontaneous recovery occurs because learning is supported by two different adaptive processes: one fast (high error sensitivity, low retention), and the other slow (low error sensitivity, high retention). Here, we searched for signatures of these hypothesized processes in the commands that guided single movements. We examined human saccadic eye movements and observed that following experience of a visual error, there was an adaptive change in the motor commands of the subsequent saccade, partially correcting for the error. However, the error correcting commands were expressed only during the deceleration period. If the errors persisted, the acceleration period commands also changed. Adaptation of acceleration period commands exhibited poor sensitivity to error, but the learning was resistant to forgetting. In contrast, the deceleration period commands adapted with high sensitivity to error, and the learning suffered from poor retention. Thus, within a single saccade, a fast-like process influenced the deceleration period commands, whereas a slow-like process influenced the acceleration period commands. Following extinction training, with passage of time motor memory exhibited spontaneous recovery, as evidenced by return of saccade endpoints toward their initial adapted state. The temporal dynamics of spontaneous recovery suggested that a single saccade is controlled by two different adaptive controllers, one active during acceleration, and the other during deceleration.Significance statementA feature of memory in many paradigms is the phenomenon of spontaneous recovery: learning followed by extinction inevitably leads to reversion toward the originally learned behavior. A theoretical model posits that spontaneous recovery is a feature of memory systems that learn with two independent learning processes, one fast, and the other slow. However, there have been no direct measures of these putative processes. Here, we found potential signatures of the two independent adaptive processes during control of a single saccade. The results suggest that distinct adaptive controllers contribute to the acceleration and deceleration phases of a saccade, and that each controller is supported by a fast and a slow learning process.

We prefer to decline effortful rewards, but if the circumstances require it, we will move only slowly to harvest them. Why should economic variables such as reward and effort affect movement vigor? In theory, our decisions and movements both contribute to a measure of fitness in which the objective is to maximize rewards minus efforts, divided by time. To test this idea, we engaged marmosets in a foraging task in which on each trial they decided whether to work by making saccades to visual targets, thus accumulating food, or to harvest by licking. We varied the effort cost of harvest by moving the food tube with respect to the mouth. Theory predicted that the subjects would respond to the increased effort costs by working longer, stockpiling food before commencing harvest, but reduce their movement vigor to conserve energy. Indeed, in response to the increased effort costs of harvest, marmosets increased their work duration but reduced their movement vigor. These changes in decisions and movements coincided with changes in pupil size. As the effort cost of harvest declined, work duration decreased, the pupils dilated, and lick and tongue vigor increased. Thus, when acquisition of reward became effortful, there was a global change in the state of the brain: the pupils constricted, the decisions exhibited delayed gratification, and the movements displayed reduced vigor.

We would rather decline an effortful option, but when compelled, will move only slowly to harvest. Why should economic variables such as reward and effort affect movement vigor? In theory, both our decisions and our movements contribute to a measure of fitness in which the objective is to maximize rewards minus efforts, divided by time. To explore this idea, we engaged marmosets in a foraging task in which on each trial they decided whether to work by making saccades to visual targets, thus accumulating food, or to harvest by licking what they had earned. We varied the effort cost of harvest by moving the food tube with respect to the mouth. Theory predicted that the subjects should respond to the increased effort costs by working longer, stockpiling food before commencing harvest, but reduce their movement vigor to conserve energy. Indeed, in response to the increased effort costs of harvest, marmosets increased their work duration but reduced their movement vigor. These changes in decisions and movements coincided with changes in pupil size. As the effort cost of harvest declined, work duration decreased, the pupils dilated, and lick and saccade vigor increased. Thus, when acquisition of reward became effortful, there was a global change in the state of the brain: the pupils constricted, the decisions exhibited delayed gratification, and the movements displayed reduced vigor. Why do economic variables such as reward and effort affect both the decision-making and the motor-control circuits of the brain? Our results suggest that as the brainstem neuromodulatory circuits that control pupil size respond to effort costs, they alter computations in the brain regions that control decisions, encouraging work and delaying gratification, and the brain regions that control movements, suppressing energy expenditure and reducing vigor. This coordinated response may improve a variable relevant to fitness: the capture rate.