Summary In humans, training in which good performance is rewarded or bad performance punished results in transient behavioral improvements [1–3]. Their relative effects on consolidation and long-term retention, critical behavioral stages for successful learning [4, 5], are not known. Here, we investigated the effects of reward and punishment on these different stages of human motor skill learning. We studied healthy subjects who trained on a motor task under rewarded, punished, or neutral control conditions. Performance was tested before, and immediately, 6 hs, 24 hs and 30 days after training in the absence of reward or punishment. Performance improvements immediately after training were comparable in the three groups. At 6 hs, the rewarded group maintained performance gains while the other two groups experienced significant forgetting. At 24 hs, the reward group showed significant offline (posttraining) improvements while the other two groups did not. At 30 days, the rewarded group retained the gains identified at 24 hs, while the other two groups experienced significant forgetting. We conclude that training under rewarded conditions is more effective than training under punished or neutral conditions in eliciting lasting motor learning, an advantage driven by offline memory gains that persist over time.
Although our understanding of the mechanisms underlying motor adaptation has greatly benefited from previous computational models, the architecture of motor memory is still uncertain. On one hand, two-state models that contain both a fast-learning-fast-forgetting process and a slow-learning-slow-forgetting process explain a wide range of data on motor adaptation, but cannot differentiate whether the fast and slow processes are arranged serially or in parallel and cannot account for learning multiple tasks simultaneously. On the other hand, multiple parallel-state models learn multiple tasks simultaneously but cannot account for a number of motor adaptation data. Here, we investigated the architecture of human motor memory by systematically testing possible architectures via a combination of simulations and a dual visuomotor adaptation experimental paradigm. We found that only one parsimonious model can account for both previous motor adaptation data and our dual-task adaptation data: a fast process that contains a single state is arranged in parallel with a slow process that contains multiple states switched via contextual cues. Our result suggests that during motor adaptation, fast and slow processes are updated simultaneously from the same motor learning errors.
Previous animal experiments have shown that serotonin is involved in the control of impulsive choice, as characterized by high preference for small immediate rewards over larger delayed rewards. Previous human studies under serotonin manipulation, however, have been either inconclusive on the effect on impulsivity or have shown an effect in the speed of action-reward learning or the optimality of action choice. Here, we manipulated central serotonergic levels of healthy volunteers by dietary tryptophan depletion and loading. Subjects performed a "dynamic" delayed reward choice task that required a continuous update of the reward value estimates to maximize total gain. By using a computational model of delayed reward choice learning, we estimated the parameters governing the subjects' reward choices in low-, normal, and high-serotonin conditions. We found an increase of proportion in small reward choices, together with an increase in the rate of discounting of delayed rewards in the low-serotonin condition compared with the control and high-serotonin conditions. There were no significant differences between conditions in the speed of learning of the estimated delayed reward values or in the variability of reward choice. Therefore, in line with previous animal experiments, our results show that low-serotonin levels steepen delayed reward discounting in humans. The combined results of our previous and current studies suggest that serotonin may adjust the rate of delayed reward discounting via the modulation of specific loops in parallel corticobasal ganglia circuits.
. Although there is converging experimental and clinical evidences suggesting that mental training with motor imagery can improve motor performance, it is unclear how humans can learn movements through mental training despite the lack of sensory feedback from the body and the environment. In a first experiment, we measured the trial-by-trial decrease in durations of executed movements (physical training group) and mentally simulated movements (motor-imagery training group), by means of training on a multiple-target arm-pointing task requiring high accuracy and speed. Movement durations were significantly lower in posttest compared with pretest after both physical and motor-imagery training. Although both the posttraining performance and the rate of learning were smaller in motor-imagery training group than in physical training group, the change in movement duration and the asymptotic movement duration after a hypothetical large number of trials were identical. The two control groups (eye-movement training and rest groups) did not show change in movement duration. In the second experiment, additional kinematic analyses revealed that arm movements were straighter and faster both immediately and 24 h after physical and motor-imagery training. No such improvements were observed in the eye-movement training group. Our results suggest that the brain uses state estimation, provided by internal forward model predictions, to improve motor performance during mental training. Furthermore, our results suggest that mental practice can, at least in young healthy subjects and if given after a short bout of physical practice, be successfully substituted to physical practice to improve motor performance.
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