Abstract
The timescales of adaptation to novel dynamics are well explained by a dual-rate model with slow and fast states. This model can predict interference, savings and spontaneous recovery, but cannot account for adaptation to multiple tasks, as each new task drives unlearning of the previously learned task. Nevertheless, in the presence of appropriate contextual cues, humans are able to adapt simultaneously to opposing dynamics. Consequently this model was expanded, suggesting that dual-adaptation occurs through a single fast process and multiple slow processes. However, such a model does not predict spontaneous recovery within dual-adaptation. Here we assess the existence of multiple fast processes by examining the presence of spontaneous recovery in two experimental variations of an adaptation-de-adaptation-error-clamp paradigm within dual-task adaptation in humans. In both experiments, evidence for spontaneous recovery towards the initially learned dynamics (A) was found in the error-clamp phase, invalidating the one-fast-two-slow dual-rate model. However, as adaptation is not only constrained to two timescales, we fit twelve multi-rate models to the experimental data. BIC model comparison again supported the existence of two fast processes, but extended the timescales to include a third rate: the ultraslow process. Even within our single day experiment, we found little evidence for decay of the learned memory over several hundred error-clamp trials. Overall, we show that dual-adaptation can be best explained by a two-fast-triple-rate model over the timescales of adaptation studied here. Longer term learning may require even slower timescales, explaining why we never forget how to ride a bicycle.
Highlights
The generation of smooth and accurate movements requires predictive compensation of both internal and external dynamics, thought to arise through the formation of an internal model [1–4]
We demonstrate that learning any task involves adaptation of independent fast, slow and ultraslow processes to build a motor memory
We simulate both the one-fast-two-slow-state model and the two-fast-two-slow-state model of motor adaptation in order to determine whether these two models have specific differences in their predictions of the classic adaptation-de-adaptation-error-clamp experiment (A-B-error-clamp paradigm) [16]
Summary
The generation of smooth and accurate movements requires predictive compensation of both internal and external dynamics, thought to arise through the formation of an internal model [1–4] This model, a neural representation of our interaction with the environment termed here a motor memory, is formed through repeated practice and driven by error and reward signals: a form of motor adaptation [5–9]. Motor adaptation has been modelled by a two-state model with different timescales [16,21,22] Within this framework, short-term motor memory is composed of one fast system that learns quickly but forgets quickly, and one slow system that learns slowly but retains more of the learning. Short-term motor memory is composed of one fast system that learns quickly but forgets quickly, and one slow system that learns slowly but retains more of the learning This architecture of motor memory is able to reproduce many aspects of motor adaptation, including interference and spontaneous recovery [16], but not savings [23]
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