Interacting Adaptive Processes with Different Timescales Underlie Short-Term Motor Learning

By Maurice A. Smith, Ali Ghazizadeh, Reza Shadmehr. PLoS Biology, 2006

1,130 citations recorded by the source corpus.

Multiple processes may contribute to motor skill acquisition, but it is thought that many of these processes require sleep or the passage of long periods of time ranging from several hours to many days or weeks. Here we demonstrate that within a timescale of minutes, two distinct fast-acting processes drive motor adaptation. One process responds weakly to error but retains information well, whereas the other responds strongly but has poor retention. This two-state learning system makes the surprising prediction of spontaneous recovery (or adaptation rebound) if error feedback is clamped at zero following an adaptation-extinction training episode. We used a novel paradigm to experimentally confirm this prediction in human motor learning of reaching, and we show that the interaction between the learning processes in this simple two-state system provides a unifying explanation for several different, apparently unrelated, phenomena in motor adaptation including savings, anterograde interference, spontaneous recovery, and rapid unlearning. Our results suggest that motor adaptation depends on at least two distinct neural systems that have different sensitivity to error and retain information at different rates.

Subfields: Biomedical Engineering, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/f78806f9df8949610bd3dbf7526e2e4dd7013c10

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Citations in the corpus

Flexible Cognitive Strategies during Motor Learning (2011, methodology): “Thus, the final version of our model is a variant of a two-rate state space model [28], but with the two rates reflecting different error sources.”
Computational mechanisms of sensorimotor control. (2011, neutral): “Specifically, this algorithm may explain the mechanism behind the fast adaptation process of the multirate model (Smith et al., 2006).”
Explicit and Implicit Contributions to Learning in a Sensorimotor Adaptation Task (2014, background): “We also suggested that, at least for rotation adaptation, these two processes may map onto the slow and fast distinction made by Smith et al. (2006) for force-field adaptation.”
Neuromechanics of muscle synergies for posture and movement. (2007, background): “It is therefore reasonable to expect that adaptation may occur differently – and at different rates – for muscle synergy patterns and for descending commands [38 ].”
Thinking fast and slow: Optimization decomposition across timescales (2017, background): “Even human sensorimotor control exhibits the same phenomenon, with slow timescale behaviors such as trajectory planning and fast timescale behaviors such as involuntary reflexes [19], [31], [42], [46].”
Explicit and Implicit Processes Constitute the Fast and Slow Processes of Sensorimotor Learning (2015, methodology): “We believe that our lack of observed rebound decay may be due to the fact that we used a longer R1 training session relative to Smith et al. (2006) and this extra training could act to buoy adaptation rebound, perhaps due to the directional biases mentioned above.”
Motor Learning and Consolidation: The Case of Visuomotor Rotation (2009, background): “The observation that savings can occur after full washout is of interest in the light of a recently proposed two-state model of adaptation, which posits the existence of two distinct processes or modules (Smith et al. 2006).”
A computational neuroanatomy for motor control (2008, background): “Indeed, maintaining performance in something as simple as a saccade or a reach probably requires constant adjustment of this internal model (Smith et al. 2006; Kording et al. 2007).”
Motor learning. (2010, background): “Recent work on force-field and visuomotor adaptation has provided evidence that learning involves (at least) two parallel processes, a fast process that adapts and de-adapts quickly and a slower process that adapts and de-adapts more gradually [9].”
Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment (2008, background): “Biological observations on motor adaptation, such as for example saccade adaptation and force field adaptation, likewise suggest that these processes involve distinct subsystems with differing timescales [21,22].”
Learning from Sensory and Reward Prediction Errors during Motor Adaptation (2011, background): “adaptation [4,5,11,12,13,14], to our knowledge all current models”
Sensory prediction errors drive cerebellum-dependent adaptation of reaching. (2007, background): “Recent work has shown evidence for two processes with different timescales contributing to force-field reaching and saccade adaptation paradigms (Smith et al. 2006).”
Principles of sensorimotor learning (2011, methodology): “This approach is based on state–space models in which, typically, the internal state represents the estimate of the perturbation 44,48,79–81 .”
Motor Adaptation as a Process of Reoptimization (2008, neutral): “For example, in channel trials in which we and others have measured the forces that subjects produce, the force trajectory is at most 80 – 82% of the imposed field (Scheidt et al., 2000; Hwang et al., 2006; Smith et al., 2006).”
Dissociating the roles of the cerebellum and motor cortex during adaptive learning: the motor cortex retains what the cerebellum learns. (2011, background): “acquisition of new motor commands resulting from movement errors (Smith et al. 2006; Hadipour-Niktarash et al. 2007).”