Environmental Consistency Determines the Rate of Motor Adaptation

Castro, L Nicolas Gonzalez; Hadjiosif, Alkis M.; Hemphill, Matthew A.; Smith, Maurice A.

doi:10.1016/j.cub.2014.03.049

Cited by 109 publications

(92 citation statements)

References 36 publications

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“…The nervous system detects the prediction error e between the perturbation and adaptation then applies voluntary correction to further reduce this error and improve performance. It is possible that adaptation alone fails to correct this error more completely because adaptation is conservative and depends on prior knowledge of environmental consistency [16]. …”

Section: Resultsmentioning

confidence: 99%

Seeing the Errors You Feel Enhances Locomotor Performance but Not Learning

2016

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SUMMARY In human motor learning, it is thought that the more information we have about our errors, the faster we learn. Here we show that additional error information can lead to improved motor performance without any concomitant improvement in learning. We studied split-belt treadmill walking that drives people to learn a new gait pattern using sensory prediction errors detected by proprioceptive feedback. When we also provided visual error feedback, participants acquired the new walking pattern far more rapidly and showed accelerated restoration of the normal walking pattern during washout. However, when the visual error feedback was removed during either learning or washout, errors reappeared with performance immediately returning to the level expected based on proprioceptive learning alone. These findings support a model with two mechanisms: a dual-rate adaptation process that learns invariantly from sensory prediction error detected by proprioception and a visual feedback dependent process that monitors learning and corrects residual errors, but shows no learning itself. We show that our voluntary correction model accurately predicted behavior in multiple situations where visual feedback was used to change acquisition of new walking patterns while the underlying learning was unaffected. The computational and behavioral framework proposed here suggests that parallel learning and error correction systems allow us to rapidly satisfy task demands without necessarily committing to learning, as the relative permanence of learning may be inappropriate or inefficient when facing environments that are liable to change.

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Section: Resultsmentioning

confidence: 99%

Seeing the Errors You Feel Enhances Locomotor Performance but Not Learning

2016

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“…Interpreted from a Bayesian perspective, one can better infer what enduring behaviors are desired when more information about the environment is collected over longer time periods [2, 30–33] (Figure S4B). A Skinnerian behaviorist would notice an analogy between our adaptation curves and behavioral training and extinction curves [34] (Figure S4C).…”

Section: Discussionmentioning

confidence: 99%

Multiple Time Courses of Vestibular Set-Point Adaptation Revealed by Sustained Magnetic Field Stimulation of the Labyrinth

et al. 2016

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SUMMARY A major focus in neurobiology is how the brain adapts its motor behavior to changes in its internal and external environments [1, 2]. Much is known about adaptively optimizing the amplitude and direction of eye and limb movements, for example, but little is known about another essential form of learning, “set-point” adaptation. Set-point adaptation balances tonic activity so that reciprocally acting, agonist and antagonist muscles have a stable platform from which to launch accurate movements. Here, we use the vestibulo-ocular reflex—a simple behavior that stabilizes the position of the eye while the head is moving—to investigate how tonic activity is adapted toward a new set point to prevent eye drift when the head is still [3, 4]. Set-point adaptation was elicited with magneto-hydrodynamic vestibular stimulation (MVS) by placing normal humans in a 7T MRI for 90 min. MVS is ideal for prolonged labyrinthine activation because it mimics constant head acceleration and induces a sustained nystagmus similar to natural vestibular lesions [5, 6]. The MVS-induced nystagmus diminished slowly but incompletely over multiple timescales. We propose a new adaptation hypothesis, using a cascade of imperfect mathematical integrators, that reproduces the response to MVS (and more natural chair rotations), including the gradual decrease in nystagmus as the set point changes over progressively longer time courses. MVS set-point adaptation is a biological model with applications to basic neurophysiological research into all types of movements [7], functional brain imaging [8], and treatment of vestibular and higher-level attentional disorders by introducing new biases to counteract pathological ones [9].

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“…2 C ). Design of the gradual perturbation was optimized to provide a “rich” input for system identification, without sacrificing the consistency of the signal too much, as this has been shown to negatively affect the adaptation rate (Gonzalez Castro et al, 2014; Herzfeld et al, 2014), and is similar to the perturbation used by Cheng and Sabes (2007). The experiment was briefly paused every 150 trials.…”

Section: Methodsmentioning

confidence: 99%

Individual Differences in Motor Noise and Adaptation Rate Are Optimally Related

Vliet¹,

Frens

Vreede³

et al. 2018

eNeuro

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Individual variations in motor adaptation rate were recently shown to correlate with movement variability or “motor noise” in a forcefield adaptation task. However, this finding could not be replicated in a meta-analysis of adaptation experiments. Possibly, this inconsistency stems from noise being composed of distinct components that relate to adaptation rate in different ways. Indeed, previous modeling and electrophysiological studies have suggested that motor noise can be factored into planning noise, originating from the brain, and execution noise, stemming from the periphery. Were the motor system optimally tuned to these noise sources, planning noise would correlate positively with adaptation rate, and execution noise would correlate negatively with adaptation rate, a phenomenon familiar in Kalman filters. To test this prediction, we performed a visuomotor adaptation experiment in 69 subjects. Using a novel Bayesian fitting procedure, we succeeded in applying the well-established state-space model of adaptation to individual data. We found that adaptation rate correlates positively with planning noise (β = 0.44; 95% HDI = [0.27 0.59]) and negatively with execution noise (β = –0.39; 95% HDI = [–0.50 –0.30]). In addition, the steady-state Kalman gain calculated from planning and execution noise correlated positively with adaptation rate (r = 0.54; 95% HDI = [0.38 0.66]). These results suggest that motor adaptation is tuned to approximate optimal learning, consistent with the “optimal control” framework that has been used to explain motor control. Since motor adaptation is thought to be a largely cerebellar process, the results further suggest the sensitivity of the cerebellum to both planning noise and execution noise.

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Environmental Consistency Determines the Rate of Motor Adaptation

Cited by 109 publications

References 36 publications

Seeing the Errors You Feel Enhances Locomotor Performance but Not Learning

Seeing the Errors You Feel Enhances Locomotor Performance but Not Learning

Multiple Time Courses of Vestibular Set-Point Adaptation Revealed by Sustained Magnetic Field Stimulation of the Labyrinth

Individual Differences in Motor Noise and Adaptation Rate Are Optimally Related

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