Biology subjects

Gribble, P.

Publications and source records attributed to Gribble, P..

5 recordsLinked to original sources

bioRxiv2018

A rapid visuomotor response on the human upper limb is selectively influenced by implicit, but not explicit, motor learning

How do humans learn to adapt their motor actions to achieve task success? Recent behavioral and patient studies have challenged the classic notion that motor learning arises solely from the errors produced during a task, suggesting instead that explicit cognitive strategies can act in concert with the implicit, error\"based, motor learning component. Here, we show that the earliest wave of directionally-tuned neuromuscular activity that occurs within ~100 ms of peripheral visual stimulus onset is selectively influenced by the implicit component of motor learning. In contrast, the voluntary neuromuscular activity associated with reach initiation, which evolves ~100 to 200 ms later is influenced by both the implicit and explicit components of motor learning. The selective influence of the implicit, but not explicit, component of motor learning on the earliest cascade of neuromuscular activity supports the notion that these components of motor learning can differentially influence descending motor pathways.

neuroscience↗

bioRxiv2018

The effects of habits on motor skill learning

Skill learning involves the formation of stable motor patterns. In musical and athletic training, however, these stable motor habits can also impede the attainment of higher levels of performance. We developed an experimental paradigm to induce a specific motor pattern in the context of a discrete sequence production task and to investigate how these habits affect performance over a 3-week training period. Participants initially practiced small segments of 2 to 3 finger movements ("chunks") and then learned longer sequences composed of these chunks. This initial training induced a persistent temporal pattern during execution, with shorter inter-press-intervals within a chunk and longer ones at chunk boundaries. This pattern remained stable during the subsequent 10 days of training, in which participants were asked to produce the sequence as fast as possible from memory. The habit was also preserved when the sequences were directly displayed, removing the need for memory recall. We were able to induce chunking patterns that were either beneficial or detrimental to performance by taking into consideration the biomechanical constraints of the sequences. While we observed an overall reduction in the detrimental effect of the disadvantageous chunking instructions with training, our results show that the degree to which these detrimental chunk structures were maintained, was predictive of lower levels of final performance. In sum, we were able to induce beneficial and detrimental motor habits in a motor sequence production task and show that these initial instructions influenced performance outcomes over a prolonged period of time. Significance StatementA habit is defined as an automatized action that resists modification once sufficiently established. Preventing bad habits, while reinforcing good habits, is a key objective when teaching new motor skills. While habit formation is an integral part of motor skill acquisition, previous research has focused on habit formation in terms of action selection. In this paper, we examine habit formation in terms of motor skill execution, after the action has been selected. We were able to induce beneficial or detrimental motor habits in the production of motor sequences. Habits were stable over a prolonged training period. Our results demonstrate how cognitive instruction can lead to persistent motor habits and we explore how these habits are potentially modified with training.

neuroscience↗

bioRxiv2018

Somatosensory Perceptual Training Enhances Motor Learning by Observing

Neuroimaging and neurophysiological studies in humans have demonstrated that action observation activates brain regions involved in sensory-motor control. A growing body of work has shown that action observation can also facilitate motor learning; observing a tutor undergoing motor learning results in functional plasticity within the motor system and gains in subsequent motor performance. However, the effects of observing motor learning extend beyond the motor domain. Converging evidence suggests that learning also results in somatosensory functional plasticity and somatosensory perceptual changes. This work has raised the possibility that the somatosensory system is also involved in motor learning that results from observation. Here we tested this hypothesis using a somatosensory perceptual training paradigm. If the somatosensory system is indeed involved in motor learning by observing, then improving subjects' somatosensory function before observation should enhance subsequent observation-related gains in motor performance. Subjects performed a proprioceptive discrimination task in which a robotic manipulandum moved the subjects passive upper limb and he or she made judgments about the position of the hand. Subjects in a Trained Learning group received trial-by-trial feedback to improve their proprioceptive acuity. Subjects in an Untrained Learning group performed the same task without feedback. All subjects then observed a learning video showing a tutor adapting her reaches to a left force field (FF). We found that subjects in the Trained Learning group, who had superior proprioceptive acuity prior to observation, benefited more from observing learning compared to subjects in the Untrained Learning group. Improving somatosensory function can therefore enhance subsequent observation-related gains in motor learning. This study provides further evidence in favor of the involvement of the somatosensory system in motor learning by observing.\n\nAbbreviationsThe authors report no financial interests or conflicts of interests.

neuroscience↗

bioRxiv2018

Distinct Neural Signatures of Reward and Sensory Prediction Error in Motor Learning

At least two distinct processes have been identified by which motor commands are adapted according to movement-related feedback: reward based learning and sensory error based learning. In sensory error based learning, mappings between sensory targets and motor commands are recalibrated according to sensory error feedback. In reward based learning, motor commands are associated with subjective value, such that successful actions are reinforced. We designed two tasks to isolate reward and sensory error based motor adaptation, and recorded electroencephalography (EEG) from humans to identify and dissociate the neural correlates of reward and sensory error processing. We designed a visuomotor rotation task to isolate sensory error based learning which was induced by altered visual feedback of hand position. In a reward learning task, we isolated reward based learning induced by binary reward feedback that was decoupled from the visual target. We found that a fronto-central event related potential called the feedback related negativity (FRN) was elicited specifically by reward feedback but not sensory error feedback. A more posterior component called the P300 was evoked by feedback in both tasks. In the visuomotor rotation task, P300 amplitude was increased by sensory error induced by perturbed visual feedback, and was correlated with learning rate. In the reward learning task, P300 amplitude was increased by reward relative to non reward and by surprise regardless of feedback valence. We propose that during motor adaptation, the FRN might specifically mark reward prediction error while the P300 might reflect processing which is modulated more generally by prediction error.\n\nNew and NoteworthyWe studied the event related potentials evoked by feedback stimuli during motor adaptation tasks that isolate reward and sensory error learning mechanisms. We found that the feedback related negativity was specifically elicited by reward feedback, while the P300 was observed in both tasks. These results reveal neural processes associated with different learning mechanisms and elucidate which classes of errors, from a computational standpoint, elicit the FRN and P300.

neuroscience↗

bioRxiv2017

Changes in corticospinal excitability associated with motor learning by observing

While many of our motor skills are acquired through physical practice, we can also learn how to make movements by observing others. For example, individuals can learn how to reach in novel dynamical environments ( force fields, FF) by observing the movements of a tutor. Previous neurophysiology and neuroimaging studies in humans suggest a role for the motor system in motor learning by observing. Here we tested the role of primary motor cortex (M1) in motor learning by observing. We used single-pulse transcranial magnetic stimulation (TMS) to elicit motor evoked potentials (MEPs) in right hand muscles at rest. MEPs were elicited before and after participants observed either a video adapting her reaches to a FF or a control video showing a tutor performing reaches in an unlearnable FF. We predicted that observing motor learning would increase M1 excitability to a greater extent than observing movements that did not involve learning. We found that observing FF learning increased MEP amplitudes recorded from right first dorsal interosseous (FDI) and right abductor pollicis brevis (APB) muscles. There were no changes in MEP amplitudes for control participants who observed a tutor performing reaches in an unlearnable, randomly varying FF. The observed MEP changes can thus be specifically linked to observing motor learning. These results are consistent with the idea that observing motor learning produces functional changes in M1, or corticospinal networks or both.

neuroscience↗