Biology subjects

Gribble, P.

Publications and source records attributed to Gribble, P..

9 recordsLinked to original sources

bioRxiv2026

Sensory expectations and prediction error during feedback control in the human brain

External disturbances to the body are better counteracted when their nature can be predicted in advance. Here, we investigated the neural mechanisms through which probabilistic predictions shape feedback responses using functional magnetic resonance imaging (fMRI) in humans. We show that, prior to a mechanical perturbation applied to a finger, the primary motor (M1) and somatosensory (S1) cortices receive a signal that linearly encodes the expected sensory input. When perturbations reach these areas, expectations are combined with the sensory input through a simple additive mechanism, yielding motor commands that reflect a weighted sum of the two signals. At the same time, M1 and S1 receive a prediction error signal encoding the difference between expectations and actual sensory input. This signal is visible in fMRI data in humans and in the local field potentials in non-human primates, but not in M1 or S1 spiking activity.

neuroscience↗

bioRxiv2021

Sensorimotor feedback loops are selectively sensitive to reward

Although it is well established that motivational factors such as earning more money for performing well improve motor performance, how the motor system implements this improvement remains unclear. For instance, feedback-based control, which uses sensory feedback from the body to correct for errors in movement, improves with greater reward. But feedback control encompasses many feedback loops with diverse characteristics such as the brain regions involved and their response time. Which specific loops drive these performance improvements with reward is unknown, even though their diversity makes it unlikely that they are contributing uniformly. We systematically tested the effect of reward on the latency (how long for a corrective response to arise?) and gain (how large is the corrective response?) of seven distinct sensorimotor feedback loops in humans. Only the fastest feedback loops were insensitive to reward, and the earliest reward-driven changes were consistently an increase in feedback gains, not a reduction in latency. Rather, a reduction of response latencies only tended to occur in slower feedback loops. These observations were similar across sensory modalities (vision and proprioception). Our results may have implications regarding feedback control performance in athletic coaching. For instance, coaching methodologies that rely on reinforcement or "reward shaping" may need to specifically target aspects of movement that rely on reward-sensitive feedback responses.

neuroscience↗

bioRxiv2021

Sensitivity to Error During Visuomotor Adaptation is Similarly Modulated by Abrupt, Gradual and Random Perturbation Schedules

It has been suggested that sensorimotor adaptation involves at least two processes (i.e., fast and slow) that differ in retention and error sensitivity. Previous work has shown that repeated exposure to an abrupt force field perturbation results in greater error sensitivity for both the fast and slow processes. While this implies that the faster relearning is associated with increased error sensitivity, it remains unclear what aspects of prior experience modulate error sensitivity. In the present study, we manipulated the initial training using different perturbation schedules, thought to differentially affect fast and slow learning processes based on error magnitude, and then observed what effect prior learning had on subsequent adaptation. During initial training of a visuomotor rotation task, we exposed three groups of participants to either an abrupt, a gradual, or a random perturbation schedule. During a testing session, all three groups were subsequently exposed to an abrupt perturbation schedule. Comparing the two sessions of the control group who experienced repetition of the same perturbation, we found an increased error sensitivity for both processes. We found that the error sensitivity was increased for both the fast and slow processes, with no reliable changes in the retention, for both the gradual and structural learning groups when compared to the first session of the control group. We discuss the findings in the context of how fast and slow learning processes respond to a history of errors. New & NoteworthyWe investigated what aspects of prior experience modulate error sensitivity, within the framework of a two-state model of short-term sensorimotor adaptation. We manipulated initial training on a visuomotor adaptation reaching task using specific perturbation schedules that are thought to differentially affect fast and slow learning processes, and we tested what effect these had on subsequent adaptation. We found that sensitivity to adaptation error was similarly modulated by abrupt, gradual, and random perturbation schedules.

neuroscience↗

bioRxiv2020

EEG correlates of physical effort and reward processing during reinforcement learning

Effort-based decision making is often described by choices according to subjective value, a function of reward discounted by effort. We asked whether a neural reinforcement learning signal, the feedback related negativity (FRN), is modulated not only by reward outcomes but also physical effort. We recorded EEG from human participants while they performed a task in which they were required to accurately produce target levels of muscle activation to receive rewards. Participants performed isometric knee extensions while quadriceps muscle activation was recorded using EMG. Real-time feedback indicated muscle activation relative to a target. On a given trial, the target muscle activation required either low or high effort. The effort was determined probabilistically according to a binary choice, such that the responses were associated with 20% and 80% probability of high effort. This contingency could only be known by experience, and it reversed periodically. After each trial binary reinforcement feedback was provided to indicate whether participants were sufficiently accurate in producing the target muscle activity. Participants adaptively avoided effort by switching responses more frequently after choices that resulted in hard effort. Feedback after participants choices which revealed the resulting effort requirement for the subsequent knee extension did not elicit an FRN component. However, the neural response to reinforcement feedback after the knee extension was increased during and after the time period of the FRN by preceding physical effort. Thus, retrospective effort modulates reward processing which may underlie paradoxical behavioral findings whereby rewards requiring more effort to obtain can become more powerful reinforcers. Significance StatementWhen making decisions, we typically select more rewarding and less effortful options. Neural reinforcement learning signals reinforce rewarding actions and deter punishing actions. When participants received feedback that their choices would require easy or hard physical effort, we did not observe reinforcement learning signals that are typically observed in response to feedback predicting reward and punishment. Thus, the reinforcement learning system does not strictly treat effort as loss or punishment. However, when the effort was completed and participants received feedback indicating whether they successfully achieved a reward or not, reinforcement learning signals were amplified by preceding effort. Thus, retrospective effort can affect neural responses to reinforcement outcomes, which may explain how effort can enhance the motivational effect of reinforcers.

neuroscience↗

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↗