Dataset Information

Adaptive Regulation of Motor Variability.

ABSTRACT: Trial-to-trial movement variability can both drive motor learning and interfere with expert performance, suggesting benefits of regulating it in context-specific ways. Here we address whether and how the brain regulates motor variability as a function of performance by training rats to execute ballistic forelimb movements for reward. Behavioral datasets comprising millions of trials revealed that motor variability is regulated by two distinct processes. A fast process modulates variability as a function of recent trial outcomes, increasing it when performance is poor and vice versa. A slower process tunes the gain of the fast process based on the uncertainty in the task's reward landscape. Simulations demonstrated that this regulation strategy optimizes reward accumulation over a wide range of time horizons, while also promoting learning. Our results uncover a sophisticated algorithm implemented by the brain to adaptively regulate motor variability to improve task performance. VIDEO ABSTRACT.

SUBMITTER: Dhawale AK

PROVIDER: S-EPMC7339968 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Adaptive Regulation of Motor Variability.

Dhawale Ashesh K AK Miyamoto Yohsuke R YR Smith Maurice A MA Ölveczky Bence P BP

Current biology : CB 20191017 21

Trial-to-trial movement variability can both drive motor learning and interfere with expert performance, suggesting benefits of regulating it in context-specific ways. Here we address whether and how the brain regulates motor variability as a function of performance by training rats to execute ballistic forelimb movements for reward. Behavioral datasets comprising millions of trials revealed that motor variability is regulated by two distinct processes. A fast process modulates variability as a ...[more]

PMID: 31630947

Similar Datasets

Project description:Despite recent advances in robot-assisted training, the benefits of haptic guidance on motor (re)learning are still limited. While haptic guidance may increase task performance during training, it may also decrease participants' effort and interfere with the perception of the environment dynamics, hindering somatosensory information crucial for motor learning. Importantly, haptic guidance limits motor variability, a factor considered essential for learning. We propose that Model Predictive Controllers (MPC) might be good alternatives to haptic guidance since they minimize the assisting forces and promote motor variability during training. We conducted a study with 40 healthy participants to investigate the effectiveness of MPCs on learning a dynamic task. The task consisted of swinging a virtual pendulum to hit incoming targets with the pendulum ball. The environment was haptically rendered using a Delta robot. We designed two MPCs: the first MPC-end-effector MPC-applied the optimal assisting forces on the end-effector. A second MPC-ball MPC-applied its forces on the virtual pendulum ball to further reduce the assisting forces. The participants' performance during training and learning at short- and long-term retention tests were compared to a control group who trained without assistance, and a group that trained with conventional haptic guidance. We hypothesized that the end-effector MPC would promote motor variability and minimize the assisting forces during training, and thus, promote learning. Moreover, we hypothesized that the ball MPC would enhance the performance and motivation during training but limit the motor variability and sense of agency (i.e., the feeling of having control over their movements), and therefore, limit learning. We found that the MPCs reduce the assisting forces compared to haptic guidance. Training with the end-effector MPC increases the movement variability and does not hinder the pendulum swing variability during training, ultimately enhancing the learning of the task dynamics compared to the other groups. Finally, we observed that increases in the sense of agency seemed to be associated with learning when training with the end-effector MPC. In conclusion, training with MPCs enhances motor learning of tasks with complex dynamics and are promising strategies to improve robotic training outcomes in neurological patients.

Project description:Motor learning studies demonstrate that an individual's natural motor variability predicts the rate at which she learns a motor task. Individuals exhibiting higher variability learn motor tasks faster, presumably because variability fosters exploration of a wider space of motor parameters. However, it is unclear how individuals regulate variability while learning a motor task together with a partner who perturbs their movements. In the current study, we investigated whether and how variability affects performance and learning in such joint actions. Participants learned to jointly perform a sequence of movements with a confederate who was either highly variable or less variable in her movements. A haptic coupling between the actors led to translation of partner's movement variability into a force perturbation. We tested how the variability and predictability of force perturbations coming from a partner foster or hamper individual and joint performance. In experiment 1, the confederate produced more or less variable range of force perturbations that occurred in an unpredictable order. In experiment 2, the confederate produced more or less variable force perturbations in a predictable order. In experiment 3, the confederate produced more or less variable force perturbations in which the magnitude of force delivered was predictable whereas the direction of the force was unpredictable. We analysed individual performance, measured as movement accuracy and joint performance, measured as interpersonal asynchrony. Results indicated that in all three experiments, participants successfully regulated the variability of their own movements. However, individual performance was worse when partner produced highly variable force perturbations in an unpredictable order. Interestingly, predictability of force perturbations offset the detrimental effects of variability on individual performance. Furthermore, participants in the high variability condition achieved higher flexibility and resilience for a wide range of force perturbations, when the partner produced predictable movements. Participants improved their joint performance with a highly variable partner only when the partner produced partially predictable movements. Our results indicate that individuals involved in a joint action selectively rely on either their own or their partner's variability (or both) for benefitting individual and joint action performance, depending on the predictability of the partner' movements.

Dataset Information

Adaptive Regulation of Motor Variability.

Publications

Adaptive Regulation of Motor Variability.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure