Types of learning: adaptation, error base, skill learning
Haar, S., & Faisal, A. A. (2020). Brain Activity Reveals Multiple Motor-Learning Mechanisms in a Real-World Task. Frontiers in human neuroscience, 14, 354. https://doi.org/10.3389/fnhum.2020.00354
The neurobehavioral mechanisms of human motor-control and learning evolved in free behaving, real-life settings, yet this is studied mostly in reductionistic lab-based experiments. Here we take a step towards a more real-world motor neuroscience using wearables for naturalistic full-body motion-tracking and the sports of pool billiards to frame a real-world skill learning experiment. First, we asked if well-known features of motor learning in lab-based experiments generalize to a real-world task. We found similarities in many features such as multiple learning rates, and the relationship between task-related variability and motor learning. Our data-driven approach reveals the structure and complexity of movement, variability, and motor learning, enabling an in-depth understanding of the structure of motor learning in three ways: First, while expecting most of the movement learning is done by the cue-wielding arm, we find that motor learning affects the whole body, changing motor-control from head to toe. Second, during learning, all subjects decreased their movement variability and their variability in the outcome. Subjects who were initially more variable were also more variable after learning. Lastly, when screening the link across subjects between initial variability in individual joints and learning, we found that only the initial variability in the right forearm supination shows a significant correlation to the subjects' learning rates. This is in-line with the relationship between learning and variability: while learning leads to an overall reduction in movement variability, only initial variability in specific task-relevant dimensions can facilitate faster learning.
Haar, S., van Assel, C. M., & Faisal, A. A. (2020). Motor learning in real-world pool billiards. Scientific reports, 10(1), 20046. https://doi.org/10.1038/s41598-020-76805-9
We found similarities in many features such as multiple learning rates, and the relationship between task-related variability and motor learning. Our data-driven approach reveals the structure and complexity of movement, variability, and motor learning, enabling an in-depth understanding of the structure of motor learning in three ways: First, while expecting most of the movement learning is done by the cue-wielding arm, we find that motor learning affects the whole body, changing motor-control from head to toe. Second, during learning, all subjects decreased their movement variability and their variability in the outcome. Subjects who were initially more variable were also more variable after learning. Lastly, when screening the link across subjects between initial variability in individual joints and learning, we found that only the initial variability in the right forearm supination shows a significant correlation to the subjects’ learning rates. This is in-line with the relationship between learning and variability: while learning leads to an overall reduction in movement variability, only initial variability in specific task-relevant dimensions can facilitate faster learning.
Movement vigor
Reppert, T. R., Rigas, I., Herzfeld, D. J., Sedaghat-Nejad, E., Komogortsev, O., & Shadmehr, R. (2018). Movement vigor as a traitlike attribute of individuality. Journal of neurophysiology, 120(2), 741–757. https://doi.org/10.1152/jn.00033.2018
A common aspect of individuality is our subjective preferences in evaluation of reward and effort. The neural circuits that evaluate these commodities influence circuits that control our movements, raising the possibility that vigor differences between individuals may also be a trait of individuality, reflecting a willingness to expend effort. In contrast, classic theories in motor control suggest that vigor differences reflect a speed-accuracy trade-off, predicting that those who move fast are sacrificing accuracy for speed. Here we tested these contrasting hypotheses. We measured motion of the eyes, head, and arm in healthy humans during various elementary movements (saccades, head-free gaze shifts, and reaching). For each person we characterized their vigor, i.e., the speed with which they moved a body part (peak velocity) with respect to the population mean. Some moved with low vigor, while others moved with high vigor. Those with high vigor tended to react sooner to a visual stimulus, moving both their eyes and arm with a shorter reaction time. Arm and head vigor were tightly linked: individuals who moved their head with high vigor also moved their arm with high vigor. However, eye vigor did not correspond strongly with arm or head vigor. In all modalities, vigor had no impact on end-point accuracy, demonstrating that differences in vigor were not due to a speed-accuracy trade-off. Our results suggest that movement vigor may be a trait of individuality, not reflecting a willingness to accept inaccuracy but demonstrating a propensity to expend effort.
NEW & NOTEWORTHY A common aspect of individuality is how we evaluate economic variables like reward and effort. This valuation affects not only decision making but also motor control, raising the possibility that vigor may be distinct between individuals but conserved across movements within an individual. Here we report conservation of vigor across elementary skeletal movements, but not eye movements, raising the possibility that the individuality of our movements may be driven by a common neural mechanism of effort evaluation across modalities of skeletal motor control.
Labaune, O., Deroche, T., Teulier, C., & Berret, B. (2020). Vigor of reaching, walking, and gazing movements: on the consistency of interindividual differences. Journal of neurophysiology, 123(1), 234–242. https://doi.org/10.1152/jn.00344.2019
Movement vigor is an important feature of motor control that is thought to originate from cortico-basal ganglia circuits and processes shared with decision-making, such as temporal reward discounting. Accordingly, vigor may be related to one's relationship with time, which may, in turn, reflect a general trait-like feature of individuality. While significant interindividual differences of vigor have been typically reported for isolated motor tasks, little is known about the consistency of such differences across tasks and movement effectors. Here, we assessed interindividual consistency of vigor across reaching (both dominant and nondominant arm), walking, and gazing movements of various distances within the same group of 20 participants. Given distinct neural pathways and biomechanical specificities of each movement modality, a significant consistency would corroborate the trait-like aspect of vigor. Vigor scores for dominant and nondominant arm movements were found to be highly correlated across individuals. Vigor scores of reaching and walking were also significantly correlated across individuals, indicating that people who reach faster than others also tend to walk faster. At last, vigor scores of saccades were uncorrelated with those of reaching and walking, reaffirming that the vigor of stimulus-elicited eye saccades is distinct. These findings highlight the trait-like aspect of vigor for reaching movements with either arms and, to a lesser extent, walking.NEW & NOTEWORTHY Robust interindividual differences of movement vigor have been reported for arm reaching and saccades. Beyond biomechanics, personality trait-like characteristics have been proposed to account for those differences. Here, we examined for the first time the consistency of interindividual differences of vigor during dominant/nondominant arm reaching, walking, and gazing to assess the trait-like aspect of vigor. We found a significant consistency of vigor within our group of individuals for all tested tasks/effectors except saccades.
Perception-action cycles
Corbetta D, DiMercurio A, Wiener RF, Connell JP, Clark M. How Perception and Action Fosters Exploration and Selection in Infant Skill Acquisition. Adv Child Dev Behav. 2018;55:1-29. https://www.sciencedirect.com/science/article/pii/S0065240718300120
In this chapter, we discuss how perception and action are intimately linked to the processes of exploration and selection. Exploration, which we define as trying several variations of the behavior, and selection, which involves attempting to reproduce the behaviors that work, are essential for learning about the environment, discovering the properties of objects, and for acquiring skills in relation to goals. Exploration and selection happen in the moment and over time as behaviors are repeated, hence leading to their fine-tuning to the goal. We illustrate this time-dependent developmental process using several examples from infants reaching for objects, to discovering object properties, to learning about the functionality of tool use, and even to word learning. As we present those examples, we introduce a more detailed perception-action loop to illustrate those moment-to-moment behaviors and show how they contribute to the acquisition of perceptual, motor, and cognitive skills in infancy.
Williams, J. L., & Corbetta, D. (2016). Assessing the Impact of Movement Consequences on the Development of Early Reaching in Infancy. Frontiers in psychology, 7, 587. doi:10.3389/fpsyg.2016.00587
Through repeated opportunities to explore movement consequences, infants discover and select movements that are most successful to the task-at-hand. This study further demonstrates that distinct sensory-motor experiences can have a significant impact on developmental trajectories and can influence the skills young infants will discover through their interactions with their surroundings.
Concept of savings
Through experience, we develop a rich repertoire of movements tailored for different environments and situations. This ability requires the capacity to learn new motor patterns and form memories of them that can be quickly called on when re-experiencing the same situation.
Savings, or faster relearning after initial learning, demonstrates humans' remarkable ability to retain learned movements amid changing environments.
Roemmich, R. T., & Bastian, A. J. (2015). Two ways to save a newly learned motor pattern. Journal of Neurophysiology, 113(10), 3519–3530. http://doi.org/10.1152/jn.00965.2014
Savings, or faster relearning after initial learning, demonstrates humans' remarkable ability to retain learned movements amid changing environments. This is important within the context of locomotion, as the ability of the nervous system to “remember” how to walk in specific environments enables us to navigate changing terrains and progressively improve gait patterns with rehabilitation. Here, we used a split-belt treadmill to study precisely how people save newly learned walking patterns. In Experiment 1, we investigated savings by systematically varying the learning and unlearning environments. Savings was predominantly influenced by 1) previous exposure to similar abrupt changes in the environment and 2) the amount of exposure to the new environment. Relearning was fastest when these two factors coincided, and we did not observe savings after the environment was introduced gradually during initial learning. In Experiment 2, we then studied whether people store explicit information about different walking environments that mirrors savings of a new walking pattern. Like savings, we found that previous exposure to abrupt changes in the environment also drove the ability to recall a previously experienced walking environment accurately. Crucially, the information recalled was extrinsic information about the learning environment (i.e., treadmill speeds) and not intrinsic information about the walking pattern itself. We conclude that simply learning a new walking pattern is not enough for long-term savings; rather, savings of a learned walking pattern involves recall of the environment or extended training at the learned state.