Moving outside the lab: The viability of conducting sensorimotor learning studies online

et al. 2021

Front. Hum. Neurosci.

Motor learning occurs over long periods of practice during which motor acuity, the ability to execute actions more accurately, precisely, and in less time, improves. Laboratory-based studies of motor learning are typically limited to a small number of participants and a time frame of minutes to several hours per participant. There is a need to assess the generalizability of theories and findings from lab-based motor learning studies on larger samples and time scales. In addition, laboratory-based studies of motor learning use relatively simple motor tasks which participants are unlikely to be intrinsically motivated to learn, limiting the interpretation of their findings in more ecologically valid settings (“in the wild”). We studied the acquisition and longitudinal refinement of a complex sensorimotor skill embodied in a first-person shooter video game scenario, with a large sample size (N = 7174, 682,564 repeats of the 60 s game) over a period of months. Participants voluntarily practiced the gaming scenario for up to several hours per day up to 100 days. We found improvement in performance accuracy (quantified as hit rate) was modest over time but motor acuity (quantified as hits per second) improved considerably, with 40–60% retention from 1 day to the next. We observed steady improvements in motor acuity across multiple days of video game practice, unlike most motor learning tasks studied in the lab that hit a performance ceiling rather quickly. Learning rate was a non-linear function of baseline performance level, amount of daily practice, and to a lesser extent, number of days between practice sessions. In addition, we found that the benefit of additional practice on any given day was non-monotonic; the greatest improvements in motor acuity were evident with about an hour of practice and 90% of the learning benefit was achieved by practicing 30 min per day. Taken together, these results provide a proof-of-concept in studying motor skill acquisition outside the confines of the traditional laboratory, in the presence of unmeasured confounds, and provide new insights into how a complex motor skill is acquired in an ecologically valid setting and refined across much longer time scales than typically explored.

Section: Discussionmentioning

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Long-Term Motor Learning in the “Wild” With High Volume Video Game Data

Listman

Tsay

et al. 2021

Front. Hum. Neurosci.

“…A linear mixed model (LMM) did not show effects of Mouse Type on single-trial learning (STL), although we observed that participants using a trackpad exhibited longer reaction times than others, consistent with a previous report. 43 Mouse position sampling rates depended on the exact hardware that each participant used to complete the task. Sampling rates were likely affected by features of the specific mouse used, along with features of the specific computer used, as computers may limit the rate at which the browser samples data in order to cope with limited processing power.…”

Section: Methodsmentioning

confidence: 99%

Motor learning without movement

Forrence

McDougle

2021

Preprint

Current theories of motor control emphasize forward models as a critical component of the brain's motor execution and learning networks. These internal models are thought to predict the consequences of movement before sensory feedback from these movements can reach the brain, allowing for smooth, continuous online motor performance and for the computation of prediction errors that drive implicit motor learning. Taking this framework to its logical extreme, we tested the hypothesis that movements are not necessary for the generation of predictions, the computation of prediction errors, and implicit motor adaptation. Human participants were cued to move a computer mouse to a visually displayed target and were subsequently cued to withhold those movements on a subset of trials. Visual errors displayed on both trials with and without movements to the target induced single-trial learning. Furthermore, learning on trials without movements persisted when accompanying movement trials were never paired with errors and when movement and sensory feedback trajectories were decoupled. These data provide compelling evidence supporting an internal model framework in which forward models generate sensory predictions independent of the generation of movements.

“…Similarly, in error-based learning (e.g., sensorimotor adaptation), the weights are altered such that the same motor command will result in an output that should reduce the error. Critically, in both learning processes, behavioral changes attributed to alterations to the sensorimotor map are limited in magnitude (Avraham, Ryan Morehead, Kim, & Ivry, 2021;Bond & Taylor, 2015;Kim, Morehead, Parvin, Moazzezi, & Ivry, 2018;Morehead, Taylor, Parvin, & Ivry, 2017;Tsay, Avraham, et al, 2020;Tsay, Ivry, Lee, & Avraham, 2021;Tsay, Kim, Parvin, Stover, & Ivry, 2021;; instead, behavior changes are primarily attributed to changes in motor planning (e.g., strategic re-aiming) (McDougle, Ivry, & Taylor, 2016). This last point is relevant when considering the contribution of use-dependent learning to skill acquisition.…”

Section: Biases That Arise From Use-dependent Changes In Motor Executionmentioning

confidence: 99%

Use-dependent biases primarily originate from a contaminated motor plan

Saxena

Parvin

et al. 2021

Preprint

Self Cite

Repetition of a specific movement biases subsequent actions towards the recently practiced movement, a phenomenon referred to as use-dependent learning (UDL). UDL has been attributed to shifts in the tuning of neurons in the motor cortex. However, recent studies employing a forced reaction time task, including the eLife article by Marinovic et al (2017), indicate that these biases may also arise from a contaminated motor plan, one that is biased towards the practiced direction. We advanced this line of inquiry, seeking to establish the relative contribution of execution and planning processes to UDL in a center-out reaching task in which participants were able to initiate movements of their own volition. On most trials, the target appeared at a designated “frequent” location; on other trials, the target appeared at one of six “rare” locations. In Experiment 1, participants exhibited a robust movement bias towards the frequent target when movements were self-initiated quickly, but a small movement bias when movements were self-initiated slowly – the signature of a contaminated motor plan. Strikingly, the heading angles were bimodally distributed, with one peak at the frequent target location and the other at the rare target location – a finding reinforced by a re-analysis of two widely cited studies on UDL. Notably, the latter peak was shifted in the frequently practiced direction, a signature of a motor execution bias. To eliminate the contribution of planning-related UDL, we imposed a delay between target onset and movement initiation in Experiment 2. As predicted, the heading angles became unimodally distributed around the rare target. The peak of this distribution was again shifted towards the location of the frequent target, indicative of a persistent bias in motor execution. Taken together, these results highlight two distinct components of UDL even when movements are self-initiated: First, the temporal dynamics underlying movement planning, in which a default plan is progressively overridden by a new plan, produces a pronounced motor planning bias. Second, there is a small, temporally stable bias that may reflect shifts in motor unit tuning.