Interactions between sensory prediction error and task error during implicit motor learning

Haith, Adrian M.; Ivry, Richard B; Kim, Hyosub

doi:10.1101/2021.06.20.449180

Cited by 21 publications

(37 citation statements)

References 72 publications

(132 reference statements)

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“…Second, whereas in-lab tasks typically focus on isolating and dissociating one learning mechanism from another (Pellizzer and Georgopoulos, 1993;Hegele and Heuer, 2010;Nikooyan and Ahmed, 2015;Morehead et al, 2017;Kim et al, 2018;Leow et al, 2018;Tsay et al, , 2021aAvraham et al, 2021), our task lies on the opposite end of the spectrum, where a wide range of learning processes are likely involved. For instance, cognitive strategies related to how to play the game (e.g., planning sequences of movements), motor adaptation (recalibrating movements with respect to mouse sensitivity), and skill learning (i.e., increasing the speed of both wrist and finger motions without sacrificing accuracy) all contribute to success in the game.…”

Section: Discussionmentioning

confidence: 99%

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

Listman

Tsay

Kim

et al. 2021

Front. Hum. Neurosci.

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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.

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Section: Discussionmentioning

confidence: 99%

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

Listman

Tsay

Kim

et al. 2021

Front. Hum. Neurosci.

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“…Design of the in-person study (Tsay, Haith, et al, 2021): Participants (n = 12) performed reaching movements to the 90º target (straight ahead). There were 100 baseline reaching trials with veridical feedback.…”

Section: Experiments 3: Adaptation In Response To Variable Non-contingent Rotated Visual Feedbackmentioning

confidence: 99%

“…Following the in-person method used in Tsay et al ( 2021) (Tsay, Haith, Ivry, & Kim, 2021), we varied the size of the noncontingent clamped feedback across trials. Each participant was exposed to a set of eight rotation sizes between 0 -60°, with four of these involving clockwise rotations and the other four involving counterclockwise rotations of the same size.…”

Section: Experiments 3: Adaptation In Response To Variable Non-contingent Rotated Visual Feedbackmentioning

confidence: 99%

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

Lee

Ivry

Avraham

2021

Preprint

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Collecting data online via crowdsourcing platforms has proven to be a very efficient way to recruit individuals from a large diverse sample. While many fields in psychology have embraced online studies, the field of motor learning has lagged behind. We suspect this is because of an implicit assumption that the loss of experimental control with online data collection will be problematic for kinematic data. As a first foray to bring motor learning online, we developed a web-based platform to collect kinematic data, serving as a template for researchers to create their own online sensorimotor control and learning experiments. As a proof-of-concept, we present three visuomotor rotation experiments conducted with this platform, asking if fundamental motor learning phenomena discovered in the lab could be reproduced online. In all experiments, there was a close correspondence between the results obtained online with those previously reported from research conducted in the laboratory. As such, our web-based motor learning platform can serve as a powerful tool to exploit the benefits of crowdsourcing approaches and extend research on motor learning beyond the confines of the traditional laboratory.

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“…Second, whereas in-lab tasks typically focus on isolating and dissociating one learning mechanism from another (Avraham et al, 2021;Hegele & Heuer, 2010;Kim et al, 2018;Leow et al, 2018;Morehead et al, 2017;Nikooyan & Ahmed, 2015;Pellizzer & Georgopoulos, 1993;Tsay, Haith, et al, 2021;, our task lies on the opposite end of the spectrum, where a wide range of learning processes are likely involved. For instance, cognitive strategies related to how to play the game (e.g., planning sequences of movements), motor adaptation (recalibrating movements with respect to mouse sensitivity), and skill learning (i.e., increasing the speed of both wrist and finger motions without sacrificing accuracy) all contribute to success in the game.…”

Section: Discussionmentioning

confidence: 99%

Long-term Motor Learning in the Wild with High Volume Video Game Data

Listman

Kim

Mackey

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Motor learning occurs over long periods of practice during which motor acuity (the ability to execute actions more accurately, precisely, and within a shorter amount of time) improves. Laboratory-based motor learning studies are typically limited to a small number of participants and a time frame of minutes to several hours per participant. Thus, there is a need to assess the generalizability of theories and findings from lab-based motor learning studies on much larger samples across longer 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. 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 participants, 682,564 repeats of the 60 sec game) over a period of months. Participants voluntarily practiced the gaming scenario for as much as 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 one 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 nonlinear 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 minutes per day. Taken together, these results provide a proof-of-concept in studying motor skill acquisition outside the confines of the traditional laboratory 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.

show abstract

Interactions between sensory prediction error and task error during implicit motor learning

Cited by 21 publications

References 72 publications

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

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

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

Long-term Motor Learning in the Wild with High Volume Video Game Data

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