The Consistency of Prior Movements Shapes Locomotor Use-Dependent Learning

Wood, Jonathan M; Morton, Susanne M.; Kim, Hyosub

doi:10.1523/eneuro.0265-20.2021

Cited by 8 publications

(23 citation statements)

References 65 publications

(109 reference statements)

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“…If so, the current results contrast somewhat with findings in sensorimotor adaptation (Huang et al, 2011;Shmuelof et al, 2012;Galea et al, 2015;Therrien et al, 2016) and use dependent learning (Floel et al, 2008;Mawase et al, 2017) in which reward boosts implicit aftereffects caused by other learning processes. However, the current results align with work suggesting that increased movement variability attenuates use-dependent learning (Verstynen and Sabes, 2011;Marinovic et al, 2017;Wood et al, 2021;Tsay et al, 2022). Indeed, when we match individuals across groups based on motor variability, the difference in implicit aftereffects disappears (n = 6 per group;…”

Section: Reinforcement Learning During Locomotion Is Accomplished By ...supporting

confidence: 89%

“…For comparison, we recruited a group that performed the same walking pattern using explicit, target error feedback, which does not induce significant exploration. Prior work has demonstrated that use-dependent learning is the predominant source of implicit aftereffects in this target error paradigm (Wood et al, 2021(Wood et al, , 2020, and neither implicit aftereffects nor explicit retention is contaminated by sensory prediction errors. We hypothesized that the reward prediction error group would learn the new walking pattern using greater exploration (a greater increase in motor variability above baseline), and that they would have larger implicit aftereffects and more accurate explicit retention compared to the target error group.…”

Section: Introductionmentioning

confidence: 86%

“…Therefore, contrary to our hypothesis, learning from reward prediction errors did not increase implicit aftereffects more than learning from target error, rather, the evidence strongly supported the opposite. This finding could reflect a reduced amount of usedependent learning for the RPE group caused by the increased variability (Wood et al, 2020(Wood et al, , 2021.…”

Section: Reinforcement Learning Did Not Increase the Magnitude Of Imp...mentioning

confidence: 95%

“…A computer monitor placed in front of the treadmill provided real-time feedback of participants’ left step lengths (The Motion Monitor Toolbox, Innovative Sports Training Inc., Chicago IL). We chose to focus on step length because it is a robustly modifiable gait parameter that has frequently been the focus of locomotor learning paradigms (Reisman et al, 2005; Roemmich et al, 2016; French et al, 2018, 2021; Wood et al, 2020, 2021), and because both implicit and explicit forms of motor memory have been observed after learning an altered step length pattern (French et al, 2018, 2021; Wood et al, 2020, 2021).…”

Section: Methodsmentioning

confidence: 99%

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Reinforcement learning during locomotion

Wood,

Kim,

Morton

2023

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When learning a new motor skill, people often must use trial and error to discover which movement is best. In the reinforcement learning framework, this concept is known as exploration and has been observed as increased movement variability in motor tasks. For locomotor tasks, however, increased variability decreases upright stability. As such, exploration during gait may jeopardize balance and safety, making reinforcement learning less effective. Therefore, we set out to determine if humans could acquire and retain a novel locomotor pattern using reinforcement learning alone. Young healthy male and female humans walked on a treadmill and were provided with binary reward feedback (success or failure only) to learn a novel stepping pattern. We also recruited a comparison group who walked with the same novel stepping pattern but did so by correcting for target error, induced by providing real time veridical visual feedback of steps and a target. In two experiments, we compared learning, motor variability, and two forms of motor memories between the groups. We found that individuals in the binary reward group did, in fact, acquire the new walking pattern by exploring (increased variability). Additionally, while reinforcement learning did not increase implicit motor memories, it resulted in more accurate explicit motor memories compared to the target error group. Overall, these results demonstrate that humans can acquire new walking patterns with reinforcement learning and retain much of the learning over 24 hours.Significance StatementHumans can learn some novel movements by independently discovering the actions that lead to success. This discovery process, exploration, requires increased motor variability to determine the best movement. However, in bipedal locomotion especially, increasing motor variability decreases stability, heightening the risk of negative outcomes such as a trip, injury, or fall. Despite this stability constraint, the current study shows that individuals do use exploration to find the most rewarding walking patterns. This form of learning led to improved explicit retention but not implicit aftereffects. Thus, the reinforcement learning framework can explain findings across a wide range of motor and cognitive tasks, including locomotion.

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Section: Reinforcement Learning During Locomotion Is Accomplished By ...supporting

confidence: 89%

Section: Introductionmentioning

confidence: 86%

Section: Reinforcement Learning Did Not Increase the Magnitude Of Imp...mentioning

confidence: 95%

Section: Methodsmentioning

confidence: 99%

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Reinforcement learning during locomotion

Wood,

Kim,

Morton

2023

Preprint

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“…Repetition can bias future movements to resemble recently repeated ones [1], a phenomenon referred to as use-dependent learning (UDL). The effects of UDL can be seen in features such as the direction and speed of the current movement, and has been observed in movements ranging from single-joint actions to whole-body locomotion [2][3][4][5][6][7][8][9][10]. Theoretically, these movement biases have been attributed to shifts in the tuning of neurons towards the direction of a frequently practiced movement, a form of plasticity that alters the sensorimotor map underlying movement execution [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Dissociable use-dependent processes for volitional goal-directed reaching

et al. 2022

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Repetition of specific movement biases subsequent actions towards the practiced movement, a phenomenon known as use-dependent learning (UDL). Recent experiments that impose strict constraints on planning time have revealed two sources of use-dependent biases, one arising from dynamic changes occurring during motor planning and another reflecting a stable shift in motor execution. Here, we used a distributional analysis to examine the contribution of these biases in reaching. To create the conditions for UDL, the target appeared at a designated ‘frequent’ location on most trials, and at one of six ‘rare’ locations on other trials. Strikingly, the heading angles were bimodally distributed, with peaks at both frequent and rare target locations. Despite having no constraints on planning time, participants exhibited a robust bias towards the frequent target when movements were self-initiated quickly, the signature of a planning bias; notably, the peak near the rare target was shifted in the frequently practiced direction, the signature of an execution bias. Furthermore, these execution biases were not only replicated in a delayed-response task but were also insensitive to reward. Taken together, these results extend our understanding of how volitional movements are influenced by recent experience.

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