Abstract:Exploration in reward-based motor learning is observable in experimental data as increased variability. In order to quantify exploration, we compare three methods for estimating other sources of variability: sensorimotor noise. We use a task in which participants could receive stochastic binary reward feedback following a target-directed weight shift. Participants first performed six baseline blocks without feedback, and next twenty blocks alternating with and without feedback. Variability was assessed based o… Show more
“…With the virtual training platform, successful performance is rewarded with special currency, experience points and levels that can be used to make in-game purchases, e.g., bike frames and wheelsets with properties (better aerodynamics or lighter weight) that can improve performance. As has been shown in connection with many exercise tasks (Van Der Kooij et al, 2019;Van Mastrigt et al, 2020), such rewards may motivate users and encourage them to exercise at higher speeds, climb more meters or ride for longer periods to accumulate even greater rewards…”
Virtual online training has emerged as one of the top 20 worldwide fitness trends for 2021 and continues to develop rapidly. Although this allows the cycling community to engage in virtual training and competition, critical evaluation of virtual training platforms is limited. Here, we discuss the strengths, weaknesses, opportunities and threats associated with virtual training technology and cycling in an attempt to enhance awareness of such aspects. Strengths include immersive worlds, innovative drafting mechanics, and versatility. Weaknesses include questionable data accuracy, inadequate strength and reliability of power-speed algorithms. Opportunities exist for expanding strategic partnerships with major cycling races, brands, and sponsors and improving user experience with the addition of video capture and “e-coaching.” Threats are present in the form of cheating during competition, and a lack of uptake and acceptance by a broader community.
“…With the virtual training platform, successful performance is rewarded with special currency, experience points and levels that can be used to make in-game purchases, e.g., bike frames and wheelsets with properties (better aerodynamics or lighter weight) that can improve performance. As has been shown in connection with many exercise tasks (Van Der Kooij et al, 2019;Van Mastrigt et al, 2020), such rewards may motivate users and encourage them to exercise at higher speeds, climb more meters or ride for longer periods to accumulate even greater rewards…”
Virtual online training has emerged as one of the top 20 worldwide fitness trends for 2021 and continues to develop rapidly. Although this allows the cycling community to engage in virtual training and competition, critical evaluation of virtual training platforms is limited. Here, we discuss the strengths, weaknesses, opportunities and threats associated with virtual training technology and cycling in an attempt to enhance awareness of such aspects. Strengths include immersive worlds, innovative drafting mechanics, and versatility. Weaknesses include questionable data accuracy, inadequate strength and reliability of power-speed algorithms. Opportunities exist for expanding strategic partnerships with major cycling races, brands, and sponsors and improving user experience with the addition of video capture and “e-coaching.” Threats are present in the form of cheating during competition, and a lack of uptake and acceptance by a broader community.
“…We previously developed a method for quantifying exploration as the additional variability following non-successful trials as compared to successful trials (van Mastrigt et al 2020 ). Here, we tested whether this method could be applied to reward-based motor learning.…”
Section: Discussionmentioning
confidence: 99%
“…This way, we use five parameters in our simulations. Based on our recommendation (van Mastrigt et al 2020 ), the task consists of 500 trials. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…We quantify exploration as total variability following non-successful trials minus the total variability following successful trials (van Mastrigt et al 2020 ). In this method, we estimate the variances in Eq.…”
Section: Methodsmentioning
confidence: 99%
“…We developed a method for quantifying exploration in the presence of motor noise (van Mastrigt et al 2020 ). Like Therrien et al ( 2016 , 2018 ), Cashaback et al ( 2019 ) and Dhawale et al ( 2019 ), we assumed that motor noise and exploration are two independent random processes of which the variances can thus be summed to total observed variance.…”
When learning a movement based on binary success information, one is more variable following failure than following success. Theoretically, the additional variability post-failure might reflect exploration of possibilities to obtain success. When average behavior is changing (as in learning), variability can be estimated from differences between subsequent movements. Can one estimate exploration reliably from such trial-to-trial changes when studying reward-based motor learning? To answer this question, we tried to reconstruct the exploration underlying learning as described by four existing reward-based motor learning models. We simulated learning for various learner and task characteristics. If we simply determined the additional change post-failure, estimates of exploration were sensitive to learner and task characteristics. We identified two pitfalls in quantifying exploration based on trial-to-trial changes. Firstly, performance-dependent feedback can cause correlated samples of motor noise and exploration on successful trials, which biases exploration estimates. Secondly, the trial relative to which trial-to-trial change is calculated may also contain exploration, which causes underestimation. As a solution, we developed the additional trial-to-trial change (ATTC) method. By moving the reference trial one trial back and subtracting trial-to-trial changes following specific sequences of trial outcomes, exploration can be estimated reliably for the three models that explore based on the outcome of only the previous trial. Since ATTC estimates are based on a selection of trial sequences, this method requires many trials. In conclusion, if exploration is a binary function of previous trial outcome, the ATTC method allows for a model-free quantification of exploration.
During reward-based motor tasks, performance failure leads to an increase in movement variability along task-relevant dimensions. These increases in movement variability are indicative of exploratory behaviour in search of a better, more successful motor action. It is unclear whether failure also induces exploration along task-irrelevant dimensions that do not influence performance. In this study, we ask whether participants would explore the task-irrelevant dimension while they performed a stencil task. With a stylus, participants applied downward, normal force that influenced whether they received reward (task-relevant) as they simultaneously made erasing-like movement patterns along the tablet that did not influence performance (task-irrelevant). In this task, the movement pattern was analyzed as the distribution of movement directions within a movement. The results showed significant exploration of task-relevant force and task-irrelevant movement patterns. We conclude that failure can induce additional movement variability along a task-irrelevant dimension.
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