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

Abstract

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.

Understanding implicit sensorimotor adaptation as a process of proprioceptive re-alignment

“…Although the size of the visual error never changes, adaptation eventually reaches an upper bound, averaging between 15° - 25° away from the target. Consistent with the results of Bond and Taylor (Bond & Taylor, 2015), this asymptote does not vary across a wide range of clamped rotation sizes (Kim et al, 2018; Neville & Cressman, 2018; Tsay, Lee, et al, 2021).…”

“…In the following section, we formalize how sensory recalibration during visuomotor adaptation drives implicit adaptation. Third, as noted above, while the extent of recalibration is a fixed ratio for small visuo-proprioceptive discrepancies (Zaidel et al, 2011), the magnitude of the change within each modality exhibits marked saturation (Salomonczyk et al, 2013; Tsay, Kim, Parvin, et al, 2021; Tsay, Parvin, et al, 2020).…”

“…As noted previously, the size of the proprioceptive shift saturates at a common value (∼5°) across a wide range of visuo-proprioceptive discrepancies (Mostafa et al, 2015; Ruttle et al, 2021; Salomonczyk et al, 2013; Synofzik et al, 2008, 2010, 2006; Tsay, Parvin, et al, 2020). For example, the proprioceptive shift is essentially the same following the introduction of a 15° rotation or a 30° rotation (Tsay, Kim, Parvin, et al, 2021). Since the size of the proprioceptive shift dictates the size of the proprioceptive error, we should expect an invariant rate and extent of motor adaptation in response to visual errors of different sizes (Eq.…”

Understanding implicit sensorimotor adaptation as a process of proprioceptive re-alignment

“…As noted throughout this review, the extent of this recalibration process is limited, likely reflecting the natural statistics of proprioceptive errors. The model does not capture the full range of motor capabilities we exhibit as humans ( Listman et al, 2021 ). Skill learning requires a much more flexible system, one that can exploit multiple sources of information and heuristics to create novel movement patterns ( Yang et al, 2021 ).…”

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks

“…Nevertheless, we here suggest that, for the purpose of research transfer, the direction of increased complexity should be explored as well. A good start would be to gradually increase the motor task complexity to better match the diversity of real world motor behavior, such as adding the third dimension to the motor task [ 238 , 239 ], allowing unconstrained movement [ 246 ], investigating tool use [ 247 , 248 ] or increasing the task training time [ 249 ]. Increasing the complexity can be done at many levels; here, we propose some options ( Figure 8 ).…”