The influence of robotic guidance on error detection and correction mechanisms.

New technologies have expanded the available methods to help individuals learn or re-learn motor skills. Despite equivocal evidence for the impact of robotic guidance for motor skill acquisition (Marchal-Crespo, McHughen, Cramer, & Reinkensmeyer, 2010), we have recently shown that robotic guidance mixed with unassisted practice can significantly improve the learning of a golf putting task (Bested & Tremblay, 2018). To understand the mechanisms associated with this new mixed approach (i.e., unassisted and robot-guided practice) for the learning of a golf putting task, the current study aimed to determine if such mixed practice extends to one's ability to detect errors. Participants completed a pre-test, an acquisition phase, as well as immediate, delayed (24-h), and transfer post-tests. During the pre-test, kinematic data from the putter was converted into highly accurate, consistent, and smooth trajectories delivered by a robot arm. During acquisition, 2 groups performed putts towards 3 different targets with robotic guidance on either 0% or 50% of acquisition trials. Only the 50% guidance group significantly reduced ball endpoint distance and variability, as well as ball endpoint error estimations, between the pre-test and the post-tests (i.e., immediate retention, 24-h retention, and 24-h transfer). The current study showed that allowing one to experience both robotic guidance and unassisted (i.e., errorful) performances enhances one's ability to detect errors, which can explain the beneficial motor learning effects of a mixed practice schedule.

Rapid online corrections for upper limb reaches to perturbed somatosensory targets: evidence for non-visual sensorimotor transformation processes.

When performing upper limb reaches, the sensorimotor system can adjust to changes in target location even if the reaching limb is not visible. To accomplish this task, sensory information about the new target location and the current position of the unseen limb are used to program online corrections. Previous researchers have argued that, prior to the initiation of corrections, somatosensory information from the unseen limb must be transformed into a visual reference frame. However, most of these previous studies involved movements to visual targets. The purpose of the present study was to determine if visual sensorimotor transformations are also necessary for the online control of movements to somatosensory targets. Participants performed reaches towards somatosensory and visual targets without vision of their reaching limb. Target positions were either stationary, or perturbed before (~ 450 ms), or after movement onset (~ 100 ms or ~ 200 ms). In response to target perturbations after movement onset, participants exhibited shorter correction latencies, larger correction magnitudes, and smaller movement endpoint errors when they reached to somatosensory targets as compared to visual targets. Because reference frame transformations have been shown to increase both processing time and errors, these results indicate that hand position was not transformed into visual reference frame during online corrections for movements to somatosensory targets. These findings support the idea that different sensorimotor transformations are used for the online control of movements to somatosensory and visual targets.

Amending Ongoing Upper-Limb Reaches: Visual and Proprioceptive Contributions?

In order to maximize the precise completion of voluntary actions, humans can theoretically utilize both visual and proprioceptive information to plan and amend ongoing limb trajectories. Although vision has been thought to be a more dominant sensory modality, research has shown that sensory feedback may be processed as a function of its relevance and reliability. As well, theoretical models of voluntary action have suggested that both vision and proprioception can be used to prepare online trajectory amendments. However, empirical evidence regarding the use of proprioception for online control has come from indirect manipulations from the sensory feedback (i.e., without directly perturbing the afferent information; e.g., visual-proprioceptive mismatch). In order to directly assess the relative contributions of visual and proprioceptive feedback to the online control of voluntary actions, direct perturbations to both vision (i.e., liquid crystal goggles) and proprioception (i.e., tendon vibration) were implemented in two experiments. The first experiment employed the manipulations while participants simply performed a rapid goal-directed movement (30 cm amplitude). Results from this first experiment yielded no significant evidence that proprioceptive feedback contributed to online control processes. The second experiment employed an imperceptible target jump to elicit online trajectory amendments. Without or with tendon vibration, participants still corrected for the target jumps. The current study provided more evidence of the importance of vision for online control but little support for the importance of proprioception for online limb-target regulation mechanisms.

An optimal velocity for online limb-target regulation processes?

The utilization of visual information for the control of ongoing voluntary limb movements has been investigated for more than a century. Recently, online sensorimotor processes for the control of upper-limb reaches were hypothesized to include a distinct process related to the comparison of limb and target positions (i.e., limb-target regulation processes: Elliott et al. in Psychol Bull 136:1023-1044. doi: 10.1037/a0020958 , 2010). In the current study, this hypothesis was tested by presenting participants with brief windows of vision (20 ms) when the real-time velocity of the reaching limb rose above selected velocity criteria. One experiment tested the perceptual judgments of endpoint bias (i.e., under- vs. over-shoot), and another experiment tested the shifts in endpoint distributions following an imperceptible target jump. Both experiments revealed that limb-target regulation processes take place at an optimal velocity or "sweet spot" between movement onset and peak limb velocity (i.e., 1.0 m/s with the employed movement amplitude and duration). In contrast with pseudo-continuous models of online control (e.g., Elliott et al. in Hum Mov Sci 10:393-418. doi: 10.1016/0167-9457(91)90013-N , 1991), humans likely optimize online limb-target regulation processes by gathering visual information at a rather limited period of time, well in advance of peak limb velocity.