Taking aim at the perceptual side of motor learning: exploring how explicit and implicit learning encode perceptual error information through depth vision

Abstract

Motor learning in visuomotor adaptation tasks results from both explicit and implicit processes, each responding differently to an error signal. While the motor output side of these processes is extensively studied, their visual input side is relatively unknown. We investigated if and how depth perception affects the computation of error information by explicit and implicit motor learning. Two groups of participants threw virtual darts at a virtual dartboard while receiving perturbed endpoint feedback. The Delayed group was allowed to re-aim and their feedback was delayed to emphasize explicit learning, while the Clamped group received clamped cursor feedback which they were told to ignore, and continued to aim straight at the target to emphasize implicit adaptation. Both groups played this game in a highly detailed virtual environment (Depth condition) and in an empty environment (No-Depth condition). The Delayed group showed an increase in error sensitivity under Depth relative to No-Depth conditions. In contrast, the Clamped group adapted to the same degree under both conditions. The movement kinematics of the Delayed participants also changed under the Depth condition, consistent with the target appearing more distant, unlike the Clamped group. A comparison of the Delayed behavioral data with a perceptual task from the same individuals showed that the effect of the Depth condition on the re-aiming direction was consistent with an increase in the scaling of the error distance and size. These findings suggest that explicit and implicit learning processes may rely on different sources of perceptual information.

Increased cognitive load in immersive virtual reality during visuomotor adaptation is associated with decreased long-term retention and context transfer

“…A ventral mode of control is thought to be dependent on visual perception and increased cognitive processes and therefore could potentially explain increased cognitive load in HMD-VR during motor learning [ 44 ]. Separately, depth information has been found to uniquely affect explicit processes and therefore could also explain why HMD-VR may rely more on explicit, cognitive strategies [ 45 ]. Further research is needed to examine whether HMD-VR relies more on a ventral mode of control and whether this shift in control could explain a greater reliance of explicit processes in HMD-VR.…”

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

“…The decay of error over the trials indicated that learning was achieved during the task. The recent study by Campagnoli et al investigated the effect of 3D perception on explicit and implicit motor adaptation using a virtual reality environment [ 239 ]. Their findings suggest that explicit and implicit learning may rely on different sources of perceptual information, but they also stressed that more work is required to detect how depth cues influence the different learning principles.…”

“…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 ).…”

“…This approach to investigate the effect of the task parameters on motor adaptation is ongoing and should be continued for all possible task parameters: error size [ 250 , 252 , 253 ], error consistency [ 254 , 255 ], feedback timing [ 256 , 257 ], dimensionality [ 239 ], inter-trial time, movement speed, degrees of freedom of motion [ 258 ], reaction time [ 259 ] or continuous vs. discrete control [ 260 ].…”