The role of motor learning in spatial adaptation near a tool

Abstract

Some visual-tactile (bimodal) cells have visual receptive fields (vRFs) that overlap and extend moderately beyond the skin of the hand. Neurophysiological evidence suggests, however, that a vRF will grow to encompass a hand-held tool following active tool use but not after passive holding. Why does active tool use, and not passive holding, lead to spatial adaptation near a tool? We asked whether spatial adaptation could be the result of motor or visual experience with the tool, and we distinguished between these alternatives by isolating motor from visual experience with the tool. Participants learned to use a novel, weighted tool. The active training group received both motor and visual experience with the tool, the passive training group received visual experience with the tool, but no motor experience, and finally, a no-training control group received neither visual nor motor experience using the tool. After training, we used a cueing paradigm to measure how quickly participants detected targets, varying whether the tool was placed near or far from the target display. Only the active training group detected targets more quickly when the tool was placed near, rather than far, from the target display. This effect of tool location was not present for either the passive-training or control groups. These results suggest that motor learning influences how visual space around the tool is represented.

Figure 1. Experimental set up.

The layout of the start position and targets for the motor learning task is shown in Panel A. The arrangement of the fixation cross and cue-target placeholders for the visual detection task is shown in Panel B.

Figure 2. Motor learning results.

Training trajectories from a representative participant in the active (participant JD) and passive training groups (participant LP) are shown in Panels A and B, respectively. Panel C shows mean end-point variability of the training movements for the active and passive training groups over trial bins of 23 trials. Error bars represent 95% confidence intervals.

Figure 3. Motor learning results for the test phase.

Panel A: Movement time (ms) as a function of training group, Panel B: mean end-point variability (mm) as a function of training group, Panel C: mean signed error (mm) as a function of training group. Error bars represent 95% confidence intervals.

Figure 4. Reaction time (RT; ms) as a function of training condition, tool location, and target location.

The active training group responded to targets more quickly when the tool was held near the display rather than far from it (training condition x tool location interaction: p = .014). This effect did not interact with target location (3-way interaction, p = .114). Error bars represent 95% confidence intervals.

Figure 5. Reaction time (RT; ms) as a function of cue location, target location and training condition.

RT was lower when the target appeared in the cued location than in the uncued location, p <.001 . This effect did not interact with training condition (p = .861) or tool location (p = .145). Error bars represent 95% confidence intervals.