Online Vision as a Function of Real-Time Limb Velocity: Another Case for Optimal Windows

ABSTRACT

The efficiency of online visuomotor processes was investigated by manipulating vision based on real-time upper limb velocity. Participants completed rapid reaches under two control (full vision, no vision) and three experimental visual window conditions. The experimental visual windows were early: 0.8–1.4 m/s, middle: above 1.4 m/s, and late: 1.4 to 0.8 m/s. The results indicated that endpoint consistency comparable to that of full-vision trials was observed when using vision from the early (43 ms) and middle (89 ms) windows, but vision from the middle window entailed a longer deceleration phase (i.e., a temporal cost). The late window was not useful to implement online trajectory amendments. This study provides further support for the idea of early visuomotor control, which may involve multiple online control processes during voluntary movement.

Keywords:

• online control
• optimal
• vision
• reaching
• real-time

Funding

This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) as well as the Canada Foundation for Innovation (CFI) and the Ontario Research Fund (ORF).

Notes

1. The online correction measure was taken from aggregate correlation coefficients between limb position at 25%, 50%, and 75% of the movement with limb position at movement end (see Heath, 2005). Lower correlation coefficients later in the trajectory indicate more variable rescaling of trajectories from trial-to-trial that can indicate a greater likelihood of limb reaccelerations or limb decelerations during trajectories.

2. It must be recognized that the employed liquid-crystal goggles manipulate the entire visual field (i.e., target, limb and surrounding visual cues). As a result, the theoretical arguments relative to impulse and limb-target regulation processes still need to be empirically tested through independent manipulations of vision of the limb and the target (e.g., Elliott, 1988; Heath, 2005), which is the goal of future investigations.

3. It is important to note that lower Z scores do not allow the inference of online control at all movement time proportions. For example, lower Z scores within the first 100 ms of a movement would rather reflect more variable movement planning. Nevertheless, the observed differences in online correction measures at 80% of movement time were consistent with other studies employing the same measurements (e.g., Heath, 2005).