The Control of Memory-Guided Reaching Movements in Peripersonal Space

Abstract

The goal of the present investigation was to explore the putative contributions of feedforward- and feedback-based processes in the control of memory-guided reaching movements. Participants (N = 4) completed an extensive number of reaching movements (2700) to 3 midline targets (20, 30, 40 cm) in 6 visual conditions: full-vision, open-loop, and four memory-guided conditions (0, 200, 400, and 600 ms of delay). To infer limb control, we used a regression technique to examine the within-trial correspondence between the spatial position of the limb at peak acceleration, peak velocity, peak deceleration, and the ultimate movement endpoint. A high degree of within-trial correspondence would suggest that the final position of the limb was largely specified prior to movement onset and not adjusted during the action (i.e., feedforward control); conversely, a low degree of within-trial correspondence would suggest that movements were modified during the reaching trajectory (i.e., feedback control). Full-vision reaches were found to be more accurate and less variable than open-loop and memory-guided reaches. Moreover, full-vision reaches demonstrated only modest within-trial correspondence between the spatial position of the limb at each kinematic marker and the ultimate movement endpoint, suggesting that reaching accuracy was achieved by adjusting the limb trajectory throughout the course of the action. Open-loop and memory-guided movements exhibited strong within-trial correspondence between final limb position and the position of the limb at peak velocity and peak deceleration. This strong correspondence indicates that the final position of the limb was largely determined by processes that occurred before the reach was initiated; errors in the planning process were not corrected during the course of the action. Thus, and contrary to our previous findings in a video-based aiming task, it appears that stored target information is not extensively (if at all) used to modify the trajectory of reaching movements to remembered targets in peripersonal space.

Different damping responses explain vertical endpoint error differences between visual conditions

“…Quick or slow goal-directed movements to visually remembered target locations often result in different endpoint locations when compared to reaching to veridical targets (Darling and Miller 1993;Westwood et al 2001;Heath and Westwood 2003;Heath et al 2004;Hondzinski and Cui 2006;Hondzinski and Kwon 2009). Relatively large endpoint errors can occur when the movement to a remembered target location is performed without vision of the hand (Prablanc et al 1986;Admiraal et al 2003;Heath and Westwood 2003;Admiraal et al 2004;Heath 2005;Sarlegna and Blouin 2010) or when inaccurate, indiscernible visual feedback of hand position is provided during the movement (Sarlegna et al 2004).…”

Quantifying online visuomotor feedback utilization in the frequency domain

“…Elliott et al, 1991), although the mean difference between vision conditions was in the expected direction for TAPV (i.e., FV = 251 ms vs. NV = 243 ms). Notably, other studies have also failed to detect TAPV differences between FV and NV conditions (e.g., Heath et al, 2004).…”

Visual regulation of manual aiming: A comparison of methods

“…Building on the Elliott et al (1999) procedure, Heath, Westwood, and Binsted (2004) calculated cumulative amplitudes at a number of kinematic markers (i.e., PA, PV, PD) and then separately correlated these amplitudes with the amplitude achieved at the end of the movement. Because the amplitudes were cumulative, early amplitude error was posited to predict late amplitude error if there was limited online regulation of the movement based on feedback (i.e., a robust r 2 value).…”

“…Although Heath and colleagues have shown reliable vision-no-vision differences using this method, the main thrust of much of their work has been to identify differences in limb control associated with memory-driven movements. In their protocols, they have also employed multiple target amplitudes (Heath et al, 2004) and conditions in which the target remained illuminated while the limb was occluded (Heath, 2005). In this context, they have found that even very brief no-vision periods prior to movement initiation result in decreased online control.…”