Energy minimization within target-directed aiming: the mediating influence of the number of movements and target size

Abstract

In target-directed aiming, performers tend to more greatly undershoot targets when aiming down compared to up because they try to avoid an overshoot error and subsequently minimize the time and energy expenditure that is required to suddenly combat gravitational forces. The present study aims to further examine this principle of time and energy minimization by directly mediating the perceived cost of potential errors as well as the likelihood of their occurrence by manipulating the number of movements and target size, respectively. Participants executed rapid aiming movements in the up/down direction as part of a one-/two-target movement towards a small/large target. Primary movement endpoints showed greater undershooting when aiming in the downward compared to upward direction and small compared to large targets. Meanwhile, the overall movement time showed that slower movements were generated for down compared to up, but only when aiming toward large targets. The failure to mediate the central tendency as a function of the number of movements and target size indicates that the feature of minimization is highly prominent within the performers' pre-response planning. However, the continued minimization of energy in the presence of large targets may inadvertently cost the movement time.

Optimization in Manual Aiming: Relating Inherent Variability and Target Size, and Its Influence on Tendency

“…To date, the available evidence appears to be consistent with the minimization model as undershoots are more prevalent in the presence of small compared to large targets (Roberts, 2020; for an alternative perspective, see Dounskaia et al, 2005;Fradet et al, 2008a). That said, there is also evidence of inversely more undershooting that lands nearer the target edges for very large targets (e.g., 8 cm) (taken with respect to the terminal movement endpoint; Slifkin & Eder, 2017).…”

“…In the former instance, an assistive elastic band that was attached to a manipulandum caused the limb to be propelled toward the target resulting in greater overshooting because it avoids the energy-expenditure that would be required to dampen the velocity and clamp the limb (Oliveira et al, 2005). In the latter instance, aiming downward within the vertical axis caused even greater undershooting than normal because it avoids the cost of reversing the limb against gravitational forces (Lyons et al, 2006; see also, Burkitt et al, 2017;Elliott et al, 2014;Roberts, 2020;Roberts et al, 2016). Within the context of the present study, the failure to capture this minimization may be at least partially attributed to the absence of a penalty.…”

“…A power analysis was conducted using G*Power (version 3.1.9.4; see Faul et al, 2007) with input parameters including α = 0.05, 1-β = 0.80, and f = 0.57 (large) (based on the smallest effect size available for primary movement endpoints from a collection of recent related studies; Roberts, 2020;Roberts et al, 2016;Slifkin & Eder, 2017). The minimum estimated sample size was 6 participants.…”

The multiple process model of goal-directed aiming/reaching: insights on limb control from various special populations

“…Thus, more forceful movements to far targets will be associated with greater primary movement undershooting than less forceful movements to near targets (see Elliott et al 2001Elliott et al , 2004Elliott et al , 2017. Likewise movements INSIGHTS ON LIMB CONTROL FROM VARIOUS SPECIAL POPULATIONS 5 to small targets, that have an increased chance of missing, typically generate greater primary movement undershooting than larger targets (Roberts 2020). If the performer is able to reduce endpoint variability due to improved force-time specification over practice, then the mean of the primary movement endpoint distribution will move closer to the target.…”

Examining the equivalence between imagery and execution within the spatial domain – Does motor imagery account for signal-dependent noise?

“…This trend concurs with computational models of sensorimotor control, where the nervous system converges onto a movement approach or central tendency that compensates for the distribution and associated likelihood of movement outcomes (Harris and Wolpert 1998 ; Wolpert and Ghahramani 2000 ). For example, it has been shown that individuals typically undershoot intended target locations to avoid the cost of overshooting when they initially perceive an increased likelihood of missing the target (Elliott et al 2004 ; Roberts 2020 ).…”