Are Sub-Movements Induced Visually in Discrete Aiming Tasks?

Abstract

There is a long-held view that discrete movements aimed to a target are composed of a sequence of movement units (sub-movements) that have different roles in motor control (e.g., initial impulse, error correction and movement termination) depending on the task constraints (e.g., spatial-temporal requirements). Here we report findings from the manipulation of vision/no-vision on the prevalence and type of sub-movements in discrete movement tasks over a range of space-time task criteria. The presence of vison resulted in longer movement times compared to the no-vision counterpart in time-matching tasks. A similar vision effect was observed in the highest Index of Difficulty for time-minimization tasks. Conditions that resulted/required longer movement times demonstrated more prevelocity-peak and post-velocity-peak types of sub-movements whereas short movement times increased the likelihood of overshooting sub-movements. The present study results are consistent with the idea that movement time is the variable associated with changes in submovement profiles.

The type 1 submovement conundrum: an investigation into the function of velocity zero-crossings within two-component aiming movements

“…For further insights, we may adapt this logic to the study of submovements following vision and no vision conditions, where we can more definitely disclose the use of online visual feedback for a correction (e.g. Hsieh et al 2022 ). If indeed there is a corrective process underpinning the functional category, then these submovements should present themselves more under vision compared to no vision conditions, although the incidence of non-functional submovements should not necessarily differ.…”

“…To examine the potential of type 1 submovements being generally coincident with more rapid movements, and type 2 and/or type 3 submovements being coincident with slower movements (Fradet et al 2008a ; Hsieh et al 2017 , 2019 , 2022 ), we compared each of these types of submovement using a one-way repeated-measures ANOVA for measures of movement time, time to the end of the primary submovement, time spent completing the secondary submovement, and magnitude of the initial velocity peak.…”

“…It is these precise submovement features that underpin the speed-accuracy trade-off, where there is an inverse relation between the movement speed/impulse magnitude and capacity to hit the target/movement precision (Fitts 1954 ; Fitts and Peterson 1964 ; Schmidt et al 1979 ). Along these lines, it has been shown that a higher proportion of submovements manifest when aiming to small compared to large targets (Dounskaia et al 2005 ; Fradet et al 2008a , b ; Roberts 2020 ), and with vision compared to no vision (Hsieh et al 2022 ; Khan and Franks 2003 ; Woodworth 1899 ; cf. Elliott et al 1991 ).…”

“…Likewise, the higher proportion of type 1 submovements found for young healthy adults compared to the elderly (Fradet et al 2008b ) and Parkinson’s patients (Dounskaia et al 2009 ) may be attributed to their much shorter movement times. Meanwhile, despite finding fewer type 1 submovements when aiming with vision compared to no vision (Hsieh et al 2022 ), further inspection highlighted how this trend was primarily contaminated by variations in movement time as there was an incidentally shorter movement time for the latter. As a result, it seems the previously identified “clamping” at movement termination is even further evident when it follows faster movements due to the need for a greater counter-acting force to dampen the higher magnitude velocity.…”

Psychometric Properties of a New Measure of Upper Limb Performance in Post-Stroke Individuals: Trunk-Based Index of Performance

“…The classic IP has been used in numerous experiments in motor control, psychology, and neuroscience. 9,10 However, it has received less attention in studies of the recovery of reaching skill in the neurorehabilitation literature 11 (but see Smits-Engelsman et al 12 ), in which motor skill is usually characterized as smaller endpoint error and/or faster movement speed. 13 The new measure, IPt, may be better able to capture real-world manifestations of motor skill learning, where increased skill is inferred if both variables change in the expected direction (faster speed, increased precision), 14 with less trunk compensation.…”