Inferring online and offline processing of visual feedback in target-directed movements from kinematic data

Khan, Michael A.; Franks, Ian M.; Elliott, Digby; Lawrence, Gavin P.; Chua, Romeo; Bernier, Pierre‐Michel; Hansen, Steve; Weeks, Daniel J.

doi:10.1016/j.neubiorev.2006.05.002

Cited by 146 publications

(146 citation statements)

References 77 publications

(135 reference statements)

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“…In line with our previous work, though, the effect of physical activity levels on aiming behavior appeared rather limited ). Only reaction times were shown to be generally longer in non-active adults, an outcome which is commonly interpreted as more elaborate planning processes (e.g., Khan et al 2006). However, slightly prolonging reaction time was not considered meaningful, especially since none of the other variables of interest differed significantly between active and non-active adults.…”

Section: Discussionmentioning

confidence: 96%

“…The primary movement corresponds to the initial pulse toward the vicinity of target. Although this preprogrammed movement phase is traditionally associated with open-loop control (Woodworth 1899), recent work has shown that vision is being used more continuously for the planning and control of limb movements (i.e., impulse control; see also Heath 2005;Khan et al 2002Khan et al , 2006Saunders and Knill 2003). While corrections may occur very early in the movement, the main body of closed-loop control takes place during the homing-in phase.…”

Section: Introductionmentioning

confidence: 99%

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Manual aiming in healthy aging: does proprioceptive acuity make the difference?

Helsen

Halewyck

Levin

et al. 2016

AGE

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The present study examines whether nonactive older adults are more dependent on visual information when executing aiming movements and whether age-related declines in proprioception play a mediating role herein. Young (N = 40) and older adults (N = 38) were divided into physically active and non-active subgroups based on self-reported sports participation levels. In experiment 1, participants executed wrist-aiming movements with and without visual feedback. In experiment 2, passive proprioceptive acuity was assessed using wrist motion detection and position matching tests. Results showed similar aiming accuracy across age groups both with and without visual feedback, but older adults exhibited longer movement times, prolonged homing-in phase, and made more corrective submovements. Passive proprioceptive acuity was significantly affected by physical activity level and age, with participants in the active group scoring better than their non-active peers. However, these declines did not predict performance changes on the aiming task. Taken together, our observations suggest that decline in proprioceptive acuity did not predict performance changes on the aiming task and older adults were able to compensate for their decreased motion and position sense when allowed sufficient time. In line with these observations, we proposed that older adults are able to compensate for their decline in proprioception by increasing their reliance on predictive models.

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Manual aiming in healthy aging: does proprioceptive acuity make the difference?

Helsen

Halewyck

Levin

et al. 2016

AGE

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“…An increased time to contact the grasp target when dependent on peripheral vision has also been reported in a study of volitional reach-to-grasp (Sivak and MacKenzie 1990), and it has been proposed in numerous studies of volitional reaching and pointing movements that a prolongation of the time after peak velocity serves to 'buy' the additional time needed for online corrections to the arm trajectory (for a review, see: Khan et al 2006). In Sivak and Mackenzie's study, as in ours, the majority of the additional time taken to grasp the target when dependent on peripheral vision occurred after the peak wrist velocity.…”

Section: Discussionmentioning

confidence: 77%

“…These trajectories appear to have been less direct primarily because of a tendency for a lateral undershoot early in the movement (first 20%), as reflected in the initial trajectory angle data. While the differences in initial trajectory did not lead to any significant declines in the accuracy of the final grasp, the increased time-afterpeak-velocity may have been related to a need to compensate for a less accurately aimed reach by allowing for the use of online corrections (Chua and Elliott 1993;Elliott et al 2001;Khan et al 2006). …”

Section: Discussionmentioning

confidence: 86%

“…Trajectory data were used to determine (a) the angle of the reaching response at 10, 20, and 30% of the total distance to the handhold and (b) the deviation from a 'direct path' to the target region on the handle, using a method adapted from studies of volitional reaching and sweeping movements and described by Khan et al (2006). The 'direct path' was defined as the straight-line path connecting the initial hand position (at movement initiation) to the target region of the handhold.…”

Section: Data Collectedmentioning

confidence: 99%

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The use of peripheral vision to guide perturbation-evoked reach-to-grasp balance-recovery reactions

et al. 2010

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For a reach-to-grasp reaction to prevent a fall, it must be executed very rapidly, but with sufficient accuracy to achieve a functional grip. Recent findings suggest that the CNS may avoid potential time delays associated with saccade-guided arm movements by instead relying on peripheral vision (PV). However, studies of volitional arm movements have shown that reaching is slower and/or less accurate when guided by PV, rather than central vision (CV). The present study investigated how the CNS resolves speed-accuracy trade-offs when forced to use PV to guide perturbation-evoked reach-to-grasp balance-recovery reactions. These reactions were evoked, in 12 healthy young adults, via sudden unpredictable anteroposterior platform translation (barriers deterred stepping reactions). In PV trials, subjects were required to look straight-ahead at a visual target while a small cylindrical handhold (length 25%> hand-width) moved intermittently and unpredictably along a transverse axis before stopping at a visual angle of 20°, 30°, or 40°. The perturbation was then delivered after a random delay. In CV trials, subjects fixated on the handhold throughout the trial. A concurrent visuo-cognitive task was performed in 50% of PV trials but had little impact on reach-to-grasp timing or accuracy. Forced reliance on PV did not significantly affect response initiation times, but did lead to longer movement times, longer timeafter-peak-velocity and less direct trajectories (compared to CV trials) at the larger visual angles. Despite these effects, forced reliance on PV did not compromise ability to achieve a functional grasp and recover equilibrium, for the moderately large perturbations and healthy young adults tested in this initial study.

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Getting Off to a Shaky Start: Specificity in Planning and Feedforward Control During Sensorimotor Learning in Autism Spectrum Disorder

et al. 2019

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Whilst autistic individuals develop new internal action models during sensorimotor learning, the acquired movements are executed less accurately and with greater variability. Such movement profiles are related to differences in sensorimotor integration and/or altered feedforward/feedback sensorimotor control. We investigated the processes underlying sensorimotor learning in autism by quantifying accuracy and variability, relative timing, and feedforward and feedback control. Although autistic individuals demonstrated significant sensorimotor learning across trials, which was facilitated by processing knowledge‐of‐results feedback, motor execution was less accurate than non‐autistic individuals. Kinematic analysis indicated that autistic individuals showed significantly greater spatial variability at peak acceleration, but comparable spatial variability at peak velocity. These kinematic markers suggest that autistic movement profiles are driven by specific differences in sensorimotor control processes (i.e., internal action models) associated with planning and regulating the forces required to execute the movement. The reduction of variability at peak velocity indicates intact early feedback‐based sensorimotor control in autism. Understanding how feedforward and feedback‐based control processes operate provides an opportunity to explore how these control processes influence the acquisition of socio‐motor actions in autism. Autism Res 2020, 13: 423–435. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary Autistic adults successfully learned a new movement skill by physically practising it, and using feedback about how well they had done to become more accurate. When looking at the movements in detail, autistic adults were more variable than non‐autistic adults when planning (e.g., how much force to use), and performing, the movement. These differences impact how autistic individuals learn different types of movement skills, which might influence how other behaviours (e.g., imitation) are acquired that support social interaction.

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Inferring online and offline processing of visual feedback in target-directed movements from kinematic data

Cited by 146 publications

References 77 publications

Manual aiming in healthy aging: does proprioceptive acuity make the difference?

Manual aiming in healthy aging: does proprioceptive acuity make the difference?

The use of peripheral vision to guide perturbation-evoked reach-to-grasp balance-recovery reactions

Getting Off to a Shaky Start: Specificity in Planning and Feedforward Control During Sensorimotor Learning in Autism Spectrum Disorder

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