December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
The effects of perceptual uncertainty on reach to grasp movements
Author Affiliations
  • William Chapman
    University of Bristol
  • Casimir Ludwig
    University of Bristol
Journal of Vision December 2022, Vol.22, 4028. doi:https://doi.org/10.1167/jov.22.14.4028
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      William Chapman, Casimir Ludwig; The effects of perceptual uncertainty on reach to grasp movements. Journal of Vision 2022;22(14):4028. https://doi.org/10.1167/jov.22.14.4028.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Perceptual decision making is often modelled as a stochastic process in which a noisy decision variable is accumulated until a boundary is reached and decision is “made”. These models are successful in accounting for choice reaction times and accuracy. Requiring a fast reaching movement, rather than a button press, may reveal the state of evidence accumulation earlier on if reach programming has access to the decision variable. We assessed the linkage between the decision process and reaching dynamics in a simple visual-motor reaching task. We conducted three experiments (total N=32, N=29, and N=5). Participants selected and retrieved a grey block from a pair of blocks under conditions of low uncertainty (high contrast difference) or high uncertainty (low contrast difference). Trial onset was signaled by an occlusion screen switching to clear. We used motion capture to record reach-to-grasp movement trajectories. In experiment 2 a time limit for movement initiation was implemented; in experiment 3 a “no decision” baseline condition was added and the per-subject number of trials was increased to facilitate model comparison with data. Normalised signed curvature measures were calculated for each trajectory and compared across difficulty and target location. Reaching trajectories showed increased curvature in conditions of higher uncertainty, especially when a time limit for movement onset was applied. The distributions of trajectory curvature from Experiment 3 were analysed in detail and compared to model simulations that directly link evidence accumulation and reach generation. These analyses suggest that most of the variability in trajectory curvature stems from basic motor noise, biomechanical and workspace constraints. The modelling suggests that additional variability introduced by the decision process consists of introducing a small population of reaches with strong attraction to the non-target, possibly reflecting “changes of mind”.

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