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Annabel Wing-Yan Fan, Lin Lawrence Guo, Adam Frost, Robert L. Whitwell, Matthias Niemeier, Jonathan S. Cant; Grasping real-world objects along ambiguous dimensions is not biased by ensemble perception. Journal of Vision 2020;20(11):750. doi: https://doi.org/10.1167/jov.20.11.750.
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© ARVO (1962-2015); The Authors (2016-present)
The visual system extracts summary representations of visually similar objects which can bias the perception of individual objects towards the ensemble average. The visual system also plays a dominant role in guiding action, which has been shown to resist the illusion-inducing backgrounds of classic pictorial illusions. These findings suggest that actions resist ensemble-based biases of visual scenes, in support of the view that different visual systems underlie scene perception and visually-guided action. Here we test whether ensemble statistics can influence visually-guided action when the target object’s orientation, a crucial object feature for planning the hand’s grasp posture, is visually ambiguous. To do this, we recorded the hand kinematics and electromyographic activity of ten participants who reached-out to grasp a circular 3D target that was placed in a background ensemble of 3D ellipses. Importantly, the average orientation and size of the ensemble was systematically varied (counter-clockwise vs. clockwise; small vs. large) across trials, with the prediction that ensemble statistics may affect grasping towards ambiguous (orientation) but not unambiguous (size) visual information. As a perceptual control, participants performed, in a separate block of trials, a manual-adjustment task in which they estimated the average size and average orientation of the ensemble displays. A univariate analysis using the kinematic data showed that neither the maximum grip aperture nor grasp orientation were biased by the average size and orientation of the ensemble displays, respectively, despite both summary statistics biasing their respective perceptual measures in the explicit estimation tasks. Furthermore, support vector machine classification of ensemble statistics achieved above-chance classification accuracy when trained on kinematic and electromyographic data from the perceptual but not grasping conditions, supporting our univariate findings. These results suggest that even along ambiguous grasping dimensions, visually guided behaviors towards real-world objects are not strongly biased by ensemble processing.
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