Maruya, Akihito

Zaidi, Qasim

English

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Feature tracking and geometrical priors counteract illusory non-rigidities from outputs of motion-energy cells  Akihito Maruya and Qasim Zaidi   Graduate Center for Vision Research, State University of New York, New York, USA   Why do objects appear rigid when projected retinal images are deformed non-rigidly by object or observer motion? We used rotating rigid objects that can appear rigid or non-rigid to test whether tracking of salient features and prior assumptions counteract nonrigidity. When two circular rings are rigidly linked at an angle and rotated (Fig. 1a), at moderate speeds (6.0 deg/sec) they appear to be wobbling and not linked rigidly (e.g. Superman II Zod’s trial).  To understand the nonrigidity we computed responses of arrays of motion-energy cells. Despite the object being rigidly rotated the pre-dominant motion energy vectors were not in the rotation direction but orthogonal to the contours of the rings, thus compatible with the rings moving closer or away from each other nonrigidly. We trained a convolutional neural network on 9000 motion flows to classify wobbling and rotation, simulating MST and STS cell responses to motion flows. Motion-energy flows from the rings to the trained CNN gave a high probability of wobbling consistent with the percept.  When the link was painted or replaced by a gap, or if the rings were polygons with vertices, the rings appeared rigidly rotating. Phenomenologically, the motion of painted segments, gaps, or vertices provides cues for rotation and against wobbling (Fig. 1b and c). These salient features can be tracked by arrays of MT pattern-motion cells or by a feature-tracking model. Motion flows from feature tracking to the trained CNN gave high probabilities of rotation. However, at higher speeds, all configurations appeared non-rigid. Salient feature-tracking thus contributes to rigidity at slower speeds, but not at faster speeds.  Combinations of CNN outputs from motion energy and feature tracking were not sufficient to fully explain differences in percepts between circular and polygonal rings. To test if prior knowledge based on feature geometries influences the percepts (e.g. a gap moves up and down for wobble but horizontally for rotation, and an object with corners is less likely to wobble than a smooth object), we simulated frequencies of wobble with a physics engine to use as prior probabilities. A Bayesian model that combined the geometrical priors with greater weight to CNN outputs from motion energy at faster speeds, and to CNN outputs from feature tracking at slower speeds, explained the proportions of rigid and nonrigid percepts (Fig.2).  Our results identify a previously unrecognized role for feature-tracking in object perception. Fig. 1 (a) Circular rings (b) Rings with a gap (c) Octagon  Fig. 2 Model estimates versus human judgments

Feature tracking and geometrical priors counteract illusory non-rigidities from outputs of motion-energy cells

https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1190&context=modvis

Feature tracking and geometrical priors counteract illusory non-rigidities from outputs of motion-energy cells

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