Neural computation of surface border ownership and relative surface depth from ambiguous contrast inputs

TL;DR

This paper investigates how the brain perceives surface borders and depth from ambiguous contrast images, combining psychophysical experiments with neural modeling to understand border ownership and surface segregation.

Contribution

It provides new psychophysical data on border ownership from ambiguous images and explains these findings using the FACADE and 3D LAMINART neural models.

Findings

Border ownership judgments depend on contrast orientation, not relative contrast.

Psychophysical data challenge the response of border ownership cells to relative contrast.

Models successfully explain how the brain segregates surfaces despite ambiguous contrast cues.

Abstract

The segregation of image parts into foreground and background is an important aspect of the neural computation of 3D scene perception. To achieve such segregation, the brain needs information about border ownership; that is, the belongingness of a contour to a specific surface represented in the image. This article presents psychophysical data derived from 3D percepts of figure and ground that were generated by presenting 2D images composed of spatially disjoint shapes that pointed inward or outward relative to the continuous boundaries that they induced along their collinear edges. The shapes in some images had the same contrast (black or white) with respect to the background gray. Other images included opposite contrasts along each induced continuous boundary. Psychophysical results show that figure vs ground judgment probabilities in response to these ambiguous displays are determined by the orientation of contrasts only, not by their relative contrasts, despite the fact that many border ownership cells in cortical area V2 respond to a preferred relative contrast. The FACADE and 3D LAMINART models are used to explain these data.