Abstract
A core challenge in perception is recognizing objects across the highly variable retinal input that occurs when objects are viewed from different directions (e.g., front vs side views). It has long been known that certain views are of particular importance, but it remains unclear why. We reasoned that characterising the computations underlying visual comparisons between objects could explain the privileged status of certain qualitatively special views. We measured pose discrimination for a wide range of objects, finding large variations in performance depending on the object and the view angle, with front and back views yielding particularly good discrimination. Strikingly, a simple and biologically plausible computational model based on measuring the projected 3D optical flow between views of objects accurately predicted both successes and failures of discrimination performance. This provides a computational account of why certain views have a privileged status.
Significance statement Some viewpoints of objects are qualitatively and perceptually special, making them easier to recognize and remember. We show that qualitatively special viewpoints of familiar and novel 3D objects can be predicted by an optical-flow model that measures how points on the surface shift in the image as viewpoint changes. This provides a quantitative account for why some viewpoints of objects are perceptually special.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵** Shared senior authorship.
Competing Interest Statement: There are no competing interests.
Some points clarified in intro and discussion.
