Abstract
One of the main functions of vision is to represent object shape. Most theories of shape perception focus exclusively on geometrical computations (e.g., curvatures, symmetries, axis structure). Here, however, we find that shape representations are also profoundly influenced by an object’s causal origins: the processes in its past that formed it. Observers placed dots on objects to report their perceived symmetry axes. When objects appeared ‘complete’—created entirely by a single generative process—responses closely approximated the object’s geometrical axes. However, when objects appeared ‘bitten’—as if parts had been removed by a distinct causal process—the responses deviated significantly from the geometrical axes, as if the bitten regions were suppressed from the computation of symmetry. This suppression of bitten regions was also found when observers were not asked about symmetry axes but about the perceived front and back of objects. The findings suggest that visual shape representations are more sophisticated than previously appreciated. Objects are not only parsed according to what features they have, but also to how or why they have those features.
Highlights
One of the main functions of vision is to represent object shape
To test whether causal inferences can influence the perceptual organization of shape, we created a set of 2D objects with different causal histories (Fig. 3), and asked observers to indicate where they perceived the central axis of the objects, by freely distributing ten dots along the axis’ length
The observers readily reported the axis that would correspond to the shape of the complete version of the object, as if they explicitly compensated for the missing portion of the object
Summary
One of the main functions of vision is to represent object shape. Most theories of shape perception focus exclusively on geometrical computations (e.g., curvatures, symmetries, axis structure). When objects appeared ‘bitten’—as if parts had been removed by a distinct causal process—the responses deviated significantly from the geometrical axes, as if the bitten regions were suppressed from the computation of symmetry. In order to make higher-level inferences about objects (e.g., identifying symmetry axes, identifying front and back ends, predicting likely physical weight, or parsing the object into functional parts, like the cup and handle of a mug), the brain must somehow pool and organize information from distant locations across the object into more global quantities—a process known as perceptual organization[17,18]. We are interested in how shapes are perceived and represented depending on higher-level inferences about the causal origin of objects and their features. Leyton’s framework is potentially powerful in capturing the visual representation of causal origin, it cannot explain differences in causal attribution for objects that are geometrically very similar (Fig. 1)
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