For example, what we know about a peacock butterfly (e.g., fragile, able to fly, nectar eater, harmless), we can use to make inferences about other butterfly varieties. We mostly infer these properties from our previous experiences about other objects from the same or similar class.
This requires inferring an object’s properties-such as its material, potential usages, dangerousness and so on. We live in a world in which our survival depends critically on successful interactions with objects. This demonstrates how we establish correspondence between very different objects by evaluating similarity between semantic parts, combining perceptual organization and cognitive processes. We present a zero-parameter model based on labeled semantic part data (obtained from a different group of participants) that well explains our data and outperforms an alternative model based on contour curvature. Despite identical geometries, correspondences were different across the interpretations, based on semantics (e.g., matching ‘Head’ to ‘Head’, ‘Tail’ to ‘Tail’). We then measured correspondence between ambiguous objects with different labels (e.g., between ‘duck’ and ‘rabbit’ interpretations of the classic ambiguous figure). Responses show correspondence is established based on similarities between semantic parts (such as head, wings, or legs). In each trial, a dot was placed on the contour of one object, and participants had to place a dot on ‘the corresponding location’ of the other object. We measured point-to-point correspondence between such object pairs. However, we can also identify ‘similar parts’ on extremely different objects, such as butterflies and owls or lizards and whales. Previous studies measured point-to-point correspondence between objects before and after rigid and non-rigid shape transformations. Establishing correspondence between objects is fundamental for object constancy, similarity perception and identifying transformations.
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