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We see the external world as consisting not only of objects and their parts, but also of relations that hold between them. Visual analogy, which depends on similarities between relations, provides a clear example of how perception supports reasoning. Here we report an experiment in which we quantitatively measured the human ability to find analogical mappings between parts of different objects, where the objects to be compared were drawn either from the same category (e.g., images of two mammals, such as a dog and a horse), or from two dissimilar categories (e.g., a chair image mapped to a cat image). Humans showed systematic mapping patterns, but with greater variability in mapping responses when objects were drawn from dissimilar categories. We simulated the human response of analogical mapping using a computational model of mapping between 3D objects, visiPAM (visual Probabilistic Analogical Mapping). VisiPAM takes point-cloud representations of two 3D objects as inputs, and outputs the mapping between analogous parts of the two objects. VisiPAM consists of a visual module that constructs structural representations of individual objects, and a reasoning module that identifies a probabilistic mapping between parts of the two 3D objects. Model simulations not only capture the qualitative pattern of human mapping performance cross conditions, but also approach human-level reliability in solving visual analogy problems.
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A nonparametric model of object discovery
A detailed model of the outside world is an essential ingredient of human cognition, enabling us to navigate, form goals, exe- cute plans, and avoid danger. Critically, these world models are flexible—they can arbitrarily expand to introduce previously- undetected objects when new information suggests their pres- ence. Although the number of possible undetected objects is theoretically infinite, people rapidly and accurately infer un- seen objects in everyday situations. How? Here we investigate one approach to characterizing this behavior—as nonparamet- ric clustering over low-level cues—and report preliminary re- sults comparing a computational model to human physical in- ferences from real-world video.
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- Award ID(s):
- 2121102
- PAR ID:
- 10576867
- Publisher / Repository:
- Cognitive Science Society
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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