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Title: Modeling Gestalt visual reasoning on Raven’s Matrices using generative image inpainting techniques.
Psychologists recognize Raven’s Progressive Matrices as a useful test of general human intelligence. While many computational models investigate various forms of top-down, deliberative reasoning on the test, there has been less research on bottom-up perceptual processes, like Gestalt image completion, that are also critical in human test performance. In this work, we investigate how Gestalt visual reasoning on the Raven’s test can be modeled using generative image inpainting techniques from computer vision. We demonstrate that a reasoning agent that has access to an off- the-shelf inpainting model trained only on photorealistic images of objects achieves a score of 27/36 on the Colored Progressive Matrices, which corresponds to average performance for nine-year-old children. We also show that when our agent uses inpainting models trained on other datasets (faces, places, and textures), it does not perform as well. Our results illustrate how learning visual regularities in real-world images can translate into successful reasoning about artificial test stimuli. On the flip side, our results also highlight the limitations of such transfer, which may contribute to explanations for why intelligence tests like the Raven’s are often sensitive to people’s individual sociocultural backgrounds.
Authors:
;
Award ID(s):
1730044
Publication Date:
NSF-PAR ID:
10209969
Journal Name:
Proceedings of the Eighth Annual Conference on Advances in Cognitive Systems (ACS)
Sponsoring Org:
National Science Foundation
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