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Modeling Visual Problem-solving as Analogical Reasoning

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Modeling visual problem-solving as analogical reasoning

Andrew Lovett

Northwestern University Chicago, US andrew@cs.northwestern.edu

Abstract

Visual problem-solving tasks are powerful tools for evaluating intelli- gence and creative thinking in humans. For example, the Ravens Pro- gressive Matrices is one of the best single-test predictors of a persons spatial, verbal, and mathematical ability. To better understand the skills that allow people to succeed at problem-solving, I have developed a computational model. The model builds on the claim that analog- ical reasoning lies at the heart of visual problem-solving. Images are compared via structure-mapping, aligning the common relational struc- ture in two images to identify commonalities and differences. These commonalities or differences can themselves be reified and used as the input for future comparisons. When images fail to align, the model re-represents them to facilitate the comparison. In this talk, I describe what the model has taught me, in terms of the challenges faced dur- ing problem-solving and the skills that can be used to overcome those challenges.

Copyrightc by the paper’s authors. Copying permitted for private and academic purposes.

In: A.M. Olteteanu, Z. Falomir (eds.): Proceedings of ProSocrates 2017: Symposium on Problem-solving, Creativity and Spatial Reasoning in Cognitive Systems, at Delmenhorst, Germany, July 2017, published at http://ceur-ws.org

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