{"pk":29941,"title":"Modeling Gestalt Visual Reasoning on Ravens Progressive Matrices UsingGenerative Image Inpainting Techniques","subtitle":null,"abstract":"Psychologists recognize Raven’s Progressive Matrices as an effective test of general intelligence. While many computa-tional models investigate top-down, deliberative reasoning on the test, there has been less research on bottom-up perceptualprocesses, like Gestalt image completion, that are also critical in human test performance. We investigate how Gestalt vi-sual reasoning on the Raven’s test can be modeled using generative image inpainting techniques from computer vision.We demonstrate that a reasoning agent using an off-the-shelf inpainting model trained on object photographs achieves ascore of 27/36 on the Colored Progressive Matrices, which corresponds to average performance for nine-year-old chil-dren. When our agent uses inpainting models trained on other datasets (faces, places, and textures), performance is lower.Our results illustrate how learning visual regularities in real-world images can translate into successful reasoning aboutartificial test stimuli, and also how different learning inputs translate into different levels of performance.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Poster Session 3","is_remote":true,"remote_url":"https://escholarship.org/uc/item/45q0g1j5","frozenauthors":[{"first_name":"Tianyu","middle_name":"","last_name":"Hua","name_suffix":"","institution":"Vanderbilt University","department":""},{"first_name":"Maithilee","middle_name":"","last_name":"Kunda","name_suffix":"","institution":"Vanderbilt University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2020-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/29941/galley/19795/download/"}]}