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{ "pk": 27884, "title": "Using Deep-Learning Representations of Complex Natural Stimuli as Input to Psychological Models of Classification", "subtitle": null, "abstract": "Tests of formal models of human categorization have\ntraditionally been restricted to artificial categories because\nderiving psychological representations for large numbers of\nnatural stimuli has been an intractable task. We show that deep\nlearning may be used to solve this problem. We train an\nensemble of convolutional neural networks (CNNs) to produce\nthe multidimensional scaling (MDS) coordinates of images of\nrocks. We then show that not only are the CNNs able to predict\nthe MDS coordinates of a held-out test set of rocks, but that the\nCNN-derived representations can be used in combination with\na formal psychological model to predict human categorization\nbehavior on a completely new set of rocks.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [ { "word": "Deep learning" }, { "word": "multidimensional scaling" }, { "word": "Categorization" }, { "word": "psychological representations" } ], "section": "Publication-based-Talks", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/3vj7j48f", "frozenauthors": [ { "first_name": "Craig", "middle_name": "A", "last_name": "Sanders", "name_suffix": "", "institution": "Indiana University, Bloomington", "department": "" }, { "first_name": "Robert", "middle_name": "M", "last_name": "Nosofsky", "name_suffix": "", "institution": "Indiana University, Bloomington", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2018-01-01T18:00:00Z", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/27884/galley/17522/download/" } ] }