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{ "pk": 28797, "title": "The contrasting roles of shape in human vision and convolutional neural networks", "subtitle": null, "abstract": "Convolutional neural networks (CNNs) were inspired by hu-man vision and, in some settings, achieve a performance com-parable to human object recognition. This has lead to the spec-ulation that both systems use similar mechanisms to performrecognition. In this study, we conducted a series of simulationsthat indicate that there is a fundamental difference between hu-man vision and vanilla CNNs: while object recognition in hu-mans relies on analysing shape, these CNNs do not have sucha shape-bias. We teased apart the type of features selectedby the model by modifying the CIFAR-10 dataset so that, inaddition to containing objects with shape, the images concur-rently contained non-shape features, such as a noise-like mask.When trained on these modified set of images, the model didnot show any bias towards selecting shapes as features. In-stead it relied on whichever feature allowed it to perform thebest prediction – even when this feature was a noise-like maskor a single predictive pixel amongst 50176 pixels.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [], "section": "Papers with Poster Presentations", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/12g3p0hn", "frozenauthors": [ { "first_name": "Gaurav", "middle_name": "", "last_name": "Malhotra", "name_suffix": "", "institution": "University of Bristol", "department": "" }, { "first_name": "Jeffrey", "middle_name": "", "last_name": "Bowers", "name_suffix": "", "institution": "University of Bristol", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2019-01-01T18:00:00Z", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/28797/galley/18668/download/" } ] }