{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predictive-coding-feedback-results-in","title":"Predictive coding feedback results in perceived illusory contours in a recurrent neural network","arxiv_id":"2102.01955","date":"2021-02-03","proceeding":"NeurIPS Workshop SVRHM 2020 12","authors":["Zhaoyang Pang","Callum Biggs O'May","Bhavin Choksi","Rufin VanRullen"],"abstract":"Modern feedforward convolutional neural networks (CNNs) can now solve some computer vision tasks at super-human levels. However, these networks only roughly mimic human visual perception. One difference from human vision is that they do not appear to perceive illusory contours (e.g. Kanizsa squares) in the same way humans do. Physiological evidence from visual cortex suggests that the perception of illusory contours could involve feedback connections. Would recurrent feedback neural networks perceive illusory contours like humans? In this work we equip a deep feedforward convolutional network with brain-inspired recurrent dynamics. The network was first pretrained with an unsupervised reconstruction objective on a natural image dataset, to expose it to natural object contour statistics. Then, a classification decision layer was added and the model was finetuned on a form discrimination task: squares vs. randomly oriented inducer shapes (no illusory contour). Finally, the model was tested with the unfamiliar ''illusory contour'' configuration: inducer shapes oriented to form an illusory square. Compared with feedforward baselines, the iterative ''predictive coding'' feedback resulted in more illusory contours being classified as physical squares. The perception of the illusory contour was measurable in the luminance profile of the image reconstructions produced by the model, demonstrating that the model really ''sees'' the illusion. Ablation studies revealed that natural image pretraining and feedback error correction are both critical to the perception of the illusion. Finally we validated our conclusions in a deeper network (VGG): adding the same predictive coding feedback dynamics again leads to the perception of illusory contours.","url_abs":"https://arxiv.org/abs/2102.01955v2","url_pdf":"https://arxiv.org/pdf/2102.01955v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predictive-coding-feedback-results-in","repo_url":"https://github.com/rufinv/Illusory-Contour-Predictive-Networks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"predictive-coding-feedback-results-in","repo_url":"https://github.com/SagarDollin/Computer_Vision_to_perceive_illusion","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2102.01955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.01955"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/SagarDollin/Computer_Vision_to_perceive_illusion","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rufinv/Illusory-Contour-Predictive-Networks","reach":{"status":"unanswered"}}],"summary":{"ran_violates":1,"ran_draft_wrong":1},"by_repo_kind":{"listed":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":2,"samples":[{"code_sha256_prefix":"ebcd07e902665755","entry":"normalize","repo":"SagarDollin/Computer_Vision_to_perceive_illusion","repo_kind":"listed","path":"model_utilities.py","file_url":"https://github.com/SagarDollin/Computer_Vision_to_perceive_illusion/blob/HEAD/model_utilities.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ebcd07e902665755"}},{"code_sha256_prefix":"30e606ad0d8b9dd6","entry":"predict_encoder","repo":"SagarDollin/Computer_Vision_to_perceive_illusion","repo_kind":"listed","path":"Model_and_train.py","file_url":"https://github.com/SagarDollin/Computer_Vision_to_perceive_illusion/blob/HEAD/Model_and_train.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"30e606ad0d8b9dd6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}