{"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/quantifying-task-relevant-representational","title":"Quantifying task-relevant representational similarity using decision variable correlation","arxiv_id":"2506.02164","date":"2025-06-02","proceeding":null,"authors":["Yu","Qian","Wilson S. Geisler","Xue-Xin Wei"],"abstract":"Previous studies have compared the brain and deep neural networks trained on image classification. Intriguingly, while some suggest that their representations are highly similar, others argued the opposite. Here, we propose a new approach to characterize the similarity of the decision strategies of two observers (models or brains) using decision variable correlation (DVC). DVC quantifies the correlation between decoded decisions on individual samples in a classification task and thus can capture task-relevant information rather than general representational alignment. We evaluate this method using monkey V4/IT recordings and models trained on image classification tasks. We find that model--model similarity is comparable to monkey--monkey similarity, whereas model--monkey similarity is consistently lower and, surprisingly, decreases with increasing ImageNet-1k performance. While adversarial training enhances robustness, it does not improve model--monkey similarity in task-relevant dimensions; however, it markedly increases model--model similarity. Similarly, pre-training on larger datasets does not improve model--monkey similarity. These results suggest a fundamental divergence between the task-relevant representations in monkey V4/IT and those learned by models trained on image classification tasks.","url_abs":"https://arxiv.org/abs/2506.02164v1","url_pdf":"https://arxiv.org/pdf/2506.02164v1.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":[],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2506.02164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.02164"}},"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":"deterministic:regex_extraction","url":"https://github.com/wei-bbc-lab/DVC","reach":null}],"summary":{"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"found_in_text":{"samples":2,"ran":1,"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":"a574fdab02370c60","entry":"calculate_dv_correlation","repo":"wei-bbc-lab/DVC","repo_kind":"found_in_text","path":"DVC.py","file_url":"https://github.com/wei-bbc-lab/DVC/blob/HEAD/DVC.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"a574fdab02370c60"}},{"code_sha256_prefix":"f6a02920cd7f1975","entry":"landmark_mds","repo":"wei-bbc-lab/DVC","repo_kind":"found_in_text","path":"DVC.py","file_url":"https://github.com/wei-bbc-lab/DVC/blob/HEAD/DVC.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f6a02920cd7f1975"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}