{"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/learning-neural-representations-of-human","title":"Learning Neural Representations of Human Cognition across Many fMRI Studies","arxiv_id":"1710.11438","date":"2017-10-31","proceeding":"NeurIPS 2017 12","authors":["Arthur Mensch","Julien Mairal","Danilo Bzdok","Bertrand Thirion","Gaël Varoquaux"],"abstract":"Cognitive neuroscience is enjoying rapid increase in extensive public\nbrain-imaging datasets. It opens the door to large-scale statistical models.\nFinding a unified perspective for all available data calls for scalable and\nautomated solutions to an old challenge: how to aggregate heterogeneous\ninformation on brain function into a universal cognitive system that relates\nmental operations/cognitive processes/psychological tasks to brain networks? We\ncast this challenge in a machine-learning approach to predict conditions from\nstatistical brain maps across different studies. For this, we leverage\nmulti-task learning and multi-scale dimension reduction to learn\nlow-dimensional representations of brain images that carry cognitive\ninformation and can be robustly associated with psychological stimuli. Our\nmulti-dataset classification model achieves the best prediction performance on\nseveral large reference datasets, compared to models without cognitive-aware\nlow-dimension representations, it brings a substantial performance boost to the\nanalysis of small datasets, and can be introspected to identify universal\ntemplate cognitive concepts.","url_abs":"http://arxiv.org/abs/1710.11438v2","url_pdf":"http://arxiv.org/pdf/1710.11438v2.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":"learning-neural-representations-of-human","repo_url":"https://github.com/arthurmensch/cogspaces","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-2-Clause"}}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1710.11438","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.11438"}},"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. 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