{
  "abstract": "Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Over time, task-relevant information became concentrated in fewer neurons, unlike with fixed decoders. At the population level, task information also became largely confined to a few neural modes that accounted for an unexpectedly small fraction of the population variance. A neural network model suggests the adaptive decoders directly contribute to forming these more compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Bioengineering, University of Washington, Seattle, WA, USA",
    "Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada",
    "Mila - Québec Artificial Intelligence Institute, Montréal, QC, Canada",
    "Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada. g.lajoie@umontreal.ca",
    "Mila - Québec Artificial Intelligence Institute, Montréal, QC, Canada. g.lajoie@umontreal.ca",
    "Department of Bioengineering, University of Washington, Seattle, WA, USA. aorsborn@uw.edu",
    "Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA. aorsborn@uw.edu",
    "Washington National Biomedical Research Center, Seattle, WA, USA. aorsborn@uw.edu"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Pavithra Rajeswaran",
      "orcid": "0009-0008-6441-9835"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Alexandre Payeur",
      "orcid": "0000-0002-2437-8249"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Guillaume Lajoie",
      "orcid": "0000-0003-2730-7291"
    },
    {
      "affs": [
        5,
        6,
        7
      ],
      "name": "Amy L. Orsborn",
      "orcid": "0000-0003-4131-5781"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6ayrhahb2jeqr.bib",
  "cited_by": 11,
  "doi": "10.1038/s41467-026-76109-y",
  "fulltext_url": null,
  "id": "6ayrhahb2jeqr",
  "issns": [
    "2041-1723"
  ],
  "landing_url": "https://doi.org/10.1038/s41467-026-76109-y",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7213337904",
  "pdf_url": "https://www.nature.com/articles/s41467-026-76109-y.pdf",
  "pmcid": "PMC10983972",
  "pmid": "38562772",
  "published": "2026-09-15",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/6ayrhahb2jeqr.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T13:21:11Z",
      "native_id": "W7213337904",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41467-026-76109-y"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|EEG and Brain-Computer Interfaces",
      "subfield": "neuroscience"
    }
  ],
  "title": "Assistive algorithms influence neural representations in motor brain-computer interfaces",
  "title_text": "Assistive algorithms influence neural representations in motor brain-computer interfaces",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:12Z",
  "url": "https://research.draconitic.com/paper/6ayrhahb2jeqr",
  "venue": "Nature Communications",
  "versions": []
}