{
  "abstract": "Motor learning is shaped by history, context, and task structure. Such phenomena are commonly explained using state-space models of adaptation or contextual-inference accounts, the latter proposing that inferred latent contexts or task states select or combine internal models or control policies. Here we propose a complementary control-theoretic account grounded in control feasibility.\n\nWe argue that effective motor control and learning may depend on the availability, construction, persistence, and decay of control-relevant variables: quantities available to or constructed within the controller that contribute to the generation, selection, or regulation of action through feedback, feedforward, or combined control pathways. Stabilizing variables form the subset of these control-relevant variables whose use within a controller supports stable and reliable regulation. This role is conditional, and the same variable may stabilize one controller or parameter regime yet be ineffective or destabilizing in another.\n\nUsing simplified plant-coupled simulations, we examine compensatory variables and dynamic states within feedback and feedforward architectures. Crossing force-bias and velocity-linked variables with constant-load and viscous perturbations shows that benefit depends on structural matching, while retained dynamic states produce aftereffect-like behavior after perturbation removal. An augmented-state analysis identifies a finite compensatory-gain boundary beyond which the constructed state destabilizes the closed loop. A curl-field reaching model shows how delay, noise, and contextual identifiability constrain compensation.\n\nThe framework provides a control-feasibility level of analysis. It suggests that effective motor control and learning may depend not only on error, reward, or contextual inference, but on whether variables are available, identifiable, timely, and compatible with stable control.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Ian S. Howard"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1kvwzkm24hyb6.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/hs7bt_v2",
  "fulltext_url": null,
  "id": "1kvwzkm24hyb6",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/hs7bt_v2/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Behavioral Neuroscience"
    ],
    [
      "bepress",
      "Cognitive Neuroscience"
    ],
    [
      "bepress",
      "Cognitive Psychology"
    ],
    [
      "bepress",
      "Neuroscience"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "openalex",
      "2805|Motor Control and Adaptation"
    ]
  ],
  "openalex": "W7212261137",
  "pdf_url": "https://osf.io/hs7bt/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1kvwzkm24hyb6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:30Z",
      "native_id": "hs7bt",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/hs7bt_v2/"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "bepress:Cognitive Neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Stabilizing Variables in Motor Control and Learning: A Control-Theoretic Account",
  "title_text": "Stabilizing Variables in Motor Control and Learning: A Control-Theoretic Account",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:30Z",
  "url": "https://research.draconitic.com/paper/1kvwzkm24hyb6",
  "venue": "PsyArXiv",
  "versions": []
}