{
  "abstract": "We investigate a theory-informed attention framework combining Cognitive Load Theory (CLT)-inspired resource regulation with Token–Context Attention (TCA) and a TCA-specific application of root-mean-square (RMS) branch normalization. Seven primary conditions were evaluated across SNLI, SST-2, IMDb, AG News, and DBPedia 14 using 10 paired seeds and locked data manifests, with a validation-calibrated fixed-gate control for adaptive TCA-RMS. A separately locked secondary ablation added entropy-only CLT (CLT-E) to self-attention, TCA, and TCA-RMS under the same records and seeds. RMS branch normalization increased mean raw-TCA accuracy on all five datasets, with Holm-adjusted raw-TCA accuracy support on IMDb and AG News. In contrast, none of the prespecified direct mixed-CLT accuracy additions survived the within-dataset Holm correction; CLT-related evidence was localized to particular architectures, datasets, and metrics. No adaptive-versus-fixed accuracy contrast was detected after correction, and the entropy-only ablation shows that useful attenuation can arise without the full entropy–uncertainty–IDF composition. Accordingly, the results support branch-scale normalization and bounded attention-output gating as separable architectural interventions, while neither universal accuracy improvement nor human cognitive-load construct validity is claimed. These conclusions are restricted to lightweight, from-scratch classifiers trained under locked limited-data protocols; whether pretrained encoders change the need for TCA branch normalization or the effects of the CLT-inspired gate remains an open empirical question.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, Texas Tech University, Lubbock, TX 79409, USA"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jarrod Graham"
    },
    {
      "affs": [
        0
      ],
      "name": "Victor S. Sheng",
      "orcid": "0000-0003-4960-174X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/encdtj36n6c1q.bib",
  "cited_by": 0,
  "doi": "10.3390/math14214076",
  "fulltext_url": null,
  "id": "encdtj36n6c1q",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/21/4076",
  "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": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/encdtj36n6c1q.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T00:21:18Z",
      "native_id": "10.3390/math14214076",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/21/4076"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Cognitive Load-Informed Attention Budgets Across Self-Attention and Token–Context Attention",
  "title_text": "Cognitive Load-Informed Attention Budgets Across Self-Attention and Token–Context Attention",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T00:21:18Z",
  "url": "https://research.draconitic.com/paper/encdtj36n6c1q",
  "venue": "Mathematics",
  "versions": []
}