Human Performance Modeling with Natural Language (HPM-NL) for Upper-limb Prostheses: Generative pre Trained Transformer (GPT)-Based Rapid HPM Under Low Hallucination
Bibliographic record
Abstract
This preliminary study presents HPM-NL, a natural-language-based cognitive modeling tool developed to estimate task completion time (TCT) for upper-limb prosthesis tasks, specifically the clothespin relocation test (CRT). HPM-NL integrates logic from established human performance modeling frameworks (GOMS, CPM-GOMS, ACT-R, QN-MHP, and SOAR) and returns citation-anchored predictions based on user-input task descriptions. A Wilcoxon signed-rank test revealed no statistically significant difference between HPM-NL and Cogulator estimates for a single CRT cycle, suggesting comparable TCT outputs for this specific task. However, HPM-NL’s current scope is limited to single-task modeling in a structured experimental setting, with no assessment of its predictions across diverse tasks, real users, or broader cognitive measures such as workload. Further limitations include reliance on a proprietary large language model, potential citation errors, lack of empirical validation against human-subject performance data, and uncertainty about generalizability. Despite these constraints, HPM-NL provides an early-stage tool for exploring task modeling in prosthesis research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".