Assessing the quality of supplementary sensory feedback using a crossmodal congruency task
Bibliographic record
Abstract
Abstract Peripheral nerve interfaces show promise in making prosthetic limbs more biomimetic and ultimately more intuitive and useful for patients. However, approaches to assess these emerging technologies are limited in their scope and the insight they provide. When outfitting a prosthesis with a new feedback system it would be helpful to quantify its physiological correspondence, i.e. how well the experimental feedback mimics the perceived feedback in an intact limb. Here we present an approach to quantify physiological correspondence using a modified crossmodal congruency task. We trained 60 able-bodied subjects to control a bypass prosthesis under different feedback conditions and training durations. We find that the crossmodal congruency effect (CCE) score is sensitive to changes in feedback modality (multi-way ANOVA; F(2,48) = 6.02, p<0.05). After extended training, the CCE score increased as the spatial separation between expected and perceived feedback decreased (unpaired t-test, p<0.05). We present a model that can quantitatively estimate physiological correspondence given the CCE result and the measured spatial separation of the feedback. This quantification approach gives researchers a tool to assess an aspect of emerging augmented feedback systems that is not measurable with current motor assessments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".