A new psychometric questionnaire for reporting of somatosensory percepts
Bibliographic record
Abstract
OBJECTIVE: There have been remarkable advances over the past decade in neural prostheses to restore lost motor function. However, restoration of somatosensory feedback, which is essential for fine motor control and user acceptance, has lagged behind. With an increasing interest in using electrical stimulation to restore somatosensory sensations within the peripheral (PNS) and central nervous systems (CNS), it is critical to characterize the percepts evoked by electrical stimulation in a standardized manner with a validated psychometric questionnaire. This will allow comparison of results from applications at various nervous system levels in multiple settings. APPROACH: We compiled a summary of published reports of somatosensory percepts that were elicited by electrical stimulation in humans and used these to develop a new psychometric questionnaire. RESULTS: This new questionnaire was able to characterize subjective evoked sensations with good test-retest reliability (Spearman's correlation coefficients ranging 0.716 ⩽ ρ ⩽ 1.000, p ⩽ 0.005) in 13 subjects receiving stimulation through neural implants in both the CNS and PNS. Furthermore, the new questionnaire captured more descriptors (M = 2.65, SD = 0.91) that would have been missed by being categorized as 'other sensations', using a previous questionnaire (M = 1.40, SD = 0.77, t(12) = -10.24, p < 0.001). Lastly, the new questionnaire was able to capture different descriptors within subjects using different patterns of electrical stimulation (Wilk's Lambda = 0.42, F(3, 10) = 4.58, p = 0.029). SIGNIFICANCE: This new somatosensory psychometric questionnaire will aid in establishing consistency and standardization of reporting in future studies of somatosensory neural prostheses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".