User confidence and electrodermal activity during haptic exploration for perceptual comparisons using a robotic system
Bibliographic record
Abstract
Children with physical impairments may have trouble effectively performing the hand movements used in haptic exploration and may miss information about object properties. Assistive robotic systems with haptic feedback may enable children with physical impairments to haptically explore objects. However, it is important to understand if they might be encountering difficulty in assessing object properties with the system. As such, we examined two methods to ascertain a user's uncertainty or stress when doing the exploration, user confidence and electrodermal activity (EDA). Twenty adults and ten children without physical impairments manipulated four pairs of objects to examine size, roughness, hardness and shape. All participants performed the manipulation by controlling a robotic system and by manual exploration in a randomized crossover design. Adults' confidence was lower when using the robotic system and correlated with lower accuracy at determining object properties. Children's responses indicated that they may not have understood how to self-rate confidence. EDA, a potentially objective measure of stress during the task, was actually lower for adults when they used the robotic system, suggesting less physiological arousal than when using their hands. Children's EDA was variable.
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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 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".