Identification of walked-upon materials in auditory, kinesthetic, haptic, and audio-haptic conditions
Bibliographic record
Abstract
Locomotion generates multisensory information about walked-upon objects. How perceptual systems use such information to get to know the environment remains unexplored. The ability to identify solid (e.g., marble) and aggregate (e.g., gravel) walked-upon materials was investigated in auditory, haptic or audio-haptic conditions, and in a kinesthetic condition where tactile information was perturbed with a vibromechanical noise. Overall, identification performance was better than chance in all experimental conditions and for both solids and the better identified aggregates. Despite large mechanical differences between the response of solids and aggregates to locomotion, for both material categories discrimination was at its worst in the auditory and kinesthetic conditions and at its best in the haptic and audio-haptic conditions. An analysis of the dominance of sensory information in the audio-haptic context supported a focus on the most accurate modality, haptics, but only for the identification of solid materials. When identifying aggregates, response biases appeared to produce a focus on the least accurate modality--kinesthesia. When walking on loose materials such as gravel, individuals do not perceive surfaces by focusing on the most accurate modality, but by focusing on the modality that would most promptly signal postural instabilities.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".