Non-visual identification of walking grounds
Bibliographic record
Abstract
We investigated the role of haptic, proprioceptive and auditory information in the non-visual identification of walking grounds. We selected four solid materials (e.g., marble) and four aggregate materials (e.g., fine gravel). Five observers identified the materials in each of four experimental conditions: multisensory, haptic, proprioceptive, and auditory. In the auditory condition, they were presented with walking sounds they produced. In the other conditions, observers walked blindfolded on the materials. In the haptic and proprioceptive conditions auditory information was masked. In the proprioceptive condition haptic information was masked. No masking took place in the multisensory condition. In all conditions, solids and aggregates were seldom confused, and aggregates were better identified than solids. Chance identification performance was observed only for solids in the presence of simultaneous haptic and auditory masking, suggesting a secondary role of proprioceptive information. In the proprioceptive condition, identification of aggregates was better than chance, suggesting a significant role of proprioception. Identification of aggregates was at its best in the haptic condition, and not in the multisensory condition, suggesting either an inconsistency of information between auditory and non-auditory channels, or conflicting cognitive strategies for the exploitation of these sources of information in multisensory contexts.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".