Sensory substitution: Using a vibrotactile device to orient and walk to targets.
Bibliographic record
Abstract
This study investigates how active exploration helps users of sensory substitution devices (SSDs) to detect action-relevant information. A vibrotactile SSD was developed that generates stimulation that is contingent on the users' movements. Target direction was specified by the location of the vibratory stimulation, and target distance by the size and intensity of the pattern of stimulation. A series of experiments was performed with blindfolded participants. In Experiments 1a to 1c, participants used the SSD to align their central body axis with prespecified targets. These experiments differed in the number of actuators that were used and whether online perception-action coupling was present. In Experiment 2, participants approached targets with forward locomotion along a straight line. Experiment 3 combined the previous experiments and studied the concomitant walking and steering toward targets. Oscillatory movements, which facilitated information pickup, were observed in all experiments. The exploratory oscillations were shown to depend on the online perception-action coupling and were related to cases of hyperacuity, for which absolute errors were found to be smaller than the areas of sensitivity of the actuators. It is concluded that, to improve the utility of SSDs, future research with SSDs should pay more attention to the role of active information detection. (PsycINFO Database Record
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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 | 0.002 |
| 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 | 0.000 |
| 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".