Cognitive map formation in the blind is enhanced by three-dimensional tactile information
Bibliographic record
Abstract
For blind individuals, tactile maps are useful tools to form cognitive maps through touch. However, they still experience challenges in cognitive map formation and independent navigation. Three-dimensional (3D) tactile information is thus increasingly being considered to convey enriched spatial information, but it remains unclear if it can facilitate cognitive map formation compared to traditional two-dimensional (2D) tactile information. Consequently, the present study investigated the impact of the type of sensory input (tactile 2D vs. tactile 3D vs. a visual control condition) on cognitive map formation. To do so, early blind (EB, n = 13), late blind (LB, n = 12), and sighted control (SC, n = 14) participants were tasked to learn the layouts of mazes produced with different sensory information (tactile 2D vs. tactile 3D vs. visual control) and to infer routes from memory. Results show that EB manifested stronger cognitive map formation with 3D mazes, LB performed equally well with 2D and 3D tactile mazes, and SC manifested equivalent cognitive map formation with visual and 3D tactile mazes but were negatively impacted by 2D tactile mazes. 3D tactile maps therefore have the potential to improve spatial learning for EB and newly blind individuals through a reduction of cognitive overload. Installation of 3D tactile maps in public spaces should be considered to promote universal accessibility and reduce blind individuals' wayfinding deficits related to the inaccessibility of spatial information through non-visual means.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".