Cognitive design in action: developing assistive technology for situational awareness for persons who are blind
Bibliographic record
Abstract
Cognitive design constitutes a cognitively-informed engineering method for developing assistive technologies. The approach is challenging in that it involves matching key cognitive principles for a given problem domain to engineering principles, and that an independent validation procedure is required for the cognitive component. In addition, we argue for a broad set of evaluation criteria and adapt a participatory design framework, one that involves the client population throughout the design process. After laying out the main precepts of the approach, we illustrate these via a particular design process, seeking to provide situational awareness and navigational assistance to persons who are blind. The problem domain is described in some detail. A solution is then presented that involves matching the need for configural knowledge about the person's surroundings with a hierarchical organisation in the spatial database so that information may be presented to the user at different levels of detail. The process involved to implement this solution is then outlined, and appropriate validation experiments described. It is noted that the cognitive design process as presented here is in use now in a number of initiatives, and that it involves a high degree of collaboration between experts from different disciplines.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".