The Role of Choice in Longitudinal Recall of Meaningful Tactile Signals
Bibliographic record
Abstract
Haptic icons (brief tactile stimuli with associated meanings) have the potential to convey abstract information through touch; however, there has been little systematic investigation of how sets of perceptually distinct tactile signals can be best utilized to convey meanings, nor of how enduring these associations can be. We hypothesized that when users can choose the signals which will represent specific concepts, their learning and recall will be eased and enhanced. Taking future embedded interfaces as context, we used two sets of 10 distinct tactile signals to compare recall of concept-meaning associations in two conditions: (1) arbitrarily assigned and (2) participant-chosen associations. Participants learned associations in under 20 minutes at 80% accuracy; at 2 weeks, recall of the associations previously learned was 86% with no significant effect of assignment condition. Subjective confidence levels sharply lagged actual performance, with zero expectation of ability to recall at 2 weeks. To the best of our knowledge, this is the first study of either longitudinal recall or the role of user choice on synthesized stimulus-meaning leamability. Its results underscore the eminent practicality of using haptic icons in everyday interface design, suggesting high learnability and a surprising user ability to find their own mnemonics for carefully composed stimuli, regardless of how associations are assigned.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".