The haptic crayola effect: Exploring the role of naming in learning haptic stimuli
Bibliographic record
Abstract
A haptic icon is a short physical stimulus attached to a simple meaning, which provides information and feedback to a user. To scale the utility demonstrated for small icon sets to larger ones, we need efficient strategies to help users learn subtle distinctions among stimuli, in a modality for which they may not hold detailed descriptive percepts. This paper investigates the effect of naming haptic stimuli - i.e. explicitly creating a linguistic marker - on the accuracy with which users are able to identify, distinguish, and recall stimuli. We conducted a between-subjects experiment using 60 participants equally divided among three naming conditions: no names, pre-selected non-descriptive names, and self-selected names. The experiment examined the impact of naming strategy on the ability of participants to identify stimuli in a nonverbal matching test, and on remembering stimulus names. For this challenging task and the degree of learning afforded, naming did not significantly impact accuracy of matching stimuli to meanings for all participants. However, more than twice of many of those allowed to choose names reported the ability to remember and distinguish stimuli than those required to use non-descriptive names, and many participants felt that the names were useful. Of middle-performing participants, the self-selected names group performed significantly better than the non-descriptive names group, and appeared to progress more quickly in learning. We summarize evidence for a trend that might widen with refined naming strategies and more extensive learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".