Exploring the interplay of visual and haptic modalities in a pattern-matching task
Bibliographic record
Abstract
It is not well understood how working memory deals with coupled haptic and visual presentation modes. Present theoretical understandings of human cognition indicate that these modes are processed by the visuospatial sketchpad. If this is accurate, then there may be no efficiency in distributing information between the haptic and visual modalities in situations of visual overload. However, this needs to be empirically explored. In this paper, we describe an evaluation of human performance in a pattern-matching task involving a fingertip interface that can present both haptic and visual information. Our purpose was to explore the interplay of visual and haptic processing in working memory, in particular how presentation mode affects performance. We designed a comparative study involving a pattern-matching task. Users were presented with a sequence of two patterns through different modalities using a fingertip interface and were asked to differentiate between them. While no significant difference was found between the visual and visual+haptic presentation modes, the results indicate a strong partiality for the coupling of visual and haptic modalities. This suggests that working memory is not hampered by using both visual and haptic channels, and that recall may be strengthened by dual-coding both visual and haptic modes.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".