Multimodal Ternus: Visual, Tactile, and Visuo — Tactile Grouping in Apparent Motion
Bibliographic record
Abstract
Gestalt rules that describe how visual stimuli are grouped also apply to sounds, but it is unknown if the Gestalt rules also apply to tactile or uniquely multimodal stimuli. To investigate these rules, we used lights, touches, and a combination of lights and touches, arranged in a classic Ternus configuration. Three stimuli (A, B, C) were arranged in a row across three fingers. A and B were presented for 50 ms and, after a delay, B and C were presented for 50 ms. Subjects were asked whether they perceived AB moving to BC (group motion) or A moving to C (element motion). For all three types of stimuli, at short delays, A to C dominated, while at longer delays AB to BC dominated. The critical delay, where perception changed from group to element motion, was significantly different for the visual Ternus (3 lights, 162 ms) and the tactile Ternus (3 touches, 195 ms). The critical delay for the multimodal Ternus (3 light-touch pairs, 161 ms) was not different from the visual or tactile Ternus effects. In a second experiment, subjects were exposed to 2.5 min of visual group motion (stimulus onset asynchrony = 300 ms). The exposure caused a shift in the critical delay of the visual Ternus, a trend in the same direction for the multimodal Ternus, but no shift in the tactile Ternus. These results suggest separate but similar grouping rules for visual, tactile, and multimodal stimuli.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".