Combining visual and haptic cues when judging a rod’s verticality
Bibliographic record
Abstract
The Subjective Visual (SVV) and Haptic (SHV) Vertical indicate the perceived direction of gravity. Previous methods have relied on participants adjusting a rod manually, potentially introducing movement-related confounds. Here we used robust psychophysical techniques to directly compare the SVV, SHV, and a novel multimodal paradigm. Participants judged if a probe rod (30.5 cm long, 0.9 cm diameter) was tilted to the right (clockwise) or left (counterclockwise) with respect to gravity, during whole-body tilt 30° leftwards in the roll plane. A motor set the orientation of the rod; tested orientations were chosen using a PSI adaptive staircase optimized to estimate the reliability of each judgment. For SVV, the central 5 cm of the rod was illuminated and viewed through a diffusing screen that reduced the reliability of judgments to a level comparable to SHV judgments. For SHV, participants explored the rod by touch. In a third, multimodal condition, participants used both visual and haptic cues simultaneously. The standard deviations of the SVV, SHV and multimodal estimates were 3.9° ± 0.2°, 5.5° ± 0.9°, and 2.9° ± 0.2° respectively. Across subjects, the variability and orientation of combined-cue estimates were consistent with the maximum likelihood estimate model. We conclude that, as when comparing haptic and visual information for shape (Ernst and Banks, 2002), perceived orientation also results from optimal combination of available cues and that this estimate is available for making cross-modal judgements with the perceived direction of gravity.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".