Visual and Haptic Perception of 3D Shape
Bibliographic record
Abstract
In the early 1960's Gibson and Caviness performed various experiments on visual and haptic shape discrimination — experiments whose data and detailed results were, unfortunately, never published. Recently, we have acquired and duplicated the original Gibson 'feelie' stimuli using 3D scanning and printing technologies. In these experiments we examine the visual and haptic perception of the feelies along with the well-studied, naturalistic stimuli (bell peppers) of Norman et al. Method: The stimuli consisted of the 10 Gibson feelies and the original set of Norman's 12 bell peppers. The task was a simple same/different shape discrimination using pairs of objects selected randomly on each trial. There were 52 subjects; each judged 50 vision trials and 50 haptic trials. Modality was varied within-subjects while object type was varied between-subjects. For both modalities stimuli were presented sequentially and exploration was limited to three seconds per stimulus. Visual objects were presented via OpenGL depicted with motion, shading, and specular highlights. Haptic objects were explored behind an occluding curtain. For all presentations the objects had a randomly-determined orientation. Results: In terms of discriminability, performance for the bell peppers was higher than for feelies (d’ of 2.62 vs 2.03, respectively). Judging the shape of the feelies was more difficult than for the bell peppers (F1, 50 = 39.7, p <.0001). With regards to modality there was no effect: haptics were equivalent to vision (d’ = 2.35 haptic vs. d’ = 2.3 vision, F1, 50 = 0.34, p = .56). Finally, there was no interaction — overall effect of object type was similar for both modalities. Discussion: For these classes of stimuli there is apparently no effect of modality on performance but the type of object matters. This is likely due to the relative complexities of the stimuli which would be consistent with Phillips et al. previous findings. Meeting abstract presented at VSS 2012
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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 | 0.006 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".