How Simultaneous is the Perception of Binocular Depth and Rivalry in Plaid Stimuli?
Bibliographic record
Abstract
Psychophysical experiments have demonstrated that it is possible to perceive both binocular depth and rivalry in plaids (Buckthought and Wilson 2007, Vision Research47 2543-2556). In a recent study, we investigated the neural substrates for depth and rivalry processing with these plaid patterns, when either a depth or rivalry task was performed (Buckthought and Mendola 2011, Journal of Vision11 1-15). However, the extent to which perception of the two stimulus aspects was truly simultaneous remained somewhat unclear. In the present study, we introduced a new task in which subjects were instructed to perform both depth and rivalry tasks concurrently. Subjects were clearly able to perform both tasks at the same time, but with a modest, symmetric drop in performance when compared to either task carried out alone. Subjects were also able to raise performance levels for either task by performing it with a higher priority, with a decline in performance for the other task. The symmetric declines in performance are consistent with the interpretation that the two tasks are equally demanding of attention (Braun and Julesz 1998, Perception & Psychophysics60 1-23). The results demonstrate the impressive combination of binocular features that supports coincident depth and rivalry in surface perception, within the constraints of presumed orientation and spatial frequency channels.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".