Perceived depth modulates perceptual resolution
Bibliographic record
Abstract
Humans constantly use depth information to support perceptual decisions about object size and location in space, as well as planning and executing actions. It was recently reported that perceived depth modulates perceptual performance even when depth information is not relevant to the task, with faster shape discrimination for objects perceived as being close to the observer. However, it is yet to be determined if the observed “close advantage” reflects differences in psychophysical sensitivity or response bias. Moreover, it is unclear whether this advantage is generalizable to other viewing situations and tasks. To address these outstanding issues, we evaluated whether visual resolution is modulated by perceived depth defined by 2D pictorial cues. In a series of experiments, we used the method of constant stimuli to measure the precision of perceptual judgements for stimuli positioned at different perceived distances. In Experiment 1, we found that size discrimination was more precise when the object was perceived to be closer to the observers. Experiments 2a and 2b extended this finding to a visual property orthogonal to depth information, by showing superior orientation discrimination for “close” objects. Finally, Experiment 3 demonstrated that the close advantage also occurs when performing high-level perceptual tasks such as face perception. Taken together, our results provide novel evidence that the perceived depth of an object, as defined by pictorial cues, modulates the precision of visual processing for close objects.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".