Perceived depth modulates the precision of visual processing
Bibliographic record
Abstract
Humans constantly use depth information to support perceptual decisions about object size and location, as well as planning and executing actions. Given the unique role of depth information in human vision, it has been proposed that perceived depth might influence visual processing. In particular, objects that are perceived as closer to the observer are processed by dedicated neural resources because they are more behaviorally relevant for both perception and action. Consistent with this proposal, there is evidence that shape discrimination is better for objects perceived as being closer to the observer. However, it is not clear from these studies if the reported processing advantage reflects changes in psychophysical sensitivity or bias. Here we evaluate whether visual resolution is modulated by perceived depth defined by 2D pictorial cues (perspective and size). In a series of experiments, we used the method of constant stimuli to measure discrimination thresholds for the length (Experiment 1) and orientation (Experiment 2) of pairs of lines. Just Noticeable Differences (JND) as well as Reaction Times (RT) were measured for pairs of stimuli positioned either on the ‘near’ or ‘far’ portion of the Ponzo Illusion, as well as a neutral version with no depth cues ‘flat’. In both experiments, despite the fact that all stimuli were physically at the same distance, we found enhanced discrimination for objects perceived as closer in depth. Importantly, the improvement associated with location in depth was observed for both the JND and RT measures. Taken together, our results provide novel evidence that the location of an object in depth, as defined by pictorial cues, modulates the precision of visual processing.
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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 | 0.003 |
| 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 | 0.000 |
| 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".