Temporal evolution from retinal image size to perceived size in human visual cortex
Bibliographic record
Abstract
Abstract Our visual system affords a distance-invariant percept of object size by integrating retinal image size with viewing distance (size constancy). Single-unit studies with animals have shown that real changes in distance can modulate the firing rate of neurons in primary visual cortex and even subcortical structures, which raises an intriguing possibility that the required integration for size constancy may occur in the initial visual processing in V1 or even earlier. In humans, however, EEG and brain imaging studies have typically manipulated the apparent (not real) distance of stimuli using pictorial illusions, in which the cues to distance are sparse and not congruent. Here, we physically moved the monitor to different distances from the observer, a more ecologically valid paradigm that emulates what happens in everyday life. Using this paradigm in combination with electroencephalography (EEG), we were able for the first time to examine how the computation of size constancy unfolds in real time under real-world viewing conditions. We showed that even when all distance cues were available and congruent, size constancy took about 150 ms to emerge in the activity of visual cortex. The 150-ms interval exceeds the time required for the visual signals to reach V1, but is consistent with the time typically associated with later processing within V1 or recurrent processing from higher-level visual areas. Therefore, this finding provides unequivocal evidence that size constancy does not occur during the initial signal processing in V1 or earlier, but requires subsequent processing, just like any other feature binding mechanisms.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".