Electrophysiological correlates of size constancy
Bibliographic record
Abstract
Size constancy is the ability of the visual system to achieve a stable experience of perceived size despite the fact that the image projected onto the retina varies continuously with viewing distance. To compute the perceived size of an object, our visual system needs to combine together retinal image size with distance information. To date, there has been little investigation on the neural mechanisms that underlie size constancy in the human brain in a situation in which the real, rather than the apparent, distance of the stimulus is manipulated. In the present study, event-related potentials (ERPs) were measured to investigate the temporal dynamics of size-distance scaling. The viewing distance and the retinal image size of a series of filled black circles were varied to create four experimental conditions: 'small-near', 'big-near', 'small-far', and 'big-far'. The critical conditions were those in which the stimuli were perceived as different in size but subtended the same retinal angle as a result of their different distance from the observer (i.e. 'small-near' vs. 'big-far') as well as those in which the stimuli were perceived as constant but their retinal image size decreased with distance (i.e. small-near vs. small-far, big-near vs. big-far). Participants were asked to maintain their gaze steadily on a fixation point throughout the experiment while EEG was recorded from 28 scalp electrodes. We focused on the first visual evoked ERP component peaking at approximately 100 ms after stimulus onset. We found earlier latencies in response to larger than smaller stimuli, regardless of their distance. Moreover, we observed that the amplitude was greater in the far than in the near condition, regardless of stimulus size. These findings provide novel evidence that size constancy involves operations that take place at the earliest cortical stages in conditions in which the real, rather than the apparent, distance changes. Meeting abstract presented at VSS 2014
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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) | 0.002 | 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".