The temporal features of size constancy in two- and three-dimensional stimuli reveals a real-world advantage
Bibliographic record
Abstract
Size constancy is the ability to maintain a stable percept of object size despite variations in the retinal image due to changes in viewing distance. Recent research using real-world objects at real distances has demonstrated that this phenomenon emerges at the earliest stages of cortical processing. It remains unclear, however, whether these findings are applicable to both 3D and 2D stimuli. Here, participants were presented with either 3D or 2D stimuli placed at different distances and asked to perform a manual size estimation using their right thumb and index finger. The stimulus physical size was scaled with respect to distance to yield a constant retinal angle. Concurrently, electroencephalographic (EEG) data were recorded using a 64-channel scalp electrode array. Results revealed an advantage for real objects in the computation of size constancy, as indicated by an earlier difference in neural responses to small versus large stimuli, observable in the first positive-going component, peaking at ~80 ms after stimulus onset. In contrast, size constancy for 2D stimuli emerged approximately 150 ms after stimulus onset. Furthermore, stimulus predictability played a role in enabling faster size-distance integration. These findings provide electrophysiological evidence for a ‘real-object advantage’ in size constancy. This advantage may be partially explained by top-down mechanisms, such as affordance—the potential to physically interact with an object—which could enhance the perceptual processing of real 3D objects relative to 2D representations. Additionally, 3D objects may provide visual cues to distance that are not available in 2D stimuli, further contributing to size constancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".