Neural Signals Underlying the Convexity Context Effect
Bibliographic record
Abstract
Convexity is relevant to figure assignment, but the relationship is display-dependent: Convex regions are more likely to be perceived as figures in 8- than 2-region displays, provided concave regions are homogeneously colored. This convexity context effect (CCE) may occur because multiple homogeneous concave regions (8-region displays) are more likely than the single concave region (2-region displays) to be interpolated into a surface behind the convex regions, making convex regions stand out as figures in 3D. Without such interpolation, 2-region displays may appear flat. Here we examined the neural signals underlying CCEs using electroencephalography to determine the effect of region number and perceptual interpretation on those signals. On each trial, a small probe appeared on one of two central regions of 2- or 8-region black & white displays. Observers (N=10) indicated whether the region containing the probe was figure or ground. Overall behavioural results were in the direction predicted by the CCE, although there was considerable variability across individuals. Our most consistent result was an effect of region number on the P2 amplitude for the grand average evoked response potentials (ERPs), with greater amplitudes for 8- than 2-region displays (p<0.0001). We found additional, but smaller, effects of region number on P1 and N1 latency: 8-region displays led to earlier peaks than 2-region. At the group level, there were no effects of figure-ground perception up to about 300 ms. Highly variable behavioural CCEs obtained across subjects led us to examine effects of perceptual differences on ERPs. When considering just subjects with strong behavioural CCEs, P1 and N1 components showed an effect of perceptual interpretation in the 8- but not 2-region displays. Overall, our results suggest that the magnitude of CCEs may be correlated with differences in early neural signals, and that the P2 may be linked to the perception of 3-dimensionality. 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 | 0.001 |
| 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".