Afterimage size is modulated by size-contrast illusions
Bibliographic record
Abstract
Traditionally, the perceived size of negative afterimages has been examined in relation to E. Emmert's law (1881), a size-distance equation that states that changes in perceived size of an afterimage are a function of the distance of the surface on which it is projected. Here, we present evidence that the size of an afterimage is also modulated by its surrounding context. We employed a new version of the Ebbinghaus-Titchener illusion with flickering surrounding stimuli and a static inner target that generated a vivid afterimage of the latter but not the former. Observers were asked to give an initial manual estimate of the size of the inner target during the adaptation phase followed by another manual estimate of the size of the afterimage during the test phase. Manual estimates were affected by the size-contrast illusion both when the surrounding contextual elements were present during afterimage induction and when the surrounding elements were absent during the viewing of the afterimage (Experiment 1). Such a modulation in perceived size, however, did not occur when observers viewed only the flickering surrounding context for a prolonged period of time and then estimated the size of a static target presented on the monitor afterward, demonstrating that flickering stimuli by themselves did not produce any aftereffect on perceived size (Experiment 2). Furthermore, in a final experiment, we showed that the modulation observed in the test phase of Experiment 1 was not due to memory of the manual estimates that had been performed during the adaptation phase (Experiment 3). These findings provide clear evidence for the role of high-level cognitive processes on the perceived size of an afterimage beyond the retinal level. Thus, although retinal stimulation is required to induce an afterimage, post-retinal factors influence its perceived size.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".