Semantic priming modulates the strength and direction of the Kanizsa illusion
Bibliographic record
Abstract
Visual illusions are considered key examples for cognitive impenetrability, as they are held not to be affected by non-perceptual processes. We revisit this claim in five experiments (N = 1148; four preregistered) focused on the Kanizsa illusion, where a nonexistent shape is experienced within illusory contours. Pac-Man-like shapes inducing the illusion were presented after primes that were either semantically related to the Pac-Man game or not. We hypothesized that semantic primes would promote interpreting the shapes as individual Pac-Man characters, thus biasing participants away from the holistic Kanizsa illusion. Indeed, we found that the Kanizsa shape was detected less when participants were primed with Pac-Man-related stimuli. We then also demonstrated the opposite effect: a prime indexing the illusory shape (“Triangle”) enhanced the probability of seeing the illusion. Together, our results suggest that semantic priming can both reduce and increase the probability of experiencing the Kanizsa illusion, thus supporting claims of cognitive penetrability. This study demonstrates that semantic priming can either increase or decrease perception of the Kanizsa illusion. The results support cognitive penetrability, demonstrating top-down influences on illusory perceptual experience.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".