Motion-induced blindness and microsaccades: Cause and effect
Bibliographic record
Abstract
It has been suggested that subjective disappearance of visual stimuli results from a spontaneous reduction of microsaccade rate causing image stabilization, enhanced adaptation, and a consequent fading. In motion-induced blindness (MIB), salient visual targets disappear intermittently when surrounded by a moving pattern. We investigated whether changes in microsaccade rate can account for MIB. We first determined that the moving mask does not affect microsaccade metrics (rate, magnitude, and temporal distribution). We then compared the dynamics of microsaccades during reported illusory disappearance (MIB) and physical disappearance (Replay) of a salient peripheral target. We found large modulations of microsaccade rate following perceptual transitions, whether illusory (MIB) or real (Replay). For MIB, the rate also decreased prior to disappearance and increased prior to reappearance. Importantly, MIB persisted in the presence of microsaccades although sustained microsaccade rate was lower during invisible than visible periods. These results suggest that the microsaccade system reacts to changes in visibility, but microsaccades also modulate MIB. The latter modulation is well described by a Poisson model of the perceptual transitions assuming that the probability for reappearance and disappearance is modulated following a microsaccade. Our results show that microsaccades counteract disappearance but are neither necessary nor sufficient to account for MIB.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".