Perceptual filling-in dispels the veridicality problem of conscious perception research
Bibliographic record
Abstract
Conscious perceptual experiences are expected to correlate with content-specific brain activity. A veridicality problem arises when attempting to disentangle unconscious and conscious brain processes if conscious perceptual contents accurately match the physical nature of the stimulus. We argue that perceptual filling-in, a phenomenon whereby visual information inaccurately spreads across visual space, is a promising approach to circumvent the veridicality problem. Filling-in generates non-veridical although unambiguous percepts dissociated from stimulus input. In particular, the radial uniformity illusion induces a filling-in experience between a central disk and the surrounding periphery. We discuss how this illusion facilitates both the detection of neurophysiological responses and subjective phenomenological monitoring. We report behavioral effects from a large-sample (n = 200) psychophysics study and examine key stimulus parameters that drive the conscious filling-in experience. We propose that these data underpin future hypothesis-driven studies of filling-in to further delineate the neural mechanisms of conscious perception.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 teacher head, 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".