Familiarity Alters the Bandwidth of Perceptual Awareness
Bibliographic record
Abstract
Results from paradigms like change blindness and inattentional blindness indicate that observers are unaware of numerous aspects of the visual world. However, intuition suggests that perceptual experience is richer than these results indicate. Why does it feel like we see so much when the data suggests we see so little? One possibility stems from the fact that experimental studies always present observers with stimuli that they have never seen before. Meanwhile, when forming intuitions about perceptual experience, observers reflect on their experiences with scenes with which they are highly familiar (e.g., their office). Does prior experience with a scene change the bandwidth of perceptual awareness? Here, we asked if observers were better at noticing alterations to the periphery in familiar scenes compared with unfamiliar scenes. We found that observers noticed changes to the periphery more frequently with familiar stimuli. Signal detection theoretic analyses revealed that when observers are unfamiliar with a stimulus, they are less sensitive at noticing (d') and are more conservative in their response criterion (c). Taken together, these results suggest that prior knowledge expands the bandwidth of perceptual awareness. It should be stressed that these results challenge the widely held idea that prior knowledge fills in perception. Overall, these findings highlight how prior knowledge plays an important role in determining the limits of perceptual experience and is an important factor to consider when attempting to reconcile the tension between empirical observation and personal introspection.
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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 | 0.010 |
| 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 | 0.001 | 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".