Examining the effect of regularity learning on object-substitution masking
Bibliographic record
Abstract
The recurrent processing theory proposes that cortical feedback mechanisms serve to match perceptual hypotheses with incoming visual information. Behaviorally, this recurrent processing can be studied using object-substitution masking (OSM: Di Lollo & Enns, 1998; Di Lollo, Enns & Rensink, 2000). Typically, OSM is generated by presenting observers with a brief visual display in which a target is surrounded by four dots (the mask) and observers make a perceptual judgement about the target. The common finding is that accuracy is worse when the mask remains on for 50-100 ms after the target than when the mask either disappears with the target or remains on for longer periods of time. This decline in accuracy (i.e., the OSM effect) is taken as evidence for a recurrent processing mechanism that substitutes the target representation with the trailing mask representation. It is known that OSM can be weakened by manipulations that encourage segmentation of the target from the mask, such as spatial precuing, focal attention, and relational cues. Statistical learning can also produce segmentation effects, spatial cuing, and shifts in attention, suggesting that regularity learning may also be capable of guiding perceptual hypotheses during recurrent processing. Thus, in two experiments, we investigated whether statistical learning could alter object-substitution masking. The first was an online experiment in which we found a typical OSM effect using novel shape stimuli. In the second experiment, we introduced a probabilistic relationship between adjacent shapes. We found that regularity learning weakened the masking effect, in line with the proposal that statistical learning refines perceptual hypotheses via an implicit prediction mechanism.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".