Differential effect of visual masking in perceptual categorization.
Bibliographic record
Abstract
This article explores the visual information used to categorize stimuli drawn from a common stimulus space into verbal and nonverbal categories using 2 experiments. Experiment 1 explores the effect of target duration on verbal and nonverbal categorization using backward masking to interrupt visual processing. With categories equated for difficulty for long and short target durations, intermediate target duration shows an advantage for verbal categorization over nonverbal categorization. Experiment 2 tests whether the results of Experiment 1 can be explained by shorter target duration resulting in a smaller signal-to-noise ratio of the categorization stimulus. To test for this possibility, Experiment 2 used integration masking with the same stimuli, categories, and masks as Experiment 1 with a varying level of mask opacity. As predicted, low mask opacity yielded similar results to long target duration while high mask opacity yielded similar results to short target duration. Importantly, intermediate mask opacity produced an advantage for verbal categorization over nonverbal categorization, similar to intermediate target duration. These results suggest that verbal and nonverbal categorization are affected differently by manipulations affecting the signal-to-noise ratio of the stimulus, consistent with multiple-system theories of categorizations. The results further suggest that verbal categorization may be more digital (and more robust to low signal-to-noise ratio) while the information used in nonverbal categorization may be more analog (and less robust to lower signal-to-noise ratio). This article concludes with a discussion of how these new results affect the use of masking in perceptual categorization and multiple-system theories of perceptual category learning.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".