A Tale of Two Processes: Categorization Accuracy and Attentional Learning Dissociate with Imperfect Feedback
Bibliographic record
Abstract
The present study used eye-tracking to examine the relationship between attention and category learning in probabilistic environments.While training, participants received either perfect feedback (100% accurate), or one of three different levels of probabilistic feedback (87.5%, 75% or 62.5% accurate).It was found that participants in the 87.5% condition were more accurate than participants in the other two probabilistic feedback conditions.However, despite their greater accuracy, participants in the 87.5% condition continued to attend to irrelevant information as frequently as those in the other two probabilistic conditions.This shows that: (1) cues that are not utilized in making a categorization decision may still be frequently attended to, and (2) attentional learning is not as tightly coupled to improving accuracy as current formal models suggest.
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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 | 0.000 | 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 | 0.002 |
| 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".