Reinforcement learning and the acquisition of perceptual expertise in ERPs
Bibliographic record
Abstract
In a category learning task, people are initially unaware when they have committed an error and therefore, require corrective feedback to modify their category decisions. Once the categories are learned, however, external feedback is no longer necessary. Electrophysiologically, the two phases of category learning are indicated by different event-related brain potentials (ERPs): the feedback ERN that is elicited following the presentation of negative feedback and the response ERN that is generated following an incorrect response. In a study of perceptual categorization, participants were asked to discriminate between very similar families of novel geometric shapes (blobs). Participants who learned the perceptual categories (i.e., expert learners) demonstrated a shift from a feedback ERN to the response ERN. The expert learners also showed an enhanced N250 response to the blob families — a component that is thought to index subordinate level representations. For the experts, the buildup of the N250 component was correlated with the shift in the ERN. In contrast, participants who were unable to learn the object families (i.e., novice learners ) failed to show a shift in their feedback-to-response ERN nor did they show an increased N250. Collectively, these results suggest that accompanying the acquisition of the subordinate categories, there is a change from an external source of error monitoring to an internal source.
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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 | 0.008 |
| 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 | 0.001 |
| 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 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".