Spatiotemporal dynamics of reward and punishment effects induced by associative learning
Bibliographic record
Abstract
While reward associative learning has been studied extensively across different species, punishment avoidance learning has received far less attention. Of particular interest is how the two types of learning change perceptual processing of the learned stimuli. We designed a task that required participants to learn the association of emotionally neutral images with reward, punishment, and no incentive value outcomes through trial-and-error. During learning, participants received monetary reward, neutral outcomes or avoided punishment by correctly identifying corresponding images. Results showed an early bias in favor of learning reward associations, in the form of higher accuracy and fewer trials needed to reach learning criterion. We subsequently assessed electrophysiological learning effects with a task in which participants viewed the stimuli with no feedback or reinforcement. Critically, we found modulation of two early event-related potential components for reward images: the frontocentral P2 (170-230 ms) and the anterior N2/Early Anterior Positivity (N2/EAP; 210-310 ms). We suggest that reward associations may change stimuli detection and incentive salience as indexed by P2 and N2/EAP. We also reported, on an exploratory basis, a late negativity with frontopolar distribution enhanced by punishment images.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".