Social feedback interferes with implicit rule learning: Evidence from event-related brain potentials
Bibliographic record
Abstract
The human brain can learn contingencies built into stimulus sequences unconsciously. The quality of such implicit learning has been connected to stimulus social relevance, but results so far are inconsistent. We engaged participants in an implicit-intentional learning task in which they learned to discriminate between legal and illegal card triads on the sole basis of feedback provided within a staircase procedure. Half of the participants received feedback from pictures of faces with a happy or sad expression (social group) and the other half based on traffic light icons (symbolic group). We hypothesised that feedback from faces would have a greater impact on learning than that from traffic lights. Although performance during learning did not differ between groups, the feedback error-related negativity (fERN) was delayed by ~20 ms for social relative to symbolic feedback, and the P3b modulation elicited by infrequent legal card triads within a stream of illegal ones during the test phase was significantly larger in the symbolic than the social feedback group. Furthermore, the P3b mean amplitude recorded at test negatively correlated with the latency of the fERN recorded during learning. These results counterintuitively suggest that, relative to symbolic feedback, socially salient feedback interferes with implicit 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".