Reversal rewards drive language switching during observational learning: Evidence from a dual-brain EEG study
Bibliographic record
Abstract
Abstract Research on the cognitive neural mechanisms of language control often overlooks the role of rewards. To investigate how reversal rewards affect bilingual language switching during observational learning, we conducted a dual-brain electroencephalography (EEG) study. Participants, classified as direct learners or observers, performed a voluntary language-switching task under dynamic reward conditions. Our results demonstrated that both direct learners and observers exhibited high correct acquisition rates for the switch and non-switch behaviors in both pre- and post-reversal phases. Notably, direct learners and observers showed reduced switch costs in the post-reversal phase, highlighting enhanced language control efficiency. EEG analyses revealed that direct learners exhibited late positive component (LPC) switch costs in both pre- and post-reversal phases, while observers showed LPC switch costs only in the post-reversal phase. These findings support the Adaptive Control Hypothesis by highlighting the adaptability of language control mechanisms in response to dynamic reward environments during direct and observational learning.
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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 | 0.002 |
| 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) | 0.001 | 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".