The right superior temporal gyrus plays a role in semantic-rule learning: Evidence supporting a reinforcement learning model
Bibliographic record
Abstract
In real-life communication, individuals use language that carries evident rewarding and punishing elements, such as praise and criticism. A common trend is to seek more praise while avoiding criticism. Furthermore, semantics is crucial for conveying information, but such semantic access to native and foreign languages is subtly distinct. To investigate how rule learning occurs in different languages and to highlight the importance of semantics in this process, we investigated both verbal and non-verbal rule learning in first (L1) and second (L2) languages using a reinforcement learning framework, including a semantic rule and a color rule. Our computational modeling on behavioral and brain imaging data revealed that individuals may be more motivated to learn and adhere to rules in an L1 compared to L2, with greater striatum activation during the outcome phase in the L1. Additionally, results on the learning rates and inverse temperature in the two rule learning tasks showed that individuals tend to be conservative and are reluctant to change their judgments regarding rule learning of semantic information. Moreover, the greater the prediction errors, the greater activation of the right superior temporal gyrus in the semantic-rule learning condition, demonstrating that such learning has differential neural correlates than symbolic rule learning. Overall, the findings provide insight into the neural mechanisms underlying rule learning in different languages, and indicate that rule learning involving verbal semantics is not a general symbolic learning that resembles a conditioned stimulus-response, but rather has its own specific characteristics.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".