Bibliographic record
Abstract
The error-likelihood model (ELM) postulates that activity of the anterior cingulate cortex is not only modulated by the commitment of an error but is rather dependent on the perceived ELM within task context. In this event-related potential (ERP) study we challenge these assumptions with a word-recognition paradigm. While learning phases were constant, ELM was modulated during recognition by varying the ratio of old and new words: old and new words had the same probability in low-risk sessions and the number of new words was tripled in high-risk sessions. Response-locked ERPs of correct yes-responses compared to no-responses revealed two different components with fronto-central distributions. The first negativity (time-range of the error-related negativity) elicited differentiations between yes- and no-responses. Yes-responses in high-risk compared to low-risk sessions resulted in an enlarged negativity. These results support the ELM which states that the activation of the anterior cingulate cortex is also modulated by the participant's perceived likelihood to commit an error and not the correctness of the response per se.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".