Characterizing an ERP correlate of intentions understanding using a sequential comic strips paradigm
Bibliographic record
Abstract
Chronometric properties of theory of mind and intentions understanding more specifically are well documented. Notably, it was demonstrated using magnetoencephalography that the brain regions involved were recruited as soon as 200 ms post-stimulus. We used event-related potentials (ERPs) to characterize an electrophysiological marker of attribution of intentions. We also explored the robustness of this ERP signature under two conditions corresponding to either explicit instructions to focus on others' intentions or implicit instructions with no reference to mental states. Two matched groups of 16 healthy volunteers each received either explicit or no instructions about intentions and performed a nonverbal attribution of intentions task based on sequential four-image comic strips depicting either intentional or physical causality. A bilateral posterior positive component, ranging from 250 to 650 ms post-stimulus, showed greater amplitude in intentional than in physical condition (the intention ERP effect). This effect occurs during the third image only, suggesting that it reflects the integration of information depicted in the third image to the contextual cues given by the first two. The intention effect was similar in the two groups of subjects. Overall, our results identify a clear ERP marker of the first hundreds of milliseconds of intentions processing probably related to a contextual integrative mechanism and suggest its robustness by showing its blindness to task demands manipulation.
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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) | 0.002 | 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".