Assessing the way people look to judge their intentions.
Bibliographic record
Abstract
Faces of unknown persons are processed to infer the intentions of these persons not only when they depict full-blown emotions, but also at rest, or when these faces do not signal any strong feelings. We explored the brain processes involved in these inferences to test whether they are similar to those found when judging full-blown emotions. We recorded the event-related brain potentials (ERPs) elicited by faces of unknown persons who, when they were photographed, were not asked to adopt any particular expression. During the ERP recording, participants had to decide whether each face appeared to be that of a positively, negatively, ambiguously, or neutrally intentioned person. The early posterior negativity, the EPN, was found smaller for neutrally categorized faces than for the other faces, suggesting that the automatic processes it indexes are similar to those evoked by full-blown expressions and thus that these processes might be involved in the decoding of intentions. In contrast, in the same 200-400 ms time window, ERPs were not more negative at anterior sites for neutrally intentioned faces. Second, the peaks of the late positive potentials (LPPs) maximal at parietal sites around 700 ms postonset were not significantly smaller for neutrally intentioned faces. Third, the slow positive waves that followed the LPP were larger for faces that took more time to categorize, that is, for ambiguously intentioned faces. These three series of unexpected results may indicate processes similar to those triggered by full-blown emotions studies, but they question the characteristics of these processes.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".