Association with emotional information alters subsequent processing of neutral faces
Bibliographic record
Abstract
The processing of emotional as compared to neutral information is associated with different patterns in eye movement and neural activity. However, the 'emotionality' of a stimulus can be conveyed not only by its physical properties, but also by the information that is presented with it. There is very limited work examining the how emotional information may influence the immediate perceptual processing of otherwise neutral information. We examined how presenting an emotion label for a neutral face may influence subsequent processing by using eye movement monitoring (EMM) and magnetoencephalography (MEG) simultaneously. Participants viewed a series of faces with neutral expressions. Each face was followed by a unique negative or neutral sentence to describe that person, and then the same face was presented in isolation again. Viewing of faces paired with a negative sentence was associated with increased early viewing of the eye region and increased neural activity between 600 and 1200 ms in emotion processing regions such as the cingulate, medial prefrontal cortex, and amygdala, as well as posterior regions such as the precuneus and occipital cortex. Viewing of faces paired with a neutral sentence was associated with increased activity in the parahippocampal gyrus during the same time window. By monitoring behavior and neural activity within the same paradigm, these findings demonstrate that emotional information alters subsequent visual scanning and the neural systems that are presumably invoked to maintain a representation of the neutral information along with its emotional details.
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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 | 0.001 |
| 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".