The brain basis of emotional aftereffects: An ERP study
Bibliographic record
Abstract
Aftereffects have been demonstrated for various types of visual stimuli including faces and emotional facial expressions. Aftereffects are assumed to be mediated by neural adaptations, but brain responses during the perception of facial expressions aftereffects have not been measured. In the current study we measure event related potential (ERP) brain responses in an emotion aftereffect paradigm with happy and sad faces. Participants were 22 undergraduate students (13 females) (Mean age = 19.6 years; SD = 1.98). First, we replicated previous behavioural results of emotion aftereffects: after fixating a happy face, a neutral face was more likely to be labelled sad, and vice versa. We also found that ERP amplitude was predicted by the strength of the aftereffect, when the percept was happy. Interestingly this was not found with neutral faces perceived as sad, which may indicate different processing mechanisms for positive and negative facial expressions. The fact that the brain response in viewers who perceive the neutral face as happy resembles that of the brain response to a happy image, rather than a neutral image, suggests a brain basis for facial aftereffects. Meeting abstract presented at VSS 2012
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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".