What are you looking at? The necessity of Eye-tracking use in ERP face-research
Bibliographic record
Abstract
The present study aimed to determine whether the N170, the most studied face-sensitive ERP component, and its known face inversion effect (FIE) are modulated by fixation within the face and whether eye-tracking should be employed as standard practice in face-research. Two ERP groups were tested in a face-orientation discrimination task. Faces were presented in such a way that the centered fixation cross was on specific facial features. Participants were instructed not to move their eyes away from the fixation cross. In the "trigger group", fixation was forced using eye-tracking fixation-triggers and trials contaminated by eye movements beyond 0.75° of visual angle around fixation were rejected. In the "natural group", no fixation-triggers were used and natural eye movements were recorded by the eye tracker. The second group thus mimicked classic ERP face studies where subjects are told to fixate on the cross yet eye position is not enforced. The eye-tracking data from the natural group revealed that although instructed to fixate on the cross, when not forced to do so participants’ eyes strayed widely. Most importantly, the ERP data showed that fixation-location within the face modulated N170 amplitude and interacted with the FIE in the trigger group but not in the natural group. These results point to an increased probability of type-II error in classic face ERP studies due to the lack of eye-tracking use. Given the theoretical importance of modulations such as the N170 FIE for understanding face perception, these findings are not trivial methodological concerns. These data highlight the importance of recording EEG and eye-tracking simultaneously in face-research. Meeting abstract presented at VSS 2013
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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 | 0.007 | 0.023 |
| 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) | 0.001 | 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".