Face and object neural processing using Mass Univariate Statistics
Bibliographic record
Abstract
Recent Event Related Potentials (ERPs) studies using eye-tracking and a gaze contingent approach to control gaze fixation location have shown that the face-sensitive N170 ERP component was modulated when participants’ gaze was fixated on the different facial features of upright or inverted faces (Nemrodov, et al. 2014; Itier & Preston, 2018). In the first experiment we re-analyzed these data sets with Mass-Univariate statistics using the LIMO toolbox. These statistics are more resistant to Type I & II errors and allow the investigation of all time points and electrodes (Pernet, et al. 2011). The results revealed a significant effect of fixation location, orientation, as well as their interaction. These significant differences were widespread across the scalp and were maximal before and after the N170 ERP component. Both main effects were largest during the P1 to N170 and N170 to P2 windows. The face inversion effect was maximal for the nasion fixation around 130ms, i.e. about 20ms before the N170 peak. In the second experiment (unpublished) the exact same experimental paradigm was used, except the images were of upright and inverted houses. The results showed a fixation and orientation effect of houses as well as a small interaction between the two. However, these effects occurred over a much smaller time window and were considerably smaller in magnitude with F values much lower than those seen with faces. These results suggest that face and house processing are differentially impacted by orientation and fixation location. Critically, most of these processes occurred outside of the N170 peak, emphasizing the need to move towards robust statistics to unravel the time course of face and object neural processing.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".