From eye to face: support for neural inhibition in holistic processing
Bibliographic record
Abstract
The N170 is an early face-sensitive ERP component that has been shown to be sensitive to face configuration disruptions but also to eyes presented in isolation. The Lateral Inhibition Face Template and Eye Detector (LIFTED – Nemrodov et al., 2014) model proposes that the N170 reflects both the activity of an eye detector and holistic processing of the face; holistic processing would be achieved through the inhibition of neurons coding foveal information by neurons coding parafoveal information. Here we investigated this possible inhibition mechanism by monitoring the variations of the N170 to the presentation of facial stimuli ranging from an isolated eye to a full face, encompassing all the intermediate stages of configuration disruption where the rest of the facial features were added one by one (e.g. eye with nose, eye with mouth, eye with nose and mouth etc.). Fixation was always enforced on one or the other eye using an eye-tracker. The N170 was largest for the isolated eye condition and decreased substantially with the sole addition of the face outline. The progressive addition of the other facial features linearly reduced its amplitude which was smallest for the full face. Similar reductions in latency were found with a remarkable 30-40ms decrease in latency between the isolated eye and the full face conditions. Variations in amplitude and latency reductions were seen between hemispheres as a function of which eye was fixated. Results overall support the idea of an inhibition process that depends on the type of features situated in parafovea and their distance from the fixated eye, with the face outline as a major contributor to holistic face processing. Meeting abstract presented at VSS 2015
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".