Task-modulated integration of facial features in the brain
Bibliographic record
Abstract
The presence of intermodulation frequencies (IM) in an EEG frequency-tagging paradigm indicates non-linear integration of multiple tagged visual features by the brain (Norcia et al., 2015). Despite its growing use in high-level vision, the efficiency of IM as an index of non-linear processing remains unclear, mostly because the importance of the non-linear integration for the task is typically unknown. We assessed the efficiency of IM using a realistic face processing task which we know implements a simple XOR non-linear function—wink detection. On each trial, EEG activity was recorded while each feature of a face flickered at a specific frequency (e.g. left eye: 6 Hz, right eye: 3Hz and mouth: 8 Hz). Subjects had to fixate a central cross and detect winks (one eye closed rather than no eyes/both eyes closed) in the non-linear condition, and the closing of one of the two eyes in the linear condition. Comparisons of brain responses between tasks during identical visual stimulations revealed that left/right-eye tagged IM—the neural response imputable to the non-linear integration of both features—were stronger in occipito-temporal electrodes when this particular feature integration was useful for the task at hand (i.e. wink condition, F(1,362)=13.98,p< .001). The magnitude of the eye-pair IM was also associated with faster response time (RT) in the non-linear wink detection condition (r= -.73,p< .05), but not in the linear control task (r=-.10,p>.70). Oppositely, the magnitude of the mouth tagged neural responses (unrelated to both tasks) was associated with longer RT in both conditions (r1=.67,p1< .05;r2 =.85,p2< .05), most likely reflecting a distractor effect. While the magnitude of feature frequency-tags clearly outweighed that of IM (average SNR were ~15 and ~1.75, respectively), the present results clearly demonstrates that IM can be an effective neural correlate of non-linear visual integration processing. Meeting abstract presented at VSS 2017
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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) | 0.001 | 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".