Receptive Fields for Flexible Face Categorizations
Bibliographic record
Abstract
Abstract-Examining the receptive fields of brain signals can elucidate how information impinging on the former modulates the latter. We applied this time-honored approach in early vision to the higher-level brain processes underlying face categorizations. Electroencephalograms in response to face-information samples were recorded while observers resolved two different categorizations (gender, expressive or not). Using a method with low bias and low variance, we compared, in a common space of information states, the information determining behavior (accuracy and reaction time) with the information that modulates emergent brain signals associated with early face encoding and later category decision. Our results provide a time line for face processing in which selective attention to diagnostic information for categorizing stimuli (the eyes and their second-order relationships in gender categorization; the mouth in expressive-or-not categorization) correlates with late electrophysiological (P300) activity, whereas early face-sensitive occipito-temporal (N170) activity is mainly driven by the contralateral eye, irrespective of the categorization task.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".