Inverse Mapping the Neuronal Substrates of Face Categorizations
Bibliographic record
Abstract
Face perception is a complex process involving a network of brain structures, dynamically processing information to enable judgments about a face to be made (e.g., familiarity, identity, and expression). Here we introduce an analysis methodology that makes it possible to directly study this information processing in the brain from spatially and temporally resolved magnetoencephalographic signals. We apply our methodology to the study of 2 face categorization tasks, gender and expressiveness, and track the processing of 3 key visual features that underlie behavioral performance, over time and throughout the cortex. We find information processing correlates beginning from 90 ms following stimulus onset, where features are processed in isolation in occipital extrastriate regions. Over time, processing of successively more features and feature combinations takes place in occipitotemporal regions, with maximal information processing of visual information coinciding with the well-established face-selective M170 component at 170 ms. Later still, around 250-400 ms, cortical activity responds significantly more to task-specific features and their complex combinations. These results indicate a complex process of visual information processing during face perception with face parts processed in isolation at very early stages, and task-specific processing of combinations of features taking place within 300 ms. Crucially, our approach specifically establishes which information in the visual stimulus the brain signal is responding to and how this varies with time, cortical location, and task demands to establish a more precise tracking of information processing mechanisms in the cortex during face perception.
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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 | 0.002 |
| 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) | 0.002 | 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".