Spatio temporal Dynamics of Face Recognition
Bibliographic record
Abstract
To better understand face recognition, it is necessary to identify not only which brain structures are implicated but also the dynamics of the neuronal activity in these structures. Latencies can then be compared to unravel the temporal dynamics of information processing at the distributed network level. To achieve high spatial and temporal resolution, we used intracerebral recordings in epileptic subjects while they performed a famous/unfamiliar face recognition task. The first components peaked at 110 ms in the fusiform gyrus (FG) and simultaneously in the inferior frontal gyrus, suggesting the early establishment of a large-scale network. This was followed by components peaking at 160 ms in 2 areas along the FG. Important stages of distributed parallel processes ensued at 240 and 360 ms involving up to 6 regions along the ventral visual pathway. The final components peaked at 480 ms in the hippocampus. These stages largely overlapped. Importantly, event-related potentials to famous faces differed from unfamiliar faces and control stimuli in all medial temporal lobe structures. The network was bilateral but more right sided. Thus, recognition of famous faces takes place through the establishment of a complex set of local and distributed processes that interact dynamically and may be an emergent property of these interactions.
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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) | 0.002 | 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".