Spatio temporal Dynamics of Face Recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
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 it