Bibliographic record
Abstract
The fusiform face area, or FFA, is a small region found on the inferior (bottom) surface of the temporal lobe. It is located in a gyrus called the fusiform gyrus.Studies in humans have shown that the FFA is sensitive to both face parts and face configurations. Recoding activity in the FFA showed that most of the neurons in the FFA are active in response to facial imagery, but not in response to images of other body parts or objects. Visual sensory neurons sensitive to a face feature and possessing a related firing rate activate an associated cluster of neurons in the FFA. This results in a partition of the FFA into clusters that respond to the various facial features. Once an entire face stimulus activates the FFA, interneurons redistribute the initial activation via the neural network. In this article a novel approach to modelling the function of the network is presented. We define by a transition matrix that describes probabilistically how one cluster, firing at a synchronous rate, affects the others in the FFA. The initial face stimulation in the FFA together with the transition matrix defines a dynamical system which possesses a stationary probability function. We claim that a stationary probability function uniquely represents a face. Among the properties of this probability function are: 1) response magnitude invariance, 2) repurposing of clusters to define new stationary probability function on the FFA partition; 3) stability of stationary probabilities under perturbations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".