Modeling prosopagnosia using dynamic artificial neural networks
Bibliographic record
Abstract
Prosopagnosia is a brain disorder causing the inability to recognize faces. Previous studies have shown that the lesions producing the disorder can occur in diverse areas of the brain. However, the most common region is the “fusiform face area” (FFA). In order to model the basic properties of prosopagnosia two networks have been used concurrently: the Feature Extracting Bidirectional Associative Memory (FEBAM-SOM) and the Bidirectional Associative Memory (BAM). The FEBAM-SOM creates a 2D topological map from correlated inputs through the categorization of various exemplars (faces and various objects). This model has the advantage of using a sparse representation which encompass both localist and distributed encoding. This process simulates the FFA in the brain by exhibiting attractor-like behavior for the categorization of all faces. Once the faces have been learned, the BAM model associates specific faces (and objects) to their corresponding semantic labels. Simulations were performed to study the recall performance in function of the size of the lesions. Results show that the recall performance of the names associated with faces decrease with the size of lesion without affecting the performance of the objects.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".