A Neurocomputational Model of Analogical Reasoning and its Breakdown in Frontotemporal Lobar Degeneration
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Bibliographic record
Abstract
Analogy is important for learning and discovery and is considered a core component of intelligence. We present a computational account of analogical reasoning that is compatible with data we have collected from patients with cortical degeneration of either their frontal or anterior temporal cortices due to frontotemporal lobar degeneration (FTLD). These two patient groups showed different deficits in picture and verbal analogies: frontal lobe FTLD patients tended to make errors due to impairments in working memory and inhibitory abilities, whereas temporal lobe FTLD patients tended to make errors due to semantic memory loss. Using the "Learning and Inference with Schemas and Analogies" model, we provide a specific account of how such deficits may arise within neural networks supporting analogical problem solving.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it