Dentate Gyrus Integrity Is Necessary for Behavioral Pattern Separation But Not Statistical Learning
Bibliographic record
Abstract
Pattern separation, the creation of distinct representations of similar inputs, and statistical learning, the rapid extraction of regularities across multiple inputs, have both been linked to hippocampal processing. It has been proposed that there may be functional differentiation within the hippocampus, such that the trisynaptic pathway (entorhinal cortex > dentate gyrus > CA3 > CA1) supports pattern separation, whereas the monosynaptic pathway (entorhinal cortex > CA1) supports statistical learning. To test this hypothesis, we investigated the behavioral expression of these two processes in B. L., an individual with highly selective bilateral lesions in the dentate gyrus that presumably disrupt the trisynaptic pathway. We tested pattern separation with two novel auditory versions of the continuous mnemonic similarity task, requiring the discrimination of similar environmental sounds and trisyllabic words. For statistical learning, participants were exposed to a continuous speech stream made up of repeating trisyllabic words. They were then tested implicitly through a RT-based task and explicitly through a rating task and a forced-choice recognition task. B. L. showed significant deficits in pattern separation on the mnemonic similarity tasks and on the explicit rating measure of statistical learning. In contrast, B. L. showed intact statistical learning on the implicit measure and the familiarity-based forced-choice recognition measure. Together, these results suggest that dentate gyrus integrity is critical for high-precision discrimination of similar inputs, but not the implicit expression of statistical regularities in behavior. Our findings offer unique new support for the view that pattern separation and statistical learning rely on distinct neural mechanisms.
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".