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Journal of Cognitive Neuroscience

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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 BL, an individual with highly selective bilateral lesions in the dentate gyrus that presumably disrupts 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 reaction time-based task and explicitly through a rating task and a forced-choice recognition task. BL showed significant deficits in pattern separation on the Mnemonic Similarity Tasks and on the explicit rating measure of statistical learning. In contrast, BL 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 behaviour. Our findings offer unique new support for the view that pattern separation and statistical learning rely on distinct neural mechanisms.


This is the author’s final version, and the article has been accepted for publication in Journal of Cognitive Neuroscience.

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This work is licensed under a Creative Commons Attribution 4.0 License.