The Role of the Hippocampus in Pattern Separation and Statistical Learning

Helena Shizhe Wang, The University of Western Ontario

Abstract

It has been theorized that pattern separation and statistical learning implicate two separate circuitries within the hippocampus, one that involves the dentate gyrus subregion and one that does not, respectively. Here, we tested whether the two processes are dissociable computations that rely on separate neural pathways by examining the effect of healthy aging (Chapter 2) and dentate gyrus lesion (Chapter 3) on participants’ ability to differentiate similar trisyllabic words and extract trisyllabic words from a continuous speech stream. Neither healthy aging nor dentate gyrus damage affected the implicit expression of statistical learning, but they both impaired pattern separation and the explicit expression of statistical learning. These results suggest that the implicit expression of statistical learning and pattern separation are dissociable computations that rely on separate neural pathways whereas the explicit, high-resolution expression of statistical learning in part shares the same process and neural architecture as pattern separation.