Honors Theses and Capstones

Date Completed

Spring 2026

Abstract

Abstract

            Implicit statistical learning is a crucial cognitive function that is highly involved in the learning and processing of language. Since both language and implicit statistical learning are supported by the basal ganglia through cortico-striatal networks, disruptions to this network could result in their impairment. Parkinson’s Disease (PD) can therefore be used to model disruptions to this system as it is primarily characterized by dysfunction in the basal ganglia. Thus, this review aims to examine the current literature studying the performance of individuals with PD on artificial grammar tasks in order to investigate the impact of impaired basal ganglia function on implicit statistical learning abilities. After conducting a literature screening using PRISMA guidelines, five studies were compared which showed in general a preservation of this ability in spite of factors such as disease severity or medication status. However, results showed significant impairment on performance exclusively during feedback-based trials. These findings suggest a reliance on alternative systems beyond the impaired cortico-striatal circuits. Parallel circuitry might allow for functions less reliant on the basal ganglia to be compensated while others, such as feedback, which are more highly reliant on cortico-striatal networks remain impaired.

Document Type

Undergraduate Thesis

First Advisor

Amy Ramage

Creative Commons License

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

College or School

COLSA

Department or Program

Neuroscience and Behavior

Degree Name

Bachelor of Science

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