Learning “How to Learn”: Super Declarative Motor Learning Is Impaired in Parkinson’s Disease

Learning new information is crucial in daily activities and occurs continuously during a subject’s lifetime. Retention of learned material is required for later recall and reuse, although learning capacity is limited and interference between consecutively learned information may occur. Learning proc...

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Bibliographic Details
Main Authors: Lucio Marinelli, Carlo Trompetto, Stefania Canneva, Laura Mori, Flavio Nobili, Francesco Fattapposta, Antonio Currà, Giovanni Abbruzzese, Maria Felice Ghilardi
Format: Article
Language:English
Published: Wiley 2017-01-01
Series:Neural Plasticity
Online Access:http://dx.doi.org/10.1155/2017/3162087
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Summary:Learning new information is crucial in daily activities and occurs continuously during a subject’s lifetime. Retention of learned material is required for later recall and reuse, although learning capacity is limited and interference between consecutively learned information may occur. Learning processes are impaired in Parkinson’s disease (PD); however, little is known about the processes related to retention and interference. The aim of this study is to investigate the retention and anterograde interference using a declarative sequence learning task in drug-naive patients in the disease’s early stages. Eleven patients with PD and eleven age-matched controls learned a visuomotor sequence, SEQ1, during Day1; the following day, retention of SEQ1 was assessed and, immediately after, a new sequence of comparable complexity, SEQ2, was learned. The comparison of the learning rates of SEQ1 on Day1 and SEQ2 on Day2 assessed the anterograde interference of SEQ1 on SEQ2. We found that SEQ1 performance improved in both patients and controls on Day2. Surprisingly, controls learned SEQ2 better than SEQ1, suggesting the absence of anterograde interference and the occurrence of learning optimization, a process that we defined as “learning how to learn.” Patients with PD lacked such improvement, suggesting defective performance optimization processes.
ISSN:2090-5904
1687-5443