Implicit Learning of Sequential Regularities and Spatial Contexts in Corticobasal Syndrome

Abstract

The present study investigated two forms of implicit learning in patients with corticobasal syndrome (CBS): contextual cueing and sequence learning. The former primarily implicates the medial temporal lobe system, and the latter, fronto-striatal-cerebellar circuits. Results revealed relatively preserved contextual cueing in patients with CBS. By contrast, sequence learning showed impairments, which seemed to reflect inability to execute correct responses in the presence of intact learning of the sequence. These findings provide the first characterization of implicit learning systems in CBS, and show that the two systems are differentially affected in patients with CBS.

Keywords:

• Implicit learning
• corticobasal syndrome
• spatial contexts
• sequence learning
• dementia

This work was supported by grants P50 AG16574, U01 AG06786, R37 AG15450, K01 MH 68351, and the Robert H. and Clarice Smith and Abigail Van Buren Alzheimer's Disease Research Program of the Mayo Foundation, and by Postdoctoral Diversity Program in Neuroscience Fellowship from the American Psychological Association. We are indebted to our colleagues with whom we work on corticobasal syndrome and corticobasal degeneration research, namely Keith A. Josephs, M.D., Daniel A. Drubach, M.D., Josephs Y. Matsumoto, M.D., J. Eric Ahlskog, Ph.D, M.D., Joseph E. Parisi, M.D., and Dennis W. Dickson, M.D. We thank our staff in the Mayo Alzheimer's Disease Research Center for their evaluation and education/counseling for many of the patients and families included in this report. We particularly extend our gratitude to the patients and their families for participating in research on neurodegenerative disease.

Disclosure Statement: The authors have no actual or potential conflicts of interest to disclose. The research is approved by the Mayo Institutional Review Board.

Notes

¹We determined chance by counting the number of possible ways to make a structure-consistent error and dividing by the total number of errors that can occur. To calculate the denominator we note that there are three possible responses to each of the 27 event triplets, two of which produce an error. Hence, there are 54 possible errors (27 triplets × 2 errors). For the numerator, we determine the number of errors that will be structure-consistent. Of the 27 possible triplets, 9 are structure-consistent (3 × 3 × 1) and 18 are structure-inconsistent (3 × 3 × 2). Errors occurring for the 9 structure consistent event triplets are structure-inconsistent by definition. For the 18 possible inconsistent event triplets only one of the txwo error responses will be structure-consistent, making a total of 18 structure-consistent errors. Therefore, chance responding will produce one of the 18 structure-consistent errors with probability .33 (i.e., 18/54).