Abstract

Parkinson’s Disease can be understood as a disorder of motor habits. A prediction of this theory is that early stage Parkinson’s patients will display fewer errors caused by interference from previously over-learned behaviours. We test this prediction in the domain of skilled typing, where actions are easy to record and errors easy to identify. We describe a method for categorizing errors as simple motor errors or habit-driven errors. We test Spanish and English participants with and without Parkinson’s, and show that indeed patients make fewer habit errors than healthy controls, and, further, that classification of error type increases the accuracy of discriminating between patients and healthy controls. As well as being a validation of a theory-led prediction, these results offer promise for automated, enhanced and early diagnosis of Parkinson’s Disease.

Details

Title
Reduced habit-driven errors in Parkinson’s Disease
Author
Bannard, Colin 1 ; Leriche Mariana 2 ; Bandmann Oliver 3 ; Brown, Christopher H 4   VIAFID ORCID Logo  ; Ferracane Elisa 4 ; Sánchez-Ferro Álvaro 5 ; Obeso José 5 ; Redgrave, Peter 6 ; Stafford, Tom 6   VIAFID ORCID Logo 

 University of Liverpool, Department of Psychological Sciences, Liverpool, UK (GRID:grid.10025.36) (ISNI:0000 0004 1936 8470) 
 University of Otago, Department of Anatomy, Dunedin, New Zealand (GRID:grid.29980.3a) (ISNI:0000 0004 1936 7830) 
 University of Sheffield, Sheffield Institute for Translational Neuroscience (SITraN), Sheffield, UK (GRID:grid.11835.3e) (ISNI:0000 0004 1936 9262) 
 University of Texas at Austin, Department of Linguistics, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924) 
 HM Hospitales, Centre for Integrative Neuroscience AC, Hospital Universitario HM Puerta del Sur, Mostoles and CEU San Pablo University. Center for Networked Biomedical Research on Neurodegenerative Diseases, Institute Carlos III, Madrid, Spain (GRID:grid.413448.e) (ISNI:0000 0000 9314 1427) 
 University of Sheffield, Department of Psychology, Sheffield, UK (GRID:grid.11835.3e) (ISNI:0000 0004 1936 9262) 
Publication year
2019
Publication date
Dec 2019
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2187941554
Copyright
This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.