Analysis of probabilistic classification learning in patients with Parkinson's disease before and after pallidotomy surgery.
This study examined the characteristics of probabilistic classification learning, a form of implicit learning previously shown to be impaired in patients with basal ganglia dysfunction (e.g., Parkinson's disease). In this task, subjects learn to predict the weather using associations that are formed gradually across many trials, because of the probabilistic nature of the cue-outcome relationships. Patients with Parkinson's disease, both before and after pallidotomy, and age-matched control subjects, exhibited evidence of probabilistic classification learning across 100 training trials. However, pallidotomy appears to hinder the learning of associations most implicit in nature (i.e., weakly associated cues). Although subjects were most sensitive to single-cue associations when learning the task, there is evidence that cue combinations contribute significantly to probability learning. The utility of multiple dependent measures is discussed.
Sage
Jennifer R
JR
Department of Psychology, University of California, Los Angeles, California 90095, USA. jsage@emory.edu
Anagnostaras
Stephan G
SG
Mitchell
Shawn
S
Bronstein
Jeff M
JM
De Salles
Antonio
A
Masterman
Donna
D
Knowlton
Barbara J
BJ
D000368:Aged; D001245:Association Learning / Q000502:physiology*; D016022:Case-Control Studies; D005260:Female; D005917:Globus Pallidus / Q000503:physiopathology* / Q000601:surgery*; D006801:Humans; D007859:Learning Disabilities / Q000209:etiology / Q000503:physiopathology; D008297:Male; D016555:Matched-Pair Analysis; D015233:Models, Statistical; D010300:Parkinson Disease / Q000150:complications / Q000503:physiopathology* / Q000601:surgery*; D010364:Pattern Recognition, Visual / Q000502:physiology; D011337:Probability Learning; D011340:Problem Solving / Q000502:physiology; D012016:Reference Values