The contribution of the striatum to category learning was examined
by having patients with Parkinson's disease (PD) and matched
controls solve categorization problems in which the optimal
rule was linear or nonlinear using the perceptual categorization
task. Traditional accuracy-based analyses, as well as quantitative
model-based analyses were performed. Unlike accuracy-based
analyses, the model-based analyses allow one to quantify and
separate the effects of categorization rule learning from
variability in the trial-by-trial application of the
participant's rule. When the categorization rule was linear,
PD patients showed no accuracy, categorization rule learning,
or rule application variability deficits. Categorization accuracy
for the PD patients was associated with their performance on
a test believed to be sensitive to frontal lobe functioning.
In contrast, when the categorization rule was nonlinear, the
PD patients showed accuracy, categorization rule learning, and
rule application variability deficits. Furthermore, categorization
accuracy was not associated with performance on the test of
frontal lobe functioning. Implications for neuropsychological
theories of categorization learning are discussed. (JINS,
2001, 7, 710–727.)
Quantitative, model-based estimates of variability in the generation and serial intervals of Plasmodium falciparum malaria
