The impact of irrelevant dimensional variation on rule-based category learning in patients with Parkinson's disease

This study examined the impact of irrelevant dimensional variation on rule-based category learning in patients with Parkinson's disease (PD), older controls (OC), and younger controls (YC). Participants were presented with 4-dimensional, binary-valued stimuli and were asked to categorize each into 1 of 2 categories. Category membership was based on the value of a single dimension. Four experimental conditions were administered in which there were zero, 1, 2, or 3 randomly varying irrelevant dimensions. Results indicated that patients with PD were impacted to a greater extent than both the OC and YC participants when the number of randomly varying irrelevant dimensions increased. These results suggest that the degree of working memory and selective attention requirements of a categorization task will impact whether PD patients are impaired in rule-based category learning, and help to clarify recent discrepancies in the literature. (JINS, 2005, 11, 503–513.)

[1]  W. R. Garner,et al.  Selective attention to attributes and to stimuli. , 1978, Journal of experimental psychology. General.

[2]  J Jonides,et al.  Spatial, but not object, delayed response is impaired in early Parkinson's disease. , 1997, Neuropsychology.

[3]  W Todd Maddox,et al.  Disrupting feedback processing interferes with rule-based but not information-integration category learning , 2004, Memory & cognition.

[4]  T. Salthouse,et al.  Assessing the age-related effects of proactive interference on working memory tasks using the Rasch model. , 2003, Psychology and aging.

[5]  Shawn W. Ell,et al.  Category learning deficits in Parkinson's disease. , 2003, Neuropsychology.

[6]  V. A. Bradley,et al.  Visuospatial working memory in Parkinson's disease. , 1989, Journal of neurology, neurosurgery, and psychiatry.

[7]  P. Pollak,et al.  The relation of putamen and caudate nucleus 18F-Dopa uptake to motor and cognitive performances in Parkinson’s disease , 1999, Journal of the Neurological Sciences.

[8]  Corey J Bohil,et al.  Observational versus feedback training in rule-based and information-integration category learning , 2002, Memory & cognition.

[9]  J. Mink THE BASAL GANGLIA: FOCUSED SELECTION AND INHIBITION OF COMPETING MOTOR PROGRAMS , 1996, Progress in Neurobiology.

[10]  M. Hoehn,et al.  Parkinsonism , 1967, Neurology.

[11]  P. Praamstra,et al.  Failed Suppression of Direct Visuomotor Activation in Parkinson's Disease , 2001, Journal of Cognitive Neuroscience.

[12]  Edward E. Smith,et al.  Similarity- versus rule-based categorization , 1994, Memory & cognition.

[13]  W. T. Maddox,et al.  Cortical and subcortical brain regions involved in rule-based category learning , 2005, Neuroreport.

[14]  Gregory Ashby,et al.  A neuropsychological theory of multiple systems in category learning. , 1998, Psychological review.

[15]  William M. Smith,et al.  A Study of Thinking , 1956 .

[16]  J. Kropotov,et al.  Selection of actions in the basal ganglia-thalamocortical circuits: review and model. , 1999, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[17]  Corey J. Bohil,et al.  Delayed feedback effects on rule-based and information-integration category learning. , 2003, Journal of experimental psychology. Learning, memory, and cognition.

[18]  W T Maddox,et al.  Quantitative modeling of visual attention processes in patients with Parkinson's disease: effects of stimulus integrality on selective attention and dimensional integration. , 1999, Neuropsychology.

[19]  G. E. Alexander,et al.  Parallel organization of functionally segregated circuits linking basal ganglia and cortex. , 1986, Annual review of neuroscience.

[20]  J Jonides,et al.  PET evidence for multiple strategies of categorization , 2001, Cognitive, affective & behavioral neuroscience.

[21]  Shawn W. Ell,et al.  Procedural learning in perceptual categorization , 2003, Memory & cognition.

[22]  W. Maddox,et al.  Effects of stimulus integrality on visual attention in older and younger adults: a quantitative model-based analysis. , 1998, Psychology and aging.

[23]  F. Collette,et al.  Brain imaging of the central executive component of working memory , 2002, Neuroscience & Biobehavioral Reviews.

[24]  W Todd Maddox,et al.  Information-integration category learning in patients with striatal dysfunction. , 2005, Neuropsychology.

[25]  T Jones,et al.  The nigrostriatal dopaminergic system assessed in vivo by positron emission tomography in healthy volunteer subjects and patients with Parkinson's disease. , 1990, Archives of neurology.

[26]  Faith M. Gunning-Dixon,et al.  Differential aging of the human striatum: a prospective MR imaging study. , 1998, AJNR. American journal of neuroradiology.

[27]  M. Yamada,et al.  [Dementia rating scale]. , 1997, Nihon rinsho. Japanese journal of clinical medicine.

[28]  C. Spence,et al.  Orienting of attention and Parkinson's disease: tactile inhibition of return and response inhibition. , 2003, Brain : a journal of neurology.

[29]  Maddox Wt,et al.  Quantitative modeling of visual attention processes in patients with Parkinson's disease: effects of stimulus integrality on selective attention and dimensional integration. , 1999 .

[30]  R. West,et al.  Visual distraction, working memory, and aging , 1999, Memory & cognition.

[31]  C. Marsden,et al.  'Frontal' cognitive function in patients with Parkinson's disease 'on' and 'off' levodopa. , 1988, Brain : a journal of neurology.

[32]  W. Gibb Neuropathology of Parkinson's disease and related syndromes. , 1992, Neurologic clinics.

[33]  W. T. Maddox,et al.  Striatal contributions to category learning: Quantitative modeling of simple linear and complex nonlinear rule learning in patients with Parkinson's disease , 2001, Journal of the International Neuropsychological Society.

[34]  A. Benton,et al.  Visuospatial judgment. A clinical test. , 1978, Archives of neurology.

[35]  Patrick Dupont,et al.  Human Brain Regions Involved in Visual Categorization , 2002, NeuroImage.

[36]  D. Salat,et al.  Selective preservation and degeneration within the prefrontal cortex in aging and Alzheimer disease. , 2001, Archives of neurology.

[37]  Edward E. Smith,et al.  The Neural Basis for Categorization in Semantic Memory , 2002, NeuroImage.

[38]  F. Ashby,et al.  The effects of concurrent task interference on category learning: Evidence for multiple category learning systems , 2001, Psychonomic bulletin & review.

[39]  E. A. Berg,et al.  A simple objective technique for measuring flexibility in thinking. , 1948, The Journal of general psychology.

[40]  Dean C. Delis,et al.  Visual selective attention deficits in patients with Parkinson's disease: A quantitative model-based approach. , 1996 .

[41]  Robert K. Heaton,et al.  Wisconsin Card Sorting Test Manual – Revised and Expanded , 1993 .

[42]  R. Heaton Wisconsin Card Sorting Test manual , 1993 .

[43]  Terry L. Jernigan,et al.  Cerebral structure on MRI, Part I: Localization of age-related changes , 1991, Biological Psychiatry.

[44]  Edward E. Smith,et al.  Alternative strategies of categorization , 1998, Cognition.

[45]  David L. Faigman,et al.  Human category learning. , 2005, Annual review of psychology.

[46]  W Todd Maddox,et al.  Category number impacts rule-based but not information-integration category learning: further evidence for dissociable category-learning systems. , 2004, Journal of experimental psychology. Learning, memory, and cognition.

[47]  J. Assad,et al.  Neural coding of behavioral relevance in parietal cortex , 2003, Current Opinion in Neurobiology.

[48]  C. Fennema-Notestine,et al.  Effects of age on tissues and regions of the cerebrum and cerebellum , 2001, Neurobiology of Aging.

[49]  W. T. Maddox,et al.  Dissociating explicit and procedural-learning based systems of perceptual category learning , 2004, Behavioural Processes.