Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-25T05:14:44.988Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  26 August 2005

J. VINCENT FILOTEO ,
W. TODD MADDOX ,
A. DAVID ING ,
VANESSA ZIZAK  and
DAVID D. SONG
J. VINCENT FILOTEO
Affiliation:
Department of Psychology, University of California, San Diego Veterans Administration San Diego Healthcare System, San Diego, California
W. TODD MADDOX
Affiliation:
Department of Psychology, University of Texas, Austin, Texas
A. DAVID ING
Affiliation:
Department of Psychology, University of Texas, Austin, Texas
VANESSA ZIZAK
Affiliation:
Department of Psychology, University of California, San Diego Veterans Administration San Diego Healthcare System, San Diego, California
DAVID D. SONG
Affiliation:
Department of Psychology, University of California, San Diego Veterans Administration San Diego Healthcare System, San Diego, California
Get access Rights & Permissions [Opens in a new window]

Abstract

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.)

Keywords

Basal gangliaStriatumConcept learningMemoryAgingSelective attention
Type
Research Article
Information
Journal of the International Neuropsychological Society , Volume 11 , Issue 5 , September 2005 , pp. 503 - 513
Copyright
© 2005 The International Neuropsychological Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Alexander, G.E., DeLong, M.R., & Strick, P.L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9, 357–381.CrossRefGoogle Scholar
Ashby, F.G., Alfonso-Reese, L.A., Turken, A.U., & Waldron, E.M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105, 442–481.CrossRefGoogle Scholar
Ashby, F.G., Ell, S.W., & Waldron, E.M. (2003a). Procedural learning in perceptual categorization. Memory and Cognition, 31, 1114–1125.Google Scholar
Ashby, F.G. & Maddox, W.T. (2005). Human category learning. Annual Review of Psychology, 56, 149–178.CrossRefGoogle Scholar
Ashby, F.G., Maddox, W.T., & Bohil, C.J. (2002). Observational versus feedback training in rule-based and information-integration category learning. Memory & Cognition, 30, 666–677.CrossRefGoogle Scholar
Ashby, F.G., Noble, S., Filoteo, J.V., Waldron, E., & Ell, S. (2003b). Category learning deficits in Parkinson's disease. Neuropsychology, 17, 115–124.Google Scholar
Assad, J.A. (2003). Neural coding of behavioral relevance in parietal cortex. Current Opinions in Neurobiology, 13, 194–197.CrossRefGoogle Scholar
Benton, A.L., Varney, N.R., & Hamsher, K. (1978). Visuospatial judgment: A clinical test. Archives of Neurology, 35, 364–367.CrossRefGoogle Scholar
Berg, E.A.A. (1948). A simple objective test for measuring flexibility in thinking. Journal of General Psychology, 39, 15–22.CrossRefGoogle Scholar
Bowles, R.P. & Salthouse, T.A. (2003). Assessing the age-related effects of proactive interference on working memory tasks using the Rasch model. Psychology and Aging, 18, 608–615.CrossRefGoogle Scholar
Bradley, V.A., Welch, J.L., & Dick, D.J. (1989). Visuospatial working memory in Parkinson's disease. Journal of Neurology, Neurosurgery, and Psychiatry, 52, 1228–1235.CrossRefGoogle Scholar
Broussolle, E., Centresangle, C., Landais, P., Garcia-Larrea, L., Pollak, P., Croisile, B., Hibert, O., Bonnefoi, F., Galy, G., Froment, J.C., & Comar, D. (1999). The relation of putamen and caudate nucleus 18F-Dopa uptake to motor and cognitive performances in Parkinson's disease. Journal of Neurological Sciences, 166, 141–151.CrossRefGoogle Scholar
Bruner, J.S., Goodnow, J.J., & Austin, G.A. (1956). A study of thinking. New York: John Wiley & Sons, Inc.
Collette, F. & Van der Linden, M. (2002). Brain imaging of the central executive component of working memory. Neuroscience and Biobehavioral Review, 26, 105–125.CrossRefGoogle Scholar
Delis, D.C., Kramer, J.H., Kaplan, E., & Ober, B.A. (2000). California Verbal Learning Test–Second Edition, Adult Version manual. San Antonio, TX: The Psychological Corporation.
Filoteo, J.V. & Maddox, T.W. (1999). Quantitative modeling of visual attention processes in patients with Parkinson's disease: Effects of stimulus integrality on selective attention and dimensional integration. Neuropsychology, 13, 206–222.CrossRefGoogle Scholar
Filoteo, J.V., Maddox, W.T., Salmon, D.P., & Song, D.D. (2005). Information-integration category learning in patients with striatal dysfunction. Neuropsychology, 19, 212–222.CrossRefGoogle Scholar
Filoteo, J.V., Maddox, W.T., Simmons, A.N., Ing, A.D., Cagigas, X.E., Matthews, S., & Paulus, M.P. (in press). Cortical and subcortical brain regions involved in rule-based category learning. NeuroReport.
Garner, W.R. (1978). Selective attention to attributes and to stimuli. Journal of Experimental Psychology: General, 107, 287–308.CrossRefGoogle Scholar
Gibb, W.R.G. (1992). Neuropathology of Parkinson's disease and related syndromes. Neurologic Clinics, 10, 361–376.CrossRefGoogle Scholar
Gotham, A.M., Brown, R.G., & Marsden, C.D. (1988). ‘Frontal’ cognitive function in patients with Parkinson's disease ‘on’ and ‘off’ levodopa. Brain, 111, 299–321.CrossRefGoogle Scholar
Grossman, M., Smith, E.E., Koenig, P., Glosser, G., DeVita, C., Moore, P., & McMillan, C. (2002). The neural basis for categorization in semantic memory. Neuroimage, 17, 1549–1561.CrossRefGoogle Scholar
Gunning-Dixon, F.M., Head, D., McQuain, J., Acker, J.D., & Raz, N. (1998). Differential aging of the human striatum: A prospective MR imaging study. American Journal of Neuroradiology, 19, 1501–1507.Google Scholar
Heaton, R.K., Chelune, G.J., Talley, J.L., Kay, G.G., & Curtiss, G. (1993). Wisconsin Card Sorting Test manual. Odessa, FL: Psychological Assessment Resources, Inc.
Hoehn, M.M. & Yahr, M.D. (1967). Parkinsonism: Onset, progression and mortality. Neurology, 17, 427–442.CrossRefGoogle Scholar
Jernigan, T.L., Archibald, S.L., Berhow, M.T., Sowell, E.R., Foster, D.S., & Hesselink, J.R. (1991). Cerebral structure on MRI. 1. Localization of age-related changes. Biological Psychiatry, 29, 55–67.Google Scholar
Jernigan, T.L., Archibald, S.L., Fennema-Notestine, C., Gamst, A.C., Stout, J.C., Bonner, J., & Hesselink, J.R. (2001). Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiology of Aging, 22, 581–594.CrossRefGoogle Scholar
Kropotov, J.D. & Etlinger, S.C. (1999). Selection of actions in the basal ganglia-thalamocortical circuits: Review and model. International Journal of Psychophysiology, 31, 197–217.CrossRefGoogle Scholar
Leenders, K.L., Salmon, E.P., Pippa, T., Perani, D., Brooks, D.J., Sager, H., Jones, T., Marsden, C.D., & Frackowiak, R.S.J. (1990). The nigrostriatal dopaminergic system assessed in vivo by positron emission tomography in healthy volunteer subjects and patients with Parkinson's disease. Archives of Neurology, 47, 1290–1298.CrossRefGoogle Scholar
Maddox, W.T. & Ashby, F.G. (2004). Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioral Processes, 66, 309–332.CrossRefGoogle Scholar
Maddox, W.T., Ashby, F.G., & Bohil, C.J. (2003). Delayed feedback effects on rule-based and information-integration category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 650–662.Google Scholar
Maddox, W.T., Ashby, F.G., Ing, A.D., & Pickering, A.D. (2004a). Disrupting feedback processing interferes with rule-based, but not information-integration category learning. Memory & Cognition, 32, 582–591.Google Scholar
Maddox, W.T. & Filoteo, J.V. (2001). Striatal contribution to category learning: Quantitative modeling of simple linear and complex non-linear rule learning in patients with Parkinson's disease. Journal of the International Neuropsychological Society, 7, 710–727.CrossRefGoogle Scholar
Maddox, W.T., Filoteo, J.V., Delis, D.C., & Salmon, D.P. (1996). Visual selective attention deficits in patients with Parkinson's disease: A quantitative model based approach. Neuropsychology, 10, 197–218.CrossRefGoogle Scholar
Maddox, W.T., Filoteo, J.V., Hejl, K.D., & Ing, A.D. (2004b). Category number impacts rule-based but not information-integration category learning: Further evidence for dissociable category learning systems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 227–235.Google Scholar
Maddox, W.T., Filoteo, J.V., & Huntington, J.R. (1998). Effects of stimulus integrality on visual attention in older and younger adults: A quantitative model-based analysis. Psychology and Aging, 13, 472–485.CrossRefGoogle Scholar
Mattis, S. (1988). Dementia Rating Scale. Odessa, FL: Psychological Assessment Resources.
Mink, J.W. (1996). The basal ganglia: Focused selection and inhibition of competing motor programs. Progress in Neurobiology, 50, 381–425.CrossRefGoogle Scholar
Patalano, A.L., Smith, E.E., Jonides, J., & Koeppe, R.A. (2001). PET evidence for multiple strategies of categorization. Cognitive Affective and Behavioral Neuroscience, 1, 360–370.CrossRefGoogle Scholar
Poliakoff, E., O'Boyle, D.J., Moore, A.P., McGlone, F.P., Cody, F.W., & Spence, C. (2003). Orienting of attention and Parkinson's disease: Tactile inhibition of return and response inhibition. Brain, 126, 2081–2092.CrossRefGoogle Scholar
Postle, B.R., Jonides, J., Smith, E.E., Corkin, S., & Growdon, J.H. (1997). Spatial, but not object, delayed response is impaired in early Parkinson's disease. Neuropsychology, 11, 171–179.CrossRefGoogle Scholar
Praamstra, P. & Plat, F.M. (2001). Failed suppression of direct visuomotor activation in Parkinson's disease. Journal of Cognitive Neuroscience, 13, 31–43.CrossRefGoogle Scholar
Salat, D.H., Kaye, J.A., & Janowsky, J.S. (2001). Selective preservation and degeneration within the prefrontal cortex in aging and Alzheimer disease. Archives of Neurology, 58, 338–344.Google Scholar
Smith, E.E., Patalano, A.L., & Jonides, J. (1998). Alternative strategies of categorization. Cognition, 65, 167–196.CrossRefGoogle Scholar
Smith, E.E. & Sloman, S.A. (1994). Similarity- versus rule-based categorization. Memory and Cognition, 22, 377–386.CrossRefGoogle Scholar
Vogels, R., Sary, G., Dupont, P., & Orban, G.A. (2002). Human brain regions involved in visual categorization. Neuroimage, 16, 401–414.CrossRefGoogle Scholar
Wager, T.D. & Smith, E.E. (2003). Neuroimaging studies of working memory: A meta-analysis. Cognitive and Affective Behavioral Neuroscience, 3, 255–274.CrossRefGoogle Scholar
Waldron, E.M. & Ashby, F.G. (2001). The effects of cocurrent task interference on category learning. Psychonomic Bulletin & Review, 8, 168–176.CrossRefGoogle Scholar
West, R. (1999). Visual distraction, working memory, and aging. Memory and Cognition, 27, 1064–1072.CrossRefGoogle Scholar