Category learning deficits in Parkinson's disease.

Ashby, F. Gregory; Noble, Sharon; Filoteo, J. Vincent; Waldron, Elliott M.; Ell, Shawn W.

doi:10.1037/0894-4105.17.1.115

Cited by 129 publications

(165 citation statements)

References 62 publications

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“…This apparent dissociation between learning and insight has been taken as evidence for two separate learning systems (Ashby, Noble, Filoteo, Waldron, & Ell, 2003;Knowlton, Mangels, & Squire, 1996;Reber & Squire, 1999;Squire, 1994): (a) an implicit (or procedural) system that operates in the absence of awareness or conscious control and is inaccessible to self-report; and (b) an explicit (or declarative) system that requires awareness and involves analytic processing. Tasks such as the WP task, which require the gradual learning and integration of probabilistic information, are generally considered to involve (a).…”

Section: The Apparent Dissociation Of Learning and Insightmentioning

confidence: 99%

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Insight and strategy in multiple-cue learning.

Lagnado

Newell

Kahan

et al. 2006

Journal of Experimental Psychology: General

114

167

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In multiple-cue learning (also known as probabilistic category learning) people acquire information about cue-outcome relations and combine these into predictions or judgments. Previous researchers claimed that people can achieve high levels of performance without explicit knowledge of the task structure or insight into their own judgment policies. It has also been argued that people use a variety of suboptimal strategies to solve such tasks. In three experiments the authors reexamined these conclusions by introducing novel measures of task knowledge and self-insight and using "rolling regression" methods to analyze individual learning. Participants successfully learned a four-cue probabilistic environment and showed accurate knowledge of both the task structure and their own judgment processes. Learning analyses suggested that the apparent use of suboptimal strategies emerges from the incremental tracking of statistical contingencies in the environment.Keywords: multiple-cue learning, self-insight, strategy, rolling regression, implicit versus explicit learning A fundamental goal of cognition is to predict the future on the basis of past experience. In an uncertain world this involves learning about the probabilistic relations that hold between the available information and an outcome of interest and integrating this information into a singular judgment. Thus, a stockbroker draws on various market indicators to predict the future price of a share, and a race-course pundit uses factors such as form and track condition to pick the likely winner of a race.The underlying structure of this kind of prediction is captured in the multiple-cue learning framework (Brunswik, 1943;Hammond, 1955), which focuses on how people learn from repeated exposure to probabilistic information. This paradigm, also known as probabilistic category learning, has been applied in numerous areas of psychology, including human judgment (Brehmer, 1979;

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Section: The Apparent Dissociation Of Learning and Insightmentioning

confidence: 99%

“…It is also argued that these two systems are subserved by distinct brain regions that can be differentially impaired (Ashby et al, 2003;Knowlton, Mangels, & Squire, 1996;Poldrack et al, 2001). Thus, the WP task has been used to reveal a distinctive pattern of dissociations among patient populations.…”

Section: The Apparent Dissociation Of Learning and Insightmentioning

confidence: 99%

Insight and strategy in multiple-cue learning.

Lagnado

Newell

Kahan

et al. 2006

Journal of Experimental Psychology: General

114

167

View full text Add to dashboard Cite

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“…PD is characterized by death of midbrain dopamine cells projecting to the BG (Kish et al, 1988), and associated motor symptoms including tremor, rigidity, and slowness of movement (McAuley, 2003). PD patients also have a variety of cognitive deficits, ranging from procedural learning to working memory, decision making and attention (Ashby, Noble, Ell, Filoteo, & Waldron, 2003;Cools, Barker, Sahakian, & Robbins, 2001bFrank, 2005a;Frank, Seeberger, & O'Reilly, 2004;Gotham, Brown, & Marsden, 1988;Jackson, Jackson, Harrison, Henderson, & Kennard, 1995;Knowlton, Mangels, & Squire, 1996;Maddox & Filoteo, 2001;Rogers et al, 1998;Shohamy et al, 2005). Given the proposed BG role in selecting among various competing low-level motor responses by modulating frontal motor activity, and the parallel circuits linking the BG with more frontal cognitive areas (Alexander et al, 1986;Middleton & Strick, 2000, 2002, it is natural to extend this action selection functionality to include higher-level cognitive decisions (Frank, 2005a;Frank & Claus, 2006;Frank et al, 2001;Houk, 2005).…”

Section: A Model Of Reinforcement Learning and Decision Making In Parmentioning

confidence: 99%

Hold your horses: A dynamic computational role for the subthalamic nucleus in decision making

2006

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The basal ganglia (BG) coordinate decision making processes by facilitating adaptive frontal motor commands while suppressing others. In previous work, neural network simulations accounted for response selection deficits associated with BG dopamine depletion in Parkinson's disease. Novel predictions from this model have been subsequently confirmed in Parkinson patients and in healthy participants under pharmacological challenge. Nevertheless, one clear limitation of that model is in its omission of the subthalamic nucleus (STN), a key BG structure that participates in both motor and cognitive processes. The present model incorporates the STN and shows that by modulating when a response is executed, the STN reduces premature responding and therefore has substantial effects on which response is ultimately selected, particularly when there are multiple competing responses. Increased cortical response conflict leads to dynamic adjustments in response thresholds via cortico-subthalamicpallidal pathways. The model accurately captures the dynamics of activity in various BG areas during response selection. Simulated dopamine depletion results in emergent oscillatory activity in BG structures, which has been linked with Parkinson's tremor. Finally, the model accounts for the beneficial effects of STN lesions on these oscillations, but suggests that this benefit may come at the expense of impaired decision making.

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“…Mit Fortschreiten der Erkrankung kann es jedoch zu deutlichen Defiziten im Bereich der kognitiven Schnelligkeit und Aufmerksamkeit, des Gedächtnisses der visuo-konstruktiven Fähigkeiten sowie der Exekutivfunktionen kommen (z.B. Taylor et al, 1987;Wassenstein et al, 1987;Levin et al, 1991;Bondi et al, 1993;Bublak et al, 2002;Ashby et al, 2003 (Posner & Boies, 1971;Posner & Rafal, 1987 (Howes & Boller, 1975;Mesulam, 1985;Posner et al, 1987;Ladavas, 1987).…”

Section: Patienten Mit Parkinson-syndrom Scheinen Im Vergleich Zur Geunclassified