Rule-based category learning in patients with Parkinson's disease

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Neuropsychologia 47 (2009) 1213–1226

Contents lists available at ScienceDirect

Neuropsychologia

journa l homepage: www.e lsev ier .com/ locate /neuropsychologia

eviews and perspectives

ule-based category learning in patients with Parkinson’s disease

manda Pricea,∗, J. Vincent Filoteob, W. Todd Maddoxc

Department of Psychology, Elizabethtown College, Elizabethtown, PA 17022, United StatesVeterans Administration, San Diego Healthcare System, San Diego, CA, United StatesDepartment of Psychology, University of Texas, Austin, TX, United States

r t i c l e i n f o

rticle history:eceived 7 May 2008eceived in revised form 21 January 2009ccepted 25 January 2009vailable online 2 February 2009

eywords:

a b s t r a c t

Measures of explicit rule-based category learning are commonly used in neuropsychological evaluationof individuals with Parkinson’s disease (PD) and the pattern of PD performance on these measures tendsto be highly varied. We review the neuropsychological literature to clarify the manner in which PD affectsthe component processes of rule-based category learning and work to identify and resolve discrepancieswithin this literature. In particular, we address the manner in which PD and its common treatmentsaffect the processes of rule generation, maintenance, shifting and selection. We then integrate the neu-

arkinson’s diseasetriatumategory learningopaminexecutive function

ropsychological research with relevant neuroimaging and computational modeling evidence to clarify theneurobiological impact of PD on each process. Current evidence indicates that neurochemical changesassociated with PD primarily disrupt rule shifting, and may disturb feedback-mediated learning pro-cesses that guide rule selection. Although surgical and pharmacological therapies remediate this deficit,it appears that the same treatments may contribute to impaired rule generation, maintenance and selec-tion processes. These data emphasize the importance of distinguishing between the impact of PD and its
common treatments when considering the neuropsychological profile of the disease.
© 2009 Elsevier Ltd. All rights reserved.

ontents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12131.1. Neurobiological impact of PD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12141.2. Component processes of rule based category learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1214

2. Rule generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12162.1. Impact of PD medication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1216

3. Rule maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12163.1. Neurobiological mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12173.2. Impact of PD medication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1218

4. Rule shifting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12184.1. Variability across tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12194.2. Dopaminergic involvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12204.3. Noradrenergic involvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1220

5. Rule selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12205.1. Ineffective use of feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12215.2. Impact of PD medication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1222

6. Summary and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1222Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1223References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1223

∗ Corresponding author. Tel.: +1 717 475 9789.E-mail address: [email protected] (A. Price).

028-3932/$ – see front matter © 2009 Elsevier Ltd. All rights reserved.oi:10.1016/j.neuropsychologia.2009.01.031

1. Introduction

As a fundamental aspect of human cognition, categorizationenables appropriate responses to a variety of familiar and novelstimuli. The same categorization processes that govern decisionsof dire importance, such as whether or not the street sign ahead

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selection, maintenance, and shifting of classification rules. Here,we elaborate on the COVIS model to better address patterns of vari-ation in the performances of PD patients on these particular aspects

214 A. Price et al. / Neuropsy

ignals a road hazard, also govern more mundane behaviors suchs filing papers or sorting laundry. The ubiquitous nature of catego-ization has led to extensive examination of the cognitive and, moreecently, neurobiological processes underlying its performance.

Much work in the area of categorization has focused on therocesses underlying the acquisition of new categories and it is gen-rally accepted that multiple learning systems operate in parallelo support such learning (Ashby, Alfonso-Reese, Turken, & Waldron,998; Ashby & Valentin, 2005; Erickson & Kruschke, 1998; Maddox

Ashby, 2004; Reber & Squire, 1994; Smith & Grossman, 2008;mith, Patalano, & Jonides, 1998; but see Nosofsky & Johansen, 2000or discussion of a unitary system of categorization). At least one ofhese systems supports implicit category learning, which developsradually with little intention, and at least one other system sup-orts explicit category learning, which requires effortful hypothesisesting and memorization processes (Ashby et al., 1998; Maddox &shby, 2004; Poldrack et al., 2001; Poldrack & Packard, 2003; SmithGrossman, 2008; Smith et al., 1998).In this review, we focus specifically on the neurobiological

spects of explicit, rule-based category learning as a detailed exam-nation of both implicit and explicit systems is outside the scopef the present article (for details on the neural mechanisms ofmplicit category learning, see Ashby & Ennis, 2006; Shohamy,

yers, & Kalanithi, 2008). Explicit rule-based category structuresre learned using logical reasoning and hypothesis testing pro-esses that demand working memory (WM) resources. Typically,he optimal classification rule is easily verbalized, although rulesary in complexity (Bruner, Goodnow, & Austin, 1956). Often,ategorization depends upon a single dimension that must beppropriately mapped onto each stimulus. One of the most com-only used measures of explicit rule-based category learning is

he Wisconsin Card Sorting Task (WCST; Berg, 1948; Heaton, 1981),n which stimuli vary along several dimensions and participants

ust sort according to one while ignoring the others. Addition-lly, some tasks rely upon multi-dimensional rules that require theonsideration of several stimulus dimensions.

.1. Neurobiological impact of PD

Much of the current knowledge regarding the cognitive neuro-cience of explicit rule-based category learning stems from researchith individuals with damage to specific brain areas. In particu-

ar, considerable work has focused on patients with fronto-striatalysfunction due to Parkinson’s disease (PD). PD is associated withegeneration of dopamine (DA) neurons in the substantia nigra parsompacta (SNPC), leading to a substantial drop in DA within the dor-al striatum (putamen and caudate) (Agid, Javoy-Agid, & Ruberg,987; Albin, Young, & Penney, 1989; Robertson & Robertson, 1988).his results in excessive inhibition of thalamic projections to cor-ical regions, namely premotor and prefrontal structures (DeLong,990; Scatton, Worms, Lloyd, & Bartholini, 1982). As PD progresses,A neurons in the ventral tegmental area (VTA) are also lost,

esulting in dysfunction of the ventral striatum and its corticalrojections, including orbitofrontal cortex (OFC) and anterior cin-ulate (ACC) (Farley, Price, & Hornykiewicz, 1977; Kish, Shannak, &ornykiewicz, 1988; Uhl, Hedreen, & Price, 1985). Additionally, sig-ificant degeneration of the locus ceruleus results in dysfunctionf the noradrenergic (NA) system, including the ceruleo-corticalrojections to PFC (see Rommelfanger & Weinshenker, 2007 for aeview).

The neurochemical changes associated with PD often lead to
ysfunction of a number of cognitive processes (Brown & Marsden,990; Salmon, Lineweayer, & Heindel, 1998). This includes impair-ent in WM, selective attention and cognitive flexibility, each ofhich are fundamental to explicit, rule-based category learning.
atients with PD commonly exhibit rule-based category learning

ia 47 (2009) 1213–1226

impairment but the nature of this impairment is highly variable. Inthe present review, we examine the particular conditions in whichrule-based category learning deficits emerge in PD and considerhow these deficits are explained by the neurobiological dysfunc-tion associated with the disease. While dysfunction in a numberof brain regions and neurotransmitter systems might contribute torule-based category learning deficits in PD, in the present review weprimarily focus on the potential role of the caudate nucleus, nucleusaccumbens, prefrontal cortex, and dopaminergic systems as thesebrain regions and neurotransmitter have been highly implicated inexplicit rule-based category learning.

A better understanding of the mechanisms that underlie rule-based category learning impairments in PD holds several benefits.Although it is the case that deficits on other cognitive tasks pre-dict future cognitive decline in PD, measures of rule-based categorylearning, such as performance on the WCST, are highly predic-tive as well (Dujardin, Degreef, Rogelet, Defebvre, & Destee, 1999;Jacobs et al., 1995; Janvin, Aarsland, & Larsen, 2005; Levy et al.,2002; Maddox, Filoteo & Zeithamova, in press; Mahieux et al., 1998;Picirilli, D’Alessandro, Finali, Piccinin, & Agostini, 1989; Woods &Troster, 2003). Thus from a clinical perspective, increased under-standing of PD-related deficits may enhance knowledge regardingthe predictors and causes of dementia. Such knowledge may alsostimulate the development of additional neuropsychological toolsto predict the onset of dementia.

Further, integration of findings across multiple paradigmsenriches our understanding of the neurobiological mechanismsthat support category learning and helps to clarify the sources ofrule-based category learning impairment in PD. The evidence wereview here indicates that particular components of explicit rule-based category learning are differently affected by PD. Variabilityin PD performance appears to reflect deficits attributable to thedisease itself and its commonly used treatments. Accumulating evi-dence indicates that pharmacological therapies ameliorate certaincognitive deficits while creating other forms of cognitive impair-ment (Cools, 2006; Cools, Barker, Sahakian, & Robbins, 2001; Cools,Barker, Sahakian, & Robbins, 2003; Cools, Altamirano, & D’Esposito,2006; Cools, Lewis, Clark, Barker, & Robbins, 2007; Gotham, Brown,& Marsden, 1988; Owen et al., 1993b). Increased knowledge regard-ing currently available PD treatments and their impact on cognitivefunction informs clinical decision making and improves under-standing of the potential cognitive impact of emerging therapeuticinterventions (e.g. stem cell therapy, novel drug therapies, and genetherapy).

1.2. Component processes of rule based category learning

Several computational models of category learning haveaddressed the explicit, rule-based system, and each tends to invokesimilar components, namely WM and hypothesis testing processes(Ashby et al., 1998; Ashby & Valentin, 2005; Erickson & Kruschke,1998; Erickson & Kruschke, 2002). One of the most successfulneurobiological models of rule-based category learning is theCOmpetition between Verbal and Implicit Systems (COVIS) modelproposed by Ashby and colleagues (Ashby et al., 1998; Ashby &Valentin, 2005; Maddox & Ashby, 2004)1. According to the COVISmodel, rule-based category learning depends upon hypothesis test-ing processes that place heavy demand on WM, including the

of hypothesis testing. In addition, we extend the model to address

1 The COVIS model also details implicit category learning but we focus only on thecomponents devoted to explicit, rule-based learning.

A. Price et al. / Neuropsychologia 47 (2009) 1213–1226 1215

gory learning, specifically rule generation, maintenance, shifting and selection.

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Fig. 2. Neural architecture of explicit, rule-based category learning: Solid lines indi-cate excitatory input; dashed lines indicate inhibitory input. Generation of rulesdepends upon dopamine (DA) activity within the ventral cortico-striatal circuit,including nucleus accumbens (NAc), orbitofrontal cortex (OFC), and anterior cin-gulate cortex (ACC). Active maintenance of the rule in use depends upon recurrentexcitation of the fronto-thalamic loop that represents that rule. Positive feedbacksupport rule maintenance through phasic DA bursts from substantia nigra pars com-pacta (SNpc) to caudate. This increases excitation of the “Go” pathway and inhibitionof the “NoGo” pathway and enables disinhibition of the relevant fronto-thalamicloop. Negative feedback triggers a phasic dip in DA availability in caudate, resultingin enhanced inhibitory output from GPi. This destabilizes the relevant rule repre-sentation in WM and increases the likelihood of a shift. Release of norepinephrine

Fig. 1. Summary of the component processes supporting rule-based cate

he process of rule generation and further articulate the process ofule selection, each of which are often overlooked in empirical andheoretical examinations of category learning.

Although each hypothesis-testing component represents a dis-inct phase within the category learning process, the componentsre interdependent. Fig. 1 summarizes the manner in which theserocesses interact to support rule-based category learning. On theCST, for example, participants must sort a deck of cards accord-

ng to one of three dimensions (color, shape or number), but theyre not told which dimension is relevant and must learn thisnformation through feedback. Before beginning to sort, most par-icipants will readily generate several possible sorting rules (e.g.ort according to color). The process of rule generation, which referso the initial activation of one or more rule representations in WM,emands inductive reasoning and spontaneous cognitive flexibil-

ty, but places little demand on WM. Although several rules may beaintained in WM, one must select a particular rule to guide clas-

ification. Initially, rule selection is likely to be fairly random buts the task progresses, the pattern of recent feedback will increaser decrease activation of a particular rule representation in WM.he most strongly activated rule is selected to guide responding.f the selected rule remains associated with a consistent historyf positive reinforcement, that rule should be maintained in WM,rotected from interference by irrelevant information/rules. If theelected rule is not associated with a recent history of positive rein-orcement, then one should shift away from that rule. Once thencorrect rule is abandoned, one must either guess until a newule can be generated or select a new rule based upon its relativectivation in WM.

In the sections that follow, we examine the impact of PD onhe component processes of rule-based category learning, namely
ule generation, maintenance, shifting and selection. In doing so,e have worked to identify and, where possible, resolve discrepan-
ies within the neuropsychological literature to clarify the mannern which PD affects each component process. Often, variability inerformance may be attributable to whether patients are tested

from the locus ceruleus may also govern shifting activity. Selection of the new ruledepends upon the pattern of activation within ACC and PFC. Specifically, unexpectednegative or positive feedback triggers a phasic dip or burst, respectively, in DA releasefrom the ventral tegmental area (VTA). These phasic changes alter activation in NAand ACC and these error-driven changes in ACC activation regulate activation ofrelevant rule representations in PFC.

1216 A. Price et al. / Neuropsychologia 47 (2009) 1213–1226

Table 1Summary of studies examining rule generation in PD.

Source Task Patient sample Result

Beatty and Monson (1990) California Card Sort Task Delis, Bihrle, Janowsky,Squire, and Shimamura (1989)

16 PD; Stages 1–4; M = 2.6 PD = NC (d = .57)

Channon (1997) Unidimensional classification learning 20 PD PD < NC (d = .41)Dimitrov et al. (1999) California Card Sort Task 8 PD; Stages 1–4; M = 3.0 PD = NC (d = .93)Farina (1994) Picture sorting task, Incissa della Rocchetta (1986) 22 PD; Stages 1–2 PD < NCHanes, Andrewes, and Pantelis (1995) Verbal solutions task, Reitan (1972) 25 PD; Stages 1–4; M = 2.76 PD < NC (d = .41)Price (2006) Multi-dimension classification learning 16 PD; Stages 1–3; M = 2.2 PD < NC (d = 1.0)S s (20

N of PD

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ote: Stages refers to Hoehn and Yahr (1967) ratings of PD severity; M = mean level

hile on or off their usual medication and we have worked topecify the potential role of PD-related medication on patients’erformance.2 For each component process of hypothesis testing,e integrate relevant neuropsychological evidence from patientsith PD with neuroimaging and computational modeling evidence

o clarify the neural architecture of rule-based category learningnd the neurobiological impact of PD. The proposed neural archi-ecture of rule-based category learning is illustrated in Fig. 2.

. Rule generation

Examinations of rule generation in PD, which are summarized inable 1, typically employ one of two techniques. The first requiresarticipants to state the rule they are testing as they perform

eedback-mediated category learning. On these tasks, patients withD tend to verbalize fewer hypotheses, regardless of whether theask involves unidimensional (e.g. respond according to shape;hannon, 1997) or multi-dimensional rules (e.g. respond basedpon the shape, color and number of stimuli showing; Price, 2006).nfortunately, this measure of rule generation is confounded by

he operation of rule shifting and selection processes. Further, it isot clear whether deficits reflect impairment to rule generation or

eedback-mediated learning, which some have argued is disruptedn PD (Charbonneau, Riopelle, & Beninger, 1996; Frank, 2005; Frank,eeberger, & O’Reilly, 2004; Shohamy, Myers, Grossman, Sage, &luck, 2005; Vriezen & Moscovitch, 1990).

To more specifically target rule generation, the second type ofeasure presents participants with a set of stimuli that vary alongultiple dimensions and participants must generate as many sort-

ng rules as possible without receiving any form of feedback. Studiessing this type of measure have produced evidence of normalBeatty & Monson, 1990; Dimitrov, Grafman, Soares, & Clark, 1999)nd impaired rule generation performance (Farina, 1994; Swainsont al., 2006) in PD. One recent study suggests this discrepancy mayeflect variations in disease severity and patients’ medication sta-us (Swainson et al., 2006). Specifically, medication worsened ruleeneration performance in patients with early PD but improvederformance among patients with moderate or severe PD.

.1. Impact of PD medication

Detrimental effects of medication in PD have been observedith other tasks and it has been argued that medication may

overdose” particular neural processes (Cools, 2006; Cools et al.,003, 2007; Gotham et al., 1988; Swainson et al., 2000). Morepecifically, the “overdose hypothesis” posits that the medicationequired to treat motor impairment, associated with degradation

2 We use the general abbreviation PD when discussing research that did notpecifically examine the impact of PD medication. When addressing research thatpecifically examined the impact of medication, we distinguish between PD patientsn and off medication.

01) 11 mild PDOFF; 27 mild PDON; 14severe PDON

Mild PDOFF = NC; Mild PDON < NC;Severe PDON = NC

severity; d = Cohen’s d measure of effect size.

of the dorsal striatum, results in excessive DA in ventral striatum,which is relatively unaffected in early PD (Kish et al., 1988; Farley etal., 1977). Consistent with this “overdose hypothesis”, medicationin early PD has been shown to disrupt activity within the ventralstriatum (Cools et al., 2007). As PD advances, DA neurons in theventral striatum degenerate and the ventral cortico-striatal circuitsbecome increasingly compromised (Farley et al., 1977; Kish etal., 1988). Among moderately affected PD patients, medicationappears to sufficiently overcome this deficit and improve per-formance on tasks mediated by the ventral circuit (Cools et al.,2001, 2006; Swainson et al., 2006). However, once the ventralstriatum is heavily compromised, as in severe PD, medication maybe insufficient to support normal task performance, resulting inimpaired performance both on and off medication.

The overdose hypothesis posits that for processes mediated byventral cortico-striatal circuitry, the effect of medication in PDinteracts with disease severity according to an “inverted U” func-tion. Specifically, such processes are impaired in medicated, earlyPD patients, due to excessive DA, and medicated, late PD patients,due to insufficient DA; medicated patients with moderate PD per-form normally (Cools et al., 2001, 2006; Swainson et al., 2006).This “inverted U” pattern has been observed with rule generation(Swainson et al., 2006) and may explain the variation in rule gen-eration performance across studies. Those studies demonstratingnormal rule generation involved medicated patients of moderateseverity (Beatty & Monson, 1990; Dimitrov et al., 1999) whereasthose studies reporting impairment involved medicated patientsof mild severity (Farina, 1994; Price, 2006).

The possibility that rule generation depends upon ventralcortico-striatal circuitry is supported by converging neuroimagingand neuropsychological evidence from a variety of problem solvingtasks indicating involvement of the OFC and ACC in hypothesis gen-eration (Burgess, 2000; Dimitrov et al., 1999; Elliot & Dolan, 1998;Goel & Dolan, 2000; Goel & Grafman, 2000; Miller & Tippett, 1996;Reverberi, D’Agostini, & Skrap, 2005; Reverberi, Lavaroni, Gigli,Skrap, & Shallice, 2005; Vartanian & Goel, 2005). Anti-Parkinsonianmedications may “overdose” the ventral striatum, thereby creat-ing dysfunction in its cortical targets, namely OFC and ACC. Takentogether, the general pattern of evidence suggests that PD patientsexperience difficulty generating rules, a problem that may stemfrom PD-related treatments rather than the disease itself.

3. Rule maintenance

Once a rule is generated, it must be maintained in WM to guideresponding. Maintaining rule representations within WM demandsselective attention, which consists of two general processes: (a)
active maintenance of relevant information and (b) inhibition ofirrelevant information. In this context, active maintenance refersto processes which sustain activation of a distinct pattern of neu-ral activity associated with relevant information. This pattern ofactivation persists unless destabilized by a signal to update WM.


Table 2Summary of studies examining rule maintenance in PD.


Beatty and Monson (1990) WCST, Heaton (1993) 27 PD; Stages 1–4 M = 3.3 PD = NC (d = .07)Beatty, Staton, Weir, Monson, and Whitaker (1989) WCST Heaton (1993) 25 PD; Stages 2–3 PD < NC (d = .86)Channon et al. (1993) Unidimensional classification learning task

(single rule; no switching)12 PD; Stages 2–3 PD = NC (d = .13)

Cooper et al. (1992) WCST, Heaton (1993) 61 PD; newly diagnosed; unmedicated PD = NC (d = .06)Filoteo et al. (2005) Unidimensional classification learning task

(single rule; no switching)19 PD; Stages 1–3 PD < NC

Filoteo et al. (2005) WCST, Heaton (1993) 19 PD; Stages 1–3 PD = NCFlowers and Robertson (1985) Odd-Man-Out task 49 PD PD < NCGauntlett-Gilbert et al. (1999) ID/ED shift task; Owen et al. (1993b) 10 PD; Stages 1–3 M = 2.26 PD < NCGreen et al. (2002) WCST, Heaton (1993) 61 PD; Stage 3–5 PD < NC (d = 1.5)Monchi et al. (2004) WCST, Heaton (1993) 8 PD; Stages 1–2 PD < NC off medsPaolo et al. (1995) WCST, Heaton (1993) 181 PD; Stages 1–4 PD = NC (d = .12)Price (2006) Multi-dimensional classification learning task 16 PD; Stage 1–2 PD = NCPrice (2006) WCST, Hart et al. (1988) 16 PD; Stages 1–2 PD = NCR and RT es et

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ichards et al. (1993) Odd-Man-Out task, Flowersomer et al. (2002) ID/ED task of CANTAB, Down


n the other hand, inhibition in this context refers to those pro-esses that are responsible for actively reducing the likelihood thatrrelevant items will achieve a representation in WM and poten-ially disrupt the maintenance of relevant information. Together,ctive maintenance of relevant information and inhibition of irrel-vant information support rule maintenance. Generally, PD hasittle impact on WM maintenance (Lewis et al., 2003a; Lewis, Dove,obbins, Barker, & Owen, 2004; Lewis, Salbosz, Robbins, Barker, &wen, 2005; Owen et al., 1993a), whereas inhibitory processes are

ess stable (Dujardin et al., 1999; Filoteo & Maddox, 1999; Maddox,iloteo, Delis, & Salmon, 1996; McDowell & Harris, 1997; Sharpe,990). Thus, rule maintenance deficits in PD may emerge underonditions of high interference by irrelevant information.

In category learning tasks, rule maintenance is typically mea-ured by the frequency of set loss errors, which occur when onebandons a rule that has been consistently reinforced. Table 2 sum-arizes findings from examinations of rule maintenance in PD.ost commonly, rule maintenance has been examined using theCST and a large scale analysis of set loss errors on the WCST,

nvolving over 180 PD patients, revealed normal patterns of ruleaintenance (Paolo, Troster, Axelrod, & Koller, 1995). A number

f studies involving the WCST and other category learning tasksave reported similar findings in PD, although a few have reported

mpairment (see Table 2). In contrast to generally preserved ruleaintenance on the WCST, PD patients exhibit deficits on the sim-

larly structured Odd-Man-Out task (OMO; Flowers & Robertson,985; Richards, Cote, & Stern, 1993). On the OMO, participants mustndicate which of three stimuli is different from the others using aimple categorization rule (e.g. letter or shape) and this rule changeseveral times over the course of the task. PD patients show normalule maintenance for the first few rules but set loss errors increaseollowing several rule switches. Previously reinforced rules createonsiderable proactive interference and increased inhibition is nec-ssary to prevent these rules from disrupting maintenance of theurrently relevant rule. This may be especially problematic for PDatients, who tend to be more susceptible to proactive interferenceHelkala, Laulumaa, Soininen, & Riekkinen, 1989; Rouleau, Imbault,aframboise, & Bedard, 2001), a deficit that likely stems from pre-rontal dysfunction associated with PD (Feredoes, Tononi, & Postle,006; Turner, Cipolatti, Yousry, & Shallice, 2007).

If set loss errors increase following multiple rule switches, it is
ossible that rule maintenance deficits may be underestimated byhe WCST. Due to a number of issues, PD patients tend to achieveewer WCST categories than controls, and therefore experienceewer rule switches. Patients who would have difficulty inhibitingreviously successful rules may also fail to achieve many previ-
obertson (1985) 45 PD; Stage M = 2.6 PD < NCal. (1989) 24 PD; Stage M = 1.6 PD = NC


ous rules and therefore experience little proactive interference. Itwould be worthwhile to systematically examine whether set losserrors increase as a function of previously reinforced rules. On taskslike the WCST, where the rule frequently changes, set loss errorsshould increase as patients achieve more categories. In contrast,if the rule remains the same throughout the task, set loss errorsshould be fairly low. Several studies that examined category learn-ing using a single-rule task found no evidence of increased set losserrors among PD patients (Channon, Jones, & Stephenson, 1993;Filoteo, Maddox, Ing, Zizak, & Song, 2005; Price, 2006), suggestingthat rule maintenance is more challenging under conditions of highproactive interference.

Additionally, it appears that high levels of concurrent interfer-ence within a task create problems for PD patients. On single-ruletasks, set loss errors among PD patients are more common whenirrelevant dimensions vary randomly (Filoteo, Maddox, Ing, & Song,2007), and this occurs even when participants are explicitly toldwhich rule to use (Filoteo & Maddox, 1999; Maddox et al., 1996). Incontrast, set loss errors are not as prevalent in PD patients whenthere is no irrelevant dimension variability, despite an increasein active maintenance demands within WM (Filoteo et al., 2007).These data suggest that PD has relatively little impact on rule main-tenance unless conditions require a high level of selective attention.

3.1. Neurobiological mechanisms

Computational models of WM typically depict active mainte-nance as a function of recurrent excitation among frontal neurons(Braver & Cohen, 1999; Braver & Cohen, 2000; Frank, Loughry,& O’Reilly, 2001; Moody, Wise, & di Pellegrino, 1998; O’Reilly &Frank, 2006; O’Reilly & Munakata, 2000) or recurrent connectionsbetween PFC and thalamus (Ashby et al., 1998; Ashby, Ell, Valentin,& Casale, 2005; Dominey, Arbib, & Joseph, 1995; Goldman-Rakic &Friedman, 1991; Houk & Wise, 1995; f, 2000; Taylor & Taylor, 2000;Zipser, 1991). If WM maintenance depends upon low levels of tonicDA stimulation in PFC, then among PD patients, available DA shouldbe sufficient to support the maintenance of WM. Alternatively, theFROntal-Striatal-Thalamic model of WM (FROST; Ashby et al., 2005),proposes that WM maintenance depends upon excitatory projec-tions from PFC to associated neurons within the head of the caudate.When WM load is high or complex, the caudate becomes necessary
to inhibit the GP and support prolonged maintenance. Striatal dys-function, such as in PD, limits the caudate’s ability to inhibit theGP from disrupting active thalamo-cortical loops. Thus, the FROSTmodel predicts that PD impairs active maintenance only when theWM load is especially high.

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In contrast to rule maintenance, which occurs when one per-sists with a successful rule, rule shifting occurs when one ceases


Whereas active maintenance is generally preserved in PD,atients may have greater difficulty inhibiting irrelevant infor-ation from accessing WM. This type of inhibition is commonly

ttributed to some type of WM gating mechanism (Ashby et al.,005; Braver & Cohen, 2000; Cohen, Braver, & O’Reilly, 1996; Frankt al., 2001; Moody et al., 1998; O’Reilly, Braver, & Cohen, 1999;’Reilly & Munakata, 2000; Zipser, 1991). Rougier, Noelle, Braver,ohen, and O’Reilly (2005) recently demonstrated the necessity ofgating mechanism in rule-based category learning through a sim-lation of WCST performance. When WM was modeled to include aating mechanism, relevant features were rapidly encoded, result-ng in activation of the correct rule. Simpler models without a gating

echanism tended to activate new rule representations with eachtimulus presentation and failed to settle on a particular rule.

A number of WM models emphasize the importance of someype of gating mechanism, which is thought to rely upon phasichanges in DA. Brief, phasic changes in DA are known to follow rein-orcement prediction errors, which reflect differences between anxpected and actual outcome (Bayer & Glimcher, 2005; Delgado,ocke, Stenger, & Fiez, 2003; Frank, Woroch, & Curran, 2005;olroyd & Coles, 2002; Schultz, Dayan, & Montague, 1997; Schultz,007). In addition, DA appears to enhance the contrast between theelevant and irrelevant dimensions of a stimulus by enhancing thering rate of currently active cells and suppressing less active cellsCohen, Braver, & Brown, 2002; Cohen & Servan-Schreiber, 1992;oote & Morrison, 1987; Hernandez-Lopez, Bargas, Surmeier, Reyes,Galarraga, 1997; Nicola, Surmeier, & Malenka, 2000; Rolls, Thorpe,oytim, Szabo, & Perrett, 1984). Thus, gating supports selectivettention via active suppression of the representation(s) associatedith the irrelevant dimension(s) and maintenance of the represen-

ation of the relevant dimension(s).Several WM models posit that gating is accomplished by DA

nput to PFC (Braver & Cohen, 1999; O’Reilly et al., 1999). Consis-ent with this, profound DA depletion within PFC leads to highistractibility and great difficulty maintaining mental set (Collins,ilkinson, Everitt, Robbins, & Roberts, 2000; Crofts et al., 2001).

he impact of DA on WM performance appears to be largely dose-ependent, such that especially high or low levels of DA increasene’s susceptibility to distraction (Kimberg, D’Esposito, & Farah,007; Williams & Goldman-Rakic, 1995). In early PD, prefrontalA availability is relatively normal (Brück et al., 2006; Sawamotot al., 2008) and medication may yield excessive DA activation inFC. Among healthy adults, WM under conditions of high distrac-ion is impaired by D2 agonists that target receptors abundant inFC and striatum, presumably because the excessive DA disruptshe gating mechanism, allowing both relevant and irrelevant infor-

ation to access WM (Frank & O’Reilly, 2006). Pharmacologicalreatments for PD, which increase the tonic availability of DA withinFC and caudate, may similarly reduce the discriminatory power ofhasic changes in DA (Frank et al., 2004). Excessive prefrontal DAay disrupt the WM gating mechanism by lowering the thresh-

ld for updating the contents of WM, resulting in rule maintenanceeficits, especially under conditions of high interference.

Alternatively, difficulty filtering irrelevant information mayeflect disturbances to a DA-mediated gating mechanism withinhe striatum (Frank & O’Reilly, 2006; Frank et al., 2001; Hazy,rank, & O’Reilly, 2007). According to the Prefrontal, Basal ganglia,orking Memory (PBWM) model, each viable cognitive response

s separately maintained in the PFC as a distinct stripe, or “iso-ated group of interconnected neurons” (Hazy et al., 2007, p. 4).ach PFC stripe is interconnected with striatal neurons that govern
M updating based upon recent feedback. Receipt of unexpected
ositive feedback triggers a phasic burst of DA within the caudate.his excites the direct, or “Go” pathway of the basal ganglia andnhibits the indirect, or “NoGo”, pathway, thereby preventing thePi from inhibiting relevant fronto-thalamic activity and increasing

ia 47 (2009) 1213–1226

the likelihood that relevant information accesses WM. Irrelevantstimulus dimensions are not updated despite receiving intermit-tent reinforcement. This is because the frequent non-reinforcementof irrelevant dimensions results in inhibition of the direct, “Go”pathway and excitation of the indirect, or “NoGo” pathway, whichindirectly allows GPi to inhibit the thalamus and decreases the like-lihood that the irrelevant dimension(s) will access WM. In this way,striatal DA selectively gates only the most relevant representations,namely those consistently associated with positive reinforcement.Consistent with this, McNab and Klingberg (2008) demonstratedthat high levels of within-task distraction were associated withincreased GPi activation, presumably reflecting the gating mech-anisms of the “Go” and “NoGo” pathways. Moreover, the extent ofGPi activation was associated with WM capacity across participants,suggesting that this gating mechanism within the basal ganglia hasimportant implications for selective attention processes in WM.

3.2. Impact of PD medication

In PD, significant DA depletion results in too little inhibition ofthe “NoGo” pathway and excessive inhibition of the thalamus bythe GPi (Frank, 2005; Frank & O’Reilly, 2006). Animal research indi-cates that DA depletion in the caudate leads to decreased updatingand enhanced resistance to distraction (Crofts et al., 2001; Collinset al., 2000). Thus, it is unlikely that the increased susceptibility tointerference among PD patients stems from striatal DA depletion.Instead, patients’ increased distractibility could reflect elevated DAlevels due to levodopa or DA agonist medication, which increasetonic DA stimulation within the caudate. Although a phasic increasein DA should selectively enhance only the most active synapses,excessive tonic DA stimulation, due to medication, may enhanceall active synapses, thereby reducing the discriminatory powerof phasic changes in DA and lowering the threshold for updatingWM. Notably, prolonged levodopa ingestion may create a secondaryproblem by sensitizing striatal DA receptors, resulting in exces-sive response to DA stimulation (Calon et al., 2002; Deogaonkar,Piallat, & Subramanian, 2005; Obeso, Rodriguez-Oroz, Rodriguez,DeLong, & Olanow, 2000; Papa, Desimone, Fiorani, & Oldfield, 1999).Once this occurs, medications that target DA receptors will inappro-priately increase excitation along the “Go” pathway and decreaseinhibition along the “NoGo” pathway. Consonant with this hypoth-esis, prolonged use of levodopa is associated with inappropriatemotor activity, specifically uncontrollable, choreatic movementsknown as dyskinesias. The mechanism that causes drug-induceddyskinesias may also be reducing the ability to inhibit inappropri-ate cognitive responses. Future research is needed to clarify thepossible relationship between WM impairment in PD and specificforms of PD treatment.

To summarize, neuropsychological evidence suggests thatPD patients exhibit increased susceptibility to irrelevant stim-ulus dimensions during category learning, which disrupts rulemaintenance under conditions of high concurrent or proactiveinterference. It appears this deficit may stem from excessive tonicDA stimulation in PFC and/or caudate. Additional work is necessaryto clarify how pharmacological therapies impact rule maintenanceperformance in PD.

4. Rule shifting

with an unsuccessful rule.3 Discussions of executive function in

3 The present discussion focuses specifically on rule shifting within categorylearning tasks and does not address the substantial literature related to task switch-


Table 3Summary of studies examining rule shifting in PD.


Beatty and Monson (1990) WCST, Heaton, Chelune, Talley, Kay, and Curtiss(1993)

27 PD; Stages 1–4; M = 2.6 PD = NC (d = .34)

Beatty et al. (1989) WCST, Heaton et al. (1993) 43 PD; Stages 1–4 PD = NC (d = .56)Beatty et al. (1989) WCST, Heaton et al. (1993) 25 PD; Stages 2–3 PD = NC (d = .43)Bowen et al. (1975) WCST, Berg (1948) 18 PDOFF; 53 PDON PDOFF = NC; PDON = NCCanavan et al. (1989) WCST Nelson (1976) 19 PD; M = 1.5 PD < NCChannon et al. (1993) Unidimensional classification learning 12 PD; Stages 2–3, M = 2.75 PD < NC (d = .50)Cools, van den Bercken, Horstink,

van Spaendonck, and Berger(1984)

Block sorting task 18 PD; Stages 1–4; M = 2.3 PD < NC

Cools et al. (2001) ID/ED task, Downes et al. (1989) 14 PDON; M = 1.75 15 PDOFF; M = 1.80 PDOFF = PDON < NCCooper et al. (1991) WCST Milner (1963) 60 PD; newly diagnosed;

unmedicatedPD = NC (d = .43)

Cooper et al. (1992) WCST, Heaton et al. (1981) 82 newly diagnosed PD; randomlyassigned on or off meds

PD = NC in each case

Dalrymple-Alford, Kaldters, Jones,and Watson (1994)

WCST, Heaton et al. (1993) 8 PD PD = NC (d = 0)

Downes et al., 1989 ED/ID task 16 PDOFF M = 1.1; 6 PDON M = 2.4 PDOFF < NC; PDON < NCFimm, Barti, Zimmerman, and

Wallesch (1994)Modified ID/ED task 19 PDON; Stages 2–3 M = 2; 8 PDOFF;

Stages 1–3; M = 2.PDON = NC; PDOFF < NC

Gauntlett-Gilbert et al. (1999) Modified ID/ED task 10 PD; Stages 1–3; M = 2.2 PD < NCGotham et al. (1988) WCST Nelson (1976) 16 PD; Compared ON and OFF PDON < NC (d = .90); PDOFF < NC

(d = 1.1)Green et al. (2002) WCST, Heaton et al. (1993) 61 PDOFF; Stage 3–4 PDOFF < NCKulisevsky et al. (1996) WCST, Heaton et al. (1993) 20 PD; Compared ON and OFF PDON = NC; PDOFF = NCLange et al. (1992) ID/ED task, Downes et al. (1989) 10 PD; Stages 3–5, M = 3.5; Compared

ON and OFFPDOFF = PDON < NC

Lees and Smith (1983) WCST, Nelson (1976) 12 PD PD = NCLewis et al. (2005) Modified ID/ED task 20 PD; M = 1.9; Compared ON and OFF PDOFF = PDON < NCMonchi et al. (2004) WCST 8 PDOFF PDOFF < NCOwen et al. (1993b) ID/ED task, Downes et al. (1989) 26 PDOFF (Stages 1–3) PDOFF < NC (d = 2.4)

23 PDON (Stages 1–4) PDON = NCPaolo et al. (1995) WCST, Heaton et al. (1993) 181 PD; Stage 1–4 PD < NC (d = 1.03)Price, 2006 WCST, Hart et al. (1988) 17 PD; Stages 1–4, M = 2.3 PD = NCSlabosz et al. (2006) Modified ID/ED task 20 PD; Stage M = 1.9; Compared ON

and OFFPDOFF = PDON < NC

Starkstein et al. (1989) WCST, Heaton et al. (1993) 48 mild PD; 20 moderate PD 26severe PD

Severe < moderate < mild PD; mildPD = NC

Tomer et al. (2007) ID/ED task of CANTAB Robbins et al. (1998) 35 PD PD < NC (d = 1.08)van Spaendonck et al. (1995) WCST and 2 variants following Nelson (1976) 45 PD PD = NCZ

N of PD

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akzanis and Freedman (1999) WCST, Heaton et al. (1993)


D commonly assert that patients exhibit rule-shifting deficits (e.g.rown & Marsden, 1990; Lees & Smith, 1983; Salmon et al., 1998;aylor, Saint-Cyr, & Lang, 1986). However, as shown in Table 3, thiseneralization fails to capture the variability across studies.

Neuropsychological examinations of rule shifting have typicallyelied upon the WCST and the Intra-Extra Dimensional Set Shift taskID/ED task; Downes et al., 1989). These tasks require a participanto categorize simple stimuli according to some rule that is not spec-fied. Following a certain number of correct sorts, the experimenterhanges the rule without notice. (The exception to this is the modi-ed WCST, Nelson, 1976, in which participants are told that a switchas occurred.) Since the previously relevant rule no longer receiveseinforcement, it should be abandoned. However, participants mayccasionally perseverate with that rule and a high frequency ofreservative responses indicates rule-shifting impairment.

As detailed in Table 3, patients with PD often exhibitncreased perseverative responses during category learning. One

eta-analysis of rule shifting on the WCST found an increase in per-everative responding among PD patients of moderate effect size,

ng. Task switching differs from rule shifting in that it does not emphasize learningnd involves changing stimulus-response mappings. In addition, task switching isore consistently affected by DA withdrawal than rule shifting, which suggests the

wo processes are sufficiently different to not be discussed as relying on the sameechanisms.

Meta-analysis (N = 11) Mean (d) = .46; Range(d) = −.66 − +.92


although considerable variability existed across studies (Zakzanis& Freedman, 1999, N = 11, mean (d) = .46, range (d) = −.66 − +.92).This variability may reflect differences in the patient samples acrossstudies. In particular, increased disease severity may be associatedwith increased shifting impairment (Starkstein et al., 1989; but seeGotham et al., 1988). Medication, however, appears to have littleeffect as it neither improves (Gotham et al., 1988) nor worsens shift-ing performance on the WCST (Bowen, Kamienny, Burns, & Yahr,1975; Cooper, Sagar, Jordan, Harvey, & Sullivan, 1991; Cooper et al.,1992; Kulisevsky et al., 1996).

4.1. Variability across tasks

Though variation across patient samples may explain some ofthe inconsistencies across studies, shifting deficits also appear tovary depending upon the WCST version in use. In the original 128-card version of the WCST (Berg, 1948; Heaton, 1981; Milner, 1963),a sort could be correct along several dimensions. In contrast, sim-plified versions (Hart, Kwentus, Wade, & Taylor, 1988; Nelson, 1976)include only those cards that can only be matched along a single
dimension. Although healthy older adults perform comparably onthe two versions (Greve & Smith, 1991), it appears that PD patientshave greater difficulty with the simplified version. Studies usingthe simpler version often indicate rule-shifting deficits among PDpatients, whereas the majority of studies using the original WCST

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eport normal shifting performance (see Table 3). On the simplifiedersions of the WCST, rule-shifting deficits emerge in patients ofarious stages of severity; on the original WCST, deficits primarilymerge among patients with more severe PD.

It can be difficult to interpret a group’s performance on theCST, however, because the task measures myriad processes. For

his reason, researchers also rely on the ID/ED task (Downes et al.,989). On this task, participants view two stimuli and must deter-ine which is correct based upon trial-by-trial feedback. The task

roceeds through a series of stages, each of which is designed toeasure a particular type of shift. On the final, extra-dimensional

hift (EDS), stage participants must begin to respond according tohe previously irrelevant dimension and ignore the previously rel-vant dimension. Rule shifting is determined either through theumber of errors made on this phase, the number of trials requiredefore the participant reaches criterion, or the percentage of par-icipants who reach criterion. Each of these methods indicatesule-shifting impairment in PD (Downes et al., 1989; Gauntlett-ilbert, Roberts, & Brown, 1999; Lange et al., 1992; Slabosz et al.,006; Tomer, Aharon-Peretz, & Tsitrinbaum, 2007).

The generally poor rule shifting performance of patients on theD/ID task mirrors the poor performance of patients on the simpli-ed versions of WCST and contrasts with the generally preservederformance on the traditional WCST (see Table 3). In contrast tohe traditional WCST, positive feedback on the simpler WCST (Hartt al., 1988; Nelson, 1976) and the ID/ED (Downes et al., 1989) taskeinforces only the appropriate dimension. By insuring that irrele-ant dimensions do not receive any reinforcement, the modificationas designed to simplify the WCST. Unfortunately, this change may

reate particular difficulty for patients with PD. Slabosz et al. (2006)emonstrated that PD patients experience shifting impairment, butnly when the newly relevant dimension had previously been com-letely irrelevant, as with the simplified WCST and ID/ED tasks.hen the newly relevant dimension had previously received some

einforcement, as it does on the traditional WCST, patients shiftedormally.

.2. Dopaminergic involvement

As discussed in the previous section, medicated PD patientsre more susceptible to distraction by irrelevant dimensions thatandomly vary with the relevant dimension. Although this mayisrupt rule maintenance, it also enables patients to more readilyhift toward the dimension once it becomes relevant. Conse-uently, medicated patients shift normally when the newly relevantimension had previously received intermittent reinforcementKulisevsky et al., 1996; Slabosz et al., 2006). In contrast, when

newly relevant dimension has no history of reinforcement, orhistory of complete non-reinforcement, patients have difficulty

dentifying the newly relevant rule (Channon et al., 1993; Price,006; van Spaendonck, Berger, Horstink, Brom, & Cools, 1995). Thisossibility is discussed further in Section 5. These findings sug-est that medicated PD patients may not necessarily have problemshifting away from an inappropriate rule. Instead they may have dif-culty selecting the newly appropriate rule, especially if the newule has no prior history of reinforcement.

By contrast, rule shifting is generally impaired among PDatients off medication, which suggests a role for striatal DA inule shifting. This possibility is illustrated in the PBWM modelFrank et al., 2001; Frank & O’Reilly, 2006), outlined in the previ-us section. According to the model, the occurrence of unexpected
egative feedback triggers a phasic dip of DA within the caudatehat decreases excitation of the “Go” pathway and inhibition ofhe “NoGo” pathway. The net effect is inhibition and destabiliza-ion of the stripe of prefrontal cortex that is associated with theule in use. Negative feedback decreases the likelihood that the
ia 47 (2009) 1213–1226

rule will be updated into WM the next time it is presented, andincreases the likelihood of a rule shift (Frank et al., 2001; Hazyet al., 2007; O’Reilly & Frank, 2006). Among PD patients off med-ication, the model further predicts that depletion of striatal DAreduces the efficacy of phasic changes in DA following reinforce-ment prediction errors (Frank, 2005; Frank & O’Reilly, 2006). In thecontext of category learning, this means PD patients off medicationshould exhibit reduced distractibility and increased perseverativebehavior. However, DA depletion within the dorsal striatum of non-human animals has no impact on set shifting, although animalsdid exhibit reduced distractibility (Crofts et al., 2001; Collins et al.,2000). These data call into question the assertion that rule shiftingprocesses are mediated by DA activity within the caudate.

Instead, shifting may depend upon DA activity throughout thedorsal fronto-striatal circuit. Though rule shifting is unaffected byselective DA depletion in PFC (Crofts et al., 2001; Roberts et al., 1994)or caudate (Crofts et al., 2001; Collins et al., 2000), it is possible thatDA in either region compensates for loss in the other. Depletion ofprefrontal DA is associated with upregulation of DA activity in thecaudate (Roberts et al., 1994), which may explain why selective DAlesions in the caudate had little impact on shifting performance.This possibility is supported by evidence that the D2/D3 antagonistsulpiride, which targets DA receptors in the PFC and NoGo pathwayof the striatum, impairs set shifting performance in healthy volun-teers (Mehta, Sahakian, McKenna, & Robbins, 1999; Mehta, Hinton,Montgomery, Bantick, & Grasby, 2005; Mehta, Manes, Magnolfi,Sahakian, & Robbins, 2004). Thus, rule shifting in PD may vary withthe degree to which prefrontal dopaminergic activity and medi-cation can compensate for striatal losses. This may explain whyrule-shifting impairment in PD patients may persist among opti-mally medicated patients (see Table 3).

4.3. Noradrenergic involvement

In addition to the involvement of the dopaminergic system,accumulating evidence indicates a role for the NA system in sup-porting rule shifting performance. NA projections from the locusceruleus target diverse forebrain sites, including thalamus, PFCand ACC. Pharmacological manipulations of prefrontal NA havebeen shown to alter set shifting performance in rats (Lapiz &Morilak, 2006; Lapiz, Bondi, & Morilak, 2007; Newman, Darling,& McGaughy, 2008; Tait et al., 2007). Although the mechanismby which cortical NA supports set shifting is unclear, it is possi-ble that repeated failure to achieve positive reinforcement resultsin a switch towards explorative behaviors, which would includeshifting away from the rule governing classification responses. Thisswitch is postulated to depend upon changes in the ceruleo-corticalNA system (Aston-Jones, Rajkowski, & Cohen, 1999). Patients withPD typically experience significant degeneration of locus ceruleus(Rommelfanger & Weinshenker, 2007 for which would result inaltered NA activity and may cause a failure to switch towards moreexplorative behaviors). Further, given that many of the pharmaco-logical treatments for PD, including l-dopa, increase NA productionin locus ceruleus (Fornai, di Poggio, Pellegrini, Ruggieri, & Paparelli,2007), medication may improve shifting performance to the extentthat locus ceruleus integrity is preserved.

In summary, rule-shifting performance may reflect activity ofthe dopaminergic and/or NA systems, both of which are compro-mised in PD. Among PD patients off medication, decreased DA andNA availability appears to disrupt rule shifting and this impairmentmay be ameliorated by pharmacological treatment.

5. Rule selection

The preceding section addressed the neural architecture ofrule shifting, which should occur following negative feedback. The


Table 4Summary of studies examining rule selection in PD.


Channon (1997) Unidimensional classification learning 21 PD PD < NC d = .62Channon et al. (1993) Unidimensional classification learning 12 PD; Stages 2–3, M = 2.75 PD < NC; (d = .64); PD tested fewer correct

hypothesesFiloteo et al. (2007) Classification learning (unidimensional,

conjunctive or disjunctive rules)12 PD; M = 1.8 Unidimensional rule: PD < NC; other rules:

PD = NCFiloteo et al. (2005) Unidimensional classification learning

tasks19 PD; M = 1.7 <2 irrelevant dimensions: PD = NC; >2

irrelevant dimensions: PD < NCGauntlett-Gilbert et al. (1999) Learned irrelevance shift on ID/ED task 10 PD; Stages 1–3; M = 2.25 PD < NCLewis et al. (2005) Learned irrelevance shift on ID/ED task 20 PD; M = 1.9, Compared ON and OFF PDON < NC; PDOFF < NCOwen et al. (1993a) Learned irrelevance shift on ID/ED task 26 PDOFF (Stages 1–3); 23 PDON (Stages 1–4) PDON < NC; PDOFF < NCPrice (2006) Multi-dimensional classification learning 17 PD; Stages 1–4, M = 2.3 PD < NCS PD MS mild P

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labosz et al. (2006) Learned irrelevance shift on ID/ED task 20wainson et al. (2006) Five dimension task, Swainson and Robbins

(2001)11

eceipt of negative feedback must not only trigger a shift away fromhe rule in use, but the selection of a new rule to guide responding.he possible impact of PD on rule selection was initially suggestedy Downes et al. (1989), who argued that PD deficits on the ID/EDask reflect difficulty identifying the newly relevant dimensionather than an inability to abandon the previous rule. The specificrocess of rule selection has received relatively little attention in theategory learning literature, although other lines of research offervidence relevant to the present discussion. Table 4 summarizesvidence relevant to rule selection in PD.

Arguably, the first examination of rule selection processes wasompleted by Owen et al. (1993b), who modified the traditionalD/ED task to include an additional shift stage, termed the learnedrrelevance shift. In this shift, the relevant dimension was replaced

ith a novel dimension and participants had to sort according to thereviously irrelevant dimension. PD patients have difficulty withhis “learned irrelevance” shift, regardless of whether they weren medication (Gauntlett-Gilbert et al., 1999; Lewis et al., 2005;wen et al., 1993b; Slabosz et al., 2006). This deficit was interpreteds reflecting enhanced latent inhibition (LI) (Owen et al., 1993b;labosz et al., 2006), which refers to slowed attention to previouslygnored stimuli relative to novel stimuli (De La Casa, Ruiz, & Lubow,993; Lubow, Dressler, & Kaplan, 1999; Lubow & Gewirtz, 1995).

Enhanced LI is likely among PD patients off medication, givenreviously discussed evidence of reduced distractibility and sethifting ability. The inability to appropriately update the contents of

M may similarly impair the capacity to disinhibit previously irrel-vant rules. This deficit may be especially pronounced if a rule hadreviously been fully irrelevant to classification and thus not repre-ented in WM (Slabosz et al., 2006). Among medicated PD patients,owever, enhanced LI is unlikely. As previously detailed, medicationay reduce inhibitory processes and medicated PD patients exhibit

iminished LI in other paradigms (Filoteo, Rilling, & Strayer, 2002;rande et al., 2006; but see Wylie & Stout, 2002). Further, medicatedD patients have difficulty selecting the appropriate rule even whenonditions are manipulated to favor enhanced LI (Gauntlett-Gilbertt al., 1999). Finally, if rule selection deficits in medicated PD pri-arily reflect enhanced LI, then selection should be unimpaired on

ategory learning tasks that involve a single rule. However patientserform poorly on such tasks, even though the relevant stimulusimension would not have been inhibited at any point in the taskChannon et al., 1993; Filoteo et al., 2005, 2007; Price, 2006). Takenogether, these data suggest that difficulty on the learned irrele-ance shift among medicated PD patients is not due to enhanced
I.
Instead, medicated PD patients may have difficulty with theearned irrelevance shift because they have difficulty identify-ng which stimulus dimension is most relevant for categorization.ategory learning performance is normal among medicated PD

= 1.9 Compared ON and OFF PDON < NC; PDOFF < NCDOFF; 27 mild PDON; 14 severe PDON Mild PDOFF = NC; Mild PDON < NC; Severe

PDON < NC

patients when all dimensions are equally relevant to classification,but impaired when tasks include stimulus dimensions of varyingrelevance to classification (Channon et al., 1993; Channon, 1997;Filoteo et al., 2005, 2007; Knowlton, Mangels, & Squire, 1996;Maddox & Filoteo, 2001; Maddox, Aparicio, Marchant, & Ivry, 2005;Price, 2006; Swainson et al., 2006). Moreover, when medicatedPD patients identify the relevant dimension, they have difficultyselecting an appropriate decisional criterion along that dimension(Filoteo et al., 2007; Maddox et al., 2005).

5.1. Ineffective use of feedback

Recent evidence suggests that selection deficits among med-icated PD patients may reflect an inability to make proper useof negative feedback to identify the relevant dimension for clas-sification (Cools et al., 2006; Frank et al., 2004; Schott, Niehaus,& Wittmann, 2007). Feedback-mediated learning is driven byreinforcement prediction errors, which are known to rely upon mid-brain DA signals (Hollerman & Schultz, 1998). Whereas unexpectedpositive feedback (positive prediction error) triggers a phasic DAburst, unexpected negative feedback (negative prediction error)triggers a phasic dip. These signals are thought to drive learningby biasing activity in a number of target regions (Schultz, 2007;Schultz & Dickinson, 2000), including the NAc (Aron et al., 2004;Rodriguez, Aron, & Poldrack, 2006) and ACC (Holroyd & Coles, 2002;Ridderinkhof, Ullsperger, Crone, & Nieuwenhuis, 2004). Functionalneuroimaging examinations of category learning have indicatedthat unexpected negative feedback is associated with increasedactivation of NAc and ACC (Aron et al., 2004; Rodriguez et al., 2006).This activity is thought to integrate reinforcement values of con-secutive events to bias changes in cognitive or overt responding(DePisapia, Slomski, & Braver, 2007; Mogenson, 1987).

At present, it is unclear whether ACC activation varies as a func-tion of NAc activity or if the two regions respond in parallel toreinforcement prediction errors. It is possible that a negative pre-diction error triggers increased NAc activation, which would brieflyinhibit the GPi, resulting in increased ACC activity. In this way, error-driven changes within NAc may govern the relative activation of ACCneurons associated with a given rule representation. Alternatively,since midbrain DA signals directly bias ACC activation, it is possiblethat reinforcement-mediated changes in the NAc and ACC occurindependently. Regardless, ACC is commonly believed to supportresponse selection, including cognitive responses (Barch, Braver, &Noll, 2000; Botvinick, Braver, Carter, Barch, & Cohen, 2001; Buckner,
Raichle, Miezin, & Petersen, 1996; Corbetta, Miezin, Dobmeyer,Shulman, & Petersen, 1992; Elliot & Dolan, 1998; Peterson, Fox,Posner, Mintan, & Raichle, 1988; Raichle et al., 1994).
The role of ACC in supporting rule selection is supported by sev-eral lines of evidence. First, functional neuroimaging with healthy

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ontrols performing the WCST found ACC activation following neg-tive feedback, when rule selection must occur, but not followingositive feedback, when the rule in use can simply be maintainedMonchi, Petrides, Petre, Worsley, & Dagher, 2001; Monchi et al.,004). Further, evidence from other decision making tasks indicateshat ACC activation is highest on learning conditions where feed-ack must be integrated over a series of trials, pointing to the role ofCC in the temporal integration of feedback (Rushworth, Buckley,ehrens, Walton, & Bannerman, 2007; Yarkoni et al., 2005). Finally,CC activity occurs with category learning only when stimuli varylong multiple dimensions; unidimensional variation is not asso-iated with ACC activation (Corbetta et al., 1992). These data ledshby et al. (1998) to conclude that the ACC facilitates rule selec-

ion on multi-dimensional tasks by biasing the relative strength ofule representations within prefrontal cortex.

.2. Impact of PD medication

To summarize, rule selection appears to depend uponeinforcement-mediated activation of the ACC, which biases theelative activation of rule representations in PFC such that theost consistently reinforced rule is selected to guide respond-

ng. Recent evidence suggests that medication in early PD disruptseinforcement-mediated activity within ACC (Holroyd, Praamstra,lat, & Coles, 2002; Stemmer, Segalowitz, Dywan, Panisset,

Melmed, 2007; Willemssen, Müller, Schwarz, Hohnsbein, &alkenstein, 2008), which may explain findings of impaired ruleelection performance in this population (Channon, 1997; Price,006; Swainson et al., 2006). Medication in early PD likely results

n an excessive level of tonic DA activity in the ventral cortico-triatal circuit, including NAc and ACC, an area that is typicallypared until advanced PD (Cools, 2006; Farley et al., 1977; Kish etl., 1988). Increased tonic DA stimulation associated with PD medi-ation may “fill in” the phasic dip in DA release that is triggered by aegative prediction error (Frank et al., 2004; Hollerman & Schultz,998). If this is the case, then negative feedback would fail to triggerhe learning signal within ACC that should weaken the associatedule representation in PFC. Thus, insensitivity to negative feedbackmong medicated PD patients may stem from excessive DA activityn the ventral cortico-striatal circuit (Cools, 2006).

Alternatively, as detailed in Section 3.2, medication in early PDay disrupt dorsal striatal function in certain patients. According

o the PBWM model (Frank et al., 2001; Frank & O’Reilly, 2006),eedback shapes the strength of individual rule representationshrough patterns of activity along the “Go” and “NoGo” striatalathways. Medication in PD may lead to abnormally high levelsf tonic DA stimulation such that the phasic dip triggered by neg-tive feedback is insufficient to counteract tonic excitation of theGo” pathway. Consonant with this prediction, a pharmacologicallynduced increase in DA release in healthy volunteers was associated

ith greater inhibition of the “NoGo” pathway and impairments inearning from negative feedback (Frank & O’Reilly, 2006). Conse-uently, selection deficits among medicated PD patients may reflectecreased sensitivity to negative feedback due to excessive DA inhe dorsal striatum. It is possible that decreased response to nega-ive feedback may explain observed deficits in rule shifting among

edicated PD patients. However, rule shifting and selection pro-esses have been dissociated in several studies; although medicatedatients have no difficulty shifting away from an inappropriate rulehey tend to select a new rule that should have been ruled out byegative feedback (Channon, 1997; Price, 2006). This would suggest
hat although negative feedback triggers rule shifting and selectionrocesses, the two depend upon distinct mechanisms.
In contrast to medicated PD patients, PD patients off medicationave no difficulty making use of negative feedback to identify theelevant stimulus dimension (Cools et al., 2006; Frank et al., 2004;

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Holroyd et al., 2002; Swainson et al., 2006). Among PD patientsoff medication, DA levels within the NAc are relatively normal andthe pattern of NAc and ACC activation during the period followingfeedback is normal (Cools et al., 2007; Monchi et al., 2004). Despitethis, PD patients off medication have difficulty with tasks designedto assess rule selection, specifically the learned irrelevance phaseof the ED/IDS task (Owen et al., 1993b). Such deficits may stemfrom an insensitivity to positive feedback (Frank et al., 2004; Schottet al., 2007; but see Cools et al., 2006; Frank, Samanta, Moustafa,& Sherman, 2007). Normally, positive feedback is associated withphasic increases in DA within the dorsal stratum but DA deple-tion minimizes the size and efficacy of such bursts (Pizzagalli et al.,2008; Schott et al., 2007), thereby reducing sensitivity to positivefeedback among PD patients off medication.

Although limited research exists on the impact of PD on ruleselection, the evidence we have reviewed here suggests impairmentamong patients on and off medication. It appears that PD patientsoff medication may be less sensitive to positive feedback, whereasmedicated PD patients are less sensitive to negative feedback. Ineither case, rule selection fails to proceed normally.

6. Summary and conclusions

In this review, we explored PD patient performance on rule-based category learning with specific focus on the componentprocesses of rule generation, maintenance, shifting and selection.This neuropsychological evidence was then integrated with neu-roimaging and computational modeling research to clarify theneural architecture of explicit rule-based category learning (seeFig. 2).

Successful rule-based category learning demands the genera-tion of one or more viable rules to guide responding. The datareviewed here suggests that rule generation relies upon activitywithin the ventral cortico-striatal circuit, namely ACC and OFC. Thiscircuit also appears to support rule selection through feedback-mediated changes in the relative activation of rule representationswithin WM. Specifically, reinforcement prediction errors triggermidbrain DA signals that govern activity within the ACC. The pat-tern of ACC activation biases activity within PFC towards thoserules that have not received recent negative feedback. Whereas rulegeneration and selection appear to rely on ventral cortico-striatalcircuitry, rule maintenance and shifting rely primarily on dorsal cir-cuitry, namely head of the caudate and dorsolateral PFC. Individualrule representations are maintained in WM via distinct loops ofrecurrent excitation between PFC and thalamus. Rule maintenanceis most likely governed by a DA-mediated gating mechanism thatinsures WM access only to those cognitive responses that are con-sistently reinforced by positive feedback; irrelevant responses arereinforced only intermittently and are therefore not maintained.Receipt of unexpected negative feedback triggers a phasic dip inDA release, which destabilizes the rule representation in WM andincreases the likelihood of a shift away from that rule. Normally,coordinated activity of these cortico-striatal systems supports suc-cessful learning of rule-based category structures.

Among patients with PD, however these systems become dys-functional due to disruption of the dopaminergic and, possibly, theNA systems. When not on medication, early PD patients largely suf-fer rule-based category learning deficits that stem from a lack ofdorsal striatal DA availability and cortical norepinephrine activity.Because of these changes, patients off medication have difficultyupdating WM, failing to readily shift away from unsuccessful rules.
Although the tendency to perseverate with an unsuccessful rulelimits the necessity of selecting new rules, early PD patients offmedication may have little difficulty generating potential rules andmaking use of feedback to identify the most relevant among them.As PD advances and the ventral striatum begins to suffer a signif-

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cant loss of DA availability, advanced PD patients off medicationay begin to experience difficulty with those processes mediated

y the ventral striatum and its cortical targets, specifically rule gen-ration and rule selection.

The nature of rule-based category learning deficits in medi-ated PD patients, specifically those in the earlier stages of disease,s quite different from those observed in patients off medication.

edication should improve patients’ ability to update WM, therebyemediating rule-shifting deficits. However, it appears that medi-ation may also disrupt the gating mechanism that supports WMpdating, causing increased distractibility among medicated PDatients. Presently, it is unclear if increased distractibility occurs

n all medicated PD patients or if it stems from particular coursesf treatment. Pharmacological therapies tend to also impact theentral cortico-striatal circuits, which are relatively spared untildvanced PD. Among earlier PD patients, medication may create aild “DA overdose” within the ventral striatum and its cortical tar-

ets (e.g. OFC; ACC). Consistent with this overdose hypothesis, ruleeneration and selection processes, which we argue depend uponentral cortico-striatal circuitry, are often impaired in PD patientshen they are on, but not off, medication.

In summary, the available data indicate that rule-based cate-ory learning depends upon a network of striatal structures androntal cortical regions. Learning to classify novel stimuli dependspon multiple processes, including the generation, maintenance,hifting and selection of classification rules. Dysfunction in any ofhese components, due to PD or its common treatments, will disruptategory learning, albeit in slightly different ways.

cknowledgement

We would like to thank Shawn Ellis and the reviewers for theirnsightful comments on earlier drafts of the manuscript.

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Rule-based category learning in patients with Parkinson's disease

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