Serial reaction time learning and Parkinson's disease: Evidence for a procedural learning deficit

Pergamon Neuropsychologm, Vol. 33, No. 5, pp. 577-593, 1995

Copyright © 1995 Elsevier Science Ltd Printed in Great Britain All rights reserved

0028-3932/95 $9 50+0 00

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SERIAL REACTION TIME LEARNING AND PARKINSON'S DISEASE: EVIDENCE FOR A PROCEDURAL LEARNING

DEFICIT

GEORGINA M. JACKSON,*t STEPHEN R. JACKSON, t JOHN HARRISON,++ LESLIE HENDERSON++ and CHRISTOPHER KENNARD:~

tSchool of Psychology, University of Wales, Bangor, Gwynedd LL57 2DG, U.K.; and ++Clinical Neuroscience Unit, Charing Cross and Westminster Medical School, Charing Cross Hospital,

London W6 8RF, U.K.

(Received 29 June 1994; accepted 17 December 1994)

Abstract--This paper presents evidence in support of a serial reaction time (SRT) deficit associated with Parkinson's disease, and related to the acquisition or execution of serial-order information. Eleven patients with idiopathic Parkinson's disease, and 10 age-matched but otherwise healthy control subjects, were compared on a variant of the SRT task introduced by Nissen and Bullemer (Cognit. Psychol. 19, 1-32, 1987). The results of this study clearly demonstrate that PD patients produce a quite different pattern of RT performance to that of control subjects. Such a pattern of results may reflect either: (1) a deficit in the patients' ability to learn the temporal order information provided by a repeating sequence of target locations in the SRT task; or (2) a deficit in the patients' ability to express temporal order information provided by the repeating sequence of target locations in the SRT task.

Key Words: Parkinson's disease; reaction time; procedural learning; sequencing; motor control.

INTRODUCTION

Over the past two decades Cognitive Psychology has seen the fractionation of memory into a number of modular systems [2]. This paper focuses on the distinction between explicit and implicit (procedural) forms of learning, and presents evidence in support of a procedural learning deficit, associated with Parkinson's disease, and related to the acquisition of temporal order information.

The distinction between implicit and explicit learning has been used to distinguish between learning with and without awareness 1-31]. Explicit learning is thought to be similar to the processes which operate during conscious problem-solving, and includes; conscious attempts to construct a representation of the task; directed search of memory for similar or analogous task relevant information, and conscious attempts to derive and test hypotheses. This type of learning has been distinguished from implicit or procedural learning, in which task relevant information is acquired automatically and without conscious awareness of what is being learnt [31].

A growing number of empirical studies have examined implicit learning using the serial

*Address for correspondence: Human Movement Laboratory, School of Psychology, University of Wales, Bangor, Gwynedd LL57 2DG, U.K.

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578 G. M JACKSON et al.

reaction time task (SRT) established by Nissen and Bullemer 1-29] [e.g. 6, 23, 37]. In this task, subjects are presented on each trial with a simple target stimulus which can appear, apparently at random, in one of several different locations. Subjects are instructed to indicate the spatial location of each target by making a manual response appropriate to the stimulus position. However, unbeknown to subjects, a series of successive trials may be organised into a sequence of locations which repeats cyclically over a number of trials. Use of this paradigm has consistently demonstrated: (i) That the RTs of subjects trained on a repeating sequence decrease significantly more than those of control subjects trained on a random pattern of locations; and (ii) When subjects trained on a repeating sequence are transferred to a random sequence, their RTs increase significantly. Furthermore, the facilitation afforded by the sequential condition has been found: in young subjects with no explicit knowledge of the pattern [37]; in groups who frequently exhibit impaired performance on direct tests of memory (e.g. Korsakoff's amnesics [29]; Alzheimer patients [23]; normal elderly subjects [15, 16]; and in groups of young subjects in which explicit memory has been temporarily impaired through the administration of drugs such as scopolamine or lorazepam [22, 30]).

Support for the distinction between explicit and implicit learning has been obtained in a variety of neuropsychological investigations of different clinical populations. Such investigations are of major importance as they frequently allow rather fine-grained behavioural distinctions to be drawn, e.g. between different aspects of procedural learning [5]. One particularly important group in this respect are Parkinson's disease (PD) patients. PD is often described as a disease of the motor system, characterized by limb tremor when in a resting state, and an inability to initiate and control voluntary movement (akinesia). These physical symptoms are associated with neural degeneration of the nigrostriatal dopamine projections within the basal ganglia, although other neurotransmitter abnormalities are frequently observed later in the course of the disease. The importance of this group within the context of procedural learning studies, stems from the fact that there is increasing support for the view that the basal ganglia play a critical role in skill learning in general, and more particularly, in the procedural learning of movement sequences [10, 21, 34]. Both PD and Huntington's (HD) patients (a disease leading to atrophy of the striatum) have been shown to perform poorly on skill learning paradigms such as rotary pursuit [ 14] and mirror reading [26], and it has been observed that PD patients have difficulty in repeating a sequence of hand gestures [3]. Moreover, in tasks that directly involve subjects learning sequential order, such as the SRT task outlined above, HD patients have been consistently shown to be impaired [24, 38]. Finally, in a recently reported study of SRT learning in PD patients, Ferraro et al. I-9] report that PD patients show a deficit in sequential learning as measured by the SRT task.

The results reported by Ferraro et al. 1-9] provide the first published account of PD patients performance on the SRT task, and their finding of a SRT learning deficit in this group provides important information regarding behavioural deficits associated with PD, and the potential role played by the basal ganglia in motor learning. While their results are broadly consistent with previously reported findings on procedural learning in PD patients [e.g. 3, 10], and also more specifically with reports of SRT learning in HD patients [e.g. 24, 38], the interpretation of these findings is problematic for two important reasons. Firstly, the authors did not use the most appropriate control group to test for 'sequence' learning; secondly, the authors did not report any attempts to evaluate the extent to which their subjects had expl ic i t ly learnt the sequential order of the pattern presented in the SRT task. These points are outlined in more detail below.

SERIAL REACTION TIME LEARNING AND PARKINSON'S DISEASE

x , . ( B \

@v,@. Fig. 1. Illustration of a finite-state grammar (see text for details).

579

The use of a pseudorandom control condition

As noted above, most published studies of SRT learning have chosen to compare subjects' RT performance for a repeating sequence of locations against subjects' performance on a 'pesudorandom' pattern in which stimuli appear at random locations (subject to the rule that a stimulus always moves to a new location). However, the validity of the pseudorandom pattern as an appropriate control condition has recently been questioned by several different research groups [e.g. 17, 32, 35], who point out that there are several important differences between the statistical structure of the sequences typically used in the SRT task and that of the pseudorandom pattern employed as a control. As the term 'statistical structure' can be used to describe many different kinds of relationship between the individual elements making up a serially-ordered sequence (e.g. lag-0, lag-l, lag-2, etc.), this point may best be conveyed by considering an analogy with another implicit learning paradigm, namely the artificial- grammar learning task.

Figure 1 illustrates a finite-state grammar of the kind used by Reber and colleagues to investigate learning of artificial grammars [31]. Within this task, subjects are presented with a series of letter strings constructed according to a series of rules embodied by a finite-state grammar, and through experience with numerous such exemplars, learn to differentiate grammatical from non-grammatical letter strings. Grammatical exemplars are produced by following a particular path through the finite-state grammar. Thus, the strings TXSVS, PVS, and TXT can all be produced by following a particular route through the grammar. In this way the grammar can be viewed as a set of rules specifying the complete set of legal and non- legal, sequential relationships between individual elements of the strings that can be constructed using the grammar. We have suggested that the pattern of stimulus locations occurring in the SRT paradigm can also be viewed in this fashion. That is, the relationship between sequentially adjacent elements in the sequence (transitions) can be viewed as conforming to a grammar in which certain transitions, e.g. A--+B are legal while others, e.g. A--+A or A--+D are non-legal (i.e. they do not occur in the sequence) [17].

It is important to note two points with regard to our use of the term transition here. Firstly, we define the term transition to mean the relationship between two sequentially adjacent sequence elements, e.g. B--+ C. Secondly, we assume that knowledge of this relationship does not require knowledge of other, earlier, elements in the sequence. That is, given the sequence ABC, knowledge of the transition B--+C does not depend upon knowing that element A precedes the transition B--+ C. Thus, we explicitly wish to distinguish knowledge of individual transitions and their relative probabilities, from more complex representations of serial- order, which may involve knowledge of the statistical relationship between many sequence

580 G.M. JACKSON et al.

elements [e.g. 7]. Henceforth in this paper we will refer to all such representations as serial- order information.

Why should we consider the relationship between serially adjacent elements (transitions) rather than any other form of statistical relationship? Transitions have traditionally been of particular importance in theorising about the serial-order problem. For instance, such relationships were central to early associationist ideas about how sequences were both learnt and expressed [e.g. 8, 25]. Furthermore, such relationships continue to be of critical importance for theories of serial-order (e.g. the distinction between Unique, Hybrid, and Ambiguous patterns [6] is based upon a consideration of the relative probability of particular transitions).

When viewed in terms of the number and the relative probabilities of the transitions to be learnt, it can be clearly demonstrated that there are important differences between sequence and pseudo-random patterns. Table 1.1 shows the transition table for the sequence used by Nissen and Bullemer 1-29] and many other researchers subsequently. Table 1.2 shows the transition table for the pseudo-random condition. Comparison of these tables dearly demonstrates two very important differences between these patterns:

(1) The Nissen and Bullemer sequence contains nine legal transitions (i.e. transitions which actually occur within the sequence) and seven non-legal transitions (i.e. transitions that do not occur within the sequence). In contrast, the pseudo-random condition contains 12 legal transitions and only four non-legal transitions.

(2) In the Nissen and Bullemer [29] sequence some transitions occur more frequently than others, e.g. C ~ B (66%) vs C ~ A (33%). In contrast, in the pseudo-random condition all legal transitions occur with the same frequency (33%).

Taken together points 1 and 2 suggest that the pseudo-random pattern may be more difficult to learn than the experimental sequence, and thus an inappropriate control condition. More importantly however in the context of the current study, point 2 suggests that the use of the pseudo-random condition as a control, does not rule out the possibility that subjects may simply be learning a subset of the most probable transitions when presented with the SRT task. That is, speeded responses on the SRT task might not reflect subjects' knowledge of serial-order of the entire sequence as has often been thought [15]. Instead, speeded SRT performance may simply reflect subjects' knowledge of the fact that the transition from C ~ B occurs more frequently than the transition from C~A.

Empirical support for this view that differences in the number of transitions to be learnt, and their relative probabilities, may be particularly important was reported by Cohen et al.

[6] who demonstrated that sequences having unique, hybrid, or ambiguous structures differed in the ease and circumstances in which they could be learnt. More recent support for this view has been provided in an important study by Stadler [36], who manipulated aspects of the statistical relationship between sequence elements to produce sequences which differed in their 'probabilistic regularity'. Stadler demonstrated that the statistical relationship between sequence elements produced observable consequences on subjects' performance on the SRT task. More specifically, Stadler demonstrated that sequences having 'high' levels of statistical structure were easier to learn than sequences having 'low' levels of statistical structure.

As was pointed out above, the term statistical structure could be used to describe a vast number of different kinds of relationship between the elements of sequence of 10 or more items in length. Table 2 shows transition tables for the three sequences used in the Stadler study (Experiment 1). Note that the first six elements are common to each sequence (the

SERIAL REACTION TIME LEARNING AND PARKINSON'S DISEASE 581

Table 1. Pattern 1 shows the transition table for the 10-element sequence adopted by Nissen and Bullemer [29]. This pattern is an example of an

'ambiguous' sequence [6] and has been used in previous studies investigating sequence learning in the elderly [15]. Pattern 2 shows the

transition table for the frequently adopted pseudo-random control condition in which the overall probability of each element is equal

.

1st element

A

A

B 0.33

C 0.33

D

D B C A C B D C B A

2nd element

B C

0.66

0.5

D

0.5 0.5

0.33 0.33

0.5

.

A

P S E U D O - R A N D O M

2nd element

1st B 0.33 element

C 0.33 0.33

D 0.33 0.33

B C D

0.33 0.33 0.33

0.33 0.33

0.33

0.33

sequence 'root'), while the final four elements (the sequence 'tail') are unique. Examination of Table 2 illustrates those transitions that occur within the sequence root (indicated 'R'), and those transitions which occur within the sequence tail (indicated 'T'). In the pattern that Stadler refers to as having 'low structure' all of the transitions which occur in the tail differ from those in the root. In contrast, in the pattern referred to as having 'high structure', only one of the transitions appearing in the sequence tail is not contained in the sequence root.

Stadler distinguished between two classes of sequence representation: verbatim representations in which sequences are represented as a series of serially-ordered events, literally event by event; and aggregate representations in which sequences are represented as a collection of partial information rather than a sequence per se [36]. Based upon these studies he concluded that subjects were not learning serial-order information (verbatim representations) but were in fact learning about the probabilistic s tucture of the sequence. One important consequence of point 2 above, is that it suggests that the use of a pseudo-random control condition is not an appropriate control condition for differentiating between learning these two forms of representation. To distinguish between the situation where subjects are learning the grammatical structure of the sequence (i.e. the set of legal and non-legal transitions which occur within a particular sequence), from that where subjects are learning serial-order information (which we take to involve more complex sequential relationships


Table 2. Sequences used by Stadler [36] to investigate the role of statistical structure in SRT learning. All three sequences begin with the same initial

pattern (the sequence 'root') but each pattern has a unique end pattern (the sequence 'tail'). Each sequence is presented as a transition table. Transit ions which appear in the root are marked 'R'. Transit ions that appear in the tail

are marked 'T'. The relative probability of each transition is given in parentheses

Low.

A

1st B T (0.33) element

C R (0.5) T (0.5)

D T (0.5) R (0.5)

B D B C A B A D A C

2nd element

B C D

R (0.33) T (0.33) T (0.33)

R (0.33) R (0.33)

Medium.

A

B D B C A B C D B C

2nd element

A B C D

R (0.l)

1st B R/T (0.75) R (0.25) element

C R (0.33) T (o.33) T (0.33)

D R/T (o.1)

High. B D B C A B C D B C

2nd element

A B C D

A R (O.l)

1st B R/T (0.05) R/T (0.05) element

C R (0.05) T (0.O5)

D R/T (o.1)

than that between a pair of adjacent elements), we need a control condition which leads to quite different effects in each case. As the pseudo-random condition changes varies both the serial-order and the grammatical information of the sequence, it obviously cannot be used to differentiate between these two alternatives. Instead, a more appropriate control condition, is one in which the grammatical structure of the sequence is unchanged, but the more complex relationships between sequence elements (i.e. serial-order information) is disrupted.


The importance of reporting explicit measures of sequence learning

The SRT task is frequently assumed to assess implicit learning, as distinct from explicit learning, which has been viewed as an independent source of knowledge [311. Thus, a second problem in interpreting the findings of Ferraro et al. [91 is that these authors do not report on the levels of explicit knowledge that subjects may have gained about the sequence. Given that PD patients are known to show deficits on direct measures of memory, including visuospatial working memory [271, it remains entirely possible that the findings of the Ferraro et al. C91 study do not reflect a specific deficit in learning about serially-ordered sequences of responses, so much as a general deficit in learning and/or memory.

Summary

To summarise the arguments presented above. Previous studies of procedural learning have indicated that PD patients may be impaired on tasks assessing procedural forms of learning. Furthermore, Ferraro et al. [9] have demonstrated that PD patients are impaired on the SRT task. This impairment could result from two distinctly different forms of impairment: Firstly, if subjects in the SRT task are simply learning about the statistical structure of the sequence, as has been suggested by Stadler 1-361, then PD patients may exhibit a deficit in forming new associations (as concluded by Ferraro et al. [91). Alternatively, if subjects in the SRT task are learning serial-order information, i.e. information about more complex statistical structure involving more than just adjacent elements [36], then PD patients may exhibit more specificity in learning about serially- ordered events. Currently it is not clear whether these two forms of sequence representation are represented at an implicit or an explicit level [ 18]. However, what is clear is that the use of a pseudo-random condition cannot differentiate between these two alternative forms of representation. In this paper we seek to extend the observation of Ferraro organization in two ways: firstly, in Experiment 1 we directly test the effects of using a control condition in which the grammatical information is identical to that of the sequence, by comparing it to a pseudo-random control condition; and secondly, in Experiment 2 we examine SRT learning in a population of idiopathic PD patients, when tested with the alternative (grammar- preserved) control condition.

EXPERIMENT 1

METHOD Subjects

Twenty elderly control subjects (mean age of 66.1 [S.D. = 4.3] years) were recruited from amongst the subject panel of the Department of Psychology, University of Wales (Bangor) and randomly assigned to each of two conditions. None of these subjects had a history of head injury or neurological disorder; none were taking psychoactive medication or anti-depressants, and none had regularly consumed excessive amounts of alcohol. All subjects had normal or corrected to normal vision, and all were paid the sum of £3 50 for their participation.

Apparatus and stimulus display The experiments took place in a sound attenuated room under dimmed lighting conditions. Stimuh were

presented on an Apple 14" RGB colour monitor controlled by an Apple LCII microcomputer. Responses were collected from an Apple extended keyboard.

Figure 2(1 ) shows an example of the stimulus display used for the SRT task. The display consisted of a single large (15 cm × 15 cm) square presented in the centre of the monitor, within which were situated four smaller squares measuring 4.5 cm × 4.5 cm. The smaller squares indicated each of the four possible locations within which target stimuh could appear. The target in both the SRT task and the Generate task consisted of large letter 'X'. At a viewing

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G. M. JACKSON et al.

2b.

V h | ~ v | l | ~ Cram IJD~Lt" AeI~ ?

Fig. 2. Examples of the stimuli used in the SRT task (1) and the 'Generate' task (2a-2b),

distance of 65 cm the target measured approximately 3.52 x 3.52 deg. In the SRT task, squares which did not contain the target for the current trial remained blank. Responses were made by depressing one of four adjacent keys on the numeric keypad of the extended keyboard. These keys (Nos 4, 5, 1 and 2) were arranged in a spatial pattern corresponding to the spatial arrangement of the display. Each of the keys selected as a response key was covered by a plain yellow label. The remaining keys were blanked off with visually distinctive (red) labels thereby isolating the response keys. Subjects were instructed to make a response by depressing the selected key with the index finger of their preferred hand. There was no time limit on the presentation of a stimulus, and each trial was terminated by a response. The response-to-stimulus interval (RSI) was 200 msec.

The stimulus displays used for the Generate task were similar to that used for the SRT task. Two types of display were presented on each generate trial. The first display in each trial, shown in Fig. 2(2a), signified the location of the target on the previous trial (or the first trial). This display was identical to that used in the SRT task save that non- target locations contained a large question mark. This stimulus measures approximately 3.08 x 2.20 deg. The second display on each trial, shown in Fig. 2(2b) was identical to the first save that it contained the instruction "Where will the cross appear next?". As in the SRT task, subjects indicated the location in which they predicted the target would next appear by depressing one of the four keys described above.

Tasks and design

The experiment utilised two distinct behavioural tasks: an SRT task in which subjects were required to indicate the location of an 'X' target by pressing one of four response keys, and a Generate task in which subjects were presented with the current location of the stimulus, and were required by means ofa keypress, to indicate where they predicted the stimulus would move on the next trial.

S R T task. The SRT task was divided into several separate blocks of trials. The first two blocks consisted of 160 pseudo-random trials (80 trials per block). In these trials the X target appeared at random in each of the four possible locations, subject to the following constraints: (1) The target never appeared at the same location on two successive trials; and (2) Across a block of pseudo-random trials, the target appeared at each of the four locations with an equal probability. Subjects then went on to complete a further eight blocks of experimental trials. Blocks 3-8 and block 10 consisted of four pseudo-random trials, followed by six repetitions of an 11-item sequence of target locations (Table 3A). The experiment consisted of two conditions: a Repeated Grammar (RG) condition; and a Pseudo-random (PR) condition. Blocks 3-8 and block 10 were identical for all subjects, and consisted of six repetitions of the 11-item unbalanced sequence shown in Table 3. The two conditions differed only at Block 9 which we have called the test block. In the case of the RG condition, the test block consisted of four pseudo-random trials, followed by one presentation only of six different 11-item patterns which each shared an identical grammatical structure to the sequence presented in blocks 3-8 and block 10. In the case of the pseudo-random condition, after the initial four pseudo-random trials, subjects went on to complete a further 66 pseudo-random trials. Finally, after completing the test block all subjects went on to complete block 10 which contained a further six repetitions of the sequence used in blocks 3-8.

The rationale of the above design is as follows: if subjects' performance is dependent upon serial-order information about the sequence (blocks 3-8), then their RTs should be slowed when presented with either the RG or PR trials in the test block. Alternatively, if subjects' can utilise grammatical information alone (e.g. the probability of particular transitions), then the presentation of the RG test block should produce significantly less disruption compared to the PR condition. That is, subjects In the RG condition should be able to utilise any knowledge of the sequence grammar they have to facilitate their responses.

Generate task. In the Generate task subjects were presented with the current location of the target stimulus, and were required, by means ofa keypress, to indicate the location where they predicted that the stimulus would move to on the next trial. The task consisted of five repetitions of the 11-item sequence. However, only the first two repetitions (22 trials) were analysed.


Table 3. Transition table for the 11-item unbalanced sequence used in this study. Table I(A) shows the sequence used in blocks 3-8 and block 10.

Table 1 (B) illustrates three examples of the six patterns used in the repeated- grammar test block. Of the 16 transitions that are possible, this sequence

uses nine

A. A B D C A D B A C D C

2nd element

A B C D

A 0.33 0.33 0.33

1st B 0.5 0.5 element

C 0.66 0.33

D 0.33 0.66

B. D C A D C D B A C A B

D C D C A C A D B A B

D B A B D C A C D C A

Procedure The experiment was divided into three phases. In the First phase, following a set of standardised instructions and a

short period of practice with a random set of trials, subjects completed the SRT task. Subjects were provided with a short break at the end of each block of trials. At the conclusion of the SRT task subjects took part in the second phase, a brief structured interview. This procedure was identical to that described in Willingham et al. [38].

The final phase of the experiment consisted of the Generate task. After a further set of standardised instructions and a very short period of practice (on a random pattern), subjects carried out the Generate task. Subjects were instructed to try to predict where the target stimulus would appear on the next trial, and were informed as to how they would receive feedback on the accuracy of their prediction.

R E S U L T S A N D D I S C U S S I O N

Preliminary analyses--Group comparability

The overal l compa rab i l i t y of the subjects assigned to the R G and P R groups was verified by several p re l iminary analyses . These analyses revealed: (1) tha t the two g roups d id not differ s ignif icant ly in age [mean ages: R G = 66.4 years; P R = 65.8 years; t (18) = 0.3, P > 0.1 ]; (2) tha t the mean RTs of the two groups were no t significantly different at the end of the two b locks of pract ice tr ials: [ R G = 7 2 0 . 1 [162.7] msec, PR =685 .6 [144.1] msec; t (18)=0 .5 , P = 0.1]; and (3) tha t the mean RTs of the two groups were not significantly different at the end of sequence learn ing tr ials (block $6): [ R G = 563.2.1 [176.7] msec, P R = 576.3 [142.2] msec; t (18)= - 0 . 2 , P = 0 . 9 ] . M e a n RTs are presented in Fig. 3.

Serial reaction time task--Initial analyses

As previous ly no ted , b locks 1 and 2 each consis ted of 80 pract ice (p seudo - r andom) trials, whereas b locks 3-10 each consis ted of 70 tr ials , an init ial four p s e u d o - r a n d o m trials fol lowed by six repet i t ions of an 11-element sequence of loca t ions (or equivalent number of tr ials in the

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P S E U D O - R A N D O M

R'I R'Z S'I s'2 s'3 s'4 s'5

r

S'6 TI3 $7

BLOCK

Fig. 3. SRT task performance for the RG and PR groups. Note RTs are absolute RTs, not RT savings.

test block). Data were collapsed in the following manner: Firstly, for each subject, median RTs were obtained for each set of eight consecutive trials in blocks 1 and 2, and for each set of 11 consecutive sequence trials (i.e. trials 5-70) in blocks 3-10. Secondly, the median RTs were further collapsed to provide, for each subject, a mean RT for each block of trials. Mean scores were then entered into 2 x 10 ANOVA with group (RG vs PR) a between-subject variable and block (1-10) a repeated measure. Relevant means are presented in Fig. 2. The analysis revealed a significant main effect of block [F (9, 162) = 17.9, MSe = 3424.2, P<0.0001] . The effect of presenting the test block was examined by carrying out a linear contrast between the mean RTs for block $6 and mean RTs for the test block (TB). This analysis revealed that RTs were substantially slo wed following presentation of the test block [F ( 1 ) = 12.4, P < 0.01 ]. No other effects were statistically significant. Specifically. there was no effect of Condition, and no Condition by Block interaction effect.

As it was predicted that any differences between the RG and PR groups would be confined to the magnitude of the increase in RT following the presentation of the test block, we compared the RG and PR groups directly on a measure of RT slowing at the test block. For each group a difference score was computed in which RTs at block $6 were subtracted from RTs at the test block. Relevant means were: RG = 68.3 [38.7] msec; PR = 62.3 [63.7] msec. Analyses revealed that these means were not reliably different from one another It (18) = 0.3, P =0 . 8 ] , confirming that the effects of presenting the RG and PR test blocks produced equivalent levels of slowing.

The Generate task and the effects of explicit knowledge on S R T performance

An analysis of subjects performance on the Generate task (i.e. the percentage of correctly predicted locations within the first 22 trials) revealed that there were no significant between- group difference It (18)= 1.2, P>0 .1 ] . Means: RG=41 .8 [1.5] %; PR=39.1 [12.5] %).


RESULTS

It was predicted that if subjects were learning associations between adjacent sequence elements (i.e. the probability of particular transitions), then the presentation of the RG test block would produce considerably less disruption (RT slowing) than presentation of the PR condition. Alternatively, if subjects were learning about more complex relationships between sequence elements (i.e. serial-order information), then the presentation of the RG and PR test blocks should lead to equivalent levels of RT slowing. The results clearly demonstrated that the presentation of both the RG and PR test blocks produced significant increases in mean RTs. However, the magnitude of RT slowing was equivalent for both kinds of test block.

EXPERIMENT 2

Within the context of PD research it remains a particularly important issue to distinguish between a specific deficit in the learning or expression of a serially-order sequence of actions [3-1, and a more general deficit in the learning of new associations. Ferraro [9] clearly demonstrated a PD-related deficit on the SRT task, and concluded that PD patients were impaired at learning new associations. However as we have already indicated, one consequence of their use of a pseudo-random test condition, was that they could not differentiate between a failure to learn probabilistic information about adjacent sequence elements, and more complex serial-order information. In Experiment 1 we demonstrated that a test block which had an identical grammatical structure to the sequence, but which disrupted serial-order relationships [e.g. 7-1, produced equivalent levels of slowing to that of a pseudo-random test block. This strongly suggests that subjects are not simply learning about associations between adjacent element pairs as element-chaining models would predict, but are learning more complex relationships between several sequence elements such as that suggested by Cleeremans and McCleland [7].

In Experiment 2 we examine SRT learning in a group of non-demented PD patients, and explicitly test whether the PD related SRT deficit observed by Ferraro et al. [9] extends to the situation in which the test block is grammatically identical to the sequence.

METHOD Parkinson's disease oroup

Eleven non-demented patients with idiopathic Parkinson's disease were recruited from amongst patients at tending at the Depar tment of Neurology, Charing Cross Hospital. This group consisted of seven males and four females, and had a mean age of 67.0 years. Demographic details are presented m Table 4. None of these patients had a history of head injury or neurological disorder (other than PD); none were taking psychoactive medication or anti- depressants, and none had regularly consumed excesswe amounts of alcohol. Patients were not selected on the basis of any behavioural criteria, and all patients were paid for their participation

Control subjects

A further 10 elderly control (HCS) subjects (mean age of 67.5 years) were recruited from amongst the subject panel of the Depar tment of Psychology, University of Wales (Bangor). None of these subjects had a history of head injury or neurological disorder; none were taking psychoactive medication or anti-depressants, and none had regularly consumed excessive amounts of alcohol. All subjects had normal or corrected to normal vision, and all were paid the sum of £3.50 for their participation.

Apparatus and stimulus display

The apparatus and stimuli were identical to those described for Experiment 1.

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BLOCK

Fig. 4. SRT task performance for the HCS and PD groups. Note RTs are absolute RTs, not RT savings.

Tasks and design and procedure

Details of the tasks and design and experimental procedure were identical to those outlined for Experiment I save for one change. In Experiment 2, there was no pseudo-random test block. All subjects were presented with a repeated g rammar (RG) test block.

RESULTS AND DISCUSSION

Preliminary analyses--Group comparability

The overall comparability of the HCS and PD groups was verified by several preliminary analyses. These revealed that the two groups did not differ significantly in age [mean ages: HCS = 67.0 years; PD = 65.6 years; t (19)= 0.19, P > 0.1], and more importantly, that the mean RTs of the two groups were equivalent at the end of the two blocks of practice (pseudo- random) trials: HCS = 744.7 [103.5] msec, PD = 670.7 [119.2] msec; [t (19)= 1.5, P > 0.1].

Serial reaction time task--Initial analyses

Data were collapsed in the manner outlined for Experiment 1 and mean of median RTs were entered into 2 x 10 ANOVA with group (HCS vs PD) a between-subject variable and block (1-10) a repeated measure. Relevant means are presented in Fig. 4. The analysis revealed a significant main effect of Block IF (9, 171) = 20.0, M Se = 2750.9, P < 0.0001], and a significant Group x Block interaction IF (9, 171) = 3.8, MSe = 2750.9, P<0.01] . The basis of this interaction is apparent from an examination of Fig. 4. While the HCS group show a consistent reduction in mean RT between blocks 1 and 8, RTs for the PD group appear to flatten out after block 2.

The key issue addressed in this experiment is the extent to which both the HCS and PD groups show equivalent levels of RT slowing when the test block was introduced. Examination of Fig. 4 suggests that the introduction of the test block produced a considerable slowing of mean RT for the control subjects, but did not do so for the PD group. This observation was tested by computing, for each subject, a difference score in which RTs at Block $6 were subtracted from RTs at the test block. Relevant means were: HCS = 74.0 [41.9] msec; P D = 9 . 3 [33.9] msec. Analyses of these means revealed that they were significantly different It (19)= 3.9, P<0.001] , confirming that the introduction of the TB produced significant slowing for the HCS group, but not for the PD group.

590 G. M, JACKSON et al.

The Generate task and the effects of explicit knowledge on SRT performance

An analysis of subjects performance on the Generate task (i.e. the proportion of correct predictions for the first 22 trials) revealed that there were no significant between-group differences [t (19)= 1.2, P>0.1J. Means: HCS=0.44 [0.13]; PD=0.37 [0.16].

To examine the effects of explicit knowledge, the SRT data were re-analysed having first removed subjects who were scoring greater than one standard deviation above chance on the Generate task. Chance performance was estimated from the data obtained from subjects who were trained on several blocks of pseudo-random trials before being transferred to the Generate task in which the same 11-item pattern was presented as was used in the current study [Ref. 18, Exp. 3]. In that instance, the mean proportion of correct predictions was 0.33, and the standard deviation was 0.12. This procedure resulted in the removal of six subjects from the analyses (5 from the HCS group and 1 from the PD group). However, in spite of this reduction in sample size, the between-group difference in the magnitude of the RT difference between block $6 and the test block remained clear, and highly significant [t=3.8, P < 0.0025].

Serial reaction time task--Secondary analyses

Several recent reports have speculated that the integrity of the frontal lobes may be a key factor in accounting for many of the cognitive deficits associated with PD [e.g. 4, 11, 33]. As all of the PD patients participating in this study had been assessed using the most widely used neuropsychological measure of frontal lobe function, the Wisconsin card sort task (WCST), we were able to evaluate the effects of frontal impairment by examining the SRT performance of sub-groups of our PD patients, who were grouped according to performance on the WCST. Patients were considered to exhibit frontal deficits if they were unable to sort more than three categories in the Nelson version of the WCST [28]. Relevant means are presented in Fig. 5.

In view of the small number of patients within the frontal sub-group, we did not consider it appropriate to subject these data to statistical analyses. However, examination of Fig. 5 clearly suggests that the integrity of frontal lobe function (as measured by the WCST) may be an important factor with respect to SRT learning. Furthermore, the data suggest that while the non-frontal PD patients appear to be doing far better than the frontal sub-group, even this group do not appear to be learning as well as the control group.

GENERAL DISCUSSION

The results of this study confirm and extend the findings of Feraro et al. [29]. Firstly, they confirm that PD patients are impaired on the SRT task. Secondly, they extend the findings of the previous study by demonstrating that this deficit is apparent even under circumstances where subjects could utilise information about the associations between adjacent sequence elements. These results suggest that the PD group are impaired at either: (1) learning about complex serial-order information; or (2) expressing such knowledge, i.e. producing a serially- ordered set of actions.

In a previous demonstration of impaired SRT learning in patients with basal ganglia disease (HD patients), Willingham and Koroshetz suggested that this deficit is not simply a consequence of impaired stimulus-response mapping, but may be specific to the acquisition of information about the sequential order of a sequence of responses [38]. In the current study we controlled against the possibility that patients might simply be learning stimulus-


E |

E

l= @

1000

900

800'

700"

600'

500

Practice

I ' I

R1 R2

_- Frontal PD [] Non-frontal PD ~, HCS

I " I ' I • I ' | ' I ' I " I "

S1 $2 $3 S4 S5 S6 TB S7

BLOCK

Fig. 5. SRT task performance for the HCS, frontally-impaired, and frontally non-inpalred PD patients.

response probability biases, by presenting a test block in which the statistical structure of the sequence was maintained, and only the sequential order information disrupted. Taken together these results suggest a role for the Basal Ganglia in SRT learning or the expression of serially-ordered action.

Several recent studies provide convergent evidence for this view. Firstly, in a recent study of sequencing in primates, Kermadi et al. [21] reported neuronal activity in the caudate which appeared to be consistent with the neural coding of serial order. In this study monkeys observed a sequence of LED illuminations, and were required to produce a sequence of manual responses which maintained the serial order of the illuminations. They found a number of neurons in the caudate which fired in relation to a specific response (button), only if it preceded or was followed by, other specific responses. Furthermore, this activity was independent of either the direction of the movement, or its absolute location.

Secondly, Aldridge and colleagues have studied the role of the neostriatum in controlling serially-ordered sequences of grooming behaviour in rodents [ 1 ]. These authors suggest that two types of evidence are required to demonstrate that a neural system controls the serial order of behavioural sequences: (1) lesions should disrupt the sequential behaviour but leave the individual elements intact; (2) activation of neurons within the neural system should be correlated with the behavioural sequence but not the individual elements. They report that lesions to the neostriatum disrupt rodent grooming sequences but do not disrupt the performance of individual behavioural elements performed outside the sequence. Further- more, they report that in a single-cell recording study of the neostriatum, 85% of the cells


sampled responded differently according to whether a specific behavioural movement performed as part of a sequence of grooming behaviours or outside of a syntactic grooming chain [1].

Finally, in a recent PET study, Seitz and Roland examined learning of a sequence of finger movements in healthy adults [14]. They found a differential pattern of blood flow activity early on compared to the later stages of learning. Early in learning they observed significant activity in parietal cortex and inferior frontal cortex. In contrast, during the later stages of learning, significant activity was observed in the putamen and globus pallidus. These results suggest that the basal ganglia may participate in the execution of a learned sequence of movements. A broadly similar pattern of results has also been observed in a recent PET investigation of skill learning [12].

At present findings such as these are only suggestive of a basal ganglia function linked to sequence learning. The basal ganglia form but one part of a number of cortico-striato- cortical loops, which receive projections from cortical areas, e.g. supplementary motor cortex, which are themselves linked to the sequencing of action [ 13]. The above findings are consistent however, with the view proposed by Jackson and colleagues [19, 20] that the basal ganglia play a critical role in the selection of sensorimotor events, and thus participate in the selection, at the appropriate point, of sequence elements. Furthermore, these data are also consistent with Gabrieli's suggestion [10] that while the basal ganglia may participate in the execution of skilled behaviour, the long-term representation underlying skilled behaviour may reside in the cortex.

In conclusion, the results of this study provide additional evidence for a SRT learning deficit associated with basal ganglia disease, and extend previous demonstrations by showing that such deficits may particularly relate to information about the serial-order of the sequence. These findings suggest an important role for the Basal Ganglia in the execution of sequential behaviour.

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Serial reaction time learning and Parkinson's disease: Evidence for a procedural learning deficit