Motor deficits cannot explain impaired cognitive associative learning in cerebellar patients

Neuropsychologia 40 (2002) 788–800

Motor deficits cannot explain impaired cognitive associative learning in cerebellar patients D. Timmann a,∗ , J. Drepper a , M. Maschke a , F.P. Kolb b , D. Böring c , A.F. Thilmann c , H.C. Diener a a

Department of Neurology, University of Essen, Hufelandstrasse 55, D-45122 Essen, Germany b Department of Physiology, University of Munich, Munich, Germany c Department of Neurology, Fachklinik Rhein/Ruhr, Essen, Germany Received 19 March 2001; accepted 24 August 2001

Abstract There is a strong evidence that the cerebellum is involved in associative motor learning. The exact role of the cerebellum in motor learning and whether it is involved in cognitive learning processes too, are still controversially discussed topics. A common problem of assessing cognitive capabilities of cerebellar patients is the existence of additional motor demands in all cognitive tests. Even if the patients are able to cope well with the motor requirements of the task, their performance could still involve compensating strategies which cost them more attentional resources than the normal controls. To investigate such interaction effects of cognitive and motor demands in cerebellar patients, we conducted a cognitive associative learning paradigm and varied systematically the motor demands and the cognitive requirements of the task. Nine patients with isolated cerebellar disease and nine matched healthy controls had to learn the association between pairs of color squares, presented centrally on a computer monitor together with a left or right answer button. In the simple motor condition, the answer button had to be pressed once and in the difficult condition three times. We measured the decision times and evaluated the correctly named associations after the test was completed. The cerebellar subjects showed a learning deficit, compared to the normal controls. However, this deficit was independent of the motor difficulty of the task. The cerebellum seems to contribute to motor-independent processes, which are generally involved in associative learning. © 2002 Elsevier Science Ltd. All rights reserved. Keywords: Cerebellum; Human; Associative learning; Conditioning; Cognition

1. Introduction There is a general agreement that the cerebellum is involved in motor coordination and learning, although the exact role of the cerebellum is still a matter of discussion [42,71]. More recently, an additional role of the cerebellum in pure cognitive function has been proposed [41,64]. In the last decade, an increasing number of studies in cerebellar patients and in healthy subjects using functional brain imaging techniques claimed evidence for an involvement of the cerebellum in mental skills [17,21,26,35,65,73]. However, various criticisms have been raised [19]. For example, several studies are based on studies of single cases or small sample sizes often including patients with extracerebellar damage [16,24,25,79,80]. Control groups were frequently not IQ-matched [10,12] and cognitive ∗ Corresponding author. Tel.: +49-201-723-3816; fax: +49-201-723-5901. E-mail address: [email protected] (D. Timmann).

background variables, e.g. short-term memory, were not assessed. Furthermore, the motor requirements of the tasks were frequently not considered. Given that all behavior includes cognitive, perceptual and motor parts [1], cognitive learning paradigms are never purely mental but include some sort of motor requirements, e.g. pressing a target button, eye movements, talking or inner speech. Motor performance is commonly impaired in cerebellar patients (e.g. cerebellar ataxia). Therefore, motor demands need to be carefully assessed in cognitive tasks. Some of these methodological shortcomings have been addressed in a recent study of our group [23]. A cognitive associative learning task was examined in patients with pure cerebellar disease. Patients were matched to controls in respect to age, education, IQ and visual memory. A simple reaction time and visual scanning task were performed to assess motor background variables. The patients’ ability to learn the association of a color and a numeral was significantly impaired, regardless of the amount of motor performance deficits. A reasonable assumption was that the

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D. Timmann et al. / Neuropsychologia 40 (2002) 788–800

cerebellum is involved in this kind of associative cognitive learning. However, another possibility remained. It may be that the execution of the motor component of the task (to push a button) took up more attentional resources as compared to controls which in turn reduced the resources for the cognitive components of the task. Although, a possible role of the cerebellum has been suggested in some attentional tasks [2,3,5,18,40], the influence of motor attentional demands on cognitive learning ability has not been assessed in previous studies in cerebellar patients. One aim of the present study was to confirm our previous findings of impaired cognitive associative learning despite careful control of impairments in motor performance. The second aim was to investigate whether deficits in a cognitive associative learning paradigm are due to increased attentional demands during executing of the motor part of the task in cerebellar patients. To evaluate possible interactions between the motor and cognitive requirements of a task, a paradigm was designed according to Sternberg’s model of additive factors [49,70]. Donders [22] postulated that mental skills can be measured as long as two tasks differ in only one aspect. If the motor aspects remain the same and only the cognitive parts differ, differences in reaction times are a reasonable measure of the cognitive abilities. Donders’ model, however, assumes that the motor and cognitive parts of the task are independent. To control possible interactions between motor and cognitive demands, Sternberg [70] proposed a model where two components of a task are independently changed. If motor and cognitive components of a task are systematically changed, the influence of the two factors as well as interactions between them can be assessed. In the present study, subjects had to learn the association between pairs of color squares. The cognitive requirements of the task were changed by varying the frequency of presentation of each color pair (low versus high frequency). To change the motor demands of the task subjects had to press a target button once or three times (one versus three key

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presses). If motor execution of the task affects the ability to learn the association of color pairs, deficits in associative learning should be more pronounced in the more difficult motor condition. In this case, statistical analysis should yield significant interactions of motor and cognitive requirements of the task. A significant interaction may already be present in healthy subjects. Possible interaction effects are likely to be more distinct in cerebellar patients as a result of motor performance deficits. If effects of motor execution on cognitive learning ability were more pronounced in cerebellar patients, statistical analysis should show significant interactions of the motor difficulty and group of subjects.

2. Method 2.1. Subjects Nine patients with degenerative cerebellar ataxia were compared with nine healthy controls. Their average age was 56.67 ± 9.41 years (range 41–72). Three patients were female, six male, seven right-handed and two left-handed. All patients presented with isolated cerebellar disease based upon neurological examination, except one patient (Gl) showing additional signs of polyneuropathy in the lower limbs. Brain MRI scans revealed diffuse cerebellar atrophy in all cases without additional brain stem or cerebral involvement. The neurological examination included the ataxia rating scale from Trouillas et al. [78]. The clinical data of the patients are summarized in Table 1. Color blindness was ruled out by testing with the color plates described by Ishihara [32]. Four of the cerebellar patients (Bn, Hn, Ip, Os; Table 1) did partake in the previous cited study by our group [23]. Nine healthy controls were selected which matched the patients with regard to age, level of school education, IQ, visual and short-term memory capabilities. Three of them were female, six male, eight right-handed and one left-handed. Their mean age was 55.0 ± 8.46 years (range 47–71). None

Table 1 Patient informationa Cerebellar subjects

Cerebellar disorder

Age Sex Posture and gait ataxia (range 0–34)

Kinetic functions (range 0–52)

Speech disorders (range 0–8)

Oculomotor disorders (range 0–6)

Total score range (0–100)

Bn Hn Ip Bo Sp Hö Pf Gl Os

IDCAb SCA6c IDCA IDCA SCA6 IDCA ADCA IIId IDCA ADCA III

72 54 52 41 57 69 57 50 58

26 29 17 23 7 18 17.5 17 17.5

4 5 1 4 3 2.5 4 1 2

5 5 1 5 2 1 3 3 5

48 56 32 45 23 37 36.5 30.5 39.5

a

M M F M M M M F F

13 17 13 13 11 15.5 12 9.5 15

Scoring of cerebellar symptoms according to the ataxia rating scale [78]. Idiopathic cerebellar atrophy. c Spinocerebellar ataxia type 6. d Autosomal dominant cerebellar ataxia type 3 (pure cerebellar). b

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of them had a history of neurological disease or revealed neurological signs based upon neurological examination. The local ethical committee of the University of Essen approved the study. All subjects gave informed written consent. 2.2. Procedure The mean duration of the tests was approximately 4 h and the whole neuropsychological test battery was carried out over 2 days in each control and five patients. Four patients completed the battery on day 1, interrupted only by an extended lunch-break. Due to organizational requirements, the tests carried out by all subjects were not in any specific order. All subjects completed all the tests (see below). 2.2.1. Associative learning task Subjects had to learn the association of pairs of colored squares and to express this learnt association via a motor response. This task included two levels of motor difficulties and two levels of cognitive difficulties. To rule out specific effects, different types of control conditions also had been performed. Fig. 1 illustrates the experimental setup. Subjects were comfortably seated in front of a 15-in. color monitor (distance 60 cm) connected to a ×86-compatible PC, controlling the experiment. Subjects started each trial by using a specialized keyboard with three buttons, i.e. a central home button and two target buttons, left and right to the home button (surface area of 1 cm2 , distance 1 cm). All subjects were

asked to use only one finger of the dominant hand (tested before) for all keyboard operations. If the central button was pressed long enough, a new trial was presented. The background color (light grey) was presented for 500 ms followed by a black cross in the middle of the screen. The black cross ensured that subjects turned their eyes to the center of the screen. In this place, after 1 s, a pair of colored squares (4 cm high × 3.5 cm wide, 1.5 cm apart) was seen with two black crosses to its right and left (Fig. 1c). After 3 s, a filled black circle was growing on the right or left side for 1500 ms. Subjects were instructed to release the home button as soon as they saw a growing black circle on the left or right side of the computer screen and hit the target button on this particular side. A growing black circle was chosen to trigger the motor response to increase the alertness of the subjects during the task. They had to watch carefully in order to be able to respond quickly, i.e. at a time when the circle was still small. Subjects were informed that color pairs presented in the middle of the screen predicted the correct side. Each color pair was regularly followed on the same side of the screen by a black circle. Subjects, therefore, had to learn the association between pairs of colors and between a given color pair and side of the response. The side of the response, however, was known to the subjects independent of their ability of associative learning. During the experiment, motor and cognitive requirements were systematically changed. In the simple motor condition, the answer button had to be pressed once and in the difficult motor condition three times. In order to change the difficulty

Fig. 1. Experimental setup: subjects started each trial by pressing the central home button of a specialized keyboard. After 1.5 s either a filled circle (a), an arrow (b) or a color pair (c) were presented on a computer screen with a black cross to the right and left. Three seconds later, a lateral black circle was growing on the left or right side of the stimulus. Subjects were instructed to hit the target button on this particular side. Association of a pair of colors enabled subjects to predict the side of the response. The centered circle and arrow conditions served as control tests, with the arrow always pointing to the correct side of the response and the circle carrying no information about the response side. In the simple motor condition, the answer button had to be pressed once and in the difficult condition three times. Total response times were shown after each trial for feedback.

D. Timmann et al. / Neuropsychologia 40 (2002) 788–800

of the cognitive task, color pairs were presented at a low and high frequency. Subjects had to learn the association of four pairs of colors, with two pairs being presented more frequently than the others. Two different color sets were used. One color set consisted of four red and yellow colors, the other of four blue and green colors. The background color of the monitor was always a light grey. The configuration of the color pairs, i.e. whether the two colors were next or on top of each other, changed randomly from trial to trial, but was identical for all subjects. To control floor and ceiling effects, two control conditions were implemented. Instead of a color pair, an arrow or a filled black circle were shown in the middle of the screen. An arrow always pointed to the correct position of the lateral circle (Fig. 1b), whereas, a centered circle (diameter 2.5 cm) carried no information about the expected side of the lateral circle to grow (Fig. 1a). The arrow condition provides the fastest possible reaction time without learning, whereas the circle condition measures the time it takes without any predictive information. These control conditions help to decide if cerebellar patients are able to change their reaction times based on their motor deficits regardless of any cognitive learning requirements. The experiment had two parts marked out by one of the motor conditions (one versus three key presses) and one of the color sets (green–blue versus yellow–red). Each motor condition and color set was presented once to each subject. The combination of motor requirements and color set of the first part was chosen at random. The setup of the first part of the experiment determined the second. Four of the cerebellar and control subjects started with the simple motor condition, five with the difficult motor condition. Five controls and four cerebellar patients started with the green–blue color-set, four controls and five patients with the yellow–red color set. Each of the two experimental parts consisted of eight blocks. In each block, 16 trials were presented. 4/16 trials were control conditions (two arrows and two circles followed by a key press to the left or right). From the four colors of each set, four pairs were chosen with each color being part of two color pairs. One of the two color pairs was followed by a key press to the right, the other by a key press to the left. The frequency of the color pairs predicting the left side was 5/16 and 1/16, the frequency of the color pairs predicting the right side 4/16 and 2/16. In our previous study, subjects had to learn the association between a color and a numeral [23]. Although not significant, there was a tendency of associative learning deficits to increase with the severity of dysarthria. In the present study, to further reduce the influence of subvocal strategies and verbal abilities, subjects had to learn the association between two colors. The colors were different shades of green and blue or red and yellow, such that it was difficult to distinguish between them and describe them verbally. Colors were chosen as stimuli in order to make their visuospatial encoding unnecessary.

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For reasons of analysis, the temporal sequence of a given trial was separated in a decision time, a moving time and a tapping time. Decision time was defined as the time between stimulus onset and releasing the central home button, whereas, moving time was the time between releasing the home button and first press of the correct target button. Tapping time was defined as the mean time difference between first and second, and second and third key press. The sum of decision time and moving time corresponded to the response time (time between stimulus onset and first key press), whereas, the sum of the response time and tapping time corresponded to the total response time (time between stimulus onset and last key press). Subjects were informed that all response times were measured and that speed and accuracy were equally important characteristics of the task. After each trial subjects were shown their total response time as feedback in dark, bold letters which were easy to read for all the subjects (Fig. 1). If the total response time (time to last key press) extended 1.5 times ± of the mean of the previous 10 trials a warning tone was applied and a message appeared on the screen (“Bitte schneller antworten”: “Please respond more quickly”). At the end of one experimental part, each of the four color pairs was shown on the monitor and subjects were asked if the color pair was associated with a response to the right or left. Finally, subjects were asked if any colors or color pairs were shown twice to ensure that all subjects could differentiate between them. 2.2.2. Neuropsychological tests In keeping with our previous study [23], a series of neuropsychological tests were performed to control possible differences in IQ, visual memory and motor deficits. In addition, visual and verbal short-term memory abilities were assessed. As a reliable measure of intelligence, the standard progressive matrices (SPM) test [61] without time restrictions were chosen, thus minimizing the potential influence on the IQ of the patients’ motor performance deficits. Furthermore, this test gives extensive information about intelligence in addition to what is indicated by the level of education. In order to measure visual memory capabilities, the recurring figures test (RFT) [29,36] had to be performed. In addition, the Corsi block tapping span [63] and the simple digit span [81] were assessed to control spatial and verbal short-term memory capabilities. To control the influence of motor and oculomotor performance deficits in cerebellar patients, two subtests of a German computerized neuropsychological battery for evaluating attentional deficits were carried out [85]. The first subtest, “alertness”, is a simple visual reaction time task where in some trials an acoustic warning occurs before the visual stimulus. The two conditions differentiate between phasic and tonic alertness deficits [60]. The test consists of four blocks with 20 trials each. In blocks 2 and 3, the acoustic warning occurred. The second subtest was a

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visual scanning task in which the subject had to decide and respond as quickly as possible whether or not a specific stimulus occurred in a visually presented matrix.

3. Results The results of the associative learning paradigm are presented first, and those of neuropsychological background variables next. For the associative learning paradigm, parameters of the control conditions are shown first, followed by a more detailed analysis of the motor requirements of the task and results of the learning color pair conditions. 3.1. Associative learning task If not otherwise indicated, statistical analysis was based on decision times (time between stimulus onset and releasing the home button). Only decision times from correctly answered trials were considered. Decision times longer than 2 s and shorter than 150 ms were excluded. The number of trials which have been discarded from analysis did not differ significantly between groups (control group (incorrect responses): 67 out of a total of 2304 responses (2.91%), cerebellar group: 76/2304 responses (3.3%); control group (outliers): 4/2304 responses (0.17%); cerebellar group: 2/2304 responses (0.09%); P > 0.25, Mann–Whitney-U-test). For data reduction, the means of a given condition in 32 consecutive trials (i.e. two blocks of 16 trials) were calculated and therefore block effects were based on four blocks. The sequence of the two experimental parts (marked out by one of the motor conditions and one of the color sets) was included as covariate because both control and cerebellar subjects were significantly faster in the second condition as compared to the first. Based on the arrow and circle control conditions, controls and cerebellar patients were 37.6 ms faster in the second part than in the first (P = 0.024). There was no significant difference between groups. The color set had no significant influence on the decision times (P >

0.25). Being implemented as covariate, statistical results are independent of any linear effect of the sequence of the tasks. 3.1.1. Arrow and circle control conditions To control floor and ceiling effects, differences between the arrow and circle condition were considered. A univariate analysis of variance was calculated with group as between-subjects factor (controls versus patients) and motor demands (one versus three key presses), stimulus condition (arrow versus circle) and block (n = 4) as repeated measures. Decision times were significantly increased in the cerebellar group in both the circle and arrow condition compared to controls (P = 0.001; Fig. 2). This finding demonstrates a clear motor deficit in the cerebellar patients. In both groups, decision times were significantly longer in the circle condition compared to the arrow condition (controls (arrow condition): 328.5 ms ± 68.1, cerebellar: 563.5 ms ± 165.5; controls (circle condition): 512.8 ms ± 96.4, cerebellar: 703.7 ms ± 174.9; P < 0.001). The difference between the circle and arrow condition was not significantly different between groups (group by condition interaction: P = 0.2). Thus, cerebellar patients were not impaired in their ability to reduce decision times based on knowledge of the side of the target button, despite their motor impairment. These findings were independent of the motor demands (one versus three key presses; P = 0.64) and number of blocks (P = 0.91). There were no significant interactions (all P > 0.25). The motor performance deficit did not prevent cerebellar patients from reducing their decision times. Therefore, any deficits in learning the association of color pairs based on decision times were unlikely to be due to inability to reduce decision times based on performance deficits (“floor effects”). 3.1.2. Motor demands are increased in three versus one key press condition Changes in motor demands in the three versus one key press condition were assessed in the control arrow condition. Decision time (time to release the home button), movement

Fig. 2. Control conditions to test for floor and ceiling effects: mean decision times and S.D. for the arrow and circle conditions in controls (left) and patients (right). Each small bar represents the value of one subject. The position of each subject remains the same in all graphs, including those in Figs. 5 and 6. Although, mean decision time were prolonged in the patient group (indicating their motor performance deficit), both controls and cerebellar patients showed significantly shorter decision time in the arrow compared to the circle condition.

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time (time from release of home button to first key press) and tapping time (mean time difference between first and second and second and third key press) were calculated as well as the response time (time between stimulus onset and first key press). Both control and cerebellar subjects showed a slightly reduced decision time comparing the one and three key press conditions (cerebellar group (one key press): 571.12 ± 185.1 ms, three key presses: 555.16 ± 185.1 ms; controls (one key press): 339.71 ± 70.12 ms, three key presses: 338.67 ± 59.33 ms). Decision time was significantly increased in cerebellar patients (P = 0.01). There was no effect of the number of key presses and number of group × condition interaction (P > 0.5). Similarly, the response time was larger in the patient group (P = 0.003), but there was no significant effect of number of key presses and no significant interaction (P > 0.4; controls (one key press): 453.1 ± 43.89; three key presses: 482.61 ± 88.42 ms; cerebellar patients (one key press): 758.34±270.96; three key presses: 777.17±235.22). Movement time, however, increased in the three key press condition in both groups (controls (one key press): 113.39 ± 45.5 ms, three key presses: 143.94 ± 43.89 ms; cerebellar group (one key press): 187.23±146.6 ms, three key presses: 222.01±127.41 ms). Although, there was a clear tendency of the movement time to be prolonged in the cerebellar group, the group difference did not reach statistical significance (P = 0.124). There was a significant effect of condition (P = 0.01), but no significant interaction (P = 0.85). The tapping time was significantly increased in the cerebellar patients (controls: 218.71 ± 37.56 ms; cerebellar group: 350.35 ± 105.77 ms; P = 0.006 unpaired t-test). In sum, there was a clear motor impairment in the cerebellar group, with significantly increased decision, response and tapping times. Furthermore, there was a significant effect of the number of key presses (one versus three) on the movement time in both the control and the cerebellar group. As expected, motor performance was impaired in cerebellar patients, and the task of three key presses changed the motor demands in both the control and the cerebellar groups. 3.1.3. Associative learning of color pairs To quantify the effects of associative learning of color pairs changes of decision times per block were analyzed. An univariate analysis of variance was calculated with group (control versus cerebellar) as between-subjects factor and block (n = 4), cognitive demands (frequently versus rarely shown color pairs) and motor requirements (one versus three key presses) as repeated measures. For all effects including the factor block, we adjusted the degrees of freedom according to the proposals of Greenhouse and Geisser if appropriate. As for control conditions, patients showed significantly increased decision times indicating motor performance deficits (patients: 679.4±169.8, controls: 475.6±86.9; P = 0.003). There was a significant block effect (P < 0.001) reflecting

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Fig. 3. Effects of motor demands in the associative learning conditions: mean decision times and standard deviations per block (n = 4) for the one key press (left) and three key presses (right) motor conditions for controls (open columns) and cerebellar patients (black columns): decision time (i) were generally longer in cerebellar patients (P = 0.003); (ii) showed a significant reduction per block (P < 0.001) which tended to be larger in the control group (P = 0.83); and (iii) the amount of change per block did not differ between motor conditions in the control and cerebellar group (P > 0.25).

the effects of associative learning (i.e. decrease of decision time per block) (Fig. 3). The amount of change per block was the same in controls and cerebellar patients regardless of the number of key presses. Neither effects of motor demands (one versus three key presses), nor motor demand versus group, nor motor demands versus cognitive demands, nor motor versus cognitive demands versus group interactions reached statistical significance (all P > 0.25). The difference between frequently and rarely shown color pairs was significant with less effects of learning in the more difficult cognitive task (rarely shown color pairs) (P = 0.012; Fig. 4). Close inspection of Figs. 3 and 4 shows that there was a tendency of decrease of decision time per block to be less in cerebellar patients than in controls. However, there was no significant block by group interaction (P = 0.83). To control confounding motor performance deficits and non-specific learning effects (e.g. due to familiarization with the task), further analysis took into account results of the control circle condition. Differences were calculated between the mean decision time in the control circle condition and decision times in the color pair conditions for trials of each block and subject. We report results for the frequently shown color pair condition, because of the more pronounced learning effects compared to the rarely shown color pair condition. Effects of associative learning resulted in increasing differences in decision times between the circle and color pair conditions per block (P = 0.007). There was a significant group effect (P = 0.037) suggesting inequalities in

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Fig. 4. Effects of cognitive demands in the associative learning conditions: mean decision time and S.D. per block (n = 4) for the low frequency (left) and high frequency (right) color pair conditions for controls (open columns) and cerebellar patients (black columns): decision times (i) were generally longer in cerebellar patients (P = 0.003); (ii) showed a significant reduction per block (P < 0.001) which tended to be larger in the control group (P = 0.83); and (iii) the amount of change per block was larger in the high frequency than the low frequency condition in the controls and cerebellar patients (P = 0.012).

cognitive learning in controls and cerebellar patients. This group difference is unlikely to reflect differences in motor performance, because (i) decision times expressed as difference between the control circle and color pair condition are normalized for performance deficits, and (ii) cerebellar patients were able to reduce decisions times in the control arrow condition compared to the circle condition as much as controls (see above). Fig. 5 shows that the mean difference of all blocks was smaller in the patients than in the controls. Therefore, effects of associative learning were significantly less in the cerebellar group. Again, there was no significant influence

Fig. 5. Effects of associative learning assessed as differences in decision times in the control circle and color pair conditions per block: mean differences and S.D. are shown for all blocks (n = 4) in the control (left) and cerebellar (right) group. Each small bar represents the value of one subject. Note smaller values in the cerebellar group representing significantly decreased learning effects as compared to the control group (P = 0.037).

of the motor difficulty (motor demands (one versus three key presses): P = 0.52; motor demand versus group interaction: P = 0.65; motor demands versus block: P = 0.27; motor demands versus block versus group: P = 0.073). The number of correctly named associations of the frequently shown color pairs and side of the response appeared to be less in the cerebellar group compared to the controls. After the one key press condition was completed, four patients were unable to name any correct associations, whereas, five were able to name two out of two. In the control group, three subjects were able to name one correct association and six were able to name two out of two. A chi-square test with Fisher’s exact statistic for small sample sizes revealed a significant group difference (P = 0.021). For the three key press condition, however, no significant group difference was found (P = 0.157). The motor background variables visual scanning time, simple reaction time and ataxia scores were not significantly correlated with the difference of the circle versus color pair conditions (all P > 0.2). In sum, cerebellar patients were less able to learn the association between pairs of colors. Cerebellar patients exhibited clear motor performance deficits. However, the associative learning deficit was independent of motor performance deficits and changes in motor requirements (one versus three key presses). 3.2. Neuropsychological tests The patients and controls were compared with respect to age, the cognitive (i.e. intelligence, visual memory, spatial and verbal short-term memory) and motor background variables (i.e. simple reaction time, visual scanning). To evaluate the group differences, t-tests for independent samples were calculated. The results are summarized in Table 2. As a measure of intelligence, the percentiles of the SPMs test were used. SPM percentiles were slightly lower in the patient group (85.56 ± 11.3 years) than in the controls (90.56±7.68 years). Capabilities of visual memory were indicated by the percentiles of the RFT. RFT percentiles were slightly higher in the patients (patient group: 65.6 ± 32.16 years; control group: 53.14 ± 28.9 years). Spatial short-term memory capabilities were indicated by the number of correctly indicated positions in the Corsi block tapping test (controls: 5.00 ± 0.5; cerebellar group: 4.67 ± 0.87). Verbal short-term memory was quantified by the number of correctly recalled digits in the simple digit span test (controls: 6.22 ± 0.83; cerebellar group: 5.89 ± 1.27). There were no significant group differences with regard to age, intelligence, visual, spatial or verbal short-term memory (all P > 0.25). As a measure of simple reaction time, the mean reaction times of all four blocks in the alertness test were used. This test permits calculation of the difference in reaction times for trials with and without an alerting stimulus, indicating phasic alertness. Phasic alertness was calculated as

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Table 2 Comparison of the two samples with respect to neuropsychological background variablesa Patients (six male, three female; six school level Ib and three school level II)

Controls (six male, three female, five school level I, one school level II and three school level III)

t-test (d.f. = 16)

Mean

S.D.

Mean

S.D.

t

p

Age (years)

56.67

9.41

55.0

8.46

−0.395

0.698

SPM Score (percentile) Time (min: s) RFT (percentile) Block span Digit span

85.56 43:20 65.6 4.67 5.89

11.3 14:21 32.16 0.87 1.27

90.56 43:20 53.14 5 6.22

7.68 12:29 28.9 0.5 0.83

1.097 0.000 −0.864 1.000 0.659

0.289 1.000 0.400 0.332 0.520

Alertness Reaction (ms) Score for phasic alertness

269.85 −0.023

83.38 0.112

227.77 0.028

31.34 0.034

−1.417 1.324

0.176 0.204

Visual scanning Time (ms) Number of errors

6400.3 7.67

3912.6 5.98

4896.1 5.78

1767.6 2.82

−1.051 −0.857

0.309 0.404

a b

d.f.: Degrees of freedom; RFT = recurring figures test [36]; SPMs = standard progressive matrices [61]. The German school system has three levels of examination; level III qualifies for university entrance.

the difference of the median reaction time of the blocks with and without alerting stimulus, divided by the median reaction time of all four blocks [85]. Although, there was a clear tendency of the simple reaction time task to be longer in the patients (269.85±83.3 ms) than in controls (227.77±31.3 ms), this did not reach statistical significance (P = 0.17) (Fig. 6). The score for phasic alertness did not differ between the control and patient group (P = 0.2). In the visual scanning task, the mean reaction times were calculated for those correctly answered trials in which no critical stimulus was in the matrix. This measure was used as an indicator of visual scanning speed, because the subjects had to scan the whole matrix in these trials in order to find the correct answer. Additionally, the number of false reactions was also counted. The mean visual scanning time for the whole presented matrix was longer in the patient group (6400.3 ± 3912.6 ms) than in controls (4896.1 ± 1767.6 ms).

However, group comparison did not show a significant difference (P = 0.3). The number of errors did not differ between groups (P = 0.4). 4. Discussion Our previous finding on impaired learning in a cognitive associative learning task in cerebellar patients was confirmed in the present study [23]. As before, differences in age, intelligence and visual memory did not account for the learning deficits. The effects of impaired motor execution on cognitive learning were carefully assessed. No evidence was found that impaired motor performance or increased attentional demands during execution of the task were related to deficits in cognitive associative learning in cerebellar patients.

Fig. 6. Mean reaction times and S.D. in (A) the simple reaction time task (“alertness”) and mean decision time and S.D. in (B) the control arrow conditions (“arrow condition”) in controls and cerebellar patients. Each small bar represents the value of one subject. In both tasks, reaction times were longer in the cerebellar group compared to the controls. However, increases in cerebellar patients were more pronounced in the control arrow condition than in the simple reaction time task.

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4.1. Assessment of motor performance deficits Motor performance deficits were assessed in a control condition with similar motor requirements as in the learning paradigm (i.e. control arrow condition) and a number of background tasks (i.e. simple reaction time task, visual scanning, clinical ataxia score). In the control arrow condition, decision times were significantly increased in cerebellar patients compared to controls. However, although, mean values were clearly larger in cerebellar patients than in controls, reaction times in the simple reaction time task did not show a statistically significant group difference (Fig. 6). The significant impairment of simple reaction times in cerebellar patients in our previous study [23] most likely reflects a larger degree of upper limb ataxia in the previous patient group compared to the present one. There are two possible reasons for the increase of motor demands in the associative learning paradigm compared to the simple reaction time task. First, in the simple reaction time task subjects viewed the screen and responded to a sudden visual stimulus with pressing one big response button using all fingers of the hand. The answer button in the associative learning paradigm was much smaller and subjects had to release (not press) the button in response to a slowly growing visual stimulus. It may be more demanding for cerebellar patients to hold a small button with a certain force of one finger and to release it than to press a big button with the whole hand. Finger force control is known to be impaired in cerebellar patients [43,51,52,66]. Second, in the associative learning paradigm, but not the simple reaction time task, the initial motor response was followed by another motor task, i.e. pressing a small target button one or three times with one finger. Cerebellar patients are known to be impaired in planning of sequential movements [15,31,67]. Furthermore, a differential role of the cerebellum in simple and compound movements has been emphasized by others [72,73]. In sum, motor performance deficits were more pronounced in the associative learning paradigm than in the simple reaction time task. The difference most likely reflects increased requirements of motor planning and execution, which are both impaired in cerebellar patients. 4.2. Deficits in cognitive learning cannot be explained by disordered motor execution Association of color pairs enabled subjects to predict the correct side of a motor response. Effects of associative learning were assessed by changes in decision times, i.e. the time between stimulus onset and releasing a home button (prior to pressing a target button; Fig. 1). Cerebellar patients were less able than controls to learn the association between pairs of colors and, therefore, less able to reduce decision times based on predictive knowledge of the side of the response. Cerebellar patients, however, were able to reduce decision times as much as controls in a control condition which did not require associative learning. Decision times were

shorter in the arrow condition, where the correct side of the response was indicated by an arrow, as compared to the circle condition, where no information was available about the correct side of the response. Therefore, deficits in cerebellar patients’ ability to reduce decision times in the associative learning part were not due to a general inability to further reduce decision times based on motor performance deficits. Furthermore, motor performance deficits were not related to deficits in cognitive associative learning. First, there was no significant interaction between motor demands (one versus three key presses) and effects of learning. Second, effects of impaired cognitive learning in the cerebellar group were most prominent when reaction times were normalized for motor performance deficits (i.e. significant group effect comparing differences in the control circle and color pair conditions; see Fig. 5). Finally, the findings of our previous study had assured that background motor variables, i.e. simple reaction times, visual scanning times and clinical ataxia scores, did not relate to impaired cognitive associative learning. Again, no significant correlation between these motor variables and cognitive learning deficits could be demonstrated. Although measures of motor impairment did not significantly correlate with deficits in cognitive associative learning, motor deficits might have interacted with cognitive demands by using up more attentional resources. Broadbent [14] defined attention as the ability to respond to specific events in the environment, whereas simultaneous stimuli are suppressed. This implies a limited capacity of the brain to process sensory information and the need for information selection (bottleneck or filter theory). Current theories propose a central attentional control system which regulates the information flow to different subsystems (“supervisory attentional system”, [68]; “central executive”, [7]). The Baddeley and Hitch [6] model of working memory postulates a central executive which coordinates the operation of two subsidiary slave systems, the phonological loop for language and the visuospatial sketchpad for vision and action. If two tasks are performed at the same time, interference can occur if tasks involve the same perceptual or motor systems, or if they involve the common attentional control system [58]. In the present study, verbal and visuospatial short-term memory capabilities were intact in cerebellar patients. Performance of the motor elements of the cognitive learning paradigm, however, might have required more central attentional resources in cerebellar patients than in controls. Early in learning, subjects need to pay attention to use the specialized keyboard. In controls, after practicing the task for a couple of trials, the motor part becomes more automatic. It can be performed with little central interference at the same time as cognitive learning. In cerebellar patients, motor performance deficits are likely to impede a more automatic use of the keyboard [73]. Less automatic motor execution may require more central attentional resources in cerebellar patients than in controls. Given the limited capacity of the “central executive” [7], its resources may be lacking to accomplish the cognitive part.

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In this case, one would expect increasing cognitive learning deficits with increasing motor demands. However, increasing difficulty of the motor responses (one versus three key presses) had no significant effect on the results of the cognitive part of the task in both healthy controls and cerebellar patients. There were no significant interactions of motor and cognitive demands or of motor demands and group. Therefore, deficits in cognitive associative learning could not be explained by increased attentional demands of the motor part in cerebellar patients. Although, the cerebellum may be involved in certain aspects of attention (e.g. shifting attention: [2,3,5]), motor performance deficits in mildly or moderately ataxic cerebellar patients seem not to interfere with attentional resources required in cognitive associative learning. This may be different in more severely affected patients. Furthermore, it has to be noted that the lack of significant increase of decision time in the three key press condition versus one key press condition in both the control and cerebellar groups indicates that subjects at least in part did not start to plan the tapping movement until after they have released the home button. Therefore, it cannot be excluded that the three key press condition was not complex enough to divert sufficient attentional resources from the cognitive component of the task. Cerebellar patients’ deficits in the cognitive task were less pronounced than in our previous study [23]. One possible reason are differences in the patient population. Patients in the present study appeared to be on average less affected. For example the difference between controls and patients in the simple reaction time task did not reach statistical significance. Second, associative demands were smaller in the present study as compared to the previous one. Previously, subjects responded by hitting two answer buttons for correct and false presentations. Subjects had to associate the correct presentation of a numeral and a color with one of the two answer buttons. Therefore, the motor part of the task contained aspects of associative learning. In the present study, the correct side of the response was indicated by a lateral circle following the color pair. The side of the response was known to the subjects independent of their ability of associative learning. This constitutes a likely reason for a third difference between the two studies. In the present study, both control and cerebellar subjects were less able to name the correct association of color pairs after the test was completed compared to the previous one. In sum, we were unable to explain cognitive associative learning deficits in cerebellar patients by the observed impaired motor performance itself or by increased attentional demands during faulty motor execution. 4.3. Possible role of the cerebellum in associative learning A role of the cerebellum in associative motor learning is widely accepted. Animal and human studies provide evidence that the cerebellum is involved in classical conditional learning of various aversive motor reactions [9,74,76,82,84].

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In the present study, a cognitive paradigm was used, that is closely related to classical conditional learning of motor reactions: The correct side of the response (“unconditioned response, UR”) was indicated by a lateral circle (“unconditioned stimulus, US”) following the color pair. Predictive knowledge of the side of the lateral circle (and therefore the target button) based on the learned association of a color pair (“conditioned stimulus, CS”) enabled the subjects to respond earlier to the growing circle (“conditioned response, CR”). The present results, therefore, extend the known findings on impaired conditional associative motor learning to cognitive associative learning in cerebellar patients. Despite a growing number of studies on cognitive abilities in cerebellar patients (for review [21,65,73]), only a few studies looked at conditional associative learning [17,79]. Canavan et al. [17] and Tucker et al. [79] examined visual motor associative tasks, which required the linkage of a visual stimulus and motor response. Canavan et al. [17] concluded that this form of conditional associative learning was generally impaired in cerebellar patients, whereas Tucker et al. [79] pointed out that associative learning was impaired in some but not all cerebellar patients. In more recent monkey cerebellar lesions studies, authors of the latter group found that visuomotor associative learning was not impaired when controlling for motor performance deficits [53,54]. The findings in our study may be different for two possible reasons: first, the role of the cerebellum in conditional learning may be different in the association of two stimuli and the association of a stimulus and a motor response. Our study required the association of two visual stimuli and the linkage of the correct color pair and motor response. In Nixon and Passingham’s tasks the association of a visual stimulus and motor response was learnt by trial and error. Besides, in the present study cerebellar patients were not affected in the control arrow condition which required the association of a stimulus and a motor response when controlling for motor performance deficits. Second, in Nixon and Passingham’s studies the deep cerebellar nuclei were lesioned. In our study, patients with diffuse cerebellar atrophy and, therefore, predominantly cortical cerebellar lesions were tested. The role of the cerebellar cortex and nuclei may be different in conditional associative learning [62,84]. The present finding of impaired cognitive learning in cerebellar patients assumes a general role of the cerebellum in conditional learning regardless of the specific type of the task. Given its homogenous internal structure, the cerebellum may provide a basic operation needed both in motor and cognitive conditional associative learning [9,72]. Depending on their afferent and efferent projections, different parts of the cerebellum may be involved in associative learning of various effector systems. One idea is that the cerebellar cortex and/or nuclei are the place where the associative neural connection is built [4,44]. Despite several theoretical considerations and certain

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experimental evidence, this hypothesis is a matter of ongoing discussion [8,11,30,55,56,69,75,84]. Another possibility is that the cerebellum is needed for correct sequencing or timing of incoming stimuli and outgoing responses [13,27,33,47,57]. In their comprehensive animal lesion, human PET and fMRI studies, Passingham’s group found that the cerebellum was involved in learning of visually guided motor sequences but not (see above) the association of a visual stimulus and motor response (review in [59]). Involvement of the human cerebellum in procedural learning of a motor sequence has been shown by others [28,34,50,67]. One may conjecture that disorders in cognitive learning are due to disordered knowledge of the sequence of incoming stimuli. Further experiments are needed to differentiate between a role of the cerebellum in sequencing incoming stimuli during cognitive conditional learning and its role in helping to build an association between them. The underlying function of the cerebellum, however, may be the ability to learn to make correct predictions in both sequence and associative learning. Previous animal and human lesion studies suggest a functional compartimentalization of the cerebellum for conditional learning of specific and non-specific aversive reactions [39]. The cerebellar vermis appears to be important for associative learning of non-specific aversive reactions (e.g. fear-conditioned potentiation of the startle-response; [45]) and the intermediate zone for conditioning of specific aversive reactions (e.g. eyeblink: [20,77,83,84]; limb flexion reflex: [37,38,76]; jaw-opening reflex: [46]). Given the known anatomical connections of the dentate nucleus to various association areas of the cerebral cortex [48], it appears likely that parts of the lateral cerebellar hemispheres are involved in cognitive associative learning. However, nothing on this matter can be concluded from the present study, since its patients had diffuse cortical cerebellar lesions, and not circumscribed lesions of the cerebellar hemispheres.

5. Conclusions The present study extents on our previous finding that deficits in cognitive associative learning cannot be explained by motor performance deficits. We were able to show that neither disordered motor execution nor increased attentional motor demands correlated with impaired conditional learning. The cerebellum may be of general importance both in motor and cognitive conditional associative learning. The cerebellum’s function remains to be determined concerning its role in sequencing or association of stimulus-stimulus and/or stimulus-response linkages in conditional learning.

Acknowledgements The authors wish to thank all subjects for participation, H.G. Elles for construction of the specialized keyboard and

Maren Erichsen and Vanessa Drepper for editorial help. The study was supported by grants of the Deutsche Forschungsgemeinschaft (DFG Ti 239/2-2 and 2-3).

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