NOTE INTERMANUAL TRANSFER IN A SIMPLE MOTOR TASK

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1 NOTE INTERMANUAL TRANSFER IN A SIMPLE MOTOR TASK Katrin Schulze 1, Eileen Lüders 1 and Lutz Jäncke 2 ( 1 Otto-von-Guericke-University Magdeburg, Department of Experimental and General Psychology, Magdeburg, Germany; 2 University Zürich, Department of Neuropsychology, Zürich, Switzerland) ABSTRACT The present study examines the effects of a four-week training session in a pegboard task on uni- and bimanual performance. Of particular interest was whether practice transfer from the trained to the untrained hand takes place. Twenty-five consistently right-handed subjects were trained to perform the task with the dominant hand, or the subdominant hand, or with both hands. After this training, the learning effects for the trained and untrained hands were analysed. To summarise, we obtained the following findings: (1) After training, movement times were considerably reduced for all hands and for all training conditions (practice effects); (2) practice effects were found for the hand trained and also for the untrained hand; (3) there was not a great difference in the size of the practice effects for the right hand after left hand training or for the left hand after right hand training; (4) task difficulty had no clear influence on the practice effect; (5) and finally, we discovered that bimanual movements not only profit from bimanual training but also from unimanual training and conversely unimanual movements benefit from bimanual training. These findings are discussed in the context of different motor control models and in the light of recent brain imaging findings. INTRODUCTION A wealth of studies have demonstrated that there is information transfer between both cortical hand motor areas. A classical paradigm used to study intermanual information transfer is to practice a specific motor task with one hand, and to examine whether the opposite untrained hand benefits from this training. In general it is found that not only the trained hand but also the untrained hand shows performance improvements in the practiced task. Although these findings have largely been replicated, there is some inconsistency as to whether this information transfer is symmetrical or asymmetrical. For example, several studies have demonstrated that the subdominant hand benefits more from dominant hand training than does the dominant hand from subdominant hand training (Halsband, 1992; Laszlo et al., 1970; Milisen and Riper, 1939; Parlow and Kinsbourne, 1989), while other studies found the opposite effect (Hicks, 1974; Taylor and Heilman, 1980). Some studies examined intermanual training effects in subjects of different ages and found that four-year-olds showed poor transfer from the dominant to the nondominant hand, whereas older subjects showed transfer effects in both directions (Uehara, 1998). Intermanual transfer effects are explained in the context of three different models (Thut et al., 1996): (i) the callosal access model, (ii) the proficiency model, and (iii) the crossactivation model. The callosal access model assumes that motor programs are stored in the dominant (mainly left) hemisphere, irrespective of the hand used for hand skill training. As a consequence, the right hand will have direct access to these motor programs while the left hand will only have indirect access via the corpus callosum (Taylor and Heilman, 1980). According to this model the dominant (usually right) hand will benefit more from subdominant hand training than vice versa. Cortex, (2002) 38,

2 806 Katrin Schulze and Others The two other models are based on those studies which revealed a greater benefit for the subdominant (usually left) hand after dominant hand training. The proficiency model (Laszlo et al., 1970) postulates the formation of unilateral engrams which are stored in hand motor areas contralateral to the trained hand. Because of the general advantage of the left-hemispheric motor area controlling the right hand, improving right hand skill through training will improve the superior left-sided motor control centre, leading to optimal information transfer to the hand motor centres located in the opposite hemisphere. In contrast, training with the subdominant hand will only improve the inferior motor programs. Therefore, the dominant hand motor area cannot benefit from this inferior motor control information. The cross activation model (Parlow and Kinsbourne, 1989) suggests the generation of two motor programs, one located in each hemisphere. However, both programs are thought to operate in a coupled manner. When subjects learn with their dominant hand, the two motor programs are stored independently in the dominant and non dominant motor cortex. Because the dominant motor cortex has access to superior motor programs, learning with the dominant hand will rely on these superior motor programs. Thus, the nondominant motor cortex will receive a copy of this updated motor program which will work independently from the dominant motor cortex when the nondominant hand is required to perform the practiced task. Therefore, learning with the dominant hand will facilitate subsequent performance of the nondominant hand. Conversely, when learning with the subdominant hand, formation of the motor program should rely on the inferior motor program located in the subdominant motor cortex, resulting in inferior performance of the dominant hand. Currently, there is no consensus as to which of these models might explain intermanual transfer, because of conflicting findings in the literature. These conflicting findings may be due to several factors. Among them are heterogenous samples, different cognitive requirements necessary to perform the motor tasks, or different durations of the training sessions. The present study was designed to re-examine the question of whether there is symmetrical or asymmetrical intermanual transfer of information during the course of unimanual motor learning. Because most of the aforementioned studies applied relatively short training sessions which might only have moderate or even transient practice effects, we introduced a long and relatively intensive training session lasting about four weeks. A second aspect of our study concerns the difficulty of the motor task, a factor which has not been controlled for in recent experiments of this type. In our study we applied four different levels which are quantified according to the formula given by Fitt and Seeger (1954). In so doing, we were in the position to study intermanual practice effects in the context of different difficulty levels. We then asked the following questions: (1) Are there practice effects contralateral to the trained hand? (2) Which of the above-mentioned models is supported? (3) Do the practice effects for unimanual movements after bimanual training differ from practice effects after unimanual training? (4) Does task difficulty influence the practice effects? MATERIALS AND METHODS Subjects The study was carried out with 25 female neurologically intact students (mean age = 22 years, range years). We have examined only female subjects in order to exclude possible between-gender differences. Handedness Measurements All subjects were strongly right-handed (no subject with a history of familial lefthandedness), as determined by two preference questionnaires and one performance test. One preference test was the Annett-handedness-questionnaire (AHQ) with 12 items used to

3 Intermanual transfer 807 detect consistent right-handed (CRH) subjects. Consistent right-handedness was determined when all six primary tasks were carried out with the right hand, in accordance with Annett (1972, 1996). All subjects were classified as CRH. For the second preference test, a hand preference test based on the Bryden-Steenhuis handedness test (Steenhuis et al., 1990) with eight preference questions was used. All subjects indicated right-hand preference on these items. Finally, the performance test Handdominanztest HDT, originally designed by Steingrüber (1971) for the assessment of children, was used. For the purpose of studying adults we applied appropriate time limits (Jäncke, 1996). This test consists of three dexterity tasks (tracing lines, dotting circles and tapping on squares), each of which has to be performed with maximum speed and precision for 15 secs. Laterality coefficients (R L)/(R+L) were determined for each test and rounded to two decimal places. Negative total scores indicate left-handedness and positive values right-handedness. Subjects with scores < 0.05 were excluded, because a previous study has shown that scores lower than 0.05 are typical of consistent left-handers or mixed-handers (Jancke, 1996). The Motor Task We used a pegboard apparatus consisting of 2 bars (bar length 140mm) each containing eight holes oriented parallel to each other (Figure 1). The centres of the holes were separated by 15 mm. The bars were fixed at a distance of 205 mm from each other. We used four different sets of bars containing holes of different diameter (1.5, 3, 6, or 12 mm). The subjects were required to place pegs of appropriate diameters into the holes as quickly as possible. The dependent variable was the time it took to place the peg successively into all eight holes (timing was not interrupted in the event of a dropped peg). The subjects had to start the movements from a predetermined starting position in front of the apparatus (Figure 1). The movement distance from this starting position to each hole varied from 150 mm to 245 mm. Because we used four different sets of bars with different hole diameters, we had motor tasks with different levels of movement difficulty. We estimated movement difficulty according to the formula given by Fitts and Peterson (1964). That is, movement difficulty Fig. 1 Schematic drawing of the peg-board task used in this study.

4 808 Katrin Schulze and Others was estimated by the width of the holes (W) and the movement distances (D) [log(2) (2*(D/W)]. The obtained average estimates for movement difficulty were 1.17 (12 mm diameter), 1.86 (6 mm diameter), 2.56 (3 mm diameter), and 3.25 (1.5 mm diameter) for the four different holes ranked from small to large diameter. This resulted in considerable differences in movement difficulty between these subtasks. Motor Training Before the procedure started, subjects were randomly assigned to three groups: the first group was required to practice only with the dominant right hand (right hand practice: RHP, n = 9); the second group was instructed to practice only with the subdominant left hand (left hand practice: LHP, n = 9); and the third group practiced with both hands simultaneously (both hand practice: BHP, n = 7). Practicing consisted of eight sessions (each session lasting 15 minutes, 2 sessions a week, resulting in a four-week practicing period with a total of 120 minutes). During each training session the order of the different difficulty levels was arranged randomly. Before the first and after the last practice session, we measured dominant and subdominant hand performance (in the time it took to complete the task in seconds) as well as bimanual performance in this pegboard task. Statistical Analysis The basic design was four factorial with one between-subjects factor (group: RHP, LHP, and BHP) and three within-subjects factors (task difficulty: four levels; hand with three levels: right, left, and both; and training: pre vs. post). The dependent variable was the time it took to complete the motor tasks. Since the data did not entirely fulfil the prerequisites for conventional linear ANOVA analysis (normal distribution, homogeneity of variances, reasonable number of subjects), distribution-free statistical models were applied (Krauth, 1988). The results of these analyses are not interpreted in terms of statistical significance, but are interpreted using p-values as a measure of effect. The p-value is defined as the lowest significance level at which one would still have obtained a significant result for a given data set, a given significance test, and a given test problem. This has the advantage that other researchers can decide for themselves whether the results are significant at the significance level they find acceptable. Since we have to take into consideration the fact that p-values depend on sample size we also calculated effect sizes according to Cohen (1969). Here, the d-value was used, which is the difference between two means divided by the accompanying standard deviation. A d > 0.5 is considered as being moderate, while a d > 0.8 is considered as being large (Cohen, 1969). All statistical analyses were performed using SPSS for Windows version We will only comment on effects associated with a p 0.05 or a d > 0.5 (moderate effect size). In order to handle the different conditions and the possible interactions between them, as well as to avoid an unnecessarily large number of statistical tests, we performed a series of statistical tests which were strongly guided by a priori hypotheses. (1) To answer our first question we first tested for the existence of significant practice effects. The averaged performance (averaged across all difficulty levels) for the pre- and post-training sessions were compared separately for each practice group and for each hand by applying the Wilcoxon matched-pairs signed-rank test. Based on published data (Peters, 1981), we predicted that our training would cause a reduction in the time spent for each task, and so all tests were one-tailed tests. (2) In a second step, we calculated the time difference (in secs) between the movement time for the pre- and post-training conditions (this difference measure will be designated the practice effect). This measure was subjected to betweengroups comparisons by applying Kruskal-Wallis tests. In case of a p-value 0.05 for the Kruskal-Wallis analysis, subsequent Mann-Whitney U-tests were performed. (3) In a third step, the influence of task difficulty on the practice effects was examined. For this the practice effect was subjected to Friedman s tests separately for each training group and hand used. In case of a p-value 0.05 for these analyses, subsequent Wilcoxon-tests were applied. (4) General influences of task difficulty and hands used were described only at a general level and not separately for each training group or task difficulty level, because the

5 Intermanual transfer 809 influence of task difficulty and hand used on movement time has already been described in numerous papers (Annett, 1992a; Doyen and Carlier, 2002; Rigal, 1992). RESULTS As expected, task difficulty had a strong influence on movement time in the predicted direction with the shortest movement times for the easiest task and the longest times for the most difficult task. The movement times were ranked from easiest to the most difficult task (14.9 secs, 18.8 secs, 21.6 secs, 28.6 secs; Friedman s test comparing the four difficulty levels, p < 0.001; effect sizes obtained for the comparisons between the mean movement times under each task difficulty ranged between 1.4 to 6.1 altogether indicating strong effects). In addition, movement times were fastest for the dominant right hand (mean movement times for the right and left hand: 18.2 secs, 20.1 secs; mean effect size for the difference between right and left hand = 2.4, p < 0.001) and slowest for bimanual movements (mean movement time for bimanual movement: 29.4 secs, mean effect size for the difference between bimanual and unimanual movements = 5.5, p < 0.001). Furthermore, movement times were similar across groups before the training (Kruskal-Wallis analysis separately for right hand, left hand, and bimanual movements revealed no significant difference, all p-values 0.72). 1. Test for General Practice Effects The mean pre- and post-training scores for each practice group and for each hand (calculated across all task difficulty conditions, see Table I) were subjected to a series of Wilcoxon matched-pairs signed-rank tests. These tests revealed very low p-values with eight practice effects associated with a p-value One test (for right hand performance of the left-hand practice group) revealed a p-value of However, this practice effect is associated with an effect size of d = 0.88 indicating a strong effect. As can be seen in Table 1, the remaining effect sizes are larger than 1.45, indicating very strong effects. Thus, we can conclude that training results in strong practice effects even for the untrained hand (left hand for the right hand training group and the right hand for the left hand training group). 2. Test for Between- and Within-Group Differences for the Practice Effects The between-group analysis for the practice effects revealed a clear between-group difference only for bimanual movements (p = 0.006). For right and left hand movements there were only weak or moderate between-group differences (see Table 1). Bimanual performance improved more after bimanual training than after unimanual training (effect size for bimanual vs. the averaged left and right hand training d = 2.01, p = 0.002). The moderate differences for right and left hand performance were due to the slightly larger practice effects for the trained hand compared to the untrained hand (right hand: RHP vs. LHP: d = 0.47, p = 0.04; left hand: RHP vs. LHP: d = 0.7, p = 0.03). The difference between the practice effects of the untrained hands was small and in the range of a small effect (d = 0.27, p = 0.13, see also Figure 2). 3. Test for the Influence of Task Difficulty The practice effects obtained for the different difficulty levels, training groups, and hands used are depicted in Table 2. As one can see in this table, the practice effects are mostly strong and exceed the effect size criterion of d = 0.8, therefore indicating strong effects. The only exception was found for the practice effects of the left hand for the right hand training group. In the easiest task ( 12 mm), movement times basically did not change after training (d = 0.04), while for the second most difficult task ( 3 mm) there was a moderate effect size (d = 0.69) for the practice effect. By subjecting these practice effects (differences between pre- and post-training scores) to a series of Friedman s tests separately for each training group

6 810 Katrin Schulze and Others TABLE I Mean Movement Times and Standard Deviations (in brackets) before (pre) and after (post) Training, as well as pre-post Differences, Effect Sizes (d), and p-values Broken down for the Three Training Groups (RHP: Right Hand Practice, LHP: Left Hand Practice, BHP: Bimanual Practice) pre post pre-post d P Right hand RHP (1.50) (1.45) 2.88 (0.6) LHP (1.88) (1.03) 2.38 (1.4) BHP (2.36) (1.65) 2.90 (1.2) K-W result p = 0.72 p = 0.15 p = 0.13 Left hand RHP (2.78) (1.46) 1.86 (2.7) LHP (1.86) (1.03) 3.42 (1.7) BHP (2.46) (1.80) 3.90 (2.7) K-W result p = 0.72 p = p = 0.08 Bimanual RHP (1.42) (1.52) 2.74 (1.6) LHP (3.11) (1.41) 3.29 (2.3) BHP (3.18) ( (1.5) K-W result p = 0.78 p = p = Fig. 2 Mean practice effects and standard errors of the mean (as vertical bars) for the untrained hands obtained in the present study (LH-RHP: left hand after right hand practice; RH- LHP: right hand after left hand practice). In addition, predicted practice effects according to the callosal access model and the other two models (see Introduction) are also presented. and hand used (nine tests, see also Table II), it was revealed that the practice effects were generally not influenced by task difficulty. The only exception was found for the bimanual training group performing bimanual movements (p = 0.04). In terms of effect sizes there were strong differences between the easiest task and the other difficulty levels (d-values ranging between 1.4 and 1.53, P-values from p = 0.05 to p = 0.01). DISCUSSION The main purpose of this study was to examine whether intensive and long lasting training (which was longer than in any previously published study of this kind) of the

7 Intermanual transfer 811 TABLE II Mean Movement Times and Standard Deviations (in brackets) before (pre) and after (post) Training, as well as pre-post Differences, and Effect Sizes (d) Broken down for the Three Training Groups [(a) RHP: Right Hand Practice, (b) LHP: Left hand Practice, (c) BHP: Bimanual Practice], the Hand Used (Right Hand, Left Hand, and Bimanual: RH, LH, BH), and Movement Difficulty Indexed as the Diameter of the Holes (a) Difficulty pre post pre-post d RHP RH 12 mm 13.4 (1.9) 11.1 (1.6) 2.3 (1.6) mm 17.5 (2.2) 14.8 (1.8) 2.7 (1.6) mm 18.4 (2.1) 15.3 (1.3) 3.1 (1.9) mm 23.6 (2.1) 20.2 (2.6) 3.4 (3.1) 1.43 p-values* p = 0.76 LH 12 mm 14.6 (1.8) 14.6 (0.9) 0.06 (1.1) mm 18.3 (1.8) 16.3 (1.7) 2.0 (2.1) mm 20.8 (2.6) 19.2 (1.9) 1.6 (2.5) mm 13.4 (1.9) 11.1 (1.5) 2.3 (1.6) 1.33 p-values p = 0.16 BH 12 mm 20.9 (1.6) 18.9 (1.8) 1.9 (1.9) mm 26.1 (1.4) 23.0 (1.7) 3.1 (2.1) mm 30.7 (2.3) 27.8 (2.3) 2.8 (2.8) mm 40.4 (2.2) 37.4 (4.0) 3.1 (3.9) 0.99 p-values p = 0.60 (b) Difficulty pre post pre-post d LHP RH 12 mm 13.4 (2.0) 12.1 (0.9) 1.4 (2.1) mm 17.3 (2.2) 15.6 (1.5) 1.8 (2.9) mm 19.2 (2.5) 16.8 (0.9) 2.4 (2.6) mm 24.9 (2.9) 21.0 (2.4) 3.9 (2.6) 1.47 p-values p = 0.18 LH 12 mm 14.5 (1.3) 12.1 (1.4) 2.3 (2.1) mm 18.0 (1.9) 14.6 (1.7) 3.5 (1.7) mm 19.9 (2.1) (.7) 3.9 (1.9) mm 13.4 (2.0) 12.1 (0.9) 1.4 (2.1) 0.93 p-values p = 0.35 BH 12 mm 19.8 (1.9) 17.1 (1.4) 2.7 (1.7) mm 26.6 (2.9) 22.3 (1.9) 4.3 (3.0) mm 30.8 (3.3) 28.1 (1.9) 2.7 (2.1) mm 39.5 (4.7) 36.1 (2.3) 3.4 (3.9) 0.98 p-values p = 0.51 (c) Difficulty pre post pre-post d BHP RH 12 mm 13.6 (2.3) 11.3 (1.3) 2.3 (1.6) mm 16.2 (2.1) 13.9 (1.5) 2.3 (1.2) mm 18.2 (2.4) 15.1 (1.5) 3.1 (2.3) mm 23.1 (3.2) 19.2 (3.0) 3.9 (2.5) 1.25 p-values p = 0.54 LH 12 mm 14.7 (1.8) 12.6 (1.6) 2.1 (1.1) mm 18.2 (2.7) 14.8 (1.7) 3.6 (2.2) mm 20.4 (2.2) 15.9 (3.8) 4.5 (5.1) mm 13.6 (2.3) 11.3 (1.3) 2.3 (1.6) 1.28 p-values p = 0.48 BH 12 mm 19.3 (1.8) 14.9 (1.6) 4.4 (1.1) mm 25.3 (4.3) 18.9 (2.2) 6.5 (2.9) mm 30.9 (4.2) 23.6 (1.6) 7.4 (2.9) mm 42.2 (5.4) 34.2 (2.8) 8.0 (3.6) 1.95 p-values p = 0.04 *p-values are from Friedman-ANOVAs testing for a general difference between the different movement difficulty levels.

8 812 Katrin Schulze and Others Fig. 3 Mean practice effects and standard errors of the mean (as vertical bars) for the untrained hands obtained in the present study broken down for the different difficulty levels. (no difference between the size of the practice effects for LH-RHP p = 0.15 and RH-LHP p = 0.17). pegboard task results in clear practice effects (reduced movement time to perform the task). Apart from wanting to study practice effects, we were also interested in studying the possible benefits gained by the untrained hands as a result of training the opposite hands in the context of different task difficulty levels. A further question was whether bimanual movements might benefit from unimanual training and vice versa. To summarise, we obtained the following findings: (1) After training, movement times were considerably reduced for all hands and for all training conditions, i.e. there were strong practice effects. (2) Practice effects were found for the hand trained and also for the untrained hand. Therefore, some kind of interhemispheric information transfer might have occurred, which may have positively influenced the untrained hand. (3) Interestingly, there was no clear sign of an asymmetric information transfer as predicted by the models discussed in the introduction. However, there was a weak trend for the right hand to benefit a little bit more from left hand training than the left hand from right hand training (effect size d = 0.27, p = 0.13), thus tentatively supporting the callosal access model. (4) Task difficulty had no clear influence on the practice effect. (5) Finally, we found that bimanual movements not only profit from bimanual training but also from unimanual training, and conversely unimanual movements benefit from bimanual training. In the following section we will firstly discuss the motor paradigm before we go on with the discussion of the behavioural findings and how they relate to published findings in the neuropsychological literature. The Motor Task In this study we used a variant of the pegboard task which is widely used in neuropsychology for the examination of complex uni- and bimanual motor functions (Annett, 1985; Doyen and Carlier, 2002). Although this task had originally been designed for the examination of motor functions, several authors emphasize that the obtained measures correlate with various normal and deviant cognitive functions and with an anatomical marker related to language processing (Annett, 1992b, 1994, 1996; Bouquet et al., 1999; Bowler et al., 2001; Rosselli et al., 2001). Although this task is associated with a variety of functions, it is definitely a specific kind of sensorimotor task in which particular visual stimuli require a specific response. Previous neuroimaging studies have shown that motor tasks triggered or guided by visual cues evoked strong cortical activations bilaterally

9 Intermanual transfer 813 in the ventral and dorsal premotor cortex (vpmc and dpmc), in the left sensorimotor cortex (M1 and S1), in the left supplementary motor cortex (SMA), bilaterally in the inferior parietal lobe (IPL), in the right inferior cerebellum, and in the left ventro-lateral thalamus (Iacoboni et al., 1998; Jäncke et al., 2000a; Lutz et al., 2000; Penhune et al., 1998). We will not interpret or explain any of these activations because that has already been done in the above-mentioned papers. We would simply like to draw attention to the fact that the pegboard task is most likely accompanied by distributed activations in a bilateral frontoparietal network, the cerebellum, the basal ganglia, and the thalamus. This distributed activation requires time-consuming intrahemispheric and interhemispheric transfer of information and also time-consuming processing within each node of this network. The Practice Effect There were strong practice effects for each hand and for each training condition. But most interestingly for the present paper is the fact that there were practice effects for the untrained hands. Therefore, one might assume that some kind of interhemispheric transfer had occurred. There was however no clear sign of asymmetric information transfer. Both untrained hands (the dominant and the subdominant hand) benefited from training of the opposite hands. The reason for the lack of asymmetric information transfer might be the nature of the task used and the neural structures involved. As mentioned, the pegboard task is a typical visuo-motor task most likely accompanied by neural activations bilaterally in the vpmc and dpmc. The most critical part of this task is not the movement itself, which is controlled by the primary motor cortex, but rather the adjustment of movement parameters (e.g., the diameter of the hole) by means of visual information. According to the abovementioned brain imaging data, we know that visual information associated with motor functions is transferred from the visual cortex bilaterally via the dorsal stream to the dpmc. Thus, even when the pegboard task is performed unimanually with the right hand (causing strong activations within the left dpmc and the left M1), the right dpmc and possibly the adjacent right M1 is also activated. It may be the case that there is no interhemispheric transfer, but rather that bilateral adjustment of visuo-motor associations occurs during unimanual movements. Learning to perform the pegboard task with the right hand will therefore automatically lead to an improvement of visuo-motor associations in both hemispheres, in which case performing the motor task with the untrained hand will rely on improved visuo-motor associations. This line of argument does not exclude the possibility that the improvement of visuo-motor association within the PMC is the only mechanism stimulated by our training. There may be further processes such as the training of motor programs located in M1 or PMC, or the interhemispheric exchange of information between both PMCs which are also stimulated. However, the present study design (and most of the previous studies of this kind) does not allow us to disentangle the contribution of the different processes to motor learning. Although task difficulty had the predicted influence on movement time with faster movements for easy tasks and slower movements for difficult tasks, we could not identify a consistent and clear influence of task difficulty on the practice effects. As can be seen in Table 2, there is a large variability in the practice effects across the different task difficulty levels, which are indexed as standard deviations. The reasons for this variability are difficult to explain and might include different skill levels or different strategies of the subjects. A further interesting finding from our study was that bimanual training resulted in strong practice effects for the unimanual tasks. Conversely, unimanual training resulted in strong practice effects for the bimanual tasks. The practice effect of bimanual training on unimanual training was similar to the effect obtained when the subjects had been practising unimanually. However, the practice effect of unimanual training on bimanual movements was smaller than the practice effect of bimanual training on bimanual movements. These findings might point to the fact that several mechanisms operative during unimanual training are also active during bimanual training. For example, during bimanual training there is simultaneous training of the visuo-motor associations and motor programs located on the dominant left (for right hand movements) and non-dominant right hemisphere (for left hand movements). Bimanual training however resulted in very strong practice effects for the

10 814 Katrin Schulze and Others bimanual task, which were about twice as large as the practice effects for the unimanual tasks were. This finding may indicate that bimanual movements are controlled in a different way than unimanual movements. Recent neuroimaging studies have shown that bimanual movements require more processing demands bilaterally in the primary motor areas but also in the lateral and mesial premotor areas, suggesting that additional motor control processes are involved (Jäncke et al., 1998, 2000a, 2000b; Stephan et al., 1999). Thus, there are more degrees of freedom to control in bimanual movements. Intensive bimanual practice might help to adjust and synchronize these different motor control processes. In contrast, relatively easy unimanual movements, as were used in this study, are controlled by relatively small neural assemblies within M1 and the lateral premotor areas, therefore resulting in fewer degrees of freedom than in bimanual movements. In addition, unimanual movements are more often performed in everyday-life than synchronous bimanual movements like those used in our study, suggesting that unimanual performance has already reached a relatively high skill level which can only be furthered a small amount. Nevertheless, we are facing conflicting results in this research area. While our study does not support any of the models already mentioned, other studies report data which support the callosal access, the proficiency, or the cross-activation models. These different findings are difficult to explain because the studies differ with respect to the motor tasks used, sample size, or heterogeneity of the studied sample, and with respect to various aspects such as age, sex, and motor proficiency. A further important difference between these studies and our study is the duration and intensity of the applied motor training. In our study we used a relatively long training period of four weeks, which is longer than any other training period used in previous experiments of the same type. It is known from previous studies that variability of motor performance decreases with increasing duration and intensity of movement training. Therefore, we might have measured stable motor performance dependent upon cortical reorganization of the hand motor areas while previous studies measured increased motor proficiency primarily due to attentional effects. Acknowledgements. We thank Dr. Alan Beaton and an anonymous reviewer for most helpful suggestions. REFERENCES ANNETT M. The distribution of manual asymmetry. British Journal of Psychology, 63: , ANNETT M. Left, Right, Hand and Brain: The Right Shift Theory. London: Erlbaum, ANNETT M. Five tests of hand skill. Cortex, 28: , 1992a. ANNETT M. Parallels between asymmetries of planum temporale and of hand skill. Neuropsychologia, 30: , 1992b. ANNETT M. Handedness as a continuous variable with dextral shift: Sex, generation, and family handedness in subgroups of left- and right- handers. Behavioral and Genetics, 24: 51-63, ANNETT M. The right shift theory of a genetic balanced polymorphism for cerebral dominance and cognitive processing. Cahiers de Psychologie Cognitive, 14: , BOUQUET CA, GARDETTE B, GORTAN C and ABRAINI JH. Psychomotor skills learning under chronic hypoxia. Neuroreport, 10: , BOWLER RM, LEZAK M, BOOTY A, HARTNEY C, MERGLER D, LEVIN J and ZISMAN F. Neuropsychological dysfunction, mood disturbance, and emotional status of munitions workers. Applied Neuropsychology, 8: 74-90, COHEN J. Statistical Power Analysis for the Behavioral Sciences. New York: Academic Press, DOYEN AL and CARLIER M. Measuring handedness: A validation study of Bishop s reaching card test. Laterality, 7: , FITTS PM and PETERSON JR. Information capacity of discrete motor responses. Journal of Experimental Psychology, FITTS PM and SEEGER CM. SR compatibility: Spatial characteristics of stimulus and response codes. Journal of Experimental Psychology, 46: , HALSBAND U. Left hemisphere preponderance in trajectorial learning. Neuroreport, 3: , HICKS RE. Asymmetry of bilateral transfer. American Journal of Psychology, 87: , IACOBONI M, WOODS RP and MAZZIOTTA JC. Bimodal (auditory and visual) left frontoparietal circuitry for sensorimotor integration and sensorimotor learning. Brain, 121: , JÄNCKE L. The hand performance test with a modified time limit instruction enables the examination of hand performance asymmetries in adults. Perceptual and Motor Skills, 82: , 1996.

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