Chapter 15 A Modular Approach to Individual Differences in Skill and Coordination

COGNITIVE ISSUES IN MOTOR EXPERTISE J.L. Stakes and F. A l l 4 (Editors) 0 1993 Elsevier Science Publishers B.V. All rights mewed.

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CHAPTER 15 A MODULAR APPROACH TO INDIVIDUAL DIFFERENCES IN SKILL AND COORDINATION STEVEN K. JONES Department of Psychology. University of Oregon Eugene, Oregon 97403-1227 Much of the field of motor learning has developed as a reaction to the rather appealing idea that individual differences in the performance of complex skills are due to differences in simpler, underlying abilities. In essence, this idea suggests that there are a small number of general abilities which support a wide variety of complex activities. Individual differences in these few abilities, then, contribute to the differences we see in a vast array of complex tasks. Those who have spent time on playgrounds are surely familiar with the concept of the "all-around athlete," a person who is believed to have a high degree of general athletic ability. As a result of his or her general ability, the all-around athlete is able to excel in many different types of sporting activities. There are several advantages to the idea that complex motor performance is determined by a small number of underlying abilities. First, it is theontically elegant. One of the major goals of developing scientific theory is to reduce the number of consmcts required to explain a variety of phenomena (Underwood, 1975). If one is able to explain differences in seemingly diverse motor behaviors by appealing to a few underlying abilities, one can develop a rather powerfill theory. The notion of general underlying abilities is of practical sigmfkance as well. If such general abilities exist, there should be a high correlation between an individual's performance on a complex task and his or her performance on a simpler one that relies on the samc underlying ability. This being the case, one could use performance on the simpler task to predict performance on the more complex one. Clearly, this would be of use to those involved in fields such as personnel selection, where prediction of future task perfonnance is of the utmost importance. In addition, to the extent that future perfonnance of complex skills is dependent upon general underlying abilities, one could argue for the development of teaching techniques that attempted to foster these abilities, especially in children (Fleishman, 1967). Despite the intuitive appeal of the "abilities" approach, much of the work in the area of motor control suggests that individual differences in task performance cannot be explained by

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appealing to a small number of general abilities. This literature extends back at least 70 years, and has a very rich history. While it is not appropriate to include a thorough review here, several of the key studies are discussed briefly below. Mort thorough reviews are available elsewhere (e.g., Adams, 1987; Marteniuk, 1974; Schmidt, 1988). Correlational Studies If individual differences in motor skills could be explained by differences in "general ability," then one would expect high positive correlations between an individual's performance on different tasks. Those individuals who have the ability necessary to perform well on one motor task should also perform well on a separate motor task. However, numerous studies show that high correlations are difficult to find. For example, Pemn (1921) tested subjects on 17 different motor tasks, ranging from stacking blocks to a choice reaction time task. While most of the correlations between these tasks were positive, they were very low. often near zero. Based on these results, Pemn argued that there cannot be a single "general motor ability." More recently, similar studies have been conducted by Henry and his colleagues (Henry, 1961; Henry & Whitley, 1960). In these studies, near-zero correlations were found between movement time and reaction time. If individual differences between even these two seemingly similar tasks are undated, it is difficult to argue that a general motor ability underlies all of motor behavior. To account for these types of results, Henry (1958; cited in Schmidt, 1988) proposed that motor skill is specific to a particular task. According to this "specificity hypothesis." there need not be high positive correlations between individuals' performances on different tasks. Factor Analytic Studies Given the findings described above, it is apparent that motor skill cannot be predicted by a single underlying ability. Therefore, one may wish to look for a somewhat larger set of more specific underlying abilities. This task was undertaken by Fleishman and his colleagues, who relied on factor analytic techniques in an attempt to describe a taxonomy of motor abilities (see Fleishman. 1967, for a review). Over the course of several studies, Fleishman tested thousands of subjects on several batteries of motor tests. By examining the patterns of correlations between these tests, he attempted to outline a relatively small number of abilities that could describe subjects' performance. While this approach was reasonably successful, Fleishman still lists no fewer than 19 factors, ranging from "aiming" to "trunk strength", that are important in describing differences in motor behavior. In addition to developing a taxonomy of basic abilities, Fleishman was also interested in examining the importance of these different factors at different stages of learning a complex skill (see Fleishman, 1967, for a review). Interestingly, he found that the pattern of abilities which contribute to overall task performance systematically changes with practice. While his proposed set of general abilities accounts for a fairly large percentage of variance in the early phases of practice, their total contribution tends to diminish with practice. In the later stages of practice, the largest percentage of variance in task performance is accounted for by a factor specific to the task itself. This suggests that, as subjects become proficient at a particular task, the role of general abilities may decrease, and the importance of task-specific knowledge becomes more

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important. Taken together, the results described above deal a serious blow to the "abilities" approach described earlier, at least in its simplest form. These results suggest that motor expertise is largely specific to a particular task. Therefore, outstanding performance on one task will not necessarily imply outstanding performance on a different task. As an example, world-class gymnasts may be highly skilled at gymnastics, but they may not necessarily be able to excel at other motor activities. The idea that expertise is largely specific to a particular task is not unique to the field of motor learning. Similar conclusions have been drawn by Chase and Simon (1973) in their well-known study of chess experts. In this study, chess masters were found to be superior to weaker players in their ability to reproduce a pattern of chess pieces on a board after a few seconds of viewing. Importantly, the superiority of the chess masters' memories was limited to the domain of chess; they did not generally have better memories. This provides further confirmation that a large component of expertise involves skills specific to a particular task. It is undeniable that much of what we consider to be expertise within a domain involves extensive knowledge or experience in that domain. However, this does not preclude the possibility that more general abilities play an important role in determining the level of individual performance. After all, Fleishman's (1967) general factors did account for approximately one-third of the variance in subjects' performance, even after many trials of practice. Rather than claiming that general abilities play no role in determining the level of task performance, it may be more accurate to assume that some kind of general abilities serve as limiting factors of that performance. Certainly, one cannot become an "expert" at some task without a large body of task-specific experience. However, the degree to which an individual possesses the requisite underlying abilities may constrain the level of expertise to which one can aspire. The challenge, then, is to identify the basic building blocks that may constmin task performance. The early work by Pemn (1921) and Henry (1961) suggests that we should not be searching for a single "athletic"ability. Instead, it is likely that "ability" can be broken down into several components. Fleishman (1967) has proposed several candidate components, but his list may not be entirely sufficient. For one, Fleishman's factors were determined as a result of posr hoc analysis of task performance; they were not derived on the basis of a systematic theoretical approach. Second, a reliance on Fleishman's techniques would not take full advantage of our current understanding of cognitive neuroscience. It seems that any taxonomy of underlying abilities must be based on knowledge concerning the ways in which the central nervous system controls movement.

In this paper, I will introduce a different approach to uncovering the basic abilities that may constrain complex motor skills. This approach is based on the idea that motor skills can be broken down into a small number of separable components, each of which is controlled by

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a different module in the brain. In the next section, I will describe this modular view in more detail, and use it to develop a general approach to the study of motor skill and coordination. In

the last section, I will discuss how this approach can be applied to the study of individual differences. In particular, I will describe recent applications of this view to the study of clumsiness in children. The Modular View A fundamental assumption in the area of motor control is that movements are organized

centrally in the form of a motor program. Evidence for this idea comes from studies that show that entire movements can be executed in the absence of peripheral feedback. These findings undermined early S-Rlearning theories which posited that each component of a given movement was elicited by the feedback caused by the component movement immediately preceding it. Instead, these results suggested that the central nervous system creates a sort of "master plan" for each coordinated sequence of movements it wishes to perform. This "master plan," then. can be implemented, often without a great deal of on-line control (see Keele, 1981 for a complete discussion). Ivry and Keele (1989) liken the motor program to the software used by computers. It is a detailed description of the job the system is to perform in a particular situation.. Given this metaphor, one might be interested in examining the specific functions and procedures that make up the motor program. These elementary components may serve as basic abilities, the building blocks of more complex task performance.

The modular view of motor control, developed by Keele and Ivry (e.g., Keele & Ivry, 1987) attempts to clarify the nature of the elementary components that make up the motor program. According to the modular view, the brain is organized by function rather than by task. Therefore, two very different tasks may call upon the same module to perform a given function. Take, for instance, bicycle riding and piano playing. These are clearly very different tasks, but their performance is dependent upon many of the same functions. In both activities, the brain must calculate each of the following: the sequence of movements that must take place and where they will occur, the relative times at which each movement should occur; and the force with which the movement should be performed. Keele and Ivry postulate that these three functions -- sequencing, timing, and force regulation -- are separate modules in the brain.' These modules, then, are utilized regardless of the specific task (bicycle riding, piano playing, etc.) that needs to be performed.

'The reader should be aware that this is certainly not an exhaustive list of functions used in the construction of motor programs. Additional computations surely must be accomplished. In addition, it is likely that these functions can be further broken down into separable sub-components. For example, the module of sequential spatial specification probably involves several component operations. Still, an examination of the modules proposed here should Serve as a useful starting point for funher discussion.

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The sense in which the term "module" is used here is somewhat different from other descriptions of mental modularity (e.g., Fodor, 1983). Basically, I am proposing that a "module" represents an anatomically distinct neural computation. One important feature of this view was alluded to above: the same computation may be called upon in a number of different tasks. Therefore, one might expect similar tasks to be influenced by at least some of the same modules. This idea is supported by Kosslyn (1987). who has argued that many diverse perceptual tasks. such as object recognition, navigation, and imagery, all rely on common modular processing systems. A second feature of the view described here is that separate modules m c o n s i d e d to be functionally independent. Therefore, the performance of one module may be unrelated to the performance of a separate module. Consistent with this view are the results of Favilla. Hening, and Ghez (1989) suggesting that the amplitude and k t i o n of voluntary movements are specified independently. Recent work by Keele, Ivry, and their colleagues has provided further evidence consistent with this modular view. For instance, a series of correlational analyses has provided support for the proposed module of timing (see Keele & Ivry, 1987). In one study, subjects were asked to synchronize key-presses with a regularly-occumng auditory signal. After synchronization the tone stopped, and the subject was required to continue tapping the key at the same speed. In some trials, subjects were asked to tap the key by moving only their index finger. In other trials, subjects were to tap the key by moving the entire forearm. Variability of inter-tap intervals serves as a measure of timing ability. To the extent that subjects' timing abilities correlate across the different effectors, one could say that timing is a general-purpose module and not one that is dependent upon the effector system used to make the response. In the above experiment. the correlation was .90. Therefore, it appears as if central control of timing is independent of the effector system used to perform the movement. One possible criticism of the key tapping experiment reported above is that the two tasks (tapping with finger and tapping with arm) are too similar to serve as a true test of the modular view. If timing is a general-purpose module that is used in many diverse skills, one should be able to see its effects in other tasks as well. In a separate study, Keele, Pokomy, Cmos, and Ivry (1985) examined the correlation between tapping accuracy (using the finger tapping task described above) and perceptual acuity. In the perceptual task, subjects heard two sets of two tones. The first pair was always separated by 400 msec.. while the second pair was separated by a variable amount of time. The subject's task was to indicate whether the interval separating the second pair of tones was longer or shorter than the fmt interval. Thresholds were calculated for each subject, indicating each subject's ability to distinguish small differences in the two intervals. The perception task, like the tapping one, requires the subject to make a computation of time. However, the structure of the task is very different; it is purely perceptual, rather than being based upon production. Still, Keele et al. reported a significant positive correlation (-53) between these two tasks. This result provides further evidence that timing is a rather general ability that is independent of the particular task that subjects are asked to perform.

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To demonstrate that force control may be its own separate module, a similar strategy was

used (Keele, Ivry, & Pokomy, 1987). Subjects were to press on a force uansducer and try to match a target force. In some trials, this was done only with the index finger, while in other trials it was done by moving the entire forearm. Variability of responses gave an index of force control. To the extent that subjects’ force control abilities correlate across the different effectors, one could say that force regulation is also a general-purpose module. In this experiment, the correlation was .76. Similar correlations were found between finger and foot, further suggesting that force control is independent of the particular effector system used to respond. The above results suggest that both timing and force control correlate very highly across different effectors. This suppons the modular view. The more important result, however, is that the correlations between timing ability and force control are rather low (ranging from . I 8 to .34; see Figure 15.1). This is m e even

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Figure 15.1. Swnmary of Correlations between Timing Ability and Force Control (from Keele CG Ivry, 1987).

when responses are made with the same effector (i.e., when both the timing task and the force control task are done with the finger, or both are done with the arm). This suggests that the modules that regulate timing and force are separable components. That is, they are independent of each other. Recent work by Keele and his colleagues has begun to investigate the modular nature of sequencing (e.g., Keele, Cohen, & Ivry, 1990). This work has used a simple key-pressing task (based on Nissen & Bullemer, 1987). in which subjects are asked to respond to x-marks presented on a computer screen. The x-marks can appear in one of three different locations on the screen, and subjects are asked to press a key corresponding to the location of the mark as quickly as possible. Unbeknownst to subjects, the x-marks appear in a repeating sequence in

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some blocks of trials, while in other blocks they appear in random order. Learning of the sequence can be inferred by decreasingreaction times in the sequence blocks (relative to random blocks) over the course of the experiment (see top panel of Figure 15.1). One critical test of the modular view is whether learning a sequence of key presses is independent of the effector used to make the response. Therefore, some of the subjects switched effectors halfway through the experiment. For example, subjects responded during the first half of the experiment with their index fmger and during the second half of the experiment with their arm. Other subjects continued responding with the same effector throughout the entire experiment. Results indicated that there is no difference between the performance of those subjects who switched effectors (bottom panel of Figure 15.1) and those who used the same effector throughout (top panel). Therefore, knowledge of the sequence acquired during performance with a particular effector is independent of that effector system. This supports the view that sequence representation is a general-purpose module that is not dependent upon the specific task performed. A more powerful test of the modular view asks if sequence knowledge transfers to responses of a completely different kind, rather than just between effectors. In a recent study (Keele, Jennings, Jones, & Cohen, IWl), subjects performed the sequence learning task described above, either by making manual responses (i.e., pressing keys corresponding to target location) or verbal responses (i.e., indicating the location of the x-mark by speaking into a microphone). Half of the subjects performed the entire experiment by making verbal responses, while the other half of the subjects switched from manual to verbal response midway through the experiment. If sequencing is a central processing module, one would expect that sequence knowledge would transfer between different response conditions. The results demonstrated partial support for this prediction. Subjects in the manuaverbal condition did show a significant sequence learning effect, even after transfering to a different response modality (see Figure 15.2). However, it should be noted that the sequence learning effect in this condition was smaller than the sequence learning effect shown by the group who gave verbal responses throughout.

Given that timing, force, and sequence representation appear to be separate modules, it may be reasonable to assume that different mas of the brain perform each of these computations. This indeed appears to be the case. Timing seems to be controlled by the cerebellum (Ivry & Keele, 1989; Ivry, Keele, & Diener, 1988). Ivry and Keele compared the performance of Parkinson, cerebellar, cortical, and peripheral neuropathy patients with age-matched controls on both the time production and time perception tasks described earlier. Only the cerebellar patients were impaired on both timing tasks. They showed increased variability in the tapping task, and they were less accurate on the perceptual task. Importantly. the latter result is not a function of any perceptual difficulties, since these patients were not impaired in a control task measuring the perception of loudness.

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SAME EFFECTOR DURING TRANSFER 400 T TRAINING

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Figure 15.3. Reaction time to visual signals appearing at one of three locations on a screen. I n blocks 1-8, the signals appear in a f i e d sequence. I n blocks 9-12, one group retains the sequence, and for the other group, the signals appear at random. For data in the top panel, subjects use the same effector in all blocks. For data in the bottom panel, subjects use different effectors in blocks 1-8 than they do in blocks 9-12. Based on Keele, Jennings, Jones & Cohen (1992).

Force regulation seems to be a function of the basal ganglia. Patients with Parkinson’s disease (which affects the basal ganglia) are impaired, relative to normal controls and other types of patients, in force regulation tasks like those described in Keele, Ivry. and Pokorny (1987)

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(Ivry, 1989; cited in Lundy-Ekman, 1990). Parkinson's patients have also shown impaired performance in a number of other similar force regulation tasks (e.g., Stelmach Br Woningham, 1988; Wing, 1988).

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Figure 152. Reaction time to visual signals appearing on a computer screen. In blocks 1, 2. 11, 12, and 14, the signals are presented randomly. In blocks 3-10 an 13, the signals are presented in a repeating sequence. The effect of sequence learning is represented by the difference between reaction times in block 13 and the random blocks surrounding it. Subjects in one condition made verbal responses throughout the entire expenentent (open circles). In the other condition, subjects learned the sequence by making manual responses (blocks 3-10), but were tested in blocks 11-14 using verbal responses.

The neural underpinnings of sequence representation art not as clear-cut. One problem is that the idea of a single "sequencing" module is probably too general. It is likely that sequence representation can be broken down into several separable sub-components. For instance, one component may be the identification of places in space where movements should occur. This is similar to the idea described by Hogan (1984), who proposes that the central nervous system defines a series of equilibriumpoints through which a given limb is programmed to pass. Hogan called this sequence of equilibrium points the "virtual trajectory." The idea is

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that part of the brain creates an abstract spatial image of the forthcoming sequence of movements, and uses this image to coordinate the effectors required to cany out the action (see also Berkenblit & Feldman, 1988; Bizzi & Mussa-Ivaldi, 1989). Given the spatial nature of this computation, the posterior parietal cortex is a possible neural substrate. This region of the brain is known to play a critical role in the visual localization of objects (Mishkin, Ungerleider, & Macko, 1983). Also, Roland and his colleagues have used various imaging techniques to implicate the parietal lobe in movement planning (Roland, Skinhoj, Lassen, & Larsen, 1980). Increases in regional blood flow were found in this region when subjects were asked to move their hands toward a target in space. but not when movements were not directed at external targets. Roland et al. concluded that parietal regions were important in providing motor neurons with information about the spatial configuration of the prescribed movement. Thus far, I have discussed three proposed modules -- timing control, force control, and sequence representation -- that may form the foundation for complex motor skills. In particular, I have proposed that these three modules contribute basic computations that are used in forming a motor program. While the evidence reviewed here does suggest that there are reliable individual differences in the performance of these modules, it is unlikely that these three modules, by themselves, account for all of the underlying abilities that constrain complex task performance. In the paragraphs that follow, I will propose two other modular computations that may play a role in determining the quality of motor performance.

Coordinating Information from Multiple Sources The approach proposed above suggests that motor programs are made up of several elementary components. That is, when constructing a particular motor program, an individual must specify where, when, and with how much force each movement will occur. Before implementing this plan, however, the central nervous system must integrate the information that is given by the timing, force, and sequencing modules. Recent research by Hunt and his colleagues (see Hunt, 1991) suggests that reliable individual differences exist in the ability to integrate information from multiple domains in performing a task. “Coordination”ability was most noticeable when subjects were required to integrate linguistic information (i.e., in the form of a question) and perceptual information (i.e., a visual display on a computer screen). If a coordination ability exists, then performance on the dual task should not be predicted by performance on the two tasks (linguistic or perceptual) separately; an additional component should be needed to explain inter-subject variation in task performance. Results of these studies suggest that a coordination ability does exist. An important concept to consider is that Hunt’s results showed an ability to coordinate information from two external domains. Both linguistic and perceptual information was presented to the subject within the context of the experiment. The ability to coordinate these types of information may or may not be the same as the ability to coordinate different

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computationswithin the motor system. Therefore, while the proposal of a general "coordinating" ability is not far-fetched in light of Hunt's results, one should interpret Hunt's evidence as merely suggestive. Clearly, more research could be done in this m a to enlighten the issue.

Attention Switching When performing complex skills, individuals often have to pay attention to perceptual input corning from several different locations in space. For example, when you ride your bicycle down a crowded street, you must pay attention to the contours of the road,the movements of people on the nearby sidewalk, and the movements of passing cars. If you are unsuccessful in monitoring information from any of these channels, your chances of an unsafe mp gnatly increase. Within psychology, the study of attention is based on the simple assumption that people are limited in their ability to process incoming information (Posner, 1982). In other words, people can pay attention to only so much information at one time. Therefore, in instances where the information load exceeds normal capacity, an individual will be forced to switch his or her attention back and forth between the multiple sources of input. To the extent that there are individual differences in the ability to switch attention between different sources, then these differences may be predictive of success or failure in complex task performance. Several studies have shown that individual differences exist in the ability to switch attention from one source to another (Gopher & Kahneman, 1971; Keele & Hawkins, 1982). The Gopher and Kahneman study was based on a dichotic listening task, in which subjects monitored a cued ear for the occurrence of digits among strings of words. Midway through each trial, subjects received a second cue indicating which ear they were to monitor. In some trials, then, subjects were required to switch their attention from one ear to the other. Results showed that more emrs, particularly intrusions, occurred after the second attentional cue. This suggests that subjects found it relatively difficult to rapidly re-orient their attention. The ability to do so, however, was predictive of success in a flight school training program in which the subjccts were enrolled. In a separate study, the ability to switch attention was also negatively correlated with the number of traffic accidents among Israeli bus drivers (Kahneman, Ben-Ishai, & Lotan, 1973). Taken together, the results discussed above suggest that attention switching may be a fairly general ability that is necessary for complex task performance. This is consistent with the work of Posner and his colleagues (e.g.. Posner, Inhoff, Friedrich, & Cohen. 1987). who have described a modular view of the attentional system. Generally, this view suggests that a general attentional system is used as a "command system" to orient and re-orient attention between different sources of perceptual input. To the extent that differences exist in the performance of this command system, one might expect them to have important behavioral consequences. I have outlined five modular abilities that may play a role in the performance of complex

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skills. The proposal is that each of these basic modules perform separable computations within the brain, and that these modules are called upon in the performance of many different skilled behaviors. That is. each module serves a particular function, regardless of the specific task that is being performed. This modular view provides an alternative to the view that expertise at a certain task is entirely task-specific. Rather than saying that expert skill relies completely on task-specific experience, it is proposed that some general underlying abilities also contribute to task performance. The modules of timing, force control, and sequence representation are proposed to serve as "building blocks" in the consnuction of motor programs. Before the motor program can be implemented, these basic parameters must be integrated. perhaps by a "coordination" module. Specific task knowledge may have an effect on the performance of tasks only after these elementary computations are in place. Finally, the ability to switch attention between competing sources of information may also contribute to success or failure in complex real-world activities. According to the modular view presented here, individual differences in the performance of each module should be predictive of ultimate task performance. That is, people with high levels of these basic abilities should be able to achieve high performance in a number of tasks (assuming, of course, that they practice enough to gain necessary task-specific knowledge). In contrast, people with low levels of one or more of these basic abilities are likely to be limited in how well they can perform certain tasks. A critical test of the modular view, then, is an examination of individuals with relatively low levels of one or more of these abilities. The modular view predicts that these people should be in the low end of the continuum of motor skill performance. Especially among children, they should be characterized as "clumsy." It is to the topic of clumsiness that I now turn. Clumsiness While most children have very little difficulty executing motor movements, a small percentage do. These are children whom we typically call "clumsy." Clumsy children are often slower and more awkward in their movements than normal children. As a result, they may have troubk with many common childhood tasks, like writing, hopping, or catching a ball (Dellen & Geuze, 1988). The problems that clumsiness can cause for a child, both socially and in terms of physical development, are obvious.

Past research has suggested several possible causes of clumsiness. These have included perceptual difficulties (Dare & Gordon, 1970), problems in response selection (Dellen & Geuze, 1988). and deficits in response planning (Cermak, 1985). While it is likely that clumsiness may be caused by failures with any of these aspects of information processing, the modular view focuses our attention on the aspect of response planning. Specifically, clumsiness may result from relatively poor functioning of one or more of the basic computational modules that constitute the motor program. For example, failures in the cerebellum may lead to poor timing, which, in tum, could lead to clumsy behavior. If problems exist in the functioning of the basal

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ganglia, force regulation may be imperfect; and so on. Two important points need to be made with respect to the above suggestion. First, imperfect processing in any of the modules does not necessarily imply problems as severe as those experienced by, for example. stroke patients. Neurological patients have more seven problems than those discussed here; clumsy children typically have no detectable signs of brain damage. I am merely suggesting that clumsy children lie in the lower extreme of the normal distribution. Second, it should be remembered that the modules proposed earlier axt thought to be independent of one another. Therefore, an individual who has problems in one module may not necessarily be impaired in any of the other modules. With this in mind, it should be possible to isolate the source of a clumsy child's dysfunction empirically. This would be very beneficial, both in terms of testing the proposed modular approach and in tern of clinical assessment. Recent studies have begun to address the issue of whether clumsy children suffer from impaired functioning of one of the proposed modules. For example. Williams, Woollacott. and Ivry (1989) tested the timing abilities of clumsy subjects and normal conmls using a paradigm identical to the one reported in Ivry and Keele (1989). That is. subjects were asked to (i) produce regularly-spacedtemporal intervals by tapping a key;(ii) discriminate between tones that vary in their duration; and (iii) discriminate between tones that vary in their loudness. Results indicated that clumsy subjects were significantly poorer than normal children at both the production task and the perception of duration task. No differences existed between &roupson the perception of loudness task. The results of the Williams et al. paper are important in two respects. First, they confirm that clumsy children may have difficulties representing time. Importantly, these subjects were impaired at both a time-based motor task and a time-based perceptual one. Therefore, the problems experienced by these children may involve a higher-oder process of temporal representation. Second, notice that the results of the clumsy children are very similar to the results of the cerebellar patients reported in Ivry and Keele (1989). While it is impossible to make direct comparisons between these two groups (since subjects were of different ages, etc.), it is interesting that the same pattern of results emerged. perhaps the clumsy subjects are experiencing impaired functioning of the "cerebellar module." This, in turn. could be an explanation of their clumsiness. One thing missing from the Williams et al. paper is a demonstration that other modules of the motor system are independent of the timing module. The modular approach suggests that the functioning of the force module, for example, is independent of the timing one. Therefore, clumsy subjects with timing difficulties may perform perfectly fine in tasks that require force regulation. Conversely,clumsy children who experiencedifficultiesin force regulation may have normal timing abilities. A demonstration of this double dissociation is needed to provide further evidence for the modular approach.

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A m e n t paper (Lundy-Ekman, Ivry, Keele, & Woollacott, 1991) provides evidence for this type of double dissociation. Lundy-Ekman et al. tested the idea that clumsy children with basal ganglia soft signs differed from those with cerebellar soft signs in timing and force regulation tasks. A "soft sign" is a milder version of a "hard sign," which typically indicates nervous system disorder. Examples of basal ganglia soft signs are choreiform movements (jerky, incgular). athetotiform movements (small, slow, writhing), and associated movements (extranous, nonrandom motions which accompany purposeful movements). Examples of soft signs associated with the cerebellum are dysmetria (inability to control the distances of movement), dysdiadochokinesia(inability to perform rapid, alternating movements), and intention tremor (shaking during intended movements).

If a soft sign is indicative of disorders localized in a particular brain region. one might expect that different soft signs would predict difficulties in different modules of the motor system. Specifically, those children with basal ganglia soft signs may have difficulties regulating force, but may have no trouble representing time. Subjects with cerebellar soft signs may have trouble representing time, but may perform normally on force regulation tasks. Lundy-Ekman et al. (1991) provided tests of these hypotheses. They tested two groups of 7-8 year-old clumsy children, one group with basal ganglia soft signs and one group with cerebellar soft signs. None of them had unequivocal evidence of neurological damage (i.e.. in the form of a hard sign). The performance of these two groups on both timing and force regulation tasks was compared with a group of age-matched controls. The force regulation task was similar to that reported by Keele, Ivry, & Pokomy (1987). Subjects were to press a force transducer with a target amount of force. Variability around the target force is taken as a measure of force regulation ability. As expected, the basal ganglia children performed significantly worse than both cerebellar subjects and controls on this task. Timing tasks were similar to those presented in Ivry and Keele (1989). In the production task, subjects hied to tap a key at regular intervals of 550 msec. Variability of inter-tap intervals provides a measure of timing ability. As expected, cerebellar subjects performed significantly worse than both basal ganglia subjects and controls on this task? In the perception of duration task, subjects were asked to discriminate between tones that vary in their duration. A control task of perception of loudness was also included. As expected, cerebellar subjects perfonned significantly worse than both basal ganglia subjects and controls in the perception of duration task. There were no differences between any of the groups in the

*Funher analysis shows that differences between children are isolated to central mechanisms of timing. "his is shown by partitioning the total variance of inter-tap intervals into two independent components: Clock variance (which is due to central mechanisms) and Motor variance (which is due to peripheral mechanisms); see Wing and Kristofferson (1973) for details of this partitioning. The timing deficits of clumsy children are evident by the large repolted differences in clock variance.

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control task of perception of loudness. The results of the timing task provide a nice dissociation between the timing module and the force module. Cerebellar subjects appear to have difficulty performing the tasks requiring control of timing, while basal ganglia subjects do not. Taken together with the results of the force regulation task, this suggests a functional independence between these two modules, each of which may contribute equally to clumsiness. A third module that might contribute to clumsiness is that of sequence representation. Recall that Hogan (1984) introduced the idea of a virtual trajectory, a spatial image of the forthcoming sequence of movements. Hogan suggested that the underlying principle behind the creation of a virtual trajectory is to minimize jerk (the rate of change of acceleration) in a movement. In other words, the goal of planning a sequence of movements is to make them as smooth as possible. hsumably, problems in refining one's virtual trajectory or in matching one's movements to the planned trajectory will reduce one's ability to perform optimally smooth movements.

The idea that clumsy children should produce ''jerkier" movements than normal childrtn is hardly profound; it is one common notion of what typifies clumsiness. It is, however, a testable prediction. For instance, clumsy children often produce relatively poor handwriting (Cermak. 1985). Poor writing is characterized by movements that are much less smooth than those produced by good writers (wann, 1987). Wann posits that this is because poor writem have not yet made the transition to effectively producing "planned trajectories." This type of failure (i.e., inability to plan trajectories for a forthcoming sequence of movements) may be a general characteristic of at least some clumsy children. Research has not yet examined the possible links between clumsiness and the performance of a "coordination" module. However, it is possible that such a link may exist. Hunt (1991) found a relationship between the ability to integrate different sources of perceptual information and expertise in competitiveorienteering. Specifically,expen orienteers were better able to link together a succession of visual scenes, giving them the ability to form what Hunt called a "surveyor's form" of their surroundings. Perhaps an important component of high-level motor performance is the ability to link together information from different sources. If true, some clumsy people may be characterized by relatively low levels of this ability. Therefore, they may be less able to integrate information from their surmundings, as well as less able to coordinate the modular computations that make up their motor programs. It is also unclear whether clumsiness is related to an "attention-switching''ability. Again, however, previous research is at least suggestive that it may be. Recall that Gopher and Kahneman (1971) found the ability to switch attention from one source to another to predict success in an air force training program. This is in agreement with the intuition that expert flyers are better able to monitor information coming from several different channels simultaneouslythan are lesser pilots. Perhaps the general ability to switch attention is predictive

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in other motor tasks as well. This would suggest that some forms of clumsiness are due to a relative inability to switch back and forth between several sources of information.

In summary. I have suggested that clumsiness may be caused, at least in some children, by imperfect functioning of one or more general-purpose modules. If true, this would suggest several important insights into clumsiness. First, it suggests that at least some clumsy children can be associated with an identifiable problem. For instance, the cerebellar subjects in Lundy-Ekman et al. (1991) may be characterized as having problems in timing. Importantly, this single problem may be the root cause of many of these children’s motor problems. Second, this approach suggests that clumsiness is a multidimensional phenomenon. Not all clumsy children should be charactenzed, or treated, in the same way. Indeed, the underlying problem (e.g., timing) associated with some children may be very different than the underlying problem (e.g., attention switching) associated with other children. Finally, this approach suggests a theoretical impetus for the development of remediation techniques. If the underlying problems associated with clumsiness can be limited to a relatively small number of candidate modules, then improvement in the functioning of these modules may lead to general improvement in motor functioning. It is to this final topic of remediation that I now turn.

Remediation Of course, the ultimate goal of research dealing with the topic of clumsiness is to develop

intervention techniques that may help clumsy children successfully participate in normal motor activities. Unfortunately, past research in this area does not offer much insight into the development of these techniques (Sugden & Keogh, 1990). The main problem is that therapy is often not based on theoretical grounds. Rather than base treatment on what is considered to be the underlying cause(s) of clumsiness, many practitioners focus their remediation techniques on individual motor tasks that the child has been shown to be unable to perform (Laszlo & Bairstow, 1989). While extensive practice on a particular task may improve performance on that task, it is not clear that this approach is the best way to improve the general motor performance of clumsy children. A more appropriate approach to remediation may be one in which therapists attack the cause(s) of the problem. According to the modular view presented here, clumsiness may be caused (at least in some cases) by imperfect functioning of a general motor module. The best way to treat this type. of clumsiness may be to attempt to improve the the functioning of the module (or modules) in question. The idea that general motor performance can be improved by training the basic abilities that underlie them flies in the face of traditional views of motor control. It is generally believed that abilities represent stable traits that cannot be easily modified (see Schmidt, 1988). However, it is ultimately an empirical question whether the modular abilities proposed here can be trained. In a recent unpublished study, we have begun to examine whether the module of timing can be trained. A small group of normal subjects (n=6) came to the lab on ten different days, over the course of a two-week period. On the first day, they were administered a long series of tests

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designed to assess their timing abilities. These tasks included a time production task similar to that described above and in Ivry 8c Keele (1989). Rather than always tapping the same interval, however, subjects tapped three different intervals: 400 msec., 550 msec., and 700 msec. In addition, subjects performed a perception of time task, and a perception of loudness task. each of which was identical to that described previously. The former test is used to assess timing independent of motor systems, and the latter task is used as a control. On each of the next eight sessions, each subject practiced the tapping task, but only by tapping the 550 msec. interval. Each session was filled with enough trials to last approximately half an hour. Finally, on the tenth day, each subject repeated the tests performed during the f r s t session. If the general ability of timing can be trained, we would expect performance on all of the timing tasks to improve from the first session to the last one. In other words, the variance of subjects’ tapping should be lower on the tenth day than on the fmt, even for the intervals (400 msec. and 700 msec.) that the subject did not practice. Performance on the time perception task should also improve. That is, subjects should improve their ability to discriminate small differences in the durations of two tones. This would be evident by finding smaller thresholds on the tenth day than on the first. Finally, because the perception of loudness task is unrelated to timing, we would expect no improvement on the performance of this task over time. Results of this experiment are summarized in Figure 15.5. Generally, practice in the key tapping task did lead to improvements in that task. Importantly, this improvement was not limited to the interval that subjects actually practiced, they improved at all three intervals. This is in agreement with the predictions outlined above. However, subjects did not improve at the perception of time task. This presents a challenge for the modular view, since it predicts that improvements in the tapping task would be linked to improvements in the perceptual one. Apparently, the improvement gained in the tapping task does not transfer to the perceptual task.

(Training = 8 sessions of tapping 550 msec intervals) Pretest Posttest Improvement Tapping (400msec)

14.20

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p < .05

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14.92

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8.50

ns

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Figure 15.5. Measurement of timing ability before and after training.

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Cell entries for each of the three tapping tasks represent estimates of clock variance, based on the techniques described in Wing and Kristofferson (1973). Clock variance represents the portion of variance in intertap intervals that is due to central mechanisms. Enmes in the perception tasks represent estimates of discrimination thresholds. Larger numbers represent larger thresholds (i.e., subjects less able to distinguish between two similar tones). In order to further examine this issue, a second study was run, in which six different subjects performed the same pre- and post-test described above, but received practice at the perception of time task in the interim. Analogous results were obtained. Subjects showed significant improvement on the perceptual task, but, in general, they showed no significant improvement in the tapping tasks. The benefit of training on the perceptual task does not appear to transfer to the tapping task. Several possible explanations exist for these perplexing results. First, it may be that, as Schmidt (1988) suggested, basic abilities cannot be trained. While training may give subjects enough task-specific knowledge to improve at a particular task, that training may not transfer to related tasks. Second, these results suggest that timing may not be a singular ability. Perhaps the production and perception tasks involve slightly different neural computations, even though both rely on similar substrates and reflect correlated abilities. For instance, the perceptual task may put greater emphasis on the representation of time, while the production task may emphasize the implementation of specific intervals. Finally. these results may be explained, in part, because of the subject population used. It is possible that training of the sort described here is only useful for individuals with extreme timing difficulties, which normal college students tested in these studies are not likely to have. Our future research in this area will attempt to address these general issues.

Summary and Conclusions A long-standing debate in the field of motor control concerns the role of general underlying abilities in constraining task performance. While a great deal of research suggests that motor expertise is largely task-specific, we have argued that a great deal can be learned by looking for more general abilities as well. By examining individual differences in the performance of these various modules, it is hoped that we can gain further insight into important topics such as clumsiness and motor expertise. References Adams, J.A. (1987). Historical review and appraisal of research on learning. retention, and transfer of human motor skills. Psychological Bulletin, 12. 41-74. Berkenblit, M.B., & Feldman, A.G. (1988). Some problems of motor control. Journal of Motor Behavior, 20, 369-373. Bizzi, E., & Mussa-Ivaldi, F.A. (1989). Geomemcal and mechanical issues in movement planning and control. In M. Posner (Ed.), Foundations of Cognitive Science. Cambridge, MA: MITRess.

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Cermak, S. (1985). Developmental dyspraxia. In E.A. Roy (Ed.), Neuropsychological Srudies of Apraxia and Related Disorders (pp. 225-248). Amstedam: Elsevier Science Publishers. Chase, W.G., & Simon, H.A. (1973). Perception in chess. Cognitive Psychology, 4.55-81. Dare, M.T., & Gordon, N. (1970). Clumsy children: A disorder of perception and motor organisation. Developmental Medicine and Child Neurology, 12, 178-185. Dellen, T. van, & Geuze, R.H. (1988). Motor response processing in clumsy children. Journal of Child Psychology and Psychiatry, 29,489400. Favilla, M., Hening, W., & Ghez, C. (1989). Trajectory control in targeted force impulses: VI. Independent specification of response amplitude and direction. Experimental Brain Research, 75, 280-294. Fleishman, E.A. (1967). Individual differences and motor learning. In R.M. Gagne (Ed.), Learning and individual differences @p. 165-191). Columbus. OH: Memll. Fodor, J.A. (1983). The modularity of mind. Cambridge, MA: MIT Ress. Gopher, D., & Kahneman, D. (1971) Individual differences in attention and the prediction of flight criteria. Perceptual and Motor Skills, 33, 1335-1342. Henry, F.M. (1961). Reaction time-movement time correlations. Perceptual and Motor Skills, 12, 63-66. Henry, F.M., & Whitley, J.D. (1960). Relationships between individual differences in strength, speed, and mass in arm movement. Research Quarrerly, 31, 24-33. Hogan, N. (1984). An organizing principle for a class of voluntary movements. The Journal of Neuroscience, 4, 2745-2754. Hunt, E. (1991). Computerized assessment of individual differences (ID No. N00014-86-C-0065). Alexandria, VA: m i c e of Naval Research. Ivry, R.I., & Keele, S.W.(1989). Timing function of the cerebellum. Journal of Cognitive Neuroscience, I, 136-152. Ivry, R.I., Keele, S.W., & Diener, H. (1988). Dissociation of the lateral and medial cerebellum in movement timing and movement execution. Experimental Brian Research, 73, 167-180. Kahneman, D., Ben-Ishai, R., & Lotan, M. (1973). Relation of a test of attention to road accidents. Journal of Applied Psychology, 58, 113-115. Keele. S.W. (1981). Behavioral analysis of movement. In V. Brooks (Ed.),Handbook of Physiology. Section 1: The Nervous System, Vol. 2: Motor Control. (pp. 1391-1414). Baltimore: Williams & Williams. Keele. S.W., Cohen, A., & Ivry, R. (1990). Motor programs: Concepts and issues. In M. Jeannerod (Ed.), Attention and Performance XIII: Motor Representation and Control. (pp. 77-110). Lawrence Erlbaum Associates. Keele. S.W., & Hawkins, H.L. (1982). Explorations of individual differences relevant to high level skill. Journal of Motor Behavior, 14, 3-23. Keele. S.W., & Ivry, R.I. (1987). Timing and force control: A modular analysis. Paper presented at IREX meeting, Moscow, USSR. Keele, S., Ivry, R., & Pokomy, R. (1987). Force control and its relation to timing. Journal of Motor Behavior, 19, 96-1 14.

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Keele. S.W., Jennings, P., Jones, S., & Cohen. A. (1991). On the modularity of sequence representation. Paper submitted for publication. Keele, S., Pokomy, R., Cmos, D.. & Ivry, R. (1985). Do perception and motor production share common timing mechanisms: A correlation analysis. Acra Psychologica, 60, 173-191. Kosslyn, S.M. (1987). Seeing and imaging in the cerebral hemispheres: A computational approach. Psychological Review, 95, 148-175. Laszlo, J.I., & Bairstow, P.J. (1989). Process-oriented assessment and treatment of children with perceptuo-motor dysfunction. In P. Lovibond & P. Wilson (Eds.), Clinical and Abnormul Psychology. (pp. 31 1-318) Amsterdam: Elsevier Science Publishers. Lundy-Ekman, L. (1990). Soft neurological signs as indicators of timing and force Unpublished doctoral dissertation, University of Oregon, Eugene, Oregon. Lundy-Ekman, L., Ivry R., Keele, S., & Woollacott, M. (1991). Timing and force control deficits in clumsy children. Journal of Cognitive Neuroscience, 3, 367-376. Marteniuk, R.G. (1974). Individual differences in motor performance and learning. In J .H . Wilmore (Ed.), Exercise and sports sciences reviews, Vol. 2. (pp. 103-130) New York Academic Press. Mishkin, M.,Ungerleider, L.G., & Macko, K.A. (1983). Object vision and spatial vision: two cortical pathways. Trends in Neuroscience, 6, 414-417. Nissen, M.J., & Bullemer, P. (1987). Attentional requirements of learning: Evidence from performance measures. Cognirive Psychology, 19, 1-32. Perrin, F.A.C. (1921). An experimental study of motor ability. Journul of Experimental P~chology,4,24-56. Posner, M.I. (1982). Cumulative development of attentional theory. American Psychologisr, 37, 168-179. Posner, M.I., Inhoff, A.W., Friedrich, F.J., & Cohen, A. (1987). Isolating attentional systems: A cognitive-anatomical analysis. Psychobiology, 15, 107-121. Roland, P.E., Skinhoj, E., Lassen, N.A., & Larsen, B. (1980). Different cortical areas in man in organization of voluntary movements in extrapersonal space. Journal of Neurophysiology, 43, 137-150. Schmidt, R.A. (1988). Motor control and learning. Champaign, L Human Kinetics Publishers. Stelmach, G.E., & Womngham. C.J. (1988). The preparation and production of isometric force in Parkinson’s disease. Neuropsychologia, 26, 93- 103. Sugden, D.A., & Keogh, J.F. (1990). Problems in movement skill development. Columbia, S C University of South Carolina Press. Underwood, B.J. (1975). Individual differences as a crucible of theory construction. American Psychologist, 30, 128-134. Wann, J.P. (1987). Trends in the refinement and optimization of fine-motor trajectories: Observations from an analysis of the handwriting of primary school children. J o u r ~ l of Motor Behavior, 19, 13-37. Williams, H., Woollacott, M., & Ivry, R. (1989). Perceptual-motor timing problems in clumsy children. Paper presented at the 19th annual meeting of the Society for Neuroscience,

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Acknowledgements Much of the research reported in this chapter was supported by the Pew Memorial Trust to the Center for the Cognitive Neuroscience of Attention, University of Oregon. The author gratefully acknowledges the assistance of Steve Keele, Mike Posner, Kris Jones, and Marjorie Woollacott, each of whom offered helpful comments on earlier drafts of this chapter. Requests for reprints should be sent to Steven K. Jones, Department of Psychology, University of Oregon, Eugene, Oregon, 97403.