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Effects of model's skill level and model's knowledge of results on the performance of a dart throwing task
Human Movement North-Holland
Science 9 (1990) 369-383
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EFFECTS OF MODEL’S SKILL LEVEL AND MODEL’S KNOWLEDGE OF RESULTS ON THE PERFORMANCE OF A DART THROWING TASK * Patricia L. WEIR and Jack L. LEAVITT University of Winah,
Ontario, Canada
Weir, P.L. and J.L. Leavitt, 1990. Effects of model’s skill level and model’s knowledge of results on the performance of a dart throwing task. Human Movement Science 9, 369-383.
The experiment was designed to examine how the effects of the model’s skill level and the model’s knowledge of results affected performance of an aiming task. Thirty novice, female dart throwers were randomly assigned to six conditions: four groups to complete a 2 x 2 factorial of model’s skill level (skilled vs. unskilled) and model’s knowledge of results (KR vs. No-KR), plus two control groups: one to control for the number of prior task experiences and one to control for the number of practice trials. Practice resulted in more accurate (AE) initial performance than did merely observing a model, and observing a model was not better than receiving no prior task experiences. Subjects observing the skilled model required the model’s knowledge of results to perform with the same degree of accuracy (AE) as that obtained from observing the unskilled model. Observing the unskilled model resulted in a more consistent (VE) performance throughout practice. There were no performance changes over a 24-hour retention interval. These findings provide preliminary support for the contention that a correct model may not be the best for creating an accurate conceptual representation; and that the effects of observing a model are short lived.
It is generally believed that observing a model facilitates the acquisition of motor skills, and although verbal instructions also can convey task demands, language is limiting when describing complex movements. Modelling as an instructional technique is commonly employed * This study was submitted as a master’s thesis at the University of Windsor by Patricia L. Weir. Patricia L. Weir is now a doctorate student at the University of Waterloo, Ontario, Canada and Jack L. Leavitt is a professor in the Dept. of Kinesiology, University of Windsor, Ontario, Canada, N9B 3P4. Requests for reprints should be sent to J.L. Leavitt, Dept. of Kinesiology, University of Windsor, 401 Sunset, Windsor, Ontario, Canada N9B 3P4.
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in industry and education, such as demonstrating the use of machinery, or the execution of sport skills. This technique is beneficial not only for the novice performer but also for the skilled, as a model can quickly and efficiently convey an image of the act (Whiting and Den Brinker 1982), or what Gentile refers to as ‘getting the idea’ (1972: 5). Instructors and educators rely heavily on demonstration as a means of communicating movement patterns. The recent surge of interest in modelling research acknowledges both its practical and theoretical significance (Adams 1986; Carroll and Bandura, 1982, 1985, 1987; Doody et al. 1985; Landers and Landers 1973; Little and McCullagh 1987; Martens et al. 1976; McCullagh and Little 1989; McCullagh et al. 1989; Ross et al. 1985). It is well acknowledged that motor skill learning benefits from augmented information (Salmoni et al. 1984). This information can be provided prior to, during, and/or after movement, although researchers have typically focused on the role of information provided during and following movement. However, Carroll and Bandura (1982) and Adams (1986) have pointed out the limitations of this dependence on the instrumental learning paradigm, and have suggested a shift of attention to information provided prior to performance, namely the observational learning paradigm. The literature considered in the present paper, on the observational learning of motor skill acquisition examines three issues: first, the concerns related to the skill level of the model, second the observer’s use of the model’s knowledge of results (vicarious knowledge of results) and third, the concerns associated with the type of prior task experience (e.g. physically practicing versus observing a model practice). Two studies have claimed to manipulate skill level of the model. Landers and Landers (1973) used skilled versus unskilled teacher and peer models, and in 1976, Martens et al. employed a correct, incorrect, and learning sequence model. However, in both studies all models were really skilled at performing the task, but pretended to be unskilled when the conditions of the experiment demanded. Consequently, the model possibly used the same movement patterns to achieve different response outcomes. This protocol has its limitations as Bandura (1969) and Williams (1986, 1989) have pointed out, in that it is unclear what information the observer extracts and uses from the model’s performance; and as McCullagh et al. have concluded from their excellent review, ‘Model characteristics do affect performance’ (1989: 493).
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Subsequent to these studies only correct models have been used (Carroll and Bandura 1982, 1985, 1987; Doody et al. 1985; Little and McCullagh 1987; McCullagh and Little 1989; Ross et al. 1985). In 1986 Adams reversed this trend by having a novice learn the task while being observed by a subject. Another issue related to the model is use of the model’s knowledge of results. Receiving knowledge of results following performance of a motor skill is thought to be a critical variable for learning to occur (b-ion 1966: 34). The guidance function of knowledge of results presumes detection and correction of movement errors which will lead to improved performance on the next trial. In observational learning, the detection and correction of errors can be linked to the theoretical framework of Bandura’s (1969, 1977, 1986) social learning theory, and Carroll and Bandura’s (1982, 1985, 1987) adaptation to motor learning. This adaptation requires the construction of a conceptual representation of the modelled pattern, formed by changing observed sequences of behavior into symbolic codes that provide the internal plan for movement production. Furthermore, this plan serves as the standard for response execution. Supposedly, response patterns are initially organized at the cognitive level and the conceptual representation of the behavior enables the learner to produce a rough approximation of the movement. If the movement is a novel one, overt practice becomes necessary to both detect mismatches between the conceptual representation and the performance feedback, and to make appropriate corrections in response execution. Adams (1986) expanded upon this idea and combined it with the instrumental paradigm where it is presumed that knowledge of results detects movement errors and serves as a guidance function in their correction. He stated that when the movement sequence to be learned is complex, information provided in addition to observation may be beneficial to the learning of the task. Employing a timing task, Adams hypothesized that the observer would not only form a conceptual representation but become experienced in response appraisal from observing the novice model and receiving the model’s knowledge of results. However, he found no significant difference in the performances between those who did and those who did not receive the model’s knowledge of results. While Adams (1986), Landers and Landers (1973) and Martens et al. (1976) have provided evidence in support of skill learning through
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observation, little is known about the relative potency of modelling as compared to the traditional physical practice with knowledge of results approach to learning a motor skill. While, receiving information prior to practice is beneficial, is this information more beneficial than practice itself? This third concern was addressed by Ross et al. in 1985. They added knowledge of results as a variable in the observational learning of a timing task to test the hypothesis that the acquisition of a motor skill through observational learning should generate better retention than physical practice with knowledge of results. Over acquisition, all groups performed with equivalent accuracy (AE) and consistency (VE). However, following the retention interval all groups, with the exception of those who observed a correct model, displayed a significant loss in accuracy of performance. Ross et al. concluded that observing a correct model throughout the acquisition period resulted in a stronger conceptual representation. Using the same task, Little and McCullagh (1987) failed to replicate this finding, as their correct model group also experienced a loss in accuracy (CE) over the retention interval. Because the subjects in these two studies received their own knowledge of results accompanying each physical practice trial, it is not clear whether they could construct an adequate conceptual representation based on modelling alone. This was addressed by McCullagh and Little (1989) who provided subjects with no knowledge of results during the physical practice trials. Over acquisition, their Physical Practice No-Knowledge of Results Group was significantly more accurate (AE) than the modelling groups. However, on immediate transfer the modelling groups were significantly more consistent (VE), while following the retention interval all groups were equal. The present experiment attempted to correct some of the problems inherent in these studies. First, by using both a skilled and unskilled model it was possible to eliminate the possible commonalities in the model’s movement pattern who was performing under different instruction conditions (Landers and Landers 1973; Martens et al. 1976). It also allowed an uncontaminated comparison of model superiority. Second, to eliminate the confounding of modelling trials and physical practice trials, a procedure similar to that used by Adams (1986) was employed whereby all modelling trials occurred prior to the practice trials. This allowed the effects of modelling to be assessed independent of the effects of physical practice. This was not possible in previous studies where modelling trials were interspersed with physical practice
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trials. Third, to eliminate the problems of unequal physical practice trials and unequal task exposures (Little and McCullagh 1987; McCullagh and Little 1989; Ross et al. 1985) two control groups were incorporated into the design. The first control group received no observational trials, but was equated to the modelling groups in terms of total number of physical practice trials. The second control group was equated to the modelling groups by performing an initial eight practice trials rather than eight observation trials, thereby receiving the same number of task exposures. Fourth, because within a block of trials only the first is uninfluenced by practice, single trials analyses were performed when appropriate to eliminate the confound of practice. The purpose of this study was to assess an observer’s subsequent performance in a dart throwing task, after observing either a skilled or an unskilled model and either receiving or not receiving the model’s knowledge of results. Similar to previous research (Doody et al. 1985; McCullagh and Little 1989; Ross et al. 1985) the measure of interest was performance, not whether the subjects were able to produce the movement pattern used by the model.
Method Subjects and models Thirty female volunteers with a mean age of 19.47 years (SD = 2.09 years) were randomly assigned to one of six experimental conditions with the provision of equal group sizes. All subjects had normal or corrected vision, were right handed and had never before thrown a dart. The two models were right handed female volunteers. On eight trials, the skilled model had an absolute constant error of 1.7 cm that was 3.0 times more accurate than the novice model, and a variable error score of 5.7 cm that was 2.7 times more consistent. These differences reflected the 15 years of recreational and competitive experience held by the skilled model. Design Two levels of model’s skill (skilled and unskilled) and two levels of model’s knowledge of results (KR and No-KR) were factorially com-
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bined with practice (15 blocks of 4 trials) to yield a 2 x 2 x 15 design with repeated measured on the last factor. This resulted in four modelling conditions: Skilled Model Plus KR, Skilled Model - No KR, Unskilled Model Plus KR, and Unskilled Model - No KR. To eliminate the possible confound of unequal task exposures and the unequal physical practice trials, two control groups were incorporated into the experimental design. The Control-60 (C60) Group was equated to the modelling conditions in terms of the number of physical practice trials (performed (60 throws), and the Control-68 (C68) Group was equated to the modelling conditions by providing eight initial practice trials rather than eight initial observation trials (68 throws). Performance of the dart throwing task was measured by calculating the accuracy (Absolute Constant Error - ACE), the variability (Variable Error VE), and the overall-performance error (Total Error - E) scores from radial error values blocked over four trials. Darts landing to the left of the vertical plane through the center of the bull’s_eye were assigned a neiative value, while those to the right were assigned a positive value. Accuracy on single trials was assessed using absolute error (AE). Apparatus
A regulation size NODOR bristle dart board was used. Located in the center of the bull’s eye was a light emitting diode (LED) 0.50 centimetres in diameter and 1.73 meters from the floor. Using 24-gram darts, the models and all subjects threw from a toe-line 2.37 meters from the face of the target, attempting to hit the LED. Measurement of the dart’s location from the LED was accomplished using a Numonics Corporation Digitizer. The dart’s position was marked and fed online to an Apple IIe computer. Software transformed the (x, y) coordinate into distance in centimetres of the dart from the LED. This distance’ information and the section number (l-20) of the dart board in which the dart landed was available immediately to be used as verbal knowledge of results. Procedure
This experiment was divided into three stages: (a) initial task experience, where subjects received either eight modelling trials, eight physical practice trials (C68 group), or neither (C60 group); (b) a practice
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period consisting of 60 physical practice trials during which all subjects received knowledge of results relating to their own performance; and (c) a retention stage whereby all subjects returned following a 24-hour retention interval and performed one block of four no-knowledge of results trials. During the initial task experience subjects in the four modelling conditions viewed a videotape of eight throws from a filming angle of 47 degrees to the LED by their respective model. Subjects were naive with respect to the skill level of the model they were viewing. Subjects receiving the model’s knowledge of results were requested, based on this information, to visualize the dart’s location on the viewing dartboard located on the wall beside them during the inter-trial interval. To prevent confounding visual information with the verbal knowledge of results, subjects in the C68 Group performed their initial task experience in a dark room with only the LED illuminated. During the practice and retention stages, due to the, reason stated above, all subjects performed in a dark room with only the LED illuminated. Subjects began each trial facing the target dart-board and waited for the start signal, then threw when ready. To eliminate any possiblity of seeing where the dart landed, the LED was turned off at the initiation of the forward motion of the throwing arm. This movement was detected as each subject wore a luminous bracelet on her throwing arm which was visible in the dark. Following the release of the dart, subjects turned to face the viewing dart-board at the back of the room. A light above the target dart-board was turned on to provide sufficient light for measurement and to allow the subjects to see the viewing dart-board. Knowledge of results (distance and section number) was provided following each throw. The subject used this information to visualize the dart’s location on the board. The room was then darkened, the subject turned to face the target dart-board and waited for the next signal. This procedure took 30 seconds and was standard for all subjects for all trials.
Results
Performance data of the dart throwing task were analyzed for each of the three stages of the experiment. Burford et al. have illustrated that
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blocking of trials (especially during early retention trials) may serve to mask important trends in the data’ (1987: 55), so where appropriate we have used either single trials or blocks of trials in the analyses. Single trials were used so performance could be assessed independent of the effects of practice. In the single trials analyses accuracy refers to the absolute distance (radial error) from the LED. Within the first block of trials, only the first is not confounded by practice, which allows the assessment of the effects of modelling. The block analyses allow the assessment of the interaction between physical practice and modelling. Due to the large amount of variance in the data, a Hartley F-max test of homogeneity of variance was performed on the variable error data to determine the appropriateness of using analysis of variance. Homogeneity of variance was supported, F(6, 4) = 6.06, p > 0.05. ‘
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The effects of initial task experiences on initial performance To assess whether physical practice was more effective on initial performance than modelling, a planned comparison was performed on the first trial between the C68 group and a weighted mean of the four modelling groups. This allowed an uncontaminated assessment of accuracy (AE) on initial performance. The analysis revealed that eight physical practice trials with knowledge of results resulted in a more accurate score than watching a model for eight trials, F(l, 24) = 4.80, p -C 0.05. A similar analysis was performed between the C60 group and the four modelling groups to evaluate whether modelling was more beneficial than no practice. This analysis revealed no difference in accuracy scores (AE) on the first trial, F(1, 24) = 1.66, p > 0.05. To assess initial performance variability (VE), more than one trial must be included in. the analysis. Thus, data from the first block of trials was used. The analysis for the C68 group versus the four modelling groups indicated no significant difference and neither did the C60 versus modelling comparison, with F-values being F(l,24)= 2.65, p > 0.05,and F(l,24)= 0.51,p > 0.05, respectively. The effects of model’s skill level, model’s knowledge of results and their interaction on the accuracy of initial performance were evaluated independent of the control groups using a 2 X 2 (Model’s Skill Level X Model’s Knowledge of Results) ANOVA on the first trial. A significant
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Fig. 1. The effects of the model’s skill level and the model’s knowledge of results on the accuracy of dart throwing for the first practice trial.
interaction emerged with F(1, 16) = 5.18, p -C0.05. As can be seen in fig. 1, subjects who viewed the unskilled model performed with equivalent accuracy regardless of whether or not they received the model’s knowledge of results; while those who viewed the skilled model required the model’s knowledge of results to perform as accurately. To assess the effects of model’s skill level, model’s knowledge of results and the interaction on overall-performance error (E) and variability (VE), a 2 x 2 (Model’s Skill Level X Model’s Knowledge of Results) ANOVA was performed on the first block of trials. The analysis for overall-performance revealed a significant main effect for model’s skill level with F(1, 16) = 5.37, p -C0.05.Subjects who viewed the unskilled model (22.74 cm) had significantly lower overall-performance scores (E) than those who viewed the skilled model (34.03 cm). Neither model’s skill level, nor model’s knowledge of results affected the initial performance variability (VE). In summary, practice resulted in more accurate performance than did observing a model, and observing a model was not better than receiving no prior task experiences. However, when observing a skilled model, to perform with the same degree of accuracy as that obtained from observing an unskilled model, one must receive the skilled model’s knowledge of results.
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The interaction of prior experiences and subsequent physical practice on acquisition Because the groups received different prior task experiences, but identical treatment during the practice stage, perhaps an interaction between the prior task experiences and practice occurred. This was assessed using a 6 x 15 (Group x Practice Block) ANOVA with repeated measures on the last factor for all dependent variables. These analyses revealed no significant main effects or interactions. Thus, the groups were not differentially affected by practice. The 6 x 15 ANOVA allowed a comparison of all groups, but did not allow an isolation of the independent variables. This was evaluated using a 2 X 2 X 15 (Model’s Skill Level X Model’s Knowledge of Results x Practice Block) ANOVA with repeated measures on practice for all dependent variables. The analysis for variable error revealed only a significant main effect for skill level of the model, F(1, 16) = 4.89, p c 0.05. Subjects who viewed an unskilled model performed more consistently (VE = 14.34 cm) than those subjects who viewed a skilled model (VE = 21.21 cm). There were no significant main effects or interactions for the other dependent variables. In summary, practice did not interact with prior task experiences. However, observing the unskilled model resulted in more consistent performance throughout the practice stage. The performance
level retained
Performance is not always a valid measure of acquisition, because it may be depressed by some variable, the effects of which would disappear with time. To evaluate this possibility a 6 X 2 (Group X Trial) ANOVA with repeated measures on trials was performed between the last trial of practice and the first trial of retention. This analysis revealed a significant group main effect for accuracy, F(5, 24) = 2.71, p c 0.05. The between-subject variability values were large which may explain why no significant differences among the groups emerged in the Newman-Keul’s post-hoc testing procedure. There was no change in performance over the retention interval as evidenced by the non-significant trial main effect. The interaction also was not significant. Decreased sensitivity of the experimental manipulations, because of the sample size, may have been responsible for the lack of significant
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findings. However, the F-ratio’s were so small it is unlikely doubling the sensitivity (squaring n) would change the results.
Discussion The present experiment addressed several problems common to observational learning research. First, by using both a skilled and unskilled model, the possibility of the model using the same movement pattern to accomplish different response outcomes, was eliminated. This permitted a valid comparison of which model type was most beneficial to the learner. Second, to eliminate the contamination of modelling trials with physical practice trials all modelling trials occurred prior to practice. This procedure allowed the effects of modelling to be assessed independent of the influence of physical practice. Third, to eliminate the problems of unequal task exposures and unequal physical practice trials, two control groups were incorporated into the design. Observing a model versus not observing a model Observing a model is more beneficial to performance in the initial stages of acquiring a motor task than not observing a model (Adams 1986; Landers and Landers 1973; Martens et al. 1976). This conclusion is based on a number of observation trials ranging from one (Martens et al.) to 50 (Adams). This conclusion is also based on a block of trials (ranging from 5 to lo), thus confounding the modelling effects with subjects’ practice. The results of the present investigation found no advantage over either control group of observing a model for performance accuracy (trial one) or consistency (block one). The lack of agreement between our results and the reported literature may be due to the type of task employed, the number of observation trials, or what we believe was, the contamination of modelling effects with practice effects during the period of initial performance assessment in previous experiments. The effects of physical practice with KR coupled with prior task experiences did not differentially affect the groups based on their prior experiences, as evidenced by the non-significant interactions. This equality among the groups at the end of the acquisition or practice
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period is supported by Martens et al. (1976) and Little and McCullagh (1987). The temporary advantage (first trial only) of physically practicing the task over watching a demonstration of the task, with or without the model’s knowledge of results, suggests that once experience in performing the task occurs, it makes no difference whether prior task experience is physical practice or observing a model. Unexpectedly, no change in performance occurred over practice, which is contrary to reported observational learning studies where changes in performance have been reported for a Bachman ladder task, a ball roll-up task, and various timing tasks (Landers and Landers 1973; Little and McCullagh 1987; Martens et al. 1976; McCullagh and Little 1989; Ross et al. 1985). Perhaps subjects in the current experiment were ‘real’ novices performing in the positive acceleration segment of a sigmoidal learning curve where changes in performance were so infinitesimal they v~:nt undetected by the current measurement system, or were not sensitive to accuracy and variability measures. Although subjects had vision of the target up to release of the dart, perhaps the darkened artificial environment was a contributing factor. However, it was necessary to eliminate vision to make the verbal knowledge of results relevant. There were no changes in performance over the 24-hour retention interval for any of the six groups. Little and McCullagh (1987) and Ross et al. (1985) reported equivocal findings regarding skill loss over this time. However, it is difficult to determine whether their analyses were performed over the retention interval or after the retention interval. Observing a skilled model versus an unskilled model In extending Social Learning Theory to the motor domain Carroll and Bandura (1982, 1985, 1987) used only skilled models, presumably because a more complete conceptual representation of the task could be formed from observing a skilled rather than an unskilled performer. The results of Martens et al. (1976) do not support this notion but it must be remembered that their skilled and unskilled models were the same person performing under the different conditions of the experiment. The results of the present experiment paint a different picture. Subjects who viewed the unskilled model were more accurate (AE) during the first four trials than subjects who viewed the skilled model.
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As well, over the practice period (15 blocks), subjects who viewed the unskilled model exhibited more consistent (VE) performance. Subjects, it seems, were able to pick up more meaningful cues from observing the erratic and changing behavior of the unskilled model from trial to trial. As Bandura states, ‘modeled characteristics that are highly discernible can be more readily acquired than subtle attributes which must be abstracted from heterogeneous responses differing on numerous stimulus dimensions’ (1969: 138). This is in opposition to the findings of Landers and Landers (1973) who reported no effect of model ability over the acquisition period. An alternate explanation is that subjects may have been more motivated after watching the unskilled model’s performance because they perceived it to be like their own. Landers and Landers (1973) support this idea in that subjects who viewed an unskilled peer model performed better than those who observed a skilled peer model. They were less motivated to perform since their initial performances were more discrepant than the skilled model’s performance. Although the subjects in the present experiment observed the model’s performance prior to performing, they may have been more motivated and at ease after observing the unskilled model, due to the perceived similarity of performance, whereas those who observed the skilled model may have been immediately discouraged by her accurate and consistent performance, believing that they would never be able to perform that well. In conclusion, there is probably not one single contributing factor to the superior performance of the subjects who observed the unskilled model. Rather, it was more likely a combination of reasons. Receiving model’s knowledge of results versus not receiving model’s knowledge of results Adams (1986) combined the observational and instrumental learning paradigms using the rationale that in receiving knowledge of results about the novice model’s performance the observer would be able to relate it to his subjectively perceived error and hypothesize the correction that is required on the next trial, just as the model does. The more errorful the model’s performance the more active the observer would be in forming the conceptual representation of the task. Thus, the novice model learning the task would be more beneficial to the observer than a skilled model who would commit fewer and more subtle errors. Adams
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did not confirm his beli’ef in the value of using the model’s knowledge of results. The present study, using both skilled and unskilled models, found no main effect for knowledge of results, but did find an interaction between knowledge of results and model’s skill level. We found, in effect, just the opposite of what Adams would predict; knowledge of results is redundant for the observer of the skilled model where the errors are more subtle and possibly more difficult to detect by a novice performer. We do, however, provide evidence to support Adam’s contention that the amalgamation of the instrumental and observational learning paradigm will lead to some interesting and previously unknown phenomena. References Adams, J.A., 1986. Use of the model’s knowledge of results to increase the observer’s performance. Journal of Human Movement Studies 12, 89-98. Bandura, A., 1969. Principles of behavior modification. New York: Holt, Rinehart and Winston. Bandura, A., 1977. Social learning theory. Englewood Cliffs, NJ: Prentice-Hall. Bandura, A., 1986. Social foundations of thought and action. Englewood Cliffs, NJ: Prentice-Hall. Burford, G.C., T. Lee and D. Elliott, 1987. Assessing motor learning: A case where retention effect is not a learning effect. Journal of Human Movement Studies 13, 51-55. Carroll, W.R. and A. Bandura, 1982. The role of visual monitoring in observational learning of action patterns: Making the unobservable observable. Journal of Motor Behavior 14(2), 152-167. Carroll, W.R. and A. Bandura, 1985. Role of timing of visual monitoring and motor rehearsal in observational learning of action patterns. Journal of Motor Behavior 17(3), 269-281. Carroll, W.R. and A. Bandura, 1987. Translating cognition into action: The role of visual guidance in observational learning. Journal of Motor Behavior 19(3), 385-398. Doody, S.G., A.M. Bird and D. Ross, 1985. The effect of auditory and visual models on acquisition of a timing task. Human Movement Science 4, 271-281. Gentile, A.M., 1972. A working model of skill acquisition with application to teaching. Quest 17, 3-23. Irion, A.L., 1966. ‘A brief history of research on the acquisition of skill’. In: E.A. Bilodeau (ed.), Acquisition of skill. New York: Academic Press. pp. l-47. Landers, D.M. and D.M. Landers, 1973. Teacher versus peer models: Effects of model’s presence and performance level of motor behavior. Journal of Motor Behavior 5(3), 129-139. Little, W.S. and P. McCullagh, 1987. A comparison of modeling and knowledge of results. Paper presented at the meeting of the North American Society for the Psychology of Sport and Physical Activity, Vancouver, British Columbia, June. Martens, R., L. Burwitz and J. Zuckerman, 1976. Modeling effects on motor performance. Research Quarterly 47(2), 277-291. McCullagh, P. and W.S. Little, 1989. A comparison of modalities in modeling. Human Performance 2,101-111. McCullagh, P., M.R. Weiss and D. Ross, 1989. ‘Modeling considerations in motor skill acquisition and performance: An integrated approach’. In: K.B. Pardolf (ed.), Exercise and Sport Sciences Reviews. Baltimore, MD: Williams & Wilkins. pp. 475-513.
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Ross, D., A.M. Bird, S.G. Doody and M. Zoeller, 1985. Effects of modeling and videotape feedback with knowledge of results on motor performance. Human Movement Science 4, 149-157. Salmoni, A.W., R.A. Schmidt and C.B. Walter, 1984. Knowledge of results and motor learning: A review and critical appraisal. Psychological Bulletin 95, 355-386. Whiting, H.T.A. and B. den Brinker, 1982. ‘Image of the act’. In: J.P. Das, R.F. Mulcahy and A.E. Wall (eds.), Theory and research in learning disabilities. New York: Plenum. pp. 217-235. Williams, J.G., 1986. Perceiving human movement: A review of research with implications for the use of the demonstration during motor learning. Physical Education Review 9(l), 53-58. Williams, J.G., 1989. Throwing action from full-cue and motion-only video-models of an arm movement sequence. Perceptual and Motor Skills 68, 259-266.
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EFFECTS OF MODEL’S SKILL LEVEL AND MODEL’S KNOWLEDGE OF RESULTS ON THE PERFORMANCE OF A DART THROWING TASK * Patricia L. WEIR and Jack L. LEAVITT University of Winah,
Ontario, Canada
Weir, P.L. and J.L. Leavitt, 1990. Effects of model’s skill level and model’s knowledge of results on the performance of a dart throwing task. Human Movement Science 9, 369-383.
The experiment was designed to examine how the effects of the model’s skill level and the model’s knowledge of results affected performance of an aiming task. Thirty novice, female dart throwers were randomly assigned to six conditions: four groups to complete a 2 x 2 factorial of model’s skill level (skilled vs. unskilled) and model’s knowledge of results (KR vs. No-KR), plus two control groups: one to control for the number of prior task experiences and one to control for the number of practice trials. Practice resulted in more accurate (AE) initial performance than did merely observing a model, and observing a model was not better than receiving no prior task experiences. Subjects observing the skilled model required the model’s knowledge of results to perform with the same degree of accuracy (AE) as that obtained from observing the unskilled model. Observing the unskilled model resulted in a more consistent (VE) performance throughout practice. There were no performance changes over a 24-hour retention interval. These findings provide preliminary support for the contention that a correct model may not be the best for creating an accurate conceptual representation; and that the effects of observing a model are short lived.
It is generally believed that observing a model facilitates the acquisition of motor skills, and although verbal instructions also can convey task demands, language is limiting when describing complex movements. Modelling as an instructional technique is commonly employed * This study was submitted as a master’s thesis at the University of Windsor by Patricia L. Weir. Patricia L. Weir is now a doctorate student at the University of Waterloo, Ontario, Canada and Jack L. Leavitt is a professor in the Dept. of Kinesiology, University of Windsor, Ontario, Canada, N9B 3P4. Requests for reprints should be sent to J.L. Leavitt, Dept. of Kinesiology, University of Windsor, 401 Sunset, Windsor, Ontario, Canada N9B 3P4.
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in industry and education, such as demonstrating the use of machinery, or the execution of sport skills. This technique is beneficial not only for the novice performer but also for the skilled, as a model can quickly and efficiently convey an image of the act (Whiting and Den Brinker 1982), or what Gentile refers to as ‘getting the idea’ (1972: 5). Instructors and educators rely heavily on demonstration as a means of communicating movement patterns. The recent surge of interest in modelling research acknowledges both its practical and theoretical significance (Adams 1986; Carroll and Bandura, 1982, 1985, 1987; Doody et al. 1985; Landers and Landers 1973; Little and McCullagh 1987; Martens et al. 1976; McCullagh and Little 1989; McCullagh et al. 1989; Ross et al. 1985). It is well acknowledged that motor skill learning benefits from augmented information (Salmoni et al. 1984). This information can be provided prior to, during, and/or after movement, although researchers have typically focused on the role of information provided during and following movement. However, Carroll and Bandura (1982) and Adams (1986) have pointed out the limitations of this dependence on the instrumental learning paradigm, and have suggested a shift of attention to information provided prior to performance, namely the observational learning paradigm. The literature considered in the present paper, on the observational learning of motor skill acquisition examines three issues: first, the concerns related to the skill level of the model, second the observer’s use of the model’s knowledge of results (vicarious knowledge of results) and third, the concerns associated with the type of prior task experience (e.g. physically practicing versus observing a model practice). Two studies have claimed to manipulate skill level of the model. Landers and Landers (1973) used skilled versus unskilled teacher and peer models, and in 1976, Martens et al. employed a correct, incorrect, and learning sequence model. However, in both studies all models were really skilled at performing the task, but pretended to be unskilled when the conditions of the experiment demanded. Consequently, the model possibly used the same movement patterns to achieve different response outcomes. This protocol has its limitations as Bandura (1969) and Williams (1986, 1989) have pointed out, in that it is unclear what information the observer extracts and uses from the model’s performance; and as McCullagh et al. have concluded from their excellent review, ‘Model characteristics do affect performance’ (1989: 493).
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Subsequent to these studies only correct models have been used (Carroll and Bandura 1982, 1985, 1987; Doody et al. 1985; Little and McCullagh 1987; McCullagh and Little 1989; Ross et al. 1985). In 1986 Adams reversed this trend by having a novice learn the task while being observed by a subject. Another issue related to the model is use of the model’s knowledge of results. Receiving knowledge of results following performance of a motor skill is thought to be a critical variable for learning to occur (b-ion 1966: 34). The guidance function of knowledge of results presumes detection and correction of movement errors which will lead to improved performance on the next trial. In observational learning, the detection and correction of errors can be linked to the theoretical framework of Bandura’s (1969, 1977, 1986) social learning theory, and Carroll and Bandura’s (1982, 1985, 1987) adaptation to motor learning. This adaptation requires the construction of a conceptual representation of the modelled pattern, formed by changing observed sequences of behavior into symbolic codes that provide the internal plan for movement production. Furthermore, this plan serves as the standard for response execution. Supposedly, response patterns are initially organized at the cognitive level and the conceptual representation of the behavior enables the learner to produce a rough approximation of the movement. If the movement is a novel one, overt practice becomes necessary to both detect mismatches between the conceptual representation and the performance feedback, and to make appropriate corrections in response execution. Adams (1986) expanded upon this idea and combined it with the instrumental paradigm where it is presumed that knowledge of results detects movement errors and serves as a guidance function in their correction. He stated that when the movement sequence to be learned is complex, information provided in addition to observation may be beneficial to the learning of the task. Employing a timing task, Adams hypothesized that the observer would not only form a conceptual representation but become experienced in response appraisal from observing the novice model and receiving the model’s knowledge of results. However, he found no significant difference in the performances between those who did and those who did not receive the model’s knowledge of results. While Adams (1986), Landers and Landers (1973) and Martens et al. (1976) have provided evidence in support of skill learning through
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observation, little is known about the relative potency of modelling as compared to the traditional physical practice with knowledge of results approach to learning a motor skill. While, receiving information prior to practice is beneficial, is this information more beneficial than practice itself? This third concern was addressed by Ross et al. in 1985. They added knowledge of results as a variable in the observational learning of a timing task to test the hypothesis that the acquisition of a motor skill through observational learning should generate better retention than physical practice with knowledge of results. Over acquisition, all groups performed with equivalent accuracy (AE) and consistency (VE). However, following the retention interval all groups, with the exception of those who observed a correct model, displayed a significant loss in accuracy of performance. Ross et al. concluded that observing a correct model throughout the acquisition period resulted in a stronger conceptual representation. Using the same task, Little and McCullagh (1987) failed to replicate this finding, as their correct model group also experienced a loss in accuracy (CE) over the retention interval. Because the subjects in these two studies received their own knowledge of results accompanying each physical practice trial, it is not clear whether they could construct an adequate conceptual representation based on modelling alone. This was addressed by McCullagh and Little (1989) who provided subjects with no knowledge of results during the physical practice trials. Over acquisition, their Physical Practice No-Knowledge of Results Group was significantly more accurate (AE) than the modelling groups. However, on immediate transfer the modelling groups were significantly more consistent (VE), while following the retention interval all groups were equal. The present experiment attempted to correct some of the problems inherent in these studies. First, by using both a skilled and unskilled model it was possible to eliminate the possible commonalities in the model’s movement pattern who was performing under different instruction conditions (Landers and Landers 1973; Martens et al. 1976). It also allowed an uncontaminated comparison of model superiority. Second, to eliminate the confounding of modelling trials and physical practice trials, a procedure similar to that used by Adams (1986) was employed whereby all modelling trials occurred prior to the practice trials. This allowed the effects of modelling to be assessed independent of the effects of physical practice. This was not possible in previous studies where modelling trials were interspersed with physical practice
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trials. Third, to eliminate the problems of unequal physical practice trials and unequal task exposures (Little and McCullagh 1987; McCullagh and Little 1989; Ross et al. 1985) two control groups were incorporated into the design. The first control group received no observational trials, but was equated to the modelling groups in terms of total number of physical practice trials. The second control group was equated to the modelling groups by performing an initial eight practice trials rather than eight observation trials, thereby receiving the same number of task exposures. Fourth, because within a block of trials only the first is uninfluenced by practice, single trials analyses were performed when appropriate to eliminate the confound of practice. The purpose of this study was to assess an observer’s subsequent performance in a dart throwing task, after observing either a skilled or an unskilled model and either receiving or not receiving the model’s knowledge of results. Similar to previous research (Doody et al. 1985; McCullagh and Little 1989; Ross et al. 1985) the measure of interest was performance, not whether the subjects were able to produce the movement pattern used by the model.
Method Subjects and models Thirty female volunteers with a mean age of 19.47 years (SD = 2.09 years) were randomly assigned to one of six experimental conditions with the provision of equal group sizes. All subjects had normal or corrected vision, were right handed and had never before thrown a dart. The two models were right handed female volunteers. On eight trials, the skilled model had an absolute constant error of 1.7 cm that was 3.0 times more accurate than the novice model, and a variable error score of 5.7 cm that was 2.7 times more consistent. These differences reflected the 15 years of recreational and competitive experience held by the skilled model. Design Two levels of model’s skill (skilled and unskilled) and two levels of model’s knowledge of results (KR and No-KR) were factorially com-
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bined with practice (15 blocks of 4 trials) to yield a 2 x 2 x 15 design with repeated measured on the last factor. This resulted in four modelling conditions: Skilled Model Plus KR, Skilled Model - No KR, Unskilled Model Plus KR, and Unskilled Model - No KR. To eliminate the possible confound of unequal task exposures and the unequal physical practice trials, two control groups were incorporated into the experimental design. The Control-60 (C60) Group was equated to the modelling conditions in terms of the number of physical practice trials (performed (60 throws), and the Control-68 (C68) Group was equated to the modelling conditions by providing eight initial practice trials rather than eight initial observation trials (68 throws). Performance of the dart throwing task was measured by calculating the accuracy (Absolute Constant Error - ACE), the variability (Variable Error VE), and the overall-performance error (Total Error - E) scores from radial error values blocked over four trials. Darts landing to the left of the vertical plane through the center of the bull’s_eye were assigned a neiative value, while those to the right were assigned a positive value. Accuracy on single trials was assessed using absolute error (AE). Apparatus
A regulation size NODOR bristle dart board was used. Located in the center of the bull’s eye was a light emitting diode (LED) 0.50 centimetres in diameter and 1.73 meters from the floor. Using 24-gram darts, the models and all subjects threw from a toe-line 2.37 meters from the face of the target, attempting to hit the LED. Measurement of the dart’s location from the LED was accomplished using a Numonics Corporation Digitizer. The dart’s position was marked and fed online to an Apple IIe computer. Software transformed the (x, y) coordinate into distance in centimetres of the dart from the LED. This distance’ information and the section number (l-20) of the dart board in which the dart landed was available immediately to be used as verbal knowledge of results. Procedure
This experiment was divided into three stages: (a) initial task experience, where subjects received either eight modelling trials, eight physical practice trials (C68 group), or neither (C60 group); (b) a practice
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period consisting of 60 physical practice trials during which all subjects received knowledge of results relating to their own performance; and (c) a retention stage whereby all subjects returned following a 24-hour retention interval and performed one block of four no-knowledge of results trials. During the initial task experience subjects in the four modelling conditions viewed a videotape of eight throws from a filming angle of 47 degrees to the LED by their respective model. Subjects were naive with respect to the skill level of the model they were viewing. Subjects receiving the model’s knowledge of results were requested, based on this information, to visualize the dart’s location on the viewing dartboard located on the wall beside them during the inter-trial interval. To prevent confounding visual information with the verbal knowledge of results, subjects in the C68 Group performed their initial task experience in a dark room with only the LED illuminated. During the practice and retention stages, due to the, reason stated above, all subjects performed in a dark room with only the LED illuminated. Subjects began each trial facing the target dart-board and waited for the start signal, then threw when ready. To eliminate any possiblity of seeing where the dart landed, the LED was turned off at the initiation of the forward motion of the throwing arm. This movement was detected as each subject wore a luminous bracelet on her throwing arm which was visible in the dark. Following the release of the dart, subjects turned to face the viewing dart-board at the back of the room. A light above the target dart-board was turned on to provide sufficient light for measurement and to allow the subjects to see the viewing dart-board. Knowledge of results (distance and section number) was provided following each throw. The subject used this information to visualize the dart’s location on the board. The room was then darkened, the subject turned to face the target dart-board and waited for the next signal. This procedure took 30 seconds and was standard for all subjects for all trials.
Results
Performance data of the dart throwing task were analyzed for each of the three stages of the experiment. Burford et al. have illustrated that
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blocking of trials (especially during early retention trials) may serve to mask important trends in the data’ (1987: 55), so where appropriate we have used either single trials or blocks of trials in the analyses. Single trials were used so performance could be assessed independent of the effects of practice. In the single trials analyses accuracy refers to the absolute distance (radial error) from the LED. Within the first block of trials, only the first is not confounded by practice, which allows the assessment of the effects of modelling. The block analyses allow the assessment of the interaction between physical practice and modelling. Due to the large amount of variance in the data, a Hartley F-max test of homogeneity of variance was performed on the variable error data to determine the appropriateness of using analysis of variance. Homogeneity of variance was supported, F(6, 4) = 6.06, p > 0.05. ‘
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The effects of initial task experiences on initial performance To assess whether physical practice was more effective on initial performance than modelling, a planned comparison was performed on the first trial between the C68 group and a weighted mean of the four modelling groups. This allowed an uncontaminated assessment of accuracy (AE) on initial performance. The analysis revealed that eight physical practice trials with knowledge of results resulted in a more accurate score than watching a model for eight trials, F(l, 24) = 4.80, p -C 0.05. A similar analysis was performed between the C60 group and the four modelling groups to evaluate whether modelling was more beneficial than no practice. This analysis revealed no difference in accuracy scores (AE) on the first trial, F(1, 24) = 1.66, p > 0.05. To assess initial performance variability (VE), more than one trial must be included in. the analysis. Thus, data from the first block of trials was used. The analysis for the C68 group versus the four modelling groups indicated no significant difference and neither did the C60 versus modelling comparison, with F-values being F(l,24)= 2.65, p > 0.05,and F(l,24)= 0.51,p > 0.05, respectively. The effects of model’s skill level, model’s knowledge of results and their interaction on the accuracy of initial performance were evaluated independent of the control groups using a 2 X 2 (Model’s Skill Level X Model’s Knowledge of Results) ANOVA on the first trial. A significant
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Fig. 1. The effects of the model’s skill level and the model’s knowledge of results on the accuracy of dart throwing for the first practice trial.
interaction emerged with F(1, 16) = 5.18, p -C0.05. As can be seen in fig. 1, subjects who viewed the unskilled model performed with equivalent accuracy regardless of whether or not they received the model’s knowledge of results; while those who viewed the skilled model required the model’s knowledge of results to perform as accurately. To assess the effects of model’s skill level, model’s knowledge of results and the interaction on overall-performance error (E) and variability (VE), a 2 x 2 (Model’s Skill Level X Model’s Knowledge of Results) ANOVA was performed on the first block of trials. The analysis for overall-performance revealed a significant main effect for model’s skill level with F(1, 16) = 5.37, p -C0.05.Subjects who viewed the unskilled model (22.74 cm) had significantly lower overall-performance scores (E) than those who viewed the skilled model (34.03 cm). Neither model’s skill level, nor model’s knowledge of results affected the initial performance variability (VE). In summary, practice resulted in more accurate performance than did observing a model, and observing a model was not better than receiving no prior task experiences. However, when observing a skilled model, to perform with the same degree of accuracy as that obtained from observing an unskilled model, one must receive the skilled model’s knowledge of results.
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The interaction of prior experiences and subsequent physical practice on acquisition Because the groups received different prior task experiences, but identical treatment during the practice stage, perhaps an interaction between the prior task experiences and practice occurred. This was assessed using a 6 x 15 (Group x Practice Block) ANOVA with repeated measures on the last factor for all dependent variables. These analyses revealed no significant main effects or interactions. Thus, the groups were not differentially affected by practice. The 6 x 15 ANOVA allowed a comparison of all groups, but did not allow an isolation of the independent variables. This was evaluated using a 2 X 2 X 15 (Model’s Skill Level X Model’s Knowledge of Results x Practice Block) ANOVA with repeated measures on practice for all dependent variables. The analysis for variable error revealed only a significant main effect for skill level of the model, F(1, 16) = 4.89, p c 0.05. Subjects who viewed an unskilled model performed more consistently (VE = 14.34 cm) than those subjects who viewed a skilled model (VE = 21.21 cm). There were no significant main effects or interactions for the other dependent variables. In summary, practice did not interact with prior task experiences. However, observing the unskilled model resulted in more consistent performance throughout the practice stage. The performance
level retained
Performance is not always a valid measure of acquisition, because it may be depressed by some variable, the effects of which would disappear with time. To evaluate this possibility a 6 X 2 (Group X Trial) ANOVA with repeated measures on trials was performed between the last trial of practice and the first trial of retention. This analysis revealed a significant group main effect for accuracy, F(5, 24) = 2.71, p c 0.05. The between-subject variability values were large which may explain why no significant differences among the groups emerged in the Newman-Keul’s post-hoc testing procedure. There was no change in performance over the retention interval as evidenced by the non-significant trial main effect. The interaction also was not significant. Decreased sensitivity of the experimental manipulations, because of the sample size, may have been responsible for the lack of significant
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findings. However, the F-ratio’s were so small it is unlikely doubling the sensitivity (squaring n) would change the results.
Discussion The present experiment addressed several problems common to observational learning research. First, by using both a skilled and unskilled model, the possibility of the model using the same movement pattern to accomplish different response outcomes, was eliminated. This permitted a valid comparison of which model type was most beneficial to the learner. Second, to eliminate the contamination of modelling trials with physical practice trials all modelling trials occurred prior to practice. This procedure allowed the effects of modelling to be assessed independent of the influence of physical practice. Third, to eliminate the problems of unequal task exposures and unequal physical practice trials, two control groups were incorporated into the design. Observing a model versus not observing a model Observing a model is more beneficial to performance in the initial stages of acquiring a motor task than not observing a model (Adams 1986; Landers and Landers 1973; Martens et al. 1976). This conclusion is based on a number of observation trials ranging from one (Martens et al.) to 50 (Adams). This conclusion is also based on a block of trials (ranging from 5 to lo), thus confounding the modelling effects with subjects’ practice. The results of the present investigation found no advantage over either control group of observing a model for performance accuracy (trial one) or consistency (block one). The lack of agreement between our results and the reported literature may be due to the type of task employed, the number of observation trials, or what we believe was, the contamination of modelling effects with practice effects during the period of initial performance assessment in previous experiments. The effects of physical practice with KR coupled with prior task experiences did not differentially affect the groups based on their prior experiences, as evidenced by the non-significant interactions. This equality among the groups at the end of the acquisition or practice
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period is supported by Martens et al. (1976) and Little and McCullagh (1987). The temporary advantage (first trial only) of physically practicing the task over watching a demonstration of the task, with or without the model’s knowledge of results, suggests that once experience in performing the task occurs, it makes no difference whether prior task experience is physical practice or observing a model. Unexpectedly, no change in performance occurred over practice, which is contrary to reported observational learning studies where changes in performance have been reported for a Bachman ladder task, a ball roll-up task, and various timing tasks (Landers and Landers 1973; Little and McCullagh 1987; Martens et al. 1976; McCullagh and Little 1989; Ross et al. 1985). Perhaps subjects in the current experiment were ‘real’ novices performing in the positive acceleration segment of a sigmoidal learning curve where changes in performance were so infinitesimal they v~:nt undetected by the current measurement system, or were not sensitive to accuracy and variability measures. Although subjects had vision of the target up to release of the dart, perhaps the darkened artificial environment was a contributing factor. However, it was necessary to eliminate vision to make the verbal knowledge of results relevant. There were no changes in performance over the 24-hour retention interval for any of the six groups. Little and McCullagh (1987) and Ross et al. (1985) reported equivocal findings regarding skill loss over this time. However, it is difficult to determine whether their analyses were performed over the retention interval or after the retention interval. Observing a skilled model versus an unskilled model In extending Social Learning Theory to the motor domain Carroll and Bandura (1982, 1985, 1987) used only skilled models, presumably because a more complete conceptual representation of the task could be formed from observing a skilled rather than an unskilled performer. The results of Martens et al. (1976) do not support this notion but it must be remembered that their skilled and unskilled models were the same person performing under the different conditions of the experiment. The results of the present experiment paint a different picture. Subjects who viewed the unskilled model were more accurate (AE) during the first four trials than subjects who viewed the skilled model.
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As well, over the practice period (15 blocks), subjects who viewed the unskilled model exhibited more consistent (VE) performance. Subjects, it seems, were able to pick up more meaningful cues from observing the erratic and changing behavior of the unskilled model from trial to trial. As Bandura states, ‘modeled characteristics that are highly discernible can be more readily acquired than subtle attributes which must be abstracted from heterogeneous responses differing on numerous stimulus dimensions’ (1969: 138). This is in opposition to the findings of Landers and Landers (1973) who reported no effect of model ability over the acquisition period. An alternate explanation is that subjects may have been more motivated after watching the unskilled model’s performance because they perceived it to be like their own. Landers and Landers (1973) support this idea in that subjects who viewed an unskilled peer model performed better than those who observed a skilled peer model. They were less motivated to perform since their initial performances were more discrepant than the skilled model’s performance. Although the subjects in the present experiment observed the model’s performance prior to performing, they may have been more motivated and at ease after observing the unskilled model, due to the perceived similarity of performance, whereas those who observed the skilled model may have been immediately discouraged by her accurate and consistent performance, believing that they would never be able to perform that well. In conclusion, there is probably not one single contributing factor to the superior performance of the subjects who observed the unskilled model. Rather, it was more likely a combination of reasons. Receiving model’s knowledge of results versus not receiving model’s knowledge of results Adams (1986) combined the observational and instrumental learning paradigms using the rationale that in receiving knowledge of results about the novice model’s performance the observer would be able to relate it to his subjectively perceived error and hypothesize the correction that is required on the next trial, just as the model does. The more errorful the model’s performance the more active the observer would be in forming the conceptual representation of the task. Thus, the novice model learning the task would be more beneficial to the observer than a skilled model who would commit fewer and more subtle errors. Adams
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did not confirm his beli’ef in the value of using the model’s knowledge of results. The present study, using both skilled and unskilled models, found no main effect for knowledge of results, but did find an interaction between knowledge of results and model’s skill level. We found, in effect, just the opposite of what Adams would predict; knowledge of results is redundant for the observer of the skilled model where the errors are more subtle and possibly more difficult to detect by a novice performer. We do, however, provide evidence to support Adam’s contention that the amalgamation of the instrumental and observational learning paradigm will lead to some interesting and previously unknown phenomena. References Adams, J.A., 1986. Use of the model’s knowledge of results to increase the observer’s performance. Journal of Human Movement Studies 12, 89-98. Bandura, A., 1969. Principles of behavior modification. New York: Holt, Rinehart and Winston. Bandura, A., 1977. Social learning theory. Englewood Cliffs, NJ: Prentice-Hall. Bandura, A., 1986. Social foundations of thought and action. Englewood Cliffs, NJ: Prentice-Hall. Burford, G.C., T. Lee and D. Elliott, 1987. Assessing motor learning: A case where retention effect is not a learning effect. Journal of Human Movement Studies 13, 51-55. Carroll, W.R. and A. Bandura, 1982. The role of visual monitoring in observational learning of action patterns: Making the unobservable observable. Journal of Motor Behavior 14(2), 152-167. Carroll, W.R. and A. Bandura, 1985. Role of timing of visual monitoring and motor rehearsal in observational learning of action patterns. Journal of Motor Behavior 17(3), 269-281. Carroll, W.R. and A. Bandura, 1987. Translating cognition into action: The role of visual guidance in observational learning. Journal of Motor Behavior 19(3), 385-398. Doody, S.G., A.M. Bird and D. Ross, 1985. The effect of auditory and visual models on acquisition of a timing task. Human Movement Science 4, 271-281. Gentile, A.M., 1972. A working model of skill acquisition with application to teaching. Quest 17, 3-23. Irion, A.L., 1966. ‘A brief history of research on the acquisition of skill’. In: E.A. Bilodeau (ed.), Acquisition of skill. New York: Academic Press. pp. l-47. Landers, D.M. and D.M. Landers, 1973. Teacher versus peer models: Effects of model’s presence and performance level of motor behavior. Journal of Motor Behavior 5(3), 129-139. Little, W.S. and P. McCullagh, 1987. A comparison of modeling and knowledge of results. Paper presented at the meeting of the North American Society for the Psychology of Sport and Physical Activity, Vancouver, British Columbia, June. Martens, R., L. Burwitz and J. Zuckerman, 1976. Modeling effects on motor performance. Research Quarterly 47(2), 277-291. McCullagh, P. and W.S. Little, 1989. A comparison of modalities in modeling. Human Performance 2,101-111. McCullagh, P., M.R. Weiss and D. Ross, 1989. ‘Modeling considerations in motor skill acquisition and performance: An integrated approach’. In: K.B. Pardolf (ed.), Exercise and Sport Sciences Reviews. Baltimore, MD: Williams & Wilkins. pp. 475-513.
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Ross, D., A.M. Bird, S.G. Doody and M. Zoeller, 1985. Effects of modeling and videotape feedback with knowledge of results on motor performance. Human Movement Science 4, 149-157. Salmoni, A.W., R.A. Schmidt and C.B. Walter, 1984. Knowledge of results and motor learning: A review and critical appraisal. Psychological Bulletin 95, 355-386. Whiting, H.T.A. and B. den Brinker, 1982. ‘Image of the act’. In: J.P. Das, R.F. Mulcahy and A.E. Wall (eds.), Theory and research in learning disabilities. New York: Plenum. pp. 217-235. Williams, J.G., 1986. Perceiving human movement: A review of research with implications for the use of the demonstration during motor learning. Physical Education Review 9(l), 53-58. Williams, J.G., 1989. Throwing action from full-cue and motion-only video-models of an arm movement sequence. Perceptual and Motor Skills 68, 259-266.