SELECTION AND CONTROL OF ACTION
3.5 Motor Learning and Acquisition of Skill Performance of virtually any perceptual–motor skill
3.5.2 Provision of Feedback
Intrinsic feedback arises from movement, and this sen- sory information is a natural consequence of action. For example, as described previously, several types of visual and proprioceptive feedback are typically associated with moving a limb from a beginning location to a target location. Of more concern for motor learning, though, is extrinsic, or augmented, feedback, which is information that is not inherent to performing a task itself. Two types of extrinsic feedback are typically distinguished,knowl- edge of results, which is information about the outcome of the action, andknowledge of performance, which is feedback concerning how the action was executed.
Knowledge of results (KR) is particularly important for motor learning when the intrinsic feedback for the task itself does not provide an indication of whether the goal was achieved. For example, in learning to throw darts at a target, the extrinsic KR is not of extreme importance because intrinsic visual feedback provides information about the amount of error in the throws.
However, even in this case, KR may provide motivation to the performer and reinforcement of their actions, and knowledge of performance (e.g., whether the throwing motion was appropriate) may also be beneficial. If the task is one of learning to throw darts in the dark, KR
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Figure 8 Mean score of Frederiksen and White’s (1989) subjects on eight game blocks of Space Fortress in the transfer session after receiving part- or whole-task training in a prior session.
increases in importance because there is no longer visual feedback to provide information about the accuracy of the throws. Many issues concerning KR have been investigated, including the precision of the information conveyed and the schedule by which it is conveyed.
Feedback can be given with varying precision.
For example, when performing a task that requires contact with a target at a specified time window, say, 490–500 ms after movement initiation, the person may be told whether or not the movement was completed within the time window (qualitative KR) or how many milliseconds shorter or longer the movement was than allowed by the window (quantitative KR). Qualitative feedback can be effective, particularly at early stages of practice when the errors are often large, but people tend to learn better when KR is quantitative than when it is just qualitative (Magill and Wood, 1986; Reeve et al., 1990).
Although it may seem that it is best to provide feed- back on every trial, research has indicated to the con- trary. For example, Winstein and Schmidt (1990) had people learn to produce a lever movement pattern consisting of four segments in 800 ms. Some subjects received KR after every trial during acquisition, whereas others received KR on only half of the trials. The two groups performed similarly during acquisition, but those subjects who received feedback after every trial did sub- stantially worse than the other group on a delayed reten- tion test. Similar results have been obtained for a more naturalistic golf-putting task (Ishikura, 2008). Summary KR, for which feedback about a subset of trials is not presented until the subset is completed, has also been found to be successful (Lavery, 1962; Schmidt et al., 1989). Schmidt et al. had people learn a timed lever movement task similar to that used by Winstein and Schmidt (1990), providing summary KR after 1, 5, 10,
or 15 trials. A delayed retention test showed that learn- ing was best when summary KR was provided every 15 trials and worst when KR was provided every trial. The apparent reason why it is best not to provide feedback on every performance attempt is that the person comes to depend on it. Thus, much like blocked practice of the same task, providing feedback on every trial does not force the person to engage in the more effortful infor- mation processing that is necessary to produce enduring memory traces needed for long-term performance.
4 SUMMARY AND CONCLUSIONS
Human–machine interactions involve a succession of reciprocal actions taken by the human and the machine.
For performance of the human component to be opti- mal, it is necessary not only to consider how the machine should display information regarding its states and activ- ities to the human, but also to take into account the pro- cesses by which the human selects and executes actions in the sequence of the interaction. Selection and control of action have been studied since the earliest days of research on human performance, and research in these areas continues to produce significant empirical and the- oretical advances, several of which have been summa- rized in this chapter. Because the purpose of the chapter is to provide readers with an overview of the topic of selection and control of action, readers are encouraged to refer to more detailed information on topics of inter- est in chapters by Rosenbaum (2002), Heuer and Massen (in press), and Proctor and Vu (in press); and books by Rosenbaum (2010), Proctor and Dutta (1995), Sanders (1998), and Schmidt and Lee (1999); and other sources.
This chapter showed that the relations between choice uncertainty and response time, captured by the
SELECTION AND CONTROL OF ACTION 111 Hick–Hyman law, movement difficulty and movement
time, conveyed by Fitts’s law, and amount of practice and performance time, depicted by the power law of practice, follow quantitative laws that can be applied to specific research and design issues in human factors and ergonomics. In addition, many qualitative principles are apparent from research that is directly applicable to human factors:
• The relative speed and accuracy of responding in a situation depends in part on the setting of response thresholds, or how much noisy evidence needs to be sampled before deciding which alternative action to select.
• Sequential sampling models can capture the rela- tions between speed and accuracy of perfor- mance in various task conditions.
• Response time increases as the number of alternatives increases, but the cost of additional alternatives is reduced when compatibility is high or the performer is highly practiced.
• Spatially compatible relations and mappings typically yield better performance than spatially incompatible ones.
• Compatibility effects are not restricted to spatial relations but occur for stimulus and response sets that have perceptual or conceptual similarity of any type.
• Compatibility effects occur when an irrelevant dimension of the stimulus set shares similarity with the relevant response dimension.
• For many situations in which compatible map- pings are mixed with less compatible ones, the benefit of compatibility is eliminated.
• When actions are not performed in isolation, the context of preceding events can affect performance significantly.
• Advance information can be used to prepare subsets of responses.
• Improvements in response selection efficiency with practice that occur in a variety of tasks involve primarily spatial locations of the actions and their relation to the stimuli, not the effectors used to accomplish the actions.
• Small amounts of experience with novel relations may influence performance after a long delay, even when those relations are no longer relevant to the task.
• Costs that are associated with mixing and switching tasks can be only partly overcome by advance preparation.
• It is difficult to select an action for more than one task at a time, although the costs in doing so can be reduced by using highly compatible tasks and with practice.
• Many constraints influence movement time, and the particular way in which an action will be carried out needs to be accommodated when designing for humans.
• Feedback of various types is important for motor control and acquisition of perceptual–motor skills.
• The tendency toward symmetry in preferred bimanual coordination patterns is primarily one of spatial symmetry, not of homologous muscles.
• Practice and feedback schedules that produce the best performance of perceptual–motor skills dur- ing the acquisition phase often do not promote learning and retention of the skills.
• Part-task training can be an effective means of teaching someone how to perform complex tasks.
Beyond these general laws and principles, research has yielded many details concerning the factors that are critical to performance in specific situations. More- over, models of various types, some qualitative and some quantitative, have been developed for various domains of phenomena that provide relatively accurate descriptions and predictions of how performance will be affected by numerous variables. The laws, princi- ples, and model characteristics can be incorporated into cognitive architectures such as EPIC (Meyer and Kieras, 1997) and ACT-R (Anderson et al., 2004; Byrne, 2001), along with other facts, to develop computational mod- els that enable quantitative predictions to be derived for complex tasks of the type encountered in much of human factors and ergonomics.
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