Procedural Memory and Skill Acquisition Research Paper

Skilled performance obviously depends on prior experience, but exactly what must be learned and remembered in order to develop and exercise skill? What aspects from learning episodes are important for the development of skill, and what aspects of memory are involved in this learning? These are key issues in understanding the development, maintenance, and exercise of skill. Other issues of importance are the roles of forgetting, the making of mistakes, and attention in the acquisition and execution of skilled performance. In this research paper, the roles of explicit, declarative memory in skilled performance will be considered and contrasted with the role of implicit, procedural memory.

Declarative Memory and Skill Acquisition

It is probably not too daring to say that all major models of skill acquisition, just as the acquisition of skill, itself, begin with declarative memory. Declarative memory has been described as an episodic or recollective memory system (Squire, 1992), the characterization of which overlaps with descriptions of episodic and semantic memory. Basically, declarative memory refers to a system that works with verbalizable knowledge. In his influential ACT* (and ACT-R) model of the development of cognitive skill, Anderson (1982, 1983, 1993) calls the first stage in the development of skill the declarative stage. Anderson’s work will be more fully described in a later section. At this point it is sufficient to note that the declarative stage is one in which verbal mediation is used to maintain facts in working memory so that they can be used to execute the task at hand. In other words, performance at this level depends heavily on declarative memory. Fitts (1962/1990, 1964; Fitts & Posner, 1967) called the first phase of skill acquisition by a different name, but his cognitive phase also depends heavily on declarative memory for comprehending instructions and maintaining a description of the cues that must be attended to and the relevance of the feedback that is provided during performance. In the frameworks of both Anderson and Fitts, the development of skill is characterized by reduced dependence on declarative memory.

At least one account of skill acquisition, Logan’s (1988, 1990) instance theory of automaticity, suggests that memory demands of performance do not qualitatively change as a function of skill, at least not once the basic instructions have been mastered. Logan’s theory may not apply to skill acquisition in a broad sense, but it has been to shown to provide a good description of the development of skilled performance in a range of cognitive tasks. Logan describes the development of automaticity as the shift from a dependence on general algorithms that do not rely on previous experience but that are sufficient to produce solutions to problems posed by the task, to a reliance on the retrieval of performance episodes. Memory plays a critical role in this model in which skilled, automatic performance entails a shift from algorithmbased performance to memory-based performance.

The instance theory of automaticity rests on several assumptions. The first of these assumptions is that encoding is obligatory, such that attention to an object or event is sufficient for it to be encoded into memory. The second assumption calls for obligatory retrieval, in which attention to an object or event is sufficient to cause things associated with it to be retrieved. An additional, critical assumption is that each encounter with an object or event is encoded, stored, and retrieved separately, and on every encounter. These encounters are the instances in the instance theory of automaticity. As mentioned above, the instance theory assumes that automaticity involves a transition from performance based on general rules or algorithms for performing a task to performance based on the retrieval of instances. Once performance is instance based, it continues to speed up because the number of instances continues to increase as long as the task is practiced. This speed-up is predicted on the basis of the statistical properties of the distribution of retrieval times for instances: As the number of instances increases, the minimum time to retrieve an instance decreases. Because retrieval is obligatory, according to the theory, performance time will decrease as a function of practice due to this faster retrieval time. An important aspect of the theory is that it predicts that changes in performance will follow a power function. This is consistent with the power law of practice, which reflects the finding that performance improvements in many tasks follow a power function (see Figure 18.1).

It can be argued that the early dependence on an algorithm for task performance can be likened to the declarative or cognitive phase of the frameworks of Fitts (1962/1990, 1964) and Anderson (1982, 1983). At this stage, the rules or guidelines for performing a task presumably must be active in working memory, and performance is relatively deliberative and slow. As a result of experience, and of paying attention to the right things at the right time, a collection of memory traces, or instances, builds up and gradually comes to dominate performance.

The Roles of Attention and Intention in Memory and Skill

Attention has assumed a curious place in the study of skill acquisition. Often, it seems that the goal of researchers has been to show that attention may not be necessary at all once a skill has been learned. The traditional view of attentive processing (or “controlled” processing; Atkinson & Shiffrin, 1968) is that it is relatively slow, requires effort, and involves consciousness of one’s actions. Skill is described as a gradual (or abrupt) freeing of resources and shift to a capacity-free, stimulus-driven mode of performance that is not dependent on conscious control. Posner and Snyder (1975) described automatic processes as those that may occur “without intention, without any conscious awareness and without interference with other mental activity” (p. 81). A great deal of research has been directed to exploring and confirming this view of dichotomous processing modes. For example, W. Schneider and Shiffrin (1977; Shiffrin & Schneider, 1977) performed an extensive series of hybrid memory and visual search experiments that seemed to support the idea that there are two different modes of processing and that controlled processing gives way to automatic processing if only enough practice is given.

The view that controlled and automatic processing are qualitatively distinct has, to some extent, fallen out of favor. Within the realm of visual search, where Shiffrin and Schneider (1977) carried out their influential work supporting such a dichotomy, researchers now tend speak about the efficiency of search, rather than pre-attentive and attentive search, and the role of attention in processing remains present across search types. Rather than considering it a form of processing, Neumann (1987) describes automaticity as a phenomenon arising from a conjunction of input stimuli, skill, and the desired action. In his view, it is appropriate to speak of automaticity when all the information for performing a task is present in the input information (stimulus information available in the environment) or in long-term memory. This view is not too different from Logan’s (1988, 1990), described above, in which automatic processing is based on memory retrieval, and attention forms the cues necessary for the retrieval processing. Attention remains an important process even in highly practiced tasks.

As will be discussed at more length in the section on training, automatic processing, as assessed by an apparent insensitivity to attentional resources or demands, can develop with learningwhentherightconditionsareprovided.Theimportant conditions seem to be the consistency of the discrimination and interpretation of the stimuli, and the stimulus-to-response mapping (W. Schneider & Fisk, 1982). The development of automaticity can be shown for a range of tasks. The idea that it depends more on consistency than on properties of the stimuli, such as perceptual salience, is supported by the finding that automatic processing can also be produced by training with stimuli divided into arbitrary classes (Shiffrin & Schneider, 1977).

According to the instance theory, “attention drives both the acquisition of automaticity and the expression of automaticity in skilled performance” (Logan & Compton, 1998, p. 114). Selected information enters into the instances that come to drive performance, but ignored information does not. Moreover, if attention is not paid to the right cues, associations dependent on those cues will not be retrieved (Logan & Etherton, 1994). Logan and Compton describe attention as an interface between memory and events in the world. The dependence of memory on attention means that knowing (or learning) what to attend to is a critical component in the development of skill. Other authors have emphasized that learning not to attend to irrelevant information is also a component of skill acquisition.

Learning to Ignore Irrelevant Information

One hypothesis about how learning to ignore irrelevant information contributes to performance changes with practice is the information reduction hypothesis (Haider & Frensch, 1996). According to this hypothesis, performance improvements can be attributed to learning to distinguish taskrelevant information from task-redundant (and, therefore, task-irrelevant) information and then learning to ignore the task-irrelevant information. Evidence for this hypothesis comes largely from tasks in which participants verified alphabetic strings such as E [4] J K L. The task is to determine whether the letters follow in alphabetic order, where the number in brackets corresponds to the number of letters left out of the alphabetic sequence. In most conditions, the length of the string was varied by changing the number of letters following the digit, which always occupied the second position in the string. If there was an error in the stimulus, the error was in the number of letters that was skipped (e.g., E [4] K L M). Early in practice, Haider and Frensch found an effect of string length on performance, such that verification times were slower when the number of letters after the number in brackets was increased. With practice, however, the slope of the function relating performance time to string length decreased. This finding suggests that participants in the study learned that the extra letters were not important for the task and should be ignored. Additional evidence for this hypothesis was found in a transfer condition in which errors could occur in the letters to the right of the gap (e.g., E [4] J K M). Consistent with the supposition that participants learned to ignore the extra letters during training, the error rate in detecting these invalid sequences increased as a function of practice. Haider and Frensch also showed that learning in this task was not stimulus specific by demonstrating transfer from one half of the alphabet to the other.

Haider and Frensch (1996) showed that learners were able to distinguish relevant from redundant task information and to limit their processing to the relevant information. They also showed that learning to reduce the amount of information that is processed takes time, developing over the course of practice, and that this ability appears to be largely stimulus independent. Moreover, after finding that speed instructions affect whether or not people learn to ignore irrelevant information, Haider and Frensch (1999) argued that skill acquisition is neither passive nor “low-level,” but at least partly under the influence of intention.

It seems obvious that knowing what to attend to will increase the chance that the right events are experienced such that useful instances are created, and that the allocation of attention at encoding and retrieval determines to a large extent both the nature of what is learned and the influence of previous experiences on performance in the present. There is, however, much to be said, and even more to be learned, about the interplay between intention and attention, and about how much we learn without really intending it.

Implicit Learning

Learning without intention, and without conscious awareness of what is being learned, is a topic that has received much attention in recent decades. Models of skill typically emphasize early processing of task instructions and goal-directed learning, and paying attention to the correct elements in a task situation is considered crucial to eventual skilled performance. The topic of this section is implicit learning (also referred to as incidental learning), that is, learning without intention, or the unintended by-product of experience with a task.

Consider a relatively simple task, that of pressing an assigned key whenever a stimulus appears at one of four particular locations on a screen. The instructions are simple: Press the rightmost key when the rightmost stimulus appears, the second key to the right for a stimulus in the corresponding location, and so on. One aspect of performance in such a task is that, despite the simplicity of the task, performance improves as a function of practice. Reaction times become faster and error rates lower (Dutta & Proctor, 1992; Proctor & Dutta, 1993), with improvements in accuracy and reaction time typically following a power function (Newell & Rosenbloom, 1981; see Figure 18.1). These improvements can be attributed to intentional learning of key and stimulus locations and of the stimulusresponse associations. Performance can be considerably improved if elements are repeated within the sequence of trials. One sort of repetition is just that: A particular stimulus may be repeated in two successive trials. However, even when the repetition occurs across a longer sequence of trials, benefits of repetition can occur.

Nissen and Bullemer (1987) provided practice with the task described above, in which keys are pressed according to the spatial location of targets. Within the sequence of trials, certain stimuli were repeated (designating the positions from left to right as A, B, C, and D, the repeating sequence was D-B-C-A-C-B-D-C-B-A). People who practiced this serial response time (SRT) task with the 10-element repeating sequence showed vastly more improvement than those who practiced the task with a random presentation of stimuli, even though the participants were not informed that there was a repeating sequence or instructed to look for repetitions while performing the task.

Implicit Learning and Awareness

The participants in Nissen and Bullemer’s (1987) study evidently learned something (the repeating sequence) even though they were not instructed to do so. Organizing and making sense of the environment is, however, something that comes naturally to most of us. The question is, then, whether participants in Nissen and Bullemer’s study either consciously looked for or somehow noticed that there was a repeating sequence and used this explicit knowledge to improve task performance. In order to separate intentional and incidental learning in this task, and in order to assess the role of awareness in the performance of the task, Nissen and Bullemer asked participants whether they were aware of any sequences in the stimuli. All of the participants in the repeated-sequence condition reported being aware of the sequence. Thus, awareness was coupled with the improvement of performance for this group. In order to address the question of whether awareness was necessary for the performance benefit to occur, Nissen and Bullemer repeated the experiment with a group of individuals characterized by a profound amnesia that prevented them from recognizing and recalling material to which they had been exposed: Korsakoff patients. As predicted, the Korsakoff patients reported no awareness of the repeating sequence. More interesting, their performance showed a degree of learning of the sequence comparable to that of controls (see Figure 18.2). This shows that learning can and does occur without awareness.

Later work (Willingham, Nissen, & Bullemer, 1989) showed that the degree of awareness of the sequence is correlated with performance for normal participants: People who showed more awareness (as indexed by explicit recall of the sequence) also showed more performance improvement. However, when anticipatory responses (i.e., pressing the response key before the next stimulus appeared) were eliminated from the analysis, the difference in performance between those who reported full or partial knowledge of the sequence and those who could evidence no explicit knowledge was minimal.

Other researchers using different measures of explicit knowledge have shown high correlations between explicit knowledge and performance (e.g., Perruchet & Amorim, 1992), as well as evidence of explicit knowledge of the sequence at the point where performance differences between random and sequential presentation first become evident, casting some doubt on the Willingham et al. (1989) finding of relative independence of the two measures of knowledge. However, given the evidence of sequence learning in Korsakoff amnesics, we see that learning without awareness certainly can occur.

Implicit Learning and Attention

It is interesting to ask whether the learning seen in the SRT task is dependent on the availability of attention to process relations between elements in the task. One way to assess this is to compare learning in a single-task condition, in which only the task to be learned is performed, with a dual-task condition, in which a secondary task is performed. The requirement to perform a secondary task should take attention away from the SRT task. Such an experiment was carried out by A. Cohen, Ivry, and Keele (1990) using a sequence task in combination with distractor tasks of different difficulties. They specially constructed sequences to contain either unique associations, in which each stimulus uniquely specified the following (e.g., A always followed by C), ambiguous associations, such that A might be followed by C in one case and by D in another, or both. They found that ambiguous sequences were not learned under the dual-task conditions but that unique associations were. On the basis of these results, they suggested that sequence learning depends on two processes. The first process is automatic, in that it can occur without attention’s being directed to it. This process forms associations between adjacent items. The second process, which requires attention to operate, is proposed to build hierarchical codes based on parsing the sequence at a higher level (i.e., into bigger subsequences) than associations between only two items.

Cohen et al. (1990) showed that simple associations could be learned under conditions of distraction, but the amount of learning may nonetheless be affected by attentional load. For example, Frensch, Buchner, and Lin (1994) showed that whereas learning could take place under both single- and dual-task conditions, and for both simple and ambiguous sequences, the amount of learning that took place was reduced when a distractor task was present, suggesting that sequence learning is modulated by attention.

Jiménez and Méndez (1999) attempted to resolve the issue of whether general attentional demands modulate sequence learning by using sequences that were unlikely to be explicitly learned and a secondary task that should produce little disruption. They examined the roles of both selective processing requirements (attending to to-be-associated elements) and generalized mental load (taxing attentional resources by adding a secondary task to the sequence-learning task). An SRT task was used in which the sequences of stimulus locations were generated following a noisy finite-state grammar and response keys were pressed corresponding to the position of the stimuli. In addition, the identity of a stimulus on a given trial gave probabilistic information about where the next stimulus would appear. A single-task condition was contrasted with a dual-task condition in which two of four possible target shapes had to be counted and the total reported at the end of the block. Participants in both single- and dual-task conditions exhibited sequence learning, showing faster reaction times and a lower error rate for practice with grammatical sequences than for random ones. Learning of the predictive relation between a stimulus on one trial and the location of the stimulus on the next trial was assessed by examining the difference between valid (in which the predictive relation held) and invalid (in which the position was not predicted) trials. Only in the dualtask conditions, in which participants had to attend to target identity in order to perform the counting task, were these relations learned. Thus, selective attention does seem to be necessary for such learning to occur, and this learning occurs even though (or precisely because) a secondary task must be performed. In other words, paying attention to a predictive dimension seems to be necessary for this dimension to enter into a predictive relationship.

In summary, sequence learning can occur implicitly, and this learning is at least partly the result of automatic associative processes. Associative processes can be carried out independently of mental load, but only on events that are active in working memory.

The Nature of Implicit Learning

If we accept that learning without intention (and perhaps without awareness) can occur, we can also ask what the nature of the learning is. For example, is one learning relations between stimuli, relations between stimulus-response associations, or the motor sequence? One interesting hypothesis, similar to the idea that prediction is the basis for conditioned learning, is that it is the response-effect relationship that is first implicitly learned and that this might provide the basis for the development of explicit knowledge. To test this hypothesis, Ziessler (1998) modified Nissen and Bullemer’s (1987) SRT paradigm so that the location of the stimulus on a particular trial was determined by the response made on the previous trial. Rather than responding to the location of the stimulus, as in Nissen and Bullemer’s task, participants in Ziessler’s experiments responded to the identity of a target in the presence of distractors. The location of the target was thus not relevant for the response, but knowledge of the location could be used to speed up search and, accordingly, reaction times.

By varying the predictability of the position of the following stimulus (achieved by varying whether all stimuli assigned to a certain key predicted the same position), Ziessler (1998) showed that performance improved more when the response made reliably predicted the position of the next stimulus than when it was only sometimes a valid predictor. Moreover, only the perfectly reliable response-stimulus associations condition showed negative transfer to a condition in which target position was random, as well as showing the largest increase in reaction time when stimulus-response relations were altered. None of the participants in Ziessler’s study reported noticing anything predictable in target position. Thus, it seems that knowledge of target position was only implicitly learned.

In the original Nissen and Bullemer (1987) experiments, responses were made to the location of the stimulus. Therefore, it is impossible to say whether performance improvements depended on the learning of perceptual relations (the relation of one stimulus to the next), stimulus-response associations, or response relations (the relation of one response to the next). Willingham et al. (1989) attempted to look separately at the learning of these relations using the SRT task described above in which the locations of the stimuli follow a predictable sequence, but requiring that responses be made to the color of the stimuli, rather than their locations. By assessing performance during practice and in a transfer task in which locations were responded to, they concluded that sequences in the stimulus locations were not learned when responses were based on the color of the stimuli. On the other hand, if practice was with a task with a predictable sequence of stimulusresponse pairs, considerable learning occurred, as indexed by better performance than when the stimuli were randomly presented. However, this group also showed no benefit of practice in a transfer condition in which responses were made to location,even though the responses were exactly the same as in the practice task, suggesting that the locus of learning was in the stimulus-response associations. A. Cohen et al. (1990) presented evidence that suggests that the actual motor responses made are not critical to sequence learning: After participants practiced an SRT task using the index, middle, and ring fingers, transfer was virtually perfect in a condition in which only one finger was used to make responses.

The actual responses made may, however, be a locus of learning in some types of tasks. Palmer and Meyer (2000) recently tested the separate contributions that conceptual and motor skill make to the skill of piano playing and found that the relative importance of the effectors used and the movements made changes as a function of skill level. For low- and moderately skilled piano players, transfer was greatest when the motor movements were the same (even though a different part of the keyboard was used) for pieces played in practice and transfer. Skilled players, in contrast, showed the most transfer when conceptual (melody) aspects of the transfer piece corresponded to the practice piece, even when different fingers and hands were used. This suggests that the mental plans for performing an action only become independent of the required movements at an advanced stage of practice. Findings that show independence of learning from the effectors used (e.g., A. Cohen et al., 1990) may be limited to relatively simple motor tasks.

It can be concluded that the research on sequence learning provides evidence that learning can occur without awareness, although attending to the relevant stimulus aspects does seem to be required for this learning to occur. The nature of the learning seems to depend primarily on associations between stimuli and responses (or between responses and stimuli), unless the visual demands are made more complex, in which case perceptual learning also plays a role (see Lewicki, Czyzewska, & Hoffman, 1987; Stadler, 1989).

Procedural Memory

Skill acquisition has been described as a transition from reliance on verbal, declarative knowledge to a reliance on procedures or routines for performing tasks. The distinction between performance based on explicit versus procedural knowledge has led to the conception of different ways of learning and knowing, sometimes described as knowing that versus knowing how. Explicit memory requires the conscious directing of attention to the act of recall for remembering facts (i.e., knowledge that), whereas the performance of a skilled action (i.e., knowledge how), although it also reflects past experience, does not involve active attention or conscious recall (Squire & Cohen, 1984). Much research indicates that procedural learning, indexed by improvements in the execution of task elements, may involve a different system from the declarative learning of facts and instructions. Indeed, it appears that there are different memory systems underlying declarative and procedural learning.

Tulving (1985) describedprocedural memory as a memory system that “enables organisms to retain learned connections between stimuli and responses, including those involving complex stimulus patterns and response chains, and to respond adaptively to the environment” (p. 387). In Tulving’s view, procedural memory differs from episodic and semantic memory in the nature of acquiring, representing, and expressing knowledge, as well as in the kind of conscious awareness that characterizes it. Procedural knowledge is available only in the form of overt expression and is not available for conscious introspection.Tulving describes procedural learning as “tuning” (Rumelhart & Norman, 1978), in the sense that procedural memory provides prescriptive knowledge that can be used to guide future action without containing specific information about the past. In this view, procedural learning is abstract in the sense that there is no memory of specific prior events, but it reflects the acquisition, retention, and retrieval of knowledge expressed through experience-induced changes in performance.

Evidence for Procedural Memory

One of the most convincing sources of evidence for a distinction between declarative and procedural memory comes from demonstrations of benefits of practice or learning in amnesic individuals. The observation that amnesic persons sometimes do show good memory performance across long retention intervals was made by Claperède (1911), who remarked that one of his patients’ behavior was altered by experience and that this altered behavior outlasted the patient’s memory of the experience itself. His patient, a woman with Korsakoff’s syndrome, learned not to shake hands with the doctor after he had pricked her with a pin secreted in his hand, but she was unable to tell the doctor why she declined to do so. Such patients can sometimes acquire information at a normal rate and can maintain normal performance across delays. In the absence of the ability to recognize having previously seen a particular stimulus, task, or, in some cases, even the experimenter, many amnesic persons have demonstrated the ability to acquire and retain perceptual-motor skills, such as rotory pursuit and mirror drawing, cognitive skills (e.g., solving jigsaw puzzles or the tower of Hanoi, or using a mathematical rule), and perceptual skill, such as reading mirror-reversed text (N. J. Cohen, 1984) or learning mazes (Corkin, 1965). For example, Nissen and Bullemer’s (1987) study, described above, showed that amnesic individuals evidence just as much improvement in the SRT task as do normally functioning individuals (see Figure 18.2).

Brooks and Baddeley (1976) showed that both Korsakoff patients and postencephalitic patients improved in the rotarypursuit task. Performance of amnesic individuals is often equivalenttothatofnormalcontrolsinavarietyofperceptualmotor tasks; however, they do not benefit as much as normal controls from the repetition of specific items. Although amnesic persons can show preserved memory for particular stimuli, as evidenced by facilitation of certain aspects of test performance based on prior exposure to stimulus materials (i.e., priming; e.g., Jacoby & Witherspoon, 1982; Verfaellie, Bauer, & Bowers, 1991), their recognition memory for the stimuli is poor. Thus, amnesic individuals seem to possess normal pattern-analyzing operations or encoding procedures but poor declarative memory for item-specific information that would normally be acquired from applying these operations or procedures.

A Procedural Memory System?

According to Tulving (1985), a memory system consists of memory processes and a supportive structure for those processes. Two important structures for procedural learning seem to be the basal ganglia and the cerebellum. At the moment, there are several different hypotheses about the roles of these two structures. One hypothesis is that learning repetitive motor sequences depends on the basal ganglia, whereas learning new mappings of visual cues to motor responses depends on the cerebellum (Willingham, Koroshetz, & Peterson, 1996). Another hypothesis is that the cerebellum is needed for closed-loop skill learning, in which visual feedback about errors in movement is available and must be used, whereas open-loop skill learning, in which movements are executed without feedback, depends more on the basal ganglia (Gabrieli, 1998). Hikosaka et al. (1999) stress the cerebellum’s role in the timing of movements and suggest that the basal ganglia is involved in reward-based evaluation.

Flament and Ebner (1996) propose that the role of the cerebellum as a comparator of desired motor output and actual performance may be most important during learning of a novel motor task. Both positron-emission tomography (PET) and functional magnetic resonance imaging (fMRI) data are compatible with the idea that the cerebellum is heavily involved when movement errors are common and corrective movements must be produced to compensate for them. Cerebellar activity decreases as skill increases, and there is a positive correlation between the number of errors and cerebellar activity. Interestingly, several studies have shown a decrease in cerebellar activity as a function of the learning of finger-movement sequences (e.g., Friston, Frith, Passingham, Liddle, & Frackowiak, 1992). Increased activity in motor cortical areas during motor learning indicates that these areas also contribute to the learning process, and neuroimaging studies point to a role of primary and secondary motor cortex in learning tasks such as the SRT task.

Procedural Memory, Implicit Learning, And Skill

Most scholars would agree that the distinction between procedural and explicit, episodic memory is a real one, and that different systems underlie implicit and explicit remembering. The exact nature of the relationship between implicit and explicit learning is less clear. Some have argued that implicit knowledge provides the basis for explicit knowledge (Ziessler, 1998), others have argued that explicit knowledge is converted into procedural knowledge (Anderson, 1983), and still others have argued that implicit and explicit knowledge develop independently of each other (Willingham & Goedert-Eschmann, 1999). Studies using PET imaging are consistent with the idea that explicit and implicit learning have separate foundations. Grafton, Hazeltine, and Ivry (1995; Hazeltine, Grafton, & Ivry, 1997), for example, found metabolic changes in primary and supplementary motor cortexes and the putamen that were associated with implicit learning, whereas explicit learning was associated with changes in blood flow in prefrontal and premotor cortices. Willingham and Goedert-Eschmann used transfer tasks to show that the degree of implicit learning in an SRT task did not depend on whether explicit learning instructions were given. This suggests that implicit learning is indeed independent of explicit learning and that performance that is initially dominated by conscious mediation may eventually come to rely on implicit knowledge that has quietly been developing as a direct by-product of task performance. However, further work is necessary to determine the way in which implicit and explicit learning are related.

Skilled Performance

Proctor and Dutta (1995) defined skill as “goal-directed, wellorganized behavior that is acquired through practice and performed with economy of effort” (p. 18). Thus, all skills are assumed to be acquired through practice or training, to be the result of goal-directed learning (even though incidental learning may occur as the result of performance), and to be expressed in coordinated, efficient performance. Simple skills, such as performing SRT tasks, consist of only a few basic components (perception, classification, response selection, and response) and are learned after a relatively modest amount of practice. Complex skills, such as solving physics problems, are made up of multiple components that need to be learned and integrated before skill is acquired. Such skills take more time to develop and are more dependent on the nature of training and the background of the performer. Whether the environment is open or closed also affects the acquisition of skill. In a closed environment, the conditions in which the skill is performed are always essentially the same, whereas in open environments conditions are changing and uncertain. In an open environment, the environment itself dictates to some extent how the skill must be performed. For example, given that ice conditions are perfect, a figure skater simply performs the learned skills regardless of where the arena is located. A hockey player, on the other hand, must be aware of the positions of other players in order to appropriately exercise learned skills.

Phases of Skill Acquisition

In the beginning of the research paper, Fitts’s (1962/1990, 1964; Fitts & Posner, 1967) framework was mentioned in the context of the role of memory in skilled performance. Fitts did not posit any specific mechanisms that describe changes in the role or importance of memory, attention, or other elementary processes, but his general framework is consistent with a shift from attentive, deliberative processing of the environment and task requirements to a dependence on retrieval from long-term memory, in one form or another. Fitts describes three phases of skill acquisition, the cognitive, associative or fixation, and autonomous phases. As described above, the cognitive phase emphasizes the role of declarative memory and cognitive processes in performance. In the associative, or fixation, phase, “correct patterns of behavior are fixated by continued practice” (Fitts, 1962/1990, p. 286). This phase may last for days or months before the autonomous phase is reached. At this final phase, performance is relatively free from errors (although performance time may continue to improve) and shows increasing resistance to stress and interference from concurrent activities. Fitts suggests that this stage is characterized by a shift from visual to proprioceptive feedback. He also points out that many skills can be described in terms of subskills, and that each of these subroutines may develop at its own rate. This idea provides the basis for parttask training, discussed below.

Anderson’s (1982, 1983, 1993) account of skill acquisition also consists of an early declarative phase and a later procedural phase, with an intermediary process of knowledge compilation that enables the learner to move from the declarative to the procedural phase by converting the declarative knowledge of the learner into a procedural form. Procedures, or productions, are basically if-then rules. On the basis of productions, even complex environmental conditions (if compiled) can trigger mental or overt actions without the requirement that all relevant aspects of the situation be kept active in working memory for the application of general interpretive mechanisms.

Mechanisms of Change

According to Anderson (1982), practice results in increased speed of processing of component procedures. Procedures may also be compiled or restructured through processes of chunking (Newell & Rosenbloom, 1981). Carlson, Sullivan, and Schneider (1989) investigated the relative contributions of component speed-up and restructuring for the tasks of predicting or verifying the output of logic gates (e.g.,“ifallinputs are equal to 1, the output is 1; otherwise the output is 0” [and gate]). They found that prediction judgments were faster than verification judgments and that both types of judgments were faster when the gate type evaluated whether certain elements were present rather than if theywere absent.The same relative ordering of task difficulty was maintained for the full 1,200 trials of practice, suggesting that participants were not able to automatize the procedures used to make the judgments. In order to test whether attentional resources were freed up as a function of practice, a memory load was introduced at two points during practice. The memory load consisted either of irrelevant digits (i.e., digits other than 0 or 1), digits that had to be substituted into the comparisons in order to make the judgments, or digits that could, in principle, be used in logic gate problems, but that were not needed to actually solve the problems. The memory load had an effect on logic gate performance only when it had to be accessed in order to solve the problem, and this effect was the same both early and late in practice. Thus, Carlson et al. did not find evidence for qualitative changes in how the task was performed.

When the task is more complex, requiring the formation of subgoals, evidence for restructuring and speed-up of component processes is sometimes found. Carlson, Khoo, Yaure, and Schneider (1990) devised a task in which complex circuits of logic gates had to be tested. They found that both the number of moves required to troubleshoot a circuit (an indication of the efficiency of the search strategy) and the time per move (the efficiency of operator application) decreased as a function of practice, with especially big improvements early in practice. The pattern of moves also changed with practice, indicating that learners did form subgoals and came to recognize the conditions under which these subgoals could be applied. Retention tests given after 6 months showed retention of both improvements in the speed of component processing and in the restructuring of the component steps.

Types of Skills

In order to gain more insight into the nature of learning and the conditions that promote the acquisition of skills, it is necessary to consider performance in a wide range of tasks. Most real-world skills include perceptual, cognitive, and motor components. Although the goal of skills researchers is to understand complex behavior, much can be learned by attempting to isolate these basic information processes and to look at the development of perceptual, cognitive, and motor components of skill.

Perceptual Skill

Perceptual skills are those skills that depend heavily on the ability to discriminate between and to classify stimuli on the basis of perceivable attributes of the stimuli. In some skills, such as wine tasting (Melcher & Schooler, 1996) or determining the sex of baby chicks (Lunn, 1948), the skill to be learned is clearly primarily perceptual. However, often perceptual skills are an important part of other skills. For example, copying high-speed Morse code depends on the perceptual ability to parse the dits and dahs that make up the message and to group these symbols into conceptual units, the motor ability to quickly type the message, and the strategic ability to copy behind, that is, to allow the typing of the message to lag behind the decoding of the message (Wisher, Sabol, & Kern, 1995). Sports performed in open environments also depend on perceptual skill. For example, it has been shown that skill in volleyball is associated with especially rapid visual search when a volleyball is the target (Allard & Starkes, 1980).

In order for perceptual skill to develop, features that are specific to a particular stimulus and that distinguish it from other stimuli must be learned. One factor that can influence the development of perceptual skill is labeling. Labeling forces observers to attend to the distinctive and unique features of stimuli; having attended to these features, observers can use them to improve performance (e.g., Rabin, 1988). It may be more than a matter of affectation that wine tasters have developed such an elaborate vocabulary for classifying wines. Training that directs the observer’s attention to unique features has also been shown to result in better perceptual learning (e.g., Biederman & Shiffrar, 1987).

Sowden, Davies, and Roling (2000) investigated whether improved sensitivity in detecting basic features could be a basis for improvement in reading X-ray images. Experts were found to be more sensitive than novices in detecting dots in X-ray images. Further, novices were found to improve over 4 days of training but to show no transfer to reversed contrast images when these images were simple. When more complicated images were used, transfer (although not perfect) did occur. Sowden et al. interpreted these results as evidence that, in addition to strategic components, stimulus-specific sensory learning is important in learning to read X-ray images.

Perceptual learning leads to improved recognition and classification of stimuli, but it may also reflect improved processing of stimuli. Processing may become more efficient because stimuli are unitized in a sort of visual chunking process (LaBerge, 1973), or because observers become more fluent in applying learned operations. Kolers and Roediger (1984) developed the idea that stimuli are not remembered independently of the operations performed on them. That is, learning can be viewed as reflecting both experience with the stimuli and experience processing them. Evidence for this view comes from a series of studies in which observers read geometrically inverted text (i.e., text presented upside down and from right to left; Kolers, 1975a). After about two months of practice, participants became quite proficient in the task. Because different texts were used on different days, the learning was not tied only to the particular stimuli used in the task. In fact, when participants were tested in the same task more than a year later, reading times were only 5% faster when the same passages used in training were read than when completely new passages were used (Kolers, 1976). The advantage for the previously read pages was likely due to specific practice with the analysis of the graphemic patterns and not due to prior exposure to the content of the text. This is suggested by a study from Kolers (1975b) in which prior reading of the same text in a normal orientation did not facilitate reading of inverted text. A similar result was noted by Thorndike and Woodworth (1901a, 1901b, 1901c), who trained people on simple tasks such as estimating areas of geometric shapes or crossing out specific letters in a text, and then transferred them to related tasks. They found that the benefits of practice were restricted in scope, suggesting that the benefits were partly, or even primarily, due to perceptual learning of the training stimuli.

Cognitive Skill

Cognitive skills range from learning to make simple associations between stimuli and responses to solving complex problems or flying fighter planes. Complex skills usually have perceptual or motor components or depend on background knowledge, but much can be gained by examining what is arguably the simplest form of cognitive skill, responseselection skill. Response-selection processes are those processes that are important in determining which response is to be made to which stimulus. Increased facility in response selection is often the most important determinant of improvement in task performance (Teichner & Krebs, 1974; Welford, 1968, 1976), outweighing the importance of making perceptual discriminations or executing motor responses.

Developmental studies have shown that children’s improvement as a function of age in a selective-attention task in which one stimulus dimension has to be attended and another ignored is largely attributable to increases in the speed with which stimulus-response translation can occur (Ridderinkhof, van der Molen, Band, & Bashore, 1997). Numerous studies have shown that stimulus-response translation is the locus of performance improvements in choice-reaction tasks among adults. For example, Pashler and Baylis (1991) used a number of practice and transfer conditions to determine the locus of performance improvements in choice-reaction tasks. Participants practiced pressing keys in response to stimulus category (e.g., pressing a key with the index finger if the stimulus was a letter, a middle key with the middle finger for a digit, and a left key with the ring finger for a nonalphanumeric symbol). During practice sessions, a small set of only two stimuli from each category was used. After substantial improvement in performance had occurred, two additional stimuli from each category were added. Importantly, responses were just as fast for new stimuli as for already practiced stimuli, suggesting that the locus of the practice effect was in assigning stimuli to categories and selecting the right category key. Changing the hand used to make the key presses had no effect on performance, ruling out a motor locus for improvements. However, consistent with a response-selection account of performance improvements, reassigning the categories to different keys completely eliminated the benefits of practice.

Although practice effects in choice-reaction tasks are concentrated in response-selection or stimulus-response translation processes, it does not seem to be the case that response selection becomes automatized such that stimuli automatically activate their corresponding responses. Ehrenstein, Walker, Czerwinski, and Feldman (1997) review evidence from choice-reaction tasks and visual search studies that cast doubt on the idea that, at a fundamental level, performance becomes automatic as a function of practice. For example, it has been shown that one of the variables that most directly affects response selection, stimulus-response compatibility, continues to affect performance even after much practice and after performance seems to have reached an asymptotic level (Dutta & Proctor, 1992; Fitts & Seeger, 1953).

Motor Skill

Motor skills have been extensively studied since the very beginnings of experimental psychology (e.g., Woodworth, 1899; Bryan & Harter, 1897, 1899). Whether one emphasizes “the integration of well-adjusted muscular performance” (Pear, 1948, p. 92) or “continuous interaction of response processes with input and feedback processes” (Fitts, 1962/1990, p. 275), motor performance often plays a central role in definitions of skill. There are three problems to be solved in learning to perform a motor task with skill. The degrees-of-freedom problem arises because there are many ways of performing any given action, and the performer is faced with the task of finding the best one. The serial-order problem concerns the timing and ordering of sequences of movements. Finally, the perceptual-motor integration problem involves coordinating the interactions between the perceptual and motor systems.

The Degrees-of-Freedom Problem

Degrees of freedom are, to put it simply, the dimensions of movement permitted by the joints involved in performing an action. In general, the more complex the movement, the more degrees of freedom there are available. A goal of skilled performance is to make optimal use of the available degrees of freedom. Bernstein (1967) suggested that, early in performance, the degrees-of-freedom problem may be solved by simply fixing or “freezing” some of the joints involved in the action. Vereijken, van Emmerik, Whiting, and Newell (1992) showed that as a person masters a skill (in this case, learing to ski on a ski simulator), the degrees of freedom that are initially fixed are gradually freed such that the use of these joints can also enter into performance. As yet, little research has been done on whether fixing degrees of freedom is a general strategy, and results from the studies that have been done are mixed. Broderick and Newell (1999) suggest that both the task and the skill level of the performer must be considered, because the coordination patterns observed seem to depend on an interaction of the task and performer. In some cases, novices seem rigid and stiff (Vereijken et al., 1992). In other cases, novices show much more variability than experts (Broderick & Newell, 1999). Coordination of multiple effectors is more complicated than just a restriction of the range of movement of specific joints.

The Serial-Order Problem

Original ideas about the serial-order problem focused on the relation between one response and the next. In the linear-chain hypothesis of Lashley (1951), the sensory feedback produced by a response initiates the next response in the sequence. Such a process may explain learning when the two responses involved have a unique association such that the second response always follows the first. In such a case, learning might occur automatically, as discussed for unambiguous sequences in the earlier section on sequence learning. However, such a hypothesis cannot explain the learning of ambiguous sequences. Lashley hypothesized that control can also be hierarchical, and this hypothesis is supported by studies that show that the pauses that performers make when carrying out a sequence of finger movements correspond to the hierarchical structure of the sequence (Povel & Collard, 1982).

The Perceptual-Motor Integration Problem

The perceptual-motor integration problem involves the ways perception influences action and action influences perception. Perception provides visual information, as well as sensory input from receptors in the muscles, joints, tendons, and skin. Of these information sources, the role of vision in learning has received the most study. Despite rather extensive research, however, it is difficult to make generalizations about the role of vision in skilled performance. In many cases, if vision does play an important role in performance, it continues to play an important role even after extensive practice. For example, Khan and Franks (2000) showed that a group allowed to view the cursor while performing a cursor positioning task (in which a cursor had to be moved onto a target) performed better than a group that saw the cursor only at the beginning of a trial. When transferred to a no-vision condition, however, the group that practiced with visual feedback performed much worse than the group that had practiced without such feedback.

Some studies have suggested that visual feedback sometimes becomes more important with practice (Proteau & Cournoyer, 1990). Such findings are predicted by the specificity-of-practice hypothesis (Proteau, 1992; Proteau, Marteniuk, & Levesque, 1992), according to which different sources of sensory information are integrated to form an intermodal sensorimotor representation. Performance suffers if a source of information is removed or added because the incoming sensory information is then no longer compatible with the sensorimotor representation. Because specificity develops with practice, changes in information may result in greater decrements in performance after extensive practice than after moderate levels of practice. Thus, whether reliance on visual information seems to increase could depend on when such reliance is tested. More recently, Proteau, Tremblay, and DeJaeger (1998) have suggested that, with practice, the source of afferent information best suited to ensure optimal performance progressively dominates other sources of sensory information. The withdrawal of this information will lead to a deterioration in performance only when its dominance has been firmly established. Thus, withdrawing such a source of afferent information early in practice will be less detrimental than doing so later.

Factors Influencing Skill Acquisition

Coordination of different effectors, hierarchical control, and perceptual-motor integration are all necessary for the development of skill, but what are the factors that can enhance the development of skill? Answering this question requires that we make a distinction between factors that have an effect on the performance of a task and factors that affect learning, as measured by retention of the skill or performance on transfer tasks. Factors that lead to better performance during training do not necessarily lead to better learning. Bjork (1999) has argued that immediate performance is based on the retrieval strength of newly made memories, whereas learning is based on what he calls storage strength. He warns that training conditions that support performance by providing a short-term basis for ready access to correct responses or procedures may impede the growth of the storage strength necessary to support long-term performance.

A number of factors have been identified that affect the rate and extent of learning of motor tasks, and many of these factors seem to play an equally important role in the learning of cognitive tasks. Although factors such as the motivation and ability of the performer have a big influence on the outcome of practice, the factors that have been most extensively studied are feedback and practice schedules.

Feedback

There are two major sources of feedback: intrinsic and extrinsic.Intrinsicfeedbackisfeedbackthatisdirectlyproduced by the response, and this can include proprioceptive, visual, auditory, and vestibular information. Contrary to an assumption that skilled performance is automatic and therefore increasingly less reliant on feedback, even skilled performance can be dependent on intrinsic feedback. As suggested by Proteau’s (1992) specificity-of-practice hypothesis, removing feedback from a task practiced with feedback can disrupt performance, as can adding visual feedback to a task learned without such feedback (Elliott & Jaeger, 1988; Proteau et al., 1992). The important point seems to be that, with practice, a central representation of the relevant feedback is formed and that this representation (like the stimulus-response representations in response-selection tasks) continues to be used in highly skilled performance. It should be noted, however, that some studies have found a decreased reliance on feedback. In one such study, Pew (1966) found evidence that an early reliance on visual information in a higher-order tracking task was replaced by a control strategy that was performed automatically, with only occasional monitoring. One could argue, however, that performers in Pew’s study learned to use proprioceptive feedback or other information in place of visual feedback.

Extrinsic feedback is feedback that is added to intrinsic feedback. It might include hearing a beep when a mistake is made or when a target is hit, watching a video of one’s own performance, or viewing a plot of movement dynamics. An important distinction is between knowledge of results (KR), in which the outcomes (accuracy or speed) of a movement are conveyed to the performer, and knowledge of performance, in which information about the dynamics of movement (temporal or spatial) is provided to the performer. Knowledge of performance is more effective than KR when the task is more complex than a simple pointing or tracking task.

It seems reasonable to think that KR will be most effective when it is provided immediately and on every trial. However, this is not always the case. For example, Winstein and Schmidt (1990) found that just as much learning occurred when KR was provided on 33% of trials in which a complex movement had to be made as when it was provided on 100% of the trials. Moreover, decreasing the percentage of trials on which KR was provided across the training period led to better learning. It has also been found that providing a summary of performance at the end of a block of trials can be more effective than providing feedback after every trial (Lavery, 1962; Schmidt, Young, Swinnen, & Shapiro, 1989). Schmidt and colleagues have suggested that the function of feedback is to guide the performer toward the performance goal. This guidance hypothesis states that when feedback is provided on every trial, performers become too dependent on it, which leads to poorer performance on retention or transfer tests without the feedback. It may be that the important process that underlies the benefit for reduced feedback is a greater reliance on memory. The inclusion of no-KR trials may also lead to the development of the sort of internal representation that is necessary for performers to detect errors on their own. Whether feedback is intrinsic or extrinsic, it takes time to process it: KR provided too soon after a trial can interfere with the processing of intrinsic feedback (Swinnen, 1990; Swinnen, Schmidt, Nicholson, & Shapiro, 1990).

Practice Schedules

The distinction between performance during practice and learning as measured with retention or transfer conditions has proven to be critical in evaluating the results of practice schedules. For example, massing practice, such that only a few sessions with many trials of practice are given in place of more sessions with fewer trials in each session, has been shown to have detrimental effects during acquisition but varying effects on learning. Lee and Genovese (1988) noted that studies with continuous tasks (such as tracking tasks) show a small but negative effect of massed practice on retention. Discrete tasks actually show more learning when practice is massed.

A dissociation between effects of the scheduling of task conditions on performance during practice and learning is also seen when different variations of a task must be learned. Blocking practice, such that one variation is practiced in one session and another variation in a different one, has been shown to lead to better performance than random practice, in which all variations are possible within a block of practice. However, learning, as assessed by transfer or retention tests, is better for the random conditions (see Figure 18.3; Carlson & Yaure, 1990; V. I. Schneider, Healy, Ericsson, & Bourne, 1995; Shea & Morgan, 1979). It seems that the need to recall task requirements on every trial, as in the random condition, is essential to learning (Battig, 1979; Lee & Magill, 1983).

Individual Differences in Skilled Performance

Individual differences in various abilities have formed the basis of selection and training research as well as a theoretical starting point for characterizing how skill develops. Theoretically, some models make predictions about which abilities should explain the most variance in skilled performance at different levels of skill acquisition. From a practical standpoint, the training and selection literature has focused on determining the abilities that predict success in learning particular skills.

The general progression from cognitive mediation to an associative phase to automatic performance (e.g., Fitts, 1962/1990, 1964) forms the basis for Ackerman’s (1988, 1992) account of the relationship between level of skill acquisition and cognitive ability. According to Ackerman, performance in the early, declarative stage of learning a skill is affected more than later stages by the background knowledge and general spatial, verbal, and numeric abilities of the learner. The development of more specific and streamlined procedures in the associative phase leads to less reliance on general declarative knowledge. In this stage, as speed and efficiency develop and the need for conscious mediation lessens, the dependence on general cognitive abilities is reduced and the perceptual speed of the learner, as measured by tasks such as letter matching and serial response time, becomes a more important determinant of performance. Finally, in the autonomous stage, in which task components have become more automatic and performance is relatively free of attentional demands, performance will be more subject to the psychomotor ability of the performer.

Ackerman (1992) tested his model by comparing the correlation between performance and ability at different levels of skill acquisition in a complex, computerized air traffic control simulator. The effectiveness of measures of perceptual ability as a predictor of performance was, as predicted, higher at higher levels of skill. However, measures of general ability were also better predictors at high skill levels. It may be that tasks that require the integration of new information never become independent of general ability. One reason for this could be the dependence of such performance on working memory. Another possibility, suggested by Matthews, Jones, and Chamberlain (1992), who found that tests of ability in the context of a mail-coding task showed no trade-off between predictive power of cognitive versus perceptual speed tasks, is that executive control of performance remains important in most complex tasks.

Skilled performance is generally described as being relatively fast and error-free, but in at least some situations, more skilled performers are actually more error-prone than less skilled performers. Bell, Gardner, and Woltz (1997) found individual differences in the rate of making undetected errors on a number reduction task, in which numbers were compared according to a series of rules. After a practice session in which speed was emphasized, participants received a transfer session emphasizing accuracy and the instruction to press a key whenever a performance error was detected. Bell et al. found that the people who were more skilled (as indicated by faster reaction times during the training session) also made more undetected errors in the transfer session. The number of undetected errors was correlated with measures of memory span, with a larger memory span being associated with a lower error rate. Speed of processing was also correlated with the making of undetected errors: Faster processors made fewer undetected errors. Although speed-accuracy trade-off can not be ruled out as an explanation for the finding that latency was negatively correlated with the number of undetected errors made, Bell et al. argue that fluency in a task brings with it an increased chance of making undetected errors. Furthermore, detecting errors seems to require working memory resources, as indicated by the finding that people with a greater memory span were better able to detect errors.

Expertise

Expertise in a particular domain can be viewed as the end product of skill acquisition. Unfortunately, it is an end that most of us do not reach in domains where we are nonetheless active. What enables some people to become expert in their field, whether it be playing tennis, solving physics problems, or playing the viola, and what characteristics distinguish experts and nonexperts?

One of the most debated topics in this field is whether expertise is primarily a result of learning or whether some people are genetically predisposed to become experts. Although it seems obvious that heredity can place constraints on the ability to become an expert, the major factor in developing expertise seems to be a commitment to years of dedicated practice. Newell and Simon (1972) were among the first to suggest that expertise can be explained in terms of the development of knowledge and information-processing abilities (e.g., memory span). Ericsson and Charness (1994) argued that extended training significantly alters both cognitive and physiological processes to an even greater degree than suggested by the work of Newell and Simon. They contend that differences between experts and novices primarily reflect changes brought about by practice rather than differences in aptitude or initial ability. It has even been argued that human expertise can be viewed as the result of circumventing normal limitations on human information processing (e.g., development of parallel processing in typing; Salthouse, 1991).

It may be that prodigious achievements in performance are rare because talent for a specific activity and the necessary environmental support for the development of that talent rarely coincide (Feldman, 1986). Gardner (1983), in particular, has argued that individual differences in aptitude and ability play a much greater role than that assumed by Ericsson and his colleagues. Gardner argues that the deliberate practice account of expertise ignores self-selection and the basis for the ability to engage in the training required to become an expert. However, there are many cases of exceptional performers who did not show any unusual talent early in childhood but, through sustained, intensive practice, nonetheless achieved high performance levels (Ericsson & Charness, 1994).

Chase and Simon (1973; Simon & Chase, 1973) developed both a skill-based account of expertise and methods for studying expertise. Using expertise in chess as an example, they attempted to document the expert’s knowledge structures and processes. Their influential work emphasized the roles of perception and memory in expert performance. According to Chase and Simon, the development of expertise in chess relies heavily on chunking.As a result of practice and experience with the game, experts come to recognize configurations of chess pieces as groups or chunks rather than as individual pieces.As chunks develop, increasingly larger configurations are recognized until the game configuration itself can be apprehended as one whole. Other researchers (e.g., Charness, 1991) have emphasized the importance of study, in this case, the study of games of chess masters and strategy, in the development of expertise in chess. Chess masters themselves have a great deal of knowledge acquired through study of books and magazines.

Search and evaluation processes have been shown to have a separate, and important, role in chess expertise (e.g., Charness, 1981), as have heuristic rules and knowledge of themes, openings, and so forth (Holding, 1985). As suggested by the work of Chase and Simon (1973), however, what is searched appears to be more important than how extensively or deeply the search is conducted. Chess masters seem to rely more on pattern recognition than search (although very fast search times have not been ruled out). Pattern recognition skills may develop in part because time limits in chess penalize long search times and encourage sacrificing; this factor could limit the generalizability of the characterization of chess expertise to other skills.

Studying Expertise

The original expertise approach consists of three steps. The first step is to produce and observe outstanding performance in the laboratory under relatively standardized conditions using tasks that are representative of the skills possessed by the expert. The second step is to create a detailed picture of expert performance by analyzing and describing the processes critical to the production of an outstanding performance on the tasks. The third and final step is to examine critical cognitive processes and propose explicit learning mechanisms to account for their acquisition. In other words, the object is to develop an account of the expert’s knowledge structures and processes.

A variety of knowledge acquisition techniques has been used to analyze expert knowledge structures and processes. Some techniques, such as hierarchical card sorting and general weighted networks, are based on judgments of similarity. In card sorting tasks, cards containing one piece of domain knowledge each are sorted into categories and subcategories. The resulting hierarchical structure is presumed to reflect the way the expert structures his or her actual knowledge. A limitation of this technique is that the requirement to make categories may force the expert to create a different structure from that which actually exists. The Pathfinder algorithm (Schvaneveldt, 1990) poses fewer constraints. Experts simply rate pairs of domain terms for their similarity, and then the algorithm is applied to find the network structure underlying the knowledge. Protocol analysis has also been extensively used to study expert behavior. In its most general application, experts are asked to think aloud while they solve a problem or perform a task. In this method, experts are simply asked to verbalize any thoughts that come to mind as they are performing a task. Ericsson and Simon (1993) maintain that this technique, unlike requiring people to explain their thinking, does not seem to cause any restructuring of the cognitive processes involved in task performance. On the other hand, it is limited to knowledge of which the expert is aware. Actions that are performed automatically or very quickly are likely to escape the notice of the performer.

Characteristics of Expertise

Asmentionedabove,changesinworkingmemoryspanfordomain information are often cited as a characteristic of expertise. Skilled memory theory states that at the time of encoding, experts form a set of retrieval cues that are associated in a meaningful way with the information to be stored. Retrieval then occurs via these cues. Rather than just chunking information so that more information can be stored in short-term memory, experts develop memory skills that enable them to store and retrieve information in long-term memory more quickly and efficiently, thus circumventing the limits of shortterm memory (Chase & Ericsson, 1982). Chase and Ericsson thus argue that extensive practice develops skills that lead to qualitative, and not just quantitative, differences in memory performance for the practiced type of information.

In the area of problem solving, several generalizations about expert performance can be made. As Anzai (1991) has shown in studies of physics expertise, experts work forward, novices backward. Novices are also apt to change problem representations more frequently than experts; they seem unable to decide which representation is best for solving a problem. Experts, on the other hand, generate and update a representation of a problem as they read it. By the time a question regarding the problem is presented, they are often able to retrieve a solution plan from memory based on this representation (Larkin, McDermott, Simon, & Simon, 1980). In other words, the representation cues the expert’s knowledge. Novices may lack both the organized knowledge base and the ability to build a representation that can act as a cue. Physics experts seem to possess multiple modes of representation for solving problems as well as the procedural knowledge for effective use of these multiple representations. This provides the basis for the formation of abstract or simpler representations from less abstract or more complex ones.

In the area of motor skill expertise, a distinction has been made between knowing and doing (Allard & Starkes, 1991). Knowing, in this context, involves directing the intake of environmental information in the appropriate way. The doing component is essential for the execution of actions, sport techniques, and motor-control programs. Knowing (consisting primarily of search processes) dominates in open skills, such as football, in which the environment (often including an opponent) is important and performance is directed toward an external goal. Doing (which can be characterized as skilled memory) predominates in closed skills, such as figure skating, in which the skill is performed in an invariant environment and has the production of a particular motor pattern as its goal.

Skill and Expertise

The fact that practice seems to be the most important determinant of the acquisition of expertise means that learning mechanisms that mediate increasing improvements from repeated practice trials must exist and play important roles in the acquisition of expertise. It is not merely exposure to a task that provides the basis for expert performance, but conscious, deliberate practice in which feedback is sought and used to improve performance. Just as it does in skilled performance in general, memory plays a large role in expertise. As mentioned above, experts have both a large, well-organized knowledge base and, often, specialized procedures for acquiring and storing knowledge. Also noteworthy is the specificity of the skills experts possess. For example, although chess players show an exceptional memory for the positions of chess pieces in various midgame positions, they do not score any higher on general tests of spatial ability than do controls (Doll & Mayr, 1987). As Thorndike and Woodworth (1901a, 1901b, 1901c) argued, there is little evidence for a “doctrine of formal discipline” (see Higginson, 1931) in which practice in one difficult skill leads to generalizable benefits in other domains. The study of expertise allows us to add that there is little evidence that exceptional abilities are a necessary prerequisite for the development of expertise.

Training

Training has been a topic of interest to psychologists for the past hundred years or so. The major question of interest has been how broad the effects of training can be. As suggested by instance accounts of skill acquisition, the effects of training are often quite tightly tied to the training conditions. Therefore, it is necessary to determine what aspects of the target environment need to be included in the training. A related problem is that many skills are too complex to be learned all at once. Effective training can therefore depend on learning only certain aspects of a skill at a time. For these reasons, methods of decomposing skills for training and then recombining them are needed if effective training programs are to be designed.

Effective, efficient training programs depend on the identification of those aspects of the task that are critical for improving skill. These aspects can be identified by interviewing experts, by determining the characteristics that divide good performers and bad performers, and through theoretical analysis of the task. One approach is to emphasize cueresponse relations (Cormier, 1987) and to determine which cues are necessary for the determination of responses. This approach has been used in designing simulators for training complex or dangerous tasks. Building high-fidelity simulators is expensive, so there is pressure on designers to include only those cues that lead to better transfer to the actual task.

If one can determine the relevant cue-response relations, only the cues that are necessary need be incorporated into the simulator. Unfortunately, determining these relations is not always easy. For example, it has been found that motion cues can lead to better performance in a flight simulator (e.g., Perry & Naish, 1964), but not to better transfer to actual flight (e.g., Jacobs & Roscoe, 1975). In order to understand this discrepancy, it is necessary to look at the type of motion cues presented. In general, the presence of disturbance motion cues (cues associated with outside influences) are more important for transfer of simulator training to actual flight. However, for relatively unstable, difficult-to-fly aircraft, maneuver cues (cues associated with control actions) can be important (for a review see Gawron, Bailey, & Lehman, 1995).

Most techniques for analyzing tasks start with a description of the complete human-machine system but focus on the description, analysis, and evaluation of the performance demands placed on the human. For example, the focus might be on decomposing tasks into their constituent information-processing requirements, such as the principles, rules, and goals contained in expert knowledge, the distinction between automatic and controlled processes, or the allocation of attention. An example of one such approach is principled task decomposition (Frederiksen & White, 1989). This method was used by Frederiksen and White to develop a training program for the Space Fortress game, a video game developed by researchers to study complex skill acquisition (Mané & Donchin, 1989), and it is based on task decomposition, an analysis of human information-processing requirements, and the characteristics of expert performance. Frederiksen and White first identified the hierarchical relationships between skill and knowledge components that allow the progression from novice to expert performance and then used this task decomposition to construct training activities for the component processes as well as their integration. A comparison of the performance of a group who received componential training and a control group who practiced the Space Fortress game showed an initial deficit for the componential-training group when first transferred to whole-game performance. However, the componential-training group quickly overtook the whole-game training group, suggesting that, after some initial integration of learned skills during their first experience with the whole game, the specific knowledge and heuristics taught in the componential training had benefited learning (see Figure 18.4).

In general, part-task training, such as that used by Frederiksen and White (1989), has been shown to be an effective method of training difficult tasks or tasks with independent components (Holding, 1965; Wightman & Lintern, 1985). Several methods of part-task training have been developed and evaluated. If a task consists of components with clear starting and stopping points, it can simply be segmented into the different components. If the last step in a segmented task is practiced first, with earlier components added later, the procedure is called backward chaining. Whether backward chaining is more effective than forward chaining, in which segments are trained sequentially, starting with the first one, will depend on the type of feedback needed for performance. For complex tasks in which the initial steps are far removed from the goal, there might be a benefit for backward chaining because this begins by emphasizing the steps closest to the goal. When feedback from one component influences performance on the next, forward chaining might be more effective (Wightman & Lintern, 1985).

Marmie and Healy (1995) showed that the benefits of parttask training using a segmentation and backward-chaining strategy can show long-lasting effects in a simulated tankgunnery task. In the relevant experiment, participants practiced either the whole task (searching for a target, sighting it, and firing) or, for several sessions, only the sighting and firing components. Performance in whole-task retention sessions given immediately after training or one month later showed no difference between the groups in overall performance (proportion of kills) or in time to identify the target. However, the part-task training group, which was able to devote more resources to the sighting and firing components of the task during training, showed a long-lasting benefit in time to fire.

If different task components are performed in parallel, it is not possible to segment them. In this case, we speak of fractionation of the task. This involves practicing some components, such as perceptual skills, in isolation and then combining them with other aspects of the task, such as making responses. It has been argued that fractionation can only be effective if there is relatively little time sharing or interdependence between the components (W. Schneider & Detweiler, 1988). In some cases, such as when multiple-task components must be carried out in parallel, the demands imposed by the need to recombine the separate skills counteract any benefits of part-task training. However, the view that part-task training is ineffective for tasks that must be time shared may be overly pessimistic.

Fabiani et al. (1989) compared the hierarchical training tasks developed by Frederiksen and White (1989) with whole-task training and with a so-called integrative training, in which the whole task was practiced, but performers were instructed to emphasize certain of the skills identified by Frederiksen and White. If time sharing must be practiced in order to be learned, one might expect better performance in a whole-task transfer condition for the integrative- than for the hierarchical-training group. However, although the integrative group showed more learning than a control group who practiced the whole task under normal instructions for the same amount of time, they did not do any better than the hierarchical group. A possible benefit for the integrative group was, however, found when a variety of secondary tasks were added to the game. The integrative group proved to be better in coping with these new task demands.

Another method of training is to simplify the task, teach the simplified version, and then release the constraints placed on the task until the task is restored to its original complexity. This method has been used successfully in teaching the use of software and has led to the concept of minimal training. Carroll (1997) argues that step-by-step manuals and computer tutorials are often frustrating and ineffective because they do not match the way people approach learning. According to Carroll, learners want to get started fast, which often leads them to omit critical steps, and neglect to plan tasks or predict the outcomes of their explorations. They also prefer not to follow procedures, often reason from inference—even when the similarity to the current situation is only superficial—and, finally, are often poor at recognizing, diagnosing, and recovering from errors. Recognition of these characteristics of learners led Carroll (Carroll & Carrithers, 1984) to develop a training wheels interface for a word processor that restricted what learners could do and, hence, the errors that they could make. They found a substantial benefit for the use of the training wheels interface on transfer to the full word processor. They attributed the benefit to the fact that training wheels users spent less time on error recovery and more time learning useful tasks. The lessons to be learned from Carroll and his colleagues’ work on minimalist training (summarized in Carroll, 1997) are that training environments should allow users to get started fast, permitthemtothinkandimprovise,embedinformationinreal tasks, relate new information to what people already know, and support error recognition and recovery. In other words, good instruction should enable active learning while providing enough support to keep learners involved in useful tasks.

Skill Acquisition and Attentional Strategies

Skill acquisition depends on paying attention to the right things at the right time. That is, an important aspect of skilled performance is skilled attending. In many tasks, it is important not only to know what to attend to, but how to attend to it. Complex, dynamic tasks often require performers to divide attention and processing resources among competing, dynamically changing stimuli or task demands, for which priorities must be established and trade-offs made. Important questions in the training of complex skills concern whether we are aware of attentional investments and can control and allocate attentional resources.

One example of attentional allocation is distributing visual attention across a relatively large area and number of processing items, or focusing it on a small area or number of items. Learning to focus attention appropriately could well be an important factor in performance of many skills. Most work on the training of attention comes from the study of dual-task performance in which performers had to learn to prioritize their performance of two tasks so that one was performed better at the cost of the other (e.g., Gopher, Brickner, & Navon, 1982). With the provision of augmented feedback, in which details of the nature of the performance are given, people can learn to make performance trade-offs and allocate attention according to instructions (Spitz, 1988).

The training of attentional allocation and prioritization strategies can have a strong and long-lasting influence on performance. Gopher and his colleagues (see Gopher, 1993) have shown that dual-task performance benefits more from training under variable priority settings (e.g., Task 1 priority of 25, 50, or 75%) than from training without priority instructions or with only one priority (e.g., 50%). The higher ability of performers who train under variable priorities seems to stem from an improved ability to detect changes and adjust efforts to cope with changing task demands. Gopher, Weil, and Siegel (1989) implemented variable-priority setting in a training program for the Space Fortress game (Mané & Donchin, 1989). By requiring participants to change their emphasis on different aspects of the game, they forced them to explore different strategies of performance, thus overcoming limitations that arise when learners lock onto a nonoptimal strategy early in performance. Participants who performed under emphasischange conditions also improved in their ability to evaluate their own peripheral attention abilities and thus to discover minimal control levels. Gopher, Weil, Bareket, and Caspi (1988) gave variable-emphasis training with the Space Fortress game to groups of Israeli Air Force cadets who were undergoing flight training. Although they received only 10 hours of variable-emphasis Space Fortress training, cadets in the experimental group showed a 30% increase in program completion. Practice with Space Fortress has also been shown to improve the piloting performance of U.S. helicopter pilots (Hart & Battiste, 1992).

Automaticity and Training

Attentional strategies can be trained, but to what extent can people be trained to operate without attention? Many complex tasks can only be performed because some task components have become automatized, thus freeing up resources for other components. Several researchers have shown that training in tasks similar to visual search can lead to automatic processing. Such training has been used successfully with air traffic controllers to promote automatic processing of some perceptual information, such as the distances between aircraft, and indications of certain maneuvers, such as the start of turns (W. Schneider, Vidulich, & Yeh, 1982). Shebilske, Goettl, and Regian (1999) have developed a framework for training that emphasizes the development of automaticity in task components. They suggest that by determining the components for which automaticity does not develop, one succeeds in identifying those components that play a controlling, or executive, role in the performance of a task.

Team Training

Many tasks are performed not by individuals working alone but by individuals working in teams. The basic principles of skill acquisition and training apply to the individuals, but teamwork brings with it special concerns. Some of the concerns of teamwork fall within the domain of organizational or social psychology, such as the organizational climate in the cockpit and its contribution to air disasters caused by the reluctance of copilots to contradict or question the pilot’s actions. Being a part of a team can also, however, change the way the individual carries out his or her work. Team workers must be able to predict other team members’ behavior and must be able to give and receive backup support. The performance of many tasks requires knowledge of what others are doing and of what they know. Salas and colleagues (e.g., Salas & Cannon-Bowers, 1997) refer to this knowledge as a shared mental model. They suggest that this knowledge allows team members to anticipate each other’s actions and to maintain an accurate, up-to-date picture of the current situation (i.e., situation awareness). The question arises whether there are special training procedures that promote such a shared mental model.

Just as in the training of any task, the development of a team-training program starts with task analysis. The communication flow between team members forms an important part of the analysis. One training strategy unique to team training is cross-training. In cross-training, team members receive information and training in the tasks of other team members. In addition to providing the team with backup knowledge should a team member be absent, this may also contribute to the development of a shared mental model. Volpe, CannonBowers, Salas, and Spector (1996) showed that 2-person teams who received cross-training used more efficient communication strategies and showed better task performance than teams not provided with this knowledge.

Entin and Serfaty (1999) have argued that cross-training is insufficient as a training method for teams who must function in high-workload environments. They maintain that special strategies are necessary to train team members to recognize high-stress conditions and adapt their behavior accordingly. They found that team performance improved after participating in a training program in which participants learned to recognize signs of stress and to communicate more effectively by anticipating the information needs of other team members.

Retention and Transfer of Skill

Transfer of Training

According to Logan and Compton (1998), transfer should occur between compatible tasks, where compatibility is defined as a condition in which “traces laid down in one task context can be used to support performance in another” (p. 119). Orthogonal or incompatible traces will be of no use and may even cause confusion if retrieved. This view is consistent with the long-standing view that transfer will occur when elements in the practiced task are also present in the transfer task. This identical-elements view of transfer (Thorndike & Woodworth, 1901a, 1901b, 1901c) is elegantly incorporated in production system models of learning (e.g., Singley & Anderson, 1989). Many examples of positive transfer of components of skill are given above. In fact, the presence of transfer is often considered to provide the basis for determining what has been learned in a training session.

Long-Term Retention of Skill

It is an old adage that once you learn to ride a bicycle you will never forget how to do it. In fact, even skills learned in relatively artificial laboratory environments often show surprisingly good retention. One example of this is the Kolers (1976) study mentioned above. After 1 year with presumably no practice, participants were still quite proficient in the skill of reading inverted text, and they even showed some benefit for seeing the same text again. Using the task of visual search of displays with various numbers of elements, Cooke, Durso, and Schvaneveldt (1994) showed retention of skilled search ability, including no loss of visual search rates and a minimal loss of search speed, after a 9-year period of nonuse. Furthermore, the savings were found for both consistent and variedmapping tasks. The fact that the search rate was maintained suggests that essential elements of the search process were retained. Participants also reported that they still experienced a pop-out effect, in which consistently mapped targets seemed to command attention even though 9 years had elapsed since the development of the search skill.

The retention of skilled performance depends on the conditions of training and the conditions under which retention is tested. In general, as suggested by Kolers and Roediger (1984), performance will be better to the extent that the procedures used by the performers during training are also used in retention testing. Healy et al. (1995) summarize a variety of studies on the learning and retention of simple cognitive skills and conclude that retention will be the greatest when retention requires the procedures employed during training, when information received during training can be related to previous experience and can be retrieved directly, when trained information is made distinctive, and when refresher or practice opportunities are provided (Healy et al., 1993, 1995). In other words, both procedures and information seem to provide the cues necessary for retrieving information even after long periods of nonuse.

The emphasis that Healy et al. (1995) place on direct retrieval fits nicely with instance accounts of skill (e.g., Logan, 1988) in which performance is said to be automatic when performance is governed by retrieval. In other words, if automaticity developed at the time of training, retention of the skill is more likely. This point is illustrated by a comparison of the Cooke et al. (1994) study mentioned above and a study by Fisk and Hodge (1992), which also evaluated retention of visual search skill. Contrary to the results of Cooke et al., Fisk and Hodge did not find good retention of variedmapping search. The major difference in the two studies is that the performers in the Cooke et al. study showed evidence of automaticity at the end of the initial learning of the task, whereas those in the Fisk and Hodge study did not. It appears that a shift in strategy, or simply the effects of overlearning, has significant consequences for retention.

Modeling Skill

Although response selection is the locus of learning in many tasks, it was argued above that response selection does not become automatic in the sense that, after learning, stimuli automatically trigger the correct response without regard to the nature of the relationship between stimulus and response. This finding is at odds with one of the most developed models of practice effects, the chunking hypothesis of Newell and Rosenbloom (1981; Rosenbloom & Newell, 1987). The model assumes a production-system architecture in which stimuli are related to responses by means of rules (e.g., “if the mapping is incompatible and the right light is on, find the key opposite to the light and press it”). The chunking hypothesis predicts that performance will improve as a function of practice and that the learning curve will follow a power function. Learning occurs by means of pattern-recognition processes whereby increasingly complex patterns of stimuli and responses are learned. In other words, learning is based on the chunking of stimulus and response patterns. Although it predicts the general pattern of improvement in simple tasks quite well, it contains no provision for long-lasting effects of factors such as stimulus-response compatibility.

Many models of learning have been based Anderson’s (1982, 1993) production system architecture. Productions have several properties that are consistent with empirical generalizations about skill, such as transfer based on common elements. The independence of productions, the all-ornone learning reflected in their creation and their accrual of strength, and potential abstraction make them an appropriate vehicle for the elements of learning.

Many neural network, or connectionist, models of learning have also been developed, although their scope has usually been rather limited. For example, J. D. Cohen, Dunbar, and McClelland (1990) developed a model of the Stroop effect based on the strength of learning to read words versus name colors. The model provides a demonstration of how the strength of learned associations between stimuli and particular types of responses can produce automatic behavior. Unfortunately, the model has been shown to be rather limited in scope, working in its particulars only when the maximum number of stimuli is two (Kanne, Balota, Spieler, & Faust, 1998).

New Directions

As in other areas of cognitive psychology, we can expect to see an increasing number of studies devoted to attempts to discover where in the brain learning occurs. We can expect that such studies will continue to shed light on issues such as the nature of procedural and episodic memory or whether separate systems underlie implicit and explicit learning. Just as in other areas, the degree to which this knowledge helps us to understand the processes by which skills are acquired remains to be seen. Increasingly, more emphasis is being placed not just on what is learned, but on what is not learned. Ohlsson (1996), for example, has proposed a theory of learning based on making mistakes in which what is not learned at one time becomes the basis for what is learned at another time.

Unskilled performance is characterized by ignorance of what to expect, what to do, or when to do it; lack of knowledge of interrelationships among variables and of what information is relevant; difficulty in combining information; insensitivity to relevant sensory or perceptual discriminations; and a lack of production proficiency. Progress has been made in understanding how these relations, skills, and proficiencies are acquired as a function of experience, and in understanding what sorts of experiences lead to the greatest improvements. The future of research in skill acquisition is as broad and as bright as in all of cognitive psychology. We can expect to see many more questions, and answers, as to the nature of the processes that allow the aforementioned changes to occur.

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