Theories of the representation of specific kinetic and spatiotem-poral features of movement range from the explicit assertion that temporal aspects of movement are not represented to the idea that they are represented and that they have neurophysiological correlates. Jeannerod's thesis is that mental and visual images have common mechanisms and that there is a link between the image to move and the mechanisms involved with movement. The target article takes the position that certain parameters are coded in motor representations but (...) that the duration of an action is not one of them. This position is based on the work of Gottlieb et al. and of Decety et al.. Both these studies are worth considering in detail. In Note 1, Jeannerod suggests that: “in time-constrained tasks subjects control the amplitude parameter of force impulses, whereas in spatially constrained tasks the duration of the force impulse is affected by accuracy demands.” This is not exactly correct. Excitation pulse intensity is modulated both in tasks that require spatial and those that require temporal accuracy. Excitation pulse duration is modulated for changes in movement distance and inertial load. If subjects are required to be very accurate spatially, they will move at less than maximum speed for a given distance and this is achieved by lower levels of excitation intensity. (shrink)

It has been suggested that the enterprise of developing mechanistic theories of the human cognitive architecture is flawed because the theories produced are not directly falsifiable. Newell attempted to sidestep this criticism by arguing for a Lakatosian model of scientific progress in which cognitive architectures should be understood as theories that develop over time. However, Newell's own candidate cognitive architecture adhered only loosely to Lakatosian principles. This paper reconsiders the role of falsification and the potential utility of Lakatosian principles in (...) the development of cognitive architectures. It is argued that a lack of direct falsifiability need not undermine the scientific development of a cognitive architecture if broadly Lakatosian principles are adopted. Moreover, it is demonstrated that the Lakatosian concepts of positive and negative heuristics for theory development and of general heuristic power offer methods for guiding the development of an architecture and for evaluating the contribution and potential of an architecture's research program. (shrink)

Cognitive architectures - task-general theories of the structure and function of the complete cognitive system - are sometimes argued to be more akin to frameworks or belief systems than scientific theories. The argument stems from the apparent non-falsifiability of existing cognitive architectures. Newell was aware of this criticism and argued that architectures should be viewed not as theories subject to Popperian falsification, but rather as Lakatosian research programs based on cumulative growth. Newell's argument is undermined because he failed to demonstrate (...) that the development of Soar, his own candidate architecture, adhered to Lakatosian principles. This paper presents detailed case studies of the development of two cognitive architectures, Soar and ACT-R, from a Lakatosian perspective. It is demonstrated that both are broadly Lakatosian, but that in both cases there have been theoretical progressions that, according to Lakatosian criteria, are pseudo-scientific. Thus, Newell's defense of Soar as a scientific rather than pseudo-scientific theory is not supported in practice. The ACT series of architectures has fewer pseudo-scientific progressions than Soar, but it too is vulnerable to accusations of pseudo-science. From this analysis, it is argued that successive versions of theories of the human cognitive architecture must explicitly address five questions to maintain scientific credibility. (shrink)

The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such (...) learning results in example‐independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self‐explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self‐explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation‐based learning to account for these psychological findings. (shrink)

The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such (...) learning results in example‐independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self‐explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self‐explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation‐based learning to account for these psychological findings. (shrink)

The current status of Beilock and Carr's "execution focus" theory of choking under pressure in performance of a sensorimotor skill is reviewed and assessed, mainly from the perspective of cognitive psychology, and put into the context of a wider range of issues, attempting to take philosophical analysis into account. These issues include other kinds of skills, pre-performance practice, post-performance evaluation and repair, and integrating new and creative achievements into repertoires of heavily practiced routines. The focus is on variation in the (...) demand for reflection versus automaticity across the full gamut of learning and experience, not just game-time performance. Though automaticity remains important and there are many circumstances in which being "in the zone" is good whereas “execution focus” can do harm, it appears that reflective action deserves just as much a place at the performance table as does unreflective action. (shrink)

We explore the extent to which Anderson's (1987) theory of knowledge compilation can account for the relationship between instructions and practice in learning to use a simple device. Bibby and Payne (1993) reported experimental support for knowledge compilation in this domain. This article replicates the finding of a performance cross‐over between instruction type and task type that disappears with practice on the tasks. The research is extended by using verbal protocols to model the strategies of novice and more experienced individuals. (...) Production system models of these strategies suggest that knowledge compilation only provides an adequate account of practice for one of two Instruction groups. To model the strategy shifts for the second group, we employ a “procedure modification heuristic” (after Neches, 1987), which relies on access to a declarative model of the configuration of the device. This suggests that instructionally derived declarative knowledge may have a subtle ongoing effect on the changes in procedural knowledge with practice. (shrink)

An analysis of student learning with the LISP tutor indicates that while LISP is complex, learning it is simple. The key to factoring out the complexity of LISP is to monitor the learning of the 500 productions in the LISP tutor which describe the programming skill. The learning of these productions follows the power‐law learning curve typical of skill acquisition. There is transfer from other programming experience to the extent that this programming experience involves the same productions. Subjects appear to (...) differ only on the general dimensions of how well they acquire the productions and how well they retain the productions. Instructional manipulations such as remediation, content of feedback, and timing of feedback are effective to the extent they give students more practice programming, and explain to students why correct solutions work. (shrink)

Archaeology can provide two bodies of information relevant to the understanding of the evolution of human cognition – the timing of developments, and the evolutionary context of these developments. The challenge is methodological. Archaeology must document attributes that have direct implications for underlying cognitive mechanisms. One example of such a cognitive archaeology is found in spatial cognition. The archaeological record documents an evolutionary sequence that begins with ape-equivalent spatial abilities 2.5 million years ago and ends with the appearance of modern (...) abilities in the still remote past of 400,000 years ago. The timing of these developments reveals two major episodes in the evolution in spatial ability, one, 1.5 million years ago and the other, one million years later. The two episodes of development in spatial cognition had very different evolutionary contexts. The first was associated with the shift to an open country adaptive niche that occurred early in the time range of Homo erectus. The second was associated with no clear adaptive shift, though it does appear to have coincided with the invasion of more hostile environments and the appearance of systematic hunting of large mammals. Neither, however, occurred in a context of modern hunting and gathering. Key Words: Archaeology; evolution; Homo erectus; spatial cognition; symmetry. (shrink)

Two studies examined the use of think aloud (TA) protocol as a means for collecting data of cognitive processes during performance in golf. In study 1, TA was employed to examine if different verbalisation (Level 2 or Level 3 TA) instructions influence performance of high and low skilled golfers. Participants performed 30 putts using TA at either Level 2, Level 3, or no verbalization condition. Although Level 3 verbalization produced a higher volume of verbal data than Level 2, TA at (...) either level 2 or 3 did not impair putting performance compared to no verbalization. Study 2 examined the congruence between data collected via TA at Level 3 and cued retrospective recall of cognitive processes during golf performance. Experienced golfers performed six holes of golf whilst engaging in Level 3 TA. After performance, three semi-structured retrospective interviews were conducted (ten minutes after performance, 24 hours after performance and 48 hours after performance). A comparison of the themes identified large discrepancies between the information reported during TA and at interview, with only 38-41% similarity in variables reported to influence decision making on each hole. Both studies suggest TA is a valuable method for recording cognitive processes of individuals during task performance. TA provides richer verbal data regarding decisions than cued retrospective recall, and TA does not negatively impact performance. (shrink)

Theories of skill acquisition have made radically different predictions about the role of general problem‐solving methods in acquiring rules that promote effective transfer to new problems. Under one view, methods that focus on reaching specific goals, such as means‐ends analysis, are assumed to provide the basis for efficient knowledge compilation (Anderson, 1987), whereas under an alternative view such methods are believed to disrupt rule induction (Sweller, 1988). We suggest that the role of general methods in learning varies with both the (...) specificity of the problem solver's goal and the systematicity of the strategies used for testing hypotheses about rules. In the absence of a specific goal people are more likely to use a rule‐induction learning strategy, whereas provision of a specific goal fosters use of difference reduction, which tends to be a non‐rule‐induction strategy. We performed two experiments to investigate the impact of goal specificity and systematicity of rule‐induction strategies in learning and transfer within a complex dynamic system. The results of Experiment 1 indicated that during free exploration of a problem space, greater learning occurred if participants adopted more systematic strategies for rule induction, and that participants come to favor such strategies. Experiment 2 revealed that participants who were provided with a specific goal performed well on the initial problem but were impaired on a transfer test using a similar problem with a different goal. Instruction on a systematic rule‐induction strategy facilitated solution for both the initial and transfer problems, but participants' use of this strategy declined if they had a specific goal. Our results support Sweller's (1988) proposal that general problemsolving methods applied to a specific goal foster acquisition of knowledge about an isolated solution path but do not provide an effective way of learning the overall structure of a problem space. We interpret these results in terms of dualspace theories of search through problem space. (shrink)

Research on expert‐novice differences has mainly focused on how experts solve familiar problems. We know far less about the skills and knowledge used by experts when they are confronted with novel problems within their area of expertise. This article discusses a study in which verbal protocols were taken from subjects of various expertise designing an experiment in an area with which they were unfamiliar. The results showed that even when domain knowledge is lacking, experts solve a novel problem within their (...) area of expertise by dividing the problem into a number of subproblems that are solved in a specified order. The lack of domain knowledge is compensated for by using abstract knowledge structures and domain‐specific heuristic strategies. However, the quality of their solutions is considerably lower than the quality attained by experts who were familiar with the type of problem to be solved. The results suggest that when experts are confronted with novel problems as compared with familiar problems, their form of reasoning remains intact, but the content of their reasoning suffers due to lack of domain knowledge. (shrink)

Previous accounts of cognitive skill acquisition have demonstrated how procedural knowledge can be obtained and transformed over time into skilled task performance. This article focuses on a complementary aspect of skill acquisition, namely the integration and reuse of previously known component skills. The article posits that, in addition to mechanisms that proceduralize knowledge into more efficient forms, skill acquisition requires tight integration of newly acquired knowledge and previously learned knowledge. Skill acquisition also benefits from reuse of existing knowledge across disparate (...) task domains, relying on indexicals to reference and share necessary information across knowledge components. To demonstrate these ideas, the article proposes a computational model of skill acquisition from instructions focused on integration and reuse, and applies this model to account for behavior across seven task domains. (shrink)

This article presents a computational model of the learning of diagnostic knowledge, based on observations of human operators engaged in real-world troubleshooting tasks. We present a model of problem solving and learning in which the reasoner introspects about its own performance on the problem-solving task, identifies what it needs to learn to improve its performance, formulates learning goals to acquire the required knowledge, and pursues its learning goals using multiple learning strategies. The model is implemented in a computer system which (...) provides a case study based on observations of troubleshooting operators and protocol analysis of the data gathered in the test area of an operational electronics manufacturing plant. The model not only addresses issues in human learning, but, in addition, is computationally justified as a uniform, extensible framework for multistrategy learning. (shrink)

A central goal of cognitive science is to develop a general theory of transfer to explain how people use and apply their prior knowledge to solve new problems. Previous work has identified multiple mechanisms of transfer including (but not limited to) analogy, knowledge compilation, and constraint violation. The central hypothesis investigated in the current work is that the particular profile of transfer processes activated for a given situation depends on both (a) the type of knowledge to be transferred and how (...) it is represented, and (b) the processing demands of the transfer task. This hypothesis was investigated in two laboratory training studies. The results from Experiment 1 show that each mechanism predicts specific behavioural patterns of performance across a common set of transfer tasks. The results from Experiment 2 show that people can adaptively shift between transfer mechanisms depending on their prior knowledge and the characteristics of the task environment. A framework for the development of a general theory of transfer based on multiple mechanisms is proposed and implications of the theory are discussed for measuring and understanding knowledge transfer. (shrink)

This article presents a process account of some typicality effects and related similarity-dependent accuracy and response time phenomena that arise in the context of supervised concept acquisition. We describe Symbolic Concept Acquisition (SCA), a computational system that acquires and activates category prediction rules. In contrast to gradient representations, SCA performs by probing for prediction rules in a series of discrete steps. For learning new rules, it acquires general rules but then incrementally learns more specific ones. In describing SCA, we emphasize (...) its functionality in terms of accuracy and efficiency and motivate its design within the set of symbolic mechanisms and memory structures defined by the Soar architecture (Laird, Newell & Rosenbloom, 1987). For replicating human behavior, we first show how SCA exhibits some typicality effects in the course of learning responding faster and more accurately to more typical test examples. Then, using data from human experiments, we evaluate SCA's qualitative predictions on accuracy and response time on individual dataset instances. We show how SCA's predictions correlate with human data across three experimental conditions concerning the effect of instruction on learning strategy. (shrink)