Jeannerod's target article describes support, through empirical and neurological findings, for the intriguing idea of motor imagery, a form of representation hypothesized to have levels of functional equivalence with motor preparation, while being consciously accessible. Jeannerod suggests that the subjectively accessible content of motor imagery allows it to be distinguished from motor preparation, which is unconscious. Motor imagery is distinguished from visual imagery in terms of content. Motor images are kinesthetic in nature; they are parametrized by variables such as force (...) and time and they are potentially governed by kinematic rules. Jeannerod acknowledges, however, that motor and visual imagery may not easily be separated, because actions take place in a spatial environment. I agree; in fact, I suggest here that visualization may generally be concomitant with, and may even subjectively dominate, motor imagery. (shrink)

This paper concerns how motor actions are neurally represented and coded. Action planning and motor preparation can be studied using a specific type of representational activity, motor imagery. A close functional equivalence between motor imagery and motor preparation is suggested by the positive effects of imagining movements on motor learning, the similarity between the neural structures involved, and the similar physiological correlates observed in both imaging and preparing. The content of motor representations can be inferred from motor images at a (...) macroscopic level, based on global aspects of the action and the motor rules and constraints which predict the spatial path and kinematics of movements. A more microscopic neural account calls for a representation of object-oriented action. Object attributes are processed in different neural pathways depending on the kind of task the subject is performing. During object-oriented action, a pragmatic representation is activated in which object affordances are transformed into specific motor schemas. Animal as well as human clinical data implicate the posterior parietal and premotor cortical areas in schema instantiation. A mechanism is proposed that is able to encode the desired goal of the action and is applicable to different levels of representational organization. (shrink)

A number of accounts of expert programming behavior have been advanced. These models of the programming activity have served to highlight the range of factors that are thought to underpin programming strategy. However, such accounts have tended to emphasize either the effects of the organization of the programmer's knowledge representation or the role played by features of the notation of the task language on the emergence, development, and support of particular forms of strategy. Such work has neglected to (a) provide (...) an account of the way in which these factors might interact to determine programming strategy, and (b) shed light upon the nature of the development of particular strategies as programming skill increases.This article presents the results of an empirical analysis of the strategies employed by programmers of varying skill levels using different programming languages. The development of particular forms of strategy is hypothesized to be related both to the development of systematic asymmetries in programmers' generic plan‐based representations of programming knowledge and to the way in which features of the notation of particular programming languages might differentially support particular strategies. However, the results of the study reported here suggest that these effects must be interpreted within a broad developmental context. Hence, as programming skill develops, it appears that the notation of the task language tends to take precedence as a determinant of strategy, but has less relevance at beginning stages of skill development and within the context of expert performance. This finding would not be predicted by current models of the programming activity because such models suggest that any notational effects will tend to be consistent regardless of the skill level attained by the programmer. This article endeavors to extend current models of programming by emphasizing the need to consider in detail not only the development of programming strategy, but also the way in which knowledge representation and features of the task langauge interact to give rise to particular forms of programming behavior. (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)

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)

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)

Future professionals should be prepared for scientific reasoning, i.e., to construct and apply scientific knowledge, in order to analyze and solve problems in their professional practice. Yet, future practitioners’ scientific reasoning skills often seem to be deficient when solving practical problems. This dissertation explores to what extent collaboration may foster the engagement of future practitioners in scientific reasoning: i.e., in epistemic processes and in referring to scientific content knowledge. Therefore, two studies were conducted to compare collaborative and individual problem solving (...) of pre-service teachers regarding their scientific reasoning. Study 1 investigates the effect of group heterogeneity with respect to problem solving scripts on scientific reasoning. Study 2 explores to what extent Epistemic Network Analysis can serve as a methodological approach for measuring scientific reasoning. As part of Study 1, pre-service teachers solved an educational problem either as individuals or as pairs. Collaboration showed a mixed effect on scientific reasoning processes: pairs engaged more in explaining and reasoning about the problem and drew more conclusions, while individuals engaged more in generating solutions. Additional analyses showed that the more heterogeneous pairs were regarding their members’ problem solving scripts the more they engaged in hypothesizing and evaluating evidence and the less they engaged in generating solutions. Finally, pairs less often referred to scientific content than individuals did during problem solving. Study 2 further analyzed the data by applying Epistemic Network Analysis. This method has the advantage of analyzing patterns of connections between epistemic processes, i.e., epistemic networks of scientific reasoning. The central epistemic process for pairs was evidence evaluation, which they frequently used in combination with hypothesizing and communicating and scrutinizing. On the other hand, the most characteristic process in individuals’ scientific reasoning was solution generation, which very often co-occurred with hypothesizing and evidence evaluation. The overall results indicate that if the aim is to develop a more reflective understanding of the problem, future practitioners should collaborate with each other, especially in heterogeneous settings. However, they should be supported to share knowledge regarding scientific theories and evidence as well as to reach a mutual understanding on the problem after a certain time so as to be able to have the capacity of generating solutions. Moreover, the different effects of collaboration on the process and content aspects of scientific reasoning imply that scientific reasoning might not be a unidimensional construct, and its process and content levels should be differentiated in future research. A further important methodological implication is that the process aspect of scientific reasoning can be analyzed as a network of interconnected skills and such analysis might bring more explanatory value than the mere reliance on frequencies of occurrences of isolated activities. (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)

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)

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)

Novice designers produced a sequence of sketches while inventing a logo for a novel brand of soft drink. The sketches were scored for the presence of specific objects, their local features and global composition. Self‐assessment scores for each sketch and art critics' scores for the end products were collected. It was investigated whether the design evolves in an essentially random fashion or according to an overall heuristic. The results indicated a macrostructure in the evolution of the design, characterized by two (...) stages. For the majority of participants, the first stage is marked by the introduction and modification of novel objects and their local and global aspects; the second stage is characterized by changes in their global composition. The minority that showed the better designs has a different strategy, in which most global changes were made in the beginning. Although participants did not consciously apply these strategies, their self‐assessment scores reflect the stages of the process. (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)

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)

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)

In this paper, we present a competency model for complex problem solving by building on the categories of Knowledge, Skills, Abilities, and Other components. We highlight domain-general and domain-specific components in each of these categories, review established conceptualizations of CPS, and present a new model of CPS competency that is meant to provide a starting point for systematic research on training and assessment. The model highlights the idea that complex problems differ with regard to the KSAO components they demand from (...) a problem solver and that performance in one problem does not necessarily predict performance in a different problem. Implications for research on the training and assessment of CPS competency are discussed, and a selection of well-established tests for various components of the KSAO model is provided. (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)

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)

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)