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 order to interpret the behaviour of cognitive systems, the integration into their specific cultural environment must be considered. The phenomenon of situatedness is a crucial determinant of this behaviour. We derive the notion of situatedness from the interplay between agent, situation, and context. The main objective of this paper is to connect a theoretical analysis of situatedness with its implications for empirical research. In particular, we consider processes of situated learning in natural and artificial systems.

We review the progress of naturalistic decision making in the decade since the first conference on the subject in 1989. After setting out a brief history of NDM we identify its essential characteristics and consider five of its main contributions: recognition-primed decisions, coping with uncertainty, team decision making, decision errors, and methodology. NDM helped identify important areas of inquiry previously neglected, it introduced new models, conceptualizations, and methods, and recruited applied investigators into the field. Above all, NDM contributed a new (...) perspective on how decisions are made. NDM still faces significant challenges, including improvement of the quantity and rigor of its empirical research, and confirming the validity of its prescriptive models. Copyright © 2001 John Wiley & Sons, Ltd. (shrink)

Metacognition, the observation of one's own thinking, is a key cognitive ability that allows humans to influence and restructure their own thought processes. The influence of culture on metacognitive strategies is a relatively new topic. Using Antonietti's, Ignazi's and Perego's questionnaire on metacognitive knowledge about problem-solving strategies, five strategies in three life domains were assessed among student samples in Brazil, India, and the United States, regarding the frequency, facility, and efficacy of these strategies. To investigate cross-cultural similarities and differences in (...) strategy use, nationality and uncertainty avoidance values were independent variables. Uncertainty avoidance was expected to lead to high frequency of decision strategies. However, results showed no effect of uncertainty avoidance on frequency, but an effect on facility of metacognitive strategies. Comparing the three cultural samples, all rated analogy as the most frequent strategy. Only in the U.S. sample, analogy was also rated as the most effective and easy to apply strategy. Every cultural group showed a different preference regarding what metacognitive strategy was most effective. Indian participants found the free production strategy to be more effective, and Indian and Brazilian participants found the combination strategy to be more effective compared to the U.S. participants. As key abilities for the five strategies, Indians rated speed, Brazilians rated synthesis, and U.S. participants rated critical thinking as more important than the other participants. These results reflect the embedded nature and functionality of problem solving strategies in specific cultural environments. The findings will be discussed referring to an eco-cultural framework. (shrink)

This paper discusses the role of cultural anthropology in Cognitive Science. Culture is described as a very large pool of information passed along from generation to generation, composed of learned “programs” for action and understanding. These cultural programs differ in important ways from computer programs. Cultural programs tend to be unspecified and inexplicit rather than clearly stated algorithms learned through a slow process of guided discovery, and involve the manipulation of content based rather than formal symbol systems. Cultural symbol systems (...) often have affective as well as objective referents, giving them a strong directive effect. The argument is made that in the process of repeated social transmission and use, cultural programs come to take forms which have a good “fit” to the natural capacities and constraints of the human information processing system. (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)

We investigate 1 factor that influences the success of ideas or cultural representations by proposing that they have a habitat, that is, a set of environmental cues that encourages people to recall and transmit them. We test 2 hypotheses: (a) fluctuation: the success of an idea will vary over time with fluctuations in its habitat, and (b) competition: ideas with more prevalent habitats will be more successful. Four studies use subject ratings and data from newspapers to provide correlational support for (...) our 2 hypotheses, with a negative factoid, positive rumor, catchphrases, and variants of a proverb. Three additional experimental studies manipulate the topic of actual conversations and find empirical support for our theory, with catchphrases, proverbs, and slang. The discussion examines how habitat prevalence applies to a more extensive class of ideas and suggests how habitats may influence the process by which ideas evolve. (shrink)

FOREWORD Yrjö Engeström is one of the most self-directed but certainly also most interesting representatives of contemporary activity theory. ...

This revised edition includes a new Preface outlining developments in Relevance Theory since 1986, discussing the more serious criticisms of the theory, and ...

Hutchins examines a set of phenomena that have fallen between the established disciplines of psychology and anthropology, bringing to light a new set of relationships between culture and cognition.

Simple Heuristics That Make Us Smart invites readers to embark on a new journey into a land of rationality that differs from the familiar territory of cognitive science and economics. Traditional views of rationality tend to see decision makers as possessing superhuman powers of reason, limitless knowledge, and all of eternity in which to ponder choices. To understand decisions in the real world, we need a different, more psychologically plausible notion of rationality, and this book provides it. It is about (...) fast and frugal heuristics--simple rules for making decisions when time is pressing and deep thought an unaffordable luxury. These heuristics can enable both living organisms and artificial systems to make smart choices, classifications, and predictions by employing bounded rationality. But when and how can such fast and frugal heuristics work? Can judgments based simply on one good reason be as accurate as those based on many reasons? Could less knowledge even lead to systematically better predictions than more knowledge? Simple Heuristics explores these questions, developing computational models of heuristics and testing them through experiments and analyses. It shows how fast and frugal heuristics can produce adaptive decisions in situations as varied as choosing a mate, dividing resources among offspring, predicting high school drop out rates, and playing the stock market. As an interdisciplinary work that is both useful and engaging, this book will appeal to a wide audience. It is ideal for researchers in cognitive psychology, evolutionary psychology, and cognitive science, as well as in economics and artificial intelligence. It will also inspire anyone interested in simply making good decisions. (shrink)