Chapter 15 Strategies for Knowledge Acquisition and Transfer of Knowledge in Dynamic Tasks

Putz-Osterloh, Wiebke

doi:10.1016/s0166-4115(08)62664-3

Cited by 22 publications

(17 citation statements)

References 6 publications

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“…VOTAT contrasts with less systematic strategies such as changing all factors haphazardly (CA, for Change All). (Similar strategy classifications have been suggested by Branke, 1991, andPutz-Osterloh, 1993.) As Tschirgi pointed out, the VOTAT strategy allows the logical disconfirmation of alternative hypotheses, and thus it is central to experimental design in science.…”

Section: Impact Of Systematicity On Effectiveness Of Strategiesmentioning

confidence: 69%

The Impact of Goal Specificity on Strategy Use and the Acquisition of Problem Structure

Vollmeyer¹,

Burns²,

Holyoak³

1996

Cognitive Science

169

166

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Section: Impact Of Systematicity On Effectiveness Of Strategiesmentioning

confidence: 69%

The Impact of Goal Specificity on Strategy Use and the Acquisition of Problem Structure

Vollmeyer¹,

Burns²,

Holyoak³

1996

Cognitive Science

169

166

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“…In particular, it becomes unclear whether one should attribute experimental findings to the experimenter's manipulation or to the peculiarities of the task employed. Most systems do not differ only with respect to surface features (i.e., the semantics implied by the labelling of their input and output variables) which we know to have strong influences on problem-solving behaviour in both static (e.g., Blessing & Ross, 1996;Hesse, Kauer, & Spies, 1997;Kotovsky & Fallside, 1989;Novick, 1988;Wagenaar, Keren, & Lichtenstein, 1988) and dynamic tasks (e.g., Hesse, 1982;Luc, Marescaux, & Karnas, 1989;Preußler, 1997;Putz-Osterloh, 1993). Equally important, for most systems it is not clear how to compare them with respect to the underlying formal structure.…”

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confidence: 99%

Dynamic systems as tools for analysing human judgement

Funke

2001

Thinking & Reasoning

200

230

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With the advent of computers in the experimental labs, dynamic systems have become a new tool for research on problem solving and decision making. A short review of this research is given and the main features of these systems (connectivity and dynamics) are illustrated. To allow systematic approaches to the influential variables in this area, two formal frameworks (linear structural equations and finite state automata) are presented. Besides the formal background, the article sets out how the task demands of system identification and system control can be realised in these environments, and how psychometrically acceptable dependent variables can be derived.

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“…A number of studies have shown, for example, that problem solving performance is related to the systematicity of the exploration strategy adopted, with more systematic strategies (i.e., exploring the properties of individual variables consecutively) resulting in richer structural knowledge and better performance (e.g., Putz-Osterloh, 1993;Vollmeyer, Burns, and Holyoak, 1996).…”

Section: Dynamic Decision Makingmentioning

confidence: 99%

Modelling Dynamic Decision Making with the ACT-R Cognitive Architecture

Peebles¹,

Banks²

2010

Journal of Artificial General Intelligence

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This paper describes a model of dynamic decision making in the Dynamic Stocks and Flows (DSF) task, developed using the ACT-R cognitive architecture. This task is a simple simulation of a water tank in which the water level must be kept constant whilst the inflow and outflow changes at varying rates. The basic functions of the model are based around three steps. Firstly, the model predicts the water level in the next cycle by adding the current water level to the predicted net inflow of water. Secondly, based on this projection, the net outflow of the water is adjusted to bring the water level back to the target. Thirdly, the predicted net inflow of water is adjusted to improve its accuracy in the future. If the prediction has overestimated net inflow then it is reduced, if it has underestimated net inflow it is increased. The model was entered into a model comparison competition-the Dynamic Stocks and Flows Challenge-to model human performance on four conditions of the DSF task and then subject the model to testing on five unseen transfer conditions. The model reproduced the main features of the development data reasonably well but did not reproduce human performance well under the transfer conditions. This suggests that the principles underlying human performance across the different conditions differ considerably despite their apparent similarity. Further lessons for the future development of our model and model comparison challenges are considered.

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Chapter 15 Strategies for Knowledge Acquisition and Transfer of Knowledge in Dynamic Tasks

Cited by 22 publications

References 6 publications

The Impact of Goal Specificity on Strategy Use and the Acquisition of Problem Structure

The Impact of Goal Specificity on Strategy Use and the Acquisition of Problem Structure

Dynamic systems as tools for analysing human judgement

Modelling Dynamic Decision Making with the ACT-R Cognitive Architecture

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