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Effects of goal specificity on means–ends analysis and learning

Effects of goal specificity on means–ends analysis and learning Means–ends analysis is a mechanism that is assumed to operate when people solve transformation problems. Its use is affected by the extent to which the goal is clearly specified to the problem solver as a problem state and by the extent to which learning occurs during a problem-solving episode. Five maze-tracing experiments were conducted with 116 undergraduates in which the finish point of the maze could be presented either as a specific location or in more general terms. The latter prevented the use of conventional means–ends analysis. Results indicate that on the particular maze configuration used, the nonspecific goal resulted in fewer errors and more rapid learning of the structure of the problem. Under conditions that facilitated the use of means–ends analyses, knowledge of the goal location rendered the problem insoluble. General results were replicated with the use of numerical problems. Implications for the generality of means–ends analysis as a problem-solving mechanism are discussed. (11 ref) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Experimental Psychology: Learning, Memory, and Cognition American Psychological Association

Effects of goal specificity on means–ends analysis and learning

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Publisher
American Psychological Association
Copyright
Copyright © 1982 American Psychological Association
ISSN
0278-7393
eISSN
1939-1285
DOI
10.1037/0278-7393.8.5.463
Publisher site
See Article on Publisher Site

Abstract

Means–ends analysis is a mechanism that is assumed to operate when people solve transformation problems. Its use is affected by the extent to which the goal is clearly specified to the problem solver as a problem state and by the extent to which learning occurs during a problem-solving episode. Five maze-tracing experiments were conducted with 116 undergraduates in which the finish point of the maze could be presented either as a specific location or in more general terms. The latter prevented the use of conventional means–ends analysis. Results indicate that on the particular maze configuration used, the nonspecific goal resulted in fewer errors and more rapid learning of the structure of the problem. Under conditions that facilitated the use of means–ends analyses, knowledge of the goal location rendered the problem insoluble. General results were replicated with the use of numerical problems. Implications for the generality of means–ends analysis as a problem-solving mechanism are discussed. (11 ref)

Journal

Journal of Experimental Psychology: Learning, Memory, and CognitionAmerican Psychological Association

Published: Sep 1, 1982

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