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Cited by 758

The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.

Multialternative decision field theory: A dynamic connectionst model of decision making.

Roe, Robert M., Busemeyer, Jermone R., Townsend, James T.·2001·Psychological Review

Abstract

The authors interpret decision field theory (J. R. Busemeyer & J. T. Townsend, 1993) as a connectionist network and extend it to accommodate multialternative preferential choice situations. This article shows that the classic weighted additive utility model (see R. L. Keeney & H. Raiffa, 1976) and the classic Thurstone preferential choice model (see L. L. Thurstone, 1959) are special cases of this new multialternative decision field theory (MDFT), which also can emulate the search process of the popular elimination by aspects (EBA) model (see A. Tversky, 1969). The new theory is unique in its ability to explain several central empirical results found in the multialternative preference literature with a common set of principles. These empirical results include the similarity effect, the attraction effect, and the compromise effect, and the complex interactions among these three effects. The dynamic nature of the model also implies strong testable predictions concerning the moderating effect of time pressure on these three effects.

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Metadata

Authors
Roe, R. M., Busemeyer, J. R., & Townsend, J. T.
Venue
Psychological Review
Published
2001
Volume
108
Issue
2
Pages
370-392
ISSN
  • 0033-295X (ISSN-L)
  • 1939-1471
OpenAlex
W1970035964
Cited by
758
References
77
FWCI
9.14
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Multialternative decision field theory: A dynamic connectionst model of decision making. https://app.cochat.ai/papers/10.1037/0033-295x.108.2.370
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Roe, R. M., Busemeyer, J. R., & Townsend, J. T. (2001). Multialternative decision field theory: A dynamic connectionst model of decision making. Psychological Review. https://doi.org/10.1037/0033-295x.108.2.370

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.