Linking together different measures of preference: A dynamic model of matching derived from decision field theory

Abstract Two frequently used tasks for measuring preferences among gambles are choice and selling price tasks. However, the rank orders observed with these tasks do not agree, and these disagreements are called preference reversals. In this article, we propose an extension of decision field theory that was originally designed to account for choice probability and the distribution of choice response times. In this extension, we show how this same theory can be used to derive predictions for the distribution of values produced by selling price tasks. This model not only accounts for the basic preference reversal results but also can explain the effects of various information processing factors on preference reversals including time, effort, and practice. We conclude by summarizing the advantages of the decision field matching model over two earlier models of choice and selling prices—expression theory and the contingent weighting model.

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