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It is often claimed that for any item to count as representational, it must form part of a general representational scheme or framework. Many people, though by no means all, claim that the idea of representation can be captured, in part, in terms of the concept of information. Many suppose that models of representation are subject to a teleological constraint. It is common to hold that, to be regarded as genuinely representational, a representation must be decouplable from the environment. In connection with the informational constraint, the possibility of representation is closely tied to the possibility of misrepresentation. Much recent work on cognition is characterized by an augmentation of the role of action coupled with an attenuation of the role of representation. This chapter discusses the representation and the extended mind, the first horn and the second horn.
Jerome R. Busemeyer, Professor of Psychology and Cognitive Science, Indiana University,
Joseph G. Johnson, Assistant Professor of Psychology, Miami University,
Ryan K. Jessup, Ph.D. candidate in psychology and cognitive science, Indiana University, Bloomington
Decision researchers have struggled for a long time with the fact that preferences are highly changeable and vary in complex ways across contexts and tasks. For example, reversals have been observed when preferences are measured using binary versus triadic choice sets or when preferences are measured by choice versus price methods. Several theoretical approaches have been developed to understand this puzzling variability in preferences. One approach is to modify the classic utility model by allowing the weights or values that enter the utility function to change across contexts or tasks. For example, Tversky, Sattath, and Slovic (1988) believe that the decision weights for attributes change across choice versus price tasks. A second approach is to use different heuristic rules to form preferences, depending on task and context. For example, Payne, Bettman, and Johnson (1993) propose that decision makers switch from compensatory to noncompensatory types of rules when the number of options increases or as time pressure increases. Both of these approaches are well established and have made a large impact on decision research.
This chapter presents a computational approach to understanding how preferences change across contexts and tasks. According to this approach, preferences are constructed from a dynamic process that takes decision contexts as inputs and generates task responses as outputs. Computational models are formed by a collection of microprocessing units, each of which performs an elementary cognitive or affective evaluation.
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