Object Representation in Multiattribute Choice

Abstract

We propose a theoretical framework for understanding how everyday choice objects are represented and how decisions involving these objects are made. Our framework combines insights regarding object and concept representation in semantic memory research with multiattribute choice rules proposed by scholars of decision making. We also outline computational techniques for using our framework to quantitatively predict naturalistic multiattribute choices. We test our approach in two-object and three-object forced choice experiments involving common books, movies, and foods. Despite using complex naturalistic stimuli, we find that our approach achieves high predictive accuracy rates, and is also able to provide a good account of decision time distributions.


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