Abstract
Choice models in marketing and economics are generally derived without specifying the underlying cognitive process of decision making. This approach has been successfully used to predict choice behavior. However, it has not much to say about such aspects of decision making as deliberation, attention, conflict, and cognitive limitations and how these influence choices. In contrast, sequential sampling models developed in cognitive psychology explain observed choices based on assumptions about cognitive processes that return the observed choice as the terminal state. We illustrate three advantages of this perspective. First, making explicit assumptions about underlying cognitive processes results in measures of deliberation, attention, conflict, and cognitive limitation. Second, the mathematical representations of underlying cognitive processes imply well documented departures from Luce’s Choice Axiom such as the similarity, compromise, and attraction effects. Third, the process perspective predicts response time and thus allows for inference based on observed choices and response times. Finally, we briefly discuss the relationship between these cognitive models and rules for statistically optimal decisions in sequential designs.
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References
Adamowicz, V., Bunch, D., Cameron, T. A., Dellaert, B. G. C., Hanneman, M., Keane, M., et al. (2008). Behavioral frontiers in choice modeling. Marketing Letters doi:10.1007/s11002-008-9038-1.
Ashby, F. G. (1983). A biased random-walk model for 2 choice reaction-times. Journal of Mathematical Psychology, 27, 277–297.
Ashby, F. G., & Townsend, J. T. (1986). Varieties of perceptual independence. Psychological Review, 93, 154–179.
Bogacz, R. (2007). Optimal decision-making theories: Linking neurobiology with behaviour. Trends in Cognitive Sciences, 11, 118–125.
Bogacz, R., Brown, E., Jeff, M., Holmes, J. P., & Cohen, J. D. (2006). The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks. Psychological Review, 113, 700–765.
Busemeyer, J. R., & Diederich, A. (2002). Survey of decision field theory. Mathematical Social Sciences, 43, 345–370.
Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: A dynamic cognition approach to decision making. Psychological Review, 100, 432–459.
Diederich, A. (1997). Dynamic stochastic model for decision making under time constraints. Journal of Mathematical Psychology, 41, 260–274.
Diederich, A. (2003a). MDFT account of decision making under time pressure. Psychonomic Bulletin and Review, 10, 157–166.
Diederich, A. (2003b). Decision making under conflict: Decision time as a measure of conflict strength. Psychonomic Bulletin and Review, 10, 167–176.
Diederich, A., & Busemeyer, J. R. (1999). Conflict and the stochastic dominance principle of decision making. Psychological Science, 10, 353–359.
Diederich, A., & Busemeyer, J. R. (2003). Simple matrix methods for analyzing diffusion models of choice probability, choice response time and simple response time. Journal of Mathematical Psychology, 47, 304–322.
Gilbride, T., & Allenby, G. (2006). Estimating heterogeneous EBA and economic screening rule choice models. Marketing Science, 25, 494–509.
Huang, Y., & Hutchinson, J. W. (2008). Counting every thought: Implicit measures of cognitive responses to advertising. Journal of Consumer Research, 35(1), 98–118.
Huber, J., Payne, J. W., & Puto, C. (1982). Adding asymmetrically dominated alternatives: Violations of regularity and the similarity hypothesis. Journal of Consumer Research, 9, 90–98.
Huber, J., & Puto, C. (1983). Market boundaries and product choice: Illustrating attraction and substitution effects. Journal of Consumer Research, 10, 31–44.
Johnson, J. G., & Busemeyer, J. R. (2005). A dynamic, computational model of preference reversal phenomena. Psychological Review, 112, 841–861.
Kivetz, R., Netzer, O., & Srinivasan, V. (2004). Alternative models for capturing the compromise effect. Journal of Marketing Research, 41, 237–57.
LaBerge, D. (1962). A recruitment theory of simple behavior. Psychometrika, 27, 375–396.
McMillen, T., & Holmes, P. (2005). The dynamics of choice among multiple alternatives. Journal of Mathematical Psychology, 50, 30–57.
Otter, T., Allenby, G., & van Zandt, T. (2007). An integrated model of choice and response time. Journal of Marketing Research (forthcoming).
Rieskamp, J., Busemeyer, J. R., & Mellers, B. A. (2006). Extending the bounds of rationality: Evidence and theories of preferential choice. Journal of Economic Literature, 44, 631–661.
Roe, R. M., Busemeyer, J. R., & Townsend, J. T. (2001). Multialternative decision field theory: A dynamic connectionist model of decision making. Psychological Review, 108, 370–392.
Ruan, S. (2007). Poisson race models for conjoint choice analysis: Theory and applications. Unpublished Ph.D. dissertation. Department of Statistics, The Ohio State University.
Ruan, S., MacEachern, S., Otter, T., & Dean, A. (2007). Dependent Poisson race models and modeling dependence in conjoint choice experiments. Psychometrika (forthcoming).
Simonson, I. (1989). Choice based on reasons: The case of attraction and compromise effects. Journal of Consumer Research, 16, 158–174.
Smith, P. L. (2000). Stochastic dynamic models of response time and accuracy: A foundational primer. Journal of Mathematical Psychology, 44, 408–436.
Stone, M. (1960). Models for choice-reaction time. Psychometrika, 25, 251–260.
Tanner, M. A., & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82, 528–540.
Townsend, J. T. (1972). Some results concerning the identifiability of parallel and serial processes. British Journal of Mathematical and Statistical Psychology, 25, 168–199.
Townsend, J. T., & Ashby, F. G. (1983). Stochastic modeling of elementary psychological processes. Cambridge: Cambridge University Press.
Townsend, J. T., & Nozawa, G. (1995). Spatio-temporal properties of elementary perception: An investigation of parallel, serial and coactive theories. Journal of Mathematical Psychology, 39, 321–360.
Townsend, J. T., & Schweickert, R. (1989). Toward the trichotomy method: Laying the foundation of stochastic mental networks. Journal of Mathematical Psychology, 33, 309–327.
Townsend, J. T., & Wenger, M. J. (2004). A theory of interactive parallel processing: New capacity measures and predictions for a response time inequality series. Psychological Review, 111, 1003–1035.
Tversky, A. (1972a). Elimination by aspects: A theory of choice. Psychological Review, 79, 281–299.
Tversky, A. (1972b). Choice by elimination. Journal of Mathematical Psychology, 9(4), 341–367.
Tversky, A., & Simonson, I. (1993). Context dependent preferences. Management Science, 39, 1179–1189.
Usher, M., & McClelland, J. L. (2004). Loss aversion and inhibition in dynamical models of multialternative choice. Psychological Review, 111, 757–769.
Vickers, D. (1970). Evidence for an accumulator of psychophysical discrimination. Ergonomics, 13, 37–58.
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Otter, T., Johnson, J., Rieskamp, J. et al. Sequential sampling models of choice: Some recent advances. Mark Lett 19, 255–267 (2008). https://doi.org/10.1007/s11002-008-9039-0
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DOI: https://doi.org/10.1007/s11002-008-9039-0