Abstract
Several models of choice compute the probability of selecting a given option by comparing the expected value (EV) of each option. However, a subtle but important difference between two common rules used to compute the action probability is often ignored. Specifically, one common rule type, the exponential rule, compares EVs via a difference operation, whereas another rule type, the power rule, uses a ratio operation. We tested the empirical validity of each rule type by having human participants perform a choice task in which either the difference or the ratio between the reward values was altered relative to a control condition. Results indicated that participants can compare expected rewards by either ratio or difference operations but that altering the ratio between EVs produces the most dramatic changes in behavior. We discuss implications for several related research fields.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Ashby, F. G., & Maddox, W. T. (1993). Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology, 37, 372–400.
Barron, G., & Erev, I. (2003). Small feedback-based decisions and their limited correspondence to description-based decisions. Journal of Behavioral Decision Making, 16, 215–233.
Birnbaum, M. H. (1978). Differences and ratios in psychological measurement. In N. J. Castellan & F. Restle (Eds.), Cognitive theory (Vol. 3, pp. 33–74). Hillsdale, NJ: Erlbaum.
Birnbaum, M. H., Anderson, C. J., & Hynan, L. G. (1989). Two operations for “ratios” and “differences” of distances on the mental map. Journal of Experimental Psychology: Human Perceptions & Performance, 15, 785–796.
Bridle, J. S. (1990). Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimates of parameters. In D. S. Touretzky (Ed.), Advances in neural information processing systems 2 (pp. 211–217). San Mateo, CA: Morgan Kaufmann.
Busemeyer, J. R., & Stout, J. C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task. Psychological Assessment, 14, 253–262.
Corrado, G. S., Sugrue, L. P., Seung, S. H., & Newsome, W. T. (2005). Linear-nonlinear-Poisson models of primate choice dynamics. Journal of the Experimental Analysis of Behavior, 84, 581–617.
Daw, N. D., & Doya, K. (2006). The computational neurobiology of learning and reward. Current Opinon in Neurobiology, 16, 199–204.
Daw, N. D., O’Doherty, J. P., Dayan, P., Seymour, B., & Dolan, R. J. (2006). Cortical substrates for exploratory decisions in humans. Nature, 441, 876–879.
Dehaene, S., Dupoux, E., & Mehler, J. (1990). Is numerical comparison digital? Analogical and symbolic effects in two-digit number comparison. Journal of Experimental Psychology: Human Perception & Performance, 16, 626–641.
Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107–140.
Erickson, M. A., & Kruschke, J. K. (2002). Rule-based extrapolation in perceptual categorization. Psychonomic Bulletin & Review, 9, 160–168.
Herrnstein, R. J. (1961). Relative and absolute strength of response as a function of frequency of reinforcement. Journal of the Experimental Analysis of Behavior, 4, 267–272.
Hertwig, R., Barron, G., Weber, E. U., & Erev, I. (2004). Decisions from experience and the effect of rare events in risky choices. Psychological Science, 15, 534–539.
Hinrichs, J. V., Yurko, D. S., & Hu, J.-M. (1981). Two-digit number comparison: Use of place information. Journal of Experimental Psychology: Human Perception & Performance, 7, 890–901.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrika, 47, 263–291.
Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22–44.
Lau, B., & Glimcher, P. W. (2005). Dynamic response-by-response models of matching behavior in rhesus monkeys. Journal of the Experimental Analysis of Behavior, 84, 555–579.
Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: A network model of category learning. Psychological Review, 111, 309–332.
Luce, R. D. (1959). Individual choice behavior. New York: Wiley.
Luce, R. D. (1963). Detection and recognition. In R. D. Luce, R. R. Bush, & E. Galanter (Eds.), Handbook of mathematical psychology (pp. 103–189). New York: Wiley.
Maddox, W. T., & Ashby, F. G. (1993). Comparing decision bound and exemplar models of categorization. Perception & Psychophysics, 53, 49–70.
Medin, D. L., & Schaffer, M. M. (1978). A context theory of classification learning. Psychological Review, 85, 207–238.
Minsky, M., & Papert, S. (1968). Perceptrons. Cambridge, MA: MIT Press.
Moyer, R. S., & Landauer, T. K. (1967). Time required for judgments of numerical inequality. Nature, 215, 1519–1520.
Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39–57.
Nosofsky, R. M., & Palmeri, T. J. (1998). A rule-plus-exception model for classifying objects in continuous-dimension spaces. Psychonomic Bulletin & Review, 5, 345–369.
Nosofsky, R. M., & Zaki, S. R. (2002). Exemplar and prototype models revisited: Response strategies, selective attention, and stimulus generalization. Journal of Experimental Psychology: Learning, Memory, & Cognition, 28, 924–940.
Reed, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3, 482–487.
Roberts, M. E., & Goldstone, R. L. (2006). EPICURE: Spatial and knowledge limitations in group foraging. Adaptive Behavior, 14, 291–313.
Rodrigues, P. M., & Murre, J. M. (2007). Rules-plus-exception tasks: A problem for exemplar models? Psychonomic Bulletin & Review, 14, 640–646.
Rumelhart, D. E., McClelland, J. L., & the PDP Research Group (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 1. Foundations. Cambridge, MA: MIT Press.
Sakamoto, Y., Matsuka, T., & Love, B. C. (2004). Dimension-wide vs. exemplar-specific attention in category learning and recognition. In M. Lovett, C. Schunn, C. Lebiere, & P. Munro (Eds.), Proceedings of the 6th International Conference on Cognitive Modeling (pp. 261–266). Mahwah, NJ: Erlbaum.
Shepard, R. N. (1957). Stimulus and response generalization: A stochastic model relating generalization to distance in psychological space. Psychometrika, 22, 325–345.
Stevens, S. S. (1957). On the psychophysical law. Psychological Review, 64, 153–181.
Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2004). Matching behavior and the representation of value in the parietal cortex. Science, 304, 1782–1787.
Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
Weber, E. U., Shafir, S., & Blais, A.-R. (2004). Predicting risk sensitivity in humans and lower animals: Risk as variance or coefficient of variation. Psychological Review, 111, 430–445.
Williams, R. J. (1988). On the use of back-propagation in associative reinforcement learning. In Proceedings of the IEEE International Conference on Neural Networks (pp. 263–270). San Diego: IEEE.
Worthy, D. A., Maddox, W. T., & Markman, A. B. (2007). Regulatory fit effects in a choice task. Psychonomic Bulletin & Review, 14, 1125–1132.
Yechiam, E., Busemeyer, J. R., Stout, J. C., & Bechara, A. (2005). Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits. Psychological Science, 16, 973–978.
Author information
Authors and Affiliations
Corresponding authors
Additional information
This research was supported by AFOSR Grant FA9550-06-1-0204 and NIMH Grant MH077708 to W.T.M. and A.B.M., and by a supplement to NIMH Grant MH077708 to D.A.W.
Rights and permissions
About this article
Cite this article
Worthy, D.A., Maddox, W.T. & Markman, A.B. Ratio and difference comparisons of expected reward in decision-making tasks. Memory & Cognition 36, 1460–1469 (2008). https://doi.org/10.3758/MC.36.8.1460
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.3758/MC.36.8.1460


