Abstract
Biased category payoff matrices engender separate reward- and accuracy-maximizing decision criteria. Although instructed to maximize reward, observers use suboptimal decision criteria that place greater emphasis on accuracy than is optimal. In this study, objective classifier feedback (the objectively correct response) was compared with optimal classifier feedback (the optimal classifier’s response) at two levels of category discriminability when zero or negative costs accompanied incorrect responses for two payoff matrix multiplication factors. Performance was superior for optimal classifier feedback relative to objective classifier feedback for both zero- and negative-cost conditions, especially when category discriminability was low, but the magnitude of the optimal classifier advantage was approximately equal for zero- and negative-cost conditions. The optimal classifier feedback performance advantage did not interact with the payoff matrix multiplication factor. Model-based analyses suggested that the weight placed on accuracy was reduced for optimal classifier feedback relative to objective classifier feedback and for high category discriminability relative to low category discriminability. In addition, the weight placed on accuracy declined with training when feedback was based on the optimal classifier and remained relatively stable when feedback was based on the objective classifier. These results suggest that feedback based on the optimal classifier leads to superior decision criterion learning across a wide range of experimental conditions.
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Ashby, F. G. (1992a). Multidimensional models of categorization. In F. G. Ashby (Ed.),Multidimensional models of perception and cognition (pp. 449–484). Hillsdale, NJ: Erlbaum.
Ashby, F. G. (1992b). Multivariate probability distributions. In F. G. Ashby (Ed.),Multidimensional models of perception and cognition (pp. 1–34). Hillsdale, NJ: Erlbaum.
Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., &Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning.Psychological Review,105, 442–481.
Ashby, F. G., &Lee, W. W. (1991). Predicting similarity and categorization from identification.Journal of Experimental Psychology: General,120, 150–172.
Ashby, F. G., &Maddox, W. T. (1993). Relations between prototype, exemplar, and decision bound models of categorization.Journal of Mathematical Psychology,37, 372–400.
Ashby, F. G., &Maddox, W. T. (1994). A response time theory of perceptual separability and perceptual integrality in speeded classification.Journal of Mathematical Psychology,33, 423–466.
Ashby, F. G., &Townsend, J. T. (1986). Varieties of perceptual independence.Psychological Review,93, 154–179.
Barkan, R. (2002). Using a signal detection safety model to simulate managerial expectations and supervisory feedback.Organizational Behavior & Human Decision Processes,89, 1005–1031.
Barkan, R., Zohar, D., &Erev, I. (1998). Accidents and decision making under uncertainty: A comparison of four models.Organizational Behavior & Human Decision Processes,74, 118–144.
Busemeyer, J. R., &Myung, I. J. (1992). An adaptive approach to human decision making: Learning theory, decision theory, and human performance.Journal of Experimental Psychology: General,121, 177–194.
Dusoir, A. E. (1980). Some evidence on additive learning models.Perception & Psychophysics,27, 163–175.
Erev, I. (1998). Signal detection by human observers: A cutoff reinforcement learning model of categorization decisions under uncertainty.Psychological Review,105, 280–298.
Erev, I., Gopher, D., Itkin, R., &Greenshpan, Y. (1995). Toward a generalization of signal detection theory to n-person games: The example of two person safety problem.Journal of Mathematical Psychology,39, 360–375.
Gilat, S., Meyer, J., Erev, I., &Gopher, D. (1997). Beyond Bayes’s theorem: Effects of base rate information in consensus games.Journal of Experimental Psychology: Applied,3, 83–104.
Green, D. M., &Swets, J. A. (1966).Signal detection theory and psychophysics, New York: Wiley.
Healy, A. F., &Kubovy, M. (1981). Probability matching and the formation of conservative decision rules in a numerical analog of signal detection.Journal of Experimental Psychology: Human Learning & Memory,7, 344–354.
Higgins, E. T. (1987). Self-discrepancy: A theory relating self and affect.Psychological Review,94, 319–340.
Kahneman, D., &Tversky, A. (1979). Prospect theory: An analysis of decision under risk.Econometrica,47, 263–291.
Kubovy, M., &Healy, A. F. (1977). The decision rule in probabilistic categorization: What it is and how it is learned.Journal of Experimental Psychology: General,106, 427–466.
Lee, W., &Janke, M. (1964). Categorizing externally distributed stimulus samples for three continua.Journal of Experimental Psychology,68, 376–382.
Lee, W., &Janke, M. (1965). Categorizing externally distributed stimulus samples for unequal molar probabilities.Psychological Reports,17, 79–90.
Lee, W., &Zentall, T. R. (1966). Factorial effects in the categorization of externally distributed stimulus samples.Perception & Psychophysics,1, 120–124.
Maddox, W. T. (1995). Base rate effects in multidimensional perceptual categorization.Journal of Experimental Psychology: Learning, Memory, & Cognition,21, 288–301.
Maddox, W. T. (2002). Toward a unified theory of decision criterion learning in perceptual categorization.Journal of the Experimental Analysis of Behavior,78, 567–596.
Maddox, W. T., &Ashby, F. G. (1993). Comparing decision bound and exemplar models of categorization.Perception & Psychophysics,53, 49–70.
Maddox, W. T., &Bohil, C. J. (1998). Base rate and payoff effects in multidimensional perceptual categorization.Journal of Experimental Psychology: Learning, Memory, & Cognition,3, 1459–1482.
Maddox, W. T., &Bohil, C. J. (2000). Costs and benefits in perceptual categorization.Memory & Cognition,28, 597–615.
Maddox, W. T., &Bohil, C. J. (2001). Feedback effects on cost-benefit learning in perceptual categorization.Memory & Cognition,29, 598–615.
Maddox, W. T., &Dodd, J. L. (2001). On the relation between base rate and cost-benefit learning in simulated medical diagnosis.Journal of Experimental Psychology: Learning, Memory, & Cognition,27, 1367–1384.
Maddox, W. T., & Estes, W. K. (1996, August).A dual process model of category learning. Paper presented at the 31st annual meeting of the Society for Mathematical Psychology, University of North Carolina, Chapel Hill.
Roth, A. E., &Erev, I. (1995). Learning in extensive form games: Experimental data and simple dynamic models in the intermediate term.Games & Economic Behavior,3, 3–24.
Stevenson, M. K., Busemeyer, J. R., &Naylor, J. C. (1991). Judgment and decision-making theory. In M. D. Dunnette & L. M. Hough (Eds.),Handbook of industrial and organizational psychology (2nd ed., Vol. 1, pp. 283–374). Palo Alto, CA: Consulting Psychologists Press.
Thomas, E. A. C. (1975). Criterion adjustment and probability matching.Perception & Psychophysics,18, 158–162.
Thomas, E. A. C., &Legge, D. (1970). Probability matching as a basis for detection and recognition decisions.Psychological Review,77, 65–72.
Tversky, A., &Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases.Science,185, 1124–1131.
Tversky, A., &Kahneman, D. (1980). Causal schemas in judgments under uncertainty. In M. Fishbein (Ed.),Progress in social psychology (pp. 84–98). Hillsdale, NJ: Erlbaum.
Tversky, A., &Kahneman, D. (1992). Prospect theory: An analysis of decision under risk.Econometrica,47, 276–287.
Ulehla, Z. J. (1966). Optimality of perceptual decision criteria.Journal of Experimental Psychology,71, 564–569.
Von Winterfeldt, D., &Edwards, W. (1982). Costs and payoffs in perceptual research.Psychological Bulletin,91, 609–622.
Wallsten, T. S., &Gonzalez-Vallejo, C. (1994). Statement verification: A stochastic model of judgment and response.Psychological Review,101, 490–504.
Wickens, T. D. (1982).Models for behavior: Stochastic processes in psychology. San Francisco: Freeman.
Yates, J. F. (1990).Judgment and decision making. Englewood Cliffs, NJ: Prentice-Hall.
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This research was supported in part by National Science Foundation Grant SBR-9796206 and Grant #5 R01MH59196-04 from the National Institute of Mental Health, National Institutes of Health.
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Bohil, C.J., Maddox, W.T. On the generality of optimal versus objective classifier feedback effects on decision criterion learning in perceptual categorization. Memory & Cognition 31, 181–198 (2003). https://doi.org/10.3758/BF03194378
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DOI: https://doi.org/10.3758/BF03194378