Skip to main content
PMC home page
Journal of the Experimental Analysis of Behavior logoLink to Journal of the Experimental Analysis of Behavior
. 2002 Nov;78(3):567–595. doi: 10.1901/jeab.2002.78-567

Toward a unified theory of decision criterion learning in perceptual categorization.

W Todd Maddox 1
PMCID: PMC1284916  PMID: 12507020

Abstract

Optimal decision criterion placement maximizes expected reward and requires sensitivity to the category base rates (prior probabilities) and payoffs (costs and benefits of incorrect and correct responding). When base rates are unequal, human decision criterion is nearly optimal, but when payoffs are unequal, suboptimal decision criterion placement is observed, even when the optimal decision criterion is identical in both cases. A series of studies are reviewed that examine the generality of this finding, and a unified theory of decision criterion learning is described (Maddox & Dodd, 2001). The theory assumes that two critical mechanisms operate in decision criterion learning. One mechanism involves competition between reward and accuracy maximization: The observer attempts to maximize reward, as instructed, but also places some importance on accuracy maximization. The second mechanism involves a flat-maxima hypothesis that assumes that the observer's estimate of the reward-maximizing decision criterion is determined from the steepness of the objective reward function that relates expected reward to decision criterion placement. Experiments used to develop and test the theory require each observer to complete a large number of trials and to participate in all conditions of the experiment. This provides maximal control over the reinforcement history of the observer and allows a focus on individual behavioral profiles. The theory is applied to decision criterion learning problems that examine category discriminability, payoff matrix multiplication and addition effects, the optimal classifier's independence assumption, and different types of trial-by-trial feedback. In every case the theory provides a good account of the data, and, most important, provides useful insights into the psychological processes involved in decision criterion learning.

Full Text

The Full Text of this article is available as a PDF (313.4 KB).

Selected References

These references are in PubMed. This may not be the complete list of references from this article.

  1. Ashby F. G., Alfonso-Reese L. A., Turken A. U., Waldron E. M. A neuropsychological theory of multiple systems in category learning. Psychol Rev. 1998 Jul;105(3):442–481. doi: 10.1037/0033-295x.105.3.442. [DOI] [PubMed] [Google Scholar]
  2. Ashby F. G., Ell S. W. The neurobiology of human category learning. Trends Cogn Sci. 2001 May 1;5(5):204–210. doi: 10.1016/s1364-6613(00)01624-7. [DOI] [PubMed] [Google Scholar]
  3. Ashby F. G., Gott R. E. Decision rules in the perception and categorization of multidimensional stimuli. J Exp Psychol Learn Mem Cogn. 1988 Jan;14(1):33–53. doi: 10.1037//0278-7393.14.1.33. [DOI] [PubMed] [Google Scholar]
  4. Ashby F. G., Lee W. W. Predicting similarity and categorization from identification. J Exp Psychol Gen. 1991 Jun;120(2):150–172. doi: 10.1037//0096-3445.120.2.150. [DOI] [PubMed] [Google Scholar]
  5. Ashby F. G., Maddox W. T. Integrating information from separable psychological dimensions. J Exp Psychol Hum Percept Perform. 1990 Aug;16(3):598–612. doi: 10.1037//0096-1523.16.3.598. [DOI] [PubMed] [Google Scholar]
  6. Ashby F. G., Townsend J. T. Varieties of perceptual independence. Psychol Rev. 1986 Apr;93(2):154–179. [PubMed] [Google Scholar]
  7. Bohil C. J., Maddox W. T. Category discriminability, base-rate, and payoff effects in perceptual categorization. Percept Psychophys. 2001 Feb;63(2):361–376. doi: 10.3758/bf03194476. [DOI] [PubMed] [Google Scholar]
  8. Davison M., Nevin J. Stimuli, reinforcers, and behavior: an integration. J Exp Anal Behav. 1999 May;71(3):439–482. doi: 10.1901/jeab.1999.71-439. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. ESTES W. K. The problem of inference from curves based on group data. Psychol Bull. 1956 Mar;53(2):134–140. doi: 10.1037/h0045156. [DOI] [PubMed] [Google Scholar]
  10. Erev I. Signal detection by human observers: a cutoff reinforcement learning model of categorization decisions under uncertainty. Psychol Rev. 1998 Apr;105(2):280–298. doi: 10.1037/0033-295x.105.2.280. [DOI] [PubMed] [Google Scholar]
  11. Filoteo J. V., Maddox W. T., Davis J. D. A possible role of the striatum in linear and nonlinear category learning: evidence from patients with Huntington's disease. Behav Neurosci. 2001 Aug;115(4):786–798. doi: 10.1037//0735-7044.115.4.786. [DOI] [PubMed] [Google Scholar]
  12. Filoteo J. V., Maddox W. T., Davis J. D. Quantitative modeling of category learning in amnesic patients. J Int Neuropsychol Soc. 2001 Jan;7(1):1–19. doi: 10.1017/s1355617701711010. [DOI] [PubMed] [Google Scholar]
  13. Fried L. S., Holyoak K. J. Induction of category distributions: a framework for classification learning. J Exp Psychol Learn Mem Cogn. 1984 Apr;10(2):234–257. doi: 10.1037//0278-7393.10.2.234. [DOI] [PubMed] [Google Scholar]
  14. HERRNSTEIN R. J. Relative and absolute strength of response as a function of frequency of reinforcement. J Exp Anal Behav. 1961 Jul;4:267–272. doi: 10.1901/jeab.1961.4-267. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Herbranson W. T., Fremouw T., Shimp C. P. The randomization procedure in the study of categorization of multidimensional stimuli by pigeons. J Exp Psychol Anim Behav Process. 1999 Jan;25(1):113–134. [PubMed] [Google Scholar]
  16. Herrnstein R. J., Heyman G. M. Is matching compatible with reinforcement maximization on concurrent variable interval variable ratio? J Exp Anal Behav. 1979 Mar;31(2):209–223. doi: 10.1901/jeab.1979.31-209. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Herrnstein R. J. On the law of effect. J Exp Anal Behav. 1970 Mar;13(2):243–266. doi: 10.1901/jeab.1970.13-243. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Knowlton B. J., Mangels J. A., Squire L. R. A neostriatal habit learning system in humans. Science. 1996 Sep 6;273(5280):1399–1402. doi: 10.1126/science.273.5280.1399. [DOI] [PubMed] [Google Scholar]
  19. LEE W., JANKE M. CATEGORIZING EXTERNALLY DISTRIBUTED STIMULUS SAMPLES FOR THREE CONTINUA. J Exp Psychol. 1964 Oct;68:376–382. doi: 10.1037/h0042770. [DOI] [PubMed] [Google Scholar]
  20. Lee W., Janke M. Categorizing externally distributed stimulus samples for unequal molar probabilities. Psychol Rep. 1965 Aug;17(1):79–90. doi: 10.2466/pr0.1965.17.1.79. [DOI] [PubMed] [Google Scholar]
  21. Maddox W. T., Ashby F. G. Comparing decision bound and exemplar models of categorization. Percept Psychophys. 1993 Jan;53(1):49–70. doi: 10.3758/bf03211715. [DOI] [PubMed] [Google Scholar]
  22. Maddox W. T. Base-rate effects in multidimensional perceptual categorization. J Exp Psychol Learn Mem Cogn. 1995 Mar;21(2):288–301. doi: 10.1037//0278-7393.21.2.288. [DOI] [PubMed] [Google Scholar]
  23. Maddox W. T., Bohil C. J. Base-rate and payoff effects in multidimensional perceptual categorization. J Exp Psychol Learn Mem Cogn. 1998 Nov;24(6):1459–1482. doi: 10.1037//0278-7393.24.6.1459. [DOI] [PubMed] [Google Scholar]
  24. Maddox W. T., Bohil C. J. Costs and benefits in perceptual categorization. Mem Cognit. 2000 Jun;28(4):597–615. doi: 10.3758/bf03201250. [DOI] [PubMed] [Google Scholar]
  25. Maddox W. T., Bohil C. J. Feedback effects on cost-benefit learning in perceptual categorization. Mem Cognit. 2001 Jun;29(4):598–615. doi: 10.3758/bf03200461. [DOI] [PubMed] [Google Scholar]
  26. Maddox W. T., Bohil C. J. Overestimation of base-rate differences in complex perceptual categories. Percept Psychophys. 1998 May;60(4):575–592. doi: 10.3758/bf03206047. [DOI] [PubMed] [Google Scholar]
  27. Maddox W. T., Dodd J. L. On the relation between base-rate and cost-benefit learning in simulated medical diagnosis. J Exp Psychol Learn Mem Cogn. 2001 Nov;27(6):1367–1384. [PubMed] [Google Scholar]
  28. Maddox W. T., Filoteo J. V. Striatal contributions to category learning: quantitative modeling of simple linear and complex nonlinear rule learning in patients with Parkinson's disease. J Int Neuropsychol Soc. 2001 Sep;7(6):710–727. doi: 10.1017/s1355617701766076. [DOI] [PubMed] [Google Scholar]
  29. Maddox W. T. On the dangers of averaging across observers when comparing decision bound models and generalized context models of categorization. Percept Psychophys. 1999 Feb;61(2):354–374. doi: 10.3758/bf03206893. [DOI] [PubMed] [Google Scholar]
  30. Mazur J. E. Estimation of indifference points with an adjusting-delay procedure. J Exp Anal Behav. 1988 Jan;49(1):37–47. doi: 10.1901/jeab.1988.49-37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Shimp C. P. Probabilistically reinforced choice behavior in pigeons. J Exp Anal Behav. 1966 Jul;9(4):443–455. doi: 10.1901/jeab.1966.9-443. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Ulehla Z. J. Optimality of perceptual decision criteria. J Exp Psychol. 1966 Apr;71(4):564–569. doi: 10.1037/h0023007. [DOI] [PubMed] [Google Scholar]
  33. White K. G., Wixted J. T. Psychophysics of remembering. J Exp Anal Behav. 1999 Jan;71(1):91–113. doi: 10.1901/jeab.1999.71-91. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Journal of the Experimental Analysis of Behavior are provided here courtesy of Society for the Experimental Analysis of Behavior

RESOURCES