Do humans produce the speed-accuracy trade-off that maximizes reward rate?

Quarterly Journal of Experimental Psychology

Volumn 63, Issue 5, 2010, Pages 863-891

  Bogacz, Rafal ^a   Hu, Peter T ^a   Holmes, Philip J ^b   Cohen, Jonathan D ^a  

^a PRINCETON UNIVERSITY   (United States)

^b Princeton University   (United States)

Author keywords

Drift diffusion model; Reward rate; Speed accuracy trade off

Indexed keywords

ADULT; ARTICLE; DECISION MAKING; FEMALE; HUMAN; MALE; MOVEMENT PERCEPTION; PHOTOSTIMULATION; PHYSIOLOGY; PREDICTION AND FORECASTING; PROBABILITY; PSYCHOLOGICAL MODEL; PSYCHOMOTOR PERFORMANCE; REACTION TIME; REINFORCEMENT; REWARD;

CHOICE BEHAVIOR; DECISION MAKING; FEMALE; HUMANS; MALE; MODELS, PSYCHOLOGICAL; MOTION PERCEPTION; PHOTIC STIMULATION; PREDICTIVE VALUE OF TESTS; PROBABILITY; PSYCHOMOTOR PERFORMANCE; REACTION TIME; REINFORCEMENT SCHEDULE; REWARD; YOUNG ADULT;

EID: 77951713282     PISSN: 17470218     EISSN: 17470226     Source Type: Journal    
DOI: 10.1080/17470210903091643     Document Type: Article

References (50)

1. Akaike, H. (1981). Likelihood of a model and information criteria. Journal of Econometrics, 16, 3-14.
2. Bogacz, R., Brown, E., Moehlis, J., Holmes, 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.
3. Bogacz, R., & Cohen, J. D. (2004). Parameterization of connectionist models. Behavioral Research Methods, Instruments, and Computers, 36, 732-741.
4. Brainard, D. H. (1997). The Psychophysics Toolbox. Spatial Vision, 10, 433-436.
5. Brown, E., & Holmes, P. (2001). Modeling a simple choice task: Stochastic dynamics of mutually inhibitory neural groups. Stochastics and Dynamics, 1,159-191.
6. Busemeyer, J. R., & Rapoport, A. (1988). Psychological models of deferred decision making. Journal of Mathematical Psychology, 32, 91-134.
7. Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: A dynamic-cognitive approach to decision making in uncertain environment. Psychological Review, 100, 432-459.
8. Desai, R. A., Maciejewski, P. K., Pantalon, M. V., & Potenza, M. N. (2005). Gender differences in adolescent gambling. Annals of Clinical Psychiatry, 17, 249-258.
9. Edwards, W. (1965). Optimal strategies for seeking information: Models for statistics, choice reaction times, and human information processing. Journal of Mathematical Psychology, 2, 312-329.
10. Erev, I. (1998). Signal detection by human observers: A cut-off reinforcement learning model of categorization decisions under uncertainty. Psychological Review, 105, 280-298.
11. Feller, W. (1968). An introduction to probability theory and its applications. New York: Wiley.
12. Franks, N. R., Dornhaus, A., Fitzsimmons, J. P., & Stevens, M. (2003). Speed versus accuracy in collective decision making. Proceedings of the Royal Society, B, 270, 2457-2463.
13. Gneezy, U., & Rustichini, A. (2004). Gender and competition at a young age. The American Economic Review, 94, 377-381.
14. Gold, J. I., & Shadlen, M. N. (2002). Banburismus and the brain: Decoding the relationship between sensory stimuli, decisions, and reward. Neuron, 36(2),299-308.
15. Gold, J. I., & Shadlen, M. N. (2003). The influence of behavioral context on the representation of a perceptual decision in developing oculomotor commands. Journal of Neuroscience, 23, 632-651.
16. Holmes,P.,Shea-Brown,E.,Moehlis,J.,Bogacz,R.,Gao, J., Aston-Jones, G., et al. (2005). Optimal decisions: From neural spikes, through stochastic differential equations, to behavior. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 88, 2496-2503.
17. Laming, D. R. J. (1968). Information theory of choice reaction time. New York: Wiley.
18. Maddox, W. T. (2002). Toward a unified theory of decision criterion learning in perceptual categorization. Journal of the Experimental Analysis of Behaviour, 78, 567-595.
19. Maddox, W. T., & Bohil, C. J. (1998). Base-rate and payoff effects in multidimensional perceptual categorization. Journal of Experimental Psychology: Learning, Memory and Cognition, 24, 1459-1482.
20. Maritz, J. S., & Jarrett, R. G. (1978). A note on estimating the variance of the sample median. Journal of the American Statistical Association, 73, 194-196.
21. Markman, A. B., Baldwin, G. C., & Maddox, W. T. (2005). The interaction of payoff structure and regulatory focus in classification. Psychological Science, 16, 852-855.
22. Masson, M. E. J., & Loftus, G. R. (2003). Using confidence intervals for graphically based data interpretation. Canadian Journal of Experimental Psychology, 57,203-220.
23. Mozer, M. C., Colagrosso, M. D., & Huber, D. H. (2002). A rational analysis of cognitive control is a speeded discrimination task. In T. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems (Vol. 14, pp. 51-57). Cambridge, MA: MIT Press.
24. Myung, I. J., & Busemeyer, J. R. (1989). Criterion learning in a deferred decision making task. American Journal of Psychology, 102, 1-16.
25. Newell, A., & Simon, H. A. (1988). The theory of human problem solving. In A. M. Collins & E. E. Smith (Eds.), Readings in cognitive science: A perspective from psychology and artificial intelligence (pp. 33-51). San Mateo, CA: Morgan Kaufmann.
26. Pachella, R. G. (1974). The interpretation of reaction time in information-processing research. In B. Kantowitz (Ed.), Human information processing: Tutorials in performance and recognition (pp. 41-82). Hillsdale, NJ: Lawrence Erlbaum Associates.
27. Palmer, J., Huk, A. C., & Shadlen, M. N. (2005). The effect of stimulus strength on the speed and accuracy of a perceptual decision. Journal of Vision, 5,376-404.
28. Pelli, D. G. (1997). The VideoToolbox software for visual psychophysics: Transforming numbers into movies. Spatial Vision, 10, 437-442.
29. Rapoport, A., & Burkheimer, G. B. (1971). Models for deferred decision making. Journal of Mathematical Psychology, 8, 508-538.
30. Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 83, 59-108.
31. Ratcliff, R. (2006). Modeling response signal and response time data. Cognitive Psychology, 53, 195-237.
32. Ratcliff, R., Gomez, R., & McKoon, G. (2004). A diffusion model account of the lexical decision task. Psychological Review, 111, 159-182.
33. Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20(4), 873-922.
34. Ratcliff, R., & Rouder, J. N. (2000). A diffusion model account of masking in two-choice letter identification. Journal of Experimental Psychology: Human Perception and Performance, 26, 127-140.
35. Ratcliff, R., & Smith, P. L. (2004). Comparison of sequential sampling models for two-choice reaction time. Psychological Review, 111(2), 333-367.
36. Ratcliff, R., Thapar, A., & McKoon, G. (2003). A diffusion model analysis of the effects of aging on brightness discrimination. Perception and Psychophysics, 65, 523-535.
37. Ratcliff, R., & Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability. Psychonomic Bulletin & Review, 9, 438-481.
38. Ratcliff, R., Van Zandt, T., & McKoon, G. (1999). Connectionist and diffusion models of reaction time. Psychological Review, 106, 261-300.
39. Rowan, T. (1990). Functional stability analysis of numerical algorithms. Austin, TX: University of Texas.
40. Shadlen, M. N., & Newsome, W. T. (2001). Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. Journal of Neurophysiology, 86(4), 1916-1936.
41. Simen, P. A., Cohen, J. D., & Holmes, P. (2006). Rapid decision threshold modulation by reward rate in a neural network. Neural Networks, 19, 1013-1026.
42. Simen, P. A., Contreras, D., Buck, C., Hu, P., Holmes, P., & Cohen, J. D. (in press). Reward rate optimization in two-alternative decision making: Empirical tests of theoretical predictions. Journal of Experimental Psychology: Human Perception and Performance.
43. Stone, M. (1960). Models for choice reaction time. Psychometrika, 25, 251-260.
44. Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108(3), 550-592.
45. Vandekerckhove, J., & Tuerlinckx, F. (2007). Fitting the Ratcliff diffusion model to experimental data. Psychonomic Bulletin & Review, 14, 1011-1026.
46. Vickers, D. (1970). Evidence for an accumulator model of psychophysical discrimination. Ergonomics, 13, 37-58.
47. Wagenmakers, E.-J., van der Maas, H. L. J., & Grasman, R. P. P. P. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14, 3-22.
48. Wald, A., & Wolfowitz, J. (1948). Optimum character of the sequential probability ratio test. Annals of Mathematical Statistics, 19, 326-339.
49. Wickelgren, W. A. (1977). Speed-accuracy tradeoff and information processing dynamics. Acta Psychologica, 41, 67-85.
50. Zackenhouse, M., Bogacz, R., & Holmes, P. (2009). Robust versus optimal strategies for two-alternative forced choice tasks. Manuscript submitted for publication.