Do the dynamics of prior information depend on task context? An analysis of optimal performance and an empirical test

Frontiers in Psychology

Volumn 3, Issue MAY, 2012, Pages

  van Ravenzwaaij, Don ^a   Mulder, Martijn J ^b   Tuerlinckx, Francis ^c   Wagenmakers, Eric Jan ^a,b  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b Cognitive Science Center Amsterdam ^*  (Netherlands)

^c UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Bias; Decision making; Drift diffusion model

Indexed keywords

EID: 84867095584     PISSN: None     EISSN: 16641078     Source Type: Journal    
DOI: 10.3389/fpsyg.2012.00132     Document Type: Article

References (36)

1. Bogacz, R., Brown, E., Moehlis, J., Holmes, P., and Cohen, J. D. (2006). The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced choice tasks. Psychol. Rev. 113, 700-765.
2. Brainard, D. H. (1997). The psychophysics toolbox. Spat. Vis. 10, 433-436.
3. Britten, K. H., Shadlen, M. N., Newsome, W. T., and Movshon, J. A. (1992). The analysis of visual motion: a comparison of neuronal and psychophysical performance. J. Neurosci. 12, 4745-4765.
4. Diederich, A., and Busemeyer, J. R. (2006). Modeling the effects of payoff on response bias in a perceptual discrimination task: bound-change, drift-rate-change, or two-stage-processing hypothesis. Percept. Psychophys. 68, 194-207.
5. Edwards, W. (1965). Optimal strategies for seeking information: models for statistics, choice reaction times, and human information processing. J. Math. Psychol. 2, 312-329.
6. Feng, S., Holmes, P., Rorie, A., and Newsome, W. T. (2009). Can monkeys choose optimally when faced with noisy stimuli and unequal rewards? PLoS Comput. Biol. 5, e1000284. doi:10.1371/journal.pcbi. 1000284
7. Gao, J., Tortell, R., and McClelland, J. L. (2011). Dynamic integration of reward and stimulus information in perceptual decision-making. PLoS ONE 6, e16749. doi:10.1371/journal.pone.0016749
8. Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2004). Bayesian Data Analysis, 2nd Edn. Boca Raton, FL: Chapman and Hall/CRC.
9. Gelman, A., and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press. Gold, J. I., and Shadlen, M. N. (2003). The influence of behavioral context on the representation of a perceptual decision in developing oculomotor commands. J. Neurosci. 23, 632-651.
10. Grasman, R. P. P. P., Wagenmakers, E.-J., and van der Maas, H. L. J. (2009). On the mean and variance of response times under the diffusion model with an application to parameter estimation. J. Math. Psychol. 53, 55-68.
11. Hanks, T. D., Mazurek, M. E., Kiani, R., Hopp, E., and Shadlen, M. N. (2011). Elapsed decision time affects the weighting of prior probability in a perceptual decision task. J. Neurosci. 31, 6339-6352.
12. Laming, D. R. J. (1968). Information Theory of Choice-Reaction Times. London: Academic Press.
13. Luce, R. D. (1986). Response Times.New York: Oxford University Press.
14. Mathworks. (1984). Matlab. Available at: http://www.mathworks.com/products/matlab
15. Mulder, M. J., Bos, D., Weusten, J. M. H., van Belle, J., van Dijk, S. C., Simen, P., van Engeland, H., and Durston, S. (2010). Basic impairments in regulating the speed-accuracy tradeoff predict symptoms of attentiondeficit/hyperactivity disorder. Biol. Psychiatry 68, 1114-1119.
16. Mulder, M. J., Wagenmakers, E. -J., Ratcliff, R., Boekel, W., and Forstmann, B. U. (2012). Bias in the brain: a diffusion model analysis of prior probability and potential payoff. J. Neurosci. 32, 2335-2343.
17. Newsome, W. T., Britten, K. H., and Movshon, J. A. (1989). Neuronal correlates of a perceptual decision. Nature 341, 52-54.
18. Newsome, W. T., and Paré, E. B. (1988). A selective impairment of motion perception following lesions of the middle temporal visual area (MT). J. Neurosci. 8, 2201-2211.
19. Palmer, J., Huk, A. C., and Shadlen, M. N. (2005). The effect of stimulus strength on the speed and accuracy of a perceptual decision. J. Vis. 5, 376-404.
20. Ratcliff, R. (1978). A theory of memory retrieval. Psychol. Rev. 85, 59-108.
21. Ratcliff, R. (1985). Theoretical interpretations of the speed and accuracy of positive and negative responses. Psychol. Rev. 92, 212-225.
22. Ratcliff, R. (2002). A diffusion model account of response time and accuracy in a brightness discrimination task: fitting real data and failing to fit fake but plausible data. Psychon. Bull. Rev. 9, 278-291.
23. Ratcliff, R., and McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput. 20, 873-922.
24. Ratcliff, R., and Rouder, J. N. (2000). A diffusion model account of masking in two-choice letter identification. J. Exp. Psychol. Hum. Percept. Perform. 26, 127-140.
25. Ratcliff, R., and Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: approaches to dealing with contaminant reaction times and parameter variability. Psychon. Bull. Rev. 9, 438-481.
26. Rorie, A. E., Gao, J., McClelland, J. L., and Newsome, W. T. (2010). Integration of sensory and reward information during perceptual decisionmaking in lateral intraparietal cortex (LIP) of the macaque monkey. PLoS ONE 5, e9308. doi:10.1371/journal.pone.0009308
27. Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., and Iverson, G. (2009). Bayesian t-tests for accepting and rejecting the null hypothesis. Psychon. Bull. Rev. 16, 225-237.
28. Simen, P., Contreras, D., Buck, C., Hu, P., Holmes, P., and Cohen, J. D. (2009). Reward rate optimization in twoalternative decision making: empirical tests of theoretical predictions. J. Exp. Psychol. Hum. Percept. Perform. 35, 1865-1897.
29. van Ravenzwaaij, D., Brown, S., and Wagenmakers, E.-J. (2011). An integrated perspective on the relation between response speed and intelligence. Cognition 119, 381-393.
30. van Ravenzwaaij, D., van der Maas, H. L. J., and Wagenmakers, E. -J. (2012). Optimal decision making in neural inhibition models. Psychol. Rev. 119, 201-215.
31. Vandekerckhove, J., and Tuerlinckx, F. (2007). Fitting the Ratcliff diffusion model to experimental data. Psychon. Bull. Rev. 14, 1011-1026.
32. Wagenmakers, E.-J. (2007). A practical solution to the pervasive problems of p-values. Psychon. Bull. Rev. 14, 779-804.
33. Wagenmakers, E.-J. (2009). Methodological and empirical developments for the Ratcliff diffusion model of response times and accuracy. Eur. J. Cogn. Psychol. 21, 641-671.
34. Wagenmakers, E.-J., van der Maas, H. J. L., and Grasman, R. P. P. P. (2007). An EZ-diffusion model for response time and accuracy. Psychon. Bull. Rev. 14, 3-22.
35. Wald, A., and Wolfowitz, J. (1948). Optimal character of the sequential probability ratio test. Ann. Math. Stat. 19, 326-339.
36. Yang, T., Hanks, T. D., Mazurek, M., McKinley, M., Palmer, J., and Shadlen, M. N. (2005). "Incorporating prior probability into decisionmaking in the face of uncertain reliability of evidence," Poster Presented at the Meeting of the Society for Neuroscience, Washington, DC.