Linear deterministic accumulator models of simple choice

Frontiers in Psychology

Volumn 3, Issue AUG, 2012, Pages

  Heathcote, Andrew ^a   Love, Jonathon ^a  

^a UNIVERSITY OF NEWCASTLE   (Australia)

Author keywords

Evidence accumulation; Lexical decision task; Linear ballistic accumulator; Mathematical modeling; Response time

Indexed keywords

EID: 84867119921     PISSN: None     EISSN: 16641078     Source Type: Journal    
DOI: 10.3389/fpsyg.2012.00292     Document Type: Article

References (73)

1. Boucher, L., Palmeri, T. J., Logan, G. D., and Schall, J. D. (2007). Inhibitory control in mind and brain: an interactive race model of countermanding saccades. Psychol. Rev. 114, 376-397.
2. Brown, S. D., and Heathcote, A. (2005a). A ballistic model of choice response time. Psychol. Rev. 112, 117-128.
3. Brown, S. D., and Heathcote, A. (2005b). Practice increases the efficiency of evidence accumulationinperceptual choice. J. Exp. Psychol. Hum. Percept. Perform. 31, 289-298.
4. Brown, S. D., and Heathcote, A. (2008). The simplest complete model of choice reaction time: linear ballistic accumulation. Cogn. Psychol. 57, 153-178.
5. Brown, S. D., Marley, A. A. J., Donkin, C., and Heathcote, A. (2008). An integrated model of choices and response times in absolute identification. Psychol. Rev. 115, 396-425.
6. Burnham, K. P., and Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261-304.
7. Busemeyer, J. R., and Townsend, J. T. (1993). Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. Psychol. Rev. 100, 432-459.
8. Carpenter, R. H. S. (1981). "Oculomotor procrastination," in Eye Move-ments: Cognition and Visual Per-ception, eds D. F. Fisher, R. A. Monty, and J. W. Senders (Hillsdale: Lawrence Erlbaum), 237-246.
9. Criss, A. H. (2010). Differentiation and response bias in episodic memory: evidence from reaction time distributions. J. Exp. Psychol. Learn. Mem. Cogn. 484-499.
10. Donkin, C., Averell, L., Brown, S. D., and Heathcote, A. (2009a). Getting more from accuracy and response time data: methods for fitting the linear ballistic accumulator model. Behav. Res. Methods 41, 1095-1110.
11. Donkin, C., Brown, S. D., and Heathcote, A.(2009b). Theover-constraint of response time models: rethinking the scaling problem. Psychon. Bull. Rev. 16, 1129-1135.
12. Donkin, C., Brown, S. D., and Heathcote, A. (2011a). Drawing conclusions from choice response time models: a tutorial. J. Math. Psychol. 55, 140-151.
13. Donkin, C., Brown, S., Heathcote, A., and Wagenmakers, E. J. (2011b). Diffusion versus linear ballistic accumulation: different models for response time, same conclusions about psychological mechanisms? Psychon. Bull. Rev. 55, 140-151.
14. Eidels, A., Donkin, C., Brown, S. D., and Heathcote, A. (2010). Converging measures of workload capacity. Psychon. Bull. Rev. 17, 763-771.
15. Forstmann, B. U., Dutilh, G., Brown, S. D., Neumann, J., von Cramon, D. Y., Ridderinkhof, K. R., and Wagenmakers, E. J. (2008). Striatum and pre-SMA facilitate decision-making under time pressure. Proc. Natl. Acad. Sci. U.S.A. 105, 17538-17542.
16. Forstmann, B. U., Schäfer, A., Anwander, A., Neumann, J., Brown, S. D., Wagenmakers, E.-J., Bogacz, R., and Turner, R. (2010). Cortico-striatal connections predict control over speed and accuracy in perceptual decision making. Proc. Natl. Acad. Sci. U.S.A. 107, 15916-15920.
17. Forstmann, B. U., Tittgemeyer, M., Wagenmakers, E.-J., Derrfuss, J., Imperati, D., and Brown, S. D. (2011). The speed-accuracy tradeoff in the elderly brain: a structural model-based approach. J. Neurosci. 31, 17242-17249.
18. Gao, J., KongFatt, W.-L., Holmes, P., Simen, P., and Cohen, J. D. (2009). Sequential effects in two-choice reaction time tasks: decomposition and synthesis of mechanisms. Neural Comput. 21, 2407-2436.
19. Gold, J. I., and Shadlen, M. N. (2007). The neural basis of decision making. Annu. Rev. Neurosci. 30, 535-574.
20. 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.
21. Gratton, G., Coles, M. G. H., Sirevaag, E. J., Eriksen, C. W., and Donchin, E. (1988). Pre and post stimulus activation of response channels: a psychophysiological analysis. J. Exp. Psychol. Hum. Percept. Perform. 14, 331-344.
22. Heathcote, A. (2004). Fitting the wald and ex-wald distributions to response time data. Behav. Res. Methods Instrum. Comput. 36, 678-694.
23. Heathcote, A., and Brown, S. D. (2004). Reply to speckman and rouder: a theoretical basis for QML. Psychon. Bull. Rev. 11, 577.
24. Heathcote, A., Brown, S. D., and Cousineau, D. (2004). QMPE: estimating Lognormal, Wald and Weibull RT distributions with a parameter dependent lower bound. Behav. Res. Methods Instrum. Comput. 36, 277-290.
25. Heathcote, A., and Hayes, B. (2012). Diffusion versus linear ballistic accumulation: different models for response time with different conclusions about psychological mechanisms? Can. J. Exp. Psychol. 66, 125-136.
26. Heathcote, A., Popiel, S. J., and Mewhort, D. J. K. (1991). Analysis of response-time distributions: an example using the Stroop task. Psychol. Bull. 109, 340-347.
27. Ho, T. C., Brown, S., and Serences, J. T. (2009). Domain general mechanisms of perceptual decision making in human cortex. J. Neurosci. 29, 8675-8687.
28. Ho, T. C., Brown, S. D., Abuyo, N. A., Kul, E.-H. J., and Serences, J. T. (in press). Perceptual consequences of feature-based attentional enhancement and suppression. J. Vis.
29. Huettel, S. A., Mack, P. B., and McCarthy, G. (2002). Perceiving patterns in random series:dynamic processing of sequence in prefrontal cortex. Nat. Neurosci. 5, 485-490.
30. Huk, A. C., and Shadlen, M. N. (2005). Neural activity in macaque parietal cortex reflects temporal integration of visual motion signals during perceptual decision making. J. Neurosci. 25, 10420-10436.
31. Karayanidis, F., Mansfield, E. L., Galloway, K. L., Smith, J. L., Provost, A., and Heathcote, A. (2009). Anticipatory reconfiguration elicited by fully and partially informative cues that validly predict a switch in task. Cogn. Affect. Behav. Neurosci. 9, 202-215.
32. Kleinsorge, T. (2001). The time course of effort mobilization and strategic adjustment of response criteria. Psychol. Res. 65, 216-223.
33. Laming, D. R. J. (1968). Information Theory of Choice-Reaction Times. London: Academic Press.
34. Leite, F. P., and Ratcliff, R. (2010). Modeling reaction time and accuracy of multiple-alternative decisions. Atten. Percept. Psychophys. 72, 246-273.
35. Leite, F. P., and Ratcliff, R. (2011). What cognitive processes drive response biases? A diffusion model analysis. Judgm. Decis. Mak. 7, 651-687.
36. Logan, G. D., and Cowan, W. B. (1984). On the ability to inhibit thought and action: a theory of an act of control. Psychol. Rev. 91, 295-327.
37. Luce, R. D. (1986). Response Times: Their Role in Inferring Elementary Mental Organization. New York: Oxford University Press.
38. Marley, A.A. J., and Colonius, H. (1992). The "horse race" random utility model for choice probabilities and reaction times, and its competing risks interpretation. J. Math. Psychol. 36, 1-20.
39. Matzke, D., and Wagenmakers, E.-J. (2009). Psychological interpretation of ex-Gaussian and shifted Wald parameters: a diffusion model analysis. Psychon. Bull. Rev. 16, 798-817.
40. McClelland, J. L. (1979). On the time relations of mental processes: an examination of systems of processes in cascade. Psychol. Rev. 86, 287-330.
41. Morey, R. D. (2008). Confidence intervals from normalized data: a correction to Cousineau (2005). Tutor. Quant. Methods Psychol. 4, 61-64.
42. Myung, I. J., and Pitt, M. A. (1997). Applying Occam's razor in modeling cognition: a Bayesian approach. Psychon. Bull. Rev. 4, 79-95.
43. R Development Core Team. (2012). R: A Language and Environment for Sta-tistical Computing. Vienna: R Foundation for Statistical Computing.
44. Ratcliff, R. (1978). A theory of memory retrieval. Psychol. Rev. 85, 59-108.
45. Ratcliff, R. (2001). Putting noise into neurophysiological models of simple decision making (letter). Nat. Neurosci. 4, 336.
46. Ratcliff, R., Gomez, P., and McKoon, G. (2004). A diffusion model account of the lexical decision task. Psychol. Rev. 111, 159-182.
47. Ratcliff, R., and McKoon, G. (2008). The diffusion decision model:theory and data for two choice decision tasks. Neural Comput. 20, 873-922.
48. Ratcliff, R., and Murdock, B. B. Jr. (1976). Retrieval processes in recognition memory. Psychol. Rev. 83, 190-214.
49. Ratcliff, R., and Rouder, J. N. (1998). Modeling response times for two choice decisions. Psychol. Sci. 9, 347-356.
50. Ratcliff, R., and Smith, P. L. (2004). A comparison of sequential sampling models for two-choice reaction time. Psychol. Rev. 111, 333-367.
51. 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.
52. Rinkenauer, G., Osman, A., Ulrich, R., Muller-Gethmann, H., and Mattes, S. (2004). On the locus of speedaccuracy trade-off in reaction time: inferences from the lateralized readiness potential. J. Exp. Psychol. Gen. 133, 261-282.
53. Schweikert, R. (1989). Separable effects of factors on activation function in discreet and continuous models. Psychol. Bull. 106, 318-328.
54. Shadlen, M. N., and Newsome, W. T. (1998). The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J. Neurosci. 18, 3870-3896.
55. Smith, P. L. (1995). Psychophysically principled models of visual simple reaction time. Psychol. Rev. 102, 567-591.
56. Smith, P. L., and Ratcliff, R. (2009). An integrated theory of attention and decision making in visual signal detection. Psychol. Rev. 116, 283-317.
57. Speckman, P. L., and Rouder, J. N. (2004). A comment on Heathcote, Brown, and Mewhort's QMLE method for response time distributions. Psychon. Bull. Rev. 11, 574-576.
58. Starns, J. J., Ratliff, R., and White, C. N. (2012). Diffusion model drift rates can be influenced by decision processes: an analysis of the strength-based mirror effect. J. Exp. Psychol. Learn. Mem. Cogn. PMID: 22545609. [Epub ahead of print].
59. Stone, M. (1960). Models for choicereaction time. Psychometrika 25, 251-260.
60. Townsend, J. T., and Fikes, T. (1995). A Beginning Quantitative Taxon-omy of Cognitive Activation Sys-tems and Applications to Continu-ous Flow Processes. Technical Report, 131. Cognitive Science Program, Bloomington: Indiana University.
61. Tsetsos, K., Usher, M., and McClelland, J. L. (2011). Testing multi-alternative decision models with nonstationary evidence. Front. Neurosci. 5:63. doi:10.3389/fnins.2011.00063.
62. Ulrich, R., and Miller, J. (1993). Information processing models generating lognormally distributed reaction times. J. Math. Psychol. 37, 513-525.
63. Usher, M., and McClelland, J. L. (2001). On the time course of perceptual choice: the leaky competing accumulator model. Psychol. Rev. 108, 550-592.
64. Van Maanen, L., Brown, S. D., Eichele, T., Wagenmakers, E.-J., Ho, T. C., Serences, J. T., and Forstmann, B. U. (2011). Neural correlates of trial-totrial fluctuations in response caution. J. Neurosci. 31, 17488-17495.
65. Vickers, D. (1979). Decision Processes in Visual Perception. New York, NY: Academic Press.
66. Vrieze, S. I. (2012). Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychol. Methods 17, 228-243.
67. Wagenmakers, E.-J., Ratcliff, R., Gomez, P., and McKoon, G. (2008). A diffusion model account of criterion shifts in the lexical decision task. J. Mem. Lang. 58, 140-159.
68. Wagenmakers, E.-J., van der Maas, H. L. J., and Grasman, R. P. P. P. (2007). An EZ-diffusion model for response time and accuracy. Psychon. Bull. Rev. 14, 3-22.
69. White, C. N., Ratcliff, R., and Starns, J. J. (2011). Diffusion models of the flanker task: discrete versus gradual attentional selection. Cogn. Psychol. 63, 210-238.
70. Wong, K. F., Huk, A. C., Shadlen, M. N., and Wang, X. J. (2007). Neural circuit dynamics underlying accumulation of time-varying evidence during decision-making. Front. Comput. Neurosci. 1:6. doi:10.3389/neuro.10.006.2007.
71. Woodworth, R. S., and Schlosberg, H. (1954). Experimental Psychology. New York: Holt.
72. Zhou, X., Wong-Lin, K., and Philip, H. (2009). Time-varying perturbations can distinguish among integrateto-threshold models for perceptual decision making in reaction time tasks. Neural Comput. 21, 2336-2362.
73. Zohary, E., Shadlen, M. N., and Newsome, W. T. (1994). Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370, 140-143.