Is there variation across individuals in processing? Bayesian analysis for systems factorial technology

Journal of Mathematical Psychology

Volumn 81, Issue , 2017, Pages 40-54

  Thiele, Jonathan E ^a   Haaf, Julia M ^a   Rouder, Jeffrey N ^a,b  

^a UNIVERSITY OF MISSOURI   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bayesian analysis; Cognitive architecture; Hierarchical models; Individual differences; Systems factorial designs

Indexed keywords

EID: 85031809524     PISSN: 00222496     EISSN: 10960880     Source Type: Journal    
DOI: 10.1016/j.jmp.2017.09.002     Document Type: Article

References (62)

1. Aitkin, M., Posterior bayes factors. Journal of the Royal Statistical Society. Series B. Statistical Methodology 53:1 (1991), 111–142 Retrieved from http://www.jstor.org/stable/2345730.
2. Atkinson, R., Shiffrin, R., Human memory: A proposed system system and its control processes. Spence, K.W., Spence, J.T., (eds.) The psychology of learning and motivation: Advances in research and theory (Vol. 2), 1968, Academic Press, New York, 89–195.
3. Bayarri, M., Garcia-Donato, G., Extending conventional priors for testing general hypotheses in linear models. Biometrika 94 (2007), 135–152.
4. Burbeck, S., Luce, R., Evidence form auditory simple reaction times for both change and level detectors. Perception & Psychophysics 32 (1982), 117–133.
5. Cowan, N., Attention and memory: An integrated framework. Oxford psychology series, no. 26, 1995, Oxford University Press.
6. Danker, J.F., Anderson, J.R., The ghosts of brain states past: remembering reactivates the brain regions engaged during encoding. Psychological Bulletin, 136(1), 2010, 87.
7. de Finetti, B., Theory of probability (Vol. 1). 1974, John Wiley Sons, New York.
8. Estes, W., Processes of memory loss, recovery, and distortion. Psychological Review 104 (1997), 148–169.
9. Fific, M., Little, D.R., Nosofsky, R.M., Logical-rule models of classification response times: A synthesis of mental-architecture, random-Walk, and Decision-Bound Approaches. Psychological Review 117:2 (2010), 309–348.
10. Fific, M., Nosofsky, R., Townsend, J., Information-processing architectures in multidimensional classification: A validation test of the systems factorial technology. Journal of Experimental Psychology: Human Perception and Performance 34:2 (2008), 356–375.
11. Garner, W., Felfoldy, G.L., Integrality of stimulus dimensions in various types of information processing. Cognitive Psychology 1:3 (1970), 225–241.
12. Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B., Bayesian data analysis. 2nd ed., 2004, Chapman Hall.
13. Haaf, J. M., & Rouder, J. N. (2017). Developing constraint in Bayesian mixed models. Revision submitted 3/17.
14. Heathcote, A., Popiel, S.J., Mewhort, D.J., Analysis of response time distributions: An example using the Stroop task. Psychological Bulletin 109 (1991), 340–347.
15. Hebb, D.O., Foord, E.N., Errors of visual recognition and the nature of the trace. Journal of Experimental Psychology, 35(5), 1945, 335.
16. Houpt, W., & Fific, M., A hierarchical Bayesian approach to distinguishing serial and parallel processing. Journal of Mathematical Psychology (in press). http://dx.doi.org/10.1016/j.jmp.2017.05.001.
17. Jeffreys, H., Theory of probability. 3rd ed., 1961, Oxford University Press, New York.
18. Kary, A., Taylor, R., Donkin, C., Using Bayes factors to test the predictions of models: A case study in visual working memory. Journal of Mathematical Psychology 72 (2016), 210–219.
19. Klugkist, I., Hoijtink, H., The bayes factor for inequality and about equality constrained models. Computational Statistics & Data Analysis 51:12 (2007), 6367–6379.
20. Klugkist, I., Kato, B., Hoijtink, H., Bayesian model selection using encompassing priors. Statistica NeerlandIca 59 (2005), 57–69.
21. Kruschke, J.K., Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 2012.
22. Liang, F., Paulo, R., Molina, G., Clyde, M.A., Berger, J.O., Mixtures of g-priors for Bayesian variable selection. Journal of the American Statistical Association 103 (2008), 410–423 Retrieved from http://pubs.amstat.org/doi/pdf/10.1198/016214507000001337.
23. Little, D.R., Nosofsky, R.M., Denton, S., Response time tests of logical-rule-based models of categorization. Journal of Experimental Psychology. Learning, Memory, and Cognition 37 (2011), 1–27.
24. Little, D.R., Nosofsky, R.M., Donkin, C., Denton, S.E., Logical rules and the classification of integral-dimension stimuli. Journal of Experimental Psychology. Learning, Memory, and Cognition 39:3 (2013), 801–820.
25. Liu, Y., Queueing network modeling of elementary mental processes. Psychological Review 103 (1996), 116–136.
26. Luce, R., Response times. 1986, Oxford University Press, New York.
27. Maddox, W.T., Ashby, F.G., Comparing decision bound and exemplar models of categorization. Perception & Psychophysics 53 (1993), 49–70.
28. Mandler, G., Recognizing: The judgment of previous occurence. Psychological Review 87 (1980), 252–271.
29. Masson, M.E.J., Loftus, G.R., Using confidence intervals for graphically based data interpretation. Canadian Journal of Experimental Psychology 57 (2003), 203–220.
30. McKinley, S.C., Nosofsky, R.M., Investigations of exemplar and decision bound models in large, ill-defined category structures. Journal of Experimental Psychology: Human Perception and Performance 21 (1995), 128–148.
31. Miller, G., The magical number seven plus or minus two: Some limits on our capacity for processing information. Psychological Review 63 (1956), 81–97.
32. Morey, R.D., Romeijn, J.-W., Rouder, J.N., The philosophy of bayes factors and the quantification of statistical evidence. Journal of Mathematical Psychology, 2016 retrieved from http://www.sciencedirect.com/science/article/pii/S0022249615000723.
33. Morey, R. D., & Rouder, J. N. (2015). BayesFactor 0.9.12-2. Comprehensive R Archive Network. Retrieved from http://cran.r-project.org/web/packages/BayesFactor/index.html.
34. Moyer, R.S., Landauer, T.K., Time required for judgements of numerical inequality. Nature 215 (1967), 1519–1520.
35. Mulder, J., Klugkist, I., van de Schoot, R., Meeus, W.H.J., Hoijtink, H., Bayesian model selection of informative hypotheses for repeated measurements. Journal of Mathematical Psychology, 54(530–546), 2009.
36. Rissman, J., Wagner, A.D., Distributed representations in memory: insights from functional brain imaging. Annual Review of Psychology 63 (2012), 101–128.
37. Robertson, T., Wright, F., Dykstra, R., Order restricted statistical inference. 1988, Wiley, New York.
38. Rogan, J.C., Keselman, H., Is the anova f-test robust to variance heterogeneity when sample sizes are equal? An investigation via a coefficient of variation. American Educational Research Journal 14:4 (1977), 493–498.
39. Rouder, J.N., The what, why, and how of born-open data. Behavioral Research Methods 48 (2016), 1062–1069.
40. Rouder, J.N., Lu, J., Speckman, P.L., Sun, D., Jiang, Y., A hierarchical model for estimating response time distributions. Psychonomic Bulletin and Review 12 (2005), 195–223.
41. Rouder, J.N., Morey, R.D., Relational and arelational confidence intervals: A comment on Fidler et al. (2004). Psychological Science 16 (2005), 77–79.
42. Rouder, J.N., Morey, R.D., Default bayes factors for model selection in regression. Multivariate Behavioral Research 47 (2012), 877–903.
43. Rouder, J.N., Morey, R.D., Speckman, P.L., Pratte, M.S., Detecting chance: A solution to the null sensitivity problem in subliminal priming. Psychonomic Bulletin and Review 14 (2007), 597–605.
44. Rouder, J.N., Morey, R.D., Speckman, P.L., Province, J.M., Default Bayes factors for ANOVA designs. Journal of Mathematical Psychology 56 (2012), 356–374.
45. Rouder, J.N., Morey, R.D., Wagenmakers, E.-J., The interplay between subjectivity, statistical practice, and psychological sciencecollabra. Collabra, 2, 2016, 6.
46. Rouder, J.N., Province, J.M., Morey, R.D., Gomez, P., Heathcote, A., The lognormal race: a cognitive-process model of choice and latency with desirable psychometric properties. Psychometrika 80 (2015), 491–513.
47. Rouder, J.N., Tuerlinckx, F., Speckman, P.L., Lu, J., Gomez, P., A hierarchical approach for fitting curves to response time measurements. Psychonomic Bulletin & Review, 15(1201–1208), 2008.
48. Rouder, J.N., Yue, Y., Speckman, P.L., Pratte, M.S., Province, J.M., Gradual growth vs. shape invariance in perceptual decision making. Psychological Review 117 (2010), 1267–1274.
49. Schweikert, R., A critical path generalization of the additive factor method: Analysis of a Stroop task. Journal of Mathematical Psychology 18 (1978), 105–139.
50. Schweikert, R., Townsend, J.T., A trichotomy: interactions of factors prolonging sequential and concurrent mental processes in stochastic discrete mental (PERT) networks. Journal of Mathematical Psychology 33 (1989), 328–347.
51. Spiegelhalter, D.J., Best, N.G., Carlin, B.P., van der Linde, A., Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B (Statistical Methodology) 64 (2002), 583–639.
52. Sternberg, S., The discovery of prossesing stages: extensions of Donder's method. Kosner, W.G., (eds.) Attention and performance II, 1969, North-Holland, Amsterdam, 276–315.
53. Townsend, J.T., Serial vs. parallel processing: sometimes they look like tweedledum and tweedledee but they can (and should) be distinguished. Psychological Science 1 (1990), 46–54.
54. Townsend, J.T., Ashby, F.G., Experimental test of contemporary mathematical models of visual letter recognition. Journal of Experimental Psychology: Human Perception and Performance 8 (1982), 834–864.
55. Townsend, J.T., Nozawa, G., On the spatio-temporal properties of elementary perception: An investigation on parallel, serial, and coactive theories. Journal of Mathematical Psychology 39 (1995), 321–359.
56. Townsend, J.T., Wenger, M.J., The serial-parallel dilemma: A case study in a linkage of theory and method. Psychonomic Bulletin & Review 11 (2004), 391–418.
57. Van Zandt, T., How to fit a response time distribution. Psychonomic Bulletin and Review 7 (2000), 424–465.
58. Vanpaemel, W., Prior sensitivity in theory testing: An apologia for the Bayes factor. Journal of Mathematical Psychology 54 (2010), 491–498.
59. Vanpaemel, W., Lee, M., Using priors to formalize theory: Optimal attention and the generalized context model. Psychonomic Bulletin & Review 19 (2012), 1047–1056.
60. Wagenmakers, E.J., Brown, S., On the linear relation between the mean and the standard deviation of a response time distribution. Psychological Review 114 (2007), 830–841.
61. Wenger, M.J., Gibson, B.S., Assing hazard functions to assess changes in processing capacity in an attentional cuing paradigm. Journal of Experimental Psychology: Human Perception and Performance 30 (2004), 708–719.
62. Zellner, A., Siow, A., Posterior odds ratios for selected regression hypotheses. Bernardo, J., DeGroot, M., Lindley, D., Smith, A., (eds.) Bayesian statistics: Proceedings of the first international meeting held in Valencia (Spain), 1980, University of Valencia, 585–603.