WoMMBAT: A user interface for hierarchical Bayesian estimation of working memory capacity

Behavior Research Methods

Volumn 43, Issue 4, 2011, Pages 1044-1065

  Morey, Richard D ^a   Morey, Candice C ^a  

^a UNIVERSITY OF GRONINGEN   (Netherlands)

Author keywords

Bayesian hierarchical models; Bayesian models; Capacity estimates; Multinomial models; Visual change detection; Visual short term memory; Working memory

Indexed keywords

ARTICLE; BAYES THEOREM; COMPUTER INTERFACE; COMPUTER PROGRAM; HUMAN; SHORT TERM MEMORY; STATISTICAL MODEL;

BAYES THEOREM; HUMANS; MEMORY, SHORT-TERM; MODELS, STATISTICAL; SOFTWARE; USER-COMPUTER INTERFACE;

EID: 81255150477     PISSN: 1554351X     EISSN: 15543528     Source Type: Journal    
DOI: 10.3758/s13428-011-0114-8     Document Type: Article

References (48)

1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716-723.
2. Alvarez, G. A., & Cavanagh, P. (2004). The capacity of visual short-term memory is set both by visual information load and by number of objects. Psychological Science, 15, 106-111.
3. Awh, E., Barton, B., & Vogel, E. K. (2007). Visual working memory represents a fixed regardless of complexity. Psychological Science, 18, 622-628.
4. Bays, P. M., & Husain, M. (2009). Response to comment on Dynamic shifts of limited working memory resources in human vision. Science, 323, 877. Available at http://www.sciencemag.org/cgi/content/abstract/323/5916/877d.
5. Brooks, S., & Gelman, A. (1997). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7, 434-455.
6. Cleveland, W. S. (1981). Lowess: A program for smoothing scatterplots by robust locally weighted regression. American Statistician, 35, 54.
7. Cowan, N. (2001). The magic number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87-114.
8. Cowan, N., Elliott, E. M., Saults, J. S., Morey, CC., Mattox, S., Hismjatullina, A., & Conway, A. R. A. (2005). On the capacity of attention: Its estimation and its role in working memory and cognitive aptitudes. Cognitive Psychology, 51, 42-100.
9. Cowan, N., & Rouder, J. N. (2009). Comment on Dynamic shifts of limited working memory resources in human vision. Science, 323, 877. Available at http://www.sciencemag. org/cgi/content/abstract/323/5916/877c.
10. Fougnie, D., & Marois, R. (2009). Dual-task interference in visual working memory: A limitation in storage capacity but not in encoding or retrieval. Attention, Perception, & Psychophysics, 71, 1831-1841.
11. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian data analysis (2nd ed.). London: Chapman and Hall.
12. Gelman, A., Goegebeur, Y., Tuerlinckx, F., & van Mechelen, I. (2000). Diagnostic checks for discrete data regression models using posterior predictive simulations. Journal of the Royal Statistical SocietyC, 49, 247-268. Available at http://www.jstor.org/stable/2680852.
13. Geweke, J. (1992). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In J. M. Bernardo, J. O. Berger, A. P. Dawid, & A. F. M. Smith (Eds.), Proceedings of the Fourth Valencia International Meeting on Bayesian Statistics(pp. 169-194). Oxford: Oxford University Press, Clarendon Press.
14. Gold, J. M., Fuller, R. L., Robinson, B. M., McMahon, R. P., Braun, E. L., & Luck, S. J. (2006). Intact attentional control of working memory encoding in schizophrenia. Journal of Abnormal Psychology, 115, 658-673.
15. Heidelberger, P., & Welch, P. (1983). Simulation run length control in the presence of an initial transient. Operations Research, 31, 1109-1144. Available at http://www.jstor.org/stable/170841.
16. Hocking, R. R. (1976). The analysis and selection of variables in linear regression. Biometrics, 32, 1-49. Available at http://www.jstor.org/stable/2529336.
17. Kumar, A., & Jiang, Y. (2005). Visual short-term memory for sequential arrays. Memory & Cognition, 33, 488-498.
18. Liu, J. S. (2001). Monte Carlo strategies in scientific computing. New York: Springer.
19. Logie, R. H. (1995). Visuo-spatial working memory. Hillsdale, NJ: Psychology Press.
20. Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390, 279-281.
21. Mackenzie, P. B. (1989). An improved hybrid Monte Carlo method. Physics Letters B, 226, 369-371.
22. McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (2nd ed.). London: Chapman & Hall.
23. Morey, C. C., Cowan, N., Morey, R. D., & Rouder, J. N. (2011). Flexible attention allocation to visual and auditory working memory tasks: Manipulating reward induces a trade-off. Attention, Perception, & Psychophysics, 73, 458-472.
24. Morey, R. D. (2011). A hierarchical Bayesian model for the measurement of working memory capacity. Journal of Mathematical Psychology, 55, 8-24.
25. Morey, R. D., Rouder, J. N., & Speckman, P. L. (2009). A truncated-probit item response model for estimating psychophysical thresholds. Psychometrika, 74, 603-618.
26. Ntzoufras, I. (2009). Bayesian modeling using WinBUGS. Hoboken, NJ: Wiley.
27. Olsson, H., & Poom, L. (2005). Visual memory needs categories. Proceedings of the National Academy of Sciences, 102, 8776-8780.
28. Paivio, A. (1990). Mental representations: A dual coding approach. New York: Oxford University Press.
29. Parra, M. A., Della Sala, S., Logie, R. H., & Abrahams, S. (2009). Selective impairment in visual short-term memory binding. Cognitive Neuropsychology, 26, 583-605.
30. Pashler, H. (1988). Familiarity and visual change detection. Perception & Psychophysics, 44, 369-378.
31. Phillips, W. A. (1974). On the distinction between sensory storage and short-term visual memory. Perception & Psychophysics, 16, 283-290.
32. Plummer, M., Best, N., Cowles, K., & Vines, K. (2006). CCODA: Convergence diagnosis and output analysis for MCMC. R News, 6, 7-11.
33. R Development Core Team. (2009). R: A language and environment for statistical computing [Computer software manual]. Vienna, Austria: R Foundation for Statistical Computing. Available at http://www.R-project.org.
34. Raftery, A., & Lewis, S. (1995). The number of iterations, convergence diagnostics and generic metropolis algorithms. In W. Gilks, D. Spiegelhalter, & S. Richardson (Eds.), Practical Markov chain Monte Carlo(pp. 116-130). London: Chapman and Hall.
35. Repovs, G., & Baddeley, A. (2006). The multi-component model of working memory: explorations in experimental cognitive psychology. Neuroscience, 139, 5-21.
36. Rouder, JN. & Lu, J. 2005. An introduction to Bayesian hierarchical models with an application in the theory of signal detection. Psychonomic Bulletin and Review, 12, 573-604.
37. Rouder, J. N., Morey, R. D., Cowan, N., Zwilling, C. E., Morey, C. C., & Pratte, M. S. (2008). An assessment of fixed-capacity models of visual working memory. Proceedings of the National Academy of Sciences, 105, 5976-5979.
38. Rouder, J. N., Morey, R. D., Morey, C. C., & Cowan, N. (2011). How to measure working-memory capacity in the change-detection paradigm. Psychonomic Bulletin & Review.
39. Rouder, J. N., Morey, R. D., Speckman, P. L., & Pratte, M. S. (2007). Detecting chance: A solution to the null sensitivity problem in subliminal priming. Psychonomic Bulletin & Review, 14, 597-605.
40. Rubin, D. C., & Kontis, T. C. (1983). A schema for common cents. Memory & Cognition, 11, 335-341.
41. Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & van der Linde, A. (2002). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical SocietyB, 64, 583-639.
42. Treisman, A., & Zhang, W. (2006). Location and binding in visual working memory. Memory & Cognition, 34, 1704-1719.
43. Vogel, E. K., McCollough, A. W., & Machizawa, M. G. (2005). Neural measures reveal individual differences in controlling access to working memory. Nature, 438, 500-503.
44. Wagenmakers, E.-J., Lodewyckx, T., Kuriyal, H., & Grasman, R. (2010). Bayesian hypothesis testing for psychologists: A tutorial on the Savage-Dickey method. Cognitive Psychology, 60, 158-189.
45. Wheeler, M. E., & Treisman, A. M. (2002). Binding in short-term visual memory. Journal of Experimental Psychology: General, 131, 48-64.
46. Wilken, P., & Ma, W. J. (2004). A detection theory account of change detection. Journal of Vision, 4, 1120-1135.
47. Woodman, G. F., & Vogel, E. K. (2005). Fractionating working memory: Consolidation and maintenance are independent processes. Psychological Science, 16, 106-113.
48. Xu, Y., & Chun, M. M. (2006). Dissociable neural mechanisms supporting visual short-term memory for objects. Nature, 440, 91-95.