Estimating the probability and fidelity of memory
Tóm tắt
Research on memory may benefit from paradigms that permit graded characterization of memory performance, but a simple variance-based approach to the analysis of such graded data confounds two potential sources of error: the probability of memory and the fidelity of memory. Such data are more properly modeled by a mixture distribution, thereby permitting explicit estimation of both the probability and fidelity of memory. An expectation-maximization algorithm is presented for fitting such data to a mixture model, and Monte Carlo validation of this tool reveals circumstances under which it may be expected to be most effective. Limitations of the tool are outlined with respect to potential confounds in experiment design and interpretation of results. Finally, approaches to ameliorating such confounds are discussed. An R procedure for fitting response error data to mixture models may be downloaded from http://brm.psychonomic-journals.org/content/supplemental.
Tài liệu tham khảo
Bays, P. M., Catalao, R. F. G., & Husain, M. (2009). The precision of visual working memory is set by allocation of a shared resource. Journal of Vision, 9(10, Art. 7), 1–11.
Busey, T. A., Tunnicliff, J., Loftus, G. R., & Loftus, E. F. (2000). Accounts of the confidence—accuracy relation in recognition memory. Psychonomic Bulletin & Review, 7, 26–48.
Byrd, R. H., Lu, P., Nocedal, P. L., & Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16, 1190–1208.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39, 1–38.
Jammalamadaka, S. R., & SenGupta, A. (2001). Topics in circular statistics. London: World Scientific Press.
Malmberg, K. J. (2002). On the form of ROCs constructed from confidence ratings. Journal of Experimental Psychology: Learning, Memory, & Cognition, 28, 380–387.
McLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York: Wiley.
Prinzmetal, W., Amiri, H., Allen, K., & Edwards, T. (1998). Phenomenology of attention: 1. Color, location, orientation, and spatial frequency. Journal of Experimental Psychology: Human Perception & Performance, 24, 261–282.
Prinzmetal, W., Nwachuku, I., Bodanski, L., Blumenfeld, L., & Shimizu, N. (1997). The phenomenology of attention: 2. Brightness and contrast. Consciousness & Cognition, 6, 372–412.
R Development Core Team (2009). R: A language and environment for statistical computing [Computer software]. Vienna: Author. Available from www.R-project.org.
Roy-Charland, A., Saint-Aubin, J., Lawrence, M. A., & Klein, R. M. (2009). Solving the chicken-and-egg problem of letter detection and fixation duration in reading. Attention, Perception, & Psychophysics, 71, 1553–1562.
Tsal, Y., & Meiran, N. (1993, November). Toward a resolution theory of visual attention. Paper presented at the 34th Annual Meeting of the Psychonomic Society, Washington, DC.
Tulving, E. (1985). Memory and consciousness. Canadian Psychology, 26, 1–12.
Yonelinas, A. P. (1994). Receiver-operating characteristics in recognition memory: Evidence for a dual-process model. Journal of Experimental Psychology: Learning, Memory, & Cognition, 20, 1341–1354.
Zhang, W. W., & Luck, S. J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 453, 233–235.