Wednesday 2:30-5:30, Tobin 304
|Name||Andrew L. Cohen
||M 9:40-10:30, F 2:30-3:30 or by appointment|
||acohen at psych umass edu|
Course Structure: Most classes will be composed of four segments. First, the instructor will review the basic techniques for the readings. Second, a student will present research which utilizes these techniques and lead a group discussion. Third, the instructor will give an overview of the the homework assigment and any needed programming skills. Fourth, the instructor will review any new math needed to understand the next set of readings. These segments will last roughly 1, 1, 1/2 and 1/2 hours, respectively.
Web Discussions: Please post any public questions to the appropriate discussion board on the class WebCT site (http://webct.oit.umass.edu/).
Special Needs: If you have any special academic needs, let me know the first week of class.
Octave/Matlab: The mini-projects and final project will be implemented in Octave. Octave is a numerical programming environment that is mostly compatable with Matlab. The advantage of Octave is that it is free and you may install it on any computer you wish (Windows, UNIX, LINUX, Mac). If you already have access to Matlab, feel free to use it, but I will not provide "technical support". Here are instructions for how to install Octave. Here is a beginning Octave tutorial. Here is a reference guide for Octave. We will develop proficiency with Octave as the semester progresses so don't worry if you've never used
(* = technique readings, read first)
||W 9/8||What are mathematical models and why?
|| Bjork, R. J. (1973). Why mathematical models? American Psychologist, 28, 22-27.
Chapanis, A. (1961). Men, machines, and models. American Psychologist, 16, 113-131.
Harris, R. J. (1976). The uncertain connection between verbal theories and research hypotheses in social psychology. Journal of Experimental Social Psychology, 12, 210-219.
Hintzman, D. L. (1991). Why are formal models useful in psychology? In Hockley, William E. & Lewandowsky, Stephan (Eds). Relating theory and data: Essays on human memory in honor of Bennet B. Murdock (pp. 39-56). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
Jacobs, A. M. & Grainger, J. (1994). Models of visual word recognition - sampling the state of the art. Journal of experimental psychology: Human perception and performance, 20, 1311-1334.
Myung, I. J. & Pitt, M. A. (2004). Model comparison methods. Methods in Enzymology, 383, 351-366.
Roberts, S. & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107, 358-367.
Rogers J. L. & Rowe, D. C. (2000). Theory development should begin (but not end) with good empirical fits: A comment on Roberts and Pashler (2000). Psychological Review, 109, 599-604.
||Multinomial processing tree models
|| *Batchelder, W. H. & Riefer, D. M. (1999). Theoretical and empirical review of multinomial process tree modeling. Psychonomic Bulletin & Review, 6, pp. 57-86. [Skip "Structure of MPT Models"; pick one section from "Application Areas" that interests you; read everything else.]
Batchelder, W. H. & Riefer, D. M. (1980). Separation of storage and retrieval in free recall of clusterable pairs. Psychological Review, 87, 375-397. [Skip from the paragraph which begins "In the case in which" on page 383 until "Conclusion"; read everything else]
Luce's choice axiom
*Laming, D. (1973). Mathematical Psychology. New York: Academic Press, chap 2.
Rumelhart, D. L. and Greeno, J. G. (1971). Similarity between stimuli: An experimental test of the Luce and Restle choice models. Journal of Mathematical Psychology, 8, 370-381. [Don't worry too much about the math in the last section.]
|Xingshan Li & Barbara Juhasz (jointly)||Assignment
||Stimulus sampling theory|| *Neimark, E. D. & Estes, W. K. (1967). Stimulus sampling theory. San Fransisco: Holden-Day, pp. 25-35.
Neimark, E. D. & Estes, W. K. (1967). Stimulus sampling theory. San Fransisco: Holden-Day, pp. 274-283.
*[http://en.wikipedia.org/wiki/Conditioning might be helpful.]
||W 10/6||Markov models|| Gray, R. (2002). "Markov at the Bat": A model of cognitive processing in baseball batters. Psychological Science, 13, pp. 542-547.
*Wickens, T. D. (1982). Models for behavior: Stochastic processes in psychology. San Francisco: W. H. Freeman and Company, chaps. 1-2 and sects. 3.1-3.2 [Skip optional (vertical line) sections].
||No class - Monday schedule.
|| *Borg, I. & Groenen, P. (1997). Modern multidimensional scaling: theory and applications. New York: Springer-Verlag, chaps. 1-3.
Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39-57.
||Random walk models|| *Atkinson, R. C., Bower, G. H., & Crothers, E. J. (1966). An introduction to mathematical learning theory. New York: John Wiley & Sons, Inc, sect. 4.4.
Nosofsky, R. M.; Palmeri, T. J. (1997). An exemplar-based random walk model of speeded classification. Psychological Review, 104. pp. 266-300.
*Wickens, T. D. (1982). Models for behavior: Stochastic processes in psychology. San Francisco: W. H. Freeman and Company, pp. 171-176 (from Chap. 8), 199-208 (from Chap. 9).
||Bayesian models|| Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27, 453-489.
*Durrett, R. (1994). The Essentials of Probability. CA: Duxbury Press, Sects. 2.1, 2.2, & 2.4.
*Sedlmeier, P. & Gigerenzer, G. (2001). Teaching Bayesian reasoning in less than two hours. Journal of Experimental Psychology: General, 130, pp. 380-382. [Up to the section on Teaching Bayesian Inference.]
|| *Myung, I. J., Pitt, M. A., & Kim, W. (2003). Model Evaluation, Testing, and Selection. In: K. Lamberts and R. Goldstone (Eds.), Handbook of Cognition. London: Sage. [Skip qualitative section.]
Olsson, H., Wennerholm, P., & Lyxzen, U. (2004). Exemplars, prototypes, and the flexibility of classification models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 936-941.
|| *Rich, E. and Knight, K. (1991). Artificial Intelligence. New York: McGraw Hill, chap. 18.
Van Rooy, D., Van Overwalle, F., Vanhoomissen, T., Labiouse, C., French, R. (2003). A Recurrent Connectionist Model of Group Biases. Psychological Review, 110, 536-563.
||Constraint satisfaction models|| Goldstone, R. L., & Rogosky, B. J. (2002). Using relations within conceptual systems to translate across conceptual systems, Cognition, 84, 295-320.
Thagard, P. (1989). Explanatory coherence. Behavioral and Brain Sciences, 12, 435-467.
|Kathryn Marszalek (Goldstone) & Adrian Staub (Thagard)||None|
||W 12/1||Dynamic system
|| *Abraham, F. D., Abraham, R., & Shaw, C. D. (1991). A visual introduction to dynamical systems for psychology. Santa Cruz, CA: Aerial Press, sects. I, II A, B1, B2, F1.
Haken, H., Kelso, J. A. S., and Bunz, H. (1985). A theoretical model of phase transitions in human hand movement. Biological Cybernetics, 51, 347-356. [Skim the math in Section 3.]
Tanner & Swets
| *Wickens, T. D. (2001). Elementary signal detection theory. London: Oxford University Press, pp. 3-44.
*Laming, D. (1973). Mathematical Psychology. New York: Academic Press, chap 6.
| *Anderson, J. R., Bothell, D., Byrne M. D. & Lebiere, C. (submitted). An Integrated Theory of the Mind. Psychological Review. pp. 1-28.
*Understanding Production Systems.
*Perception and Motor Actions in ACT-R.
Paper to be selected by presenter from http://act-r.psy.cmu.edu/publications/.
||No final||No Class|| None