Course Schedule

Week 1 (Mon Jan 6)
CLASS CANCELLED because of weather

 


 

Week 2 (Mon Jan 13)
Introduction to Computational Cognitive Modeling
What is a model? Why do we model? How do we model? What do we want to learn from models?

Reading:
Lewandowsky & Farrell, Chapter 1

Powerpoint Slides:
Week 2

Homework:
If you have had little MATLAB experience, you should read through the MATLAB Tutorials:
http://www.mathworks.com/academia/student_center/tutorials/launchpad.html

Recommended Matlab Textbook:
Matlab: A Practical Introduction to Programming and Problem Solving
Stormy Attaway
Butterworth-Heinemann Publisher
2013

Further Recommended Readings:
Busemeyer & Diederich, Chapter 1 [PDF]
Hintzman, D. L. (1990). Human learning and memory: Connections and dissociations. Annual Review of Psychology, 41, 109-139. [PDF]
Palmeri, T.J., & Cottrell, G. (2009). Modeling perceptual expertise. In I. Gauthier, D. Bub, & M. Tarr (Eds.), Perceptual Expertise: Bridging Brain and Behavior. Oxford University Press. [PDF]

More Recommended Readings:
Platt, J.R. (1964). Strong inference. Science, 146, 347-353. [PDF]
Marr, D. (1983). Vision. W.H. Freeman. [PDF]

 


 

Week 3 (Mon Jan 20)
Martin Luther King Day – No Class

 


 

Week 4 (Mon Jan 27)
Implementing a Computational Model
Comparing observed data with model predictions. Measures of model fit. Model parameters. Tricks and tips for effective modeling. Simulating predictions of a model. Prediction versus fitting. Using the similarity choice family of models as an example.

Readings:
Lewandowsky & Farrell, Chapter 1
Busemeyer & Diederich, Chapter 2 [PDF]

Powerpoint Slides:
Week 4

Graphs and Plots:
Note: For all homework assignments, when graphs and plots are needed, I want them to be created by Matlab code and to be properly formatted and labeled. Here is a simple example of some Matlab code for creating line and bar graphs: GraphExamples.m. For more information on creating graphs and plots in Matlab, you might check my PSY319 course (especially Week 6): http://catlab.psy.vanderbilt.edu/palmeri/psy319/

Homework:
Homework4.pdf
Homework4.m

Homework Solution:
Homework4_Solution.zip

Further Recommended Readings:
Lamberts, K. (1997). Process models of categorization. In K. Lamberts and D. Shanks (Eds.), Knowledge, Concepts, and Categories, chapter 10, pages 371-403. MIT Press. 
Logan, G.D. (2004). Cumulative progress in formal theories of attention. Annual Review of Psychology, 55, 207-234. [PDF]
Nosofsky, R.M. (1988). Exemplar-based accounts of relations between classification, recognition, and typicality. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 700-708. [PDF]
Nosofsky, R.M. (1992). Exemplar-based approach to relating categorization, identification, and recognition. In F.G. Ashby (Ed.), Multidimensional Models of Perception and Cognition (pp. 363-393), Hillsdale, NJ: Erlbaum. [PDF]
Richler, J.J., & Palmeri, T.J. (2014). Visual category learning. Wiley Interdisciplinary Reviews in Cognitive Science, 5, 75-94 [PDF]
Shepard, R.N. (1980). Multidimensional scaling, tree-fitting, and clustering. Science, 210, 390-398. [PDF]
Shepard, R.N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1318-1323. [PDF]

 


 

Week 5 (Mon Feb 3)
Fitting Models to Data; Optimization Techniques

Readings:
Lewandowsky & Farrell, Chapter 2, pp. 56-69
Lewandowsky & Farrell, Chapter 3

Powerpoint Slides:
Week 5

In-class Matlab Code:
Week5_Matlab.zip

Homework:
Homework5.pdf
Homework5.m

Homework Solution:
Homework5_Solution.zip

Further Recommended Readings:
Lewandowsky & Farrell, Chapter 2, pp. 35-56
Kelley, C.T. (1999). Iterative Methods for Optimization. SIAM. Chapters 6-8. [PDF]
Kolda, T.G., Lewis, R.M., & Torczon, V. (2003). Optimization by direct search: New perspectives on some classical and modern methods. SIAM Review, 45, 385-482. [PDF]
Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(1), 104-114. [PDF]
Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115(1), 39-57. [PDF]
Storn, R. (2008). Differential evolution research: Trends and open questions. In Advances in Differential Evolution (pp. 1-31). Springer Berlin Heidelberg. [PDF]
Turner, B.M., & Sederberg, P.B. (2012). Approximate Bayesian computation with differential evolution. Journal of Mathematical Psychology, 56(5), 375–385. [PDF]

 


 

Week 6 (Mon Feb 10)
Model Comparison Methods; Maximum Likelihood

Readings:
Lewandowsky & Farrell, Chapter 4
Lewandowsky & Farrell, Chapter 5

Powerpoint Slides:
Week 6

In-class Matlab Code:
Week6.zip

Homework:
Homework6.pdf
Homework6_Files.zip

Homework Solution:
Homework6_Solution.zip

Further Recommended Reading:
Myung, I.J. (2003). Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology, 47, 90–100. [PDF]

 


 

Week 7 (Mon Feb 17)
Continued Discussion of Model Comparison; Random Numbers and Monte Carlo Techniques

Reading:
Lewandowsky & Farrell, Chapter 5
Lewandowsky & Farrell, Chapter 7 (pp. 258-282)
Zhang, W., & Luck, S.J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 453, 233-235. [PDF] [Supplemental]

Powerpoint Slides:
Week 7

In-class Matlab Code:
Week7.zip

Homework:
No Homework This Week

Further Recommended Readings:
Bozdogan, H. (2000). Akaike's information criterion and recent developments in information complexity. Journal of Mathematical Psychology, 44, 62-91. [PDF]
Evans, M., Hastings, N., & Peacock, B. (2000). Statistical Distributions. Wiley.
Park, S. K., & Miller, K. W. (1988). Random number generators: Good ones are hard to find. Communications of the ACM, 31(10), 1192-1201. [PDF]
Random Numbers (see Chapter 7) from Numeric Recipes [PDF]
Zucchini, W. (2000). An introduction to model selection. Journal of Mathematical Psychology, 44, 41-61. [PDF]

 


 

Week 8 (Mon Feb 24)
Monte Carlo Techniques; Bootstrapping Methods; Model Complexity Measures

Reading:
Lewandowsky & Farrell, Chapter 6

Powerpoint Slides:
Week 8 

In-class Matlab Code:
Week8.zip

Homework:
Homework8.pdf

Homework Solution:
Homework8_Solution.m

Further Recommended Readings:
Efron, B., & Gong, G. (1983). A leisurely look at the bootstrap, the jackknife, and cross-validation. The American Statistician, 37, 36-48. [PDF]
McClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1, 11–38. [PDF]
Myung, I.J., Pitt, M.A., & Kim, K. (2005). Model evaluation, testing and selection. In K. Lambert and R. Goldstone (Eds.), Handbook of Cognition. Sage Publication. [PDF]
Pitt, M. A., Myung, I. J., & Zhang, S. (2002).  Toward a method of selecting among computational models of cognition. Psychological Review, 109(3), 472-491. [PDF]
Pitt, M. A., Kim, W., Navarro, D. J. & Myung, J. I. (2006). Global model analysis by parameter space partitioning. Psychological Review, 113, 57-83. [PDF]
Wagenmakers, E.-J., Ratcliff, R., Gomez, P., & Iverson, G.J. (2004). Assessing model mimicry using the parametric bootstrap. Journal of Mathematical Psychology, 48, 28-50. [PDF]
Wichman, F.A., & Hill, N.J. (2001). The psychometric function: I. Fitting, sampling, and goodness of fit. Perception & Psychophysics, 63, 1293-1313. [PDF]
Wichman, F.A., & Hill, N.J. (2001). The psychometric function: II. Bootstrap-based confidence intervals and sampling. Perception & Psychophysics, 63, 1314-1329. [PDF]

 


 

– SPRING BREAK –

 


 

Week 9 (Mon Mar 10)
Bayesian Modeling

Reading:
Shiffrin, R.M., Lee, M.D., Wagenmakers, E.-J., & Kim, W.J. (2008). A survey of model evaluation approaches with a focus on hierarchical Bayesian methods. Cognitive Science, 32(8), 1248-1284. [PDF]

Powerpoint Slides:
Week 9

In-class Matlab Code:
Week9.zip

Homework:
Homework9.pdf

Homework Solution:
Homework9_Solution.m

Further Recommended Readings:
Kruschke, J.K. (2011). Doing Bayesian Data Analysis: A Tutorial with R and Bugs. Academic Press.
Lee, M.D. (2008). Three case studies in the Bayesian analysis of cognitive models. Psychonomic Bulletin & Review, 15(1), 1-15. [PDF]
Lee, M.D., & Vanpaemel, W. (2008). Exemplars, prototypes, similarities and rules in category representation: An example of hierarchical Bayesian analysis. Cognitive Science, 32(8), 1403-1424. [PDF]
Rouder J.N., & Lu J. (2005). An introduction to Bayesian hierarchical models with an application in the theory of signal detection. Psychonomic Bulletin & Review, 12, 573-604. [PDF]
 


 

Week 10 (Mon Mar 17)
Modeling Response Times

Reading:
Nosofsky, R.M., & Palmeri, T.J. (2014). Exemplar-based random walk model. To appear in J.R. Busemeyer, J. Townsend, Z.J. Wang, & A. Eidels (Eds.), Mathematical and Computational Models of Cognition, Oxford University Press. [PDF]
Wagenmakers, E.-J. (2009). Methodological and empirical developments for the Ratcliff diffusion model of response times and accuracy. European Journal of Cognitive Psychology, 21, 641-671. [PDF]

Powerpoint Slides:
Week 10

In-class Matlab Code:
Week10.zip

Homework:
Homework10.pdf

Homework Solution:
Homework10_Solution.m

Further Recommended Readings:
Logan, G. D. (1988). Toward an instance theory of automatization. Psychological Review, 95, 492-527. [PDF]
Nosofsky, R.M., & Palmeri, T.J. (1997). An exemplar-based random walk model of speeded classification. Psychological Review, 104, 266-300. [PDF]
Palmeri, T.J. (1997). Exemplar similarity and the development of automaticity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 324-354. [PDF]
Ratcliff, R., & Rouder, J.N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9, 347-356. [PDF]

 


 

Week 11 (Mon Mar 24)
Diffusions, Random Walks, and Other Stochastic Models

Reading:
Rouder, J.N., & Speckman, P.L. (2004). An evaluation of the Vincentizing method of forming group-level response time distributions. Psychonomic Bulletin & Review, 11, 419-427. [PDF]
Van Zandt, T. (2000). How to fit a response time distribution. Psychonomic Bulletin & Review, 7, 424-465. [PDF]

Powerpoint Slides:
Week 11

In-class Matlab Code:
Week11.zip

Homework:
Homework11.pdf
Week11_Homework.zip

Homework Solution:
Homework11_Solution.zip

Further Recommended Readings:
van Ravenzwaaij, D., & Oberauer, K. (2009). How to use the diffusion model: Parameter recovery of three methods: EZ, fast-dm, and DMAT. Journal of Mathematical Psychology, 53, 463-473. [PDF]

Wagenmakers, E.-J., van der Maas, H.L.J., & Grasman, R.P.P.P. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14, 3-22. [PDF]
The EZ-diffusion model has been implemented in JavaScript (click here), R (click here), and Excel (click here). A Matlab implementation is here (courtesy of Alex Petrov, http://alexpetrov.com/).

Wagenmakers, E.-J., van der Maas, H.L.J., Dolan, C., & Grasman, R.P.P.P. (2008). EZ does it! Extensions of the EZ-diffusion model. Psychonomic Bulletin & Review, 15, 1229-1235. [PDF]
The Robust EZ software can be found here, and Robust EZ software adjusted for batch processing can be found here

Grasman, R. P. P. P., Wagenmakers, E.-J., & 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. Journal of Mathematical Psychology, 53, 55-68. [PDF]
This is the link to the EZ2 software in Excel, JavaScript, and R.

Vandekerckhove, J., & Tuerlinckx, F. (2008). Diffusion model analysis with MATLAB: A DMAT primer. Behavior Research Methods, 40, 61-72. [PDF]
Click here for a link to the DMAT toolbox. 

Ratcliff, R., & Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability. Psychonomic Bulletin & Review, 9, 438-481. [PDF]
Tuerlinckx, F., Maris, E., Ratcliff, R., & De Boeck, P. (2001). A comparison of four methods for simulating the diffusion process. Behavior Research Methods, Instruments, & Computers, 33, 443-456. [PDF]
Vandekerckhove, J., & Tuerlinckx, F. (2007). Fitting the Ratcliff diffusion model to experimental data. Psychonomic Bulletin & Review, 14, 1011-1026. [PDF]

 


 

Week 12 (Mon Mar 31)
Simulating Stochastic and Deterministic Differential Equations

Reading:
Usher, M., & McClelland, J.L. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108, 550-592. (esp. pp. 550-564) [PDF]

Powerpoint Slides:
Week 12

In-class Matlab Code:
Week12.zip

Homework:
Homework12.docx

Homework Solution:
Homework12_Solution.zip

Further Recommended Readings:
Higham, D.J., (2001). An algorithmic introduction to numerical simulation of stochastic differential equations. SIAM Review, 43, 525-546. [PDF]
Smith, P.L. (2000). Stochastic dynamic models of response time and accuracy: A foundational primer. Journal of Mathematical Psychology, 44, 408-463. [PDF]
Tuerlinckx, F., Maris, E., Ratcliff, R., & De Boeck, P. (2001). A comparison of four methods for simulating the diffusion process. Behavior Research Methods, Instruments, & Computers, 33, 443-456. [PDF]
Wilson, H.R. (1999). Spikes, Decision, and Actions: The Dynamical Foundations of Neuroscience. Oxford University Press. [PDF]

 


 

Week 13 (Mon Apr 7)
NO CLASS

 


 

Week 14 (Mon Apr 14)
Linking Cognitive Models and Neural Data

Reading:
Palmeri, T.J., Schall, J.D. & Logan, G.D. (2014). Neurocognitive modelling of perceptual decisions. To appear in J.R. Busemeyer, J. Townsend, Z.J. Wang, & A. Eidels (Eds.), Oxford Handbook of Computational and Mathematical Psychology, Oxford University Press. [PDF]

Powerpoint Slides:
Week 14

In-class Matlab Code:
Week14.m

Homework:
No Homework This Week

Further Recommended Readings:
Boucher, L., Palmeri, T.J., Logan, G.D., Schall, J.D. (2007). Inhibitory control in mind and brain: An interactive race model of countermanding saccades. Psychological Review, 114, 376-397. [PDF]
Carandini, M. (2012). From circuits to behavior: A bridge too far? Nature Neuroscience, 15, 507-509. [PDF]
Davis, T., Love, B.C., & Preston, A.R. (2012).  Learning the exception to the rule: Model-based fMRI reveals specialized representations for surprising category members. Cerebral Cortex, 22, 260-273. [PDF]
Dayan, P., & Abbott, L.F. (2005). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press.
Mack, M.L., Preston, A.R. & Love, B.C. (2013). Decoding the brain's algorithm for categorization from its neural implementation. Current Biology, 23, 2023-2027. [PDF]
Purcell, B.A., Heitz, R.P., Cohen, J.Y., Schall, J.D., Logan, G.D., & Palmeri, T.J. (2010). Neurally-constrained modeling of perceptual decision making. Psychological Review, 117, 1113-1143. [PDF]
Purcell, B.A., Schall, J.D., Logan, G.D., & Palmeri, T.J. (2012). Gated stochastic accumulator model of visual search decisions in FEF. Journal of Neuroscience, 32(10), 3433-3446. [PDF]
Smith, P.L., & Ratcliff, R. (2004). Psychology and neurobiology of simple decisions. Trends in Neurosciences, 27, 161-168. [PDF]

 


 

Week 15A (Mon Apr 21)
Tricks and Techniques to Speed up Simulations.

Reading:
None

Powerpoint Slides:
Week 15

In-class Matlab Code:
Week15.zip

Homework:
No Homework This Week

 


 

Week 15B (Tue Apr 22)
Brief In-class Project Presentations (makeup for cancelled class), 10am WH316

 


 

FINAL PROJECTS DUE MONDAY APRIL 28th