G89.3405 - Introduction to Computational Modeling

Meeting Time/Place: MW 4-5:50pm, Meyer 851
Instructor: Todd M. Gureckis
Office: Meyers 280
Office Hours: By appointment
Email: todd.gureckis@nyu.edu

Course Description

Computational modeling plays an increasingly important role in the social and behavioral sciences. This introductory course provides a broad survey of computational approaches to human behavior. Topics will be organized around interests of students in class, however, the core concepts we will cover are the goals and philosophy behind developing models and basic issues in model evaluation, testing, and fitting. Readings and lectures will survey a broad set of approaches to modeling cognitive processes with an emphasis on what are traditionally considered "higher-level" cognitive processes. Example topics include reviews of the basic properties (and limitations) of artificial neural networks/parallel distributed processing, contemporary approaches to modeling memory, learning, and decision making processes, modeling of reaction time data, developmental approaches (i.e., dynamical field theory, etc...), models of categorization, reasoning, problem solving, analogy, etc..., approaches to integrating models and findings from cognitive neuroscience (i.e., what can they tell each other), the relative merits of bayesian/rational approaches and mechanic models (a bit of modeling philosophy), other topics might include a segment on agent-based models of socio-behavioral processes (i.e., models based on interactive, distributed processing by independent components).

In other words, we'll aim to cover a relatively broad set of topics in formal modeling. In an ideal world, everyone would leave the course with a richer understanding of the role that computational model plays in contemporary cognitive science, understand how to fit/evaluate models, and how to read a modeling paper, think about the predictions it makes, and perhaps even implement it yourself.

***Please note***
This is not a quantitative course. For the most part, the focus will be developing an understanding and intuition for concepts as they relate to human behavior relative to a math skills course. If you have taken Math Tools in the psych department, or had linear algebra or calculus as an undergrad you will be in the best position for approach the material. However, we will, when needed, review some of the basic concepts needed to understand the assigned papers. For the (infrequent) homework/assignments, I will generally assume some basic familiarity with programming in something like Matlab or Python (if you know what a for loop is you'll be fine), however, if you are interested in the above topics and the programming is the main hang up, please consider enrolling. An effort will be made to adjust the assignments to people's individual (and hopefully diverse) backgrounds as much as possible.

Books:

There is no textbook to purchase for this course. Readings will be selected research articles and book chapters provided via this website (password to access the files will be provided in class).

Prerequisites:

Graduate standing or permission of instructor.

Grading:

Grading will be based on attendance/participation in lectures, a small number of homework assignments, and a final project. The final project will be a modeling project of the student's design or a proposal for such a project written as a NRSA grant proposal. The projects will be presented at the end of class and will be evaluated by the peers in the class (akin to a mini grant panel). The expectation is that the final project will apply some idea from the course to the student's own research background/project.

Attendance/participation: 40%, Final Project: 20%, 2-3 Homeworks 30%

Python tips:

By popular demand, here are a collection of helpful resources. Note please stick with python 2.5 (i'm using 2.5.4 on mac). Python 2.6 are in a bit of a transition and I'm unsure of the compatibility status of scipy/numpy (libraries we will want to use).

(Tenative) Class Schedule

Since this is my first time teaching this type of course, my goal is to refine this as we go, however, I'll always try to stay 2-3 weeks ahead so you can plan/prepare for readings.

Date Description Slides
Jan 21, 2009 Welcome, Course Policies, General Overview,
Pre-Course Survey - Please fill out and email to me
Jan 26, 2009 Week 1: Introduction to Cognitive Modeling

Lecture 1: What are the goals of modeling?

In the first week, we take a first step at thinking about the role of models in cognitive science. We'll read some classic papers from the start of the cognitive revolution that argued for why computational/mathematical models are necessary in behavioral sciences. We'll discuss some of the primary goals of modeling (i.e., accounting for past data, formalizing theories of cognitive function, and making novel predictions).

Busemeyer, J. (forthcoming book) Methods of Cognitive Modeling. Chapter 1

Marr. D. Vision (ch. 1)

Some Fun and some Beautiful Examples

Griffiths, T. L., Steyvers, M., & Firl, A. (2007). Google and the mind: Predicting fluency with PageRank. Psychological Science, 18, 1069-1076.

Anderson, J. R., & Schooler, L. J. (1991). Reflections of the environment in memory. Psychological Science, 2, 396-408.

Jan 28, 2009 Week 1 (cont): Introduction to Cognitive Modeling - Types of Models, and "Thinking in Levels"
Today we'll take an overview of types of models. In addition, I'll be walking through a bit of what is required for the first homework. Please read the following papers before class (all are interesting but you might focus on the ones that match you background/research interests)

Gluck, M.A. and Myers, C.E. (2001) Gateway to Memory: An Introduction to Neural Network Modeling of the Hippocampus and Learning. MIT Press (cht. 5 - Unsupervised Learning: Auto-associative Network and the Hippocampus)

Shelling, T. (1978) Micromotives and macrobehavior. Ch. 4 Sorting and Mixing, Race and Sex.

Wolfram, S. (2005) A new kind of science (ch. 2).

Homework 1: Hands on with a complex, interactive model
This homework we will play with a couple hands-on modeling simulators I developed for a undergraduate class. However, I think they will be useful in getting people to think about how the goal of cognitive modeling is to understand complex processes which are often hard to understand/predict without actually simulating them on a computer.

- homework 1 description *note: some parts of step III are just copied from my undergraduate course notes because i lost the original word file i used to make the PDF. Skip the steps about writing your own lesion code, and the stuff about turning in a screen shot of a input pattern. There is nothing to turn in for this really, just experiment on your own (maybe see how to run a python script).

- Zip file with python scripts for homework 1

Feb 2, 2009 Week 2: Evaluating Models
The next two classes, we will start to get our hands dirty and focus on some pragmatic issues of model evaluation and testing. Basically, how do we assess if a model is a "good" account for our data? There are both qualitative (i.e., does the model make reasonable psychological assumptions?) and quantitative answers to this kind of question (i.e., if model A fits better than model B we should prefer model A). However, model evaluation is a complex statistical question. The readings focus on currently accepted model evaluation technique in journals catering to psychology/social science.
Code for Exemplar/Prototype Model!
Python script for exemplar/prototype modesl
Readings

Pitt, M.A. and Myung, J (2002) When a good fit can be bad. Trends in Cognitive Science, 6, 10, 421-425.

Myung, I.J. (2003). Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology, 47, 90-100.

Roberts, S. & Pashler, H. (2000) How persuasive is a good fit? A comment on theory testing. Psychological Review, 107, 358-367.

Additional/Advanced Readings

Pitt, M.A., Myung, J., & Zhang, S. (2002). Toward a Method of Selecting Among Computational Models of Cognition. Psychological Review, 5, 472-491. 573-605.

Myung, J. and Pitt, M. (in press) Optimal Experimental Design for Model Discrimination.

Feb 4, 2009 Week 2: What should we compare our models to? The group or the individuals? How do these choices influence the inferences we make about cognitive processes?

Cohen, A. L., Sanborn, A. N., & Shiffrin, R. M. (2008). Model evaluation using grouped or individual data. Psychonomic Bulletin & Review, 15, 692-712.

An example (and recent debate) about inferences to group or individual data:

Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17, 767-773.

Mozer, M., Pashler, H., & Homaei, H. (in press). Optimal predictions in everyday cognition: The wisdom of individuals or crowds? Cognitive Science

A solution!!! (advanced reading, but breeze through for basic idea)

Rouder, J. and Lu, J. (2005) An introduction to Bayesian heirarchical models with an application in the theory of signal detection. Psychonomic Bulletin & Review. 12(4) 573-604.

Homework 2: Model fitting methods
Code for Exemplar/Prototype Model PLUS code for doing Nelder-Mead Simplex Parameter Searches
Python script for fitting exemplar model using RMSE.

Homework 2 description. Please let me know if anything isn't clear. Due 3/2/09.

Feb 9, 2009 Week 3: Connectionist/PDP Approaches - Lecture
This week begins the first segment focusing on particular types of models in more detail and the psychological literature which they address. In particular, we begin somewhat in the middle of the cognitive revolution with the advent of parallel distributed processing (PDP), also known as connectionist modeling, neural networks, etc.. In a couple lectures we'll review some of the basic properties of neural network, and discuss some early and important papers.
Topics:
  • Rescorla-wagner/delta rule
  • Multi-layer feedforward networks
  • Competitive specialization
  • Adaptive Resonance Theory (ART)
  • Interactive Activation (IAC)
  • Hebbian Learning (in more depth from hw. 1)
  • Recurrent Networks
Introductory readings:

Feldman, J. A., & Ballard, D. H. (1982). Connectionist models and their properties. Cognitive Science, 6, 205-254.

Rumelhart, D. E., & Zipser, D. (1985). Feature discovery by competitive learning. Cognitive Science, 9, 75-112.

Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179-211.

Feb 11, 2009 Week 3 (cont): Connectionist/PDP Approaches (cont) - Lecture
Feb 16, 2009 No Class, President's Day!
Feb 18, 2009 Week 4: Derivation of the Back prop algorithm (including how to implement it in python)

*note about mini homework*: it is true that you need a bias unit or step function in your neurons to solve the task.

Marcus, G.F (1998) Rethinking Eliminative Connectionism. Cognitive Psychology, 37, 243-282

Feb 23, 2009 Week 5: Models of Category Learning
Fresh off the debates last week about the relative merits of the PDP approach, we take a look at a particular domain in which a variety of different modeling approaches have been developed. In particular we are going to read three papers. Each account for the same data, but each make substantially different claims about the nature of cognitive representation

Medin, D. L. and Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207-238.

Nosofsky, R. M., and Palmeri, T. J., and Mckinley, S. C. (1994). Rule-plus-exception model of classification learning. Psychological Review, 104, 266-300.

Love, B.C., Medin, D.L, & Gureckis, T.M (2004). SUSTAIN: A Network Model of Category Learning. Psychological Review, 111, 309-332.

Feb 25, 2009 Week 5 (continued): Models of Category Learning
Mar 2, 2009 Week 6: Bayesian Approaches

Griffiths, T. L., & Yuille, A. (2008). A primer on probabilistic inference. In M. Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press. (manuscript pdf)

Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98, 409-429.

Hemmer, P. & Steyvers, M. (2008). A Bayesian Account of Reconstructive Memory. In V. Sloutsky, B. Love, and K. McRae (Eds.) Proceedings of the 30th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum

Mar 4, 2009 Week 6: Bayesian Approaches Continued (approximation methods, MCMC)

Andrieu, Freitas, Doucet, Jordan (2003). An Introduction to MCMC for Machine Learning. Machine Learning, 50, 5-43.

Kemp, C. and Tenenbaum, J.B. (2009). Structured statistical models of inductive reasoning. Psychological Review, 116, 1, 20-58.

Additional readings:

Sanborn, A. and Griffiths, T. (2007) Markov Chain Monte Carlo with People. NIPS 07.

Mar 9, 2009 Week 7: Rational vs. Mechanistic Approaches

Fu, W. (2008). Is a single-bladed knife enough to dissect human cognition? Cognitive Science, 32, 155-161.

Sakamoto, Y., Jones, M., & Love, B. C. (2008). Putting the Psychology Back into Psychological Models: Mechanistic vs. Rational Approaches. Memory & Cognition, 36, 1057-1065.

Mar 11, 2009 Week 7 (cont): Models of Memory

Howard, M. W. (2009). Memory: Computational models. L. R. Squire (Ed), Encyclopedia of Neuroscience, volume 5, pp. 771-777. Oxford: Academic Press.

Shiffrin, R.M. & Steyvers, M. (1997). A model for recognition memory: REM: Retrieving Effectively from Memory. Psychonomic Bulletin & Review, 4 (2), 145-166.

O'Reilly and Norman (2002) Hippocampal and neocortical contribution to memory: advnces in the complementary learning systems framework. Trends in Cognitive Science, 6(2), 505-510.

Norman, K. A., & O'Reilly, R. C. (2003). Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. Psychological Review, 110 (4), 611-46

Mar 16, 2009 No Class, Spring Break!
Mar 18, 2009 No Class, Spring Break!
Mar 23, 2009 Week 9: Science Day
Instead of class, please attend the CNS Science day on models and experiments: CNS ScienceDay
A note about presenting a paper. Some of these papers can be complex and have tons of simulations and data (As nick now knows!). More than we can have time to talk about, and more than anyone could process in 20 minutes or so. The best hope is that the paper gives someone some pointers into a literature they were interested in, and the basic background for further reading. To make things most effective, the best thing would be to present some basic background. What is the model developed for? What are the major assumptions going into it? Then talk about the data it is intended to account for. Why is this data the best place to test out a computational model? What hole exists in the literature that this model fills? Next, describe the basics of the operation of the model. Equations are useful when they contribute to understanding but not strictly necessary. We just want to have a feel for why that model predicts what it does. Then, choose a couple of the key behavior effects the model explains, and try to explain why this naturally comes out of the model. Finally, what is wrong with the model (definitely something). What assumptions are unreasonable? How might the model be applied in some other domain? What are the best things about the model? Hopefully these final questions can hope provoke some discussion. Slides are not necessary but could be helpful in some cases.
Mar 25, 2009 Week 9 (cont): Models and Cognitive Neuroscience (processes models as data analysis)

Busemeyer, J. R. & Stout, J. C. (2002) A Contribution of Cognitive Decision Models to Clinical Assessment: Decomposing Performance on the Bechara Gambling Task. Psychological Assessment, 14, 253-262 - Todd

Nathaniel D. Daw, John P. O'Doherty, Peter Dayan, Ben Seymour & Raymond J. Dolan (2006). Cortical substrates for exploratory decisions in humans. Nature, 441, 876-879. - Bob

Anderson, J. R., Albert, M. V., & Fincham, J.M. (2005) Tracing Problem Solving in Real Time: fMRI Analysis of the Subject-Paced Tower of Hanoi. Journal of Cognitive Neuroscience, 17 1261-1274. - Craig

Mar 30, 2009 Week 10: Models and Cognitive Neuroscience (cont.) - implications of models for cog. neuro findings

Nosofsky, R. M., & Zaki, S. R. (1998). Dissociations between categorization and recognition in amnesic and normal individuals: An exemplar-based interpretation. Psychological Science, 9(4), 247-255. - John

Ashby, F.G. and Alfonso-Reese, L.A. and Turken, A.U. and Waldron, E.M. (1998) A Neuropsychological Theory of Multiple System in Category Learning. Psychological Review, 105 (5), 442-481. - John

Busemeyer, J. R., Jessup, R. K., Johnson, J. G., & Townsend, J. T. (2006). Building bridges between neural models and complex decision making behavior. Neural Networks, 19, 1047-1058. - Doug

Apr 1, 2009 Week 10: Models of Object Detection and Perception

Ullman, S.(2006). Object recognition and segmentation by a fragment-based hierarchy. Trends in Cognitive Science, 11(2), 58-64. - Mordechai

Ullman, S. and Soloviev (1999). Computation of pattern invariance in brain-like structures. Neural Networks, 12, 1021-1036. - Mordechai

Hummel, J. E., & Biederman, I. (1992). Dynamic binding in a neural network for shape recognition. Psychological Review, 99, 480-517. - Todd

Apr 6, 2009 Week 11: Learning and Perception

Goldstone, R. L. (2003). Learning to perceive while perceiving to learn. in R. Kimchi, M. Behrmann, and C. Olson (Eds.) Perceptual Organization in Vision: Behavioral and Neural Perspectives. Mahwah, New Jersey. Lawrence Erlbaum Associates. (pp. 233-278) - Keyong Jin Tark

Apr 8, 2009 Week 11: Modeling Higher Level Cognition (semantic memory)

Steyvers, M and Tenenbaum, J. B. (2005). The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth, Cognitive Science, 29, 41-78. - Youssef

Griffiths, T.L., Steyvers, M., & Tenenbaum, J.B.T. (2007). Topics in Semantic Representation. Psychological Review, 114(2), 211-244. - Todd

Apr 13, 2009 Week 12: Decision Making and Learning
This week, we will look a a variety of approaches to modeling human decision making. In fact, decision making processes is probably one of the most heavily modeled behaviors.

Gigerenzer and Goldstein (1996) Reasoning the Fast and Frugal Way: Models of Bounded Rationality. Psychological Review, 103 (4), 650-669. - Jim

Gureckis, T.M. and Love, B.C. (in press) Short Term Gains, Long Term Pains: Reinforcement Learning in Dynamic Environments. Cognition - Jim

Apr 15, 2009 Week 12: Decision Making and Learning

Busemeyer, J. R., and Myung, I. J. (1992). An adaptive approach to human decision making: learning theory, decision theory, and human performance. Journal of Experimental Psychology: General, 121(2) , 177-194. - Todd

Ratcliff, R., & Rouder, J. N. (1998). Modeling response time for two-choice decisions. Psychological Sciences, 9, 347-356. - Todd

Apr 20, 2009 Week 13: Developmental Approaches

Spencer, Perone, and Johnson (in press). The Dynamic Field Theory and Embodied Cognitive Dynamics - Madeline

Gureckis, T.M. and Love, B.C. (2004). Common Mechanisms in Infant and Adult Category Learning. Infancy, vol 5, no.2, 173-198.

Elman, J.L. (2003). Learning and development in neural networks: The importance of starting small. Cognition, vol 48, no.1, 71-99.

Apr 22, 2009 Week 13: Complex Adaptive Systems
We conclude with a final segment on complex adaptive systems.

Goldsone and Janssen (2005) Computational models of collective behavior. Trends in Cognitive Science, 9(9), 424-430.

Todd and Heuvelink, in press Todd, P.M., Heuvelink, A., in press. Shaping social environments with simple recognition heuristics. In: Carruthers, P. (Ed.), The Innate Mind: Culture and Cognition.

Todd, P.M. (1997). Searching for the next best mate. In R. Conte, R. Hegselmann, and P. Terna (Eds.), Simulating social phenomena (pp. 419-436). Berlin: Springer-Verlag.

Apr 27, 2009 Week 14: Limitations of modeling

Luce, R.D. (1995). Four tensions concerning mathematical modeling in psychology. Annual Review of Psychology, 46, 1-26.

Uttal, W.R. (1990). On some two-way barriers between models and mechanisms. Perception & Psychophysics, 48, 188-203.

Apr 29, 2009 Week 14: Part 2 Grant panel for final project
May 4, 2009 Week 15: Final project presentations