G89.3404
Meeting time/place
Class meets weekly on Wednesday 2:003:50pm in Meyer 482.
Blurb
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 "higherlevel" 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 agentbased models of sociobehavioral 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. 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.
Instructor

Todd M. Gureckis
email: todd.gureckis@nyu.edu
website: here, lab
office: 859
office hours: after class Wednesday or by appt.

Textbook

Computational Modeling in Cognition: Principles and Practice by Lewandowsky and Farrell [website]
Readings from the book will be supplemented with additional research articles distributed from this website.

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%, 23 Homeworks 30%


Course Announcements
2.21.2012  Homework 2 posted! Also reading list updated with volunteered presented. A few still have '?' if you want to sign up or switch. Please contact me if you are auditing and aren't presenting (I beg of you!).

2.15.2012  For you Matlab lovers out there, Numpy for Matlab users.

2.15.2012  Don't worry if you didn't get everything in class today. We will continue this next week, and then move on. BUT please read Ch. 3 of the text before the next class.

2.15.2012  Fun fact about iPython: In iPython if you execute %who or %whos it will list the currently defined variables and functions (whos gives more information). This is an approximation to the "workspace" browser in Matlab. Also %reset will reset the current
namespace (i.e., delete existing variable definitions). This is a useful alternative to Kernel>Restart Kernel.

2.11.2012  An easier way to launch iPython Notebook on Mac OS X.

2.9.2012  Came across this interesting online course on modeling that runs somewhat similar to own. Included are video lectures, etc… From this link you can see Scott Page's description of the Schelling model we discussed in class on Wednesday. (I always find hearing things from two different sources helps me understand things).

2.8.2012  iPython installation tips (please contribute if you have any)

2.8.2012  If you are NOT getting emails from me, but think you should be on my mailing list, let me know (I sent one today after class).

1.31.2012  We're moving to a larger classroom starting tomorrow (2/1). This is Meyer 482. See you there!

1.25.2012  Programming help!
Some useful python links:

1.25.2012  See also this Cognitive Modeling Greatest Hits page by Garrison Cottrell.

1.25.2012  Poll for topics! (see the final results)

1.16.2012  This is where course announcements will
appear. Please check back often.

Schedule (with links to slides)
coming soon
Date 
Description 
Slides 
Jan. 25 
1. Introduction/Overview  Welcome, Course Policies, General Overview
Please see the poll above. Also remember to email me if you are taking or auditing the course so we can stay in touch.


Feb. 1 
2. Introduction to Cognitive Modeling  To kick things off, we will think 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).
Textbook reading: Ch. 1  Introduction

[slides]
[chaos game]

Feb. 8 
3. Introduction to Cognitive Modeling  Types of Models, and "Thinking in Levels"  Today we'll continue the previous discussion, taking an overview of types of models.Please read the following papers BEFORE class.
Marr, D. (1982) “Vision” (Chapter 1) [PDF]
Shelling, T. (1978) Micromotives and macrobehavior. Ch. 4 Sorting and Mixing, Race and Sex. [PDF]
Wolfram, S. (2005) A new kind of science (ch. 2). [PDF]
iPython installation tips (please contribute if you have any)

[hw1: python as art] 
Feb. 15 
4. From psychological theory to a model  How does one "build" a model? This is the most frequently asked question
I get from students. It often seems like a mystery how one goes from an idea of some psychological process to a set of equations
or code. This lecture (and the next one) will try to resolve some of this mystery via example.
Textbook reading: Ch. 2  "From Words to Models: Building a Toolkit"

[In Class (ipynb)]

Feb. 22 
5. Evaluating models  Now that we have a model, how do we assess if it 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 next three classes focus on currently accepted model evaluation technique in journals catering to psychology/social science.
Textbook reading: Ch. 3  Basic parameter estimation techniques
Optional: Shiffrin, R. and Nobel, P.A. (1997). The art of model development and testing. Behavior Research Methods, Instruments, & Computers, 29(1), 614. [PDF]

[In Class Phonoloop example (ipynb)]

Feb. 29 
6. Evaluating models (part II)  The theory and practice of maximum likelihood estimation
Textbook reading: Ch. 4  Maximum Likelihood Estimation
Myung, I.J. (2003). Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology, 47, 90100. [PDF]

[MLE example (ipynb)]

Mar. 7 
7. Evaluating Models (part III)  Model Comparison: which is better?
Textbook reading: Ch. 5  Parameter uncertainty and model comparisons


Mar. 14 
No Class, Spring Break


Mar. 21 
8. When a good fit can be bad/What should we fit to?  All this talk about fitting our model to data. However, what does a fit tell us in psychological terms? Today we will discuss in more detail the challenges in interpreting model fits. Also, what should we be fitting to? The group of the individuals?
Textbook reading: Ch. 6  Not everything that fits is gold: Interpreting the modeling
Pitt, M.A. and Myung, J (2002) When a good fit can be bad. Trends in Cognitive Science, 6, 10, 421425. [PDF]
Roberts, S. & Pashler, H. (2000) How persuasive is a good fit? A comment on theory testing. Psychological Review, 107, 358367.[PDF]
Cohen, A. L., Sanborn, A. N., & Shiffrin, R. M. (2008). Model evaluation using grouped or individual data. Psychonomic Bulletin & Review, 15, 692712. [PDF]

[Homework 2 (ipynb)]

Mar. 28 
9. Probabilistic Models/Bayesian Approaches
This week begins the first segment focusing on particular types of models in more detail and the psychological
literature which they address. We begin by considering probabilistic models which have recently been making
waves in many different literatures.
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. [PDF]  PATRICIA
Hemmer, P. & Steyvers, M. (2009). A Bayesian Account of Reconstructive Memory. Topics in Cognitive Science. 1, 189202. [PDF]  ELISE
Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17, 767773. [PDF]  JAY
See also:
Mozer, M., Pashler, H., & Homaei, H. (2008). Optimal predictions in everyday cognition: The wisdom of individuals or crowds? Cognitive Science, 32, 11331147. [PDF]


Apr. 4 
10. Neural Networks/Parallel Distributed Processing
This week we step back somewhat to the middle of the cognitive revolution
with the advent of parallel distributed processing (PDP), also known as connectionist modeling, neural networks,
etc. We'll review some of the basic properties of neural network, and discuss some
early and important papers.
Topics include:
 Rescorlawagner/delta rule
 Multilayer feedforward networks
 Competitive specialization
 Adaptive Resonance Theory (ART)
 Interactive Activation (IAC)
 Hebbian Learning
 Recurrent Networks
Feldman, J. A., & Ballard, D. H. (1982). Connectionist models and their properties. Cognitive Science, 6, 205254. [PDF]  XIANBIN
Rumelhart, D. E., & Zipser, D. (1985). Feature discovery by competitive learning. Cognitive Science, 9, 75112. [PDF] NICK
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179211. [PDF]  TAL
Criticisms:
Marcus, G.F (1998) Rethinking Eliminative Connectionism. Cognitive Psychology, 37, 243282. [PDF]


Apr. 11 
11. Rational versus Mechanistic Approaches
Having considered both rational/probabilistic/bayesian models and more mechanistic approaches (PDP, etc…), we'll now
discuss the relative theoretical merit of either approach. The three example articles were selected because they
show different approaches to a single psychological issue (category learning).
Jones, M. & Love, B.C. (2011). Bayesian Fundamentalism or Enlightenment? On the Explanatory Status and Theoretical Contributions of Bayesian Models of Cognition. Behavioral and Brain Sciences (target article). [PDF]  TODD
Three examples:
Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98, 409429. [PDF]  RONY
Love, B.C., Medin, D.L, & Gureckis, T.M (2004). SUSTAIN: A Network Model of Category Learning. Psychological Review, 111, 309332. [PDF]  ?
Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010). Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review, 117 (4), 11441167. [PDF]  STEPHANIE


Apr. 18 
12. Decision Making and Learning
By popular demand, this week will focus on really classic/influential models of decision making and learning.
Kording, K. and Wolpert, D.. (2006) Bayesian decision theory in sensorimotor control. Trends in Cognitive Science, 10(7), 319326. [PDF]  KARI
Ratcliff, R. and Rouder, J.N. (1998) Modeling response times for twochoice decisions. Psychological Science, 9(5), 347356. [PDF]  ZUZANNA
Niv, Y. (2009)  Reinforcement learning in the brain. The Journal of Mathematical Psychology, 53(3), 139154. [PDF]  STEPHANIE


Apr. 25 
13. Modeling and Cognitive Neuroscience
The role of models in connecting mind and brain in cognitive neuroscience.
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, 253262. [PDF]  SARAH
Nathaniel D. Daw, John P. O'Doherty, Peter Dayan, Ben Seymour & Raymond J. Dolan (2006). Cortical substrates for exploratory decisions in humans. Nature, 441, 876879. [PDF]  PAT
Anderson, J. R., Albert, M. V., & Fincham, J.M. (2005) Tracing Problem Solving in Real Time: fMRI Analysis of the SubjectPaced Tower of Hanoi. Journal of Cognitive Neuroscience, 17 12611274. [PDF]  ANDY
Mitchell et al. (2008). Predicting human brain activity associated with the meanings of nouns. Science, 320, 11911195. [PDF]  DHAYA


May 2 
14. Modeling in a broader context
Textbook reading: Ch. 8  Modeling in a broader context
Luce, R.D. (1995). Four tensions concerning mathematical modeling in psychology. Annual Review of Psychology, 46, 126. [PDF]
Uttal, W.R. (1990). On some twoway barriers between models and mechanisms. Perception & Psychophysics, 48, 188203. [PDF]


