Computational cognitive science

Lectures: 2 sessions / week, 1.5 hours / session

This course is an introduction to computational theories of human cognition. Drawing on formal models from classic and contemporary artificial intelligence, we will explore fundamental issues in human knowledge representation, inductive learning and reasoning. What are the forms that our knowledge of the world takes? What are the inductive principles that allow us to acquire new knowledge from the interaction of prior knowledge with observed data? What kinds of data must be available to human learners, and what kinds of innate knowledge (if any) must they have? Class sessions will comprise a mixture of lectures and discussion. Readings will include seminal and state-of-the-art research papers from the cognitive, AI, and machine learning literatures, as well as textbook chapters and tutorials on technical approaches. Assignments will consist of several problem sets and a final modeling project or paper.

We will cover a range of formal modeling approaches and their applications to understanding core areas of cognition. Cognitive science topics will include:

Formal modeling topics will include:

The syllabus will balance presentations of state-of-the-art material with a broad historical perspective. Depth of presentation will vary across topics, from brief overviews in some areas to more technical and detailed coverage in others.

The pre-requisite is a class in probability or statistics (e.g., 9.07, Statistical Methods in Brain and Cognitive Science, 18.05, Introduction to Probability and Statistics, 6.041, Probabilistic Systems Analysis and Applied Probability). A class in artificial intelligence or machine learning would be helpful but is not necessary, as the relevant material will be reviewed in this class. Experience in programming (particularly in a high-level language such as MATLAB®) is desirable.

There is no single required text for this class. Russell, Stuart J., and Peter Norvig. Artificial Intelligence: A Modern Approach. 2nd ed. Upper Saddle River, N.J.: Prentice Hall/Pearson Education, 2003. ISBN: 0137903952, is strongly recommended as background reading on relevant formal models. Readings will consist of papers from the cognitive literature and background material from AIMA and several texts and tutorials in machine learning.

Short (≈ 1 paragraph) responses to the readings or assigned questions are due by 10 am on the day of class. You should post these directly to the MIT server discussion board.

You must submit 20 notes for full credit, with no more than two posts counting in any one week. These can include short responses to other peoples' posts, as long as the responses are thoughtful and in some way address the assigned readings and questions.

The grade distribution is approximately 40% problem sets, 40% project, and 20% discussion/participation.

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