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 Probability and Causality in Human Cognition  posted by  duggu   on 12/19/2007  Add Courseware to favorites Add To Favorites  
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Abstract/Syllabus:

Tenenbaum, Joshua, 9.916-A Probability and Causality in Human Cognition, Spring 2003. (Massachusetts Institute of Technology: MIT OpenCourseWare), http://ocw.mit.edu (Accessed 11 Jul, 2010). License: Creative Commons BY-NC-SA

Representation of a causal model.

People’s domain theories mediate between observed statistics and causal models generated to account for the statistics. Causal models can be used to judge the probability of a variable, given that other variables have been observed. (Image by Prof. Joshua Tenenbaum.)

Course Highlights

Probability theory captures a number of essential characteristics of human cognition, including aspects of perception, reasoning, belief revision, and learning. Expressions of degree of belief were used in language long before people began codifying the laws of probability theory. This course explores the history and debates over codifying the laws of probability, how probability theory applies to specific cognitive processes, how it relates to the human understanding of causality, and how new computational approaches to causal modeling provide a framework for understanding human probabilistic reasoning.

Course Description

An introduction to the use of probability theory to capture aspects of cognitive processes. Emphasizes history of probability theory and computational approaches to probabilistic and causal inference. This class is suitable for advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields.

Prerequisites: A course in cognitive science, and a course in probability or statistics.

Syllabus

 
Overview

Probability theory captures a number of essential characteristics of human cognition, including aspects of perception, reasoning, belief revision, and learning. Expressions of degree of belief were used in language long before people began codifying the laws of probability theory. This course explores the history and debates over codifying the laws of probability, how probability theory applies to specific cognitive processes, how it relates to the human understanding of causality, and how new computational approaches to causal modeling provide a framework for understanding human probabilistic reasoning.

Prerequisites

This class is suitable for advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields. A course in cognitive science, and a course in probability or statistics, are helpful.

Class Policies

Class meets for two hours per week. Students are expected to do weekly readings in preparation for discussions during class.

Over the course of the semester, students will be required to produce an annotated bibliography based on the course readings: for each reading, students must write a short (approximately one paragraph) summary of the important points of the reading, and how that reading fits into the themes of the class.

Students will also be required to design, execute, and report on an original research experiment exploring how human behavior can be modeled using probability theory and/or causal structures.

Calendar

 

Week 1: Introduction and Organizational Meeting

Week 2: History of Probability Terminology

Week 3: History of Probability Theory

Week 4: Bayesian Probability Theory

Week 5: Probabilistic Reasoning and Human Competencies

Week 6: Behavioral Economics and Optimal Behavior

Week 7: Probabilistic Analysis of Rational Behavior

Week 8: Relevance Theory

Week 9: Causality and Probability

Week 10: Bayesian Networks

Week 11: Extensions of Bayesian Networks to Classes and Instances

Week 12: Functional Causal Models

Week 13: Causal Model Construction, Theories, and Mechanisms

Week 14: Discussion of Research Report

 



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