PSY 458 (44920) --- Experimental Psychology

T,TH 3:30 - 6:30 PM, SEA 2.116

Fall 2006 Syllabus

homepage: http://love.psy.utexas.edu/458/458.html
Click here for a week by week class schedule.

Who Office Office Hours email
Bradley Love SEA 5.202 Th 1:30-3:30 PM
brad_love@mail.utexas.edu
Marc Tomlinson SEA 5.108 M 1:00-3:00 PM
marctomlinson@mail.utexas.edu

Enrollment limitations/Prerequisites

A major in psychology, PSY 301 with a grade of at least C, PSY 418 or an equivalent statistics course with a grade of at least C, and a GPA of at least 3.0 in psychology courses taken at the University. Upper-division standing required.

General Course Description

Understanding complex systems such as economies, the weather, or human beings often requires developing computational models. In Psychology, these models allow researchers to simulate interesting behaviors, often providing insight and advancing our understanding of the mechanisms underlying our behavior. In this course, students will learn how to build models and perform simulations. We'll consider a broad range of topics ranging from memory formation to group interactions.

No prior knowledge of computer programming is assumed, but complete novices will have to have a willingness to work hard to develop these new skills. The beginning of the course will be devoted to getting students up to speed on the Python programming language which we will use to build and simulate models. Subsequently, class will focus on lab assignments in which students will develop and simulate models of their own construction. These projects will be written up as lab reports. The course will involve a final project of the student's choosing. There will be no exams and assigned readings will be light. This course emphasizes thinking about complex phenomena (particularly in Psychology) mechanistically.

Format of Classes

Classes will begin with a discussion of last week's assignment. Student involvement is encouraged. Next week's assignment will then be previewed at which point students are encouraged to ask questions. Then, students will begin work on the assignment in class with the professor and TA available to assist. In this last regard, class time is somewhat analogous to art studios, but in this case it is science or modeling studio.

Requirements

Attendance is important. Attendance and in class participation determine 20% of the grade, lab assignments determine 60% of the grade, and the final project fills out the final 20%.

Part of the participation requirement of class is to check your email somewhat frequently for course announcements. Email me if your preferred address is not your UT address or if you would like course emails directed to another account. Better yet, update your UT email address to your preferred address.

Textbook

The textbooks (available at the coop) for the class are geared toward learning to program in Python:
  • "How to Think Like a Computer Scientist: Learning with Python" by Allen B. Downey, Jeffrey Elkner and Chris Meyers
  • Learning Python, Second Edition by Mark Lutz, David Ascher

    • "How to Think Like a Computer Scientist" is also available online.

    Additional online resources are listed below.

    Useful Links

  • The main python website is at Python. From this site, you can download python for your personal computer.
  • A handy page for quick referencing for information about pythons types (strings, integers, etc...) math functions, files and the like is available here.
  • There is also an official python tutorial for students interested in reinforcing lessons learned in class from another perspective.

    Class Schedule

    Date Readings/Homework Due
    August 31 Welcome
    September 5 Read Chpt. 1 of HTTLACS, Life Assignment
    September 7 Read Chpt. 2 and 3 of HTTLACS, Chpt. 2 Assignment
    September 12 Read 4.1-4.8 and 5.1-5.3 of HTTLACS, Chpt. 3 Assignment
    September 14 Read 6 of HTTLACS, Conditional Assignment
    September 19 Read 7 of HTTLACS, Loop Assignment
    September 21 Read 8 of HTTLACS, Strings and Loops Assignment
    September 26 Come to class, work on assignment
    September 28 Lists and Stats Assignment
    October 3 Come to class, work on assignment
    October 5 Come to class, work on assignment
    October 10 Come to class, work on assignment
    October 12 Come to class, work on assignment
    October 17 Learning Lab Assignment (start of class)
    October 19 Come to class, work on assignment
    October 24 Come to class, work on assignment
    October 26 Prototype Lab Assignment (start of class)
    October 31 Come to class, work on assignment
    November 2 Come to class, work on assignment
    November 7 Come to class, work on assignment
    November 9 Hebbian Lab Assignment (start of class)
    November 14 Final Project Proposal (start of class)
    November 16 Final Project Work Period
    November 21 Final Project Work Period
    November 23 No class, Thanksgiving
    November 28 Final Project Work Period
    November 30 Final Project Work Period
    December 5 Project Presentations/Final Project
    December 7 Project Presentations