CCS: Computational Cognitive Science

Welcome

Welcome to Computational Cognitive Science.

This course introduces basic concepts and methods needed to implement and analyse computational models of cognition. It considers the fundamental advantages and challenges in taking a computational approach to explore and model cognition.

We will explore how computational models relate to, are tested against, and illuminate psychological theories and data. Our focus will be on probabilistic modelling methods, and provide practical experience with implementing models.

Following the textbook, the tutorials and assignment will use the statistical language R.

If you want to audit the course, you don't need the permission of the lecturer, just submit the audit form. This will give you access to all the course materials, including lecture recordings.

Key details:

• The Course Schedule contains the week-by-week plan of the course and links to lecture slides, tutorial materials, and readings.
• Lecture recordings can be found on Learn.
• Please check the Course Timetable for times and locations of lectures and tutorials.
• As textbook, we will use Farrell and Lewandowsky's (2018) Computational Modeling of
  Cognition and Behavior.
• Starting in week 2, I will hold office hours on Wednesdays 11:00-12:00. You can meet me using this Teams Link or in person in my office, 3.27A in the Informatics Forum.
• Tutorials start in week 3 and run until week 10, with the exception of week 7 (no tutorials)
• We will use EdStem for discussion.
• Details about the assignment (including deadlines and how to submit) are also on Learn. 

Communication

When you sign up for the course, you will have access to:

• this website: all essential course information can be found here;
• the Learn page of the course, used for the assignment and for lecture recordings;
• any course-related announcements will be posted on Learn.

We will use an EdStem discussion forum for the course.

• Link in the navigation menu to the right of the page.
• you can use it to post questions about the course content, including tutorials and the assignment;
• the main purpose is peer support: students discuss course material and help each other;
• lecturer and TA moderate the discussion and contribute.

Syllabus

Course components

Lectures

All lectures will be in-person unless there is an announcement to the contrary, e.g., for remote guest lectures.

Tutorials

Tutorials are one-hour small-group sessions led by a tutor:

• they reinforce and complement material from the lectures;
• they help you practice and apply this material, allow you to discuss and ask questions;
• a question sheet is issued for each week; please prepare for the tutorial by working through this sheet;
• tutorials start in week 3 and run until week10, with the exception of week 7 (no tutorials);
• you will be automatically assigned a tutorial group; if you have a timetable clash,
  request a group change using the MyEd Timetabling Channel. 

Required Background

We expect students to have some knowledge of probability and statistics, and enough programming experience that they know or will be comfortable learning R. See the course descriptor for details.