SCM: Seminar in Cognitive Modelling

Welcome to SCM 2026!  In this course, you will have the opportunity to explore a range of topics in computational cognitive science while honing your science communication skills.  This course follows a seminar format, with lots of student presentations, paper discussions, and interactive activities.

The course focuses on computational cognitive modelling.  Readings will touch on a variety of modelling approaches, such as Bayesian models, neural network approaches, reinforcement learning, agent based models, Markov decision processes, large language models, etc.  Readings will also traverse a very broad range of cognitive topics, such as language, reasoning, planning, theory of mind, culture, creativity, and more.

In addition to reading and discussing papers, you will also get a chance to work on your presentation and writing skills, as well as participating in many small-group discussions and activities.

Course timetabling

  • Tue/Thu 10:00am-12:00pm
  • Weeks 1-11, Semester 1
  • Same days/times/weeks
  • Locations will be posted on Learn.

Because a large portion of this course is designed around in-class discussions, presentations, and group activities, in-person attendance and participation is a key part of completing course objectives.

(It will also, hopefully, be reasonably fun!)

Learning objectives

  1. Demonstrate understanding of a range of classic and current articles in cognitive science/modelling by summarizing and critiquing their central ideas and/or results.
  2. Demonstrate understanding of the relationship between computational models and cognitive theories, by being able to critically assess the theoretical adequacy of a given model.
  3. Compare and contrast the strengths and weaknesses of different models of the same behaviour.
  4. Search the literature and synthesize information from several papers on the same topic and create a coherent oral presentation on that topic.
  5. Communicate (written and oral) key findings in cognitive science/modelling to inter-disciplinary audiences.
Please refer to the course page on Learn for up-to-date info on assigments, course materials, schedule, etc.
License
Creative Commons - Attribution Non-Commercial