Postgraduate Course: Topics in Cognitive Modelling (Level 11) (INFR11086)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Year 4 Undergraduate)
||Availability||Available to all students
|Summary||The aim of this course is to expose students to a variety of cognitive models and to discuss competing models for similar problems, i.e. to explore a small number of cognitive domains in depth rather than to aim for broad coverage, as in Computational Cognitive Science. The course will focus especially on how to evaluate and compare models against each other and experimental data. Students will be expected to present and critique classic and recent research articles from the cognitive modelling literature, chosen from a list provided by the instructor.
The syllabus consists of two parts. In the first part, the instructor will provide introductory and background material, as well as information on how to develop skills in reading scientific papers and presenting them. In the second part, students will present papers on a variety of cognitive models from the literature, chosen from a list provided by the instructor (or approved by the instructor).
Topics covered by the instructor will include
- Introduction and overview of modelling approaches/philosophies.
- Model comparison and evaluation methods.
- Necessary background in probability and information theory.
- How to read, analyse, and present research papers in cognitive modelling (including general oral presentation skills).
- Example presentation(s) of papers.
Topics available for students to present will vary depending on the instructor, but will likely emphasize language learning and processing. Other topics may include visual processing, cognitive development, categorization, memory, decision-making, reasoning, and motor control. For specific topics, see the course web page or contact the instructor directly.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
This course requires familiarity with cognitive and linguistic issues, as evidenced by having taken, e.g., [Computational Cognitive Science or Computational Cognitive Neuroscience or Cognitive Psychology] and [Advanced Natural Language Processing or Foundations of Natural Language Processing].
Some mathematics background is required:
- comfort with mathematical notation (e.g. summations, vector notation)
- basic probability theory is strongly recommended (e.g. familiarity with Bayes' Rule and probability distributions)
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Not being delivered|
On completion of this course, the student will be able to:
- Demonstrate understanding of classic and current articles on cognitive modelling by summarizing their central ideas and/or results.
- Demonstrate understanding of the relationship between computational models and psychological theories, by being able to critically assess the psychological adequacy of a given model.
- Compare and contrast the strengths and weaknesses of different models of the same behaviour.
- Synthesize information from several papers on the same topic and create a coherent presentation on that topic.
|There is no main textbook for the course. Readings will be chosen from classic and current research papers on cognitive modelling.|
|Course organiser||Dr John Lee
Tel: (0131 6)50 4420
|Course secretary||Mrs Victoria Swann
Tel: (0131 6)51 7607