Undergraduate Course: Cognitive Modelling (Level 10) (INFR10004)
Course Outline
| School |
School of Informatics |
College |
College of Science and Engineering |
| Course type |
Standard |
Availability |
Available to all students |
| Credit level (Normal year taken) |
SCQF Level 10 (Year 4 Undergraduate) |
Credits |
10 |
| Home subject area |
Informatics |
Other subject area |
None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/cm
|
Taught in Gaelic? |
No |
| Course description |
This course aims to introduce students to the basic concepts and methodology underlying cognitive modelling. The fundamental issues of using a computational approach to the exploration and modelling of cognition are considered. In particular, we explore the way that computational models relate to, are tested against, and illuminate psychological theories and data.
The course will introduce both symbolic and subsymbolic modelling methodologies. The symbolic part will focus on cognitive architectures, while the subsymbolic part will introduce probabilistic models. |
Information for Visiting Students
| Pre-requisites |
None |
| Displayed in Visiting Students Prospectus? |
No |
Course Delivery Information
| Not being delivered |
Summary of Intended Learning Outcomes
1 - Demonstrate knowledge of the basic concepts and methodology of cognitive modelling, by being able to design simple cognitive models for sample problems.
2 - Demonstrate understanding of the relationship between computational models and psychological theories, by being able to critically assess the psychological adequacy of a given model.
3 - Qualitatively and quantitatively evaluate cognitive models using a range of techniques, when given a model and a set of experimental data that it is supposed to account for.
4 - Demonstrate an awareness of the most important approaches to cognitive modelling, by being able to use these approaches to formalise theories that are couched in potentially vague and ambiguous terms (e.g., natural language).
5 - Use existing modelling tools (e.g., Cogent) to design and test computer implementations of cognitive models (both existing models from the literature and simple models they have designed themselves). |
Assessment Information
Written Examination 70
Assessed Assignments 30
Oral Presentations 0
Assessment
The assignments will require students to develop or modify cognitive models using the Cogent cognitive modelling package and software for probabilistic modelling.
If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
* An introduction to the area and the kind of problems addressed in cognitive modelling.
* The general motivations underlying the computational modelling of cognition.
* The relationship between computational models and psychological theories.
* Methodology and issues in the development and evaluation of cognitive models.
* Modelling techniques: in the assignments, students will experiment with both symbolic (rule-based) and subsymbolic (probabilistic) cognitive models.
* Example models: in a number of areas, including language processing and reasoning, we will look at the theories proposed and different ways of modelling them.
Relevant QAA Computing Curriculum Sections: Artificial Intelligence |
| Transferable skills |
Not entered |
| Reading list |
* Cooper, Richard P. 2002. Modelling High-Level Cognitive Processes. Lawrence Erlbaum Associates, Mahwah, NJ.
* Slides from lectures will be available online. Other references will also be given as required reading.
|
| Study Abroad |
Not entered |
| Study Pattern |
Lectures 20
Tutorials 0
Timetabled Laboratories 0
Non-timetabled assessed assignments 30
Private Study/Other 50
Total 100 |
| Keywords |
Not entered |
Contacts
| Course organiser |
Dr Amos Storkey
Tel: (0131 6)51 1208
Email: A.Storkey@ed.ac.uk |
Course secretary |
Miss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk |
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