Undergraduate Course: Computational Cognitive Science (INFR09035)

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 9 (Year 3 Undergraduate)	Credits	10
Home subject area	Informatics	Other subject area	None
Course website	http://course.inf.ed.ac.uk/ccs	Taught in Gaelic?	No
Course description	This course aims to introduce students to the basic concepts and methodology needed to implement and analyse computational models of cognition. It considers the fundamental issues of using a computational approach to explore and model cognition. 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, and provide practical experience with implementing models. The symbolic part will focus on cognitive architectures, while the subsymbolic part will introduce probabilistic models.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations		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. Students who have completed Year 2 of another degree will also be accepted provided they have some programming experience. Informatics 1: Cognitive Science is strongly recommended.
Additional Costs	None

Information for Visiting Students
Pre-requisites	None
Displayed in Visiting Students Prospectus?	Yes

Course Delivery Information

Delivery period: 2013/14 Semester 1, Available to all students (SV1)			Learn enabled: No	Quota: None
Web Timetable	Web Timetable
Course Start Date	16/09/2013
Breakdown of Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 20, Seminar/Tutorial Hours 8, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Additional Notes
Breakdown of Assessment Methods (Further Info)	Written Exam 70 %, Coursework 30 %, Practical Exam 0 %
Exam Information
Exam Diet	Paper Name	Hours & Minutes
Main Exam Diet S2 (April/May)		2:00
Resit Exam Diet (August)		2:00

Delivery period: 2013/14 Semester 1, Part-year visiting students only (VV1)			Learn enabled: No	Quota: None
Web Timetable	Web Timetable
Course Start Date	16/09/2013
Breakdown of Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 20, Seminar/Tutorial Hours 8, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Additional Notes
Breakdown of Assessment Methods (Further Info)	Written Exam 70 %, Coursework 30 %, Practical Exam 0 %
Exam Information
Exam Diet	Paper Name	Hours & Minutes
Main Exam Diet S1 (December)		2:00

Summary of Intended Learning Outcomes
- Demonstrate knowledge of the basic concepts and methodologies of cognitive modelling, by being able to design simple cognitive models for sample problems. - Demonstrate understanding of the relationship between computational models and psychological theories, by being able to critically assess the psychological adequacy of a given model. - Qualitatively and quantitatively evaluate computational models of cognition using a range of techniques, when given a model and a set of experimental data that it is supposed to account for. - Demonstrate an awareness of the most important computational 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). - Use existing modelling tools (e.g., Cogent or Matlab) 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 or other software for probabilistic modelling. Students will also be required to analyse the adequacy of their models with respect to psychological data, and critically evaluate models and ideas presented in course readings (e.g., Marr's three levels of analysis).

Special Arrangements
None

Additional Information
Academic description	Not entered
Syllabus	- An introduction/review of the idea of computational approaches to studying cognition; the mind as information-processing system; Marr's levels of analysis (computational, algorithmic, implementation). - The general motivations underlying the computational modelling of cognition, and different kinds of questions that can be answered (e.g., why do cognitive processes behave as they do, or what algorithms might be used to carry out this behaviour? What kinds of information are used, or how is this information processed/integrated over time?) - Mechanistic/algorithmic approaches and issues addressed by these approaches: parallel versus serial processing, flow of information, timing effects. - Rational/probabilistic approaches and issues addressed by these approaches: adaptation to the environment, behaviour under uncertainty, learning, timing effects. - General issues: top-down versus bottom-up processing, online processing, integration of multiple sources of information. - Methodology and issues in the development and evaluation of cognitive models: Which psychological data are relevant? What predictions are made by a model? How could these be tested? - Modelling techniques: in the assignments, students will experiment with both symbolic (rulebased) and subsymbolic (probabilistic) cognitive models. - Example models: in a number of areas we will look at the theories proposed and different ways of modelling them. Areas discussed will include several of the following: language processing, reasoning, memory, high-level vision, categorization. Specific models will be introduced and analysed with regard to relevant psychological data.
Transferable skills	Not entered
Reading list	Modelling High-Level Cognitive Processes, Richard Cooper, Lawrence Erlbaum
Study Abroad	Not entered
Study Pattern	Lectures: 20 Tutorials: 5 Timetabled Laboratories: 0 Non-timetabled assessed assignments: 30 Private Study/Other: 45
Keywords	Not entered

Contacts
Course organiser	Mr Vijayanand Nagarajan Tel: (0131 6)51 3440 Email: vijay.nagarajan@ed.ac.uk	Course secretary	Miss Claire Edminson Tel: (0131 6)51 7607 Email: C.Edminson@ed.ac.uk