Cognitive Modelling (Level 10) (U01943)

? Credit Points : 10 ? SCQF Level : 10 ? Acronym : INF-4-CM

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.

Entry Requirements

? Pre-requisites : At least 40 points from Schedule O at level 9/10. Introduction to Cognitive Science (ICS) or an introduction to cognition or cognitive psychology is strongly recommended, though not currently required. The ICS course may be taken in parallel. [MSc students may take ICS with permission of the Director of Teaching.]

Subject Areas

Home subject area

Artificial Intelligence, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : 4th year

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
09/01/2006	15:00	16:00	Room G.11, William Robertson Building	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Monday	15:00	15:50	Central
Lecture	Thursday	15:00	15:50	Central

Summary of Intended Learning Outcomes

After completing this course successfully, students will be able to:

-Demonstrate knowledge of the basic concepts and methodology 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 cognitive models 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 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) to design and test computer implementations of cognitive models (both existing models from the literature and simple models they have designed themselves).