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Semester 1

Computational Cognitive Science (INFR10054)

Course Website

http://course.inf.ed.ac.uk/ccs/

Subject

Informatics

College

SCE

Credits

10

Normal Year Taken

3

Delivery Session Year

2022/2023

Pre-requisites

Visiting students are required to have comparable background to that assumed by the course prerequisites listed in the Degree Regulations & Programmes of Study. If in doubt, consult the course organiser (lecturer).This course is open to full year Visiting Students only, as the course is delivered in Semester 1 and examined at the end of Semester 2.

Course Summary

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.

Course Description

- 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.

Assessment Information

Written Exam 60%, Coursework 40%, Practical Exam 0%

Additional Assessment Information

There will be 1-2 practical assignments which will require students todevelop or modify cognitive models. Students will also be required toanalyse 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).Visiting Students:This course is open to full year Visiting Students only, as the courseis delivered in Semester 1 and examined at the end of Semester 2.

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