Undergraduate Course: Computational Cognitive Science (INFR10054)

Course Outline
School	School of Informatics	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 10 (Year 3 Undergraduate)	Availability	Available to all students
SCQF Credits	10	ECTS Credits	5
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.

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 (lecturer). 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.

Information for Visiting Students
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).
High Demand Course?	Yes

Course Delivery Information

Academic year 2017/18, Available to all students (SV1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 15, Seminar/Tutorial Hours 5, Feedback/Feedforward Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 76 )
Assessment (Further Info)	Written Exam 75 %, Coursework 25 %, Practical Exam 0 %
Additional Information (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). You should expect to spend approximately 30 hours on the coursework for this course.
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S2 (April/May)			2:00
Resit Exam Diet (August)			2:00

Academic year 2017/18, Part-year visiting students only (VV1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 15, Seminar/Tutorial Hours 5, Feedback/Feedforward Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 76 )
Assessment (Further Info)	Written Exam 75 %, Coursework 25 %, Practical Exam 0 %
Additional Information (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). You should expect to spend approximately 30 hours on the coursework for this course.
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S1 (December)			2:00

Learning Outcomes
On completion of this course, the student will be able to: 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).