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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Computational Cognitive Science (INFR10054)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis 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.

This course uses the R programming language for examples and coursework.
No previous experience with R is assumed, but students must have some
previous programming experience.

Students must also be familiar with basic probability theory, and are
recommended to have some background in cognitive science, psychology, or

In particular, the following concepts from probability are assumed:

- Counting techniques: product rule, permutations, combinations
- Axioms of probability, sample space, events, De Morgan's Law
- Joint and conditional probability, independence, chain rule, law of
total probability, Bayes' Theorem
- Random variables, expectation, variance, covariance
- Common discrete and continuous distributions (e.g., Bernoulli,
binomial, Poisson, uniform, exponential, normal)

The recommended background in cognitive science can be achieved by
taking Informatics 1: Cognitive Science or courses in psychology or
Information for Visiting Students
Pre-requisitesVisiting 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.
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, 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 60 %, Coursework 40 %, Practical Exam 0 %
Additional Information (Assessment) There will be 1-2 practical assignments which will require students to
develop or modify cognitive models. 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).

Visiting Students:
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.
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
Learning Outcomes
On completion of this course, the student will be able to:
  1. Demonstrate knowledge of the basic concepts and methodologies 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 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.
  4. 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).
  5. Implement and test cognitive models, including existing models from the literature and simple models they have designed themselves.
Reading List
Computational Modeling of Cognition and Behavior
Simon Farrell and Stephen Lewandowsky, Cambridge University Press 2018
Additional Information
Course URL
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserDr Christopher Lucas
Tel: (0131 6)51 3260
Course secretaryMrs Michelle Bain
Tel: (0131 6)51 7607
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