Undergraduate Course: Informatics 1 - Cognitive Science (INFR08020)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 8 (Year 1 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course is designed as a first introduction to Cognitive Science. It will provide a selective but representative overview of the subject, suitable for all interested students, including students on the Cognitive Science degrees and external students.
The aim of the lecturing team is to present a unified view of the field, based on a computational approach to analysing cognition. The material is organized by cognitive function (e.g., language, vision), rather than by subdiscipline (e.g., psychology, neuroscience).
The course covers language, vision, memory, control and action, and reasoning and generalization. All topics will be presented from a computational point of view, and this perspective will be reinforced by lab sessions in which students implement simple cognitive models. |
| Course description |
The syllabus covers the following topics.
They are listed separately here, but in some cases they will be presented in an interleaved fashion:
1. Language
- cognitive instinct or cognitive technology?
- linguistic representations: productivity and reuse
- Connectionist and Bayesian models of language
- language acquisition: speech segmentation and word learning
- categorization and models of word meaning
2. Reasoning and generalization
- inductive reasoning
- fallacies and (ir)rationality
- models of abstraction and generalisation
- theory formation and the origins of knowledge
3. Fundamentals of cognitive neuroscience
- basic brain anatomy and function
- experimental techniques to record brain activity
- simple models of neurons
4. Vision
- the anatomy of vision, neural correlates of visual perception
- comparison of biological and artificial visual systems
5. Memory and Attention
- types of memory, memory impairments
- computational models of memory
6. Actions and behaviour
- reinforcement learning
Note that this course is intended to give a high-level introduction to the topics listed; subsequent courses (e.g., Computational Cognitive Science) will then provide a more detailed coverage.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | This course does not assume prior programming skills, but students are required to learn how to code. There will be no explicit instruction on programming in lectures. Students will receive an introduction to a contemporary programming language, Python, in the labs and use it to experiment with simple cognitive models in the assignments. While there are resources provided in labs, some students with no prior programming / maths experience have reported difficulty completing the assignments. |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2023/24, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 30,
Seminar/Tutorial Hours 10,
Supervised Practical/Workshop/Studio Hours 10,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
146 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Assessment will be based on two practical exercises in which students are provided with implemented cognitive models they have to explore and modify.
The practical exercises will be supported by lab sessions and an early formative assignment in which students learn the basics of a contemporary programming language. Students will be supported to build up programming skills gradually and will then use these skills in the assignments to implement simple cognitive models.
All assignments will be supported by tutorials in which students are able to clarify and discuss the materials covered in the lectures. |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- demonstrate knowledge of key areas of cognitive science, and be able to take an integrated, rather than disciplinary perspective on the field
- evaluate the most important conceptual problems in cognitive science and discuss the solutions that have been proposed
- analyse and modify simple computational models in a variety of modelling paradigms
- understand how cognition and cognitive science is societally situated and the ethical issues raised in researching cognition
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Reading List
| http://www.inf.ed.ac.uk/teaching/courses/inf1-cg/reading.html |
Contacts
| Course organiser | Dr Matthias Hennig
Tel: (131 6)50 3080
Email: m.hennig@ed.ac.uk |
Course secretary | Ms Kendal Reid
Tel: (0131 6)51 3249
Email: kr@inf.ed.ac.uk |
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