Undergraduate Course: Informatics 1 - Cognitive Science (INFR08020)
|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
|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 and attention, memory, motor 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. The course will also provide a basic grounding in the methods of Cognitive Science, focusing on
computational modelling and experimental design.
The syllabus covers the following topics.
They are listed separately here, but in some cases they will be presented in an interleaved fashion:
- the language faculty
- models of linguistic data, words and rules theory
- Connectionist models of language
- language acquisition: speech segmentation, word learning, learning
- categorization and models of word meaning
- understanding sentences
- the anatomy of vision, neural correlates of visual perception
- Marr's model
- fine vs. coarse coding
- face recognition
3. Memory and Attention
- types of memory, memory impairments, models of memory
- attention, neglect
4. Cognition and neuroscience
- an introduction to cognitive neuroscience
- some philosophical perspectives on the brain
5. Reasoning and generalization
- inductive reasoning
- fallacies and (ir)rationality
- models of abstraction and generalisation
- theory formation and the origins of knowledge
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.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| This course does not assume prior programming skills, but students are
required to learn how to code. They will receive an introduction to a
contemporary programming language in the labs and use it to experiment
with simple cognitive models in the assignments.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2020/21, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|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.
|No Exam Information
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.
- Analyze and modify simple computational models in a variety of modeling paradigms.
- Demonstrate understanding of experimental design and statistics and apply it to simple problems in cognitive science.
- Understand how cognition and cognitive science is societally situated and the ethical issues raised in researching cognition
|Graduate Attributes and Skills
|Additional Class Delivery Information
||Total Hours: 200 ( Lecture Hours 30, Seminar/Tutorial Hours 10,
Supervised Practical/Workshop/Studio Hours 10, Summative Assessment
Hours 2, Programme Level Learning and Teaching Hours 4, Directed
Learning and Independent Learning Hours 144 )
|Course organiser||Dr Matthias Hennig
Tel: (131 6)50 3080
|Course secretary||Miss Laura Ambrose
Tel: (0131 6)50 5194