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Semester 2

Informatics 1 - Cognitive Science (INFR08020)

Course Website

http://course.inf.ed.ac.uk/inf1-cg

Subject

Informatics

College

SCE

Credits

20

Normal Year Taken

1

Delivery Session Year

2023/2024

Pre-requisites

Course 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.

Assessment Information

Written Exam 0%, Coursework 100%, Practical Exam 0%

Additional Assessment Information

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.

view the timetable and further details for this course