- ARCHIVE for reference only

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Informatics 1 - Cognitive Science (INFR08020)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 8 (Year 1 Undergraduate) Credits20
Home subject areaInformatics Other subject areaNone
Course website Taught in Gaelic?No
Course descriptionThis course is designed as a first introduction into cognitive science. It will provide a broad overview of the subject suitable for all interested students, including students on the Cognitive Science degrees and external students.

The aim of the course is to present a unified view of the field, based on a computational approach to analyzing cognition. The material is presented grouped by cognitive function, rather than by subdiscipline.

The course covers vision and attention, memory, motor control and action, reasoning and problem solving, and language. All topics will be presented from a computational point of view, and this perspective will be reinforced by a lab sessions in which students use implementations of cognitive models. The course will also provide a basic grounding in cognitive science methodology, focusing computational modeling, experimental design, and statistics. Both symbolic and subsymbolic modeling approaches will be covered.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Additional Costs None
Information for Visiting Students
Displayed in Visiting Students Prospectus?No
Course Delivery Information
Delivery period: 2011/12 Semester 2, Available to all students (SV1) WebCT enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 14:00 - 14:50
CentralLecture1-11 14:00 - 14:50
CentralLecture1-11 15:00 - 15:50
First Class Week 1, Tuesday, 14:00 - 14:50, Zone: Central. AT LT2
No Exam Information
Summary of Intended Learning Outcomes
After completing this course successfully, students 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.
Assessment Information
Written Examination - 60%
Assessed Assignments - 40%

There will be three assessed assignments in this course, these will combine:
- practical exercises in which students are provided with implemented cognitive models they have to explore and modify them;
- essay questions in which students analyze empirical or conceptual problems in cognitive science.

The practical exercises will be supported by lab sessions in which students learn how to use implemented cognitive models. Where possible, the assignments will employ pre-existing modeling tools
(e.g., ACT-R, neural network simulators, probabilistic modeling tools).

The essay questions will be supported by tutorials in which students are able to clarify and discuss the materials covered in the lectures.
Special Arrangements
Additional Information
Academic description Not entered
Syllabus The syllabus has three parts: theories and controversies; cognitive functions; methodology. They are listed separately here, but will be presented in an interleaved fashion by in the lectures.

1. Basic Concepts
- representation: digital/analog, dual coding, propositional representations
- computation: tri-level hypothesis, classical/connectionist computation
- interdisciplinarity: disciplines and methodologies contributing to cognitive science
- controversies: modularity, Turing test, neural binding

2. Cognitive functions
- vision and attention: template matching, feature detection, Marr&ęs model, models of attention
- memory: types of memory, modal model, ACT-R model of memory
- action: motor control, planning, hierarchical/reactive paradigm in robotics
- reasoning and problem solving: inductive reasoning, reasoning paradoxes, models of problem solving, theory formation
- language: categorization, language acquisition, word recognition, semantic representations, NetTalk, Logogen model

3. Methodology
- neuroscience methods: brain imaging, neuron anatomy, brain anatomy
- experimental design, statistics: between/within subject design, levels of measurement, t-test, chi-square test, correlation
- constructing, criticizing, and evaluating models
- cognitive architectures: ACT-R
- neural networks: perceptrons, backproagation, network topologies
- probabilistic models: Bayes rule, Bayes nets

Note that this course is intended to give a high-level introduction to the topics listed; subsequent courses (e.g., Cognitive Modeling) will then provide a more detailed coverage.
Transferable skills Not entered
Reading list - Jay Daniels Friedenberg and Gordon Silverman. 2005. Cognitive Science: An Introduction to the Study of Mind. Thousand Oaks, CA: Sage.
- Neil A. Stillings, Steven E. Weisler, Christopher H. Chase, Mark H. Feinstein, Jay L. Garfield and Edwina L. Rissland. 1995. Cognitive Science: An Introduction. 2nd Edition, Cambridge MA: MIT Press.
Study Abroad Not entered
Study Pattern Lectures: 30
Tutorials: 10
Timetabled Laboratories: 10
Non-timetabled assessed assignments: 40
Private Study/Other: 110
KeywordsNot entered
Course organiserDr Ewan Klein
Tel: (0131 6)50 2705
Course secretaryMs Kirsten Belk
Tel: (0131 6)50 5194
Help & Information
Search DPTs and Courses
Degree Programmes
Browse DPTs
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Important Information
© Copyright 2011 The University of Edinburgh - 16 January 2012 6:15 am