SUBJECT

Title

Informatics

Code

CCNM17-104

Type of instruction

seminar

Level

Master

Credits

4

Recommended in

Semester 1

Typically offered in

Autumn semester

Course description

Introduction to cognitive informatics

  1. What is computational cognitive modelling, types of cognitive modelling, what is computational cognitive modelling good for, multiple levels of cognitive modelling, successes and pitfalls of cognitive modelling
  2. Introduction to symbolic modelling
  3. Introduction to connectionist type modelling
  4. Connectionist vs Symbolic vs Hybrid Modelling

Connectionist Modelling

  1. What is an artificial neuron and how it transmits information – Activation functions, connection weights, output computation
  2. McCulloch-Pitts neuronal type
  3. Learning rules
  4. Network behaviour
  5. Worked examples

Learning and memory and knowledge representation, concepts, categories

  1. Psychological studies and computational models of concept formation, concept learning and knowledge representation

Symbolic Modelling (Systems and Architectures)

  1. ACT-R
  2. Soar

  3. CLARION

Learning outcome, competences knowledge:

  • understanding computational cognitive modelling
  • has an overall view of the field of informatics

attitude:

  • is capable of cooperation and solving tasks in teams;
    skills:
  • is able to see causal relationships, can think logically, and can prepare comprehensive reviews;

Learning activities, learning methods:

Lectures and interactive discussions

Readings

Course textbook:

Polk, T. A., & Seifert, C. M. (2002). Cognitive Modelling. Cambridge, Mass.: MIT Press.
(http://api.ning.com/files/pFUGNH4chIZY4rfEDP1DSg- pM7eUjJOa- wYjcjvSp0xyhMqBucXw37KXqOPz6xkymUfvtqMbaeF3dMEmJHkR5dSTzcjWP2PS/CognitiveModelingBradfordBooks.pdf)

Suggested Readings: