�鶹��ý

Informatics

CCNM17-104

seminar

Master

Cognitive Science MSc

Computational and Cognitive Neuroscience MSc

4

Semester 1

Autumn semester

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. ���Dz���

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

Course textbook:

Polk, T. A., & Seifert, C. M. (2002). Cognitive Modelling. Cambridge, Mass.: MIT Press.
()

Suggested Readings:

• Sun, R. (2008). Introduction to computational cognitive modeling. In: R. Sun (Ed.), The Cambridge Handbook of Computational Psychology (pp.3-19). New York: Cambridge University Press. ()
• Sun, R. (2001). Artificial intelligence: Connectionist and symbolic approaches. In: N. J. Smelser, & P. B. Baltes (Eds.), International Encyclopedia of the Social and Behavioral Sciences (pp.783-789). Oxford: Pergamon/Elsevier. ()
• Plautt, D. C. (1999). Connectionist modeling. In A. Kasdin (Ed.), Encyclopedia of Psychology. Washington DC: Americal Psychological Association. ()
• Stufflebeam, R. (2006). Connectionism: An introduction. Retrieved from
• Marsalli, M. (n.d.). McCulloch-Pitts neurons. Retrieved from
• Hinton, G. (2002). How Neural Networks Learn from Experience. In: T. A. Polk, & C. M. Seifert. Cognitive Modelling (pp. 181-197). Cambridge, Mass.: MIT Press.
• Concept Learning. (2014). In Wikipedia. Retrieved from
• Semantic network. (2014). In Wikipedia. Retrieved from
• Sowa, J. F. (1992). Semantic networks. In: S. C. Shapiro (Ed.). The Encyclopedia of Artificial Intelligence (pp. ) New York: Wiley. ()
• Rogers, T. T., & McClelland, J. L. (2003). Categories, hierarchies and theories. In: T. T. Rogers & J. L. McClelland. Semantic Cognition: A Parallel Distributed Processing Approach (pp. 1-26). Cambridge, MA: MIT Press.
  )
• Rogers, T. T., & McClelland, J. L. (2003 A PDP Theory of Semantic Cognition. In: T. T. Rogers & J. L. McClelland. Semantic Cognition: A Parallel Distributed Processing Approach (pp. 27-44). Cambridge, MA: MIT Press.
  )
• Prince, A., & Smolensky, P. (2002). Adaptive Resonance Theory. In: T. A. Polk, & C. M. Seifert. Cognitive Modelling (pp. 289-316). Cambridge, Mass.: MIT Press.
• Anderson, J. R. (2002). ACT: A Simple Theory of Complex Cognition. In: T. A. Polk, & C. M. Seifert. Cognitive Modelling (pp. 49-70). Cambridge, Mass.: MIT Press.
• Lehman, J. F., Laird, J., & Rosenbloom, P. (2006). A gentle introduction to Soar: An architecture for human cognition. Retrieved from
• Allison, R. (n.d.). A short tutorial on CLARION. Retrieved from