PSYE503 Models and Simulations in Contemporary Cognitive Psychology


• Give knowledge about the contemporary approaches in modeling in cognitive psychology

• Acquaint the students with the potential of modeling and simulation in planning experiments in cognitive psychology

• Acquaint the students with the newest models in cognitive psychology and their usage in the understanding of empirical results

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Psychology (in English)


Prof. Maurice Grinberg, PhD

Course Description:



At the end of the course, students are expected to:

1) know:

• the basics of modelling in psychology

• modeling approaches and simulation envirnoments

• examples of application of models to empirical results

2) be able:

• to understand modeling and description of empirical data

• to analyze simple experiments based on various modelling approaches

• to run simple simulation and understand their results

• to design an experiment based on model expectations


Full-time Programmes

Types of Courses:

Language of teaching:


  1. Models and modeling in cognitive psychology. The methodology of cognitive modeling.
  2. Basic approaches to modeling in cognitive psychology. Symbolic, connectionist, dynamic, neuronal, and Bayesian approaches.
  3. Symbolic approach. Logic and knowledge representation. Propositional and predicate logic.
  4. Production systems. Semantic networks. Frames, schemas, scripts.
  5. The architecture ACT-R. Examples: Simple models.
  6. Connectionist approach. Basic assumptions and terms.
  7. Connectionist architectures. Learning paradigms.
  8. Test I
  9. Unsupervised neuron networks.
  10. Recurrent networks. Attractors and Hopefield networks.
  11. Example: Simulation of neural networks.
  12. Dynamical systems approach. Introduction to Dynamic Field Theory (DFT).
  13. Dynamical field theory: models and experiments.
  14. Introduction to the Bayesian approach to cognition.
  15. Test II


Anderson, J. R. (2007) How Can the Human Mind Occur in the Physical Universe? New York: Oxford University Press

Eliasmith, C. & Anderson, C.H. (2002). Neural Engineering:Computation, Representation, and Dynamics in Neurobilogical Systems, TLFeBook (и материали от сайта

The Emergent simulation environment.

John F. Sowa: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks/Cole: New York, 2000

Langley, P., Laird, J. E., & Rogers, S. (2006). Cognitive architectures: Research issues and challenges

McLeod, P., Plunkett, K., and Rolls, E.T. (1998), "Introduction to Connectionist Modelling of Cognitive Processes", Oxford Press

Michael S. C. Thomas, M.S.C., McClelland, J.L., Richardson, F.M., Shapiro, A.C., & Baughman, F. (2009). Dynamical and Connectionist Approaches to Development: Toward a Future of Mutually Beneficial Co-evolution. In Spencer, J. (Ed.) Toward a Unified Theory of Development: Connectionism and Dynamic System Theory Re-Considered. Oxford Press.

Newell, A. (1990). Unified Theories of Cognition. Harvard University Press. Cambridge, Massachusetts

O'Reilly, R. C., Munakata, Y., Frank, M. J., Hazy, T. E., and Contributors (2012). Computational Cognitive Neuroscience. Wiki Book, 1st Edition. URL: http:/ / ccnbook. colorado. edu

Plunkett, K., and Elman, J.L. (1998), "Exercises in Rethinking Innateness: A Handbook for Connectionist Simulations", MIT (в наличност в библиотеката на НБУ)

Polk, A.T. & Seifert, C.M. (Eds.) (2002). Cognitive modelling. MIT Press.

Port, R. F. and van Gelder, T. (1995), "It's About Time: An Overview of the Dynamical Approach to Cognition", ch. 1 in "Mind as Motion: Exploration in the Dynamics of Cognition", eds. Port, R. F. and van Gelder, T. (1995)

Rogers, T. T. and McClelland, J. L. (2004). Semantic Cognition: A Parallel Distributed Processing Approach. Cambridge