- An introduction/review of the idea of computational approaches to studying cognition; the mind as information-processing system; Marr's levels of analysis (computational, algorithmic, implementation).
- The general motivations underlying the computational modelling of cognition, and different kinds of questions that can be answered (e.g., why do cognitive processes behave as they do, or what algorithms might be used to carry out this behaviour? What kinds of information are used, or how is this information processed/integrated over time?)
- Mechanistic/algorithmic approaches and issues addressed by these approaches: parallel versus serial processing, flow of information, timing effects.
- Rational/probabilistic approaches and issues addressed by these approaches: adaptation to the environment, behaviour under uncertainty, learning, timing effects.
- General issues: top-down versus bottom-up processing, online processing, integration of multiple sources of information.
- Methodology and issues in the development and evaluation of cognitive models: Which psychological data are relevant? What predictions are made by a model? How could these be tested?
- Modelling techniques: in the assignments, students will experiment with both symbolic (rulebased) and subsymbolic (probabilistic) cognitive models.
- Example models: in a number of areas we will look at the theories proposed and different ways of modelling them. Areas discussed will include several of the following: language processing, reasoning, memory, high-level vision, categorization. Specific models will be introduced and analysed with regard to relevant psychological data.
