Introduction to Cognitive Science 3800-KOG-WK

The students learn the interdisciplinary project of cognitive science and its basic paradigms. Basic research problems along with proposed solutions are introduced. The topics covered include classical cognitivism, computational modelling, connectionism, computer metaphor, artificial intelligence, emergentism, dynamical systems, Bayesian accounts of cognition, logical approaches to the study of reasoning, embodied cognition, the notion of representation and its criticisms, as well as models of consciousness and the notion of information.

The lecture will be based primarily on case studies. We focus on cases that inspired many later, similar models.

We will start with the classical computational / symbolic theory of cognition, and then look at connectionist models. Then we will deal with the recent ideas: dynamic systems, Bayesian models, sensorimotor accounts and embodied cognition, and behavioural robotics. We will also look at the role of mental representation in explanations of cognition in various approaches to simulation and modelling. Some even deny that the mind represents at all. What does this mean?

The lecture is an introduction to the methodology of cognitive science. The introduction highlights the explanatory pluralism of the contemporary (and earlier) research.

Estimated number of hours a student should spend on achieving learning outcomes: 30h (lecture) + 45h self-study

Lecture topics
1. The nature of explanation in cognitive science. What is explanation? Competence and performance, functional and mechanistic explanation
2. Simulation, computation and modelling: Chinese room
3. Symbolic computation. Newell and Simon’s GPS as a model of cognition
4. Computational neuroscience. Marr’s theory of theory and three levels of explanation
5. Computational neuroscience. A connectionist model of learning of past tense for English verbs: Rumelhart and McClelland
6. Dynamic systems in explaining the developmental processes in children (Thelen and Smith)
7. Probabilistic (Bayesian) models of human rationality (Oaksford and Chater)
8. Logic and thinking: Wason's task in the light of nonmonotonic logic
9. Behavioral or cognitive robotics? Phonotaxia in robotic crickets (B. Webb)
10. Explanatory role of representation. The classic approach
11. Explanatory role of representation. Imagery debate.
12. Explanatory role of representation. Connectionism
13. Explanatory role of representation. Behavioural robotics.
14. The concept of information and representation
15. Modelling in cognitive science. Consciousness. Explanatory pluralism