On completing this module, students will have acquired the following knowledge: 1) Understanding of the theoretical and historical foundations of connectionism and artificial neural networks; 2) Understanding of the problem area in which neural networks have usefully been applied to the study of humans, with special focus on work in the 1980's and 1990's; 3) Understanding of the opportunities and limitations of connectionist simulation of human cognitive abilities, with a focus on human development, and, 4) Understanding of the relationship between models and data with specific focus on connectionist models, and 5) learn the basic concepts underlying dynamical systems theory, especially as these have been applied in modeling human behaviour, and will be able to do the following: 5) Design and apply simple neural networks using a module-specific modelling platform (BasicProp), 6) Analyze the performance of a network during and after training, and 7) Relate network performance to the specific details of an empirical problem, and 8) understand the ways in which concepts from dynamical systems theory have been employed in describing human behaviour.

History and origins of connectionist research; Fundamentals of network architecture and training; Relation of training and testing data to both model performance and to assumptions of researchers; Application of network modelling to human development and learning; Basic concepts of dynamical systems theory; Worked examples of the application of dynamical models to human behaviour.

Familiarity with elementary statistics, including distributions and the theories of linear correlation and regression will be assumed.

• Feedback individually to students, post-assessment

Written feedback on all assignments and essay will be provided in a timely manner.