Course Syllabus
Learning area
Research methods in experimental psychological sciences
Learning objectives
Knowledge and understanding
- Methodological and epistemological foundations in cognitive modelling
- Development of computational models: techniques and approaches
- Methods for the validation and assessment of the models
Applying knowledge and understanding
- Development of simple models in different domains of human cognition
- Application of toolkits to large-scale data
- Validation of computational models through behavioral data
- Critical analysis and interpretation of the model and its predictions
Contents
The course aims to provide an introduction to the use of computational modeling in cognitive sciences. The theoretical and epistemological bases of the approach will be described, as well as the main methods of developing and validating a model, with examples from different domains of human cognition. The lectures will be accompanied by hands-on practice with the techniques and methodologies introduced.
Detailed program
Introduction to computational modelling and Artificial Intelligence
Epistemological foundations of cognitive modeling
Levels of description and representation
Methods for developing models in different domains of cognition
Tuning, setting, and interpreting parameters
Training and validation of learning models
Simulation of behavioral data
Model evaluation: quantitative performance and theoretical criteria
Example: implementing an exemplar-based model for categorization
Example: implementing a model for the phonological loop
Example: training models based on the Rescorla-Wagner equations
Example: training and testing neural networks
Prerequisites
Familiarity with R. General knowledge in the field of cognitive psychology
Teaching methods
Lectures.
Discussions about the role of computational methods in psychology.
Hands-on experience with specific toolkits, implementation of simple models, and setup of simulations in the R environment.
Attendance is required.
*** Lessons will be held in presence, unless further COVID-19 related restrictions are imposed ***
Assessment methods
Individual assignments will require students to replicate the scripts developed in class, and to produce short essays concerning their views on cognitive modelling and AI.
Moreover, group assignments will require the students to apply the practical knowledge acquired during the course. These will include modifying simple scripts, evaluating the impact of different parameters on model performances, testing model predictions against human-generated data, and comparing simulations from different models.
Textbooks and Reading Materials
Reference materials:
Lewandowsky, S., & Farrell, S. (2010). Computational modeling in cognition: Principles and practice. Sage Publications.
Sun, R. (Ed.). (2008). The Cambridge handbook of computational psychology. Cambridge University Press.