At the end of this course, you are able to:
- Implement components of cognitive models in computer simulations, up to a level that you can later apply and extend such models in your own projects (e.g., for your master thesis).
- evaluate the scientific literature on cognitive models, up to a level that you can motivate what type of model is useful for a specific practical or theoretical problem (e.g., for your master thesis)
Assessment
Exam (weight: 40% of the final mark), lab assignment (40%), and presentation (20%).
Further details on the weighing of these three components and minimal grades are specified in the course manual.
To qualify for a repair of the final result the mark needs to be at least a 4.
Prerequisites
This course is a primary in the masters AINM and HCIM.
Students from other master programs are required to have
- some interest and familiarity with (cognitive) psychology (e.g., have read parts of an introductory textbook, for example “Cognitive science: An introduction to the study of mind” by Friedenberg & Silverman), and
- some experience with programming (e.g., taken a BSc level introductory programming course or an online course).
Terminology such as “if/else”, “while”, “for”, “function”, and “variable” are assumed to be known.
Experience learns that students that barely meet these requirements can continue to develop their skills and pass the course.
However, they should be prepared to invest more time per week than they do for the typical course.
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Formal models of human behavior and cognition that are implemented as computer simulations - cognitive models - play a crucial role in science and industry. In science, cognitive models formalize psychological theories.
This formalization allows one to predict human behavior in novel settings and to tease apart the parameters that are essential for intelligent behavior.
Cognitive models are used to study many domains, including learning, decision making, language use, multitasking, and perception and action.
The models take many forms including dynamic equation models, neural networks, symbolic models, and Bayesian networks. In industry, cognitive models predict human behavior in intelligent 'user models'.
These user models are used for example for human-like game opponents and intelligent tutoring systems that adaptively change the difficulty of a game or training program to a model of the human's capacities. Similarly, user models are used in the design and evaluation of interfaces: what mistakes are humans likely to make in a task, what information might they overlook on an interface, and what are the best points to interrupt a user (e.g., with an e-mail alert) such that this interruption does not overload them?
To be able to develop, implement, and evaluate cognitive models and user models, you first need to know which techniques and methods are available and what are appropriate (scientific or practical) questions to test with a model.
Moreover, you need practical experience in implementing (components of) such models.
In this course you will get an overview of various modeling techniques that are used world-wide and also by researchers in Utrecht (especially in the department of psychology and the department of linguistics).
You will learn their characteristics, strengths and weaknesses, and their theoretical and practical importance. Moreover, you will practice with implementing (components of) such models during lab sessions.
Course form
Lectures, tutorials.
Study material
Scientific papers that are accessible through Utrecht University’s repository. The references are provided in the course manual.
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