A completed (academic) BSc degree in Artificial Intelligence or related field (such as Cognitive Science, Computing Science, Cognitive Psychology, Cognitive Neuroscience, Philosophy, or Linguistics). Furthermore more the course assumes that students have some affinity with cognitive psychological research and some proficiency in algorithm design and analysis.
In this course, students learn to use methods derived from computational complexity theory for analyzing the (in)tractability of cognitive models, and for identifying sources of complexity in a model. Students also learn how this knowledge can be used to make model revisions that yield tractability. As two competing models may differ in the nature of their sources of complexity, the analyses can also yield novel empirical predictions that can be used to test the models.
|Contents (Inhoud / Omschrijving)
The functioning of the human brain can be studied and modeled at different levels of abstraction ranging from the neural implementation level to a cognitive computational level. Ideally, models postulated at the computational level are consistent with the brain resources available at the neural level. Building computational models that fit with human brain resources can be quite challenging. This is illustrated by the fact that many computational models in cognitive (neuro)science postulate brain computations that are---on closer inspection---computationally intractable. Here ‘computational intractability' means that the postulated computations require more resources (such as time, space, memory, hardware) than a human mind/brain or any computational mechanism realistically has available.
Examples of intractable computational models can be found in almost all cognitive domains, including perception, learning, language, planning, decision-making, communication, and reasoning. Intractability makes these models psychologically and neurally implausible as cognitive computational level models of brain functioning. However, there are ways to deal with this problem by identifying sources of complexity in these models and investigating if they can be removed from the model without loss of explanatory power. This course covers several concepts and techniques that can be used to this end.
- Selected readings (book chapters + original articles)
- Course manual
|Teaching methods (Werkvormen)
Lectures, practicals, assignments and individual project.
Dr. I. van Rooij, T:++31 24 3612645, E:
|Exam information (Toetsinformatie)
|Enrollment ( Inschrijving )
|Extra information (Bijzonderheden)
The course is a 6EC semester course. The first part of the course serves to introduce the conceptual framework of computational complexity analysis and its associated techniques (+/-7 lectures) and illustrate the application of these techniques to existing cognitive models (+/-4 lectures). During lectures and in take-home assignments students will have ample opportunity to practice the techniques. In the second part of the course (4-5 weeks) students perform an individual project. In the project, students can pursue an application of the learned techniques to research questions of interest for them. Topics can range from identifying sources of complexity in an existing computational model to setting up a design to test previously identified sources of complexity.
- Spring semester
- Credits: 6 EC, 112 working hours divided into: 16 contact hours, 96 hours preparation
- Level: master
- Language of instruction: The course will be given in English.
- Enrollment (exam and course): Through Student Portal