BC4 Computational Cognitive Modeling – Thinking like a Computer Scientist

Description

Attention: if you plan to participate in this course you may want to bring a laptop (with or without swi-prolog: http://www.swi-prolog.org/) to the course

In this basic course, an introduction to computational modeling of cognitive processes is given. The focus is on higher cognition, mainly on reasoning and problem solving. And the introduced models and methods address modeling on the symbol/knowledge level.
Lecture 1: Short history of cognitive modeling, early cognitive models (Teachable Language Comprehender, Spreading Activation)
Lecture 2: Logic as foundation for knowledge representation, Prolog
Lecture 3: Cognitive Architectures and stand-alone models, models of human reasoning problem solving
Lecture 4: Human concept learning and machine learning

Objectives

- Understanding what the basic research methods and evaluation criteria of computer science are and understanding the perspective of Artificial Intelligence as a subfield of computer science as well as an integral part of cognitive science.
- Overview of artificial intelligence methods for cognitive modeling.
- Ability to write simple computational models in Prolog.

Literature

Stuart Russell and Peter Norvig (2009). Artificial Intelligence: A Modern Approach (3rd Edition). Pearson. http://aima.cs.berkeley.edu/

Diedrich Dörner und Ute Schmid (2015).Modellierung psychischer Prozesse .In: A. Schütz, M. Brand, H. Selg, S. Lautenbacher (Hrsg.) : Psychologie. Eine Einführung in ihre Grundlagen und Anwendungsfelder (5. Aufl.). Stuttgart: Kohlhammer, S. 329 – 343.

Ute Schmid (2006). Computermodelle des Denkens und Problemlösens .In: J. Funke (Hrsg.), Enzyklopädie der Psychologie, Themenbereich C: Theorie und Forschung, Serie 2: Kognition (Denken und Problemlösen Bd. 8). Göttingen: Hogrefe, S. 483-547.

Course location

Forum 1

Course requirements

None

Instructor information.

Instructor's name

Ute Schmid

Email

cf. website

Vita


Ute Schmid holds a diploma in psychology and a diploma in computer science, both from Technical University Berlin (TUB), Germany. She received her doctoral degree (Dr. rer.nat.) in computer science from TUB in 1994 and her habilitation in computer science in 2002. From 1994 to 2001 she was assistant professor (wissenschaftliche Assistentin) at the AI/Machine Learning group, Department of Computer Science, TUB. Afterwards she worked as lecturer (akademische Rätin) at the Department of Mathematics and Computer Science at University Osnabrück. Since 2004 she holds a professorship of Applied Computer Science/Cognitive Systems at the University of Bamberg. Her research interests are mainly in the domain of high-level learning on structural data, especially inductive programming, knowledge level learning from planning, learning structural prototypes, analogical problem solving and learning. Further research is on various applications of machine learning (e.g., classifier learning from medical data and for facial expressions) and empirical and experimental work on high-level cognitive processes and usability evaluation.

Website

https://www.uni-bamberg.de/kogsys/team/schmid/