Information-theoretical complexity metrics

John T. Hale (Université de Cornell, invité LabEx)
Information-theoretical complexity metrics

This class takes up information-theoretical complexity metrics in their own right. We show how to use the freely-available Cornell Conditional Probability Calculator to work out what a person would expect at intermediate points in a sentence. We discuss the implications of this formalization as regards theoretical issues under discussion in Adrian Staub's class.

Recommended reading:

• Chapter 7 of Automaton Theories
• http://www.amazon.fr/Automaton-Theories-Sentence-Comprehension-Informati...
• Yun et al 2015 doi:10.1007/s10831-014-9126-6
• Chen et al 2014 http://mindmodeling.org/cogsci2014/papers/322/