Basic Principles of ERP Research, Surprise, and Probability Estimation

Antonio Kolossa ()
Additional contact information
Antonio Kolossa: Technische Universität Braunschweig, Institut für Nachrichtentechnik

Chapter Chapter 1 in Computational Modeling of Neural Activities for Statistical Inference, 2016, pp 1-13 from Springer

Abstract: Abstract This section introduces the fundamentals of this work. It starts with a description of the hardware and software which was used to conduct the experiments and further analyses. Next, a method for signal-to-noise ratio estimation of event-related potentials is described, followed by the important concept of circularity in data analyses. Then it is shown how evidence for the coding of probability distributions in the brain can be obtained, using a framework that relates random variables to neural activities. Last, an overview on probability weighting by humans is given, the role of which in probabilistic reasoning is investigated in this work.

Keywords: Probability Weighting; Probability Weighting Function; Sample Index; Selection Hold; Observable Random Variable (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-32285-8_1

Ordering information: This item can be ordered from
http://www.springer.com/9783319322858

DOI: 10.1007/978-3-319-32285-8_1

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().