Understanding the mechanisms of human decision making
is of significant importance to the cognitive science. Today’s motivation on
artificial intelligence and machine learning has been focused on more humanoid
machines. Classical machine learning algorithms are based on classical logic
and probability. However, empirical evidence shows that human decision behavior
reveals some non-classical aspects such as context effects, order effects or
ambiguity aversion. Electroencephalography (EEG) is a well-known way to obtain
and interpret the brain signals for diverse goals. This study presents standard
EEG methods to obtain the data, then some methods will be briefly surveyed
analyzing non-classical effects by using EEG data.
Primary Language | English |
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Journal Section | Articles |
Authors | |
Publication Date | December 1, 2017 |
Published in Issue | Year 2017 Volume: 2 Issue: 2 |