Discovering Processing Stages by combining EEG with Hidden Markov Models

Abstract

A new method is demonstrated for identifying processing stages in a task. Since the 1860s cognitive scientists have used different methods to identify processing stages, usually based on reaction time (RT) differences between conditions. To overcome the limitations of RT-based methods we used Hidden Markov Models (HMMs) to analyze EEG data. The HMMs indicate for how many stages there is evidence in the data, and how the durations of these stages vary with experimental condition. This method was applied to an associative recognition task in which associative strength and target/foil type were manipulated. The HMM-EEG method identified six different processing stages for targets and re-paired foils, whereas four similar stages were identified for new foils. The duration of the third, fifth and sixth stage varied with associative strength for targets and re-paired foils. We present an interpretation of the identified stages, and conclude that the method can provide valuable insight in human information processing.


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