Classification of Cognitive Strategies by the underlying processing stages using Hidden semi-Markov Models

Abstract

When doing a cognitive task, people can employ different cognitive strategies. A strategy consists of different cognitive processing stages, which is how different strategies can be differentiated. A novel machine learning method developed by Anderson et al., 2016 is able to model cognitive processing stages in EEG, MEG, and fMRI as a hidden semi-Markov model, calling it Hidden semi-Markov Model Multivariate Pattern Analysis (HsMM-MVPA). This method works across subjects, so among other things it seems to be able to deal with the inter-subject variability of EEG data. This leads to the hypothesis that HsMM-MVPA could potentially be used to predict what cognitive strategy someone used in new, unseen data. To test this hypothesis, EEG data collected from a group of subjects who performed a multiplication task with self-reported cognitive strategies was used. Subjects reported either knowing the answer to a multiplication problem from memory ("retrieval"), or had to compute the answer ("procedural"). We estimated hidden semi-Markov models on some of the subjects and tested how well these models could predict what strategy was used on the other subjects. The models are able to correctly identify retrieval-strategies, but tend to be less sensitive to the procedural-class. This seems to be because the retrieval-strategy is more consistent. HsMM-MVPA can be used for classification, but might fare better with more consistent cognitive strategies.