A Hidden semi-Markov model classifier for strategy detection in multiplication problem solving

Abstract

Self-report as a tool to understand different cognitive processing strategies has been criticised for decades, but to date there have not been many alternatives. To remedy this hiatus, we propose to apply a recently developed method for processing stage analysis (Hidden semi-Markov Model Multivariate Pattern Analysis, HsMM-MVPA) to a cognitive strategy prediction task. HsMM-MVPA uses specific patterns in EEG data to determine the most likely number of sequential processing stages. Under the assumption that cognitive processing strategies differ in the number of stages, we constructed a classifier using fitted HsMM-MVPA to try and differentiate between two cognitive strategies in unseen data. The method is applied to data from a multiplication verification task, in which participants are asked to verify the truth of a solution to a multiplication problem (3 × 9). We asked participants to indicate via self-report whether they knew the answer by heart (Strategy 1, Retrieval) or needed to compute the answer (Strategy 2, Procedural). The classifier could predict the self report labels above chance, suggesting that the number of processing stages identified using EEG can be used to track the cognitive processing strategy that are in use throughout a task.