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Tracking children's mental states while solving algebra equations

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Abstract

Behavioral and function magnetic resonance imagery (fMRI) data were combined to infer the mental states of students as they interacted with an intelligent tutoring system. Sixteen children interacted with a computer tutor for solving linear equations over a six-day period (Days 0–5), with Days 1 and 5 occurring in an fMRI scanner. Hidden Markov model algorithms combined a model of student behavior with multi-voxel imaging pattern data to predict the mental states of students. We separately assessed the algorithms' ability to predict which step in a problem-solving sequence was performed and whether the step was performed correctly. For Day 1, the data patterns of other students were used to predict the mental states of a target student. These predictions were improved on Day 5 by adding information about the target student's behavioral and imaging data from Day 1. Successful tracking of mental states depended on using the combination of a behavioral model and multi-voxel pattern analysis, illustrating the effectiveness of an integrated approach to tracking the cognition of individuals in real time as they perform complex tasks. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.

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