title: Tracking children's mental states while solving algebra equations.
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.
authors.affiliation: Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA. ja@cmu.edu
authors.forename: John R
authors.initials: JR
authors.lastname: Anderson
authors.forename: Shawn
authors.initials: S
authors.lastname: Betts
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Ferris
authors.forename: Jon M
authors.initials: JM
authors.lastname: Fincham
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001931:Brain Mapping; D002648:Child; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D011340:Problem Solving / Q000502:physiology*