Discovering the structure of mathematical problem solving

被引:27
作者
Anderson, John R. [1 ]
Lee, Hee Seung [2 ]
Fincham, Jon M. [1 ]
机构
[1] Carnegie Mellon Univ, Dept Psychol, Pittsburgh, PA 15213 USA
[2] Yonsei Univ, Dept Educ, Seoul 120749, South Korea
关键词
fMRI; Hidden Markov model; Mathematical problem solving; Multivariate pattern analysis; FORWARD-BACKWARD ALGORITHM; PREFRONTAL CORTEX; DEFAULT NETWORK; INTRAPARIETAL SULCUS; PARIETAL CORTEX; BRAIN; ACTIVATION; VISUALIZATION; DYSCALCULIA; SOFTWARE;
D O I
10.1016/j.neuroimage.2014.04.031
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The goal of this research is to discover the stages of mathematical problem solving, the factors that influence the duration of these stages, and how these stages are related to the learning of a new mathematical competence. Using a combination of multivariate pattern analysis (MVPA) and hidden Markov models (HMM), we found that participants went through 5 major phases in solving a class of problems: A Define Phase where they identified the problem to be solved, an Encode Phase where they encoded the needed information, a Compute Phase where they performed the necessary arithmetic calculations, a Transform Phase where they performed any mathematical transformations, and a Respond Phase where they entered an answer. The Define Phase is characterized by activity in visual attention and default network regions, the Encode Phase by activity in visual regions, the Compute Phase by activity in regions active in mathematical tasks, the Transform Phase by activity in mathematical and response regions, and the Respond phase by activity in motor regions. The duration of the Compute and Transform Phases were the only ones that varied with condition. Two features distinguished the mastery trials on which participants came to understand a new problem type. First the duration of late phases of the problem solution increased. Second, there was increased activation in the rostrolateral prefrontal cortex (RLPFC) and angular gyrus (AG), regions associated with metacognition. This indicates the importance of reflection to successful learning. (C) 2014 Elsevier Inc All rights reserved.
引用
收藏
页码:163 / 177
页数:15
相关论文
共 53 条
[1]   Tracking children's mental states while solving algebra equations [J].
Anderson, John R. ;
Betts, Shawn ;
Ferris, Jennifer L. ;
Fincham, Jon M. .
HUMAN BRAIN MAPPING, 2012, 33 (11) :2650-2665
[2]   Using brain imaging to track problem solving in a complex state space [J].
Anderson, John R. ;
Fincham, Jon M. ;
Schneider, Darryl W. ;
Yang, Jian .
NEUROIMAGE, 2012, 60 (01) :633-643
[3]   Cognitive and metacognitive activity in mathematical problem solving: prefrontal and parietal patterns [J].
Anderson, John R. ;
Betts, Shawn ;
Ferris, Jennifer L. ;
Fincham, Jon M. .
COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE, 2011, 11 (01) :52-67
[4]   Neural imaging to track mental states while using an intelligent tutoring system [J].
Anderson, John R. ;
Betts, Shawn ;
Ferris, Jennifer L. ;
Fincham, Jon M. .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2010, 107 (15) :7018-7023
[5]   Human symbol manipulation within an integrated cognitive architecture [J].
Anderson, JR .
COGNITIVE SCIENCE, 2005, 29 (03) :313-341
[6]  
[Anonymous], COGN SCI
[7]  
[Anonymous], 2004, INTRO COMBINATORICS
[8]   Age-related changes in the activation of the intraparietal sulcus during nonsymbolic magnitude processing: An event-related functional magnetic resonance imaging study [J].
Ansari, Daniel ;
Dhital, Bibek .
JOURNAL OF COGNITIVE NEUROSCIENCE, 2006, 18 (11) :1820-1828
[9]   Is 2+2=4? Meta-analyses of brain areas needed for numbers and calculations [J].
Arsalidou, Marie ;
Taylor, Margot J. .
NEUROIMAGE, 2011, 54 (03) :2382-2393
[10]   Rostral Prefrontal Cortex and the Focus of Attention in Prospective Memory [J].
Benoit, Roland G. ;
Gilbert, Sam J. ;
Frith, Chris D. ;
Burgess, Paul W. .
CEREBRAL CORTEX, 2012, 22 (08) :1876-1886