Using brain imaging to track problem solving in a complex state space
Using brain imaging to track problem solving in a complex state space
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DOI：
10.1016/j.neuroimage.2011.12.025
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发表时间：
2012-03-01
期刊：
影响因子：
5.7
通讯作者：
Yang, Jian
中科院分区：
文献类型：
--
作者：
Anderson, John R.;Fincham, Jon M.;Yang, Jian
本文描述了如何将行为和成像数据与隐马尔可夫模型（HMM）相结合，以跟踪参与者在复杂状态空间中的轨迹。参与者完成了一个记忆游戏的问题解决变体，涉及625个不同的状态，24个操作符，以及通过状态空间的天文数字路径。为进行分类，使用了三个信息来源。首先，使用不完全记忆模型来估计HMM的转移概率。其次，行为数据提供了有关不同事件发生时间的信息。第三，使用成像数据的多体素模式分析来识别操作者的特征。通过结合这三种信息源，HMM算法能够有效地识别参与者通过状态空间的最可能路径，准确率超过80%。这些结果支持的方法作为一种通用的方法来跟踪心理状态，发生在个人解决问题的情节。(C)2011 Elsevier Inc. All rights reserved.
This paper describes how behavioral and imaging data can be combined with a Hidden Markov Model (HMM) to track participants' trajectories through a complex state space. Participants completed a problem-solving variant of a memory game that involved 625 distinct states, 24 operators, and an astronomical number of paths through the state space. Three sources of information were used for classification purposes. First, an Imperfect Memory Model was used to estimate transition probabilities for the HMM. Second, behavioral data provided information about the timing of different events. Third, multivoxel pattern analysis of the imaging data was used to identify features of the operators. By combining the three sources of information, an HMM algorithm was able to efficiently identify the most probable path that participants took through the state space, achieving over 80% accuracy. These results support the approach as a general methodology for tracking mental states that occur during individual problem-solving episodes. (C) 2011 Elsevier Inc. All rights reserved.