Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15208, USA.
Neuroimage. 2012 Mar;60(1):633-43. doi: 10.1016/j.neuroimage.2011.12.025. Epub 2011 Dec 22.
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.
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。参与者完成了记忆游戏的一种解决问题的变体，其中涉及 625 个不同的状态、24 个运算符和通过状态空间的天文数字数量的路径。为分类目的使用了三种信息来源。首先，使用不完美记忆模型来估计 HMM 的转移概率。其次，行为数据提供了有关不同事件时间的信息。第三，对成像数据的多体素模式分析用于识别运算符的特征。通过结合三种信息来源，HMM 算法能够有效地识别参与者在状态空间中所走的最可能路径，准确率超过 80%。这些结果支持了作为跟踪个体解决问题过程中发生的心理状态的一般方法。
