启发式问题解决认知神经机制及fMRI数据分析方法研究
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摘要
人工智能经过几十年的发展,取得了一定的成绩,也面临很大的困惑。问题解决是人类思维的最一般形式,是人类高级智能的具体表现,理解与探索人脑问题解决神经机制和信息加工过程可为人工智能提供新的思路。出于“认识脑、保护脑、开发脑、创造脑”的目的,问题解决的认知神经机制以及信息加工过程的研究已经越来越多地受到了不同领域研究人员的关注。
本研究是国家自然科学基金项目“问题求解中启发式搜索的认知神经机制研究”(ID:60875075)中的主要组成部分,属于探索性研究。问题解决信息加工理论认为:问题解决就是通过不断操作算子,使问题从初始状态变化到目标状态的过程,使用启发式算子可以提高问题解决效率。依据该理论,针对问题解决过程中启发式算子搜索与运用的认知神经机制和信息加工过程进行了系统的研究,并提出了基于分类与聚类的fMRI数据分析方法。主要工作和创新点如下:
(1)针对算子搜索与运用认知神经机制,提出了新的问题解决实验范式,并设计了两个认知实验:①启发式规则运用与提取;②启发式规则搜索。第一个认知实验重点研究人脑如何提取并运用特定算子,第二个重点研究人脑合理选择和搜索合适算子进行状态空间搜索的神经机制。
(2)采用多角度fMRI数据分析方法,研究分析了算子搜索与运用的神经机制和信息加工过程。通过脑功能定位分析,发现了算子搜索与运用的激活脑区。在此基础上,通过功能连接分析探索激活脑区之间的连接模式,并提出了算子搜索与运用认知过程的假设。进一步通过ACT-R建立的认知模型验证了该假设,该模型与实际数据的拟合程度达80%以上,说明了该假设与模型比较客观地反映了实际信息加工过程,为理解启发式问题解决信息加工过程提供了有力支持。
(3)分析了现有的fMRI分类算法,提出了SVVC数据分类方法,该方法可以分析特定脑区与认知过程的相关性,能较好地预测高级思维状态。通过提取单体素BOLD时间序列,训练单体素分类器,并使用AdaBoost算法集成单体素分类结果,分类正确率高达90%。研究对比了不同特征选择方法以及不同分类算法的分类情况,结果表明使用AdaBoost算法的方法性能最好,而选择与问题解决相关的脑区作为分类特征的分类性能较好。
(4)提出了基于BOLD模式聚类的fMRI数据分析方法,并运用该方法进行大脑协同工作模式的分析。通过提取全脑所有体素的BOLD效应,并对这些BOLD模式进行聚类分析,发现了典型的BOLD模式及其在大脑中的分布情况。在此基础上,进一步从时间和空间上分析了大脑协同工作情况。该方法是一种全面揭示大脑协同工作模式的有效fMRI数据分析手段。
总之,围绕着问题解决过程中启发式算子的搜索与运用,本研究借助fMRI技术、认知心理学、数据挖掘、认知建模等方法探索了其神经机制和信息加工过程,研究结果有望为人工智能研究提供一些参考。研究过程中提出的基于分类与聚类等fMRI数据分析方法是现有方法的有益补充。
Through several decades of development, artificial intelligence has made quite achievements, but also faces considerable bottlenecks. The ablities of problem-solving are the most general form of the human mind, and a concrete manifestation of the human high-level intelligence. The understanding and exploration of human brain problem-solving and information processing process can provide new ideas for artificial intelligence. With the purpose of“understanding brain, protecting brain, developing brain, creating brain”, the study of cognitive neural mechanisms of problem-solving and information processing process has been studied in more and more different fields.
This study is the main component of the National Natural Science Foundation project“The study of cognitive neuroscience about heuristic search in problem solving (ID: 60875075)”, also is an exploratory research. Problem-solving information processing theory contends that problem solving is a process of operating different operators continuously to change the problem from the initial state to target state; the use of heuristic operator can improve the efficiency of problem-solving. Based on this theory, this study focuses on the cognitive neural mechanisms of the search and application of heuristic operator and information processing process during problem-solving process, and proposes the fMRI data analysis methods based on classification and clustering. Main tasks in this dissertation are as follows:
(1) Based on the cognitive neural mechanisms of search and application of operator in problem solving, we proposed a new paradigm which is suitable to fMRI experiment, then design two cognitive experiments using this paradigm:①the application of heuristic operators and retriving;②the seaching and selection of heuristic operators. The first cognitive experiment focuses on how human brain retrive and use a specific operator. The second experiment focuses on studying the neural mechanisms of human brain rational selection and searching for appropriate operators in problem state-space.
(2) Multi-view analysis methods were used to explore the mechanism. The activated brain regions related to search and application of operator were explored through functional location analysis. On this basis, we used functional connectivity analysis to explore the connection patterns between the activated brain regions, and proposed the hypothesis of operator searching and the using cognitive processes. Then, these hypothesis were verified through a cognitive model established based on ACT-R. The degree of fitting with actual data and the ACT-R model is over 80%, and the good fitness implies that the model objectively reflects the possible information processing process, and the ACT-R model provides strong support for understanding neural mechanisms and information processing process of problem-solving.
(3) Based on investigation of current fMRI classification method, a kind of data classification method, SVVC, has been proposed in this dissertation, researchers can use this mothod to explore the relationship between brain regions and certain cognitive process. We trained single voxel classifier and use AdaBoost algorithm to integrate classification results of single voxel classifier, and the accuracy of classification is high than 90%. This method can be used as a new ways of decoding high-level mind state based on fMRI. The study also compares classification accuracy of different feature selection methods and different classification algorithms. Experiments results show that the performance of using AdaBoost is the best, and the classification performance of selecting brain regions related to problem-solving is better than other regions.
(4) This dissertation also proposed a fMRI data analysis method based on BOLD pattern clustering. We extract BOLD effects of all voxels of the whole brain, and use clustering algorithm to analyze these BOLD patterns, and find some typical BOLD patterns and their distribution within the brain. Experiments results show that there are some intrinsic BOLD patterns of the brain during the problems solving. On this basis, we analyz the cooperation working model of different brain regions through spatio-temporal analysis, the method could be an effective means of fMRI data analysis which can reveal the cooperation working pattern of the whole brain.
In short, focusing on the cognitive neuroscience mechanism of heuristic operator searching and application while problem solving, this dissertation uses fMRI techniques and methodology of cognitive psychology, multi-methods, such as data mining and cognitive modeling, are all used to explore the neural mechanisms and information processing process. These cognitive neuroscience mechanism and information processing process of heuristic problem solving can provide some references and enlighten artificial intelligence hopely. The two new data-driven fMRI data analysis methods proposed in this dissertation can improve the level of fMRI data analysis.
本研究是国家自然科学基金项目“问题求解中启发式搜索的认知神经机制研究”(ID:60875075)中的主要组成部分,属于探索性研究。问题解决信息加工理论认为:问题解决就是通过不断操作算子,使问题从初始状态变化到目标状态的过程,使用启发式算子可以提高问题解决效率。依据该理论,针对问题解决过程中启发式算子搜索与运用的认知神经机制和信息加工过程进行了系统的研究,并提出了基于分类与聚类的fMRI数据分析方法。主要工作和创新点如下:
(1)针对算子搜索与运用认知神经机制,提出了新的问题解决实验范式,并设计了两个认知实验:①启发式规则运用与提取;②启发式规则搜索。第一个认知实验重点研究人脑如何提取并运用特定算子,第二个重点研究人脑合理选择和搜索合适算子进行状态空间搜索的神经机制。
(2)采用多角度fMRI数据分析方法,研究分析了算子搜索与运用的神经机制和信息加工过程。通过脑功能定位分析,发现了算子搜索与运用的激活脑区。在此基础上,通过功能连接分析探索激活脑区之间的连接模式,并提出了算子搜索与运用认知过程的假设。进一步通过ACT-R建立的认知模型验证了该假设,该模型与实际数据的拟合程度达80%以上,说明了该假设与模型比较客观地反映了实际信息加工过程,为理解启发式问题解决信息加工过程提供了有力支持。
(3)分析了现有的fMRI分类算法,提出了SVVC数据分类方法,该方法可以分析特定脑区与认知过程的相关性,能较好地预测高级思维状态。通过提取单体素BOLD时间序列,训练单体素分类器,并使用AdaBoost算法集成单体素分类结果,分类正确率高达90%。研究对比了不同特征选择方法以及不同分类算法的分类情况,结果表明使用AdaBoost算法的方法性能最好,而选择与问题解决相关的脑区作为分类特征的分类性能较好。
(4)提出了基于BOLD模式聚类的fMRI数据分析方法,并运用该方法进行大脑协同工作模式的分析。通过提取全脑所有体素的BOLD效应,并对这些BOLD模式进行聚类分析,发现了典型的BOLD模式及其在大脑中的分布情况。在此基础上,进一步从时间和空间上分析了大脑协同工作情况。该方法是一种全面揭示大脑协同工作模式的有效fMRI数据分析手段。
总之,围绕着问题解决过程中启发式算子的搜索与运用,本研究借助fMRI技术、认知心理学、数据挖掘、认知建模等方法探索了其神经机制和信息加工过程,研究结果有望为人工智能研究提供一些参考。研究过程中提出的基于分类与聚类等fMRI数据分析方法是现有方法的有益补充。
Through several decades of development, artificial intelligence has made quite achievements, but also faces considerable bottlenecks. The ablities of problem-solving are the most general form of the human mind, and a concrete manifestation of the human high-level intelligence. The understanding and exploration of human brain problem-solving and information processing process can provide new ideas for artificial intelligence. With the purpose of“understanding brain, protecting brain, developing brain, creating brain”, the study of cognitive neural mechanisms of problem-solving and information processing process has been studied in more and more different fields.
This study is the main component of the National Natural Science Foundation project“The study of cognitive neuroscience about heuristic search in problem solving (ID: 60875075)”, also is an exploratory research. Problem-solving information processing theory contends that problem solving is a process of operating different operators continuously to change the problem from the initial state to target state; the use of heuristic operator can improve the efficiency of problem-solving. Based on this theory, this study focuses on the cognitive neural mechanisms of the search and application of heuristic operator and information processing process during problem-solving process, and proposes the fMRI data analysis methods based on classification and clustering. Main tasks in this dissertation are as follows:
(1) Based on the cognitive neural mechanisms of search and application of operator in problem solving, we proposed a new paradigm which is suitable to fMRI experiment, then design two cognitive experiments using this paradigm:①the application of heuristic operators and retriving;②the seaching and selection of heuristic operators. The first cognitive experiment focuses on how human brain retrive and use a specific operator. The second experiment focuses on studying the neural mechanisms of human brain rational selection and searching for appropriate operators in problem state-space.
(2) Multi-view analysis methods were used to explore the mechanism. The activated brain regions related to search and application of operator were explored through functional location analysis. On this basis, we used functional connectivity analysis to explore the connection patterns between the activated brain regions, and proposed the hypothesis of operator searching and the using cognitive processes. Then, these hypothesis were verified through a cognitive model established based on ACT-R. The degree of fitting with actual data and the ACT-R model is over 80%, and the good fitness implies that the model objectively reflects the possible information processing process, and the ACT-R model provides strong support for understanding neural mechanisms and information processing process of problem-solving.
(3) Based on investigation of current fMRI classification method, a kind of data classification method, SVVC, has been proposed in this dissertation, researchers can use this mothod to explore the relationship between brain regions and certain cognitive process. We trained single voxel classifier and use AdaBoost algorithm to integrate classification results of single voxel classifier, and the accuracy of classification is high than 90%. This method can be used as a new ways of decoding high-level mind state based on fMRI. The study also compares classification accuracy of different feature selection methods and different classification algorithms. Experiments results show that the performance of using AdaBoost is the best, and the classification performance of selecting brain regions related to problem-solving is better than other regions.
(4) This dissertation also proposed a fMRI data analysis method based on BOLD pattern clustering. We extract BOLD effects of all voxels of the whole brain, and use clustering algorithm to analyze these BOLD patterns, and find some typical BOLD patterns and their distribution within the brain. Experiments results show that there are some intrinsic BOLD patterns of the brain during the problems solving. On this basis, we analyz the cooperation working model of different brain regions through spatio-temporal analysis, the method could be an effective means of fMRI data analysis which can reveal the cooperation working pattern of the whole brain.
In short, focusing on the cognitive neuroscience mechanism of heuristic operator searching and application while problem solving, this dissertation uses fMRI techniques and methodology of cognitive psychology, multi-methods, such as data mining and cognitive modeling, are all used to explore the neural mechanisms and information processing process. These cognitive neuroscience mechanism and information processing process of heuristic problem solving can provide some references and enlighten artificial intelligence hopely. The two new data-driven fMRI data analysis methods proposed in this dissertation can improve the level of fMRI data analysis.
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