Using fMRI to Test Models of Complex Cognition
Using fMRI to Test Models of Complex Cognition
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DOI：
10.1080/03640210802451588
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发表时间：
2008-01-01
影响因子：
2.5
通讯作者：
Rosenberg-Lee, Miriam
中科院分区：
文献类型：
--
作者：
Anderson, John R.;Carter, Cameron S.;Rosenberg-Lee, Miriam
本文探讨了功能磁共振成像的潜力，以测试复杂的认知任务模型中的不同组件的假设。如果一个模型的组成部分可以与特定的大脑区域相关联，人们就可以预测这些区域中BOLD反应的时间进程。一个事件锁定的过程中描述了处理时间的变化，使模型运行和个人数据试验对齐。描述了用于测试模型的统计方法，其处理BOLD信号的测量误差中的扫描到扫描相关性。这种方法是使用牺牲ACT-R模型，涉及映射6个模块到6个大脑区域的实验中，从Ravizza，安德森，卡特（在新闻中）有关方程求解。该模型的视觉编码预测梭状回的BOLD反应，其控制检索预测的BOLD反应在外侧下前额叶皮层，其子目标设置预测的BOLD反应在前扣带皮层。另一方面，它的运动编程未能预测运动皮层的预期激活，其表征变化未能预测后顶叶皮层的活动模式，其程序组件未能预测尾状核的初始尖峰。结果表明，这些数据的力量，以指导复杂问题解决的理论的发展，无论是在一个特定的任务模型的水平，以及在认知架构的水平。
This article investigates the potential of fMRI to test assumptions about different components in models of complex cognitive tasks. If the components of a model can be associated with specific brain regions, one can make predictions for the temporal course of the BOLD response in these regions. An event-locked procedure is described for dealing with temporal variability and bringing model runs and individual data trials into alignment. Statistical methods for testing the model are described that deal with the scan-to-scan correlations in the errors of measurement of the BOLD signal. This approach is illustrated using a sacrificial ACT-R model that involves mapping 6 modules onto 6 brain regions in an experiment from Ravizza, Anderson, and Carter (in press) concerned with equation solving. The model's visual encoding predicted the BOLD response in the fusiform gyrus, its controlled retrieval predicted the BOLD response in the lateral inferior prefrontal cortex, and its subgoal setting predicted the BOLD response in the anterior cingulate cortex. On the other hand, its motor programming failed to predict anticipatory activation in the motor cortex, its representational changes failed to predicted the pattern of activity in the posterior parietal cortex, and its procedural component failed to predict an initial spike in caudate. The results illustrate the power of such data to direct the development of a theory of complex problem solving, both at the level of a specific task model as well as at the level of the cognitive architecture.