Turner Brandon M, Rodriguez Christian A, Norcia Tony M, McClure Samuel M, Steyvers Mark
Department of Psychology, The Ohio State University, USA.
Department of Psychology, Stanford University, USA.
Neuroimage. 2016 Mar;128:96-115. doi: 10.1016/j.neuroimage.2015.12.030. Epub 2015 Dec 23.
The need to test a growing number of theories in cognitive science has led to increased interest in inferential methods that integrate multiple data modalities. In this manuscript, we show how a method for integrating three data modalities within a single framework provides (1) more detailed descriptions of cognitive processes and (2) more accurate predictions of unobserved data than less integrative methods. Specifically, we show how combining either EEG and fMRI with a behavioral model can perform substantially better than a behavioral-data-only model in both generative and predictive modeling analyses. We then show how a trivariate model - a model including EEG, fMRI, and behavioral data - outperforms bivariate models in both generative and predictive modeling analyses. Together, these results suggest that within an appropriate modeling framework, more data can be used to better constrain cognitive theory, and to generate more accurate predictions for behavioral and neural data.
认知科学中需要测试越来越多的理论，这使得人们对整合多种数据模态的推理方法越来越感兴趣。在本手稿中，我们展示了一种在单一框架内整合三种数据模态的方法如何（1）比整合性较差的方法提供对认知过程更详细的描述，以及（2）对未观察到的数据做出更准确的预测。具体而言，我们展示了将脑电图（EEG）和功能磁共振成像（fMRI）与行为模型相结合，在生成性建模分析和预测性建模分析中如何比仅使用行为数据的模型表现得好得多。然后我们展示了一个三变量模型——一个包括EEG、fMRI和行为数据的模型——在生成性建模分析和预测性建模分析中如何优于双变量模型。总之，这些结果表明，在一个合适的建模框架内，可以使用更多数据来更好地约束认知理论，并为行为和神经数据生成更准确的预测。
