Quantitative modeling of the neural representation of objects: How semantic feature norms can account for fMRI activation
Quantitative modeling of the neural representation of objects: How semantic feature norms can account for fMRI activation
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DOI：
10.1016/j.neuroimage.2010.04.271
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发表时间：
2011-05-15
期刊：
影响因子：
5.7
通讯作者：
Just, Marcel Adam
中科院分区：
文献类型：
--
作者：
Chang, Kai-min Kevin;Mitchell, Tom;Just, Marcel Adam
最近对 fMRI 激活的多变量分析表明，支持向量机 (SVM) 等判别分类器能够解码与各种对象类别的视觉呈现相关的 fMRI 感知的神经状态。然而，缺乏神经活动的生成模型限制了这些判别性分类器用于理解底层神经表示的通用性。在这项研究中，我们提出了一种生成分类器，它使用多元多元线性回归模型对支撑对象神经表示的隐藏因素进行建模。结果表明，源自独立行为特征规范研究的物体特征可以解释在物体沉思任务中观察到的神经活动的系统方差的很大一部分。此外，所得回归模型可用于对以前未见过的神经激活向量进行分类，表明神经活动的分布式模式编码了足够的信号来区分刺激之间的差异。更重要的是，两种分类器方法以及参与者内部和参与者之间的泛化之间似乎存在双重分离。虽然基于 SVM 的判别分类器在参与者内分析中实现了最佳分类精度，但生成分类器优于基于 SVM 的模型，后者在参与者间分析中不使用此类中间表示。这种结果模式表明，基于 SVM 的分类器可能会发现一些在参与者之间不能很好泛化的特殊模式，并且在参与者之间良好的泛化可能需要在我们的中间语义特征集中使用广泛的、大规模的模式。最后，这种中间表示允许我们将神经活动模型外推到以前未见过的单词，这是判别性分类器无法完成的。 (C) 2010 Elsevier Inc. 保留所有权利。
Recent multivariate analyses of fMRI activation have shown that discriminative classifiers such as Support Vector Machines (SVM) are capable of decoding fMRI-sensed neural states associated with the visual presentation of categories of various objects. However, the lack of a generative model of neural activity limits the generality of these discriminative classifiers for understanding the underlying neural representation. In this study, we propose a generative classifier that models the hidden factors that underpin the neural representation of objects, using a multivariate multiple linear regression model. The results indicate that object features derived from an independent behavioral feature norming study can explain a significant portion of the systematic variance in the neural activity observed in an object-contemplation task. Furthermore, the resulting regression model is useful for classifying a previously unseen neural activation vector, indicating that the distributed pattern of neural activities encodes sufficient signal to discriminate differences among stimuli. More importantly, there appears to be a double dissociation between the two classifier approaches and within- versus between-participants generalization. Whereas an SVM-based discriminative classifier achieves the best classification accuracy in within-participants analysis, the generative classifier outperforms an SVM-based model which does not utilize such intermediate representations in between-participants analysis. This pattern of results suggests the SVM-based classifier may be picking up some idiosyncratic patterns that do not generalize well across participants and that good generalization across participants may require broad, large-scale patterns that are used in our set of intermediate semantic features. Finally, this intermediate representation allows us to extrapolate the model of the neural activity to previously unseen words, which cannot be done with a discriminative classifier. (C) 2010 Elsevier Inc. All rights reserved.