标题： 模式识别中的因果和反因果学习 显示英文标题

标题： Causal and anti-causal learning in pattern recognition for neuroimaging

摘要： 模式识别在神经影像学中区分两种类型的模型：编码模型和解码模型。 这种区分基于这样的见解，即在实验范式中被发现相关的脑状态特征，在编码模型与解码模型中具有不同的含义。 在本文中，我们认为这种区分是不够的：在编码模型和解码模型中的相关特征，其含义取决于它们是否表示因果关系或反因果关系。 我们为这一论点提供了理论依据，并得出结论，因果推断对于神经影像学的解释是至关重要的。 显示英文摘要

摘要： Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models. This distinction is based on the insight that brain state features, that are found to be relevant in an experimental paradigm, carry a different meaning in encoding- than in decoding models. In this paper, we argue that this distinction is not sufficient: Relevant features in encoding- and decoding models carry a different meaning depending on whether they represent causal- or anti-causal relations. We provide a theoretical justification for this argument and conclude that causal inference is essential for interpretation in neuroimaging.