Department of Germanic Linguistics, University of Marburg, Deutschhausstr. 3, 35032, Marburg, Germany,
Neuroinformatics. 2014 Jan;12(1):143-79. doi: 10.1007/s12021-013-9198-x.
Neurophysiological data from a range of typologically diverse languages provide evidence for a cross-linguistically valid, actor-based strategy of understanding sentence-level meaning. This strategy seeks to identify the participant primarily responsible for the state of affairs (the actor) as quickly and unambiguously as possible, thus resulting in competition for the actor role when there are multiple candidates. Due to its applicability across languages with vastly different characteristics, we have proposed that the actor strategy may derive from more basic cognitive or neurobiological organizational principles, though it is also shaped by distributional properties of the linguistic input (e.g. the morphosyntactic coding strategies for actors in a given language). Here, we describe an initial computational model of the actor strategy and how it interacts with language-specific properties. Specifically, we contrast two distance metrics derived from the output of the computational model (one weighted and one unweighted) as potential measures of the degree of competition for actorhood by testing how well they predict modulations of electrophysiological activity engendered by language processing. To this end, we present an EEG study on word order processing in German and use linear mixed-effects models to assess the effect of the various distance metrics. Our results show that a weighted metric, which takes into account the weighting of an actor-identifying feature in the language under consideration outperforms an unweighted distance measure. We conclude that actor competition effects cannot be reduced to feature overlap between multiple sentence participants and thereby to the notion of similarity-based interference, which is prominent in current memory-based models of language processing. Finally, we argue that, in addition to illuminating the underlying neurocognitive mechanisms of actor competition, the present model can form the basis for a more comprehensive, neurobiologically plausible computational model of constructing sentence-level meaning.
来自各种类型学上不同语言的神经生理学数据为理解句子层面意义的跨语言有效、基于参与者的策略提供了证据。该策略旨在尽可能快速且明确地识别出主要负责事态的参与者（即参与者），因此当有多个候选者时，参与者角色就会产生竞争。由于其适用于具有极大差异的语言，我们提出参与者策略可能源于更基本的认知或神经生物学组织原则，尽管它也受到语言输入分布特征的影响（例如，给定语言中参与者的形态句法编码策略）。在这里，我们描述了参与者策略的初始计算模型以及它如何与语言特定的特征相互作用。具体来说，我们对比了从计算模型的输出中得出的两种距离度量（一种加权的和一种非加权的）作为参与者身份竞争程度的潜在度量，通过测试它们在多大程度上可以预测语言处理引发的电生理活动的调制。为此，我们进行了一项关于德语语序处理的 EEG 研究，并使用线性混合效应模型来评估各种距离度量的效果。我们的结果表明，一种加权度量，它考虑了语言中参与者识别特征的权重，优于非加权距离度量。我们得出结论，参与者竞争效应不能简化为多个句子参与者之间的特征重叠，从而不能简化为当前基于记忆的语言处理模型中突出的基于相似性的干扰概念。最后，我们认为，除了阐明参与者竞争的潜在神经认知机制外，该模型还可以作为构建句子层面意义的更全面、神经生物学上合理的计算模型的基础。
