Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands; International Max Planck Research School for Language Sciences, Nijmegen, the Netherlands.
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
Cognition. 2021 Jun;211:104636. doi: 10.1016/j.cognition.2021.104636. Epub 2021 Feb 26.
In recent years, it has become clear that attention plays an important role in spoken word production. Some of this evidence comes from distributional analyses of reaction time (RT) in regular picture naming and picture-word interference. Yet we lack a mechanistic account of how the properties of RT distributions come to reflect attentional processes and how these processes may in turn modulate the amount of conflict between lexical representations. Here, we present a computational account according to which attentional lapses allow for existing conflict to build up unsupervised on a subset of trials, thus modulating the shape of the resulting RT distribution. Our process model resolves discrepancies between outcomes of previous studies on semantic interference. Moreover, the model's predictions were confirmed in a new experiment where participants' motivation to remain attentive determined the size and distributional locus of semantic interference in picture naming. We conclude that process modeling of RT distributions importantly improves our understanding of the interplay between attention and conflict in word production. Our model thus provides a framework for interpreting distributional analyses of RT data in picture naming tasks.
近年来，人们已经清楚地认识到注意力在口语产生中起着重要作用。其中一些证据来自于对常规图片命名和图片-单词干扰中反应时（RT）的分布分析。然而，我们缺乏一种机制解释RT 分布的性质如何反映注意力过程，以及这些过程如何反过来调节词汇表征之间的冲突量。在这里，我们提出了一个计算模型，根据该模型，注意力的丧失允许在一小部分试验上不受监督地积累现有的冲突，从而调节 RT 分布的形状。我们的过程模型解决了之前关于语义干扰的研究结果之间的差异。此外，该模型的预测在一项新的实验中得到了证实，在该实验中，参与者保持注意力的动机决定了图片命名中语义干扰的大小和分布位置。我们得出结论，RT 分布的过程建模大大提高了我们对注意力和单词产生中的冲突相互作用的理解。因此，我们的模型为解释图片命名任务中 RT 数据的分布分析提供了一个框架。
