Seminar für Sprachwissenschaft, Eberhard-Karls-Universität, Tübingen, Germany.
PLoS One. 2019 Jul 31;14(7):e0218802. doi: 10.1371/journal.pone.0218802. eCollection 2019.
We present the Naive Discriminative Reading Aloud (ndra) model. The ndra differs from existing models of response times in the reading aloud task in two ways. First, a single lexical architecture is responsible for both word and non-word naming. As such, the model differs from dual-route models, which consist of both a lexical route and a sub-lexical route that directly maps orthographic units onto phonological units. Second, the linguistic core of the ndra exclusively operates on the basis of the equilibrium equations for the well-established general human learning algorithm provided by the Rescorla-Wagner model. The model therefore does not posit language-specific processing mechanisms and avoids the problems of psychological and neurobiological implausibility associated with alternative computational implementations. We demonstrate that the single-route discriminative learning architecture of the ndra captures a wide range of effects documented in the experimental reading aloud literature and that the overall fit of the model is at least as good as that of state-of-the-art dual-route models.
我们提出了朴素判别式朗读（ndra）模型。该模型与朗读任务中现有的反应时模型在两个方面存在差异。首先，单一的词汇架构负责单词和非单词命名。因此，该模型与双通路模型不同，后者由词汇通路和直接将正字法单位映射到语音单位的亚词汇通路组成。其次，ndra 的语言核心完全基于由 Rescorla-Wagner 模型提供的经过充分验证的通用人类学习算法的平衡方程运行。因此，该模型不假设语言特有的处理机制，并避免了与替代计算实现相关的心理和神经生物学不可信的问题。我们证明，ndra 的单一通路判别式学习架构可以捕捉到实验朗读文献中记录的广泛的效应，并且该模型的整体拟合度至少与最先进的双通路模型一样好。
