Department of Linguistics, The Ohio State University.
School of Foreign Languages, Shanghai Jiao Tong University.
Cogn Sci. 2024 Oct;48(10):e13500. doi: 10.1111/cogs.13500.
This article evaluates the predictions of an algorithmic-level distributed associative memory model as it introduces, propagates, and resolves ambiguity, and compares it to the predictions of computational-level parallel parsing models in which ambiguous analyses are accounted separately in discrete distributions. By superposing activation patterns that serve as cues to other activation patterns, the model is able to maintain multiple syntactically complex analyses superposed in a finite working memory, propagate this ambiguity through multiple intervening words, then resolve this ambiguity in a way that produces a measurable predictor that is proportional to the log conditional probability of the disambiguating word given its context, marginalizing over all remaining analyses. The results are indeed consistent in cases of complex structural ambiguity with computational-level parallel parsing models producing this same probability as a predictor, which have been shown reliably to predict human reading times.
本文评估了一种算法级分布式联想记忆模型的预测能力，该模型在引入、传播和解决歧义方面表现出色，并将其与计算级并行解析模型的预测进行了比较。在计算级并行解析模型中，歧义分析分别在离散分布中进行解释。通过叠加作为其他激活模式线索的激活模式，该模型能够在有限的工作记忆中保持多个句法复杂的分析，并通过多个中间词传播这种歧义，然后以一种产生可衡量的预测器的方式解决这种歧义，该预测器与给定上下文的消歧词的对数条件概率成正比，同时对所有剩余的分析进行边缘化。在复杂的结构歧义情况下，结果确实与计算级并行解析模型的预测一致，这些模型已经被证明能够可靠地预测人类阅读时间。
