New insights into the speaker’s lexicon: Computational modelling and experimental data

我们经历事件的频率决定了我们对类似事件的后续处理。语言也不例外。在语言产生的研究中，最早被发现的实验效应之一是词汇频率效应。更频繁的单词可以被更快地访问和编码。这种经验效应塑造了我们对心理词汇的理解——关于单词的信息是如何在记忆中组织起来的——以及访问这些表征所涉及的过程。有证据表明，单词在长期记忆中是与使用频率相关的信息一起呈现的。就像一些单词比其他单词更频繁一样，一些单词序列或话语比其他单词更频繁。我们会说，我更喜欢喝一杯，而不是吃盘子；我更喜欢旅行，而不是坐一架好飞机。数据块的频率是否会影响数据块的准备（即编码）？目前语言产生的心理语言学模型预测，情况并非如此。在这些模型中，短语是动态构建的。一旦产生了给定的话语，它就不会在系统中留下任何痕迹。然而，最近的研究表明，多词话语也会产生类似的影响。说话者经常使用的短语可以更快地编码。这一实证结果表明，语言产生系统不仅记录了与单个单词的接触，还记录了这些单词组合成短语的频率。这一结果要求对语言产生的心理语言学模型进行改进，或者对其一些基本假设进行重新审视。本项目利用了这一发现。其目的是实现和测试一种新的语言产生模型，该模型可以解释这些影响。该模型将词汇视为一个相互关联的词汇表征网络，该网络随着说话人对网络的使用而动态演变。人工智能工具（即线性建模，naïve辨别学习）将用于模拟该模型的不同版本，以及系统存储整个短语表示的替代模型。将进行心理语言学实验，以衡量参与者在使用语言时表现的变化。这些实验的结果将与计算模型的预测结果进行比较。这个项目有望成为语言产生过程研究的一个转折点。它将为语言产生系统的认知结构提供新的视角，并为该系统的研究开辟新的视角。

The frequency with which we experience events shapes our subsequent processing of similar events. Language is no exception. In the study of language production, one of the first experimental effects to be discovered was the lexical frequency effect. More frequent words can be accessed and encoded more quickly. This empirical effect has shaped our understanding of the mental lexicon - how information about words is organized in memory- and of the processes involved in accessing these representations. It has been taken as evidence that words are represented in long-term memory together with information about their frequency of use. Just like some words are more frequent than others, some word sequences or utterances are more frequent than others. We say I’ll take a glass more often than I’ll take a plate and a great trip more often than a great plane. Does the frequency of the chunk impact the preparation (i.e., encoding) of this chunk? Current psycholinguistic models of language production predict that this should not be the case. In these models phrases are built on the fly. Once a given utterance has been produced, it does not leave any trace in the system. Yet recent studies have shown that similar effects arise for multi-word utterances. The phrases that speakers use more often can be encoded more quickly. This empirical result demonstrates that the language production system keeps track not only of its encounters with individual words, but also of how often these words are combined into phrases. This result requires that psycholinguistic models of language production be refined or that some of their fundamental assumptions be revisited. The present project capitalizes on this discovery. Its aim is to implement and test a novel model of language production that can account for these effects. This model views the lexicon as a network of interconnected lexical representations that evolves dynamically as a results of the speaker’s use of the network. Tools of Artificial Intelligence (i.e., linear modeling, naïve discrimination learning) will be used to simulate different versions of that model as well as an alternative model in which the system stores representations for whole phrases. Psycholinguistic experiments will be conduct to measure changes in the participants’ performance as a function of their use of the language. The outcome of these experiments will be compared to the predictions of the computational models. This project is expected to mark a turning point in the study of language production processes. It will shed a new light on the cognitive architecture of the language production system and open up new perspectives in the study of that system.