A computational implementation of the Swinging Lexical Network model of language production

关于语言产生中的语义效应的行为学研究观察到了明显的相互矛盾的语境效应：在已有的研究中，语义相关的干扰因素(例如，当参与者不得不说出老虎的名字时，被试说出狮子这个词)的存在导致了便利化(即，较快的反应)，而另一些研究则观察到了干扰(即，较慢的反应)。为了用一个统一、全面的模型来解释这些影响，Abdel Rahman和Melinger(2009,2019)提出了语言产生的摆动词汇网络模型。该模型依赖于两个核心假设：(1)它假设作为扩展激活(在存在语义相关的上下文词的情况下导致更快的响应)的结果的概念-语义级的启动，但在词汇级别的词选择期间的竞争(因为只有一个词将被选择用于产生)，以及(2)它假设启动和竞争的量不仅来自目标和上下文的激活，而且还受到共同激活的队列的影响-由目标、上下文词以及在处理期间由彼此相互激活的其他概念(例如，在我们的例子中，“豹”或“猫”)。然而，正如作者所承认的那样，该模型目前是一个纯粹的口头理论，还没有在计算上实施，这使得严格评估其实际解释力和经验有效性非常困难。当前项目的目的是提供这种计算实施和经验评估。该模型由以下部分组成：(I)我们将使用分布式语义模型/词嵌入作为最先进的语义记忆计算模型，以及(Ii)使用Kintsch(1988)的构建-整合算法来模拟队列的相互共激活。我们从关于(Iii)语义和词汇层面的激活扩散和(Iv)词汇层面的选择的非常简单的假设开始。第一个工作包侧重于实现该模型，并使已实现的模型可供研究社区访问。第二个工作包侧重于根据已有的和已发表的关于语言加工中语义语境效应的研究来估计模型的自由参数。最后，第三个工作包侧重于在实验研究中对模型进行实证验证，根据模型预测生成新的项目材料，这些项目将预期特定的语境效应。

Behavioral studies on semantic effects in language production have observed apparently contradictory context effects: In come studies, the presence of semantically related distractors (such as the word "LION" when participants have to name the picture of a tiger) has resulted in facilitation (i.e., faster responses), while others have observed interference (i.e., slower responses). In order to explain these effects in a unified, comprehensive model, Abdel Rahman and Melinger (2009, 2019) have proposed the Swinging Lexical Network Model of language production. This model relies on two core assumptions: (1) it assumes priming at the conceptual-semantic level as a consequence of spreading activation (leading to faster responses in the presence of semantically related context words) but competition during word selection at the lexical level (since only one word is to be selected for production), and (2) it assumes that the amount of priming and competition does not only result from the activation of the target and context, but is also influenced by a co-activated cohort - other concepts that are mutually activated by the target, the context word, and by each other during processing (such as, in our example, "leopard" or "cat"). However, as acknowledged by the authors, the fact that this model is currently a purely verbal theory and not yet computationally implemented makes it very difficult to rigorously assess ist actual explanatory power and empirical validity. The aim of the current project is to provide this computational implementation and empirical evaluation. This model consists of the following components: (I) We will employ distributional semantic models/word embeddings as a state-of-the-art computational model of semantic memory, and (II) apply Kintsch’s (1988) construction-integration algorithm to model the mutual co-activation of the cohort. We start from very simplistic assumptions about (III) activation spread between the semantic and lexical level and (IV) selection at the lexical level. The first work package focuses on implementing this model and making the implemented model accessible to the research community. The second work package focuses on estimating the free parameters of the model from already existing and published studies on semantic context effects in language processing. Finally, the third work package focuses on empirically validating the model in experimental studies, generating new item material for which specific context effects would be expected according to the model predictions.