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

语言产生的 Swinging Lexical Network 模型的计算实现

• 批准号: 532390335
• 负责人: Professorin Dr. Rasha Abdel Rahman
• 金额: --
• 依托单位: Institut für Psychologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/532390335?language=en
• 关键词: computational; implementation; Swinging; Lexical; Network

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.

关于语言产生中语义效应的行为研究观察到了明显矛盾的语境效应：在一些研究中，语义相关干扰物的存在（例如，当参与者必须说出老虎的图片时，单词“狮子”）导致了促进作用（即，更快的响应），而其他人已经观察到干扰（即，反应较慢）。为了在一个统一的、全面的模型中解释这些效应，Abdel Rahman和Melinger（2009，2019）提出了语言产生的摆动词汇网络模型。该模型基于两个核心假设：（1）概念语义层面的启动是激活扩散的结果（导致在语义相关的上下文单词存在时反应更快），但在词汇水平上选择单词时的竞争（因为只有一个词要被选择用于产生），以及（2）它假设启动和竞争的量不仅来自于目标和背景的激活，而且还受到共同激活的群组（co-activated cohort）的影响，所述共同激活的群组是由目标、上下文词以及在处理期间彼此相互激活的其他概念（例如，在我们的示例中，“leopard”或“cat”）。然而，正如作者所承认的那样，这个模型目前是一个纯粹的口头理论，尚未在计算上实现，这使得很难严格评估其实际的解释力和经验有效性。当前项目的目的是提供这种计算实现和经验评估。该模型由以下部分组成：（I）我们将采用分布式语义模型/词嵌入作为语义记忆的最先进的计算模型，以及（II）应用Kintsch（1988）的构建整合算法来模拟队列的相互协同激活。我们从非常简单的假设（III）激活语义和词汇水平之间的传播和（IV）在词汇水平的选择。第一个工作包的重点是实现这个模型，并使实现的模型可供研究界使用。第二个工作包的重点是估计的自由参数的模型，从已经存在的和发表的研究语义上下文的影响，在语言处理。最后，第三个工作包侧重于在实验研究中实证验证模型，生成新的项目材料，根据模型预测，预期特定的上下文效果。