McRae Ken, Hare Mary, Elman Jeffrey L, Ferretti Todd
Department of Psychology, Social Science Centre, University of Western Ontario, London, Ontario, Canada.
Mem Cognit. 2005 Oct;33(7):1174-84. doi: 10.3758/bf03193221.
We explore the implications of an event-based expectancy generation approach to language understanding, suggesting that one useful strategy employed by comprehenders is to generate expectations about upcoming words. We focus on two questions: (1) What role is played by elements other than verbs in generating expectancies? (2) What connection exists between expectancy generation and event-based knowledge? Because verbs follow their arguments in many constructions (particularly in verb-final languages), deferring expectations until the verb seems inefficient. Both human data and computational modeling suggest that other sentential elements may also play a role in predictive processing and that these constraints often reflect knowledge regarding typical events. We investigated these predictions, using both short and long stimulus onset asynchrony priming. Robust priming obtained when verbs were named aloud following typical agents, patients, instruments, and locations, suggesting that event memory is organized so that nouns denoting entities and objects activate the classes of events in which they typically play a role. These computations are assumed to be an important component of expectancy generation in sentence processing.
我们探讨了基于事件的预期生成方法对语言理解的影响，表明理解者采用的一种有用策略是对即将出现的单词生成预期。我们关注两个问题：（1）除动词外的其他元素在生成预期中起什么作用？（2）预期生成与基于事件的知识之间存在什么联系？由于在许多结构中（特别是在动词后置语言中）动词跟随其论元，将预期推迟到动词出现似乎效率不高。人类数据和计算模型都表明，其他句子元素也可能在预测性处理中起作用，并且这些限制通常反映了关于典型事件的知识。我们使用短和长刺激起始异步启动来研究这些预测。当动词在典型的施事、受事、工具和位置之后被大声说出时，获得了强大的启动效应，这表明事件记忆是有组织的，以至于表示实体和对象的名词会激活它们通常在其中发挥作用的事件类别。这些计算被认为是句子处理中预期生成的一个重要组成部分。
