Towards a processing model for argument-verb computations in online sentence comprehension

The current study investigated the processing stages by which the parser incorporates different pieces of information, from clausehood to argument roles, to update predictions about the main verb. Using Mandarin to match word position across relevant conditions, we extend classic ERP findings on the impact of argument role reversals ([The millionaireSUBJECT the servantOBJECT fired] vs. #[The servantSUBJECT the millionaireOBJECT fired]), by investigating cases where one of the nouns is not an argument of the verb ([The millionaireSUBJECT the servantOBJECT fired] vs. #[The millionaire thought [the servantSUBJECT fired...]]). The pattern of N400 responses suggest a three-stage model of argument-verb computation: An initial stage demonstrates sensitivity at the verb to semantic association only. Soon after, responses show partial structure-sensitivity, differentiating whether the noun phrases are arguments of the upcoming verb or not. Only at the last stage do the arguments' roles (e.g. agent/patient) become available to impact computations at the verb.