Task and Timing Effects in Argument Role Sensitivity: Evidence From Production, EEG, and Computational Modeling

Comprehenders generate expectations about upcoming lexical items in language processing using various types of contextual information. However, a number of studies have shown that argument roles do not impact neural and behavioral prediction measures. Despite these robust findings, some prior studies have suggested that lexical prediction might be sensitive to argument roles in production tasks such as the cloze task or in comprehension tasks when additional time is available for prediction. This study demonstrates that both the task and additional time for prediction independently influence lexical prediction using argument roles, via evidence from closely matched electroencephalogram (EEG) and speeded cloze experiments. In order to investigate the timing effect, our EEG experiment used maximally simple Japanese stimuli such as Bee-nom/acc sting, and it manipulated the time for prediction by changing the temporal interval between the context noun and the target verb without adding any further linguistic content. In order to investigate the task effect, we conducted a speeded cloze study that was matched with our EEG study both in terms of stimuli and the time available for prediction. We found that both the EEG study with additional time for prediction and the speeded cloze study with matched timing showed clear sensitivity to argument roles, while the EEG conditions with less time for prediction replicated the standard pattern of argument role insensitivity. Based on these findings, we propose that lexical prediction is initially insensitive to argument roles but a monitoring mechanism serially inhibits role-inappropriate candidates. This monitoring process operates quickly in production tasks, where it is important to quickly select a single candidate to produce, whereas it may operate more slowly in comprehension tasks, where multiple candidates can be maintained until a continuation is perceived. Computational simulations demonstrate that this mechanism can successfully explain the task and timing effects observed in our experiments.