A predictive memory activation model of sentence comprehension

A well-established claim in sentence comprehension research is that the subject-verb dependency is resolved via a cue-based retrieval process: The subject noun is searched and retrieved from memory using retrieval cues, such as subject and animate. The cue-based retrieval process can be hindered by decay in the accessibility of the subject noun when the subject and the verb are kept away from each other, causing a processing difficulty at the verb, called the locality effect. The locality effect is robustly observed in reading studies from English, Spanish, Danish, and Russian. However, in verb-final languages like German and Hindi, an opposite effect is observed: When the distance between the subject and the verb increases, a speedup is observed in reading times at the verb. This effect has been called anti-locality and has been attributed to the strong predictability of the upcoming verb in rich case-marking, verb-final languages. We implement this idea as a computational model within the cue-based retrieval architecture. Our model assumes that pre-verbal nouns may preactivate the upcoming verb phrase in memory, and if the preactivation of the verb is strong enough, it can override the cost of retrieval, causing an anti-locality effect. Under the assumption that preactivation is stronger and more relied upon in verb-final languages, the model can account for locality as well as anti-locality effects across languages. We find strong evidence for the predictive activation model compared to the retrieval-only model, given data from five published studies on locality.