Predictive memory activation attenuates similarity-based interference in verb-final languages

Cue-based retrieval theories of sentence comprehension as sume that syntactically related words (e.g., the subject and the verb) are identified and linked together through a content addressable search in memory, using retrieval cues such as subject and animate. A key prediction is similarity-based interference: When multiple nouns in memory match retrieval cues at the verb, it becomes difficult to retrieve the target subject noun, causing a slowdown in processing time at the verb. Similarity-based interference is consistently observed in reading studies from English, furnishing strong evidence for cue-based retrieval theories. However, in verb-final languages such as German and Hindi, evidence is equivocal. The classi cal cue-based retrieval models fail to explain interference ef fect patterns observed in verb-final languages. Data demand a new theory that can explain interference effects in verb-final as well as verb-medial languages. We explore the predictive memory activation mechanism: the pre-verbal nouns stored in memory strongly preactivate the upcoming verb phrase, which can override the cost of retrieval interference in verb-final lan guages. We implement this mechanism within a cue-based re trieval architecture. We show that the predictive memory ac tivation model can account for interference effects observed in verb-final as well as verb-medial languages. Bayes fac tor analysis revealed strong evidence in favor of the predic tive memory activation model against the cue-based retrieval model. Our modeling results have an important theoretical im plication: Dependency completion is driven by top-down pre dictive activation and bottom-up cue-based retrieval processes.