Predictive re-activation of the upcoming verb explains(anti-)locality effects in Hindi

A well-established empirical phenomenon in human sentence comprehension is the locality effect: reading time at a verb is slower when its argument (e.g., the subject) is placed linearly farther away than when it is kept closer to the verb. The locality effect has been robustly observed across languages and attributed to working memory constraints: due to limited working memory, it becomes difficult to maintain the accessibility of the argument nouns as the distance between the argument and the verb increases. However, working memory-based theories fail to explain the anti-locality effect. In verb-final languages like German and Hindi, increased distance between the arguments and the verb causes a speedup in reading times at the verb. Anti-locality effects have been attributed to predictive processing. However, it remains unclear how predictive processing produces the anti-locality effect and why the locality effect is not found in verb-final languages. We explore the Vasishth and Lewis (2006) proposal, the VP-reactivation hypothesis: intervening items between the subject and the verb reactivate the prediction of the upcoming verb phrase. As distance increases, the verb phrase becomes highly preactivated in memory, facilitating processing at the verb and, consequently, overriding the locality effect. We computationally implement this proposal within a cue-based retrieval framework and conduct two self-paced reading experiments on Hindi that manipulate whether the intervening material directly modifies the upcoming verb or not. The reading data support the key prediction of the predictive reactivation model: anti-locality effects are stronger when preverbal material directly modifies the upcoming verb.