Great expectations: Evidence for graded prediction of grammatical gender

AbstractLanguage processing is predictive in nature. But how do people balance multiple competing options as they predict upcoming meanings? Here, we investigated whether readers generate graded predictions about grammatical gender of nouns. Sentence contexts were manipulated so that they strongly biased people's expectations towards two or more nouns that had the same grammatical gender (single bias condition), or they biased multiple genders from different grammatical classes (multiple bias condition). Our expectation was that unexpected articles should lead to elevated reading times (RTs) in the single-bias condition when probabilistic expectations towards a particular gender are violated. Indeed, the results showed greater sensitivity among language users towards unexpected articles in the single-bias condition, however, RTs on unexpected gender-marked articles were facilitated, and not slowed. Our data confirm that difficulty in sentence processing is modulated by uncertainty about predicted information, and suggest that readers make graded predictions about grammatical gender.

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