Language 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.