  The present study uses a computational approach to examine the role of
semantic constraints in normal reading. This methodology avoids confounds
inherent in conventional measures of predictability, allowing for theoretically
deeper accounts of semantic processing. We start from a definition of
associations between words based on the significant log likelihood that two
words co-occur frequently together in the sentences of a large text corpus.
Direct associations between stimulus words were controlled, and semantic
feature overlap between prime and target words was manipulated by their common
associates. The stimuli consisted of sentences of the form pronoun, verb,
article, adjective and noun, followed by a series of closed class words, e. g.
"She rides the grey elephant on one of her many exploratory voyages". The
results showed that verb-noun overlap reduces single and first fixation
durations of the target noun and adjective-noun overlap reduces go-past
durations. A dynamic spreading of activation account suggests that associates
of the prime words take some time to become activated: The verb can act on the
target noun's early eye-movement measures presented three words later, while
the adjective is presented immediately prior to the target, which induces
sentence re-examination after a difficult adjective-noun semantic integration.
