According to one view of linguistic information, a speaker can convey contextually new information in one of two ways: by asserting the content as new information; or by presupposing the content as given information which would then have to be accommodated. This distinction predicts that it is conversationally more appropriate to assert implausible information rather than presuppose it. A second view rejects the assumption that presuppositions are accommodated; instead, presuppositions are assimilated into asserted content and both are correspondingly open to (...) challenge. Under this view, we should not expect to find a difference in conversational appropriateness between asserting implausible information and presupposing it. To distinguish between these two views of linguistic information, we performed two self-paced reading experiments with an on-line stops-making-sense judgment. The results of the two experiments—using the presupposition triggers the and too—show that accommodation is inappropriate relative to non-presuppositional controls when the presupposed information is implausible but not when it is plausible. These results provide support for the first view of linguistic information: the contrast in implausible contexts can only be explained if there is a presupposition-assertion distinction and accommodation is a mechanism dedicated to reasoning about presuppositions. (shrink)

We report one production and one comprehension experiment investigating the effect of animacy in relative clause attachment in Chinese. Experiment 1 involved a fill-in-the-blank task that manipulated the order of an animate noun phrase in a complex NP construction. The results showed that while low attachment responses exceeded high attachment responses overall (cf. Shen, 2006), a tendency exists to attach a relative clause to an animate NP in Chinese (cf. Desmet et al., 2002). Experiment 2 used a rating task to (...) examine the interplay between animacy and structural information by manipulating the order of the animate NP as well as the relative clause type (i.e., subject vs. object relative clauses). The results showed that the animate NP modification tendency found in Experiment 1 was limited to subject-relative clauses and that no animacy-related effect was found with object-relative clauses. These results are incompatible with purely structural parsing strategies such as Late Closure (Frazier, 1987) and the Predicate Proximity Principle (Gibson et al., 1996). Instead, the current results suggest that attachment ambiguity resolution in Chinese relative clauses is sensitive to animacy as well as structural information. (shrink)

All other things being equal the parser favors attaching an ambiguous modifier to the most recent possible site. A plausible explanation is that locality preferences such as this arise in the service of minimizing memory costs—more distant sentential material is more difficult to reactivate than more recent material. Note that processing any sentence requires linking each new lexical item with material in the current parse. This often involves the construction of long‐distance dependencies. Under a resource‐limited view of language processing, lengthy (...) integrations should induce difficulty even in unambiguous sentences. To date there has been little direct quantitative evidence in support of this perspective. This article presents 2 self‐paced reading studies, which explore the hypothesis that dependency distance is a fundamental determinant of reading complexity in unambiguous constructions in English. The evidence suggests that the difficulty associated with integrating a new input item is heavily determined by the amount of lexical material intervening between the input item and the site of its target dependents. The patterns observed here are not straightforwardly accounted for within purely experience‐based models of complexity. Instead, this work supports the role of a memory bottleneck in language comprehension. This constraint arises because hierarchical linguistic relations must be recovered from a linear input stream. (shrink)