![]() Given the assumed link between uncertainty about previous input and overt regressions in the noisy-channel model, data from eye tracking during reading are potentially informative when it comes to dissociating the two proposed explanations. Given that the alternative parse is not available in (1b, 1c), no revision of beliefs is triggered at tossed/thrown, and the difference between conditions is thus accounted for. Levy ( 2008b) assumes that large changes in the reader’s belief about the previous input result in slowed processing and, if possible, in regressive saccades, due to lingering uncertainty about the actual identity of the critical word at. For instance, in (1a), changing at to as only requires substitution of one letter and results in the possibility of a high-probability alternative parse in which tossed is analyzed as an active verb ( The coach smiled as the player tossed a frisbee). The main intuition is that readers may change their assumptions about the identity of previously read words when subsequent input does not easily fit with the current parse. The model rests on the assumption that readers maintain some uncertainty about the words they have read, and may consider the possibility that they did not read the word at in (1a), but rather the word and or the word as, or that at may have been a typo. If the conflict between the two cannot be resolved, parsing failure may result.Īn entirely different explanation of local coherence effects, the noisy-channel model, was proposed by Levy ( 2008b) and tested by Levy et al. Slowed reading in locally coherent structures is naturally accounted for by this kind of model as the underlined local analysis in (1a) competes with the globally correct analysis. If multiple locally well-formed attachments compete with each other, processing difficulty arises. In these models, lexical items bring with them small pieces of syntactic structure that may freely combine at any point during processing and attempt to form a globally well-formed tree structure (Smith, 2018 Smith et al., 2018 Villata & Franck, 2020). A class of models that do not assume self-consistency are self-organized parsing models such as SOPARSE (Tabor & Hutchins, 2004) and Unification Space (Vosse & Kempen, 2000). ( 2004) argue that such local coherence effects may be best explained by parsing models that relax the principle of grammatical self-consistency, which requires that the parser only considers globally legal analyses at every point during the parse. This pattern is consistent with the idea that in (1a), the human sentence processor is led astray due to the substring the player tossed a frisbee being a possible active main clause, even though it appears at a position in the sentence where such an analysis is ruled out by grammar. They replicated the effect in a second self-paced reading study and in a grammaticality judgment study, where (1a) was more likely to be rejected than (1b) and (1c). ![]() ( 2004) found that the words tossed/ thrown a frisbee took longer to process in (1a) compared to (1b) and (1c). In a self-paced reading experiment Tabor et al. We discuss implications for self-organized and noisy-channel models of local coherence. There was, however, no indication that local coherence led to illusions of grammaticality (a prediction of self-organization), and only weak, inconclusive support for local coherence leading to targeted regressions to critical context words (a prediction of the uncertain-input approach). In our data, local coherence affected on-line processing immediately at the point of the manipulation. We report the results of an eye-tracking study in which subjects read German grammatical and ungrammatical sentences that either contained a locally coherent substring or not and gave binary grammaticality judgments. It has been suggested that such effects occur either because sentence processing occurs in a bottom-up, self-organized manner rather than under constant grammatical supervision, or because local coherence can disrupt processing due to readers maintaining uncertainty about previous input. Local coherence effects arise when the human sentence processor is temporarily misled by a locally grammatical but globally ungrammatical analysis ( The coach smiled at the player tossed a frisbee by the opposing team).
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