Uncertainty and Expectation in Sentence Processing: Evidence From Subcategorization Distributions

Tal Linzen, T. Florian Jaeger

    Research output: Contribution to journalArticlepeer-review

    Abstract

    There is now considerable evidence that human sentence processing is expectation based: As people read a sentence, they use their statistical experience with their language to generate predictions about upcoming syntactic structure. This study examines how sentence processing is affected by readers' uncertainty about those expectations. In a self-paced reading study, we use lexical subcategorization distributions to factorially manipulate both the strength of expectations and the uncertainty about them. We compare two types of uncertainty: uncertainty about the verb's complement, reflecting the next prediction step; and uncertainty about the full sentence, reflecting an unbounded number of prediction steps. We find that uncertainty about the full structure, but not about the next step, was a significant predictor of processing difficulty: Greater reduction in uncertainty was correlated with increased reading times (RTs). We additionally replicated previously observed effects of expectation violation (surprisal), orthogonal to the effect of uncertainty. This suggests that both surprisal and uncertainty affect human RTs. We discuss the consequences for theories of sentence comprehension.

    Original languageEnglish (US)
    Pages (from-to)1382-1411
    Number of pages30
    JournalCognitive Science
    Volume40
    Issue number6
    DOIs
    StatePublished - Aug 1 2016

    Keywords

    • Competition
    • Entropy reduction
    • Prediction
    • Sentence processing
    • Surprisal
    • Uncertainty

    ASJC Scopus subject areas

    • Experimental and Cognitive Psychology
    • Cognitive Neuroscience
    • Artificial Intelligence

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