In prevailing approaches to human sentence comprehension, the outcome of the word recognition process is assumed to be a categorical representation with no residual uncertainty. Yet perception is inevitably uncertain, and a system making optimal use of available information might retain this uncertainty and interactively recruit grammatical analysis and subsequent perceptual input to help resolve it. To test for the possibility of such an interaction, we tracked readers' eye movements as they read sentences constructed to vary in (i) whether an early word had near neighbors of a different grammatical category, and (ii) how strongly another word further downstream cohered grammatically with these potential near neighbors. Eye movements indicated that readers maintain uncertain beliefs about previously read word identities, revise these beliefs on the basis of relative grammatical consistency with subsequent input, and use these changing beliefs to guide saccadic behavior in ways consistent with principles of rational probabilistic inference.
|Original language||English (US)|
|Number of pages||5|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|State||Published - Dec 15 2009|
- Language comprehension
- Probabilistic models of cognition
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