Eye movement evidence that readers maintain and act on uncertainty about past linguistic input

Abstract

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.

Rational integration of noisy evidence and prior semantic expectations in sentence interpretation

“…[17][18][19]. Four predictions of noisy-channel models were confirmed, each of which showed that factors which should influence a rational Bayesian decoder did influence people's interpretation of sentences.…”

“…Furthermore, several researchers have observed the importance of noise in the input for the compositional, syntactic processes of sentence understanding (12)(13)(14)(15)(16), leading to the recent proposal of noisychannel models of sentence understanding (17)(18)(19). According to a noisy-channel account, the sentence comprehension mechanism rationally combines information about a priori plausible utterances with a model of the imperfect transmission of the linguistic signal across a noisy channel.…”

“…Critically, although different syntactic alternations (Table 1) share the fact that the two alternatives are identical in terms of propositional meaning, they can vary in how close the alternatives are, under simple string edits (see also refs. [17][18][19].…”

“…Note that this differs from the symmetric Levenshtein noise model (26) used in refs. [17][18][19]. Thus, semantic cues should have a stronger influence for each of the implausible structures in 3d, 4d, and 5d-in which a word has been deleted from the plausible alternation-than for the implausible structures in 3c, 4c, and 5c-in which a word has been inserted into the plausible alternation.…”

Toward a scalable holographic word-form representation

“…The BEAGLE model, augmented to include principled environmental vectors, can be applied to a variety of additional tasks beyond the fragment completion task. Perceptual-level uncertainty about printed text-even in the absence of stimulus degradation-has been shown to affect sentence comprehension (Levy, Bicknell, Slattery, & Rayner, 2009), an effect to which an extended BEAGLE model could be applied. The inclusion of orthographic information also enables inferences about semantics based on shared word-form properties: For instance, a model could come to represent the meanings of prefixes and suffixes or could generalize information across words (or to new words) on the basis of shared phonaesthemes, sublexical units that indicate semantic similarity (Otis & Sagi, 2008).…”