Neural Networks as Cognitive Models of the Processing of Syntactic Constraints.

Languages are governed by -structural rules that determine which sentences are grammatical in the language. In English, one such constraint is , which dictates that the number of a verb must match the number of its corresponding subject: "the dog run", but "the dog run". While this constraint appears to be simple, in practice speakers make agreement errors, particularly when a noun phrase near the verb differs in number from the subject (for example, a speaker might produce the ungrammatical sentence "the key to the cabinets are rusty").

Rapid syntactic adaptation in self-paced reading: Detectable, but only with many participants.

Temporarily ambiguous sentences that are disambiguated in favor of a less preferred parse are read more slowly than their unambiguous counterparts. This slowdown is referred to as a garden path effect. Recent self-paced reading studies have found that this effect decreased over the course of the experiment as participants were exposed to such syntactically ambiguous sentences.

Single-Stage Prediction Models Do Not Explain the Magnitude of Syntactic Disambiguation Difficulty.

The disambiguation of a syntactically ambiguous sentence in favor of a less preferred parse can lead to slower reading at the disambiguation point. This phenomenon, referred to as a garden-path effect, has motivated models in which readers initially maintain only a subset of the possible parses of the sentence, and subsequently require time-consuming reanalysis to reconstruct a discarded parse. A more recent proposal argues that the garden-path effect can be reduced to surprisal arising in a fully parallel parser: words consistent with the initially dispreferred but ultimately correct parse are simply less predictable than those consistent with the incorrect parse.

In Spoken Word Recognition, the Future Predicts the Past.

Speech is an inherently noisy and ambiguous signal. To fluently derive meaning, a listener must integrate contextual information to guide interpretations of the sensory input. Although many studies have demonstrated the influence of prior context on speech perception, the neural mechanisms supporting the integration of subsequent context remain unknown.

Uncertainty and Expectation in Sentence Processing: Evidence From Subcategorization Distributions.

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.

Lexical Preactivation in Basic Linguistic Phrases.

Many previous studies have shown that predictable words are read faster and lead to reduced neural activation, consistent with a model of reading in which words are activated in advance of being encountered. The nature of such preactivation, however, has typically been studied indirectly through its subsequent effect on word recognition. Here, we use magnetoencephalography to study the dynamics of prediction within serially presented adjective-noun phrases, beginning at the point at which the predictive information is first available to the reader.

The role of morphology in phoneme prediction: evidence from MEG.

There is substantial neural evidence for the role of morphology (word-internal structure) in visual word recognition. We extend this work to auditory word recognition, drawing on recent evidence that phoneme prediction is central to this process. In a magnetoencephalography (MEG) study, we crossed morphological complexity (bruis-er vs.