Interpretation-based processing: a unified theory of semantic sentence comprehension

We present interpretation-based processing—a theory of sentence processing that builds a syntactic and a semantic representation for a sentence and assigns an interpretation to the sentence as soon as possible. That interpretation can further participate in comprehension and in lexical processing and is vital for relating the sentence to the prior discourse. Our theory offers a unified account of the processing of literal sentences, metaphoric sentences, and sentences containing semantic illusions. It also explains how text can prime lexical access. We show that word literality is a matter of degree and that the speed and quality of comprehension depend both on how similar words are to their antecedents in the preceding text and how salient the sentence is with respect to the preceding text. Interpretation-based processing also reconciles superficially contradictory findings about the difference in processing times for metaphors and literals. The theory has been implemented in ACT-R [Anderson and Lebiere, The Atomic Components of Thought, Lawrence Erlbaum Associates Publishers, Mahwah, NJ, 1998].     

The developing constraints on parsing decisions: the role of lexical-biases and referential scenes in child and adult sentence processing

Two striking contrasts currently exist in the sentence processing literature. First, whereas adult readers rely heavily on lexical information in the generation of syntactic alternatives, adult listeners in world-situated eye-gaze studies appear to allow referential evidence to override strong countervailing lexical biases (Tanenhaus, Spivey-Knowlton, Eberhard, and Sedivy, 1995). Second, in contrast to adults, children in similar listening studies fail to use this referential information and appear to rely exclusively on verb biases or perhaps syntactically based parsing principles (Trueswell, Sekerina, Hill, and Logrip, 1999). We explore these contrasts by fully crossing verb bias and referential manipulations in a study using the eye-gaze listening technique with adults (Experiment 1) and five-year-olds (Experiment 2). Results indicate that adults combine lexical and referential information to determine syntactic choice. Children rely exclusively on verb bias in their ultimate interpretation. However, their eye movements reveal an emerging sensitivity to referential constraints. The observed changes in information use over ontogenetic time best support a constraint-based lexicalist account of parsing development, which posits that highly reliable cues to structure, like lexical biases, will emerge earlier during development and more robustly than less reliable cues.     

Acquiring and processing verb argument structure: distributional learning in a miniature language

Adult knowledge of a language involves correctly balancing lexically-based and more language-general patterns. For example, verb argument structures may sometimes readily generalize to new verbs, yet with particular verbs may resist generalization. From the perspective of acquisition, this creates significant learnability problems, with some researchers claiming a crucial role for verb semantics in the determination of when generalization may and may not occur. Similarly, there has been debate regarding how verb-specific and more generalized constraints interact in sentence processing and on the role of semantics in this process. The current work explores these issues using artificial language learning. In three experiments using languages without semantic cues to verb distribution, we demonstrate that learners can acquire both verb-specific and verb-general patterns, based on distributional information in the linguistic input regarding each of the verbs as well as across the language as a whole. As with natural languages, these factors are shown to affect production, judgments and real-time processing. We demonstrate that learners apply a rational procedure in determining their usage of these different input statistics and conclude by suggesting that a Bayesian perspective on statistical learning may be an appropriate framework for capturing our findings.     

Neuronal correlates of decisions to speak and act: Spontaneous emergence and dynamic topographies in a computational model of frontal and temporal areas

The neural mechanisms underlying the spontaneous, stimulus-independent emergence of intentions and decisions to act are poorly understood. Using a neurobiologically realistic model of frontal and temporal areas of the brain, we simulated the learning of perception–action circuits for speech and hand-related actions and subsequently observed their spontaneous behaviour. Noise-driven accumulation of reverberant activity in these circuits leads to their spontaneous ignition and partial-to-full activation, which we interpret, respectively, as model correlates of action intention emergence and action decision-and-execution. Importantly, activity emerged first in higher-association prefrontal and temporal cortices, subsequently spreading to secondary and finally primary sensorimotor model-areas, hence reproducing the dynamics of cortical correlates of voluntary action revealed by readiness-potential and verb-generation experiments. This model for the first time explains the cortical origins and topography of endogenous action decisions, and the natural emergence of functional specialisation in the cortex, as mechanistic consequences of neurobiological principles, anatomical structure and sensorimotor experience.