This paper investigates the role of resource allocation as a source of processing difficulty in human sentence comprehension. The paper proposes a simple informationtheoretic characterization of processing difficulty as the work incurred by resource reallocation during parallel, incremental, probabilistic disambiguation in sentence comprehension, and demonstrates its equivalence to the theory of Hale (2001), in which the difficulty of a word is proportional to its surprisal (its negative log-probability) in the context within which it appears. This proposal subsumes and clarifies findings that high-constraint contexts can facilitate lexical processing, and connects these findings to well-known models of parallel constraint-based comprehension. In addition, the theory leads to a number of specific predictions about the role of expectation in syntactic comprehension, including the reversal of locality-based difficulty patterns in syntactically constrained contexts, and conditions under which increased ambiguity facilitates processing. The paper examines a range of established results bearing on these predictions, and shows that they are largely consistent with the surprisal theory.

Although for many years a sharp distinction has been made in language research between rules and words—with primary interest on rules—this distinction is now blurred in many theories. If anything, the focus of attention has shifted in recent years in favor of words. Results from many different areas of language research suggest that the lexicon is representationally rich, that it is the source of much productive behavior, and that lexically specific information plays a critical and early role in the interpretation of grammatical structure. But how much information can or should be placed in the lexicon? This is the question I address here. I review a set of studies whose results indicate that event knowledge plays a significant role in early stages of sentence processing and structural analysis. This poses a conundrum for traditional views of the lexicon. Either the lexicon must be expanded to include factors that do not plausibly seem to belong there; or else virtually all information about word meaning is removed, leaving the lexicon impoverished. I suggest a third alternative, which provides a way to account for lexical knowledge without a mental lexicon.

This article examines cognitive process models of human sentence comprehension based on the idea of informed search. These models are rational in the sense that they strive to quickly find a good syntactic analysis. Informed search derives a new account of garden pathing that handles traditional counterexamples. It supports a symbolic explanation for local coherence as well as an algorithmic account of entropy reduction. The models are expressed in a broad framework for theories of human sentence comprehension. 1

Subsymbolic systems have been successfully used to model several aspects of human language processing. Yet, it has proven difficult to scale them up to realistic language. They have limited memory capacity, long training times, and difficulty representing the wealth of linguistic structure. In this paper, a new connectionist model, InSomNet, is presented that scales up by utilizing semantic self-organization. InSomNet was trained on semantic dependency graph representations from the Redwoods Treebank of sentences from the VerbMobil project. The results show that InSomNet learns to represent these semantic dependencies accurately and generalizes to novel structures. Further evaluation of InSomNet on the original spoken language transcripts shows that it can also process noisy input robustly, and its performance degrades gracefully when noise is added to the network weights, underscoring how InSomNet tolerates damage. It interprets sentences nonmonotonically, i.e., it generates expectations and revises them, primes future inputs based on semantics, and coactivates multiple interpretations in the output. In other words, while scaling up it still retains the cognitively valid behavior typical of subsymbolic systems. 1

This is the pre-publication manuscript. The published version may slightly differ.

Interpreting a verb-phrase ellipsis (VP ellipsis) requires accessing an antecedent in memory, and then integrating a representation of this antecedent into the local context. This problem was investigated in 4 speed-accuracy trade-off and 2 eye-tracking experiments. To investigate whether the antecedent for a VP ellipsis is accessed with a search or direct-access retrieval process, Experiments 1 and 2 measured the affect of the distance between an ellipsis and its antecedent on the speed and accuracy of comprehension. Accuracy was lower with longer distances, indicating that interpolated material reduced the quality of retrieved information about the antecedent. However, contra a search process, distance did not affect the speed of interpreting ellipsis. This pattern suggests that antecedent representations are content-addressable and retrieved with a direct-access process. To determine whether interpreting ellipsis involves copying antecedent information into the ellipsis site, Experiments 3-6 manipulated the length and complexity of the antecedent. Some types of antecedent complexity—particularly, the number of discourse entities in the antecedent—lowered accuracy. However, neither antecedent length nor complexity affected the speed of interpreting the ellipsis. This pattern is inconsistent with a copy operation, and it suggests that ellipsis interpretation may involve a pointer to extant structures in memory.

Roark, and two anonymous reviewers for their help and comments. The research presented here was funded by the Netherlands Organization for Scientific Research (NWO; grant number 277-70-006) and by the European Union 7th Framework

Human sentence processing occurs incrementally. Most models of human processing rely on parsers that always build connected tree structures. But according to the theory of Good Enough parsing (Ferreira & Patson, 2007), humans parse sentences using small chunks of local information, not always forming a globally coherent parse. This difference is apparent in the study of local coherence effects (Tabor, Galantucci, & Richardson, 2004), wherein a locally plausible interpretation interferes with the correct global interpretation of a sentence. We present a model that accounts for these effects using a wide-coverage parser that captures the idea of Good Enough parsing. Using Combinatory Categorial Grammar, our parser works bottom-up, enforcing the use of local information only. We model the difficulty of processing a sentence in terms of the probability of a locally coherent reading relative to the probability of the globally coherent reading of the sentence. Our model successfully predicts psycholinguistic results.

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The Visual World Paradigm (VWP) presents listeners with a challenging problem: They must integrate two disparate signals, the spoken language and the visual context, in support of action (e.g., complex movements of the eyes across a scene). We present Impulse Processing, a dynamical systems approach to incremental eye movements in the visual world that suggests a framework for integrating language, vision, and action generally. Our approach assumes that impulses driven by the language and the visual context impinge minutely on a dynamical landscape of attractors corresponding to the potential eye-movement behaviors of the system. We test three unique predictions of our approach in an empirical study in the VWP, and describe an implementation in an artificial neural network. We discuss the Impulse Processing framework in relation to other models of the VWP. Keywords: Dynamical systems; Self-organization; Local coherence; Artificial neural networks;