Ten years ago, researchers using event‐related brain potentials (ERPs) to study language comprehension were puzzled by what looked like a Semantic Illusion: Semantically anomalous, but structurally well‐formed sentences did not affect the N400 component—traditionally taken to reflect semantic integration—but instead produced a P600 effect, which is generally linked to syntactic processing. This finding led to a considerable amount of debate, and a number of complex processing models have been proposed as an explanation. What these models have in common is that they postulate two or more separate processing streams, in order to reconcile the Semantic Illusion and other semantically induced P600 effects with the traditional interpretations of the N400 and the P600. Recently, however, these multi‐stream models have been called into question, and a simpler single‐stream model has been proposed. According to this alternative model, the N400 component reflects the retrieval of word meaning from semantic memory, and the P600 component indexes the integration of this meaning into the unfolding utterance interpretation. In the present paper, we provide support for this “Retrieval–Integration (RI)” account by instantiating it as a neurocomputational model. This neurocomputational model is the first to successfully simulate the N400 and P600 amplitude in language comprehension, and simulations with this model provide a proof of concept of the single‐stream RI account of semantically induced patterns of N400 and P600 modulations.
The processing difficulty of each word we encounter in a sentence is affected by both our prior linguistic experience and our general knowledge about the world. Computational models of incremental language processing have, however, been limited in accounting for the influence of world knowledge. We develop an incremental model of language comprehension that constructs-on a word-by-word basis-rich, probabilistic situation model representations. To quantify linguistic processing effort, we adopt Surprisal Theory, which asserts that the processing difficulty incurred by a word is inversely proportional to its expectancy (Hale, 2001;Levy, 2008). In contrast with typical language model implementations of surprisal, the proposed model instantiates a novel comprehension-centric metric of surprisal that reflects the likelihood of the unfolding utterance meaning as established after processing each word. Simulations are presented that demonstrate that linguistic experience and world knowledge are integrated in the model at the level of interpretation and combine in determining online expectations.
What would be a good method to provide a large collection of semantically annotated texts with formal, deep semantics rather than shallow? In this talk I will argue that (i) a bootstrapping approach comprising state-of-the-art NLP tools for semantic parsing, in combination with (ii) a wiki-like interface for collaborative annotation of experts, and (iii) a game with a purpose for crowdsourcing, are the starting ingredients for fulfilling this enterprise. The result, known as the Groningen Meaning Bank, is a semantic resource that anyone can edit and that integrates various semantic phenomena, including predicate-argument structure, scope, tense, thematic roles, animacy, pronouns, and rhetorical relations. A single semantic formalism, Discourse Representation Theory, embraces all these phenonema by taking meaning representations of texts rather than sentences as the units of annotation.
Expectation-based theories of language comprehension, in particular Surprisal Theory, go a long way in accounting for the behavioral correlates of word-by-word processing difficulty, such as reading times. An open question, however, is in which component(s) of the Event-Related brain Potential (ERP) signal Surprisal is reflected, and how these electrophysiological correlates relate to behavioral processing indices. Here, we address this question by instantiating an explicit neurocomputational model of incremental, word-by-word language comprehension that produces estimates of the N400 and the P600—the two most salient ERP components for language processing—as well as estimates of “comprehension-centric” Surprisal for each word in a sentence. We derive model predictions for a recent experimental design that directly investigates “world-knowledge”-induced Surprisal. By relating these predictions to both empirical electrophysiological and behavioral results, we establish a close link between Surprisal, as indexed by reading times, and the P600 component of the ERP signal. The resultant model thus offers an integrated neurobehavioral account of processing difficulty in language comprehension.
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