Ten years ago, researchers using event-related brain potentials 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 ” 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. (shrink)

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 of the Event-Related brain Potential 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. (shrink)

Evidence from numerous studies using the visual world paradigm has revealed both that spoken language can rapidly guide attention in a related visual scene and that scene information can immediately influence comprehension processes. These findings motivated the coordinated interplay account (Knoeferle & Crocker, 2006) of situated comprehension, which claims that utterance‐mediated attention crucially underlies this closely coordinated interaction of language and scene processing. We present a recurrent sigma‐pi neural network that models the rapid use of scene information, exploiting an utterance‐mediated (...) attentional mechanism that directly instantiates the CIA. The model is shown to achieve high levels of performance (both with and without scene contexts), while also exhibiting hallmark behaviors of situated comprehension, such as incremental processing, anticipation of appropriate role fillers, as well as the immediate use, and priority, of depicted event information through the coordinated use of utterance‐mediated attention to the scene. (shrink)

Experimental research shows that human sentence processing uses information from different levels of linguistic analysis, for example, lexical and syntactic preferences as well as semantic plausibility. Existing computational models of human sentence processing, however, have focused primarily on lexico‐syntactic factors. Those models that do account for semantic plausibility effects lack a general model of human plausibility intuitions at the sentence level. Within a probabilistic framework, we propose a wide‐coverage model that both assigns thematic roles to verb–argument pairs and determines a (...) preferred interpretation by evaluating the plausibility of the resulting (verb, role, argument) triples. The model is trained on a corpus of role‐annotated language data. We also present a transparent integration of the semantic model with an incremental probabilistic parser. We demonstrate that both the semantic plausibility model and the combined syntax/semantics model predict judgment and reading time data from the experimental literature. (shrink)

A variety of mechanisms contribute to word learning. Learners can track co-occurring words and referents across situations in a bottom-up manner. Equally, they can exploit sentential contexts, relying on top–down information such as verb–argument relations and world knowledge, offering immediate constraints on meaning. When combined, CSWL and SLCL potentially modulate each other's influence, revealing how word learners deal with multiple mechanisms simultaneously: Do they use all mechanisms? Prefer one? Is their strategy context dependent? Three experiments conducted with adult learners reveal (...) that learners prioritize SLCL over CSWL. CSWL is applied in addition to SLCL only if SLCL is not perfectly disambiguating, thereby complementing or competing with it. These studies demonstrate the importance of investigating word-learning mechanisms simultaneously, revealing important characteristics of their interaction in more naturalistic learning environments. (shrink)

Beyond the observation that both speakers and listeners rapidly inspect the visual targets of referring expressions, it has been argued that such gaze may constitute part of the communicative signal. In this study, we investigate whether a speaker may, in principle, exploit listener gaze to improve communicative success. In the context of a virtual environment where listeners follow computer-generated instructions, we provide two kinds of support for this claim. First, we show that listener gaze provides a reliable real-time index of (...) understanding even in dynamic and complex environments, and on a per-utterance basis. Second, we show that a language generation system that uses listener gaze to provide rapid feedback improves overall task performance in comparison with two systems that do not use gaze. Aside from demonstrating the utility of listener gaze in situated communication, our findings open the door to new methods for developing and evaluating multi-modal models of situated interaction. (shrink)

In referential communication, Grice's Maxim of Quantity is thought to imply that utterances conveying unnecessary information should incur comprehension difficulties. There is, however, considerable evidence that speakers frequently encode redundant information in their referring expressions, raising the question as to whether such overspecifications hinder listeners’ processing. Evidence from previous work is inconclusive, and mostly comes from offline studies. In this article, we present two event‐related potential (ERP) experiments, investigating the real‐time comprehension of referring expressions that contain redundant adjectives in complex (...) visual contexts. Our findings provide support for both Gricean and bounded‐rational accounts. We argue that these seemingly incompatible results can be reconciled if common ground is taken into account. We propose a bounded‐rational account of overspecification, according to which even redundant words can be beneficial to comprehension to the extent that they facilitate the reduction of listeners’ uncertainty regarding the target referent. (shrink)