On the Proper Treatment of the N400 and P600 in Language Comprehension

Event-Related Potentials (ERPs)—stimulus-locked, scalp-recorded voltage fluctuations caused by post-synaptic neural activity—have proven invaluable to the study of language comprehension. Of interest in the ERP signal are systematic, reoccurring voltage fluctuations called components, which are taken to reflect the neural activity underlying specific computational operations carried out in given neuroanatomical networks (cf. Näätänen and Picton, 1987). For language processing, the N400 component and the P600 component are of particular salience (see Kutas et al., 2006, for a review). The typical approach to determining whether a target word in a sentence leads to differential modulation of these components, relative to a control word, is to look for effects on mean amplitude in predetermined time-windows on the respective ERP waveforms, e.g., 350–550 ms for the N400 component and 600–900 ms for the P600 component. The common mode of operation in psycholinguistics, then, is to tabulate the presence/absence of N400- and/or P600-effects across studies, and to use this categorical data to inform neurocognitive models that attribute specific functional roles to the N400 and P600 component (see Kuperberg, 2007; Bornkessel-Schlesewsky and Schlesewsky, 2008; Brouwer et al., 2012, for reviews). Here, we assert that this Waveform-based Component Structure (WCS) approach to ERPs leads to inconsistent data patterns, and hence, misinforms neurocognitive models of the electrophysiology of language processing. The reason for this is that the WCS approach ignores the latent component structure underlying ERP waveforms (cf. Luck, 2005), thereby leading to conclusions about component structure that do not factor in spatiotemporal component overlap of the N400 and the P600. This becomes particularly problematic when spatiotemporal component overlap interacts with differential P600 modulations due to task demands (cf. Kolk et al., 2003). While the problem of spatiotemporal component overlap is generally acknowledged, and occasionally invoked to account for within-study inconsistencies in the data, its implications are often overlooked in psycholinguistic theorizing that aims to integrate findings across studies. We believe WCS-centric theorizing to be the single largest reason for the lack of convergence regarding the processes underlying the N400 and the P600, thereby seriously hindering the advancement of neurocognitive theories and models of language processing

Evidence for a Tagging Model of Human Lexical Category Disambiguation.

We investigate the explanatory power of very simple statistical mechanisms within a modular model of the Human Sentence Processing Mechanism. In particular, we borrow the idea of a 'part-of-speech tagger' from the field of Naniral Language Processing, and use this to explain a number of existing experimental results in the area of lexical category disambiguation. Not only can each be explained without the need to posit extra mechanisms or constraints, but the exercise also suggests a novel account for some established data

Accessibility of Depicted Events Influences Their Priority in Spoken Comprehension

Knoeferle P, Crocker M. Accessibility of depicted events influences their priority in spoken comprehension. In: Proceedings of the 27th Annual Conference of the Cognitive Science Society. 2006: 1623-1628.Studies that monitor attention in depicted event scenes during utterance comprehension show that people prefer to rely on depicted events rather than their stereotypical knowledge. However, the presence of depicted events in the scene may have exaggerated their importance. Two eye-tracking experiments examined this issue by varying the accessibility of scene information. When we varied the visual presence of the scene (the scene disappeared before the utterance was heard), findings confirmed a greater relative priority of depicted events (Experiment 1). In contrast, when we altered the temporal extension of scene events (scene presentation emulated that they had been completed), people neither had a preference to rely on depicted events nor on their stereotypical knowledge (Experiment 2). These findings suggest that the visual presence of scene events cannot account for the preference to rely on depicted events. We discuss our findings in the context of research on the accessibility of events in discourse comprehension (e.g., Zwaan, Maaden, & Whitten, 2000)

On the predictability of event boundaries in discourse : An ERP investigation

When reading a text describing an everyday activity, comprehenders build a model of the situation described that includes prior knowledge of the entities, locations, and sequences of actions that typically occur within the event. Previous work has demonstrated that such knowledge guides the processing of incoming information by making event boundaries more or less expected. In the present ERP study, we investigated whether comprehenders’ expectations about event boundaries are influenced by how elaborately common events are described in the context. Participants read short stories in which a common activity (e.g., washing the dishes) was described either in brief or in an elaborate manner. The final sentence contained a target word referring to a more predictable action marking a fine event boundary (e.g., drying) or a less predictable action, marking a coarse event boundary (e.g., jogging). The results revealed a larger N400 effect for coarse event boundaries compared to fine event boundaries, but no interaction with description length. Between 600 and 1000 ms, however, elaborate contexts elicited a larger frontal positivity compared to brief contexts. This effect was largely driven by less predictable targets, marking coarse event boundaries. We interpret the P600 effect as indexing the updating of the situation model at event boundaries, consistent with Event Segmentation Theory (EST). The updating process is more demanding with coarse event boundaries, which presumably require the construction of a new situation model

Semantic Systematicity in Connectionist Language Production

Decades of studies trying to define the extent to which artificial neural networks can exhibit systematicity suggest that systematicity can be achieved by connectionist models but not by default. Here we present a novel connectionist model of sentence production that employs rich situation model representations originally proposed for modeling systematicity in comprehension. The high performance of our model demonstrates that such representations are also well suited to model language production. Furthermore, the model can produce multiple novel sentences for previously unseen situations, including in a different voice (actives vs. passive) and with words in new syntactic roles, thus demonstrating semantic and syntactic generalization and arguably systematicity. Our results provide yet further evidence that such connectionist approaches can achieve systematicity, in production as well as comprehension. We propose our positive results to be a consequence of the regularities of the microworld from which the semantic representations are derived, which provides a sufficient structure from which the neural network can interpret novel inputs

Splitting event-related potentials: Modeling latent components using regression-based waveform estimation

Event‐related potentials (ERPs) provide a multidimensional and real‐time window into neurocognitive processing. The typical Waveform‐based Component Structure (WCS) approach to ERPs assesses the modulation pattern of components—systematic, reoccurring voltage fluctuations reflecting specific computational operations—by looking at mean amplitude in predetermined time‐windows. This WCS approach, however, often leads to inconsistent results within as well as across studies. It has been argued that at least some inconsistencies may be reconciled by considering spatiotemporal overlap between components; that is, components may overlap in both space and time, and given their additive nature, this means that the WCS may fail to accurately represent its underlying latent component structure (LCS). We employ regression‐based ERP (rERP) estimation to extend traditional approaches with an additional layer of analysis, which enables the explicit modeling of the LCS underlying WCS. To demonstrate its utility, we incrementally derive an rERP analysis of a recent study on language comprehension with seemingly inconsistent WCS‐derived results. Analysis of the resultant regression models allows one to derive an explanation for the WCS in terms of how relevant regression predictors combine in space and time, and crucially, how individual predictors may be mapped onto unique components in LCS, revealing how these spatiotemporally overlap in the WCS. We conclude that rERP estimation allows for investigating how scalp‐recorded voltages derive from the spatiotemporal combination of experimentally manipulated factors. Moreover, when factors can be uniquely mapped onto components, rERPs may offer explanations for seemingly inconsistent ERP waveforms at the level of their underlying latent component structure

Connectionist Semantic Systematicity in Language Production

A novel connectionist model of sentence production is pre-sented, which employs rich situation model representationsoriginally proposed for modeling systematicity in comprehen-sion (Frank, Haselager, & van Rooij, 2009). The high overallperformance of our model demonstrates that such represen-tations are not only suitable for comprehension, but also formodeling language production. Further, the model is able toproduce novel encodings (active vs. passive) for a particularsemantics, as well as generate such encodings for previouslyunseen situations, thus demonstrating both syntactic and se-mantic systematicity. Our results provide yet further evidencethat such connectionist approaches can achieve systematicity,in production as well as comprehension

Teasing apart coercion and surprisal : Evidence from eye-movements and ERPs

Previous behavioral and electrophysiological studies have presented evidence suggesting that coercion expressions (e.g., began the book) are more difficult to process than control expressions like read the book. While this processing cost has been attributed to a specific coercion operation for recovering an eventsense of the complement (e.g., began reading the book), an alternative view based on the Surprisal Theory of language processing would attribute the cost to the relative unpredictability of the complement noun in the coercion compared to the control condition, with no need to postulate coercionspecific mechanisms. In two experiments, monitoring eye-tracking and event-related potentials (ERPs), respectively, we sought to determine whether there is any evidence for coercion-specific processing cost above-and-beyond the difficulty predicted by surprisal, by contrasting coercing and control expressions with a further control condition in which the predictability of the complement noun was similar to that in the coercion condition (e.g., bought the book). While the eye-tracking study showed significant effects of surprisal and a marginal effect of coercion on late reading measures, the ERP study clearly supported the surprisal account. Overall, our findings suggest that the coercion cost largely reflects the surprisal of the complement noun with coercion specific operations possibly influencing later processing stages

Single-trial neurodynamics reveal N400 and P600 coupling in language comprehension

Theories of the electrophysiology of language comprehension are mostly informed by event-related potential effects observed between condition averages. We here argue that a dissociation between competing effect-level explanations of event-related potentials can be achieved by turning to predictions and analyses at the single-trial level. Specifically, we examine the single-trial dynamics in event-related potential data that exhibited a biphasic N400–P600 effect pattern. A group of multi-stream models can explain biphasic effects by positing that each individual trial should induce either an N400 increase or a P600 increase, but not both. An alternative, single-stream account, Retrieval-Integration theory, explicitly predicts that N400 amplitude and P600 amplitude should be correlated at the single-trial level. In order to investigate the single-trial dynamics of the N400 and the P600, we apply a regression-based technique in which we quantify the extent to which N400 amplitudes are predictive of the electroencephalogram in the P600 time window. Our findings suggest that, indeed, N400 amplitudes and P600 amplitudes are inversely correlated within-trial and, hence, the N400 effect and the P600 effect in biphasic data are driven by the same trials. Critically, we demonstrate that this finding also extends to data which exhibited only monophasic effects between conditions. In sum, the observation that the N400 is inversely correlated with the P600 on a by-trial basis supports a single stream view, such as Retrieval-Integration theory, and is difficult to reconcile with the processing mechanisms proposed by multi-stream models