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

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

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

Event-related potentials index lexical retrieval (N400) and integration (P600) during language comprehension

The functional interpretation of two salient language-sensitive ERP components - the N400 and the P600 - remains a matter of debate. Prominent alternative accounts link the N400 to processes related to lexical retrieval, semantic integration, or both, while the P600 has been associated with syntactic reanalysis or, alternatively, to semantic integration. The often overlapping predictions of these competing accounts in extant experimental designs, however, has meant that previous findings have failed to clearly decide among them. Here, we present an experiment that directly tests the competing hypotheses using a design that clearly teases apart the retrieval versus integration view of the N400, while also dissociating a syntactic reanalysis/reprocessing account of the P600 from semantic integration. Our findings provide support for an integrated functional interpretation according to which the N400 reflects context-sensitive lexical retrieval - but not integration - processes. While the observed P600 effects were not predicted by any account, we argue that they can be reconciled with the integration view, if spatio-temporal overlap of ERP components is taken into consideration

When components collide : Spatiotemporal overlap of the N400 and P600 in language comprehension

The problem of spatiotemporal overlap between event-related potential (ERP) components is generally acknowledged in language research. However, its implications for the interpretation of experimental results are often overlooked. In a previous experiment on the functional interpretation of the N400 and P600, it was argued that a P600 effect to implausible words was largely obscured – in one of two implausible conditions – by an overlapping N400 effect of semantic association. In the present ERP study, we show that the P600 effect of implausibility is uncovered when the critical condition is tested against a proper baseline condition which elicits a similar N400 amplitude, while it is obscured when tested against a baseline condition producing an N400 effect. Our findings reveal that component overlap can result in the apparent absence or presence of an effect in the surface signal and should therefore be carefully considered when interpreting ERP patterns. Importantly, we show that, by factoring in the effects of spatiotemporal overlap between the N400 and P600 on the surface signal, which we reveal using rERP analysis, apparent inconsistencies in previous findings are easily reconciled, enabling us to draw unambiguous conclusions about the functional interpretation of the N400 and P600 components. Overall, our results provide compelling evidence that the N400 reflects lexical retrieval processes, while the P600 indexes compositional integration of word meaning into the unfolding utterance interpretation

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

Semantic Entropy in Language Comprehension

Language is processed on a more or less word-by-word basis, and the processing difficulty induced by each word is affected by our prior linguistic experience as well as our general knowledge about the world. Surprisal and entropy reduction have been independently proposed as linking theories between word processing difficulty and probabilistic language models. Extant models, however, are typically limited to capturing linguistic experience and hence cannot account for the influence of world knowledge. A recent comprehension model by Venhuizen, Crocker, and Brouwer (2019, Discourse Processes) improves upon this situation by instantiating a comprehension-centric metric of surprisal that integrates linguistic experience and world knowledge at the level of interpretation and combines them in determining online expectations. Here, we extend this work by deriving a comprehension-centric metric of entropy reduction from this model. In contrast to previous work, which has found that surprisal and entropy reduction are not easily dissociated, we do find a clear dissociation in our model. While both surprisal and entropy reduction derive from the same cognitive process—the word-by-word updating of the unfolding interpretation—they reflect different aspects of this process: state-by-state expectation (surprisal) versus end-state confirmation (entropy reduction)

Expectation-based Comprehension : Modeling the Interaction of World Knowledge and Linguistic Experience

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