Toward a Neurobiologically Plausible Model of Language-Related, Negative Event-Related Potentials

Language-related event-related potential (ERP) components such as the N400 have traditionally been associated with linguistic or cognitive functional interpretations. By contrast, it has been considerably more difficult to relate these components to neurobiologically grounded accounts of language. Here, we propose a theoretical framework based on a predictive coding architecture, within which negative language-related ERP components such as the N400 can be accounted for in a neurobiologically plausible manner. Specifically, we posit that the amplitude of negative language-related ERP components reflects precision-weighted prediction error signals, i.e., prediction errors weighted by the relevance of the information source leading to the error. From this perspective, precision has a direct link to cue validity in a particular language and, thereby, to relevance of individual linguistic features for internal model updating. We view components such as the N400 and LAN as members of a family with similar functional characteristics and suggest that latency and topography differences between these components reflect the locus of prediction errors and model updating within a hierarchically organized cortical predictive coding architecture. This account has the potential to unify findings from the full range of the N400 literature, including word-level, sentence-, and discourse-level results as well as cross-linguistic differences

Sleep-Dependent Memory Consolidation and Incremental Sentence Comprehension : Computational Dependencies during Language Learning as Revealed by Neuronal Oscillations

We hypothesize a beneficial influence of sleep on the consolidation of the combinatorial mechanisms underlying incremental sentence comprehension. These predictions are grounded in recent work examining the effect of sleep on the consolidation of linguistic information, which demonstrate that sleep-dependent neurophysiological activity consolidates the meaning of novel words and simple grammatical rules. However, the sleep-dependent consolidation of sentence-level combinatorics has not been studied to date. Here, we propose that dissociable aspects of sleep neurophysiology consolidate two different types of combinatory mechanisms in human language: sequence-based (order-sensitive) and dependency-based (order-insensitive) combinatorics. The distinction between the two types of combinatorics is motivated both by cross-linguistic considerations and the neurobiological underpinnings of human language. Unifying this perspective with principles of sleep-dependent memory consolidation, we posit that a function of sleep is to optimize the consolidation of sequence-based knowledge (thewhen) and the establishment of semantic schemas of unordered items (thewhat) that underpin cross-linguistic variations in sentence comprehension. This hypothesis builds on the proposal that sleep is involved in the construction of predictive codes, a unified principle of brain function that supports incremental sentence comprehension. Finally, we discuss neurophysiological measures (EEG/MEG) that could be used to test these claims, such as the quantification of neuronal oscillations, which reflect basic mechanisms of information processing in the brain

An agent-first preference in a patient-first language during sentence comprehension

The language comprehension system preferentially assumes that agents come first during incremental processing. While this might reflect a biologically fixed bias, shared with other domains and other species, the evidence is limited to languages that place agents first, and so the bias could also be learned from usage frequency. Here, we probe the bias with electroencephalograph (EEG)y in Äiwoo, a language that by default places patients first, but where sentence-initial nouns are still locally ambiguous between patient or agent roles. Comprehenders transiently interpreted non-human nouns as patients, eliciting a negativity when disambiguation was toward the less common agent-initial order. By contrast and against frequencies, human nouns were transiently interpreted as agents, eliciting a N400-like negativity when disambiguation was toward patient-initial order. Consistent with the notion of a fixed property, the agent bias is robust against usage frequency for human referents. However, this bias can be reversed by frequency experience for non-human referents

Surprisal from language models can predict ERPs in processing predicate-argument structures only if enriched by an Agent Preference principle

Language models based on artificial neural networks increasingly capture key aspects of how humans process sentences. Most notably, model-based surprisals predict event-related potentials such as N400 amplitudes during parsing. Assuming that these models represent realistic estimates of human linguistic experience, their success in modelling language processing raises the possibility that the human processing system relies on no other principles than the general architecture of language models and on sufficient linguistic input. Here, we test this hypothesis on N400 effects observed during the processing of verb-final sentences in German, Basque, and Hindi. By stacking Bayesian generalised additive models, we show that, in each language, N400 amplitudes and topographies in the region of the verb are best predicted when model-based surprisals are complemented by an Agent Preference principle that transiently interprets initial role-ambiguous NPs as agents, leading to reanalysis when this interpretation fails. Our findings demonstrate the need for this principle independently of usage frequencies and structural differences between languages. The principle has an unequal force, however. Compared to surprisal, its effect is weakest in German, stronger in Hindi, and still stronger in Basque. This gradient is correlated with the extent to which grammars allow unmarked NPs to be patients, a structural feature that boosts reanalysis effects. We conclude that language models gain more neurobiological plausibility by incorporating an Agent Preference. Conversely, theories of human processing profit from incorporating surprisal estimates in addition to principles like the Agent Preference, which arguably have distinct evolutionary roots

Cross-linguistic differences in case marking shape neural power dynamics and gaze behavior during sentence planning

Languages differ in how they mark the dependencies between verbs and arguments, e.g., by case. An eye tracking and EEG picture description study examined the influence of case marking on the time course of sentence planning in Basque and Swiss German. While German assigns an unmarked (nominative) case to subjects, Basque specifically marks agent arguments through ergative case. Fixations to agents and event-related synchronization (ERS) in the theta and alpha frequency bands, as well as desynchronization (ERD) in the alpha and beta bands revealed multiple effects of case marking on the time course of early sentence planning. Speakers decided on case marking under planning early when preparing sentences with ergative-marked agents in Basque, whereas sentences with unmarked agents allowed delaying structural commitment across languages. These findings support hierarchically incremental accounts of sentence planning and highlight how cross-linguistic differences shape the neural dynamics underpinning language use.This work was funded by Swiss National Science Foundation Grant Nr. 100015_160011 (B.B. and M.M.), the NCCR Evolving Language, Swiss National Science Foundation Agreement Nr. #51NF40_180888 (B.B. and M. M.), and the PhD Program in Linguistics and the Graduate Research Campus of the University of Zurich (A.E.). DEB is supported by a grant from the Harvard Data Science Initiative and the Branco Weiss Foundation. I.B.-S. is supported by an Australian Research Council Future Fellowship (FT160100437). I.L. is supported by grants from the Spanish Ministry of Economy and Competitiveness (Grant No. FFI2015-64183-P) and the Basque Government (IT1169-19). The authors thank Anne-Lise Giraud for the suggestion to include beta-band analyses, Vitória Piai for advice on EEG data processing, Giuachin Kreiliger for statistical consultation, Andrina Balsofiore and Edurne Petrirena for help recording the lead-in fragments, Nathalie Rieser and Debora Beuret for help with data collection and processing, and the Phonogram Archives of the University of Zurich for technical support. The authors also thank two anonymous reviewers for their helpful comments on an earlier version of the manuscript

Dynamische Aspekte der Argumentinterpretation. Eine neurokognitive Perspektive

Der vorliegende Aufsatz beschäftigt sich mit der Frage, wie Argumente während des Sprachverstehens erkannt werden, welche Eigenschaften einem Argument in Abwesenheit des Verbs zugeschrieben werden und welche Art von Vorhersagen mit der Argumentinterpretation verbunden sind. Ausgehend von der Annahme, dass beim Sprachverstehen in Echtzeit jedes Wort so maximal wie möglich interpretiert wird, werden wir argumentieren, dass die zugrunde liegenden, sprachübergreifend zu findenden Mechanismen durch die Interaktion von typologisch motivierten Prominenzskalen (z.B. Belebtheitshierarchie) beschrieben werden sollten. Diese gestatten nicht nur eine Erklärung bestehender Befunde, sondern besitzen das Potenzial, zentrale Aspekte der Sprachverstehensarchitektur modelltheoretisch abzuleiten. Experimentell liegt der Fokus des Aufsatzes auf der Erfassung elektrophysiologischer-neuronaler Aktivierungsmuster, da diese uns im Gegensatz zu Urteilen oder Korpusverteilungen einen unmittelbaren Einblick in die Verarbeitung im Echtzeitbereich gestatten