Publications

Abstract

Human language offers a variety of ways to create meaning, one of which is referring to entities, objects, or events in the world. One such meaning maker is understanding to whom or to what a pronoun in a discourse refers to. To understand a pronoun, the brain must access matching entities or concepts that have been encoded in memory from previous linguistic context. Models of language processing propose that internally stored linguistic concepts, accessed via exogenous cues such as phonological input of a word, are represented as (a)synchronous activities across a population of neurons active at specific frequency bands. Converging evidence suggests that delta band activity (1–3 Hz) is involved in temporal and representational integration during sentence processing. Moreover, recent advances in the neurobiology of memory suggest that recollection engages neural dynamics similar to those which occurred during memory encoding. Integrating from these two research lines, we here tested the hypothesis that neural dynamic patterns, especially in delta frequency range, underlying referential meaning representation, would be reinstated during pronoun resolution. By leveraging neural decoding techniques (i.e., representational similarity analysis) on a magnetoencephalogram data set acquired during a naturalistic story-listening task, we provide evidence that delta-band activity underlies referential meaning representation. Our findings suggest that, during spoken language comprehension, endogenous linguistic representations such as referential concepts may be proactively retrieved and represented via activation of their underlying dynamic neural patterns.

Abstract

When we understand language, we recognize words and combine them into sentences. In this article, we explore the hypothesis that listeners use probabilistic information about words to build syntactic structure. Recent work has shown that lexical probability and syntactic structure both modulate the delta-band (<4 Hz) neural signal. Here, we investigated whether the neural encoding of syntactic structure changes as a function of the distributional properties of a word. To this end, we analyzed MEG data of 24 native speakers of Dutch who listened to three fairytales with a total duration of 49 min. Using temporal response functions and a cumulative model-comparison approach, we evaluated the contributions of syntactic and distributional features to the variance in the delta-band neural signal. This revealed that lexical surprisal values (a distributional feature), as well as bottom-up node counts (a syntactic feature) positively contributed to the model of the delta-band neural signal. Subsequently, we compared responses to the syntactic feature between words with high- and low-surprisal values. This revealed a delay in the response to the syntactic feature as a consequence of the surprisal value of the word: high-surprisal values were associated with a delayed response to the syntactic feature by 150–190 ms. The delay was not affected by word duration, and did not have a lexical origin. These findings suggest that the brain uses probabilistic information to infer syntactic structure, and highlight an importance for the role of time in this process.

Abstract

From a brain's-eye-view, when a stimulus occurs and what it is are interrelated aspects of interpreting the perceptual world. Yet in practice, the putative perceptual inferences about sensory content and timing are often dichotomized and not investigated as an integrated process. We here argue that neural temporal dynamics can influence what is perceived, and in turn, stimulus content can influence the time at which perception is achieved. This computational principle results from the highly interdependent relationship of what and when in the environment. Both brain processes and perceptual events display strong temporal variability that is not always modeled; we argue that understanding—and, minimally, modeling—this temporal variability is key for theories of how the brain generates unified and consistent neural representations and that we ignore temporal variability in our analysis practice at the peril of both data interpretation and theory-building. Here, we review what and when interactions in the brain, demonstrate via simulations how temporal variability can result in misguided interpretations and conclusions, and outline how to integrate and synthesize what and when in theories and models of brain computation.

Abstract

Neural oscillations reflect fluctuations in excitability, which biases the percept of ambiguous sensory input. Why this bias occurs is still not fully understood. We hypothesized that neural populations representing likely events are more sensitive, and thereby become active on earlier oscillatory phases, when the ensemble itself is less excitable. Perception of ambiguous input presented during less-excitable phases should therefore be biased toward frequent or predictable stimuli that have lower activation thresholds. Here, we show such a frequency bias in spoken word recognition using psychophysics, magnetoencephalography (MEG), and computational modelling. With MEG, we found a double dissociation, where the phase of oscillations in the superior temporal gyrus and medial temporal gyrus biased word-identification behavior based on phoneme and lexical frequencies, respectively. This finding was reproduced in a computational model. These results demonstrate that oscillations provide a temporal ordering of neural activity based on the sensitivity of separable neural populations.

Abstract

Humans excel at extracting structurally-determined meaning from speech despite inherent physical variability. This study explores the brain’s ability to predict and understand spoken language robustly. It investigates the relationship between structural and statistical language knowledge in brain dynamics, focusing on phase and amplitude modulation. Using syntactic features from constituent hierarchies and surface statistics from a transformer model as predictors of forward encoding models, we reconstructed cross-frequency neural dynamics from MEG data during audiobook listening. Our findings challenge a strict separation of linguistic structure and statistics in the brain, with both aiding neural signal reconstruction. Syntactic features have a more temporally spread impact, and both word entropy and the number of closing syntactic constituents are linked to the phase-amplitude coupling of neural dynamics, implying a role in temporal prediction and cortical oscillation alignment during speech processing. Our results indicate that structured and statistical information jointly shape neural dynamics during spoken language comprehension and suggest an integration process via a cross-frequency coupling mechanism

Abstract

Electrophysiological brain activity has been shown to synchronize with the quasi-regular repetition of grammatical phrases in connected speech—so-called phrase-rate neural tracking. Current debate centers around whether this phenomenon is best explained in terms of the syntactic properties of phrases or in terms of syntax-external information, such as the sequential repetition of parts of speech. As these two factors were confounded in previous studies, much of the literature is compatible with both accounts. Here, we used electroencephalography (EEG) to determine if and when the brain is sensitive to both types of information. Twenty native speakers of Mandarin Chinese listened to isochronously presented streams of monosyllabic words, which contained either grammatical two-word phrases (e.g., catch fish, sell house) or non-grammatical word combinations (e.g., full lend, bread far). Within the grammatical conditions, we varied two structural factors: the position of the head of each phrase and the type of attachment. Within the non-grammatical conditions, we varied the consistency with which parts of speech were repeated. Tracking was quantified through evoked power and inter-trial phase coherence, both derived from the frequency-domain representation of EEG responses. As expected, neural tracking at the phrase rate was stronger in grammatical sequences than in non-grammatical sequences without syntactic structure. Moreover, it was modulated by both attachment type and head position, revealing the structure-sensitivity of phrase-rate tracking. We additionally found that the brain tracks the repetition of parts of speech in non-grammatical sequences. These data provide an integrative perspective on the current debate about neural tracking effects, revealing that the brain utilizes regularities computed over multiple levels of linguistic representation in guiding rhythmic computation.

Abstract

Research into the role of brain oscillations in basic perceptual and cognitive functions has suggested that the alpha rhythm reflects functional inhibition while the beta rhythm reflects neural ensemble (re)activation. However, little is known regarding the generalization of these proposed fundamental operations to linguistic processes, such as speech comprehension and production. Here, we recorded magnetoencephalography in participants performing a novel rule-switching paradigm. Specifically, Dutch native speakers had to produce an alternative exemplar from the same category or a feature of a given target word embedded in spoken sentences (e.g., for the word “tuna”, an exemplar from the same category—“seafood”—would be “shrimp”, and a feature would be “pink”). A cue indicated the task rule—exemplar or feature—either before (pre-cue) or after (retro-cue) listening to the sentence. Alpha power during the working memory delay was lower for retro-cue compared with that for pre-cue in the left hemispheric language-related regions. Critically, alpha power negatively correlated with reaction times, suggestive of alpha facilitating task performance by regulating inhibition in regions linked to lexical retrieval. Furthermore, we observed a different spatiotemporal pattern of beta activity for exemplars versus features in the right temporoparietal regions, in line with the proposed role of beta in recruiting neural networks for the encoding of distinct categories. Overall, our study provides evidence for the generalizability of the role of alpha and beta oscillations from perceptual to more “complex, linguistic processes” and offers a novel task to investigate links between rule-switching, working memory, and word production.

Abstract

In psycholinguistic studies using error rates as a response measure, response times (RT) are most often analyzed independently of the error rate, although it is widely recognized that they are related. In this paper we present a mixed effects logistic regression model for the error rate that uses RT as a trial-level fixed- and random-effect regression input. Production data from a translation–recall experiment are analyzed as an example. Several model comparisons reveal that RT improves the fit of the regression model for the error rate. Two simulation studies then show how the mixed effects regression model can identify individual participants for whom (a) faster responses are more accurate, (b) faster responses are less accurate, or (c) there is no relation between speed and accuracy. These results show that this type of model can serve as a useful adjunct to traditional techniques, allowing psycholinguistic researchers to examine more closely the relationship between RT and accuracy in individual subjects and better account for the variability which may be present, as well as a preliminary step to more advanced RT–accuracy modeling.

Abstract

The animacy distinction is deeply rooted in the language faculty. A key example is differential object marking, the phenomenon where animate sentential objects receive specific marking. We used event-related potentials to examine the neural processing consequences of case-marking violations on animate and inanimate direct objects in Spanish. Inanimate objects with incorrect prepositional case marker ‘a’ (‘al suelo’) elicited a P600 effect compared to unmarked objects, consistent with previous literature. However, animate objects without the required prepositional case marker (‘el obispo’) only elicited an N400 effect compared to marked objects. This novel finding, an exclusive N400 modulation by a straightforward grammatical rule violation, does not follow from extant neurocognitive models of sentence processing, and mirrors unexpected “semantic P600” effects for thematically problematic sentences. These results may reflect animacy asymmetry in competition for argument prominence: following the article, thematic interpretation difficulties are elicited only by unexpectedly animate objects.

Abstract

Successful language use requires access to products of past processing within an evolving discourse. A central issue for any neurocognitive theory of language then concerns the role of memory variables during language processing. Under a cue-based retrieval account of language comprehension, linguistic dependency resolution (e.g., retrieving antecedents) is subject to interference from other information in the sentence, especially information that occurs between the words that form the dependency (e.g., between the antecedent and the retrieval site). Retrieval interference may then shape processing complexity as a function of the match of the information at retrieval with the antecedent versus other recent or similar items in memory. To address these issues, we studied the online processing of ellipsis in Castilian Spanish, a language with morphological gender agreement. We recorded event-related brain potentials while participants read sentences containing noun-phrase ellipsis indicated by the determiner otro/a (‘another’). These determiners had a grammatically correct or incorrect gender with respect to their antecedent nouns that occurred earlier in the sentence. Moreover, between each antecedent and determiner, another noun phrase occurred that was structurally unavailable as an antecedent and that matched or mismatched the gender of the antecedent (i.e., a local agreement attractor). In contrast to extant P600 results on agreement violation processing, and inconsistent with predictions from neurocognitive models of sentence processing, grammatically incorrect determiners evoked a sustained, broadly distributed negativity compared to correct ones between 400 and 1000 ms after word onset, possibly related to sustained negativities as observed for referential processing difficulties. Crucially, this effect was modulated by the attractor: an increased negativity was observed for grammatically correct determiners that did not match the gender of the attractor, suggesting that structurally unavailable noun phrases were at least temporarily considered for grammatically correct ellipsis. These results constitute the first ERP evidence for cue-based retrieval interference during comprehension of grammatical sentences.

Abstract

The aim of this event-related fMRI study was to investigate the cortical networks involved in case processing, an operation that is crucial to language comprehension yet whose neural underpinnings are not well-understood. What is the relationship of these networks to those that serve other aspects of syntactic and semantic processing? Participants read Basque sentences that contained case violations, number agreement violations or semantic anomalies, or that were both syntactically and semantically correct. Case violations elicited activity increases, compared to correct control sentences, in a set of parietal regions including the posterior cingulate, the precuneus, and the left and right inferior parietal lobules. Number agreement violations also elicited activity increases in left and right inferior parietal regions, and additional activations in the left and right middle frontal gyrus. Regions-of-interest analyses showed that almost all of the clusters that were responsive to case or number agreement violations did not differentiate between these two. In contrast, the left and right anterior inferior frontal gyrus and the dorsomedial prefrontal cortex were only sensitive to semantic violations. Our results suggest that whereas syntactic and semantic anomalies clearly recruit distinct neural circuits, case, and number violations recruit largely overlapping neural circuits and that the distinction between the two rests on the relative contributions of parietal and prefrontal regions, respectively. Furthermore, our results are consistent with recently reported contributions of bilateral parietal and dorsolateral brain regions to syntactic processing, pointing towards potential extensions of current neurocognitive theories of language. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.

Abstract

Propositional truth-value can be a defining feature of a sentence’s relevance to the unfolding discourse, and establishing propositional truth-value in context can be key to successful interpretation. In the current study, we investigate its role in the comprehension of counterfactual conditionals, which describe imaginary consequences of hypothetical events, and are thought to require keeping in mind both what is true and what is false. Pre-stored real-world knowledge may therefore intrude upon and delay counterfactual comprehension, which is predicted by some accounts of discourse comprehension, and has been observed during online comprehension. The impact of propositional truth-value may thus be delayed in counterfactual conditionals, as also claimed for sentences containing other types of logical operators (e.g., negation, scalar quantifiers). In an event-related potential (ERP) experiment, we investigated the impact of propositional truth-value when described consequences are both true and predictable given the counterfactual premise. False words elicited larger N400 ERPs than true words, in negated counterfactual sentences (e.g., “If N.A.S.A. had not developed its Apollo Project, the first country to land on the moon would have been Russia/America”) and real-world sentences (e.g., “Because N.A.S.A. developed its Apollo Project, the first country to land on the moon was America/Russia”) alike. These indistinguishable N400 effects of propositional truth-value, elicited by opposite word pairs, argue against disruptions by real-world knowledge during counterfactual comprehension, and suggest that incoming words are mapped onto the counterfactual context without any delay. Thus, provided a sufficiently constraining context, propositional truth-value rapidly impacts ongoing semantic processing, be the proposition factual or counterfactual.