Publications

Abstract

Computation in neuronal assemblies is putatively reflected in the excitatory and inhibitory cycles of activation distributed throughout the brain. In speech and language processing, coordination of these cycles resulting in phase synchronization has been argued to reflect the integration of information on different timescales (e.g. segmenting acoustics signals to phonemic and syllabic representations; (Giraud and Poeppel 2012 Nat. Neurosci.15, 511 (doi:10.1038/nn.3063)). A natural extension of this claim is that phase synchronization functions similarly to support the inference of more abstract higher-level linguistic structures (Martin 2016 Front. Psychol.7, 120; Martin and Doumas 2017 PLoS Biol. 15, e2000663 (doi:10.1371/journal.pbio.2000663); Martin and Doumas. 2019 Curr. Opin. Behav. Sci.29, 77–83 (doi:10.1016/j.cobeha.2019.04.008)). Hale et al. (Hale et al. 2018 Finding syntax in human encephalography with beam search. arXiv 1806.04127 (http://arxiv.org/abs/1806.04127)) showed that syntactically driven parsing decisions predict electroencephalography (EEG) responses in the time domain; here we ask whether phase synchronization in the form of either inter-trial phrase coherence or cross-frequency coupling (CFC) between high-frequency (i.e. gamma) bursts and lower-frequency carrier signals (i.e. delta, theta), changes as the linguistic structures of compositional meaning (viz., bracket completions, as denoted by the onset of words that complete phrases) accrue. We use a naturalistic story-listening EEG dataset from Hale et al. to assess the relationship between linguistic structure and phase alignment. We observe increased phase synchronization as a function of phrase counts in the delta, theta, and gamma bands, especially for function words. A more complex pattern emerged for CFC as phrase count changed, possibly related to the lack of a one-to-one mapping between ‘size’ of linguistic structure and frequency band—an assumption that is tacit in recent frameworks. These results emphasize the important role that phase synchronization, desynchronization, and thus, inhibition, play in the construction of compositional meaning by distributed neural networks in the brain.

Abstract

Human thought and language have extraordinary expressive power because meaningful parts can be assembled into more complex semantic structures. This partly underlies our ability to compose meanings into endlessly novel configurations, and sets us apart from other species and current computing devices. Crucially, human behaviour, including language use and linguistic data, indicates that composing parts into complex structures does not threaten the existence of constituent parts as independent units in the system: parts and wholes exist simultaneously yet independently from one another in the mind and brain. This independence is evident in human behaviour, but it seems at odds with what is known about the brain's exquisite sensitivity to statistical patterns: everyday language use is productive and expressive precisely because it can go beyond statistical regularities. Formal theories in philosophy and linguistics explain this fact by assuming that language and thought are compositional: systems of representations that separate a variable (or role) from its values (fillers), such that the meaning of a complex expression is a function of the values assigned to the variables. The debate on whether and how compositional systems could be implemented in minds, brains and machines remains vigorous. However, it has not yet resulted in mechanistic models of semantic composition: how, then, are the constituents of thoughts and sentences put and held together? We review and discuss current efforts at understanding this problem, and we chart possible routes for future research.

Abstract

Neither neurobiological nor process models of meaning composition specify the operator through which constituent parts are bound together into compositional structures. In this paper, we argue that a neurophysiological computation system cannot achieve the compositionality exhibited in human thought and language if it were to rely on a multiplicative operator to perform binding, as the tensor product (TP)-based systems that have been widely adopted in cognitive science, neuroscience and artificial intelligence do. We show via simulation and two behavioural experiments that TPs violate variable-value independence, but human behaviour does not. Specifically, TPs fail to capture that in the statements fuzzy cactus and fuzzy penguin, both cactus and penguin are predicated by fuzzy(x) and belong to the set of fuzzy things, rendering these arguments similar to each other. Consistent with that thesis, people judged arguments that shared the same role to be similar, even when those arguments themselves (e.g., cacti and penguins) were judged to be dissimilar when in isolation. By contrast, the similarity of the TPs representing fuzzy(cactus) and fuzzy(penguin) was determined by the similarity of the arguments, which in this case approaches zero. Based on these results, we argue that neural systems that use TPs for binding cannot approximate how the human mind and brain represent compositional information during processing. We describe a contrasting binding mechanism that any physiological or artificial neural system could use to maintain independence between a role and its argument, a prerequisite for compositionality and, thus, for instantiating the expressive power of human thought and language in a neural system.

Abstract

Humans learn to represent complex structures (e.g. natural language, music, mathematics) from experience with their environments. Often such structures are latent, hidden, or not encoded in statistics about sensory representations alone. Accounts of human cognition have long emphasized the importance of structured representations, yet the majority of contemporary neural networks do not learn structure from experience. Here, we describe one way that structured, functionally symbolic representations can be instantiated in an artificial neural network. Then, we describe how such latent structures (viz. predicates) can be learned from experience with unstructured data. Our approach exploits two principles from psychology and neuroscience: comparison of representations, and the naturally occurring dynamic properties of distributed computing across neuronal assemblies (viz. neural oscillations). We discuss how the ability to learn predicates from experience, to represent information compositionally, and to extrapolate knowledge to unseen data is core to understanding and modeling the most complex human behaviors (e.g. relational reasoning, analogy, language processing, game play).

Abstract

We used ERPs to investigate the pre-activation of form and meaning in language comprehension. Participants read high-cloze sentence contexts (e.g., “The student is going to the library to borrow a…”), followed by a word that was predictable (book), form-related (hook) or semantically related (page) to the predictable word, or unrelated (sofa). At a 500 ms SOA (Experiment 1), semantically related words, but not form-related words, elicited a reduced N400 compared to unrelated words. At a 700 ms SOA (Experiment 2), semantically related words and form-related words elicited reduced N400 effects, but the effect for form-related words occurred in very high-cloze sentences only. At both SOAs, form-related words elicited an enhanced, post-N400 posterior positivity (Late Positive Component effect). The N400 effects suggest that readers can pre-activate meaning and form information for highly predictable words, but form pre-activation is more limited than meaning pre-activation. The post-N400 LPC effect suggests that participants detected the form similarity between expected and encountered input. Pre-activation of word forms crucially depends upon the time that readers have to make predictions, in line with production-based accounts of linguistic prediction.

Abstract

I argue that cue integration, a psychophysiological mechanism from vision and multisensory perception, offers a computational linking hypothesis between psycholinguistic theory and neurobiological models of language. I propose that this mechanism, which incorporates probabilistic estimates of a cue's reliability, might function in language processing from the perception of a phoneme to the comprehension of a phrase structure. I briefly consider the implications of the cue integration hypothesis for an integrated theory of language that includes acquisition, production, dialogue and bilingualism, while grounding the hypothesis in canonical neural computation.

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.