Publications

Abstract

Since the cognitive revolution, language and action have been compared as cognitive systems, with cross-domain convergent views recently gaining renewed interest in biology, neuroscience, and cognitive science. Language and action are both combinatorial systems whose mode of combination has been argued to be hierarchical, combining elements into constituents of increasingly larger size. This structural similarity has led to the suggestion that they rely on shared cognitive and neural resources. In this article, we compare the conceptual and formal properties of hierarchy in language and action using set theory. We show that the strong compositionality of language requires a particular formalism, a magma, to describe the algebraic structure corresponding to the set of hierarchical structures underlying sentences. When this formalism is applied to actions, it appears to be both too strong and too weak. To overcome these limitations, which are related to the weak compositionality and sequential nature of action structures, we formalize the algebraic structure corresponding to the set of actions as a trace monoid. We aim to capture the different system properties of language and action in terms of the distinction between hierarchical sets and hierarchical sequences and discuss the implications for the way both systems could be represented in the brain.

Abstract

In the cognitive, computational, and neuro-sciences, practitioners often reason about what computational models represent or learn, as well as what algorithm is instantiated. The putative goal of such reasoning is to generalize claims about the model in question, to claims about the mind and brain, and the neurocognitive capacities of those systems. Such inference is often based on a model’s performance on a task, and whether that performance approximates human behavior or brain activity. Here we demonstrate how such argumentation problematizes the relationship between models and their targets; we place emphasis on artificial neural networks (ANNs), though any theory-brain relationship that falls into the same schema of reasoning is at risk. In this paper, we model inferences from ANNs to brains and back within a formal framework — metatheoretical calculus — in order to initiate a dialogue on both how models are broadly understood and used, and on how to best formally characterize them and their functions. To these ends, we express claims from the published record about models’ successes and failures in first-order logic. Our proposed formalization describes the decision-making processes enacted by scientists to adjudicate over theories. We demonstrate that formalizing the argumentation in the literature can uncover potential deep issues about how theory is related to phenomena. We discuss what this means broadly for research in cognitive science, neuroscience, and psychology; what it means for models when they lose the ability to mediate between theory and data in a meaningful way; and what this means for the metatheoretical calculus our fields deploy when performing high-level scientific inference.

Abstract

To understand language, we need to recognize words and combine them into phrases and sentences. During this process, responses to the words themselves are changed. In a step towards understanding how the brain builds sentence structure, the present study concerns the neural readout of this adaptation. We ask whether low-frequency neural readouts associated with words change as a function of being in a sentence. To this end, we analyzed an MEG dataset by Schoffelen et al. (2019) of 102 human participants (51 women) listening to sentences and word lists, the latter lacking any syntactic structure and combinatorial meaning. Using temporal response functions and a cumulative model-fitting approach, we disentangled delta- and theta-band responses to lexical information (word frequency), from responses to sensory- and distributional variables. The results suggest that delta-band responses to words are affected by sentence context in time and space, over and above entropy and surprisal. In both conditions, the word frequency response spanned left temporal and posterior frontal areas; however, the response appeared later in word lists than in sentences. In addition, sentence context determined whether inferior frontal areas were responsive to lexical information. In the theta band, the amplitude was larger in the word list condition around 100 milliseconds in right frontal areas. We conclude that low-frequency responses to words are changed by sentential context. The results of this study speak to how the neural representation of words is affected by structural context, and as such provide insight into how the brain instantiates compositionality in language.

Abstract

When we comprehend language from speech, the phase of the neural response aligns with particular features of the speech input, resulting in a phenomenon referred to as neural tracking. In recent years, a large body of work has demonstrated the tracking of the acoustic envelope and abstract linguistic units at the phoneme and word levels, and beyond. However, the degree to which speech tracking is driven by acoustic edges of the signal, or by internally-generated linguistic units, or by the interplay of both, remains contentious. In this study, we used naturalistic story-listening to investigate (1) whether phoneme-level features are tracked over and above acoustic edges, (2) whether word entropy, which can reflect sentence- and discourse-level constraints, impacted the encoding of acoustic and phoneme-level features, and (3) whether the tracking of acoustic edges was enhanced or suppressed during comprehension of a first language (Dutch) compared to a statistically familiar but uncomprehended language (French). We first show that encoding models with phoneme-level linguistic features, in addition to acoustic features, uncovered an increased neural tracking response; this signal was further amplified in a comprehended language, putatively reflecting the transformation of acoustic features into internally generated phoneme-level representations. Phonemes were tracked more strongly in a comprehended language, suggesting that language comprehension functions as a neural filter over acoustic edges of the speech signal as it transforms sensory signals into abstract linguistic units. We then show that word entropy enhances neural tracking of both acoustic and phonemic features when sentence- and discourse-context are less constraining. When language was not comprehended, acoustic features, but not phonemic ones, were more strongly modulated, but in contrast, when a native language is comprehended, phoneme features are more strongly modulated. Taken together, our findings highlight the flexible modulation of acoustic, and phonemic features by sentence and discourse-level constraint in language comprehension, and document the neural transformation from speech perception to language comprehension, consistent with an account of language processing as a neural filter from sensory to abstract representations.

Abstract

Brain oscillations are prevalent in all species and are involved in numerous perceptual operations. α oscillations are thought to facilitate processing through the inhibition of task-irrelevant networks, while β oscillations are linked to the putative reactivation of content representations. Can the proposed functional role of α and β oscillations be generalized from low-level operations to higher-level cognitive processes? Here we address this question focusing on naturalistic spoken language comprehension. Twenty-two (18 female) Dutch native speakers listened to stories in Dutch and French while MEG was recorded. We used dependency parsing to identify three dependency states at each word: the number of (1) newly opened dependencies, (2) dependencies that remained open, and (3) resolved dependencies. We then constructed forward models to predict α and β power from the dependency features. Results showed that dependency features predict α and β power in language-related regions beyond low-level linguistic features. Left temporal, fundamental language regions are involved in language comprehension in α, while frontal and parietal, higher-order language regions, and motor regions are involved in β. Critically, α- and β-band dynamics seem to subserve language comprehension tapping into syntactic structure building and semantic composition by providing low-level mechanistic operations for inhibition and reactivation processes. Because of the temporal similarity of the α-β responses, their potential functional dissociation remains to be elucidated. Overall, this study sheds light on the role of α and β oscillations during naturalistic spoken language comprehension, providing evidence for the generalizability of these dynamics from perceptual to complex linguistic processes.

Abstract

Comprehension of verb-phrase ellipsis (VPE) requires reevaluation of recently processed constituents, which often necessitates retrieval of information about the elided constituent from memory. A. E. Martin and B. McElree (2008) argued that representations formed during comprehension are content addressable and that VPE antecedents are retrieved from memory via a cue-dependent direct-access pointer rather than via a search process. This hypothesis was further tested by manipulating the location of interfering material—either before the onset of the antecedent (proactive interference; PI) or intervening between antecedent and ellipsis site (retroactive interference; RI). The speed–accuracy tradeoff procedure was used to measure the time course of VPE processing. The location of the interfering material affected VPE comprehension accuracy: RI conditions engendered lower accuracy than PI conditions. Crucially, location did not affect the speed of processing VPE, which is inconsistent with both forward and backward search mechanisms. The observed time-course profiles are consistent with the hypothesis that VPE antecedents are retrieved via a cue-dependent direct-access operation. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Abstract

To study the neural bases of semantic composition in language processing without confounds from syntactic composition, recent magnetoencephalography (MEG) studies have investigated the processing of constructions that exhibit some type of syntax-semantics mismatch. The most studied case of such a mismatch is complement coercion; expressions such as the author began the book, where an entity-denoting noun phrase is coerced into an eventive meaning in order to match the semantic properties of the event-selecting verb (e.g., ‘the author began reading/writing the book’). These expressions have been found to elicit increased activity in the Anterior Midline Field (AMF), an MEG component elicited at frontomedial sensors at ∼400 ms after the onset of the coercing noun [Pylkkänen, L., & McElree, B. (2007). An MEG study of silent meaning. Journal of Cognitive Neuroscience, 19, 11]. Thus, the AMF constitutes a potential neural correlate of coercion. However, the AMF was generated in ventromedial prefrontal regions, which are heavily associated with decision-making. This raises the possibility that, instead of semantic processing, the AMF effect may have been related to the experimental task, which was a sensicality judgment. We tested this hypothesis by assessing the effect of coercion when subjects were simply reading for comprehension, without a decision-task. Additionally, we investigated coercion in an adjectival rather than a verbal environment to further generalize the findings. Our results show that an AMF effect of coercion is elicited without a decision-task and that the effect also extends to this novel syntactic environment. We conclude that in addition to its role in non-linguistic higher cognition, ventromedial prefrontal regions contribute to the resolution of syntax-semantics mismatches in language processing.