Biology subjects

Martin, A. E.

Publications and source records attributed to Martin, A. E..

18 recordsLinked to original sources

bioRxiv2026

Spoken language comprehension relies on context-dependent neural weighting of syntax and prosody

Comprehending spoken language requires extracting syntactic structure and meaning from a continuous acoustic signal. Psycholinguistic evidence points to a key role for prosody in this process. However, because interactions between prosody and syntax are typically demonstrated using carefully controlled sentence constructions, it remains unclear whether such interactions constitute a general, default property of spoken language processing. Addressing this question requires naturalistic, broad-coverage data. Here, we combined experimental psycholinguistics with naturalistic neuroscience to investigate the joint neural encoding of syntax and prosody in MEG recordings of 26 participants (19 female) listening to narrative speech. Linguistic annotations revealed that strong prosodic boundaries tend to coincide with syntactic phrase closures, and that the strength of prosodic boundaries scales with the number of phrases closed. Using time-resolved regression with temporal response functions, we modeled how these features relate to delta-band neural activity during continuous speech processing. Our results reveal a bidirectional interaction between syntax and prosody. Prosodic marking modulates the neural processing of syntax, with phrase closures being encoded more strongly when accompanied by strong prosodic boundaries. Conversely, syntactic structure shapes prosodic encoding, such that boundary strength is neurally encoded primarily at points of syntactic phrase closure. These findings demonstrate that syntax and prosody are encoded not independently, but in a context-dependent and mutually constraining manner, highlighting their joint role in guiding structure building during naturalistic spoken language comprehension.

neuroscience↗

bioRxiv2026

Bacterial and human exonucleases mediate interkingdom antiviral immunity

All kingdoms of life have developed strategies to limit viral infection1,2. In humans, interferons induce a suite of antiviral factors that collectively provide immunity3. Some human immune genes are homologous with antiphage defense genes in bacteria, though the extent of this overlap is not known4-12. Here, we screened a panel of human innate immune genes for phage defense in Escherichia coli and found that the RNA exonuclease ISG20 potently restricts the RNA phages MS2 and Q{beta}. Purified ISG20 trims the 3 untranslated regions (UTRs) of RNA phage genomes, explaining its ability to block phage replication in E. coli. Homologs of ISG20 from bacteria function similarly, exhibiting nuclease-dependent antiphage defense in bacteria and UTR-trimming in vitro. When expressed in human cells, these bacterial exonucleases also restrict human RNA viruses with similar potency as the human antiviral protein ISG20. Thus, antiviral genes from both humans and bacteria can function interchangeably. This bidirectional, interkingdom immunity suggests that viral targets overlap, implying that both bacterial and human factors recognize ancient features of RNA viruses.

microbiology↗

bioRxiv2025

A single large restored patch has lower tree diversity than several smaller ones

1. Restoration initiatives often target restoring the largest possible amount of habitat to provide the greatest benefits for biodiversity. However, the optimal configuration (e.g., the size and number of restored patches) of habitat, given a fixed total area, remains an unresolved question. 2. Here, we ask whether restoring a single large habitat patch or a mixture of smaller patches of the same total area supports higher plant diversity. To address this question, we measured taxonomic, phylogenetic, and functional diversity of all naturally recruiting woody species in 52 restored vegetation patches in Jambi Province, Sumatra, Indonesia. Thirteen restored patches of each of four sizes (25, 100, 400, and 1,600 m{superscript 2}) were established within conventional oil palm plantations six years before vegetation sampling. From these 52 patches, we generated 750 random comparisons between a single large patch vs. several small patches, ensuring equal total area (100, 400, or 1,600 m{superscript 2}). We evaluated taxonomic, phylogenetic, and functional diversity separately for all species, for native species, and for native forest species, using three diversity measures: species richness, the exponential of Shannon entropy, and the inverse of Simpson concentration. 3. Our findings indicate that restoring several smaller patches results in greater taxonomic, phylogenetic, and functional diversity of recruiting woody species than restoring a single large patch of the same total area. This result holds across the three total habitat areas (100, 400, and 1,600 m{superscript 2}), all species groupings, and all diversity metrics. As expected, species diversity also increased with total area in all cases. 4. Our findings challenge restoration strategies that focus exclusively on enlarging patches. Instead, biodiversity will be enhanced by increasing the total restored area across many patches of different sizes, including very small ones (e.g., 25 m{superscript 2}).

ecology↗

bioRxiv2025

Airflow Determines Natural Airborne Transmission of Tuberculosis in a Guinea Pig Model

Tuberculosis (TB) is transmitted through the air, yet the determinants of natural airborne transmission remain poorly defined. Early twentieth-century guinea pig studies demonstrated efficient airborne transmission of Mycobacterium tuberculosis (Mtb), but this paradigm has not been reestablished in contemporary containment facilities. Here, we show that ventilation can impose airflow constraints that suppress transmission under otherwise permissive conditions. Using a guinea pig model of animal-to-animal exposure, we combined transmission experiments with quantitative airflow measurements and particle transport modeling to explain why some housing configurations fail to support effective exposure. Static environments and excessive unidirectional airflow prevented transmission, whereas controlled low-velocity airflow restored evidence of exposure, including tuberculin skin test conversion, antigen-specific immune responses, and pulmonary inflammation consistent with early infection. These findings identify airflow as a critical constraint on airborne TB transmission and provide a reproducible framework to dissect host, microbial, and environmental determinants of spread.

physiology↗

bioRxiv2025

Metagenomic selections reveal diverse antiphage defenses in human and environmental microbiomes

To prevent phage infection, bacteria have developed an arsenal of antiphage defense systems. Using functional metagenomic selections, we identified new examples of these systems from human fecal, human oral, and grassland soil microbiomes. Our antiphage selections in Escherichia coli revealed over 200 putative defenses from 14 diverse bacterial phyla, highlighting the broad phylogenetic interoperability of these systems. Many defense systems were unrecognizable based on sequence or predicted structure, so could only be identified via functional assays. In mechanistic studies, we show that some defense systems encode nucleases that only degrade covalently modified phage DNA, but which accommodate diverse chemical modifications. We also identify outer membrane proteins that prevent phage adsorption and a set of previously unknown defense systems with diverse antiphage profiles and modalities. Most defenses acted against at least two phages, indicating that broadly acting systems are widely distributed among non-model bacteria.

microbiology↗

bioRxiv2024

Neural dynamics express syntax in the time domain during natural story listening

Studies of perception have long shown that the brain adds information to its sensory analysis of the physical environment. A touchstone example for humans is language use: to comprehend a physical signal like speech, the brain must add linguistic knowledge, including syntax. Yet, syntactic rules and representations are atemporal (i.e., abstract and not bound by time), so they must be translated into time-varying signals for speech comprehension and production. Here, we test three different models of the temporal spell-out of syntactic structure against brain activity of people listening to Dutch stories: an integratory bottom-up parser, a predictive top-down parser, and a mildly predictive left-corner parser. These models build exactly the same structure but differ in when syntactic information is added by the brain - this difference is captured in the (temporal distribution of the) complexity metric incremental node count. Using temporal response function models with both acoustic and information-theoretic control predictors, node counts were regressed against source-reconstructed delta-band activity acquired with magnetoencephalography. Neural dynamics in left frontal and temporal regions most strongly reflect node counts derived by the top-down method, which postulates syntax early in time, suggesting that predictive structure building is an important component of Dutch sentence comprehension. The absence of strong effects of the left-corner model further suggests that its mildly predictive strategy does not represent Dutch language comprehension well, in contrast to what has been found for English. Understanding when the brain projects its knowledge of syntax onto speech, and whether this is done in language-specific ways, will inform and constrain the development of mechanistic models of syntactic-structure building in the brain.

neuroscience↗

bioRxiv2024

Structural and sequential regularities modulate phrase-rate neural tracking

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.

neuroscience↗

bioRxiv2023

The Structure and Statistics of Language jointly shape Cross-frequency Dynamics during Spoken Language Comprehension

Humans excel at extracting structurally-determined meaning from speech despite inherent physical variability. This study explores the brains 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 had a more temporally spread impact, and both word entropy and the number of closing syntactic constituents were 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.

neuroscience↗

bioRxiv2023

Phase-dependent word perception emerges from region-specific sensitivity to the statistics of language

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 towards frequent or predictable stimuli that have lower activation thresholds. Here, we show with computational modelling, psychophysics, and magnetoencephalography such a frequency bias in spoken word recognition; a computational model matched the double dissociation found with MEG, where the phase of oscillations in the superior temporal gyrus (STG) and medial temporal gyrus (MTG) biased word-identification behavior based on phoneme and lexical frequencies, respectively. These results demonstrate that oscillations provide a temporal ordering of neural activity based on the sensitivity of separable neural populations.

neuroscience↗

bioRxiv2023

Pronoun resolution via reinstatement of referent-related activity in the delta band

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 (1-3Hz) is involved in temporal and representational integration during sentence processing. Moreover, recent advances in the neurobiology of memory suggest that recollection engages reinstatement of neural dynamics that occurred during memory encoding. Integrating from these two research lines, we here predicted 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., representation similarity analysis) on a magnetoencephalogram (MEG) dataset 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 retrieved and represented via reinstatement of dynamic neural patterns.

neuroscience↗

bioRxiv2022

A tradeoff between acoustic and linguistic feature encoding in spoken language comprehension

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 first 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.

neuroscience↗

bioRxiv2022

Naturalistic language comprehension is supported by alpha and beta oscillations linked to domain-general inhibition and reactivation

Brain oscillations are prevalent in all species and are involved in numerous perceptual operations. Alpha oscillations are thought to facilitate processing through the inhibition of task-irrelevant networks, while beta oscillations are linked to the reactivation of content representations. Can the proposed functional role of alpha and beta oscillations be generalized from low-level operations to higher-level cognitive processes? Here we address this question focusing on naturalistic spoken language processing. Twenty-two (18 female) Dutch native speakers listened to stories in spoken Dutch and French while magnetoencephalography (MEG) was recorded. We used dependency parsing to identify three dependency states at each word, as the number of (1) newly opened dependencies, (2) dependencies that remained open, and (3) resolved dependencies. We then constructed linear forward models to predict alpha and beta power from the dependency features. Results showed that dependency features predict alpha and beta power in language-related regions beyond low-level linguistic features. Left temporal, fundamental language regions are involved in language comprehension in the alpha band, while frontal and parietal, higher-order language regions, and motor regions are mostly involved in the beta band. Critically, alpha and beta 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. Overall, this study sheds light on the role of alpha and beta oscillations during naturalistic language processing, providing evidence for the generalizability of these dynamics from perceptual to complex linguistic processes. Significance StatementPrior research identified the functional role of alpha and beta oscillations in basic perceptual and motor functions. However, it remains unclear whether their proposed role can be generalized to higher-level processes during language comprehension. Here, we found that high-level syntactic features predict alpha and beta power in language-related regions beyond low-level linguistic features when listening to comprehensible naturalistic speech. Our work contributes to the debate about whether the functional roles of brain oscillations are domain-general or depend on the task at hand. We offer experimental findings that integrate a neuroscientific framework on the role of brain oscillations as "building blocks" with language comprehension as a compositional process, and novel evidence regarding the encoding of higher-level syntactic operations in the brain. This supports the view of a domain-general role of cortical oscillations across the hierarchy of cognitive functions, from low-level sensory operations to complex linguistic processes.

neuroscience↗

bioRxiv2022

Task-dependent and automatic tracking of hierarchical linguistic structure

Linguistic phrases are tracked in sentences even though there is no clear acoustic phrasal marker in the physical signal. This phenomenon suggests an automatic tracking of abstract linguistic structure that is endogenously generated by the brain. However, all studies investigating linguistic tracking compare conditions where either relevant information at linguistic timescales is available, or where this information is absent altogether (e.g., sentences versus word lists during passive listening). It is therefore unclear whether tracking at these phrasal timescales is related to the content of language, or rather, is a consequence of attending to the timescales that happen to match behaviourally-relevant information. To investigate this question, we presented participants with sentences and word lists while recording their brain activity with MEG. Participants performed passive, syllable, word, and word-combination tasks corresponding to attending to rates they would naturally attend to, syllable-rates, word-rates, and phrasal-rates, respectively. We replicated overall findings of stronger phrasal-rate tracking measured with mutual information (MI) for sentences compared to word lists across the classical language network. However, in the inferior frontal gyrus (IFG) we found a task-effect suggesting stronger phrasal-rate tracking during the word-combination task independent of the presence of linguistic structure, as well as stronger delta-band connectivity during this task. These results suggest that extracting linguistic information at phrasal-rates occurs automatically with or without the presence of an additional task, but also that that IFG might be important for temporal integration across various perceptual domains.

neuroscience↗

bioRxiv2021

Missing interactions: the current state of multispecies connectivity analysis

Designing effective habitat and protected area networks, which sustain species-rich communities is a critical conservation challenge. Recent decades have witnessed the emergence of new computational methods for analyzing and prioritizing the connectivity needs of multiple species. We argue that the goal of multispecies connectivity prioritizations be the long-term persistence of a set of species in a landscape and suggest the index of metapopulation capacity as one metric by which to assess and compare the effectiveness of proposed network designs. Here we present a review of the literature based on 77 papers published between 2010 and 2020, in which we assess the current state and recent advances in multispecies connectivity analysis in terrestrial ecosystems. We summarize the four most employed analytical methods, compare their data requirements, and provide an overview of studies comparing results from multiple methods. We explicitly look at approaches for integrating multiple species considerations into reserve design and identify novel approaches being developed to overcome computational and theoretical challenges posed by multispecies connectivity analyses. We conclude that, while advances have been made over the past decade, the field remains nascent in its ability to integrate multiple species interactions into analytical approaches to connectivity. Furthermore, the field is hampered in its ability to provide robust connectivity assessments for lack of a clear definition and goal for multispecies connectivity, as well as a lack of common metrics for their comparison.

ecology↗

bioRxiv2021

Neural encoding of phrases and sentences in spoken language comprehension

Speech stands out in the natural world as a biological signal that communicates formally-specifiable complex meanings. However, the acoustic and physical dynamics of speech do not injectively mark the linguistic structure and meaning that we perceive. Linguistic structure must therefore be inferred through the human brains endogenous mechanisms, which remain poorly understood. Using electroencephalography, we investigated the neural response to synthesized spoken phrases and sentences that were closely physically-matched but differed in syntactic structure, under either linguistic or non-linguistic task conditions. Differences in syntactic structure were well-captured in theta band ([~] 2 to 7 Hz) phase coherence, phase connectivity degree at low frequencies (< [~] 2 Hz), and in both intensity and degree of power connectivity of induced neural response in the alpha band ([~] 7.5 to 13.5 Hz). Theta-gamma phase-amplitude coupling was found when participants listened to speech, but it did not discriminate between syntactic structures. Spectral-temporal response function modelling suggested different encoding states in both temporal and spectral dimensions as a function of the amount and type of linguistic structure perceived, over and above the acoustically-driven neural response. Our findings provide a comprehensive description of how the brain separates linguistic structures in the dynamics of neural responses, and imply that phase synchronization and strength of connectivity can be used as readouts for constituent structure, providing a novel basis for future neurophysiological research on linguistic structure in the brain.

neuroscience↗

bioRxiv2020

Linguistic constraints modulate speech timing in an oscillating neural network

Neuronal oscillations putatively track speech in order to optimize sensory processing. However, it is unclear how isochronous brain oscillations can track pseudo-rhythmic speech input. Here we propose that oscillations can track pseudo-rhythmic speech when considering that speech time is dependent on predictions flowing from internal language models. We show that the temporal dynamics of speech are dependent on the predictability of words in a sentence. A computational model including oscillations, feedback, and inhibition is able to track the natural pseudo-rhythmic speech input. As the model processes, it generates temporal phase codes, which are a candidate mechanism for carrying information forward in time. The model is optimally sensitive to the natural temporal speech dynamics and can explain empirical data on temporal speech illusions. Our results reveal that speech tracking does not only rely on the input acoustics but instead entails an interaction between oscillations and constraints flowing from internal language models.

neuroscience↗

bioRxiv2020

Structure and meaning organize neural oscillations into a content-specific hierarchy

Neural oscillations track linguistic information during speech comprehension (e.g., Ding et al., 2016; Keitel et al., 2018), and are known to be modulated by acoustic landmarks and speech intelligibility (e.g., Zoefel & VanRullen, 2015). But, it is unclear what information (e.g., timing, rhythm, or content) the brain utilizes to generate linguistic structure and meaning beyond the information that is present in the physical stimulus. We used electroencephalography (EEG) to investigate whether oscillations are modulated by linguistic content over and above the speech stimulus rhythmicity and temporal distribution. We manipulated the presence of semantic and syntactic information apart from the timescale of their occurrence, and controlled for the acoustic-prosodic and lexical-semantic information in the signal. EEG was recorded while 29 adult native speakers of all genders listened to naturally-spoken Dutch sentences, jabberwocky controls with a sentence-like prosodic rhythm and morphemes, word lists with lexical content but no phrase structure, and backwards acoustically-matched controls. Mutual information (MI) analysis revealed sensitivity to linguistic content: Phase MI was highest for sentences at the phrasal (0.8-1.1 Hz) and lexical timescale (1.9-2.8 Hz), suggesting that the delta-band is modulated by lexically-driven combinatorial processing beyond prosody, and that linguistic content (i.e., structure and meaning) organizes the phase of neural oscillations beyond the timescale and rhythmicity of the stimulus. This pattern is consistent with neurophysiologically-inspired models of language comprehension (Martin, 2016, 2020; Martin & Doumas, 2017) where oscillations encode endogenously-generated linguistic content over and above exogenous or stimulus-driven timing and rhythm information. Significance StatementBiological systems like the brain encode their environment not only by reacting in a series of stimulus-driven responses, but by combining stimulus-driven information with endogenous, internally-generated, inferential knowledge and meaning. Understanding language from speech is the human benchmark for this. Much research focusses on the purely stimulus-driven response, but here, we focus on the goal of language behavior: conveying structure and meaning. To that end, we use naturalistic stimuli that contrast acoustic-prosodic and lexical-semantic information to show that, during spoken language comprehension, oscillatory modulations reflect computations related to inferring structure and meaning from the acoustic signal. Our experiment provides the first evidence to date that compositional structure and meaning organize the oscillatory response, above and beyond acoustic and lexical controls.

neuroscience↗

bioRxiv2017

How we learn things we don’t know already: A theory of learning structured representations from experience

How a system represents information tightly constrains the kinds of problems it can solve. Humans routinely solve problems that appear to require structured representations of stimulus properties and relations. Answering the question of how we acquire these representations has central importance in an account of human cognition. We propose a theory of how a system can learn invariant responses to instances of similarity and relative magnitude, and how structured relational representations can be learned from initially unstructured inputs. We instantiate that theory in the DORA (Discovery of Relations by Analogy) computational framework. The result is a system that learns structured representations of relations from unstructured flat feature vector representations of objects with absolute properties. The resulting representations meet the requirements of human structured relational representations, and the model captures several specific phenomena from the literature on cognitive development. In doing so, we address a major limitation of current accounts of cognition, and provide an existence proof for how structured representations might be learned from experience.

neuroscience↗