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"Marslen-Wilson, William"
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Auditory sequence processing reveals evolutionarily conserved regions of frontal cortex in macaques and humans
An evolutionary account of human language as a neurobiological system must distinguish between human-unique neurocognitive processes supporting language and evolutionarily conserved, domain-general processes that can be traced back to our primate ancestors. Neuroimaging studies across species may determine whether candidate neural processes are supported by homologous, functionally conserved brain areas or by different neurobiological substrates. Here we use functional magnetic resonance imaging in
Rhesus macaques
and humans to examine the brain regions involved in processing the ordering relationships between auditory nonsense words in rule-based sequences. We find that key regions in the human ventral frontal and opercular cortex have functional counterparts in the monkey brain. These regions are also known to be associated with initial stages of human syntactic processing. This study raises the possibility that certain ventral frontal neural systems, which play a significant role in language function in modern humans, originally evolved to support domain-general abilities involved in sequence processing.
This study uses functional magnetic resonance imaging in humans and monkeys to show similar ventral frontal and opercular cortical responses when processing sequences of auditory nonsense words. The study indicates that this frontal region is involved in evaluating the order of incoming sounds in a sequence, a process that may be conserved in primates.
Journal Article
Finding structure during incremental speech comprehension
A core aspect of human speech comprehension is the ability to incrementally integrate consecutive words into a structured and coherent interpretation, aligning with the speaker’s intended meaning. This rapid process is subject to multidimensional probabilistic constraints, including both linguistic knowledge and non-linguistic information within specific contexts, and it is their interpretative coherence that drives successful comprehension. To study the neural substrates of this process, we extract word-by-word measures of sentential structure from BERT, a deep language model, which effectively approximates the coherent outcomes of the dynamic interplay among various types of constraints. Using representational similarity analysis, we tested BERT parse depths and relevant corpus-based measures against the spatiotemporally resolved brain activity recorded by electro-/magnetoencephalography when participants were listening to the same sentences. Our results provide a detailed picture of the neurobiological processes involved in the incremental construction of structured interpretations. These findings show when and where coherent interpretations emerge through the evaluation and integration of multifaceted constraints in the brain, which engages bilateral brain regions extending beyond the classical fronto-temporal language system. Furthermore, this study provides empirical evidence supporting the use of artificial neural networks as computational models for revealing the neural dynamics underpinning complex cognitive processes in the brain.
Journal Article
A Toolbox for Representational Similarity Analysis
Neuronal population codes are increasingly being investigated with multivariate pattern-information analyses. A key challenge is to use measured brain-activity patterns to test computational models of brain information processing. One approach to this problem is representational similarity analysis (RSA), which characterizes a representation in a brain or computational model by the distance matrix of the response patterns elicited by a set of stimuli. The representational distance matrix encapsulates what distinctions between stimuli are emphasized and what distinctions are de-emphasized in the representation. A model is tested by comparing the representational distance matrix it predicts to that of a measured brain region. RSA also enables us to compare representations between stages of processing within a given brain or model, between brain and behavioral data, and between individuals and species. Here, we introduce a Matlab toolbox for RSA. The toolbox supports an analysis approach that is simultaneously data- and hypothesis-driven. It is designed to help integrate a wide range of computational models into the analysis of multichannel brain-activity measurements as provided by modern functional imaging and neuronal recording techniques. Tools for visualization and inference enable the user to relate sets of models to sets of brain regions and to statistically test and compare the models using nonparametric inference methods. The toolbox supports searchlight-based RSA, to continuously map a measured brain volume in search of a neuronal population code with a specific geometry. Finally, we introduce the linear-discriminant t value as a measure of representational discriminability that bridges the gap between linear decoding analyses and RSA. In order to demonstrate the capabilities of the toolbox, we apply it to both simulated and real fMRI data. The key functions are equally applicable to other modalities of brain-activity measurement. The toolbox is freely available to the community under an open-source license agreement (http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/).
Journal Article
Bihemispheric foundations for human speech comprehension
Emerging evidence from neuroimaging and neuropsychology suggests that human speech comprehension engages two types of neurocognitive processes: a distributed bilateral system underpinning general perceptual and cognitive processing, viewed as neurobiologically primary, and a more specialized left hemisphere system supporting key grammatical language functions, likely to be specific to humans. To test these hypotheses directly we covaried increases in the nonlinguistic complexity of spoken words [presence or absence of an embedded stem, e.g., claim (clay)] with variations in their linguistic complexity (presence of inflectional affixes, e.g., play+ed). Nonlinguistic complexity, generated by the on-line competition between the full word and its onset-embedded stem, was found to activate both right and left fronto-temporal brain regions, including bilateral BA45 and -47. Linguistic complexity activated left-lateralized inferior frontal areas only, primarily in BA45. This contrast reflects a differentiation between the functional roles of a bilateral system, which supports the basic mapping from sound to lexical meaning, and a language-specific left-lateralized system that supports core decompositional and combinatorial processes invoked by linguistically complex inputs. These differences can be related to the neurobiological foundations of human language and underline the importance of bihemispheric systems in supporting the dynamic processing and interpretation of spoken inputs.
Journal Article
Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem
There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresses over time, to the incremental 'brain states', measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.
Journal Article
Morphology, language and the brain: the decompositional substrate for language comprehension
This paper outlines a neurocognitive approach to human language, focusing on inflectional morphology and grammatical function in English. Taking as a starting point the selective deficits for regular inflectional morphology of a group of non-fluent patients with left hemisphere damage, we argue for a core decompositional network linking left inferior frontal cortex with superior and middle temporal cortex, connected via the arcuate fasciculus. This network handles the processing of regularly inflected words (such as joined or treats), which are argued not to be stored as whole forms and which require morpho-phonological parsing in order to segment complex forms into stems and inflectional affixes. This parsing process operates early and automatically upon all potential inflected forms and is triggered by their surface phonological properties. The predictions of this model were confirmed in a further neuroimaging study, using event-related functional magnetic resonance imaging (fMRI), on unimpaired young adults. The salience of grammatical morphemes for the language system is highlighted by new research showing that similarly early and blind segmentation also operates for derivationally complex forms (such as darkness or rider). These findings are interpreted as evidence for a hidden decompositional substrate to human language processing and related to a functional architecture derived from non-human primate models.
Journal Article
Neural dynamics of semantic composition
Human speech comprehension is remarkable for its immediacy and rapidity. The listener interprets an incrementally delivered auditory input, millisecond by millisecond as it is heard, in terms of complex multilevel representations of relevant linguistic and nonlinguistic knowledge. Central to this process are the neural computations involved in semantic combination, whereby the meanings of words are combined into more complex representations, as in the combination of a verb and its following direct object (DO) noun (e.g., “eat the apple”). These combinatorial processes form the backbone for incremental interpretation, enabling listeners to integrate the meaning of each word as it is heard into their dynamic interpretation of the current utterance. Focusing on the verb-DO noun relationship in simple spoken sentences, we applied multivariate pattern analysis and computational semantic modeling to source-localized electro/magnetoencephalographic data to map out the specific representational constraints that are constructed as each word is heard, and to determine how these constraints guide the interpretation of subsequent words in the utterance. Comparing context-independent semantic models of the DO noun with contextually constrained noun models reflecting the semantic properties of the preceding verb, we found that only the contextually constrained model showed a significant fit to the brain data. Pattern-based measures of directed connectivity across the left hemisphere language network revealed a continuous information flow among temporal, inferior frontal, and inferior parietal regions, underpinning the verb’s modification of the DO noun’s activated semantics. These results provide a plausible neural substrate for seamless real-time incremental interpretation on the observed millisecond time scales.
Journal Article
Is left fronto-temporal connectivity essential for syntax? Effective connectivity, tractography and performance in left-hemisphere damaged patients
Syntactic processing typically engages left inferior frontal gyrus and posterior middle temporal gyrus, and damage to these regions is associated with syntactic deficits. What has not been directly determined, however, is whether it is the effective connectivity between these regions – and therefore also the integrity of the white matter tracts that connect them – that underpins successful syntactic analysis. We addressed these related issues by obtaining measures of the psycho-physiological interaction between frontal and temporal regions and of the integrity of the major white matter tracts that directly connect them — the arcuate fasciculus and extreme capsule fibre system. We correlated these estimates with performance measures of syntax in a group of patients with left hemisphere damage and varying degrees of syntactic impairment. Good syntactic function was associated with enhanced effective connectivity and increased tract integrity, suggesting that intact connectivity between left frontal and temporal regions is essential for syntactic analysis rather than the activation of these regions per se.
► Patients with LH brain damage and variable syntactic processing abilities. ► Reduced LIFG–LpMTG structural/effective connectivity linked to syntactic deficits. ► Intact effective connectivity between LIFG and LpMTG essential for syntax. ► Intact dorsal/ventral structural fronto-temporal connectivity essential for syntax. ► Reduced structural connectivity associated with reduced effective connectivity.
Journal Article
Kymata Soto Language Dataset: an electro-magnetoencephalographic dataset for natural speech processing
The Kymata Soto Language Dataset comprises raw electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings from 15 native Russian speakers and 20 native English speakers as they listened to approximately seven minutes of conversational speech in their respective native languages. Each participant heard the same conversational speech stimulus multiple times (four repetitions for Russian speakers and eight for English speakers). The dataset includes transcriptions of the recordings, along with timestamp annotations for each phoneme and word. Organized according to the Brain Imaging Data Structure (BIDS), this dataset facilitates in-depth research into brain responses to naturalistic speech. To validate the dataset and our preprocessing pipeline, we employed Python-based analyses, revealing consistent low-level loudness perception trends across both language groups. All EEG and MEG data, audio recordings, transcriptions with timestamp annotations, and validation codes are open source, promoting transparency and reproducibility.
Journal Article