Negation mitigates rather than inverts the neural representations of adjectives.

Combinatoric linguistic operations underpin human language processes, but how meaning is composed and refined in the mind of the reader is not well understood. We address this puzzle by exploiting the ubiquitous function of negation. We track the online effects of negation ("not") and intensifiers ("really") on the representation of scalar adjectives (e.g., "good") in parametrically designed behavioral and neurophysiological (MEG) experiments. The behavioral data show that participants first interpret negated adjectives as affirmative and later modify their interpretation towards, but never exactly as, the opposite meaning. Decoding analyses of neural activity further reveal significant above chance decoding accuracy for negated adjectives within 600 ms from adjective onset, suggesting that negation does not invert the representation of adjectives (i.e., "not bad" represented as "good"); furthermore, decoding accuracy for negated adjectives is found to be significantly lower than that for affirmative adjectives. Overall, these results suggest that negation mitigates rather than inverts the neural representations of adjectives. This putative suppression mechanism of negation is supported by increased synchronization of beta-band neural activity in sensorimotor areas. The analysis of negation provides a steppingstone to understand how the human brain represents changes of meaning over time.

Hierarchical dynamic coding coordinates speech comprehension in the brain.

Speech comprehension requires the human brain to transform an acoustic waveform into meaning. To do so, the brain generates a hierarchy of features that converts the sensory input into increasingly abstract language properties. However, little is known about how these hierarchical features are generated and continuously coordinated. Here, we propose that each linguistic feature is dynamically represented in the brain to simultaneously represent successive events. To test this 'Hierarchical Dynamic Coding' (HDC) hypothesis, we use time-resolved decoding of brain activity to track the construction, maintenance, and integration of a comprehensive hierarchy of language features spanning acoustic, phonetic, sub-lexical, lexical, syntactic and semantic representations. For this, we recorded 21 participants with magnetoencephalography (MEG), while they listened to two hours of short stories. Our analyses reveal three main findings. First, the brain incrementally represents and simultaneously maintains successive features. Second, the duration of these representations depend on their level in the language hierarchy. Third, each representation is maintained by a dynamic neural code, which evolves at a speed commensurate with its corresponding linguistic level. This HDC preserves the maintenance of information over time while limiting the interference between successive features. Overall, HDC reveals how the human brain continuously builds and maintains a language hierarchy during natural speech comprehension, thereby anchoring linguistic theories to their biological implementations.

Large-scale single-neuron speech sound encoding across the depth of human cortex.

Understanding the neural basis of speech perception requires that we study the human brain both at the scale of the fundamental computational unit of neurons and in their organization across the depth of cortex. Here we used high-density Neuropixels arrays1-3 to record from 685 neurons across cortical layers at nine sites in a high-level auditory region that is critical for speech, the superior temporal gyrus4,5, while participants listened to spoken sentences. Single neurons encoded a wide range of speech sound cues, including features of consonants and vowels, relative vocal pitch, onsets, amplitude envelope and sequence statistics. Neurons at each cross-laminar recording exhibited dominant tuning to a primary speech feature while also containing a substantial proportion of neurons that encoded other features contributing to heterogeneous selectivity. Spatially, neurons at similar cortical depths tended to encode similar speech features. Activity across all cortical layers was predictive of high-frequency field potentials (electrocorticography), providing a neuronal origin for macroelectrode recordings from the cortical surface. Together, these results establish single-neuron tuning across the cortical laminae as an important dimension of speech encoding in human superior temporal gyrus.

Introducing MEG-MASC a high-quality magneto-encephalography dataset for evaluating natural speech processing.

The "MEG-MASC" dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories. Each participant performed two identical sessions, involving listening to four fictional stories from the Manually Annotated Sub-Corpus (MASC) intermixed with random word lists and comprehension questions. We time-stamp the onset and offset of each word and phoneme in the metadata of the recording, and organize the dataset according to the 'Brain Imaging Data Structure' (BIDS). This data collection provides a suitable benchmark to large-scale encoding and decoding analyses of temporally-resolved brain responses to speech. We provide the Python code to replicate several validations analyses of the MEG evoked responses such as the temporal decoding of phonetic features and word frequency. All code and MEG, audio and text data are publicly available to keep with best practices in transparent and reproducible research.

Neural dynamics of phoneme sequences reveal position-invariant code for content and order.

Speech consists of a continuously-varying acoustic signal. Yet human listeners experience it as sequences of discrete speech sounds, which are used to recognise discrete words. To examine how the human brain appropriately sequences the speech signal, we recorded two-hour magnetoencephalograms from 21 participants listening to short narratives. Our analyses show that the brain continuously encodes the three most recently heard speech sounds in parallel, and maintains this information long past its dissipation from the sensory input. Each speech sound representation evolves over time, jointly encoding both its phonetic features and the amount of time elapsed since onset. As a result, this dynamic neural pattern encodes both the relative order and phonetic content of the speech sequence. These representations are active earlier when phonemes are more predictable, and are sustained longer when lexical identity is uncertain. Our results show how phonetic sequences in natural speech are represented at the level of populations of neurons, providing insight into what intermediary representations exist between the sensory input and sub-lexical units. The flexibility in the dynamics of these representations paves the way for further understanding of how such sequences may be used to interface with higher order structure such as lexical identity.

High-density single-unit human cortical recordings using the Neuropixels probe.

The action potential is a fundamental unit of neural computation. Even though significant advances have been made in recording large numbers of individual neurons in animal models, translation of these methodologies to humans has been limited because of clinical constraints and electrode reliability. Here, we present a reliable method for intraoperative recording of dozens of neurons in humans using the Neuropixels probe, yielding up to ∼100 simultaneously recorded single units. Most single units were active within 1 min of reaching target depth. The motion of the electrode array had a strong inverse correlation with yield, identifying a major challenge and opportunity to further increase the probe utility. Cell pairs active close in time were spatially closer in most recordings, demonstrating the power to resolve complex cortical dynamics. Altogether, this approach provides access to population single-unit activity across the depth of human neocortex at scales previously only accessible in animal models.

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability.

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

Adaptation to mis-pronounced speech: evidence for a prefrontal-cortex repair mechanism.

Speech is a complex and ambiguous acoustic signal that varies significantly within and across speakers. Despite the processing challenge that such variability poses, humans adapt to systematic variations in pronunciation rapidly. The goal of this study is to uncover the neurobiological bases of the attunement process that enables such fluent comprehension. Twenty-four native English participants listened to words spoken by a "canonical" American speaker and two non-canonical speakers, and performed a word-picture matching task, while magnetoencephalography was recorded. Non-canonical speech was created by including systematic phonological substitutions within the word (e.g. [s] → [sh]). Activity in the auditory cortex (superior temporal gyrus) was greater in response to substituted phonemes, and, critically, this was not attenuated by exposure. By contrast, prefrontal regions showed an interaction between the presence of a substitution and the amount of exposure: activity decreased for canonical speech over time, whereas responses to non-canonical speech remained consistently elevated. Grainger causality analyses further revealed that prefrontal responses serve to modulate activity in auditory regions, suggesting the recruitment of top-down processing to decode non-canonical pronunciations. In sum, our results suggest that the behavioural deficit in processing mispronounced phonemes may be due to a disruption to the typical exchange of information between the prefrontal and auditory cortices as observed for canonical speech.

Recurrent processes support a cascade of hierarchical decisions.

Perception depends on a complex interplay between feedforward and recurrent processing. Yet, while the former has been extensively characterized, the computational organization of the latter remains largely unknown. Here, we use magneto-encephalography to localize, track and decode the feedforward and recurrent processes of reading, as elicited by letters and digits whose level of ambiguity was parametrically manipulated. We first confirm that a feedforward response propagates through the ventral and dorsal pathways within the first 200 ms. The subsequent activity is distributed across temporal, parietal and prefrontal cortices, which sequentially generate five levels of representations culminating in action-specific motor signals. Our decoding analyses reveal that both the content and the timing of these brain responses are best explained by a hierarchy of recurrent neural assemblies, which both maintain and broadcast increasingly rich representations. Together, these results show how recurrent processes generate, over extended time periods, a cascade of decisions that ultimately accounts for subjects' perceptual reports and reaction times.

Magnetoencephalography and Language.

This article provides an overview of research that uses magnetoencephalography to understand the brain basis of human language. The cognitive processes and brain networks that have been implicated in written and spoken language comprehension and production are discussed in relation to different methodologies: we review event-related brain responses, research on the coupling of neural oscillations to speech, oscillatory coupling between brain regions (eg, auditory-motor coupling), and neural decoding approaches in naturalistic language comprehension.

How the brain composes morphemes into meaning.

Morphemes (e.g. [tune], [-ful], [-ly]) are the basic blocks with which complex meaning is built. Here, I explore the critical role that morpho-syntactic rules play in forming the meaning of morphologically complex words, from two primary standpoints: (i) how semantically rich stem morphemes (e.g. explode, bake, post) combine with syntactic operators (e.g. -ion, -er, -age) to output a semantically predictable result; (ii) how this process can be understood in terms of mathematical operations, easily allowing the brain to generate representations of novel morphemes and comprehend novel words. With these ideas in mind, I offer a model of morphological processing that incorporates semantic and morpho-syntactic operations in service to meaning composition, and discuss how such a model could be implemented in the human brain. This article is part of the theme issue 'Towards mechanistic models of meaning composition'.

Prefix Stripping Re-Re-Revisited: MEG Investigations of Morphological Decomposition and Recomposition.

We revisit a long-standing question in the psycholinguistic and neurolinguistic literature on comprehending morphologically complex words: are prefixes and suffixes processed using the same cognitive mechanisms? Recent work using Magnetoencephalography (MEG) to uncover the dynamic temporal and spatial responses evoked by visually presented complex suffixed single words provide us with a comprehensive picture of morphological processing in the brain, from early, form-based decomposition, through lexical access, grammatically constrained recomposition, and semantic interpretation. In the present study, we find that MEG responses to prefixed words reveal interesting early differences in the lateralization of the form-based decomposition response compared to the effects reported in the literature for suffixed words, but a very similar post-decomposition profile. These results not only address a question stretching back to the earliest days of modern psycholinguistics, but also add critical support and nuance to our much newer emerging understanding of spatial organization and temporal dynamics of morphological processing in the human brain.

In Spoken Word Recognition, the Future Predicts the Past.

Speech is an inherently noisy and ambiguous signal. To fluently derive meaning, a listener must integrate contextual information to guide interpretations of the sensory input. Although many studies have demonstrated the influence of prior context on speech perception, the neural mechanisms supporting the integration of subsequent context remain unknown. Using MEG to record from human auditory cortex, we analyzed responses to spoken words with a varyingly ambiguous onset phoneme, the identity of which is later disambiguated at the lexical uniqueness point. Fifty participants (both male and female) were recruited across two MEG experiments. Our findings suggest that primary auditory cortex is sensitive to phonological ambiguity very early during processing at just 50 ms after onset. Subphonemic detail is preserved in auditory cortex over long timescales and re-evoked at subsequent phoneme positions. Commitments to phonological categories occur in parallel, resolving on the shorter timescale of ∼450 ms. These findings provide evidence that future input determines the perception of earlier speech sounds by maintaining sensory features until they can be integrated with top-down lexical information.SIGNIFICANCE STATEMENT The perception of a speech sound is determined by its surrounding context in the form of words, sentences, and other speech sounds. Often, such contextual information becomes available later than the sensory input. The present study is the first to unveil how the brain uses this subsequent information to aid speech comprehension. Concretely, we found that the auditory system actively maintains the acoustic signal in auditory cortex while concurrently making guesses about the identity of the words being said. Such a processing strategy allows the content of the message to be accessed quickly while also permitting reanalysis of the acoustic signal to minimize parsing mistakes.

Language in Context: MEG Evidence for Modality-General and -Specific Responses to Reference Resolution.

Successful language comprehension critically depends on our ability to link linguistic expressions to the entities they refer to. Without reference resolution, newly encountered language cannot be related to previously acquired knowledge. The human experience includes many different types of referents, some visual, some auditory, some very abstract. Does the neural basis of reference resolution depend on the nature of the referents, or do our brains use a modality-general mechanism for linking meanings to referents? Here we report evidence for both. Using magnetoencephalography (MEG), we varied both the modality of referents, which consisted either of visual or auditory objects, and the point at which reference resolution was possible within sentences. Source-localized MEG responses revealed brain activity associated with reference resolution that was independent of the modality of the referents, localized to the medial parietal lobe and starting ∼415 ms after the onset of reference resolving words. A modality-specific response to reference resolution in auditory domains was also found, in the vicinity of auditory cortex. Our results suggest that referential language processing cannot be reduced to processing in classical language regions and representations of the referential domain in modality-specific neural systems. Instead, our results suggest that reference resolution engages medial parietal cortex, which supports a mechanism for referential processing regardless of the content modality.

EEG can Track the Time Course of Successful Reference Resolution in Small Visual Worlds.

Previous research has shown that language comprehenders resolve reference quickly and incrementally, but not much is known about the neural processes and representations that are involved. Studies of visual short-term memory suggest that access to the representation of an item from a previously seen display is associated with a negative evoked potential at posterior electrodes contralateral to the spatial location of that item in the display. In this paper we demonstrate that resolving the reference of a noun phrase in a recently seen visual display is associated with an event-related potential that is analogous to this effect. Our design was adapted from the visual world paradigm: in each trial, participants saw a display containing three simple objects, followed by a question about the objects, such as Was the pink fish next to a boat?, presented word by word. Questions differed in whether the color adjective allowed the reader to identify the referent of the noun phrase or not (i.e., whether one or more objects of the named color were present). Consistent with our hypothesis, we observed that reference resolution by the adjective was associated with a negative evoked potential at posterior electrodes contralateral to spatial location of the referent, starting approximately 333 ms after the onset of the adjective. The fact that the laterality of the effect depended upon the location of the referent within the display suggests that reference resolution in visual domains involves, at some level, a modality-specific representation. In addition, the effect gives us an estimate of the time course of processing from perception of the written word to the point at which its meaning is brought into correspondence with the referential domain.

Involvement of prefrontal cortex in scalar implicatures: evidence from magnetoencephalography.

The present study investigated the neural correlates of the realisation of scalar inferences, i.e., the interpretation of some as meaning some but not all. We used magnetoencephalography, which has high temporal resolution, to measure neural activity while participants heard stories that included the scalar inference trigger some in contexts that either provide strong cues for a scalar inference or provide weaker cues. The middle portion of the lateral prefrontal cortex (Brodmann area 46) showed an increased response to some in contexts with fewer cues to the inference, suggesting that this condition elicited greater effort. While the results are not predicted by traditional all-or-nothing accounts of scalar inferencing that assume the process is always automatic or always effortful, they are consistent with more recent gradient accounts which predict that the speed and effort of scalar inferences is strongly modulated by numerous contextual factors.

Sensitivity to morphological composition in spoken word recognition: Evidence from grammatical and lexical identification tasks.

Access to morphological structure during lexical processing has been established across a number of languages; however, it remains unclear which constituents are held as mental representations in the lexicon. The present study examined the auditory recognition of different noun types across 2 experiments. The critical manipulations were morphological complexity and the presence of a verbal derivation or nominalizing suffix form. Results showed that nominalizations, such as "explosion," were harder to classify as a noun but easier to classify as a word when compared with monomorphemic words with similar actionlike semantics, such as "avalanche." These findings support the claim that listeners decompose morphologically complex words into their constituent units during processing. More specifically, the results suggest that people hold representations of base morphemes in the lexicon.