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Sleep is a vital need, forcing us to spend a large portion of our life unable to interact with the external world. Current models interpret such extreme vulnerability as the price to pay for optimal learning. Sleep would limit external interferences on memory consolidation 1 – 3 and allow neural systems to reset through synaptic downscaling 4 . Yet, the sleeping brain continues generating neural responses to external events 5 , 6 , revealing the preservation of cognitive processes ranging from the recognition of familiar stimuli to the formation of new memory representations 7 – 15 . Why would sleepers continue processing external events and yet remain unresponsive? Here we hypothesized that sleepers enter a ‘standby mode’ in which they continue tracking relevant signals, finely balancing the need to stay inward for memory consolidation with the ability to rapidly awake when necessary. Using electroencephalography to reconstruct competing streams in a multitalker environment 16 , we demonstrate that the sleeping brain amplifies meaningful speech compared to irrelevant signals. However, the amplification of relevant stimuli was transient and vanished during deep sleep. The effect of sleep depth could be traced back to specific oscillations, with K-complexes promoting relevant information in light sleep, whereas slow waves actively suppress relevant signals in deep sleep. Thus, the selection of relevant stimuli continues to operate during sleep but is strongly modulated by specific brain rhythms. Why do we continue processing external events during sleep, yet remain unresponsive? Legendre et al. use electroencephalography to show that sleepers enter a ‘standby mode’, continuing to track relevant signals but doing so transiently.

When motor actions (e.g., reaching with your hand) adapt to altered sensory feedback (e.g., viewing a shifted image of your hand through a prism), the phenomenon is called sensorimotor adaptation (SA). In the study reported here, SA was observed in speech. In two 2-hour experiments (adaptation and control), participants whispered a variety of CVC words. For those words containing the vowel /ε/, participants heard auditory feedback of their whispering. A DSP-based vocoder processed the participants' auditory feedback in real time, allowing the formant frequencies of participants' auditory speech feedback to be shifted. In the adaptation experiment, formants were shifted along one edge of the vowel triangle. For half the participants, formants were shifted so participants heard /a/ when they produced /ε/; for the other half, the shift made participants hear /i/ when they produced /ε/. During the adaptation experiment, participants altered their production of /ε/ to compensate for the altered feedback, and these production changes were retained when participants whispered with auditory feedback blocked by masking noise. In a control experiment, in which the formants were not shifted, participants' production changes were small and inconsistent. Participants exhibited a range of adaptations in response to the altered feedback, with some participants adapting almost completely, and other participants showing very little or no adaptation.

The recognition of spoken language has typically been studied by focusing on either words or their constituent elements (for example, low-level features or phonemes). More recently, the ‘temporal mesoscale’ of speech has been explored, specifically regularities in the envelope of the acoustic signal that correlate with syllabic information and that play a central role in production and perception processes. The temporal structure of speech at this scale is remarkably stable across languages, with a preferred range of rhythmicity of 2– 8 Hz. Importantly, this rhythmicity is required by the processes underlying the construction of intelligible speech. A lot of current work focuses on audio-motor interactions in speech, highlighting behavioural and neural evidence that demonstrates how properties of perceptual and motor systems, and their relation, can underlie the mesoscale speech rhythms. The data invite the hypothesis that the speech motor cortex is best modelled as a neural oscillator, a conjecture that aligns well with current proposals highlighting the fundamental role of neural oscillations in perception and cognition. The findings also show motor theories (of speech) in a different light, placing new mechanistic constraints on accounts of the action–perception interface.Syllables play a central role in speech production and perception. In this Review, Poeppel and Assaneo outline how a simple biophysical model of the speech production system as an oscillator explains the remarkably stable rhythmic structure of spoken language.

Historic theories of speech perception (Motor Theory and Analysis by Synthesis) invoked listeners’ knowledge of speech production to explain speech perception. Neuroimaging data show that adult listeners activate motor brain areas during speech perception. In two experiments using magnetoencephalography (MEG), we investigated motor brain activation, as well as auditory brain activation, during discrimination of native and nonnative syllables in infants at two ages that straddle the developmental transition from language-universal to language-specific speech perception. Adults are also tested in Exp. 1. MEG data revealed that 7-mo-old infants activate auditory (superior temporal) as well as motor brain areas (Broca’s area, cerebellum) in response to speech, and equivalently for native and nonnative syllables. However, in 11- and 12-mo-old infants, native speech activates auditory brain areas to a greater degree than nonnative, whereas nonnative speech activates motor brain areas to a greater degree than native speech. This double dissociation in 11- to 12-mo-old infants matches the pattern of results obtained in adult listeners. Our infant data are consistent with Analysis by Synthesis: auditory analysis of speech is coupled with synthesis of the motor plans necessary to produce the speech signal. The findings have implications for: (i) perception-action theories of speech perception, (ii) the impact of “motherese” on early language learning, and (iii) the “social-gating” hypothesis and humans’ development of social understanding.

Attention plays a fundamental role in selectively processing stimuli in our environment despite distraction. Spatial attention induces increasing and decreasing power of neural alpha oscillations (8–12 Hz) in brain regions ipsilateral and contralateral to the locus of attention, respectively. This study tested whether the hemispheric lateralization of alpha power codes not just the spatial location but also the temporal structure of the stimulus. Participants attended to spoken digits presented to one ear and ignored tightly synchronized distracting digits presented to the other ear. In the magnetoencephalogram, spatial attention induced lateralization of alpha power in parietal, but notably also in auditory cortical regions. This alpha power lateralization was not maintained steadily but fluctuated in synchrony with the speech rate and lagged the time course of low-frequency (1–5 Hz) sensory synchronization. Higher amplitude of alpha power modulation at the speech rate was predictive of a listener’s enhanced performance of stream-specific speech comprehension. Our findings demonstrate that alpha power lateralization is modulated in tune with the sensory input and acts as a spatiotemporal filter controlling the read-out of sensory content.

How do we recognize what one person is saying when others are speaking at the same time? This review summarizes widespread research in psychoacoustics, auditory scene analysis, and attention, all dealing with early processing and selection of speech, which has been stimulated by this question. Important effects occurring at the peripheral and brainstem levels are mutual masking of sounds and “unmasking” resulting from binaural listening. Psychoacoustic models have been developed that can predict these effects accurately, albeit using computational approaches rather than approximations of neural processing. Grouping—the segregation and streaming of sounds—represents a subsequent processing stage that interacts closely with attention. Sounds can be easily grouped—and subsequently selected—using primitive features such as spatial location and fundamental frequency. More complex processing is required when lexical, syntactic, or semantic information is used. Whereas it is now clear that such processing can take place preattentively, there also is evidence that the processing depth depends on the task-relevancy of the sound. This is consistent with the presence of a feedback loop in attentional control, triggering enhancement of to-be-selected input. Despite recent progress, there are still many unresolved issues: there is a need for integrative models that are neurophysiologically plausible, for research into grouping based on other than spatial or voice-related cues, for studies explicitly addressing endogenous and exogenous attention, for an explanation of the remarkable sluggishness of attention focused on dynamically changing sounds, and for research elucidating the distinction between binaural speech perception and sound localization.

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 arrays 1 – 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 gyrus 4 , 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. High-density single-neuron recordings show diverse tuning for acoustic and phonetic features across layers in human auditory speech cortex.

Musical training is associated with a myriad of neuroplastic changes in the brain, including more robust and efficient neural processing of clean and degraded speech signals at brainstem and cortical levels. These assumptions stem largely from cross-sectional studies between musicians and nonmusicians which cannot address whether training itself is sufficient to induce physiological changes or whether preexisting superiority in auditory function before training predisposes individuals to pursue musical interests and appear to have similar neuroplastic benefits as musicians. Here, we recorded neuroelectric brain activity to clear and noise-degraded speech sounds in individuals without formal music training but who differed in their receptive musical perceptual abilities as assessed objectively via the Profile of Music Perception Skills. We found that listeners with naturally more adept listening skills (“musical sleepers”) had enhanced frequency-following responses to speech that were also more resilient to the detrimental effects of noise, consistent with the increased fidelity of speech encoding and speech-in-noise benefits observed previously in highly trained musicians. Further comparisons between these musical sleepers and actual trained musicians suggested that experience provides an additional boost to the neural encoding and perception of speech. Collectively, our findings suggest that the auditory neuroplasticity of music engagement likely involves a layering of both preexisting (nature) and experience-driven (nurture) factors in complex sound processing. In the absence of formal training, individuals with intrinsically proficient auditory systems can exhibit musician-like auditory function that can be further shaped in an experience-dependent manner.

Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However, the role of prediction in language processing remains disputed, with disagreement about both the ubiquity and representational nature of predictions. Here, we address both issues by analyzing brain recordings of participants listening to audiobooks, and using a deep neural network (GPT-2) to precisely quantify contextual predictions. First, we establish that brain responses to words are modulated by ubiquitous predictions. Next, we disentangle model-based predictions into distinct dimensions, revealing dissociable neural signatures of predictions about syntactic category (parts of speech), phonemes, and semantics. Finally, we show that high-level (word) predictions inform low-level (phoneme) predictions, supporting hierarchical predictive processing. Together, these results underscore the ubiquity of prediction in language processing, showing that the brain spontaneously predicts upcoming language at multiple levels of abstraction.

The anatomy of language has been investigated with PET or fMRI for more than 20 years. Here I attempt to provide an overview of the brain areas associated with heard speech, speech production and reading. The conclusions of many hundreds of studies were considered, grouped according to the type of processing, and reported in the order that they were published. Many findings have been replicated time and time again leading to some consistent and undisputable conclusions. These are summarised in an anatomical model that indicates the location of the language areas and the most consistent functions that have been assigned to them. The implications for cognitive models of language processing are also considered. In particular, a distinction can be made between processes that are localized to specific structures (e.g. sensory and motor processing) and processes where specialisation arises in the distributed pattern of activation over many different areas that each participate in multiple functions. For example, phonological processing of heard speech is supported by the functional integration of auditory processing and articulation; and orthographic processing is supported by the functional integration of visual processing, articulation and semantics. Future studies will undoubtedly be able to improve the spatial precision with which functional regions can be dissociated but the greatest challenge will be to understand how different brain regions interact with one another in their attempts to comprehend and produce language.