Sequences of Intonation Units form a ~1 Hz rhythm

Inbar, Maya; Grossman, Eitan; Landau, Ayelet N.

doi:10.1101/765016

Cited by 6 publications

(8 citation statements)

References 44 publications

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“…Besides the low ecological validity of such stimuli, they further rely on the assumption that speech and music share similar cognitive time constants. However, speech unfolds faster than music (Ding et al, 2017), and while a linguistic phrase typically lasts less than a second (Inbar et al, 2020), a melodic phrase is an order of magnitude longer. Moreover, balancing the complexity/simplicity of linguistic and musical stimuli can be challenging, and musical stimuli are often reduced to very simple melodies played on a synthesizer.…”

Section: P R E P R I N T 1 Introductionmentioning

confidence: 99%

Speech and music recruit frequency-specific distributed and overlapping cortical networks

Rietmolen¹,

Mercier²,

Trébuchon

et al. 2022

Preprint

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To what extent do speech and music processing rely on domain-specific and domain-general neural networks? Adopting a dynamical system framework, we investigate the presence of frequency-specific and network-level selectivity and combine it with a statistical approach in which a clear distinction is made between shared, preferred, and category-selective neural responses. Using intracranial EEG recordings in 18 epilepsy patients listening to natural and continuous speech and music, we show that the majority of focal and network-level neural activity is shared between speech and music processing. Our data also reveal an absence of regional selectivity. Instead, neural selectivity is restricted to dis- tributed and frequency-specific coherent oscillations, typical of spectral fingerprints. Our work addresses a longstanding debate and redefines an epistemological posture on how to map cognitive and brain functions.

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Section: P R E P R I N T 1 Introductionmentioning

confidence: 99%

Speech and music recruit frequency-specific distributed and overlapping cortical networks

Rietmolen¹,

Mercier²,

Trébuchon

et al. 2022

Preprint

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“…We found that sequences of IUs form a ∼1 Hz rhythm in all these languages, despite substantial variation in their grammatical structures and socio-cultural profiles. The consistent temporal structure of IUs across languages provides further evidence for their importance in cognition (Inbar et al, 2020).…”

Section: Introductionmentioning

confidence: 94%

“…Based on our previous work demonstrating that sequences of IUs form ∼1 Hz rhythms (Inbar et al, 2020), we hypothesized that from a continuous perspective, the time course of responses to IUs would give rise to delta-band neural speech tracking. We use the estimated responses from the GLM model to predict the continuous EEG response to our stimuli and perform a neural speech tracking analysis on the empirical EEG and on the model-predicted EEG (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Intonation Units in spontaneous speech evoke a neural response

Inbar

Genzer

Grossman

et al. 2023

Preprint

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Spontaneous speech is produced in chunks called Intonation Units (IUs). IUs are defined by a set of prosodic cues and occur in all human languages. Linguistic theory suggests that IUs pace the flow of information and serve as a window onto the dynamic focus of attention in speech processing. IUs provide a promising and hitherto unexplored theoretical framework for studying the neural mechanisms of communication, thanks to their universality and their consistent temporal structure across different grammatical and socio-cultural conditions. In this article, we identify a neural response unique to the boundary defined by the IU. We measured the EEG of participants who listened to different speakers recounting an emotional life event. We analyzed the speech stimuli linguistically, and modeled the EEG response at word offset using a GLM approach. We find that the EEG response to IU-final words differs from the response to IU-nonfinal words when acoustic boundary strength is held constant. To the best of our knowledge, this is the first time this is demonstrated in spontaneous speech under naturalistic listening conditions, and under a theoretical framework that connects the prosodic chunking of speech, on the one hand, with the flow of information during communication, on the other. Finally, we relate our findings to the body of research on rhythmic brain mechanism in speech processing by comparing the topographical distributions of neural speech tracking in model-predicted and empirical EEG. This qualitative comparison suggests that IU-related neural activity contributes to the previously characterized delta-band neural speech tracking.

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“…Neural speech tracking has become an increasingly useful tool for studying how the brain encodes and processes continuous speech (Obleser and Kayser, 2019; Brodbeck and Simon, 2020). Importantly, characterizing linguistic attributes of speech on a continuous basis gives a new angle to auditory attention and neurolinguistics, as researchers have been able to dissociate neural responses driven by the acoustics of speech from those capturing higher-order processes in a dynamically changing speech signal (Brodbeck et al, 2018; Keitel et al, 2018; Inbar et al, 2020; Gillis et al, 2021). And yet, the speech stimuli used in these studies are generally highly scripted and edited, taken – for example – from audiobooks or TED talks, which are also extensively rehearsed and are delivered by professionals.…”

Section: Introductionmentioning

confidence: 99%

“Um…, it’s really difficult to… um… speak fluently”: Neural tracking of spontaneous speech

Agmon

Jaeger

Tsarfaty

et al. 2022

Preprint

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Spontaneous real-life speech is imperfect in many ways. It contains disfluencies and ill-formed utterances that the brain needs to contend with in order to extract meaning out of speech. Here, we studied how the neural speech-tracking response is affected by three specific factors that are prevalent in spontaneous colloquial speech: (1) the presence of non-lexical fillers, (2) the need to detect syntactic boundaries in disfluent speech and (3) the effort involved in processing syntactically complex phrases. Neural activity (EEG) was recorded from individuals as they listened to an unscripted, spontaneous narrative, which was analyzed in a time-resolved fashion to identify fillers, detect syntactic boundaries and assess the syntactic complexity of different phrases. When considering these factors in the speech-tracking analysis, we found that it was affected by all of them. The most consistent effect, observed for all three factors, was modulation of a centro-frontal negative response that peaked around 350 ms, highly resembling the well-known N400 ERP response linked to various aspects of lexical access and semantic processing. This response was observed for lexical words but not for fillers, was larger for opening vs. closing words of a clause and was enhanced in response to high-complexity phrases. These findings broaden ongoing efforts to understand neural processing of speech under increasingly realistic conditions. They highlight the importance of considering the imperfect nature of real-life spoken language, linking past research on linguistically well-formed and meticulously controlled speech to the type of speech that the brain actually deals with on a daily basis.

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Sequences of Intonation Units form a ~1 Hz rhythm

Abstract: 10

Cited by 6 publications

References 44 publications

Speech and music recruit frequency-specific distributed and overlapping cortical networks

Speech and music recruit frequency-specific distributed and overlapping cortical networks

Intonation Units in spontaneous speech evoke a neural response

“Um…, it’s really difficult to… um… speak fluently”: Neural tracking of spontaneous speech

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