Intonational speech prosody encoding in the human auditory cortex

Deficits in congenital amusia: Pitch, music, speech, and beyond

Congenital amusia is a neurodevelopmental disorder characterized by deficits of music perception and production, which are related to altered pitch processing. The present study used a wide variety of tasks to test potential patterns of processing impairment in individuals with congenital amusia (N = 18) in comparison to matched controls (N = 19), notably classical pitch processing tests (i.e., pitch change detection, pitch direction of change identification, and pitch short-term memory tasks) together with tasks assessing other aspects of pitch-related auditory cognition, such as emotion recognition in speech, sound segregation in tone sequences, and speech-in-noise perception. Additional behavioral measures were also collected, including text reading/copying tests, visual control tasks, and a subjective assessment of hearing abilities. As expected, amusics' performance was impaired for the three pitch-specific tasks compared to controls. This deficit of pitch perception had a self-perceived impact on amusics' quality of hearing. Moreover, participants with amusia were impaired in emotion recognition in vowels compared to controls, but no group difference was observed for emotion recognition in sentences, replicating previous data. Despite pitch processing deficits, participants with amusia did not differ from controls in sound segregation and speech-in-noise perception. Text reading and visual control tests did not reveal any impairments in participants with amusia compared to controls. However, the copying test revealed more numerous eye-movements and a smaller memory span. These results allow us to refine the pattern of pitch processing and memory deficits in congenital amusia, thus contributing further to understand pitch-related auditory cognition. Together with previous reports suggesting a comorbidity between congenital amusia and dyslexia, the findings call for further investigation of language-related abilities in this disorder even in the absence of neurodevelopmental language disorder diagnosis.

A comparison of EEG encoding models using audiovisual stimuli and their unimodal counterparts

Communication in the real world is inherently multimodal. When having a conversation, typically sighted and hearing people use both auditory and visual cues to understand one another. For example, objects may make sounds as they move in space, or we may use the movement of a person's mouth to better understand what they are saying in a noisy environment. Still, many neuroscience experiments rely on unimodal stimuli to understand encoding of sensory features in the brain. The extent to which visual information may influence encoding of auditory information and vice versa in natural environments is thus unclear. Here, we addressed this question by recording scalp electroencephalography (EEG) in 11 subjects as they listened to and watched movie trailers in audiovisual (AV), visual (V) only, and audio (A) only conditions. We then fit linear encoding models that described the relationship between the brain responses and the acoustic, phonetic, and visual information in the stimuli. We also compared whether auditory and visual feature tuning was the same when stimuli were presented in the original AV format versus when visual or auditory information was removed. In these stimuli, visual and auditory information was relatively uncorrelated, and included spoken narration over a scene as well as animated or live-action characters talking with and without their face visible. For this stimulus, we found that auditory feature tuning was similar in the AV and A-only conditions, and similarly, tuning for visual information was similar when stimuli were presented with the audio present (AV) and when the audio was removed (V only). In a cross prediction analysis, we investigated whether models trained on AV data predicted responses to A or V only test data similarly to models trained on unimodal data. Overall, prediction performance using AV training and V test sets was similar to using V training and V test sets, suggesting that the auditory information has a relatively smaller effect on EEG. In contrast, prediction performance using AV training and A only test set was slightly worse than using matching A only training and A only test sets. This suggests the visual information has a stronger influence on EEG, though this makes no qualitative difference in the derived feature tuning. In effect, our results show that researchers may benefit from the richness of multimodal datasets, which can then be used to answer more than one research question.

Humans use local spectrotemporal correlations to detect rising and falling pitch

To discern speech or appreciate music, the human auditory system detects how pitch increases or decreases over time. However, the algorithms used to detect changes in pitch, or pitch motion, are incompletely understood. Here, using psychophysics, computational modeling, functional neuroimaging, and analysis of recorded speech, we ask if humans detect pitch motion using computations analogous to those used by the visual system. We adapted stimuli from studies of vision to create novel auditory correlated noise stimuli that elicited robust pitch motion percepts. Crucially, these stimuli possess no persistent features across frequency or time, but do possess positive or negative local spectrotemporal correlations in intensity. In psychophysical experiments, we found clear evidence that humans judge pitch direction based on both positive and negative spectrotemporal correlations. The observed sensitivity to negative correlations is a direct analogue of illusory "reverse-phi" motion in vision, and thus constitutes a new auditory illusion. Our behavioral results and computational modeling led us to hypothesize that human auditory processing employs pitch direction opponency. fMRI measurements in auditory cortex supported this hypothesis. To link our psychophysical findings to real-world pitch perception, we analyzed recordings of English and Mandarin speech and discovered that pitch direction was robustly signaled by the same positive and negative spectrotemporal correlations used in our psychophysical tests, suggesting that sensitivity to both positive and negative correlations confers ecological benefits. Overall, this work reveals that motion detection algorithms sensitive to local correlations are deployed by the central nervous system across disparate modalities (vision and audition) and dimensions (space and frequency).

Perception and adaptation of receptive prosody in autistic adolescents

A fundamental aspect of language processing is inferring others' minds from subtle variations in speech. The same word or sentence can often convey different meanings depending on its tempo, timing, and intonation-features often referred to as prosody. Although autistic children and adults are known to experience difficulty in making such inferences, the science remains unclear as to why. We hypothesize that detail-oriented perception in autism may interfere with the inference process if it lacks the adaptivity required to cope with the variability ubiquitous in human speech. Using a novel prosodic continuum that shifts the sentence meaning gradiently from a statement (e.g., "It's raining") to a question (e.g., "It's raining?"), we have investigated the perception and adaptation of receptive prosody in autistic adolescents and two groups of non-autistic controls. Autistic adolescents showed attenuated adaptivity in categorizing prosody, whereas they were equivalent to controls in terms of discrimination accuracy. Combined with recent findings in segmental (e.g., phoneme) recognition, the current results provide the basis for an emerging research framework for attenuated flexibility and reduced influence of contextual feedback as a possible source of deficits that hinder linguistic and social communication in autism.

A simple psychophysical procedure separates representational and noise components in impairments of speech prosody perception after right-hemisphere stroke

After a right hemisphere stroke, more than half of the patients are impaired in their capacity to produce or comprehend speech prosody. Yet, and despite its social-cognitive consequences for patients, aprosodia following stroke has received scant attention. In this report, we introduce a novel, simple psychophysical procedure which, by combining systematic digital manipulations of speech stimuli and reverse-correlation analysis, allows estimating the internal sensory representations that subtend how individual patients perceive speech prosody, and the level of internal noise that govern behavioral variability in how patients apply these representations. Tested on a sample of N = 22 right-hemisphere stroke survivors and N = 21 age-matched controls, the representation + noise model provides a promising alternative to the clinical gold standard for evaluating aprosodia (MEC): both parameters strongly associate with receptive, and not expressive, aprosodia measured by MEC within the patient group; they have better sensitivity than MEC for separating high-functioning patients from controls; and have good specificity with respect to non-prosody-related impairments of auditory attention and processing. Taken together, individual differences in either internal representation, internal noise, or both, paint a potent portrait of the variety of sensory/cognitive mechanisms that can explain impairments of prosody processing after stroke.

Speech prosody enhances the neural processing of syntax

Human language relies on the correct processing of syntactic information, as it is essential for successful communication between speakers. As an abstract level of language, syntax has often been studied separately from the physical form of the speech signal, thus often masking the interactions that can promote better syntactic processing in the human brain. However, behavioral and neural evidence from adults suggests the idea that prosody and syntax interact, and studies in infants support the notion that prosody assists language learning. Here we analyze a MEG dataset to investigate how acoustic cues, specifically prosody, interact with syntactic representations in the brains of native English speakers. More specifically, to examine whether prosody enhances the cortical encoding of syntactic representations, we decode syntactic phrase boundaries directly from brain activity, and evaluate possible modulations of this decoding by the prosodic boundaries. Our findings demonstrate that the presence of prosodic boundaries improves the neural representation of phrase boundaries, indicating the facilitative role of prosodic cues in processing abstract linguistic features. This work has implications for interactive models of how the brain processes different linguistic features. Future research is needed to establish the neural underpinnings of prosody-syntax interactions in languages with different typological characteristics.

Processing of auditory feedback in perisylvian and insular cortex

When we speak, we not only make movements with our mouth, lips, and tongue, but we also hear the sound of our own voice. Thus, speech production in the brain involves not only controlling the movements we make, but also auditory and sensory feedback. Auditory responses are typically suppressed during speech production compared to perception, but how this manifests across space and time is unclear. Here we recorded intracranial EEG in seventeen pediatric, adolescent, and adult patients with medication-resistant epilepsy who performed a reading/listening task to investigate how other auditory responses are modulated during speech production. We identified onset and sustained responses to speech in bilateral auditory cortex, with a selective suppression of onset responses during speech production. Onset responses provide a temporal landmark during speech perception that is redundant with forward prediction during speech production. Phonological feature tuning in these "onset suppression" electrodes remained stable between perception and production. Notably, the posterior insula responded at sentence onset for both perception and production, suggesting a role in multisensory integration during feedback control.

Encoding of melody in the human auditory cortex

Melody is a core component of music in which discrete pitches are serially arranged to convey emotion and meaning. Perception varies along several pitch-based dimensions: (i) the absolute pitch of notes, (ii) the difference in pitch between successive notes, and (iii) the statistical expectation of each note given prior context. How the brain represents these dimensions and whether their encoding is specialized for music remains unknown. We recorded high-density neurophysiological activity directly from the human auditory cortex while participants listened to Western musical phrases. Pitch, pitch-change, and expectation were selectively encoded at different cortical sites, indicating a spatial map for representing distinct melodic dimensions. The same participants listened to spoken English, and we compared responses to music and speech. Cortical sites selective for music encoded expectation, while sites that encoded pitch and pitch-change in music used the same neural code to represent equivalent properties of speech. Findings reveal how the perception of melody recruits both music-specific and general-purpose sound representations.

Controlling Pitch for Prosody: Sensorimotor Adaptation in Linguistically Meaningful Contexts

PURPOSE

This study examined how speakers adapt to fundamental frequency (fo) errors that affect the use of prosody to convey linguistic meaning, whether fo adaptation in that context relates to adaptation in linguistically neutral sustained vowels, and whether cue trading is reflected in responses in the prosodic cues of fo and amplitude.

METHOD

Twenty-four speakers said vowels and sentences while fo was digitally altered to induce predictable errors. Shifts in fo (±200 cents) were applied to the entire sustained vowel and one word (emphasized or unemphasized) in sentences. Two prosodic cues-fo and amplitude-were extracted. The effects of fo shifts, shift direction, and emphasis on fo response magnitude were evaluated with repeated-measures analyses of variance. Relationships between adaptive fo responses in sentences and vowels and between adaptive fo and amplitude responses were evaluated with Spearman correlations.

RESULTS

Speakers adapted to fo errors in both linguistically meaningful sentences and linguistically neutral vowels. Adaptive fo responses of unemphasized words were smaller than those of emphasized words when fo was shifted upward. There was no relationship between adaptive fo responses in vowels and emphasized words, but adaptive fo and amplitude responses were strongly, positively correlated.

CONCLUSIONS

Sensorimotor adaptation occurs in response to fo errors regardless of how disruptive the error is to linguistic meaning. Adaptation to fo errors during sustained vowels may not involve the exact same mechanisms as sensorimotor adaptation as it occurs in meaningful speech. The relationship between adaptive responses in fo and amplitude supports an integrated model of prosody.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25008908.

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.

Biological principles for music and mental health

Efforts to integrate music into healthcare systems and wellness practices are accelerating but the biological foundations supporting these initiatives remain underappreciated. As a result, music-based interventions are often sidelined in medicine. Here, I bring together advances in music research from neuroscience, psychology, and psychiatry to bridge music's specific foundations in human biology with its specific therapeutic applications. The framework I propose organizes the neurophysiological effects of music around four core elements of human musicality: tonality, rhythm, reward, and sociality. For each, I review key concepts, biological bases, and evidence of clinical benefits. Within this framework, I outline a strategy to increase music's impact on health based on standardizing treatments and their alignment with individual differences in responsivity to these musical elements. I propose that an integrated biological understanding of human musicality-describing each element's functional origins, development, phylogeny, and neural bases-is critical to advancing rational applications of music in mental health and wellness.

Dissecting neural computations in the human auditory pathway using deep neural networks for speech

The human auditory system extracts rich linguistic abstractions from speech signals. Traditional approaches to understanding this complex process have used linear feature-encoding models, with limited success. Artificial neural networks excel in speech recognition tasks and offer promising computational models of speech processing. We used speech representations in state-of-the-art deep neural network (DNN) models to investigate neural coding from the auditory nerve to the speech cortex. Representations in hierarchical layers of the DNN correlated well with the neural activity throughout the ascending auditory system. Unsupervised speech models performed at least as well as other purely supervised or fine-tuned models. Deeper DNN layers were better correlated with the neural activity in the higher-order auditory cortex, with computations aligned with phonemic and syllabic structures in speech. Accordingly, DNN models trained on either English or Mandarin predicted cortical responses in native speakers of each language. These results reveal convergence between DNN model representations and the biological auditory pathway, offering new approaches for modeling neural coding in the auditory cortex.

Neural control of lexical tone production in human laryngeal motor cortex

In tonal languages, which are spoken by nearly one-third of the world's population, speakers precisely control the tension of vocal folds in the larynx to modulate pitch in order to distinguish words with completely different meanings. The specific pitch trajectories for a given tonal language are called lexical tones. Here, we used high-density direct cortical recordings to determine the neural basis of lexical tone production in native Mandarin-speaking participants. We found that instead of a tone category-selective coding, local populations in the bilateral laryngeal motor cortex (LMC) encode articulatory kinematic information to generate the pitch dynamics of lexical tones. Using a computational model of tone production, we discovered two distinct patterns of population activity in LMC commanding pitch rising and lowering. Finally, we showed that direct electrocortical stimulation of different local populations in LMC evoked pitch rising and lowering during tone production, respectively. Together, these results reveal the neural basis of vocal pitch control of lexical tones in tonal languages.

Encoding of melody in the human auditory cortex

Melody is a core component of music in which discrete pitches are serially arranged to convey emotion and meaning. Perception of melody varies along several pitch-based dimensions: (1) the absolute pitch of notes, (2) the difference in pitch between successive notes, and (3) the higher-order statistical expectation of each note conditioned on its prior context. While humans readily perceive melody, how these dimensions are collectively represented in the brain and whether their encoding is specialized for music remains unknown. Here, we recorded high-density neurophysiological activity directly from the surface of human auditory cortex while Western participants listened to Western musical phrases. Pitch, pitch-change, and expectation were selectively encoded at different cortical sites, indicating a spatial code for representing distinct dimensions of melody. The same participants listened to spoken English, and we compared evoked responses to music and speech. Cortical sites selective for music were systematically driven by the encoding of expectation. In contrast, sites that encoded pitch and pitch-change used the same neural code to represent equivalent properties of speech. These findings reveal the multidimensional nature of melody encoding, consisting of both music-specific and domain-general sound representations in auditory cortex.

Teaser

The human brain contains both general-purpose and music-specific neural populations for processing distinct attributes of melody.

Auditory cortex and beyond: Deficits in congenital amusia

Congenital amusia is a neuro-developmental disorder of music perception and production, with the observed deficits contrasting with the sophisticated music processing reported for the general population. Musical deficits within amusia have been hypothesized to arise from altered pitch processing, with impairments in pitch discrimination and, notably, short-term memory. We here review research investigating its behavioral and neural correlates, in particular the impairments at encoding, retention, and recollection of pitch information, as well as how these impairments extend to the processing of pitch cues in speech and emotion. The impairments have been related to altered brain responses in a distributed fronto-temporal network, which can be observed also at rest. Neuroimaging studies revealed changes in connectivity patterns within this network and beyond, shedding light on the brain dynamics underlying auditory cognition. Interestingly, some studies revealed spared implicit pitch processing in congenital amusia, showing the power of implicit cognition in the music domain. Building on these findings, together with audiovisual integration and other beneficial mechanisms, we outline perspectives for training and rehabilitation and the future directions of this research domain.

What we do (not) know about the mechanisms underlying adaptive speech perception: A computational framework and review

Speech from unfamiliar talkers can be difficult to comprehend initially. These difficulties tend to dissipate with exposure, sometimes within minutes or less. Adaptivity in response to unfamiliar input is now considered a fundamental property of speech perception, and research over the past two decades has made substantial progress in identifying its characteristics. The mechanisms underlying adaptive speech perception, however, remain unknown. Past work has attributed facilitatory effects of exposure to any one of three qualitatively different hypothesized mechanisms: (1) low-level, pre-linguistic, signal normalization, (2) changes in/selection of linguistic representations, or (3) changes in post-perceptual decision-making. Direct comparisons of these hypotheses, or combinations thereof, have been lacking. We describe a general computational framework for adaptive speech perception (ASP) that-for the first time-implements all three mechanisms. We demonstrate how the framework can be used to derive predictions for experiments on perception from the acoustic properties of the stimuli. Using this approach, we find that-at the level of data analysis presently employed by most studies in the field-the signature results of influential experimental paradigms do not distinguish between the three mechanisms. This highlights the need for a change in research practices, so that future experiments provide more informative results. We recommend specific changes to experimental paradigms and data analysis. All data and code for this study are shared via OSF, including the R markdown document that this article is generated from, and an R library that implements the models we present.

Relative pitch representations and invariance to timbre

Information in speech and music is often conveyed through changes in fundamental frequency (f0), perceived by humans as "relative pitch". Relative pitch judgments are complicated by two facts. First, sounds can simultaneously vary in timbre due to filtering imposed by a vocal tract or instrument body. Second, relative pitch can be extracted in two ways: by measuring changes in constituent frequency components from one sound to another, or by estimating the f0 of each sound and comparing the estimates. We examined the effects of timbral differences on relative pitch judgments, and whether any invariance to timbre depends on whether judgments are based on constituent frequencies or their f0. Listeners performed up/down and interval discrimination tasks with pairs of spoken vowels, instrument notes, or synthetic tones, synthesized to be either harmonic or inharmonic. Inharmonic sounds lack a well-defined f0, such that relative pitch must be extracted from changes in individual frequencies. Pitch judgments were less accurate when vowels/instruments were different compared to when they were the same, and were biased by the associated timbre differences. However, this bias was similar for harmonic and inharmonic sounds, and was observed even in conditions where judgments of harmonic sounds were based on f0 representations. Relative pitch judgments are thus not invariant to timbre, even when timbral variation is naturalistic, and when such judgments are based on representations of f0.

Dataset size considerations for robust acoustic and phonetic speech encoding models in EEG

In many experiments that investigate auditory and speech processing in the brain using electroencephalography (EEG), the experimental paradigm is often lengthy and tedious. Typically, the experimenter errs on the side of including more data, more trials, and therefore conducting a longer task to ensure that the data are robust and effects are measurable. Recent studies used naturalistic stimuli to investigate the brain's response to individual or a combination of multiple speech features using system identification techniques, such as multivariate temporal receptive field (mTRF) analyses. The neural data collected from such experiments must be divided into a training set and a test set to fit and validate the mTRF weights. While a good strategy is clearly to collect as much data as is feasible, it is unclear how much data are needed to achieve stable results. Furthermore, it is unclear whether the specific stimulus used for mTRF fitting and the choice of feature representation affects how much data would be required for robust and generalizable results. Here, we used previously collected EEG data from our lab using sentence stimuli and movie stimuli as well as EEG data from an open-source dataset using audiobook stimuli to better understand how much data needs to be collected for naturalistic speech experiments measuring acoustic and phonetic tuning. We found that the EEG receptive field structure tested here stabilizes after collecting a training dataset of approximately 200 s of TIMIT sentences, around 600 s of movie trailers training set data, and approximately 460 s of audiobook training set data. Thus, we provide suggestions on the minimum amount of data that would be necessary for fitting mTRFs from naturalistic listening data. Our findings are motivated by highly practical concerns when working with children, patient populations, or others who may not tolerate long study sessions. These findings will aid future researchers who wish to study naturalistic speech processing in healthy and clinical populations while minimizing participant fatigue and retaining signal quality.

Intact speech perception after resection of dominant hemisphere primary auditory cortex for the treatment of medically refractory epilepsy: illustrative case

BACKGROUND

In classic speech network models, the primary auditory cortex is the source of auditory input to Wernicke's area in the posterior superior temporal gyrus (pSTG). Because resection of the primary auditory cortex in the dominant hemisphere removes inputs to the pSTG, there is a risk of speech impairment. However, recent research has shown the existence of other, nonprimary auditory cortex inputs to the pSTG, potentially reducing the risk of primary auditory cortex resection in the dominant hemisphere.

OBSERVATIONS

Here, the authors present a clinical case of a woman with severe medically refractory epilepsy with a lesional epileptic focus in the left (dominant) Heschl's gyrus. Analysis of neural responses to speech stimuli was consistent with primary auditory cortex localization to Heschl's gyrus. Although the primary auditory cortex was within the proposed resection margins, she underwent lesionectomy with total resection of Heschl's gyrus. Postoperatively, she had no speech deficits and her seizures were fully controlled.

LESSONS

While resection of the dominant hemisphere Heschl's gyrus/primary auditory cortex warrants caution, this case illustrates the ability to resect the primary auditory cortex without speech impairment and supports recent models of multiple parallel inputs to the pSTG.

Use of explicit priming to phenotype absolute pitch ability

Musicians with absolute pitch (AP) can name the pitch of a musical note in isolation. Expression of this unusual ability is thought to be influenced by heritability, early music training and current practice. However, our understanding of factors shaping its expression is hampered by testing and scoring methods that treat AP as dichotomous. These fail to capture the observed variability in pitch-naming accuracy among reported AP possessors. The aim of this study was to trial a novel explicit priming paradigm to explore phenotypic variability of AP. Thirty-five musically experienced individuals (M_age = 29 years, range 18–68; 14 males) with varying AP ability completed a standard AP task and the explicit priming AP task. Results showed: 1) phenotypic variability of AP ability, including high-accuracy AP, heterogeneous intermediate performers, and chance-level performers; 2) intermediate performance profiles that were either reliant on or independent of relative pitch strategies, as identified by the priming task; and 3) the emergence of a bimodal distribution of AP performance when adopting scoring criteria that assign credit to semitone errors. These findings show the importance of methods in studying behavioural traits, and are a key step towards identifying AP phenotypes. Replication of our results in larger samples will further establish the usefulness of this priming paradigm in AP research.

Cortical tracking of voice pitch in the presence of multiple speakers depends on selective attention

Voice pitch carries linguistic and non-linguistic information. Previous studies have described cortical tracking of voice pitch in clean speech, with responses reflecting both pitch strength and pitch value. However, pitch is also a powerful cue for auditory stream segregation, especially when competing streams have pitch differing in fundamental frequency, as is the case when multiple speakers talk simultaneously. We therefore investigated how cortical speech pitch tracking is affected in the presence of a second, task-irrelevant speaker. We analyzed human magnetoencephalography (MEG) responses to continuous narrative speech, presented either as a single talker in a quiet background or as a two-talker mixture of a male and a female speaker. In clean speech, voice pitch was associated with a right-dominant response, peaking at a latency of around 100 ms, consistent with previous electroencephalography and electrocorticography results. The response tracked both the presence of pitch and the relative value of the speaker's fundamental frequency. In the two-talker mixture, the pitch of the attended speaker was tracked bilaterally, regardless of whether or not there was simultaneously present pitch in the speech of the irrelevant speaker. Pitch tracking for the irrelevant speaker was reduced: only the right hemisphere still significantly tracked pitch of the unattended speaker, and only during intervals in which no pitch was present in the attended talker's speech. Taken together, these results suggest that pitch-based segregation of multiple speakers, at least as measured by macroscopic cortical tracking, is not entirely automatic but strongly dependent on selective attention.

Detection of Brain Network Communities During Natural Speech Comprehension From Functionally Aligned EEG Sources

In recent years, electroencephalograph (EEG) studies on speech comprehension have been extended from a controlled paradigm to a natural paradigm. Under the hypothesis that the brain can be approximated as a linear time-invariant system, the neural response to natural speech has been investigated extensively using temporal response functions (TRFs). However, most studies have modeled TRFs in the electrode space, which is a mixture of brain sources and thus cannot fully reveal the functional mechanism underlying speech comprehension. In this paper, we propose methods for investigating the brain networks of natural speech comprehension using TRFs on the basis of EEG source reconstruction. We first propose a functional hyper-alignment method with an additive average method to reduce EEG noise. Then, we reconstruct neural sources within the brain based on the EEG signals to estimate TRFs from speech stimuli to source areas, and then investigate the brain networks in the neural source space on the basis of the community detection method. To evaluate TRF-based brain networks, EEG data were recorded in story listening tasks with normal speech and time-reversed speech. To obtain reliable structures of brain networks, we detected TRF-based communities from multiple scales. As a result, the proposed functional hyper-alignment method could effectively reduce the noise caused by individual settings in an EEG experiment and thus improve the accuracy of source reconstruction. The detected brain networks for normal speech comprehension were clearly distinctive from those for non-semantically driven (time-reversed speech) audio processing. Our result indicates that the proposed source TRFs can reflect the cognitive processing of spoken language and that the multi-scale community detection method is powerful for investigating brain networks.

Neural correlates of impaired vocal feedback control in post-stroke aphasia

We used left-hemisphere stroke as a model to examine how damage to sensorimotor brain networks impairs vocal auditory feedback processing and control. Individuals with post-stroke aphasia and matched neurotypical control subjects vocalized speech vowel sounds and listened to the playback of their self-produced vocalizations under normal (NAF) and pitch-shifted altered auditory feedback (AAF) while their brain activity was recorded using electroencephalography (EEG) signals. Event-related potentials (ERPs) were utilized as a neural index to probe the effect of vocal production on auditory feedback processing with high temporal resolution, while lesion data in the stroke group was used to determine how brain abnormality accounted for the impairment of such mechanisms. Results revealed that ERP activity was aberrantly modulated during vocalization vs. listening in aphasia, and this effect was accompanied by the reduced magnitude of compensatory vocal responses to pitch-shift alterations in the auditory feedback compared with control subjects. Lesion-mapping revealed that the aberrant pattern of ERP modulation in response to NAF was accounted for by damage to sensorimotor networks within the left-hemisphere inferior frontal, precentral, inferior parietal, and superior temporal cortices. For responses to AAF, neural deficits were predicted by damage to a distinguishable network within the inferior frontal and parietal cortices. These findings define the left-hemisphere sensorimotor networks implicated in auditory feedback processing, error detection, and vocal motor control. Our results provide translational synergy to inform the theoretical models of sensorimotor integration while having clinical applications for diagnosis and treatment of communication disabilities in individuals with stroke and other neurological conditions.

Human song: Separate neural pathways for melody and speech

Does the brain perceive song as speech with melody? A new study using intracranial recordings and functional brain imaging in humans suggests that it does not. Instead, singing, instrumental music, and speech are represented by different neural populations.

Spontaneous State Detection Using Time-Frequency and Time-Domain Features Extracted From Stereo-Electroencephalography Traces

As a minimally invasive recording technique, stereo-electroencephalography (SEEG) measures intracranial signals directly by inserting depth electrodes shafts into the human brain, and thus can capture neural activities in both cortical layers and subcortical structures. Despite gradually increasing SEEG-based brain-computer interface (BCI) studies, the features utilized were usually confined to the amplitude of the event-related potential (ERP) or band power, and the decoding capabilities of other time-frequency and time-domain features have not been demonstrated for SEEG recordings yet. In this study, we aimed to verify the validity of time-domain and time-frequency features of SEEG, where classification performances served as evaluating indicators. To do this, using SEEG signals under intermittent auditory stimuli, we extracted features including the average amplitude, root mean square, slope of linear regression, and line-length from the ERP trace and three traces of band power activities (high-gamma, beta, and alpha). These features were used to detect the active state (including activations to two types of names) against the idle state. Results suggested that valid time-domain and time-frequency features distributed across multiple regions, including the temporal lobe, parietal lobe, and deeper structures such as the insula. Among all feature types, the average amplitude, root mean square, and line-length extracted from high-gamma (60–140 Hz) power and the line-length extracted from ERP were the most informative. Using a hidden Markov model (HMM), we could precisely detect the onset and the end of the active state with a sensitivity of 95.7 ± 1.3% and a precision of 91.7 ± 1.6%. The valid features derived from high-gamma power and ERP in this work provided new insights into the feature selection procedure for further SEEG-based BCI applications.

Controversy over the temporal cortical terminations of the left arcuate fasciculus: a reappraisal

The arcuate fasciculus has been considered a major dorsal fronto-temporal white matter pathway linking frontal language production regions with auditory perception in the superior temporal gyrus, the so-called Wernicke's area. In line with this tradition, both historical and contemporary models of language function have assigned primacy to superior temporal projections of the arcuate fasciculus. However, classical anatomical descriptions and emerging behavioural data are at odds with this assumption. On one hand, fronto-temporal projections to Wernicke's area may not be unique to the arcuate fasciculus. On the other hand, dorsal stream language deficits have been reported also for damage to middle, inferior and basal temporal gyri which may be linked to arcuate disconnection. These findings point to a reappraisal of arcuate projections in the temporal lobe. Here, we review anatomical and functional evidence regarding the temporal cortical terminations of the left arcuate fasciculus by incorporating dissection and tractography findings with stimulation data using cortico-cortical evoked potentials and direct electrical stimulation mapping in awake patients. Firstly, we discuss the fibers of the arcuate fasciculus projecting to the superior temporal gyrus and the functional rostro-caudal gradient in this region where both phonological encoding and auditory-motor transformation may be performed. Caudal regions within the temporoparietal junction may be involved in articulation and associated with temporoparietal projections of the third branch of the superior longitudinal fasciculus, while more rostral regions may support encoding of acoustic phonetic features, supported by arcuate fibres. We then move to examine clinical data showing that multimodal phonological encoding is facilitated by projections of the arcuate fasciculus to superior, but also middle, inferior and basal temporal regions. Hence, we discuss how projections of the arcuate fasciculus may contribute to acoustic (middle-posterior superior and middle temporal gyri), visual (posterior inferior temporal/fusiform gyri comprising the visual word form area) and lexical (anterior-middle inferior temporal/fusiform gyri in the basal temporal language area) information in the temporal lobe to be processed, encoded and translated into a dorsal phonological route to the frontal lobe. Finally, we point out surgical implications for this model in terms of the prediction and avoidance of neurological deficit.

Attention Differentially Affects Acoustic and Phonetic Feature Encoding in a Multispeaker Environment

Humans have the remarkable ability to selectively focus on a single talker in the midst of other competing talkers. The neural mechanisms that underlie this phenomenon remain incompletely understood. In particular, there has been longstanding debate over whether attention operates at an early or late stage in the speech processing hierarchy. One way to better understand this is to examine how attention might differentially affect neurophysiological indices of hierarchical acoustic and linguistic speech representations. In this study, we do this by using encoding models to identify neural correlates of speech processing at various levels of representation. Specifically, we recorded EEG from fourteen human subjects (nine female and five male) during a "cocktail party" attention experiment. Model comparisons based on these data revealed phonetic feature processing for attended, but not unattended speech. Furthermore, we show that attention specifically enhances isolated indices of phonetic feature processing, but that such attention effects are not apparent for isolated measures of acoustic processing. These results provide new insights into the effects of attention on different prelexical representations of speech, insights that complement recent anatomic accounts of the hierarchical encoding of attended speech. Furthermore, our findings support the notion that, for attended speech, phonetic features are processed as a distinct stage, separate from the processing of the speech acoustics.SIGNIFICANCE STATEMENT Humans are very good at paying attention to one speaker in an environment with multiple speakers. However, the details of how attended and unattended speech are processed differently by the brain is not completely clear. Here, we explore how attention affects the processing of the acoustic sounds of speech as well as the mapping of those sounds onto categorical phonetic features. We find evidence of categorical phonetic feature processing for attended, but not unattended speech. Furthermore, we find evidence that categorical phonetic feature processing is enhanced by attention, but acoustic processing is not. These findings add an important new layer in our understanding of how the human brain solves the cocktail party problem.

Neural Measures of Pitch Processing in EEG Responses to Running Speech

Linearized encoding models are increasingly employed to model cortical responses to running speech. Recent extensions to subcortical responses suggest clinical perspectives, potentially complementing auditory brainstem responses (ABRs) or frequency-following responses (FFRs) that are current clinical standards. However, while it is well-known that the auditory brainstem responds both to transient amplitude variations and the stimulus periodicity that gives rise to pitch, these features co-vary in running speech. Here, we discuss challenges in disentangling the features that drive the subcortical response to running speech. Cortical and subcortical electroencephalographic (EEG) responses to running speech from 19 normal-hearing listeners (12 female) were analyzed. Using forward regression models, we confirm that responses to the rectified broadband speech signal yield temporal response functions consistent with wave V of the ABR, as shown in previous work. Peak latency and amplitude of the speech-evoked brainstem response were correlated with standard click-evoked ABRs recorded at the vertex electrode (Cz). Similar responses could be obtained using the fundamental frequency (F0) of the speech signal as model predictor. However, simulations indicated that dissociating responses to temporal fine structure at the F0 from broadband amplitude variations is not possible given the high co-variance of the features and the poor signal-to-noise ratio (SNR) of subcortical EEG responses. In cortex, both simulations and data replicated previous findings indicating that envelope tracking on frontal electrodes can be dissociated from responses to slow variations in F0 (relative pitch). Yet, no association between subcortical F0-tracking and cortical responses to relative pitch could be detected. These results indicate that while subcortical speech responses are comparable to click-evoked ABRs, dissociating pitch-related processing in the auditory brainstem may be challenging with natural speech stimuli.

Speech Computations of the Human Superior Temporal Gyrus

Human speech perception results from neural computations that transform external acoustic speech signals into internal representations of words. The superior temporal gyrus (STG) contains the nonprimary auditory cortex and is a critical locus for phonological processing. Here, we describe how speech sound representation in the STG relies on fundamentally nonlinear and dynamical processes, such as categorization, normalization, contextual restoration, and the extraction of temporal structure. A spatial mosaic of local cortical sites on the STG exhibits complex auditory encoding for distinct acoustic-phonetic and prosodic features. We propose that as a population ensemble, these distributed patterns of neural activity give rise to abstract, higher-order phonemic and syllabic representations that support speech perception. This review presents a multi-scale, recurrent model of phonological processing in the STG, highlighting the critical interface between auditory and language systems.

OUP accepted manuscript

During conversations, speech prosody provides important clues about the speaker’s communicative intentions. In many languages, a rising vocal pitch at the end of a sentence typically expresses a question function, whereas a falling pitch suggests a statement. Here, the neurophysiological basis of intonation and speech act understanding were investigated with high-density electroencephalography (EEG) to determine whether prosodic features are reflected at the neurophysiological level. Already approximately 100 ms after the sentence-final word differing in prosody, questions, and statements expressed with the same sentences led to different neurophysiological activity recorded in the event-related potential. Interestingly, low-pass filtered sentences and acoustically matched nonvocal musical signals failed to show any neurophysiological dissociations, thus suggesting that the physical intonation alone cannot explain this modulation. Our results show rapid neurophysiological indexes of prosodic communicative information processing that emerge only when pragmatic and lexico-semantic information are fully expressed. The early enhancement of question-related activity compared with statements was due to sources in the articulatory-motor region, which may reflect the richer action knowledge immanent to questions, namely the expectation of the partner action of answering the question. The present findings demonstrate a neurophysiological correlate of prosodic communicative information processing, which enables humans to rapidly detect and understand speaker intentions in linguistic interactions.

Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception

Perception is thought to be shaped by the environments for which organisms are optimized. These influences are difficult to test in biological organisms but may be revealed by machine perceptual systems optimized under different conditions. We investigated environmental and physiological influences on pitch perception, whose properties are commonly linked to peripheral neural coding limits. We first trained artificial neural networks to estimate fundamental frequency from biologically faithful cochlear representations of natural sounds. The best-performing networks replicated many characteristics of human pitch judgments. To probe the origins of these characteristics, we then optimized networks given altered cochleae or sound statistics. Human-like behavior emerged only when cochleae had high temporal fidelity and when models were optimized for naturalistic sounds. The results suggest pitch perception is critically shaped by the constraints of natural environments in addition to those of the cochlea, illustrating the use of artificial neural networks to reveal underpinnings of behavior.

Generalizable EEG Encoding Models with Naturalistic Audiovisual Stimuli

In natural conversations, listeners must attend to what others are saying while ignoring extraneous background sounds. Recent studies have used encoding models to predict electroencephalography (EEG) responses to speech in noise-free listening situations, sometimes referred to as "speech tracking." Researchers have analyzed how speech tracking changes with different types of background noise. It is unclear, however, whether neural responses from acoustically rich, naturalistic environments with and without background noise can be generalized to more controlled stimuli. If encoding models for acoustically rich, naturalistic stimuli are generalizable to other tasks, this could aid in data collection from populations of individuals who may not tolerate listening to more controlled and less engaging stimuli for long periods of time. We recorded noninvasive scalp EEG while 17 human participants (8 male/9 female) listened to speech without noise and audiovisual speech stimuli containing overlapping speakers and background sounds. We fit multivariate temporal receptive field encoding models to predict EEG responses to pitch, the acoustic envelope, phonological features, and visual cues in both stimulus conditions. Our results suggested that neural responses to naturalistic stimuli were generalizable to more controlled datasets. EEG responses to speech in isolation were predicted accurately using phonological features alone, while responses to speech in a rich acoustic background were more accurate when including both phonological and acoustic features. Our findings suggest that naturalistic audiovisual stimuli can be used to measure receptive fields that are comparable and generalizable to more controlled audio-only stimuli.SIGNIFICANCE STATEMENT Understanding spoken language in natural environments requires listeners to parse acoustic and linguistic information in the presence of other distracting stimuli. However, most studies of auditory processing rely on highly controlled stimuli with no background noise, or with background noise inserted at specific times. Here, we compare models where EEG data are predicted based on a combination of acoustic, phonetic, and visual features in highly disparate stimuli-sentences from a speech corpus and speech embedded within movie trailers. We show that modeling neural responses to highly noisy, audiovisual movies can uncover tuning for acoustic and phonetic information that generalizes to simpler stimuli typically used in sensory neuroscience experiments.

Thinking probabilistically in the study of intonational speech prosody

Speech prosody, the melodic and rhythmic properties of a language, plays a critical role in our everyday communication. Researchers have identified unique patterns of prosody that segment words and phrases, highlight focal elements in a sentence, and convey holistic meanings and speech acts that interact with the information shared in context. The mapping between the sound and meaning represented in prosody is suggested to be probabilistic-the same physical instance of sounds can support multiple meanings across talkers and contexts while the same meaning can be encoded in physically distinct sound patterns (e.g., pitch movements). The current overview presents an analysis framework for probing the nature of this probabilistic relationship. Illustrated by examples from the literature and a dataset of German focus marking, we discuss the production variability within and across talkers and consider challenges that this variability imposes on the comprehension system. A better understanding of these challenges, we argue, will illuminate how the human perceptual, cognitive, and computational mechanisms may navigate the variability to arrive at a coherent understanding of speech prosody. The current paper is intended to be an introduction for those who are interested in thinking probabilistically about the sound-meaning mapping in prosody. Open questions for future research are discussed with proposals for examining prosodic production and comprehension within a comprehensive, mathematically-motivated framework of probabilistic inference under uncertainty. This article is categorized under: Linguistics > Language in Mind and Brain Psychology > Language.

Learning nonnative speech sounds changes local encoding in the adult human cortex

Adults can learn to identify nonnative speech sounds with training, albeit with substantial variability in learning behavior. Increases in behavioral accuracy are associated with increased separability for sound representations in cortical speech areas. However, it remains unclear whether individual auditory neural populations all show the same types of changes with learning, or whether there are heterogeneous encoding patterns. Here, we used high-resolution direct neural recordings to examine local population response patterns, while native English listeners learned to recognize unfamiliar vocal pitch patterns in Mandarin Chinese tones. We found a distributed set of neural populations in bilateral superior temporal gyrus and ventrolateral frontal cortex, where the encoding of Mandarin tones changed throughout training as a function of trial-by-trial accuracy ("learning effect"), including both increases and decreases in the separability of tones. These populations were distinct from populations that showed changes as a function of exposure to the stimuli regardless of trial-by-trial accuracy. These learning effects were driven in part by more variable neural responses to repeated presentations of acoustically identical stimuli. Finally, learning effects could be predicted from speech-evoked activity even before training, suggesting that intrinsic properties of these populations make them amenable to behavior-related changes. Together, these results demonstrate that nonnative speech sound learning involves a wide array of changes in neural representations across a distributed set of brain regions.

Parallel and distributed encoding of speech across human auditory cortex

Speech perception is thought to rely on a cortical feedforward serial transformation of acoustic into linguistic representations. Using intracranial recordings across the entire human auditory cortex, electrocortical stimulation, and surgical ablation, we show that cortical processing across areas is not consistent with a serial hierarchical organization. Instead, response latency and receptive field analyses demonstrate parallel and distinct information processing in the primary and nonprimary auditory cortices. This functional dissociation was also observed where stimulation of the primary auditory cortex evokes auditory hallucination but does not distort or interfere with speech perception. Opposite effects were observed during stimulation of nonprimary cortex in superior temporal gyrus. Ablation of the primary auditory cortex does not affect speech perception. These results establish a distributed functional organization of parallel information processing throughout the human auditory cortex and demonstrate an essential independent role for nonprimary auditory cortex in speech processing.

Clinical applications of neurolinguistics in neurosurgery

The protection of language function is one of the major challenges of brain surgery. Over the past century, neurosurgeons have attempted to seek the optimal strategy for the preoperative and intraoperative identification of language-related brain regions. Neurosurgeons have investigated the neural mechanism of language, developed neurolinguistics theory, and provided unique evidence to further understand the neural basis of language functions by using intraoperative cortical and subcortical electrical stimulation. With the emergence of modern neuroscience techniques and dramatic advances in language models over the last 25 years, novel language mapping methods have been applied in the neurosurgical practice to help neurosurgeons protect the brain and reduce morbidity. The rapid advancements in brain-computer interface have provided the perfect platform for the combination of neurosurgery and neurolinguistics. In this review, the history of neurolinguistics models, advancements in modern technology, role of neurosurgery in language mapping, and modern language mapping methods (including noninvasive neuroimaging techniques and invasive cortical electroencephalogram) are presented.

The neural processing of pitch accents in continuous speech

Pitch accents are local pitch patterns that convey differences in word prominence and modulate the information structure of the discourse. Despite the importance to discourse in languages like English, neural processing of pitch accents remains understudied. The current study investigates the neural processing of pitch accents by native and non-native English speakers while they are listening to or ignoring 45 min of continuous, natural speech. Leveraging an approach used to study phonemes in natural speech, we analyzed thousands of electroencephalography (EEG) segments time-locked to pitch accents in a prosodic transcription. The optimal neural discrimination between pitch accent categories emerged at latencies between 100 and 200 ms. During these latencies, we found a strong structural alignment between neural and phonetic representations of pitch accent categories. In the same latencies, native listeners exhibited more robust processing of pitch accent contrasts than non-native listeners. However, these group differences attenuated when the speech signal was ignored. We can reliably capture the neural processing of discrete and contrastive pitch accent categories in continuous speech. Our analytic approach also captures how language-specific knowledge and selective attention influences the neural processing of pitch accent categories.

Convergence of heteromodal lexical retrieval in the lateral prefrontal cortex

Lexical retrieval requires selecting and retrieving the most appropriate word from the lexicon to express a desired concept. Few studies have probed lexical retrieval with tasks other than picture naming, and when non-picture naming lexical retrieval tasks have been applied, both convergent and divergent results emerged. The presence of a single construct for auditory and visual processes of lexical retrieval would influence cognitive rehabilitation strategies for patients with aphasia. In this study, we perform support vector regression lesion-symptom mapping using a brain tumor model to test the hypothesis that brain regions specifically involved in lexical retrieval from visual and auditory stimuli represent overlapping neural systems. We find that principal components analysis of language tasks revealed multicollinearity between picture naming, auditory naming, and a validated measure of word finding, implying the existence of redundant cognitive constructs. Nonparametric, multivariate lesion-symptom mapping across participants was used to model accuracies on each of the four language tasks. Lesions within overlapping clusters of 8,333 voxels and 21,512 voxels in the left lateral prefrontal cortex (PFC) were predictive of impaired picture naming and auditory naming, respectively. These data indicate a convergence of heteromodal lexical retrieval within the PFC.

Sufficient sampling for kriging prediction of cortical potential in rat, monkey, and human µECoG

Objective. Large channel count surface-based electrophysiology arrays (e.g. µECoG) are high-throughput neural interfaces with good chronic stability. Electrode spacing remains ad hoc due to redundancy and nonstationarity of field dynamics. Here, we establish a criterion for electrode spacing based on the expected accuracy of predicting unsampled field potential from sampled sites.Approach. We applied spatial covariance modeling and field prediction techniques based on geospatial kriging to quantify sufficient sampling for thousands of 500 ms µECoG snapshots in human, monkey, and rat. We calculated a probably approximately correct (PAC) spacing based on kriging that would be required to predict µECoG fields at≤10% error for most cases (95% of observations).Main results. Kriging theory accurately explained the competing effects of electrode density and noise on predicting field potential. Across five frequency bands from 4-7 to 75-300 Hz, PAC spacing was sub-millimeter for auditory cortex in anesthetized and awake rats, and posterior superior temporal gyrus in anesthetized human. At 75-300 Hz, sub-millimeter PAC spacing was required in all species and cortical areas.Significance. PAC spacing accounted for the effect of signal-to-noise on prediction quality and was sensitive to the full distribution of non-stationary covariance states. Our results show that µECoG arrays should sample at sub-millimeter resolution for applications in diverse cortical areas and for noise resilience.

Human cortical encoding of pitch in tonal and non-tonal languages

Languages can use a common repertoire of vocal sounds to signify distinct meanings. In tonal languages, such as Mandarin Chinese, pitch contours of syllables distinguish one word from another, whereas in non-tonal languages, such as English, pitch is used to convey intonation. The neural computations underlying language specialization in speech perception are unknown. Here, we use a cross-linguistic approach to address this. Native Mandarin- and English- speaking participants each listened to both Mandarin and English speech, while neural activity was directly recorded from the non-primary auditory cortex. Both groups show language-general coding of speaker-invariant pitch at the single electrode level. At the electrode population level, we find language-specific distribution of cortical tuning parameters in Mandarin speakers only, with enhanced sensitivity to Mandarin tone categories. Our results show that speech perception relies upon a shared cortical auditory feature processing mechanism, which may be tuned to the statistics of a given language.

Different languages rely on different vocal sounds to convey meaning. Here the authors show that language-general coding of pitch occurs in the non-primary auditory cortex for both tonal (Mandarin Chinese) and non-tonal (English) languages, with some language specificity on the population level.

Distributed source modeling of intracranial stereoelectro-encephalographic measurements

Intracranial stereoelectroencephalography (sEEG) provides unsurpassed sensitivity and specificity for human neurophysiology. However, functional mapping of brain functions has been limited because the implantations have sparse coverage and differ greatly across individuals. Here, we developed a distributed, anatomically realistic sEEG source-modeling approach for within- and between-subject analyses. In addition to intracranial event-related potentials (iERP), we estimated the sources of high broadband gamma activity (HBBG), a putative correlate of local neural firing. Our novel approach accounted for a significant portion of the variance of the sEEG measurements in leave-one-out cross-validation. After logarithmic transformations, the sensitivity and signal-to-noise ratio were linearly inversely related to the minimal distance between the brain location and electrode contacts (slope≈-3.6). The signa-to-noise ratio and sensitivity in the thalamus and brain stem were comparable to those locations at the vicinity of electrode contact implantation. The HGGB source estimates were remarkably consistent with analyses of intracranial-contact data. In conclusion, distributed sEEG source modeling provides a powerful neuroimaging tool, which facilitates anatomically-normalized functional mapping of human brain using both iERP and HBBG data.

Time-dependent discrimination advantages for harmonic sounds suggest efficient coding for memory

Perceptual systems have finite memory resources and must store incoming signals in compressed formats. To explore whether representations of a sound's pitch might derive from this need for compression, we compared discrimination of harmonic and inharmonic sounds across delays. In contrast to inharmonic spectra, harmonic spectra can be summarized, and thus compressed, using their fundamental frequency (f0). Participants heard two sounds and judged which was higher. Despite being comparable for sounds presented back-to-back, discrimination was better for harmonic than inharmonic stimuli when sounds were separated in time, implicating memory representations unique to harmonic sounds. Patterns of individual differences (correlations between thresholds in different conditions) indicated that listeners use different representations depending on the time delay between sounds, directly comparing the spectra of temporally adjacent sounds, but transitioning to comparing f0s across delays. The need to store sound in memory appears to determine reliance on f0-based pitch and may explain its importance in music, in which listeners must extract relationships between notes separated in time.

Continuous speech processing

Speech processing in the human brain is grounded in non-specific auditory processing in the general mammalian brain, but relies on human-specific adaptations for processing speech and language. For this reason, many recent neurophysiological investigations of speech processing have turned to the human brain, with an emphasis on continuous speech. Substantial progress has been made using the phenomenon of "neural speech tracking", in which neurophysiological responses time-lock to the rhythm of auditory (and other) features in continuous speech. One broad category of investigations concerns the extent to which speech tracking measures are related to speech intelligibility, which has clinical applications in addition to its scientific importance. Recent investigations have also focused on disentangling different neural processes that contribute to speech tracking. The two lines of research are closely related, since processing stages throughout auditory cortex contribute to speech comprehension, in addition to subcortical processing and higher order and attentional processes.

Speech frequency-following response in human auditory cortex is more than a simple tracking

The human auditory cortex is recently found to contribute to the frequency following response (FFR) and the cortical component has been shown to be more relevant to speech perception. However, it is not clear how cortical FFR may contribute to the processing of speech fundamental frequency (F0) and the dynamic pitch. Using intracranial EEG recordings, we observed a significant FFR at the fundamental frequency (F0) for both speech and speech-like harmonic complex stimuli in the human auditory cortex, even in the missing fundamental condition. Both the spectral amplitude and phase coherence of the cortical FFR showed a significant harmonic preference, and attenuated from the primary auditory cortex to the surrounding associative auditory cortex. The phase coherence of the speech FFR was found significantly higher than that of the harmonic complex stimuli, especially in the left hemisphere, showing a high timing fidelity of the cortical FFR in tracking dynamic F0 in speech. Spectrally, the frequency band of the cortical FFR was largely overlapped with the range of the human vocal pitch. Taken together, our study parsed the intrinsic properties of the cortical FFR and reveals a preference for speech-like sounds, supporting its potential role in processing speech intonation and lexical tones.

Transformation of a temporal speech cue to a spatial neural code in human auditory cortex

In speech, listeners extract continuously-varying spectrotemporal cues from the acoustic signal to perceive discrete phonetic categories. Spectral cues are spatially encoded in the amplitude of responses in phonetically-tuned neural populations in auditory cortex. It remains unknown whether similar neurophysiological mechanisms encode temporal cues like voice-onset time (VOT), which distinguishes sounds like /b/ and/p/. We used direct brain recordings in humans to investigate the neural encoding of temporal speech cues with a VOT continuum from /ba/ to /pa/. We found that distinct neural populations respond preferentially to VOTs from one phonetic category, and are also sensitive to sub-phonetic VOT differences within a population's preferred category. In a simple neural network model, simulated populations tuned to detect either temporal gaps or coincidences between spectral cues captured encoding patterns observed in real neural data. These results demonstrate that a spatial/amplitude neural code underlies the cortical representation of both spectral and temporal speech cues.

Dissociation of broadband high-frequency activity and neuronal firing in the neocortex

Broadband high-frequency activity (BHA; 70 to 150 Hz), also known as "high gamma," a key analytic signal in human intracranial (electrocorticographic) recordings, is often assumed to reflect local neural firing [multiunit activity (MUA)]. As the precise physiological substrates of BHA are unknown, this assumption remains controversial. Our analysis of laminar multielectrode data from V1 and A1 in monkeys outlines two components of stimulus-evoked BHA distributed across the cortical layers: an "early-deep" and "late-superficial" response. Early-deep BHA has a clear spatial and temporal overlap with MUA. Late-superficial BHA was more prominent and accounted for more of the BHA signal measured near the cortical pial surface. However, its association with local MUA is weak and often undetectable, consistent with the view that it reflects dendritic processes separable from local neuronal firing.

Periodicity Pitch Perception

This study presents a computational model to reproduce the biological dynamics of "listening to music." A biologically plausible model of periodicity pitch detection is proposed and simulated. Periodicity pitch is computed across a range of the auditory spectrum. Periodicity pitch is detected from subsets of activated auditory nerve fibers (ANFs). These activate connected model octopus cells, which trigger model neurons detecting onsets and offsets; thence model interval-tuned neurons are innervated at the right interval times; and finally, a set of common interval-detecting neurons indicate pitch. Octopus cells rhythmically spike with the pitch periodicity of the sound. Batteries of interval-tuned neurons stopwatch-like measure the inter-spike intervals of the octopus cells by coding interval durations as first spike latencies (FSLs). The FSL-triggered spikes synchronously coincide through a monolayer spiking neural network at the corresponding receiver pitch neurons.