The brain lowers its response to inputs we generate ourselves, such as moving or speaking. Essentially, our brain ‘knows’ what will happen next when we carry out these actions, and therefore does not need to react as strongly as it would to unexpected events. This is why we cannot tickle ourselves, and why the brain does not react as much to our own voice as it does to someone else’s. Quieting down the brain’s response also allows us to focus on things that are new or important without getting distracted by our own movements or sounds.

Studies in non-human primates showed that neurons in the auditory cortex (the region of the brain responsible for processing sound) displayed suppressed levels of activity when the animals made sounds. Interestingly, when the primates heard an altered version of their own voice, many of these same neurons became more active. But it was unclear whether this also happens in humans.

To investigate, Ozker et al. used a technique called electrocorticography to record neural activity in different regions of the human brain while participants spoke. The results showed that most areas of the brain involved in auditory processing showed suppressed activity when individuals were speaking. However, when people heard an altered version of their own voice which had an unexpected delay, those same areas displayed increased activity. In addition, Ozker et al. found that the higher the level of suppression in the auditory cortex, the more sensitive these areas were to changes in a person’s speech.

These findings suggest that suppressing the brain’s response to self-generated speech may help in detecting errors during speech production. Speech deficits are common in various neurological disorders, such as stuttering, Parkinson’s disease, and aphasia. Ozker et al. hypothesize that these deficits may arise because individuals fail to suppress activity in auditory regions of the brain, causing a struggle when detecting and correcting errors in their own speech. However, further experiments are needed to test this theory.

A major question in neuroscience is how do animals distinguish between stimuli originating from the environment and those produced by their own actions. Sensorimotor circuits share a common mechanism across the animal kingdom in which sensory responses to self-generated motor actions are suppressed. It is commonly hypothesized that suppressing responses to predicted self-generated stimuli increases sensitivity of the sensory system to external stimuli (Poulet and Hedwig, 2002; Poulet and Hedwig, 2006; Crapse and Sommer, 2008; Schneider and Mooney, 2018). Furthermore, it enables detection and correction of motor errors by providing a template of the predicted sensory outcome to compare with the actual sensory outcome. In the domain of speech, this mechanism is described in models which suggest that neural responses in the auditory cortex are suppressed during speech production. When there is a mismatch between the predicted auditory outcome and the actual auditory feedback, responses in the auditory regions are enhanced to encode the mismatch and inform vocal-motor regions to correct vocalization (Hickok et al., 2011; Houde and Nagarajan, 2011; Tourville and Guenther, 2011).

A common experimental strategy to generate mismatch between the predicted auditory outcome and the actual auditory feedback is to perturb auditory feedback during speech production. Auditory feedback perturbations are usually applied either by delaying auditory feedback (DAF), which disrupts speech fluency (Lee, 1950; Fairbanks, 1955; Stuart et al., 2002), or by shifting voice pitch and formants, which result in compensatory vocal changes in the opposite direction of the shift (Houde and Jordan, 1998; Jones and Munhall, 2000; Niziolek and Guenther, 2013). Numerous electrophysiological and neuroimaging studies investigated neural responses during speech production both in the absence and presence of auditory feedback perturbations. In support of speech production models, these studies have repeatedly reported suppressed responses in auditory cortex during speaking compared with passive listening to speech (Numminen et al., 1999; Wise et al., 1999; Curio et al., 2000, Houde et al., 2002; Christoffels et al., 2007, Ford et al., 2010, Niziolek et al., 2013), as well as enhanced responses when auditory feedback was perturbed indicating sensitivity to auditory feedback (Tourville et al., 2008; Behroozmand et al., 2009; Chang et al., 2013; Greenlee et al., 2013, Kort et al., 2014; Behroozmand et al., 2015; Ozker et al., 2022). However, it is not clear whether the same or distinct neural populations in the auditory cortex show speech-induced suppression and sensitivity to auditory feedback.

While auditory responses are largely suppressed during speech production, detailed investigations using neurosurgical recordings revealed that the degree of suppression was variable across cortical sites, and auditory cortex also exhibited non-suppressed and enhanced responses (albeit less common) (Creutzfeldt and Ojemann, 1989; Flinker et al., 2010; Greenlee et al., 2011), mirroring results from non-human primate studies using single-unit recordings (Eliades and Wang, 2003; Eliades and Wang, 2008). In the same non-human primate study, it was reported that neurons that were suppressed during vocalization showed increased activity when auditory feedback was perturbed (Eliades and Wang, 2008). Based on this finding, we predicted that if speech-induced suppression enables detection and correction of speech errors, suppressed auditory sites should be sensitive to auditory feedback, thus exhibit enhanced neural responses to feedback perturbations. Alternatively, if suppression and speech monitoring are unrelated processes, then suppressed sites should be distinct from the ones that are sensitive to auditory feedback.

The level of attention during speech monitoring can vary depending on the speech task. During normal speech production, speech monitoring does not require a conscious effort, however it is a controlled, attentional process during an auditory feedback perturbation task (Hashimoto and Sakai, 2003). It is well known that selective attention enhances auditory responses and improves speech perception under noisy listening conditions or when multiple speech streams are present (Mesgarani and Chang, 2012, Zion Golumbic et al., 2013). We predicted that increased attention to auditory feedback under adverse speaking conditions, such as during an auditory feedback perturbation task, should increase feedback sensitivity and elicit larger responses in the auditory cortex compared to normal speech production.

To summarize, in this study we aimed to test the hypothesis that speech-induced suppression increases sensitivity to auditory feedback in human neurophysiological recordings. We predicted that auditory sites showing speech-induced suppression would elicit enhanced responses to auditory feedback perturbations. Further, we aimed to investigate the role of attention in auditory feedback sensitivity by comparing auditory responses during an auditory feedback perturbation task compared with normal speech production.

To address these aims, we used intracranial electroencephalography (iEEG) recordings in neurosurgical participants, which offers a level of spatial detail and temporal precision that would not be possible to achieve using non-invasive techniques. We first identified the sites that show auditory suppression during speech production, and then employed a DAF paradigm to test whether the same sites show sensitivity to perturbed feedback. Our results revealed that overlapping sites in the superior temporal gyrus (STG) exhibited both speech-induced auditory suppression and sensitivity to auditory feedback with a strong correlation between the two measures, supporting the hypothesis that auditory suppression predicts sensitivity to speech errors in humans. Further, we showed that auditory responses in the posterior STG are enhanced in a DAF task compared to normal speech production, even for trials in which participants receive simultaneous auditory feedback (no-delay condition). This result suggests that increased attention during an auditory feedback perturbation task can modulate auditory feedback sensitivity and posterior STG is a critical region for this attentional modulation.

In order to assess cortical responses during perception and production of speech, and quantify speech-induced auditory suppression, participants (n=35) performed an auditory word repetition (AWR) task. We examined the response patterns in seven different cortical regions including STG, middle temporal gyrus (MTG), supramarginal gyrus (SMG), inferior frontal gyrus (IFG), middle frontal gyrus (MFG), precentral gyrus (preCG), and postcentral gyrus (postCG) (Figure 1A). As an index of the neural response, we used the high gamma broadband signal (70–150 Hz, see Materials and methods), which correlates with the spiking activity of the underlying neuronal population (Mukamel et al., 2005; Crone et al., 2006; Cardin et al., 2009; Ray and Maunsell, 2011; Lachaux et al., 2012).

Figure 1

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We analyzed the responses in two different time windows: during passive listening of the auditory stimulus (0–500 ms after stimulus onset) and during speaking when participants repeated the perceived auditory stimulus (0–500 ms after articulation onset). Average responses were larger during passive listening in STG (average % signal change ± SEM; Listen: 62.1±0.6, Speak: 29.8±0.4), MTG (32.7±0.9, 22.3±0.9), and SMG (27.4±0.8, 25.8±0.7) compared with speaking. Conversely, responses were larger during speaking in IFG (29.2±1.3, 31.2±1.3), MFG (28.3±1.6, 31.4±1.3), preCG (27.4±0.4, 37±0.5), and postCG (26±0.4, 42±0.5). These results suggested that auditory regions responded more strongly during passive listening compared to speaking, verifying previous reports of neural response suppression to self-generated speech in auditory cortex (Figure 1B–D).

In the AWR task, participants heard the same auditory stimulus twice in each trial, once from a recorded female voice and once from their own voice. It is well known that repeated presentation of a stimulus results in the suppression of neural activity in regions that process that stimulus, a neural adaptation phenomenon referred to as repetition suppression (Grill-Spector et al., 2006; Todorovic and de Lange, 2012). To ensure that our observed suppression of neural activity in auditory regions was not due to repetition suppression, but rather was induced by speech production, we performed a visual word reading (VWR) task, in which participants hear the auditory stimulus only once (from their own voice). Response magnitudes during speaking in the AWR and VWR tasks were similar (paired t-test: t(466)=0.62, p=0.53), characterized by a strong correlation across electrodes (Pearson’s correlation: r=0.9006, p=0). These results suggested that repetition of the auditory stimulus in the AWR task did not affect response magnitudes and the observed reduction in response magnitudes was induced by speech production.

To quantify the amount of speech-induced suppression, we calculated a suppression index (SuppI) for each electrode by comparing neural responses during listening versus speaking in the AWR task (SuppI = Listen-Speak/Listen+Speak; see Materials and methods). A positive SuppI indicated a response suppression during speaking compared to listening and was observed most strongly in middle to posterior parts of STG, followed by MTG and SMG. A negative SuppI indicated a response enhancement during speaking compared to listening and was observed in motor regions, most strongly in the postCG (Figure 2A and B).

Figure 2

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After mapping the topographical distribution of SuppI across the cortex, we focused on understanding the functional role of auditory suppression in speech monitoring. We hypothesized that the degree of speech-induced auditory suppression should be tightly linked to sensitivity to speech errors, as predicted by current models (Houde and Nagarajan, 2011; Tourville and Guenther, 2011) and neural data in non-human primates (Eliades and Wang, 2008). To test this hypothesis, we used an additional task, in which we delayed the auditory feedback (DAF) during speech production to disrupt speech fluency. In this task, 14 participants repeated the VWR task while they were presented with their voice feedback through earphones either simultaneously (no-delay) or with a delay (50, 100, and 200 ms; see Materials and methods). In a previous study (Ozker et al., 2022), using the same dataset, we demonstrated that participants slowed down their speech in response to DAF (articulation duration; DAF₀: 0.698, DAF₅₀: 0.726, DAF₁₀₀: 0.737, and DAF₂₀₀: 0.749 ms). Moreover, auditory regions exhibited an enhanced response that varied as a function of feedback delay, likely representing an auditory error signal encoding the mismatch between the expected and the actual feedback. However, those results were not directly linked to auditory suppression.

Here, we compared neural responses in the AWR and the DAF tasks to test whether auditory regions that exhibit strong speech-induced suppression also exhibit large auditory error responses to DAF, which would indicate strong sensitivity to speech errors. In a single participant, we demonstrated that a representative electrode on the STG with strong auditory suppression (average % signal change in 0–500 ms; Listen: 124±7, Speak: 20±3, SuppI: 0.27) exhibited significant response enhancement (DAF₀: 135±12, DAF₅₀: 134±8, DAF₁₀₀: 175±10, DAF₂₀₀: 208±17, ANOVA: F(3, 116)=8.5, p=3.7e-05) (Figure 3A and B), while a nearby electrode with weaker auditory suppression (Listen: 116±6, Speak: 80±4, SuppI: 0.06) did not exhibit significant response enhancement with feedback delays (DAF₀: 360±29, DAF₅₀: 328±24, DAF₁₀₀: 379±31, DAF₂₀₀: 419±30, ANOVA: F(3, 116)=1.73, p=0.16) (Figure 3C and D).

Figure 3

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To quantify the auditory error response and measure the sensitivity of a cortical region to DAF, we calculated a sensitivity index (SensI) for each electrode by correlating the delay condition and the average neural response across trials (see Materials and methods). A large SensI indicated a strong response enhancement (large auditory error response) with increasing delays. The degree of both speech-induced suppression and sensitivity to DAF were highly variable across the cortex, SuppI ranging from –0.46 to 0.53 and SensI ranging from –0.62 to 0.70. The largest SuppI and SensI as well as a strong overlap between the two measures were observed in the STG, suggesting that auditory electrodes that show speech-induced suppression are also sensitive to auditory feedback perturbations (Figure 4A–C). We validated this relationship by revealing a significant correlation between SuppI and SensI of auditory electrodes (n=57, Pearson’s correlation: r=0.4006, p=0.002) supporting our hypothesis and providing evidence for a common neural mechanism (Figure 4D).

Figure 4

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Our neural analysis revealed that response magnitudes in auditory cortex were much larger when participants heard their simultaneous voice feedback in a DAF paradigm compared with producing speech without any feedback (DAF₀: no-delay trials) (average % signal change in 0–500 ms; DAF₀: 113±14, VWR: 41±7, compare gray lines in Figure 3A and C with black lines in Figure 3B and D, respectively). We were interested in dissociating if these larger responses were merely an effect of perceiving voice feedback through earphones instead of air or rather were specific to our DAF design, likely due to increased attentional demands. Therefore, four participants performed an additional VWR task in which they were presented with their simultaneous voice feedback through earphones (VWR with auditory feedback [VWR-AF]). As previous studies have reported that DAF can increase voice intensity (Yates, 1963, Howell and Archer, 1984), we first verified whether participants spoke louder during the DAF task. A comparison of their voice intensity between DAF₀ (no-delay trials in the DAF task) and the VWR-AF (standard word reading with simultaneous feedback through earphones) conditions did not show a significant difference (voice intensity; DAF₀: 50±11 dB, VWR: 49±12 dB; paired t-test: t(118)=1.8, p=0.08). After verifying that the sound volume entering the auditory system is not statistically different in the two conditions, we compared the responses in the auditory cortex and found that overall response magnitudes were now on par across the two conditions (DAF₀: 89±17, VWR-AF: 82±17, Figure 5A). However, a detailed inspection of individual electrode responses revealed that some electrodes showed larger response to DAF₀, while others showed either larger responses to VWR-AF or similar responses to both conditions (Figure 5B). In a single participant, we demonstrated that adjacent electrodes in the STG that are only 5 mm apart exhibited completely different response patterns. Electrodes in the more posterior parts of STG showed larger responses to DAF₀, while electrodes in more anterior parts showed similar responses to DAF₀ and VWR-AF (Figure 5C). To determine an anatomical landmark at which the reversal of response patterns occurred in the STG, we used the lateral termination of the transverse temporal sulcus (TTS) (Greenlee et al., 2011; Nourski et al., 2016) based on the individual FreeSurfer segmentation of the participant’s preoperative MRI. Across participants, this landmark corresponded to y coordinate = –22±2.

Figure 5

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Next, we compared the response patterns in the two conditions for all electrodes across participants by calculating a t-value for each electrode (unpaired t-test: average responses from –200 to 500 ms). We demonstrated that auditory regions in posterior STG showed larger responses to DAF₀ condition, while frontal motor regions showed larger responses to VWR-AF (Figure 5D). Lastly, we examined STG electrodes alone, sorted by their anterior-to-posterior positions with respect to the TTS. In line with the results from the single participant, electrodes that were located posteriorly within a 1 cm distance from this anatomical landmark showed significantly larger responses to the DAF₀ condition (Figure 5E). These results suggest that posterior STG is more activated when participants are engaged in a speech production task that requires increased effort and attention.

Our study provides a detailed topographical investigation of speech-induced auditory suppression in a large cohort of neurosurgical participants. We found that while the strongest auditory suppression was observed in the STG, the degree of suppression was highly variable across different recording sites. To explain this variability, we considered the functional role of auditory suppression in speech monitoring. We showed that delaying auditory feedback during speech production enhanced auditory responses in the STG. The degree of sensitivity to feedback delays was also variable across different recording sites. We found a significant correlation between speech-induced suppression and feedback sensitivity, providing evidence for a shared mechanism between auditory suppression and speech monitoring. While there was no anatomical organization for auditory suppression and feedback sensitivity in the STG, we found an anterior-posterior organization for the effect of attention on feedback sensitivity. Auditory sites that lie posterior to the lateral termination of the TTS in the STG showed stronger activation during the DAF task compared to a standard word reading task, even for trials in which participants received simultaneous feedback, demonstrating attentional modulation of feedback sensitivity.

We observed the strongest speech-induced suppression in the middle and posterior parts of the STG. In line with previous iEEG studies, we found that degree of suppression was variable across different recording sites in the STG without any anatomical organization (Flinker et al., 2010; Greenlee et al., 2011; Nourski et al., 2016). So far, a clear gradient for speech-induced suppression has never been reported in the STG but only in the Heschl’s gyrus and superior temporal sulcus by studies that used comprehensive depth electrode coverage within the temporal lobe (Nourski et al., 2016; Nourski et al., 2021).

We found only a few sites with speech-induced enhancement and several sites with no response change. Based on single-unit recordings in non-human primates, it is known that majority of neurons in the non-core auditory cortex exhibits suppression, while a smaller group exhibits excitation during vocalization. It is difficult to isolate speech-induced enhancement in human studies, because measurements reflect the average response of the underlying neural population, which is dominated by suppressed responses. A previous non-human primate study suggested that there might be a division of labor between the suppressed and excited neurons. They showed that when an external auditory stimulus is presented concurrently during vocalization, neurons that showed vocalization-induced suppression did not respond to the external stimulus. In contrary, neurons that showed vocalization-induced excitation responded even more when external stimulus is concurrently presented during vocalization, suggesting a role in maintaining sensitivity to the external acoustic environment (Eliades and Wang, 2003). In humans there might be a similar division of labor between auditory sites that were suppressed and non-suppressed, such that while suppressed sites are engaged in monitoring self-generated sounds, non-suppressed sites maintain sensitivity to external sounds. But unfortunately, our study did not include the necessary experimental conditions to directly test this hypothesis.

Our broad topographical search using subdural electrodes revealed additional sites outside the canonical auditory regions in the STG that showed speech-induced suppression, mainly in the MTG, and a few others in the SMG and preCG. Sensorimotor regions in the preCG including inferior frontal and premotor cortices are known to activate during passive listening tasks (Wilson et al., 2004; Pulvermüller et al., 2006; Cogan et al., 2014), and show tuning to different acoustic properties of speech similar to the auditory regions in the STG (Mesgarani et al., 2014; Cheung et al., 2016). Our results showed that isolated sites in these frontal motor regions were sensitive to DAF, confirming their auditory properties and suggesting their involvement in speech monitoring.

Current models of speech motor control predicted a shared mechanism between auditory suppression and sensitivity to speech errors, suggesting a role for auditory suppression in speech monitoring (Houde and Nagarajan, 2011; Tourville and Guenther, 2011). Behavioral evidence in human studies showed that when auditory feedback is delayed in real time, speakers attempt to reset or slow down their speech (Lee, 1950; Fairbanks, 1955; Stuart et al., 2002). Similarly, when fundamental frequency (pitch) or formant frequencies of the voice are shifted, speakers change their vocal output in the opposite direction of the shift to compensate for the spectral perturbation (Houde and Jordan, 1998; Jones and Munhall, 2000; Niziolek and Guenther, 2013). Neurosurgical recordings and neuroimaging studies that investigate the brain mechanism of auditory feedback processing demonstrated that these feedback-induced vocal adjustments are accompanied by enhanced neural responses in various auditory regions (Tourville et al., 2008; Behroozmand et al., 2009; Behroozmand et al., 2015; Ozker et al., 2022). However, it has not been clear whether it is the same or different neural populations that exhibit speech-induced suppression and enhanced responses to auditory feedback perturbations. Only in a non-human primate study, which recorded single-unit activity in auditory neurons of marmoset monkeys, it was shown that neurons that were suppressed during vocalization exhibited increased activity during frequency-shifted feedback (Eliades and Wang, 2008). In contrast, to replicate this finding in humans, a previous iEEG study by Chang et al., 2013, used frequency-shifted feedback during vowel production and found that most suppressed auditory sites did not overlap with those sensitive to feedback alterations. Using DAF instead of frequency-shifted feedback, we demonstrated a significant overlap of two neural populations in the STG, along with a strong correlation between the degree of speech-induced suppression and sensitivity to auditory feedback. This discrepancy may be due to different methods of calculating sensitivity to altered feedback. In our study, sensitivity was determined by comparing responses to delayed and non-delayed feedback during production, whereas Chang et al. compared perturbed feedback responses during production and listening. One possibility is that our approach identifies a larger auditory neural population in the STG sensitive to altered feedback. Alternatively, it could indicate a larger population highly sensitive to temporal rather than spectral perturbations in auditory feedback. Thus, we observe a wide overlap of the two neural populations in the STG showing both speech-induced suppression and sensitivity to auditory feedback. Replaying a recording of the participants’ own delayed voice back to them, which we were unable to complete in this study, would have made the results of the two studies more comparable while also completely eliminating the possibility of a sensory explanation for the observed response enhancement.

Forward models of speech production suggest that a mismatch between the predicted and the actual auditory feedback is encoded by a response enhancement in the auditory cortex signifying an error signal (Houde and Nagarajan, 2011; Tourville and Guenther, 2011; Hickok, 2012). Our results suggested that attention to one’s own speech stream during adverse speaking conditions, such as during an auditory feedback perturbations task, might also contribute to the response enhancement in the auditory cortex. Auditory feedback control of speech was thought to be involuntary and not subject to attentional control, because several previous studies showed that participants produced compensatory responses to pitch shifts even when they were told to ignore feedback perturbations (Munhall et al., 2009; Zarate et al., 2010; Keough et al., 2013). However, prolonging pitch shift duration resulted in an early vocal response that opposes the pitch shift direction and a later vocal response that follows the pitch shift direction suggesting an interplay between reflexive and top-down processes in controlling voice pitch (Hain et al., 2000; Burnett and Larson, 2002). More recent EEG studies demonstrated that dividing attention between auditory feedback and additional visual stimuli or increasing the attentional load of the task affected vocal responses as well as the magnitude of ERP components, suggesting that attention modulates auditory feedback control on both a behavioral and a cortical level (Tumber et al., 2014; Hu et al., 2015; Liu et al., 2015; Liu et al., 2018). In our study, we found that neural responses in the posterior STG were larger for DAF₀ (randomly presented simultaneous feedback condition in the DAF task) as compared with the VWR-AF condition (consistent simultaneous feedback throughout standard word reading task), even though participants displayed similar vocal behavior in these two conditions. In light of the previous literature, we interpret these response differences as arising from an attentional load difference between the two tasks. In the DAF experiment, the auditory feedback was not consistent since no-delay trials were randomized with delay trials. This randomized structure of the paradigm with interleaved long delay trials (causing slowed speech) required conscious effort for speech monitoring and thus sustained attention. While remaining cautious about this interpretation and our study’s limitation in attentional controls, we believe that this response enhancement represents an increased neural gain driven by attention to auditory feedback (Hillyard et al., 1998), and highlights the critical role of the posterior STG in auditory-motor integration during speech monitoring (Hickok and Poeppel, 2000), with its close proximity to the human ventral attention network comprising temporoparietal junction (Vossel et al., 2014). We leave it to future studies to include additional conditions to manipulate the direction and load of attention to further validate the influence of attention on speech monitoring.

Data and code are available in GitHub (copy archived at Ozker, 2024).

1. 1. Ashburner J

   (2007) A fast diffeomorphic image registration algorithm

   NeuroImage 38:95–113.

   https://doi.org/10.1016/j.neuroimage.2007.07.007

   • PubMed
   • Google Scholar
2. 1. Behroozmand R
   2. Karvelis L
   3. Liu H
   4. Larson CR

   (2009) Vocalization-induced enhancement of the auditory cortex responsiveness during voice F0 feedback perturbation

   Clinical Neurophysiology 120:1303–1312.

   https://doi.org/10.1016/j.clinph.2009.04.022

   • PubMed
   • Google Scholar
3. 1. Behroozmand R
   2. Shebek R
   3. Hansen DR
   4. Oya H
   5. Robin DA
   6. Howard MA
   7. Greenlee JDW

   (2015) Sensory-motor networks involved in speech production and motor control: an fMRI study

   NeuroImage 109:418–428.

   https://doi.org/10.1016/j.neuroimage.2015.01.040

   • PubMed
   • Google Scholar
4. 1. Boersma P

   (2001)

   Praat, a system for doing phonetics by computer

   Glot International 5:341–345.

   • Google Scholar
5. 1. Burnett TA
   2. Larson CR

   (2002) Early pitch-shift response is active in both steady and dynamic voice pitch control

   The Journal of the Acoustical Society of America 112:1058–1063.

   https://doi.org/10.1121/1.1487844

   • PubMed
   • Google Scholar
6. 1. Cardin JA
   2. Carlén M
   3. Meletis K
   4. Knoblich U
   5. Zhang F
   6. Deisseroth K
   7. Tsai L-H
   8. Moore CI

   (2009) Driving fast-spiking cells induces gamma rhythm and controls sensory responses

   Nature 459:663–667.

   https://doi.org/10.1038/nature08002

   • PubMed
   • Google Scholar
7. 1. Chang EF
   2. Niziolek CA
   3. Knight RT
   4. Nagarajan SS
   5. Houde JF

   (2013) Human cortical sensorimotor network underlying feedback control of vocal pitch

   PNAS 110:2653–2658.

   https://doi.org/10.1073/pnas.1216827110

   • PubMed
   • Google Scholar
8. 1. Cheung C
   2. Hamiton LS
   3. Johnson K
   4. Chang EF

   (2016) The auditory representation of speech sounds in human motor cortex

   eLife 5:e12577.

   https://doi.org/10.7554/eLife.12577

   • PubMed
   • Google Scholar
9. 1. Christoffels IK
   2. Formisano E
   3. Schiller NO

   (2007) Neural correlates of verbal feedback processing: an fMRI study employing overt speech

   Human Brain Mapping 28:868–879.

   https://doi.org/10.1002/hbm.20315

   • PubMed
   • Google Scholar
10. 1. Cogan GB
    2. Thesen T
    3. Carlson C
    4. Doyle W
    5. Devinsky O
    6. Pesaran B

    (2014) Sensory-motor transformations for speech occur bilaterally

    Nature 507:94–98.

    https://doi.org/10.1038/nature12935

    • PubMed
    • Google Scholar
11. 1. Crapse TB
    2. Sommer MA

    (2008) Corollary discharge across the animal kingdom

    Nature Reviews. Neuroscience 9:587–600.

    https://doi.org/10.1038/nrn2457

    • PubMed
    • Google Scholar
12. 1. Creutzfeldt O
    2. Ojemann G

    (1989) Neuronal activity in the human lateral temporal lobe. III. Activity changes during music

    Experimental Brain Research 77:490–498.

    https://doi.org/10.1007/BF00249602

    • PubMed
    • Google Scholar
13. 1. Crone NE
    2. Sinai A
    3. Korzeniewska A

    (2006) High-frequency gamma oscillations and human brain mapping with electrocorticography

    Progress in Brain Research 159:275–295.

    https://doi.org/10.1016/S0079-6123(06)59019-3

    • PubMed
    • Google Scholar
14. 1. Curio G
    2. Neuloh G
    3. Numminen J
    4. Jousmäki V
    5. Hari R

    (2000) Speaking modifies voice-evoked activity in the human auditory cortex

    Human Brain Mapping 9:183–191.

    https://doi.org/10.1002/(sici)1097-0193(200004)9:4<183::aid-hbm1>3.0.co;2-z

    • PubMed
    • Google Scholar
15. 1. Eliades SJ
    2. Wang X

    (2003) Sensory-motor interaction in the primate auditory cortex during self-initiated vocalizations

    Journal of Neurophysiology 89:2194–2207.

    https://doi.org/10.1152/jn.00627.2002

    • PubMed
    • Google Scholar
16. 1. Eliades SJ
    2. Wang X

    (2008) Neural substrates of vocalization feedback monitoring in primate auditory cortex

    Nature 453:1102–1106.

    https://doi.org/10.1038/nature06910

    • PubMed
    • Google Scholar
17. 1. Fairbanks G

    (1955) Selective vocal effects of delayed auditory feedback

    The Journal of Speech and Hearing Disorders 20:333–346.

    https://doi.org/10.1044/jshd.2004.333

    • PubMed
    • Google Scholar
18. 1. Flinker A
    2. Chang EF
    3. Kirsch HE
    4. Barbaro NM
    5. Crone NE
    6. Knight RT

    (2010) Single-trial speech suppression of auditory cortex activity in humans

    The Journal of Neuroscience 30:16643–16650.

    https://doi.org/10.1523/JNEUROSCI.1809-10.2010

    • PubMed
    • Google Scholar
19. 1. Ford JM
    2. Roach BJ
    3. Mathalon DH

    (2010) Assessing corollary discharge in humans using noninvasive neurophysiological methods

    Nature Protocols 5:1160–1168.

    https://doi.org/10.1038/nprot.2010.67

    • PubMed
    • Google Scholar
20. 1. Greenlee JDW
    2. Jackson AW
    3. Chen F
    4. Larson CR
    5. Oya H
    6. Kawasaki H
    7. Chen H
    8. Howard MA

    (2011) Human auditory cortical activation during self-vocalization

    PLOS ONE 6:e14744.

    https://doi.org/10.1371/journal.pone.0014744

    • PubMed
    • Google Scholar
21. 1. Greenlee JDW
    2. Behroozmand R
    3. Larson CR
    4. Jackson AW
    5. Chen F
    6. Hansen DR
    7. Oya H
    8. Kawasaki H
    9. Howard MA

    (2013) Sensory-motor interactions for vocal pitch monitoring in non-primary human auditory cortex

    PLOS ONE 8:e60783.

    https://doi.org/10.1371/journal.pone.0060783

    • PubMed
    • Google Scholar
22. 1. Grill-Spector K
    2. Henson R
    3. Martin A

    (2006) Repetition and the brain: neural models of stimulus-specific effects

    Trends in Cognitive Sciences 10:14–23.

    https://doi.org/10.1016/j.tics.2005.11.006

    • PubMed
    • Google Scholar
23. 1. Hain TC
    2. Burnett TA
    3. Kiran S
    4. Larson CR
    5. Singh S
    6. Kenney MK

    (2000) Instructing subjects to make a voluntary response reveals the presence of two components to the audio-vocal reflex

    Experimental Brain Research 130:133–141.

    https://doi.org/10.1007/s002219900237

    • PubMed
    • Google Scholar
24. 1. Hashimoto Y
    2. Sakai KL

    (2003) Brain activations during conscious self-monitoring of speech production with delayed auditory feedback: an fMRI study

    Human Brain Mapping 20:22–28.

    https://doi.org/10.1002/hbm.10119

    • PubMed
    • Google Scholar
25. 1. Hickok G
    2. Poeppel D

    (2000) Towards a functional neuroanatomy of speech perception

    Trends in Cognitive Sciences 4:131–138.

    https://doi.org/10.1016/s1364-6613(00)01463-7

    • PubMed
    • Google Scholar
26. 1. Hickok G
    2. Houde J
    3. Rong F

    (2011) Sensorimotor integration in speech processing: computational basis and neural organization

    Neuron 69:407–422.

    https://doi.org/10.1016/j.neuron.2011.01.019

    • PubMed
    • Google Scholar
27. 1. Hickok G

    (2012) Computational neuroanatomy of speech production

    Nature Reviews. Neuroscience 13:135–145.

    https://doi.org/10.1038/nrn3158

    • PubMed
    • Google Scholar
28. 1. Hillyard SA
    2. Vogel EK
    3. Luck SJ

    (1998) Sensory gain control (amplification) as a mechanism of selective attention: electrophysiological and neuroimaging evidence

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 353:1257–1270.

    https://doi.org/10.1098/rstb.1998.0281

    • PubMed
    • Google Scholar
29. 1. Houde JF
    2. Jordan MI

    (1998) Sensorimotor adaptation in speech production

    Science 279:1213–1216.

    https://doi.org/10.1126/science.279.5354.1213

    • PubMed
    • Google Scholar
30. 1. Houde JF
    2. Nagarajan SS
    3. Sekihara K
    4. Merzenich MM

    (2002) Modulation of the auditory cortex during speech: an MEG study

    Journal of Cognitive Neuroscience 14:1125–1138.

    https://doi.org/10.1162/089892902760807140

    • PubMed
    • Google Scholar
31. 1. Houde JF
    2. Nagarajan SS

    (2011) Speech production as state feedback control

    Frontiers in Human Neuroscience 5:82.

    https://doi.org/10.3389/fnhum.2011.00082

    • PubMed
    • Google Scholar
32. 1. Howell P
    2. Archer A

    (1984) Susceptibility to the effects of delayed auditory feedback

    Perception & Psychophysics 36:296–302.

    https://doi.org/10.3758/bf03206371

    • PubMed
    • Google Scholar
33. 1. Hu H
    2. Liu Y
    3. Guo Z
    4. Li W
    5. Liu P
    6. Chen S
    7. Liu H

    (2015) Attention modulates cortical processing of pitch feedback errors in voice control

    Scientific Reports 5:1–8.

    https://doi.org/10.1038/srep07812

    • Google Scholar
34. 1. Jones JA
    2. Munhall KG

    (2000) Perceptual calibration of F0 production: evidence from feedback perturbation

    The Journal of the Acoustical Society of America 108:1246–1251.

    https://doi.org/10.1121/1.1288414

    • PubMed
    • Google Scholar
35. 1. Keough D
    2. Hawco C
    3. Jones JA

    (2013) Auditory-motor adaptation to frequency-altered auditory feedback occurs when participants ignore feedback

    BMC Neuroscience 14:25.

    https://doi.org/10.1186/1471-2202-14-25

    • PubMed
    • Google Scholar
36. 1. Kort NS
    2. Nagarajan SS
    3. Houde JF

    (2014) A bilateral cortical network responds to pitch perturbations in speech feedback

    NeuroImage 86:525–535.

    https://doi.org/10.1016/j.neuroimage.2013.09.042

    • PubMed
    • Google Scholar
37. 1. Lachaux JP
    2. Axmacher N
    3. Mormann F
    4. Halgren E
    5. Crone NE

    (2012) High-frequency neural activity and human cognition: past, present and possible future of intracranial EEG research

    Progress in Neurobiology 98:279–301.

    https://doi.org/10.1016/j.pneurobio.2012.06.008

    • PubMed
    • Google Scholar
38. 1. Lee BS

    (1950) Effects of delayed speech feedback

    The Journal of the Acoustical Society of America 22:824–826.

    https://doi.org/10.1121/1.1906696

    • Google Scholar
39. 1. Liu Y
    2. Hu H
    3. Jones JA
    4. Guo Z
    5. Li W
    6. Chen X
    7. Liu P
    8. Liu H

    (2015) Selective and divided attention modulates auditory-vocal integration in the processing of pitch feedback errors

    The European Journal of Neuroscience 42:1895–1904.

    https://doi.org/10.1111/ejn.12949

    • PubMed
    • Google Scholar
40. 1. Liu Y
    2. Fan H
    3. Li J
    4. Jones JA
    5. Liu P
    6. Zhang B
    7. Liu H

    (2018) Auditory-motor control of vocal production during divided attention: Behavioral and ERP correlates

    Frontiers in Neuroscience 12:113.

    https://doi.org/10.3389/fnins.2018.00113

    • PubMed
    • Google Scholar
41. 1. Mesgarani N
    2. Chang EF

    (2012) Selective cortical representation of attended speaker in multi-talker speech perception

    Nature 485:233–236.

    https://doi.org/10.1038/nature11020

    • PubMed
    • Google Scholar
42. 1. Mesgarani N
    2. Cheung C
    3. Johnson K
    4. Chang EF

    (2014) Phonetic feature encoding in human superior temporal gyrus

    Science 343:1006–1010.

    https://doi.org/10.1126/science.1245994

    • PubMed
    • Google Scholar
43. 1. Mukamel R
    2. Gelbard H
    3. Arieli A
    4. Hasson U
    5. Fried I
    6. Malach R

    (2005) Coupling between neuronal firing, field potentials, and FMRI in human auditory cortex

    Science 309:951–954.

    https://doi.org/10.1126/science.1110913

    • PubMed
    • Google Scholar
44. 1. Munhall KG
    2. MacDonald EN
    3. Byrne SK
    4. Johnsrude I

    (2009) Talkers alter vowel production in response to real-time formant perturbation even when instructed not to compensate

    The Journal of the Acoustical Society of America 125:384–390.

    https://doi.org/10.1121/1.3035829

    • PubMed
    • Google Scholar
45. 1. Niziolek CA
    2. Guenther FH

    (2013) Vowel category boundaries enhance cortical and behavioral responses to speech feedback alterations

    The Journal of Neuroscience 33:12090–12098.

    https://doi.org/10.1523/JNEUROSCI.1008-13.2013

    • PubMed
    • Google Scholar
46. 1. Niziolek CA
    2. Nagarajan SS
    3. Houde JF

    (2013) What does motor efference copy represent? Evidence from speech production

    The Journal of Neuroscience 33:16110–16116.

    https://doi.org/10.1523/JNEUROSCI.2137-13.2013

    • PubMed
    • Google Scholar
47. 1. Nourski KV
    2. Steinschneider M
    3. Rhone AE

    (2016) Electrocorticographic activation within human auditory cortex during dialog-based language and cognitive testing

    Frontiers in Human Neuroscience 10:202.

    https://doi.org/10.3389/fnhum.2016.00202

    • PubMed
    • Google Scholar
48. 1. Nourski KV
    2. Steinschneider M
    3. Rhone AE
    4. Kovach CK
    5. Banks MI
    6. Krause BM
    7. Kawasaki H
    8. Howard MA

    (2021) Electrophysiology of the human superior temporal sulcus during speech processing

    Cerebral Cortex 31:1131–1148.

    https://doi.org/10.1093/cercor/bhaa281

    • PubMed
    • Google Scholar
49. 1. Numminen J
    2. Salmelin R
    3. Hari R

    (1999) Subject’s own speech reduces reactivity of the human auditory cortex

    Neuroscience Letters 265:119–122.

    https://doi.org/10.1016/s0304-3940(99)00218-9

    • PubMed
    • Google Scholar
50. 1. Ozker M
    2. Doyle W
    3. Devinsky O
    4. Flinker A

    (2022) A cortical network processes auditory error signals during human speech production to maintain fluency

    PLOS Biology 20:e3001493.

    https://doi.org/10.1371/journal.pbio.3001493

    • PubMed
    • Google Scholar
51. Software

    1. Ozker M

    (2024) Sensitivity suppression, version swh:1:rev:c216d583151b887b671cba15cacd786dc58bcdc2

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:ed09d518f393abd3ece26dd5548208db6aa48faf;origin=https://github.com/flinkerlab/Sensitivity-Suppression;visit=swh:1:snp:3de9673b1ed419aebfcb848d93421bc6c2ddf083;anchor=swh:1:rev:c216d583151b887b671cba15cacd786dc58bcdc2
52. Book

    1. Percival DB
    2. Walden AT

    (1993) Spectral Analysis for Physical Applications

    Cambridge University Press.

    https://doi.org/10.1017/CBO9780511622762

    • Google Scholar
53. 1. Poulet JFA
    2. Hedwig B

    (2002) A corollary discharge maintains auditory sensitivity during sound production

    Nature 418:872–876.

    https://doi.org/10.1038/nature00919

    • PubMed
    • Google Scholar
54. 1. Poulet JFA
    2. Hedwig B

    (2006) The cellular basis of a corollary discharge

    Science 311:518–522.

    https://doi.org/10.1126/science.1120847

    • PubMed
    • Google Scholar
55. 1. Pulvermüller F
    2. Huss M
    3. Kherif F
    4. Moscoso del Prado Martin F
    5. Hauk O
    6. Shtyrov Y

    (2006) Motor cortex maps articulatory features of speech sounds

    PNAS 103:7865–7870.

    https://doi.org/10.1073/pnas.0509989103

    • PubMed
    • Google Scholar
56. 1. Ray S
    2. Maunsell JHR

    (2011) Different origins of gamma rhythm and high-gamma activity in macaque visual cortex

    PLOS Biology 9:e1000610.

    https://doi.org/10.1371/journal.pbio.1000610

    • PubMed
    • Google Scholar
57. 1. Rossion B
    2. Pourtois G

    (2004) Revisiting Snodgrass and Vanderwart’s object pictorial set: the role of surface detail in basic-level object recognition

    Perception 33:217–236.

    https://doi.org/10.1068/p5117

    • PubMed
    • Google Scholar
58. 1. Schneider DM
    2. Mooney R

    (2018) How movement modulates hearing

    Annual Review of Neuroscience 41:553–572.

    https://doi.org/10.1146/annurev-neuro-072116-031215

    • PubMed
    • Google Scholar
59. 1. Shum J
    2. Fanda L
    3. Dugan P
    4. Doyle WK
    5. Devinsky O
    6. Flinker A

    (2020) Neural correlates of sign language production revealed by electrocorticography

    Neurology 95:e2880–e2889.

    https://doi.org/10.1212/WNL.0000000000010639

    • PubMed
    • Google Scholar
60. 1. Stuart A
    2. Kalinowski J
    3. Rastatter MP
    4. Lynch K

    (2002) Effect of delayed auditory feedback on normal speakers at two speech rates

    The Journal of the Acoustical Society of America 111:2237–2241.

    https://doi.org/10.1121/1.1466868

    • PubMed
    • Google Scholar
61. 1. Todorovic A
    2. de Lange FP

    (2012) Repetition suppression and expectation suppression are dissociable in time in early auditory evoked fields

    The Journal of Neuroscience 32:13389–13395.

    https://doi.org/10.1523/JNEUROSCI.2227-12.2012

    • PubMed
    • Google Scholar
62. 1. Tourville JA
    2. Reilly KJ
    3. Guenther FH

    (2008) Neural mechanisms underlying auditory feedback control of speech

    NeuroImage 39:1429–1443.

    https://doi.org/10.1016/j.neuroimage.2007.09.054

    • PubMed
    • Google Scholar
63. 1. Tourville JA
    2. Guenther FH

    (2011) The DIVA model: A neural theory of speech acquisition and production

    Language and Cognitive Processes 26:952–981.

    https://doi.org/10.1080/01690960903498424

    • PubMed
    • Google Scholar
64. 1. Tumber AK
    2. Scheerer NE
    3. Jones JA

    (2014) Attentional demands influence vocal compensations to pitch errors heard in auditory feedback

    PLOS ONE 9:e109968.

    https://doi.org/10.1371/journal.pone.0109968

    • PubMed
    • Google Scholar
65. 1. Vossel S
    2. Geng JJ
    3. Fink GR

    (2014) Dorsal and ventral attention systems: distinct neural circuits but collaborative roles

    The Neuroscientist 20:150–159.

    https://doi.org/10.1177/1073858413494269

    • PubMed
    • Google Scholar
66. 1. Wilson SM
    2. Saygin AP
    3. Sereno MI
    4. Iacoboni M

    (2004) Listening to speech activates motor areas involved in speech production

    Nature Neuroscience 7:701–702.

    https://doi.org/10.1038/nn1263

    • PubMed
    • Google Scholar
67. 1. Wise RJ
    2. Greene J
    3. Büchel C
    4. Scott SK

    (1999) Brain regions involved in articulation

    Lancet 353:1057–1061.

    https://doi.org/10.1016/s0140-6736(98)07491-1

    • PubMed
    • Google Scholar
68. 1. Yang AI
    2. Wang X
    3. Doyle WK
    4. Halgren E
    5. Carlson C
    6. Belcher TL
    7. Cash SS
    8. Devinsky O
    9. Thesen T

    (2012) Localization of dense intracranial electrode arrays using magnetic resonance imaging

    NeuroImage 63:157–165.

    https://doi.org/10.1016/j.neuroimage.2012.06.039

    • PubMed
    • Google Scholar
69. 1. Yates AJ

    (1963) Delayed auditory feedback

    Psychological Bulletin 60:213–232.

    https://doi.org/10.1037/h0044155

    • PubMed
    • Google Scholar
70. 1. Zarate JM
    2. Wood S
    3. Zatorre RJ

    (2010) Neural networks involved in voluntary and involuntary vocal pitch regulation in experienced singers

    Neuropsychologia 48:607–618.

    https://doi.org/10.1016/j.neuropsychologia.2009.10.025

    • PubMed
    • Google Scholar
71. 1. Zion Golumbic EM
    2. Ding N
    3. Bickel S
    4. Lakatos P
    5. Schevon CA
    6. McKhann GM
    7. Goodman RR
    8. Emerson R
    9. Mehta AD
    10. Simon JZ
    11. Poeppel D
    12. Schroeder CE

    (2013) Mechanisms underlying selective neuronal tracking of attended speech at a “cocktail party.”

    Neuron 77:980–991.

    https://doi.org/10.1016/j.neuron.2012.12.037

    • PubMed
    • Google Scholar

Author details

1. Muge Ozker

   1. Neurology Department, New York University, New York, United States
   2. Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   mozker@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7472-4528

2. Leyao Yu

   1. Neurology Department, New York University, New York, United States
   2. Biomedical Engineering Department, New York University, New York, United States

   Contribution

   Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

3. Patricia Dugan

   Neurology Department, New York University, New York, United States

   Contribution

   Resources, Data curation, Investigation, Project administration

   Competing interests

   No competing interests declared

4. Werner Doyle

   Neurosurgery Department, New York University, New York, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

5. Daniel Friedman

   Neurology Department, New York University, New York, United States

   Contribution

   Resources, Data curation, Investigation, Methodology

   Competing interests

   No competing interests declared

6. Orrin Devinsky

   Neurology Department, New York University, New York, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

7. Adeen Flinker

   1. Neurology Department, New York University, New York, United States
   2. Biomedical Engineering Department, New York University, New York, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1247-1283

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