Human laryngeal responses to auditory stimulation

Sapir, Shimon; McClean, Michael D.; Larson, Charles R.

doi:10.1121/1.388812

Cited by 87 publications

(31 citation statements)

References 18 publications

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“…This extremely fast latency for both parameters suggests that the underlying neuronal network can function without direct auditory feedback from the bat's own voice, which indicates a direct connection between the auditory and vocal-motor systems at the brainstem level. Earlier studies showed that auditory stimulation with clicks, pure tones, or noise evoked rapid reflex responses in the recurrent laryngeal nerve and the spinal ventral root with latencies between 6 and 25 ms (15,(32)(33)(34)(35)(36). Considering that call durations are typically between 40 and 50 ms, and calls are repeated every 50-150 ms, these studies suggest that auditory inputs originating from centers early within the ascending auditory pathway could directly affect laryngeal and expiratory muscles.…”

Section: Discussionmentioning

confidence: 99%

Ambient noise induces independent shifts in call frequency and amplitude within the Lombard effect in echolocating bats

Hage

Jiang

Berquist

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

125

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The Lombard effect, an involuntary rise in call amplitude in response to masking ambient noise, represents one of the most efficient mechanisms to optimize signal-to-noise ratio. The Lombard effect occurs in birds and mammals, including humans, and is often associated with several other vocal changes, such as call frequency and duration. Most studies, however, have focused on noisedependent changes in call amplitude. It is therefore still largely unknown how the adaptive changes in call amplitude relate to associated vocal changes such as frequency shifts, how the underlying mechanisms are linked, and if auditory feedback from the changing vocal output is needed. Here, we examined the Lombard effect and the associated changes in call frequency in a highly vocal mammal, echolocating horseshoe bats. We analyzed how bandpassfiltered noise (BFN; bandwidth 20 kHz) affected their echolocation behavior when BFN was centered on different frequencies within their hearing range. Call amplitudes increased only when BFN was centered on the dominant frequency component of the bats' calls. In contrast, call frequencies increased for all but one BFN center frequency tested. Both amplitude and frequency rises were extremely fast and occurred in the first call uttered after noise onset, suggesting that no auditory feedback was required. The different effects that varying the BFN center frequency had on amplitude and frequency rises indicate different neural circuits and/or mechanisms underlying these changes.A ny transmission of signals between sender and receiver faces the challenge of being subjected to masking by noise. For acoustic signals, for example, animals have evolved several strategies that aid in increasing the signal-to-noise ratio, thus facilitating signal transmission. One of the most efficient mechanisms is the socalled Lombard effect, i.e., the involuntary rise in call amplitude in response to masking ambient noise (1). This effect was first described in human communication a century ago (2) and has since been found in several species of birds (3-11) and various mammals (12-17), including bats (18). In human speech, several vocal changes, such as a rise in fundamental frequency (19) or lengthening in word duration (20), are often accompanied with the Lombard effect; combined, these changes are referred to as Lombard speech (21). Vocal changes associated with the Lombard effect have rarely been analyzed in animal species. So far, noisedependent changes in call frequency have been observed in birds (8,11,22), and changes in call duration in birds and monkeys (8,12).Most animal studies, however, have focused on noise-dependent changes in call amplitude and do not examine other possible vocal changes (2-5, 7, 9, 10, 15-17). It is therefore still largely unknown how the adaptive changes in call amplitude relate to frequency shifts or call elongation and how the underlying mechanisms are linked. It is also unknown if auditory feedback from the changing vocal output is needed to drive the Lombard effect in general.Over...

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Section: Discussionmentioning

confidence: 99%

Ambient noise induces independent shifts in call frequency and amplitude within the Lombard effect in echolocating bats

Hage

Jiang

Berquist

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

125

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“…The Lombard effect response latency of 30 ms is even shorter than the auditory laryngeal reflex, an increase in fundamental frequency in response to the presentation of a click, with a latency of ∼50 ms in humans (18). Nevertheless, the 30-ms response latency is well within the physiological limit of audio-vocal integration, as the acoustic crycothyroid muscle reflex can be as short as 6 ms (19).…”

Section: Discussionmentioning

confidence: 99%

Sensorimotor integration on a rapid time scale

Luo

Kothari

Moss

2017

Proc. Natl. Acad. Sci. U.S.A.

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Sensing is fundamental to the control of movement: From grasping objects to speech production, sensing guides action. So far, most of our knowledge about sensorimotor integration comes from visually guided reaching and oculomotor integration, in which the time course and trajectories of movements can be measured at a high temporal resolution. By contrast, production of vocalizations by humans and animals involves complex and variable actions, and each syllable often lasts a few hundreds of milliseconds, making it difficult to infer underlying neural processes. Here, we measured and modeled the transfer of sensory information into motor commands for vocal amplitude control in response to background noise, also known as the Lombard effect. We exploited the brief vocalizations of echolocating bats to trace the time course of the Lombard effect on a millisecond time scale. Empirical studies revealed that the Lombard effect features a response latency of a mere 30 ms and provided the foundation for the quantitative audiomotor model of the Lombard effect. We show that the Lombard effect operates by continuously integrating the sound pressure level of background noise through temporal summation to guide the extremely rapid vocal-motor adjustments. These findings can now be extended to models and measures of audiomotor integration in other animals, including humans.S ensing plays a critical role in the control of movement. In humans, natural behaviors, from grasping a coffee mug to producing intelligible speech, all rely on the guidance of sensing. Similarly, sensory signals lie at the core of most animal behaviors. However, the brain mechanisms underlying sensorimotor integration are not well understood. This is particularly true regarding the control of vocalizations, which are used by a wide range of animal species for communication. At present, a dominant model for motor control is derived from the state feedback control (SFC) theory, which successfully accounts for a wide range of motor behaviors, such as visually guided arm movement (1) and speech production (2). SFC models posit that motor control is based on a comparison of sensory prediction generated from an internal forward model with actual sensory feedback, and sensory feedback is used to train and update the internal forward model. According to this SFC model, sensory feedback is not directly used to guide motor commands, due to its noisy and delayed characteristics, and this notion has been supported by a large body of experimental and theoretical work (1-7). These stand in contrast to motor reflexes, which are movements in direct response to sensory signals. Note, motor reflexes can be graded in response magnitude and can be modulated by cognitive processes (8, 9). One well-known example is the pupillary light reflex, an adjustment in pupil diameter in response to light intensity (10).The Lombard effect refers to an animal's increase in vocal signal amplitude in response to an increase in background noise (11). Evidence of the Lombard effect comes from s...

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“…While some demonstrations of auditory feedback producing rapid immediate changes in speech characteristics have been reported (Sapir et al, 1983), the timing of articulation and feedback processing is not conducive to a servomechanism. This does not imply that the timing of feedback is not crucial.…”

Section: Introductionmentioning

confidence: 99%

Modulation of auditory-motor learning in response to formant perturbation as a function of delayed auditory feedback

Mitsuya

Munhall

Purcell

2017

The Journal of the Acoustical Society of America

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The interaction of language production and perception has been substantiated by empirical studies where speakers compensate their speech articulation in response to the manipulated sound of their voice heard in real-time as auditory feedback. A recent study by Max and Maffett [(2015). Neurosci. Lett. 591,[25][26][27][28][29] reported an absence of compensation (i.e., auditory-motor learning) for frequency-shifted formants when auditory feedback was delayed by 100 ms. In the present study, the effect of auditory feedback delay was studied when only the first formant was manipulated while delaying auditory feedback systematically. In experiment 1, a small yet significant compensation was observed even with 100 ms of auditory delay unlike the past report. This result suggests that the tolerance of feedback delay depends on different types of auditory errors being processed. In experiment 2, it was revealed that the amount of formant compensation had an inverse linear relationship with the amount of auditory delay. One of the speculated mechanisms to account for these results is that as auditory delay increases, undelayed (and unperturbed) somatosensory feedback is given more preference for accuracy control of vowel formants.

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Human laryngeal responses to auditory stimulation

Cited by 87 publications

References 18 publications

Ambient noise induces independent shifts in call frequency and amplitude within the Lombard effect in echolocating bats

Ambient noise induces independent shifts in call frequency and amplitude within the Lombard effect in echolocating bats

Sensorimotor integration on a rapid time scale

Modulation of auditory-motor learning in response to formant perturbation as a function of delayed auditory feedback

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