Neural Fluctuation Contrast as a Code for Complex Sounds: The Role and Control of Peripheral Nonlinearities.

The nonlinearities of the inner ear are often considered to be obstacles that the central nervous system has to overcome to decode neural responses to sounds. This review describes how peripheral nonlinearities, such as saturation of the inner-hair-cell response and of the IHC-auditory-nerve synapse, are instead beneficial to the neural encoding of complex sounds such as speech. These nonlinearities set up contrast in the depth of neural-fluctuations in auditory-nerve responses along the tonotopic axis, referred to here as neural fluctuation contrast (NFC). Physiological support for the NFC coding hypothesis is reviewed, and predictions of several psychophysical phenomena, including masked detection and speech intelligibility, are presented. Lastly, a framework based on the NFC code for understanding how the medial olivocochlear (MOC) efferent system contributes to the coding of complex sounds is presented. By modulating cochlear gain control in response to both sound energy and fluctuations in neural responses, the MOC system is hypothesized to function not as a simple feedback gain-control device, but rather as a mechanism for enhancing NFC along the tonotopic axis, enabling robust encoding of complex sounds across a wide range of sound levels and in the presence of background noise. Effects of sensorineural hearing loss on the NFC code and on the MOC feedback system are presented and discussed.

Subcortical auditory model including efferent dynamic gain control with inputs from cochlear nucleus and inferior colliculus.

An auditory model has been developed with a time-varying, gain-control signal based on the physiology of the efferent system and subcortical neural pathways. The medial olivocochlear (MOC) efferent stage of the model receives excitatory projections from fluctuation-sensitive model neurons of the inferior colliculus (IC) and wide-dynamic-range model neurons of the cochlear nucleus. The response of the model MOC stage dynamically controls cochlear gain via simulated outer hair cells. In response to amplitude-modulated (AM) noise, firing rates of most IC neurons with band-enhanced modulation transfer functions in awake rabbits increase over a time course consistent with the dynamics of the MOC efferent feedback. These changes in the rates of IC neurons in awake rabbits were employed to adjust the parameters of the efferent stage of the proposed model. Responses of the proposed model to AM noise were able to simulate the increasing IC rate over time, whereas the model without the efferent system did not show this trend. The proposed model with efferent gain control provides a powerful tool for testing hypotheses, shedding insight on mechanisms in hearing, specifically those involving the efferent system.

Sensitivity to direction and velocity of fast frequency chirps in the inferior colliculus of awake rabbit.

Neurons in the mammalian inferior colliculus (IC) are sensitive to the velocity (speed and direction) of fast frequency chirps contained in Schroeder-phase harmonic complexes (SCHR). However, IC neurons are also sensitive to stimulus periodicity, a prominent feature of SCHR stimuli. Here, to disentangle velocity sensitivity from periodicity tuning, we introduced a novel stimulus consisting of aperiodic random chirps. Extracellular, single-unit recordings were made in the IC of Dutch-belted rabbits in response to both SCHR and aperiodic chirps. Rate-velocity functions were constructed from aperiodic-chirp responses and compared to SCHR rate profiles, revealing interactions between stimulus periodicity and neural velocity sensitivity. A generalized linear model analysis demonstrated that periodicity tuning influences SCHR response rates more strongly than velocity sensitivity. Principal component analysis of rate-velocity functions revealed that neurons were more often sensitive to the direction of lower-velocity chirps and were less often sensitive to the direction of higher-velocity chirps. Overall, these results demonstrate that sensitivity to chirp velocity is common in the IC. Harmonic sounds with complex phase spectra, such as speech and music, contain chirps, and velocity sensitivity would shape IC responses to these sounds.

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.

Representations of fricatives in subcortical model responses: Comparisons with human consonant perception.

Fricatives are obstruent sound contrasts made by airflow constrictions in the vocal tract that produce turbulence across the constriction or at a site downstream from the constriction. Fricatives exhibit significant intra/intersubject and contextual variability. Yet, fricatives are perceived with high accuracy. The current study investigated modeled neural responses to fricatives in the auditory nerve (AN) and inferior colliculus (IC) with the hypothesis that response profiles across populations of neurons provide robust correlates to consonant perception. Stimuli were 270 intervocalic fricatives (10 speakers × 9 fricatives × 3 utterances). Computational model response profiles had characteristic frequencies that were log-spaced from 125 Hz to 8 or 20 kHz to explore the impact of high-frequency responses. Confusion matrices generated by k-nearest-neighbor subspace classifiers were based on the profiles of average rates across characteristic frequencies as feature vectors. Model confusion matrices were compared with published behavioral data. The modeled AN and IC neural responses provided better predictions of behavioral accuracy than the stimulus spectra, and IC showed better accuracy than AN. Behavioral fricative accuracy was explained by modeled neural response profiles, whereas confusions were only partially explained. Extended frequencies improved accuracy based on the model IC, corroborating the importance of extended high frequencies in speech perception.

Mechanisms of masking by Schroeder-phase harmonic tone complexes in the budgerigar (Melopsittacus undulatus).

Schroeder-phase harmonic tone complexes can have a flat temporal envelope and rising or falling instantaneous-frequency sweeps within F0 periods, depending on the phase-scaling parameter C. Human tone-detection thresholds in a concurrent Schroeder masker are 10-15 dB lower for positive C values (rising frequency sweeps) compared to negative (falling sweeps), potentially due to cochlear mechanics, though this hypothesis remains controversial. Birds provide an interesting model for studies of Schroeder masking because many species produce vocalizations containing frequency sweeps. Prior behavioral studies in birds suggest less behavioral threshold difference between maskers with opposite C values than in humans, but focused on low masker F0s and did not explore neural mechanisms. We performed behavioral Schroeder-masking experiments in budgerigars (Melopsittacus undulatus) using a wide range of masker F0 and C values. Signal frequency was 2800 Hz. Neural recordings from the midbrain characterized encoding of behavioral stimuli in awake animals. Behavioral thresholds increased with increasing masker F0 and showed minimal difference between opposite C values, consistent with prior budgerigar studies. Midbrain recordings showed prominent temporal and rate-based encoding of Schroeder F0, and in many cases, marked asymmetry in Schroeder responses between C polarities. Neural thresholds for Schroeder-masked tone detection were often based on a response decrement compared to the masker alone, consistent with prominent modulation tuning in midbrain neurons, and were generally similar between opposite C values. The results highlight the likely importance of envelope cues in Schroeder masking and show that differences in supra-threshold Schroeder responses do not necessarily result in neural threshold differences.

Effects of sensorineural hearing loss on formant-frequency discrimination: Measurements and models.

This study concerns the effect of hearing loss on discrimination of formant frequencies in vowels. In the response of the healthy ear to a harmonic sound, auditory-nerve (AN) rate functions fluctuate at the fundamental frequency, F0. Responses of inner-hair-cells (IHCs) tuned near spectral peaks are captured (or dominated) by a single harmonic, resulting in lower fluctuation depths than responses of IHCs tuned between spectral peaks. Therefore, the depth of neural fluctuations (NFs) varies along the tonotopic axis and encodes spectral peaks, including formant frequencies of vowels. This NF code is robust across a wide range of sound levels and in background noise. The NF profile is converted into a rate-place representation in the auditory midbrain, wherein neurons are sensitive to low-frequency fluctuations. The NF code is vulnerable to sensorineural hearing loss (SNHL) because capture depends upon saturation of IHCs, and thus the interaction of cochlear gain with IHC transduction. In this study, formant-frequency discrimination limens (DLFFs) were estimated for listeners with normal hearing or mild to moderate SNHL. The F0 was fixed at 100 Hz, and formant peaks were either aligned with harmonic frequencies or placed between harmonics. Formant peak frequencies were 600 and 2000 Hz, in the range of first and second formants of several vowels. The difficulty of the task was varied by changing formant bandwidth to modulate the contrast in the NF profile. Results were compared to predictions from model auditory-nerve and inferior colliculus (IC) neurons, with listeners' audiograms used to individualize the AN model. Correlations between DLFFs, audiometric thresholds near the formant frequencies, age, and scores on the Quick speech-in-noise test are reported. SNHL had a strong effect on DLFF for the second formant frequency (F2), but relatively small effect on DLFF for the first formant (F1). The IC model appropriately predicted substantial threshold elevations for changes in F2 as a function of SNHL and little effect of SNHL on thresholds for changes in F1.

Inherent envelope fluctuations in forward masking: Effects of age and hearing loss.

Forward masking is generally greater for Gaussian noise (GN) than for low-fluctuation noise maskers, i.e., GN disruption. Because the minimal hearing loss that is associated with older age may affect GN disruption differently than more significant hearing loss, the current study explored the contribution of minimal hearing loss associated with older age to GN disruption. GN disruption was measured using three masker-signal delays (25, 75, and 150 ms) for three adult groups: younger participants with normal hearing (NH), older participants with minimal hearing loss, and older participants with sensorineural hearing loss. The role of underlying mechanisms was tested using a computational model for midbrain neurons. The primary result suggests that older listeners with mild threshold elevations that typically occur with age may be more susceptible to the deleterious effects of masker envelope fluctuations than younger listeners with NH. Results from the computational model indicate that there may be a larger influence of efferent feedback and saturation of inner hair cells on forward masking and GN disruption than previously considered.

Incorporating models of subcortical processing improves the ability to predict EEG responses to natural speech.

The goal of describing how the human brain responds to complex acoustic stimuli has driven auditory neuroscience research for decades. Often, a systems-based approach has been taken, in which neurophysiological responses are modeled based on features of the presented stimulus. This includes a wealth of work modeling electroencephalogram (EEG) responses to complex acoustic stimuli such as speech. Examples of the acoustic features used in such modeling include the amplitude envelope and spectrogram of speech. These models implicitly assume a direct mapping from stimulus representation to cortical activity. However, in reality, the representation of sound is transformed as it passes through early stages of the auditory pathway, such that inputs to the cortex are fundamentally different from the raw audio signal that was presented. Thus, it could be valuable to account for the transformations taking place in lower-order auditory areas, such as the auditory nerve, cochlear nucleus, and inferior colliculus (IC) when predicting cortical responses to complex sounds. Specifically, because IC responses are more similar to cortical inputs than acoustic features derived directly from the audio signal, we hypothesized that linear mappings (temporal response functions; TRFs) fit to the outputs of an IC model would better predict EEG responses to speech stimuli. To this end, we modeled responses to the acoustic stimuli as they passed through the auditory nerve, cochlear nucleus, and inferior colliculus before fitting a TRF to the output of the modeled IC responses. Results showed that using model-IC responses in traditional systems analyzes resulted in better predictions of EEG activity than using the envelope or spectrogram of a speech stimulus. Further, it was revealed that model-IC derived TRFs predict different aspects of the EEG than acoustic-feature TRFs, and combining both types of TRF models provides a more accurate prediction of the EEG response.

Incorporating models of subcortical processing improves the ability to predict EEG responses to natural speech.

The goal of describing how the human brain responds to complex acoustic stimuli has driven auditory neuroscience research for decades. Often, a systems-based approach has been taken, in which neurophysiological responses are modeled based on features of the presented stimulus. This includes a wealth of work modeling electroencephalogram (EEG) responses to complex acoustic stimuli such as speech. Examples of the acoustic features used in such modeling include the amplitude envelope and spectrogram of speech. These models implicitly assume a direct mapping from stimulus representation to cortical activity. However, in reality, the representation of sound is transformed as it passes through early stages of the auditory pathway, such that inputs to the cortex are fundamentally different from the raw audio signal that was presented. Thus, it could be valuable to account for the transformations taking place in lower-order auditory areas, such as the auditory nerve, cochlear nucleus, and inferior colliculus (IC) when predicting cortical responses to complex sounds. Specifically, because IC responses are more similar to cortical inputs than acoustic features derived directly from the audio signal, we hypothesized that linear mappings (temporal response functions; TRFs) fit to the outputs of an IC model would better predict EEG responses to speech stimuli. To this end, we modeled responses to the acoustic stimuli as they passed through the auditory nerve, cochlear nucleus, and inferior colliculus before fitting a TRF to the output of the modeled IC responses. Results showed that using model-IC responses in traditional systems analyses resulted in better predictions of EEG activity than using the envelope or spectrogram of a speech stimulus. Further, it was revealed that model-IC derived TRFs predict different aspects of the EEG than acoustic-feature TRFs, and combining both types of TRF models provides a more accurate prediction of the EEG response.x.

Responses to dichotic tone-in-noise stimuli in the inferior colliculus.

Human listeners are more sensitive to tones embedded in diotic noise when the tones are out-of-phase at the two ears (N0Sπ) than when they are in-phase (N0S0). The difference between the tone-detection thresholds for these two conditions is referred to as the binaural masking level difference (BMLD) and reflects a benefit of binaural processing. Detection in the N0Sπ condition has been explained in modeling studies by changes in interaural correlation (IAC), but this model has only been directly tested physiologically for low frequencies. Here, the IAC-based hypothesis for binaural detection was examined across a wide range of frequencies and masker levels using recordings in the awake rabbit inferior colliculus (IC). IAC-based cues were strongly correlated with neural responses to N0Sπ stimuli. Additionally, average rate-based thresholds were calculated for both N0S0 and N0Sπ conditions. The rate-based neural BMLD at 500 Hz matched rabbit behavioral data, but the trend of neural BMLDs across frequency differed from that of humans.

A comparative study of eight human auditory models of monaural processing.

A number of auditory models have been developed using diverging approaches, either physiological or perceptual, but they share comparable stages of signal processing, as they are inspired by the same constitutive parts of the auditory system. We compare eight monaural models that are openly accessible in the Auditory Modelling Toolbox. We discuss the considerations required to make the model outputs comparable to each other, as well as the results for the following model processing stages or their equivalents: Outer and middle ear, cochlear filter bank, inner hair cell, auditory nerve synapse, cochlear nucleus, and inferior colliculus. The discussion includes a list of recommendations for future applications of auditory models.

Predicting speech intelligibility in hearing-impaired listeners using a physiologically inspired auditory model.

This study presents a major update and full evaluation of a speech intelligibility (SI) prediction model previously introduced by Scheidiger, Carney, Dau, and Zaar [(2018), Acta Acust. United Ac. 104, 914-917]. The model predicts SI in speech-in-noise conditions via comparison of the noisy speech and the noise-alone reference. The two signals are processed through a physiologically inspired nonlinear model of the auditory periphery, for a range of characteristic frequencies (CFs), followed by a modulation analysis in the range of the fundamental frequency of speech. The decision metric of the model is the mean of a series of short-term, across-CF correlations between population responses to noisy speech and noise alone, with a sensitivity-limitation process imposed. The decision metric is assumed to be inversely related to SI and is converted to a percent-correct score using a single data-based fitting function. The model performance was evaluated in conditions of stationary, fluctuating, and speech-like interferers using sentence-based speech-reception thresholds (SRTs) previously obtained in 5 normal-hearing (NH) and 13 hearing-impaired (HI) listeners. For the NH listener group, the model accurately predicted SRTs across the different acoustic conditions (apart from a slight overestimation of the masking release observed for fluctuating maskers), as well as plausible effects in response to changes in presentation level. For HI listeners, the model was adjusted to account for the individual audiograms using standard assumptions concerning the amount of HI attributed to inner-hair-cell (IHC) and outer-hair-cell (OHC) impairment. HI model results accounted remarkably well for elevated individual SRTs and reduced masking release. Furthermore, plausible predictions of worsened SI were obtained when the relative contribution of IHC impairment to HI was increased. Overall, the present model provides a useful tool to accurately predict speech-in-noise outcomes in NH and HI listeners, and may yield important insights into auditory processes that are crucial for speech understanding.

Neural processing and perception of Schroeder-phase harmonic tone complexes in the gerbil: Relating single-unit neurophysiology to behavior.

Schroeder-phase harmonic tone complexes have been used in physiological and psychophysical studies in several species to gain insight into cochlear function. Each pitch period of the Schroeder stimulus contains a linear frequency sweep; the duty cycle, sweep velocity, and direction are controlled by parameters of the phase spectrum. Here, responses to a range of Schroeder-phase harmonic tone complexes were studied both behaviorally and in neural recordings from the auditory nerve and inferior colliculus of Mongolian gerbils. Gerbils were able to discriminate Schroeder-phase harmonic tone complexes based on sweep direction, duty cycle, and/or velocity for fundamental frequencies up to 200 Hz. Temporal representation in neural responses based on the van Rossum spike-distance metric, with time constants of either 1 ms or related to the stimulus' period, was compared with average discharge rates. Neural responses and behavioral performance were both expressed in terms of sensitivity, d', to allow direct comparisons. Our results suggest that in the auditory nerve, stimulus fine structure is represented by spike timing, whereas envelope is represented by rate. In the inferior colliculus, both temporal fine structure and envelope appear to be represented best by rate. However, correlations between neural d' values and behavioral sensitivity for sweep direction were strongest for both temporal metrics, for both auditory nerve and inferior colliculus. Furthermore, the high sensitivity observed in the inferior colliculus neural rate-based discrimination suggests that these neurons integrate across multiple inputs arising from the auditory periphery.

Responses to diotic tone-in-noise stimuli in the inferior colliculus: stimulus envelope and neural fluctuation cues.

Human detection thresholds in tone-in-noise (TIN) paradigms cannot be explained by the prevalent power-spectrum model when stimulus energy is made less reliable, e.g., in roving-level or equal-energy paradigms. Envelope-related cues provide an alternative that is more robust across level. The TIN stimulus envelope is encoded by slow fluctuations in auditory-nerve (AN) responses - a temporal representation affected by inner-hair-cell (IHC) saturation and cochlear compression. Here, envelope-related fluctuations in AN responses were hypothesized to be reflected in responses of neurons in the inferior colliculus (IC), which have average discharge rates that are sensitive to amplitude-modulation (AM) depth and frequency. Responses to tones masked by narrowband gaussian noise (GN) and low-noise noise (LNN) were recorded in the IC of awake rabbits. Fluctuation amplitudes in the stimulus envelope and in model AN responses decrease for GN maskers and increase for LNN upon addition of tones near threshold. Response rates of IC neurons that are excited by AM were expected to be positively correlated with fluctuation amplitudes, whereas rates of neurons suppressed by AM were expected to be negatively correlated. Of neurons with measurable TIN-detection thresholds, most had the predicted changes in rate with increasing tone level for both GN and LNN maskers. Changes in rate with tone level were correlated with envelope sensitivity measured with two methods, including the maximum slopes of modulation transfer functions. IC rate-based thresholds were broadly consistent with published human and rabbit behavioral data. These results highlight the importance of midbrain sensitivity to envelope cues, as represented in peripheral neural fluctuations, for detection of signals in noise.

Midbrain-Level Neural Correlates of Behavioral Tone-in-Noise Detection: Dependence on Energy and Envelope Cues.

Hearing in noise is a problem often assumed to depend on encoding of energy level by channels tuned to target frequencies, but few studies have tested this hypothesis. The present study examined neural correlates of behavioral tone-in-noise (TIN) detection in budgerigars (Melopsittacus undulatus, either sex), a parakeet species with human-like behavioral sensitivity to many simple and complex sounds. Behavioral sensitivity to tones in band-limited noise was assessed using operant-conditioning procedures. Neural recordings were made in awake animals from midbrain-level neurons in the inferior colliculus, the first processing stage of the ascending auditory pathway with pronounced rate-based encoding of stimulus amplitude modulation. Budgerigar TIN detection thresholds were similar to human thresholds across the full range of frequencies (0.5-4 kHz) and noise levels (45-85 dB SPL) tested. Also as in humans, thresholds were minimally affected by a challenging roving-level condition with random variation in background-noise level. Many midbrain neurons showed a decreasing response rate as TIN signal-to-noise ratio (SNR) was increased by elevating the tone level, a pattern attributable to amplitude-modulation tuning in these cells and the fact that higher SNR tone-plus-noise stimuli have flatter amplitude envelopes. TIN thresholds of individual neurons were as sensitive as behavioral thresholds under most conditions, perhaps surprisingly even when the unit's characteristic frequency was tuned an octave or more away from the test frequency. A model that combined responses of two cell types enhanced TIN sensitivity in the roving-level condition. These results highlight the importance of midbrain-level envelope encoding and off-frequency neural channels for hearing in noise.SIGNIFICANCE STATEMENT Detection of target sounds in noise is often assumed to depend on energy-level encoding by neural processing channels tuned to the target frequency. In contrast, we found that tone-in-noise sensitivity in budgerigars was often greatest in midbrain neurons not tuned to the test frequency, underscoring the potential importance of off-frequency channels for perception. Furthermore, the results highlight the importance of envelope processing for hearing in noise, especially under challenging conditions with random variation in background noise level over time.

Amplitude modulation transfer functions reveal opposing populations within both the inferior colliculus and medial geniculate body.

Based on single-unit recordings of modulation transfer functions (MTFs) in the inferior colliculus (IC) and the medial geniculate body (MGB) of the unanesthetized rabbit, we identified two opposing populations: band-enhanced (BE) and band-suppressed (BS) neurons. In response to amplitude-modulated (AM) sounds, firing rates of BE and BS neurons were enhanced and suppressed, respectively, relative to their responses to an unmodulated noise with a one-octave bandwidth. We also identified a third population, designated hybrid neurons, whose firing rates were enhanced by some modulation frequencies and suppressed by others. Our finding suggests that perception of AM may be based on the co-occurrence of enhancement and suppression of responses of the opposing populations of neurons. Because AM carries an important part of the content of speech, progress in understanding auditory processing of AM sounds should lead to progress in understanding speech perception. Each of the BE, BS, and hybrid types of MTFs comprised approximately one-third of the total sample. Modulation envelopes having short duty cycles of 20-50% and raised-sine envelopes accentuated the degree of enhancement and suppression and sharpened tuning of the MTFs. With sinusoidal envelopes, peak modulation frequencies were centered around 32-64 Hz among IC BE neurons, whereas the MGB peak frequencies skewed toward lower frequencies, with a median of 16 Hz. We also tested an auditory-brainstem model and found that a simple circuit containing fast excitatory synapses and slow inhibitory synapses was able to reproduce salient features of the BE- and BS-type MTFs of IC neurons.NEW & NOTEWORTHY Opposing populations of neurons have been identified in the mammalian auditory midbrain and thalamus. In response to amplitude-modulated sounds, responses of one population (band-enhanced) increased whereas responses of another (band-suppressed) decreased relative to their responses to an unmodulated sound. These opposing auditory populations are analogous to the ON and OFF populations of the visual system and may improve transfer of information carried by the temporal envelopes of complex sounds such as speech.

Neural fluctuation cues for simultaneous notched-noise masking and profile-analysis tasks: Insights from model midbrain responses.

Results of simultaneous notched-noise masking are commonly interpreted as reflecting the bandwidth of underlying auditory filters. This interpretation assumes that listeners detect a tone added to notched-noise based on an increase in energy at the output of an auditory filter. Previous work challenged this assumption by showing that randomly and independently varying (roving) the levels of each stimulus interval does not substantially worsen listener thresholds [Lentz, Richards, and Matiasek (1999). J. Acoust. Soc. Am. 106, 2779-2792]. Lentz et al. further challenged this assumption by showing that filter bandwidths based on notched-noise results were different from those based on a profile-analysis task [Green (1983). Am. Psychol. 38, 133-142; (1988). (Oxford University Press, New York)], although these estimates were later reconciled by emphasizing spectral peaks of the profile-analysis stimulus [Lentz (2006). J. Acoust. Soc. Am. 120, 945-956]. Here, a single physiological model is shown to account for performance in fixed- and roving-level notched-noise tasks and the Lentz et al. profile-analysis task. This model depends on peripheral neural fluctuation cues that are transformed into the average rates of model inferior colliculus neurons. Neural fluctuations are influenced by peripheral filters, synaptic adaptation, cochlear amplification, and saturation of inner hair cells, an element not included in previous theories of envelope-based cues for these tasks. Results suggest reevaluation of the interpretation of performance in these paradigms.

Identifying cues for tone-in-noise detection using decision variable correlation in the budgerigar (Melopsittacus undulatus).

Previous studies evaluated cues for masked tone detection using reproducible noise waveforms. Human results founded on this approach suggest that tone detection is based on combined energy and envelope (ENV) cues, but detection cues in nonhuman species are less clear. Decision variable correlation (DVC) was used to evaluate tone-in-noise detection cues in the budgerigar, an avian species with human-like behavioral sensitivity to many complex sounds. DVC quantifies a model's ability to predict trial-by-trial variance in behavioral responses. Budgerigars were behaviorally conditioned to detect 500-Hz tones in wideband (WB; 100-3000 Hz) and narrowband (NB; 452-552 Hz) noise. Behavioral responses were obtained using a single-interval, two-alternative discrimination task and two-down, one-up adaptive tracking procedures. Tone-detection thresholds in WB noise were higher than human thresholds, putatively due to broader peripheral frequency tuning, whereas NB thresholds were within ∼1 dB of human results. Budgerigar average hit and false-alarm rates across noise waveforms were consistent, highly correlated across subjects, and correlated to human results. Trial-by-trial behavioral results in NB noise were best explained by a model combining energy and ENV cues. In contrast, WB results were better predicted by ENV-based or multiple-channel energy detector models. These results suggest that budgerigars and humans use similar cues for tone-in-noise detection.

Sensorineural Hearing Loss Diminishes Use of Temporal Envelope Cues: Evidence From Roving-Level Tone-in-Noise Detection.

OBJECTIVES: The objective of our study is to understand how listeners with and without sensorineural hearing loss (SNHL) use energy and temporal envelope cues to detect tones in noise. Previous studies of low-frequency tone-in-noise detection have shown that when energy cues are made less reliable using a roving-level paradigm, thresholds of listeners with normal hearing (NH) are only slightly increased. This result is consistent with studies demonstrating the importance of temporal envelope cues for masked detection. In contrast, roving-level detection thresholds are more elevated in listeners with SNHL at the test frequency, suggesting stronger weighting of energy cues. The present study extended these tests to a wide range of frequencies and stimulus levels. The authors hypothesized that individual listeners with SNHL use energy and temporal envelope cues differently for masked detection at different frequencies and levels, depending on the degree of hearing loss. DESIGN: Twelve listeners with mild to moderate SNHL and 12 NH listeners participated. Tone-in-noise detection thresholds at 0.5, 1, 2, and 4 kHz in 1/3 octave bands of simultaneously gated Gaussian noise were obtained using a novel, two-part tracking paradigm. A track refers to the sequence of trials in an adaptive test procedure; the signal to noise ratio was the tracked variable. Each part of the track consisted of a two-alternative, two-interval, forced-choice procedure. The initial portion of the track estimated detection threshold using a fixed masker level. When the track continued, stimulus levels were randomly varied over a 20-dB rove range (±10 dB with respect to mean masker level), and a second threshold was estimated. Rove effect (RE) was defined as the difference between thresholds for the fixed- and roving-level tests. The size of the RE indicated how strongly a listener weighted energy-based cues for masked detection. Participants were tested at one to three masker levels per frequency, depending on audibility. RESULTS: Across all stimulus frequencies and levels, NH listeners had small REs (≈1 dB), whereas listeners with SNHL typically had larger REs. Some listeners with SNHL had larger REs at higher frequencies, where pure-tone audiometric thresholds were typically elevated. RE did not vary significantly with masker level for either group. Increased RE for the SNHL group was consistent with simulations in which energy cues were more heavily weighted than envelope cues. CONCLUSIONS: Tone-in-noise detection thresholds in NH listeners were typically elevated only slightly by the roving-level paradigm at any frequency or level tested, consistent with the primary use of level-independent cues, such as temporal envelope cues that are conveyed by fluctuations in neural responses. In comparison, thresholds of listeners with SNHL were more affected by the roving-level paradigm, suggesting stronger weighting of energy cues. For listeners with SNHL, the largest RE was observed at 4000 Hz, for which pure-tone audiometric thresholds were most elevated. Specifically, RE size at 4000 Hz was significantly correlated with higher pure-tone audiometric thresholds at the same frequency, after controlling for the effect of age. Future studies will explore strategies for restoring or enhancing neural fluctuation cues that may lead to improved hearing in noise for listeners with SNHL.