# Neural and psychoacoustic insights into amplitude modulation processing and its link with speech perception in noise

Clémence Basire, Irene Lorenzini, Laurianne Cabrera

► To cite this version:

Clémence Basire, Irene Lorenzini, Laurianne Cabrera. Neural and psychoacoustic insights into amplitude modulation processing and its link with speech perception in noise. Journal of Neurophysiology, 2025, 133 (6), pp.1828-1835. 10.1152/jn.00007.2025. hal-05444819

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Submitted on 6 Jan 2026

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SHORT REPORT

Sensory Processing

# Neural and psychoacoustic insights into amplitude modulation processing and its link with speech perception in noise

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Clémence Basire,¹*

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Irene Lorenzini,¹,²*

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Laurianne Cabrera¹

¹Integrative Neuroscience and Cognition Center, Université Paris Cité, CNRS, Paris, France and ²Laboratoire Éthologie Cognition Développement (LECD), Université Paris Nanterre, Nanterre, France

Abstract

Temporal modulations, especially amplitude modulations (AMs), play a fundamental role in speech perception in noisy environments, as highlighted by psychoacoustic research. However, the mechanisms underlying AM processing and its contribution to speech perception in noise remain unclear. The present study combined both behavioral psychophysics and electroencephalography (EEG) measures to investigate AM processing and its relationship to the perception of consonants in noise for young adults with normal hearing. Participants completed psychoacoustic measures of AM sensitivity, i.e., detection thresholds for AM fluctuations at 8 Hz, and susceptibility to AM masking (the effect on AM detection thresholds of interfering AM fluctuations carried by a masker) at 8 Hz. In addition, participants underwent an EEG recording measuring the neural AM following response (AMFR, or envelope following response) for AM tones modulated at 8 or 40 Hz presented without masking. Finally, they completed a consonant identification task in a stationary speech-shaped noise. Results showed a significant positive correlation between the signal-to-noise ratio of the AMFR recorded at 8 Hz and AM masking effects measured at the same rate. This finding is discussed in terms of both reduced selectivity of AM filters and elevated internal noise. Finally, no relationship between AM measures and consonant-in-noise perception was observed, suggesting that the mechanisms involved in AM processing may not fully capture the ability of adults with normal hearing to identify consonants in noisy backgrounds.

**NEW & NOTEWORTHY** Results showed a significant correlation between the amplitude modulation (AM) following response (AMFR) and AM masking effect. Participants who are more susceptible to AM masking exhibit higher neural tracking of AM at the same fluctuation rate (8 Hz). This result might be explained both in terms of reduced selectivity of AM filters and elevated internal noise. Our results also showed no relationship between AM measures and consonant-in-noise perception, indicating that AM sensitivity mechanisms do not fully explain speech-in-noise perception skills in young normal-hearing adults.

AM masking; amplitude modulation frequency response; amplitude modulations; envelope following response; speech-in-noise perception

INTRODUCTION

Although human communication frequently occurs in acoustically challenging environments, adults with normal hearing have developed robust mechanisms for speech-in-noise perception, allowing them to comprehend speech effectively despite background noise. However, this ability is highly impacted by cochlear damage and represents the main complaint of people with hearing difficulties (1). One current challenge in hearing research is thus to better

understand the relationship between auditory skills and efficiency in speech-in-noise perception.

Amplitude modulation (AM) processing is thought to be a good predictor of speech-in-noise perception. In particular, recent studies have observed significant correlations between AM sensitivity (the ability to detect AM depth as a function of the modulation rate, as measured by AM detection thresholds) and speech-in-noise perception performance for adults using cochlear implants (CIs; cf. Refs. 2-4). Specifically, sensitivity to slow AM fluctuations at 8 Hz

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<footnote>*C. Basire and I. Lorenzini contributed equally to this work.
Correspondence: I. Lorenzini (ilorenzi@parisnanterre.fr).
Submitted 7 January 2025 / Revised 19 March 2025 / Accepted 8 May 2025</footnote>
<figure><img src="image_7.png" /></figure>

was found to be correlated with consonant-in-noise perception in adults using CIs (3), and similar findings were observed in children with normal hearing (5). This aligns with the observation that the intelligibility of speech depends on the perception of slow amplitude modulation (AM) patterns, typically below 5–10 Hz (6–8).

A wealth of behavioral psychoacoustic studies has measured listeners' AM sensitivity: using broadband or narrow-band carriers modulated in amplitude from slow to faster rates ("Temporal Modulation Transfer Function") (9–11), it has been shown that listeners' detection thresholds typically exhibit a lowpass shape, indicating that auditory temporal resolution (acuity) is limited. Previous studies also revealed that the detection of a target AM can be affected by the AM fluctuations of a masker. This phenomenon, called "AM masking," has been interpreted as resulting from the effects of selective AM filters, tuned to specific AM rates and possibly implemented neurally within the auditory pathway (12, 13). In psychoacoustic experiments, AM masking effect can be measured by applying the masking AM to the same carrier as the signal AM (14, 15). For instance, AM masking effect can be computed as the difference in thresholds between a task completed without AM masker (e.g., a tone carrier) and a second one completed with an AM masker (e.g., narrow-band noise carrier, see Refs. 5, 16, and 17). Greater AM masking is supposed to reflect lower selectivity of the AM filters, as broader filters would be less effective at isolating specific modulation rates and would therefore be more easily influenced by masker signals with similar fluctuation rates.

Neural processing of AM has been more recently documented. Electrophysiological recordings showed phased-locked neural responses to amplitude fluctuations of external sounds (18, 19), a response called AM following-response (AMFR, or envelope following response, or auditory steady-state response). Importantly, recent studies have observed a relationship between the AMFR and speech-in-noise perception in normal hearing adults aged 18 to 63 yr, as best performers for speech perception tasks also displayed higher signal-to-noise ratio in their AMFR for higher harmonics (i.e., 360 to 600 Hz) when listening to a 4-kHz pure-tone carrier sinusoidally modulated in amplitude at 120 Hz (20).

Interestingly, the relationship between behavioral measures of AM sensitivity and selectivity, on the one side, and the AMFR on the other side, has rarely been assessed. Verhulst et al. (21) observed a significant link between AM sensitivity and AMFR in a group of young normal-hearing adults and hearing-impaired adults (n = 11 and 9, respectively). AM sensitivity and the AMFR were measured from the same participants using the same AM carrier (a 4-kHz tone) but different AM rates (100 vs. 120 Hz). Results showed a significant correlation between the two measures, as better AM detection thresholds were associated with higher AMFR amplitude. Similarly, Bharadwaj et al. (22) observed a significant correlation between AM detection thresholds, measured with a broadband noise carrier modulated at 19 Hz and the AMFR recorded using a pure tone centered at 4 kHz and modulated at 100 Hz, in a cohort of young normal-hearing adults.

Our study further explores the relationship between behavioral and neural measures of AM processing, and the relationship between these measures and speech-in-noise

perception, in young normal-hearing adults. The aims of this study are: to investigate the neural mechanisms underlying temporal processing as measured behaviorally (the relationship between the AMFR and AM sensitivity and selectivity); finding predictors of speech-in-noise processing (the relationship between AM processing measures and consonant-in-noise identification thresholds). First, using a psychoacoustic procedure, we behaviorally assessed AM sensitivity and AM selectivity using a pure tone and a narrow-band noise modulated in amplitude at 8 Hz, an AM rate that is particularly relevant for speech units (8) and that was found to be linked to consonant-in-noise perception (3, 5). For the same group of participants, we then measured AMFR for a pure tone 100% modulated in amplitude at 8 Hz, and also at 40 Hz as control condition. Finally, the participants also completed a series of behavioral tasks assessing consonant identification in noise.

Following previous studies, we expected that higher sensitivity (lower AM detection thresholds for the pure tone carrier) and higher selectivity for AM (lower masking effects) may relate to better signal-to-noise ratios for the AMFR recorded at the same rate (8 Hz). In addition, we hypothesized that, if speech-in-noise perception relates to the ability of our auditory system to extract AM fluctuations selectively, thus leading to better segregation between the noise versus speech AM, then we should observe a significant correlation between in-noise consonant identification thresholds and AM processing scores, especially at 8 Hz [i.e., the signal-to-noise ratio (SNR) of the AMFR at 8 Hz, AM detection thresholds, and AM masking effect].

## METHODS

All tasks were carried out following relevant guidelines and regulations. All experimental protocols were approved by the university ethics committee (CER-U Paris). Informed written consent forms were obtained from all participants as approved by this committee. All participants received monetary compensation for their time.

### Participants

Thirty-four adults took part in the experiment, aged between 18 and 30 yr (M age = 22.18 yr, SD = 2.35; 25 females). All participants were raised in monolingual French families (exposed less than 10% of the time to an additional language other than French). Absolute auditory thresholds were assessed with pure tones audiometry for both ears, and all participants had averaged audiometric thresholds between 0.5 and 4 kHz ≤ 20 dB hearing level (HL) (M = 3.26 dB HL, SD = 7.06), except for two participants who had an average threshold below 30 dB HL. Adults were tested in a sound-proof room at Paris Cité University and completed all tasks within a single session lasting 2 h.

### Stimuli and Procedure

#### Behavioral AM detection task.

This task followed the methodology by Lorenzini et al. (17). The MATLAB environment (v.2018b) was used to generate digitally all stimuli at a sampling rate of 44.1 kHz. Two carrier conditions were designed to assess AM detection for an 8-Hz target sinusoidal using: 1) a 500-Hz sine tone carrier
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(Tone condition) and 2) a noise carrier centered at 500 Hz with a bandwidth of 4 Hz (Noise condition). We used a total of 500 different noise carriers. All carrier stimuli were 500-ms long, including a 14-ms raised-cosine onset/offset ramps, and were generated with a fixed starting phase. The expression describing the modulated target was:

$$T(t) = [1 + m \sin(2\pi f_m t + 3\pi/2)]c(t),$$

where t is time, m is the modulation depth ($0 \le m \le 1$), $f_m$ is the modulation rate, and c is the carrier signal.

For the Noise condition, each narrowband noise carrier was generated by adding together five equal-amplitude sine tones with frequencies 498, 499, 500, 501, and 502 Hz. Sounds with “low” values of envelope SD were selected (SD ranging between 0.059 and 0.069 au, arbitrary amplitude units, the maximum value of the modulation index spectrum = 0.19). AM spectra of these carriers show the largest modulation index below ~4 Hz (see Ref. 5). All sounds were equated in terms of root-mean-square (RMS) power. The sounds were played diotically through headphones (Sennheiser HD 25-SP II) at 70 dB sound pressure level (SPL), using an external soundcard (DAC audio Audioengine D3). AM detection thresholds (AMDTs) were obtained using a two-interval, two-alternative adaptive forced-choice procedure (2I-2AFC) implemented on a touch-screen tablet on MATLAB. On each trial, one interval presented an unmodulated carrier, whereas the other presented the target (8 Hz). The interstimulus interval of 500 ms and the adaptive variable was modulation depth.

The outcome measure of this AM detection task was the AMDT (in dB) obtained in the Tone and in the Noise condition. Moreover, we computed the masking effect value, measuring AM selectivity, calculated as the difference between the thresholds obtained in the Noise versus the Tone condition. Higher (>0) masking values indicate that the noise carrier impaired AM detection compared with the tone carrier. Consequently, participants with higher masking values are more susceptible to AM masking.

## AMFR Recording in EEG

Two 100% sinusoidally AM-pure tones centered at 1,027 Hz were used as stimuli (23, 24). The AM rate was set either at 8 or at 40 Hz. The 40 Hz was used as control condition, as we were expecting a relationship between psychophysical measures for AM-8 Hz and AMFR-8 Hz but not with AMFR-40 Hz. To reduce testing time, we presented long and continuous AM-sounds as in the study by Lorenzini et al. (25). Each sound was 4 min long, including 50-ms raised-cosine onset/offset ramps, and presented in free field continuously through two loudspeakers (Companion20, Bose, Framingham, MA) positioned at 0 azimuths on each side of a computer screen. At the onset of each sound, a marker was sent to the EEG amplifier via the E-prime software to ensure synchronization for EEG analysis (E-prime 2.0). Participants sat in front of the screen at a distance of 1 m. The intensity level was calibrated at 65 dB SPL relative to the participants’ precise location. Each participant was exposed two times to each sound presented in a counterbalanced order between participants. After each sound, a short break was proposed before the next sound. Participants watched a silent cartoon throughout.

Three active central electrodes (Fz, Cz, and Pz from the 10-20 system) were positioned on a cap together with the ground (positioned at FPz), and two external electrodes (acting as online references) positioned at the mastoids. The electrophysiological recording was performed using an actiCHamp amplifier (Brain Products GmbH, Gilching, Germany) with a sampling rate of 10 kHz. Signal processing was performed using EEG laboratory (v.2020.0) in MATLAB (2018b). The continuous EEG signal was divided into four 4-min epochs, the epochs were concatenated as a function of the AM rate presented (resulting into two 8-min recordings). The signal was then band-pass filtered from 6 to 46 Hz using the finite impulse response (FIR) method. The filter was specified with -6 dB cutoff frequencies at 4 Hz and 48 Hz. A baseline correction was performed on each epoch from -100 to 0 ms. For each AM condition, the complex frequency spectrum of each epoch was calculated using a fast Fourier transform, resulting in a frequency resolution of 0.0024 Hz. The EEG signal’s maximum peak amplitude value was measured for each participant between 7.8-8.2 Hz and 39.8-40.2 Hz for the 8 and 40 Hz conditions, respectively. Following previous studies, the noise floor was computed as the mean of squares at either side of the modulation frequency ±2 Hz, excluding portions of the target signal window itself (20, 26). The SNR was calculated as the ratio between the maximum amplitude at the target frequency and the noise floor.

Visual inspection showed a better SNR at Fz in each AM condition. Following common practice in the field (20, 21), SNR analyses of the AMFR focused on one electrode, Fz here. The variable of interest was defined as the signal-to-noise ratio in each condition.

## Consonant in Noise Identification

Three native French speakers, one male (F0 = 110 Hz) and two females (F0 = 195 and 205 Hz), recorded syllables of the form/aCa/: with C = /f/, /v/, /ʃ/, /ʒ/, /s/, /z/, /b/, /p/, /d/, /t/, /g/, /k/. A steady speech-shaped noise masker was designed to match the long-term spectrum of the female syllables. The syllables were presented in an XAB task with six phonetic conditions: fricatives or stops differing in place of articulation (/f/, /-s/, /ʃ/, /-s/, /z/, /ʒ/, /v/, /-z/, /or/, /b/, /-d/, /p/, /-t/, /d/, /-g/, /t/, /-k/); unvoiced or voiced consonants differing in manner of articulation (/p/, /-f/, /t/, /-s/, /ʃ/, /-k/, /or/, /b/, /-v/, /d/, /-z/, /ʒ/, /-g/); fricative or stop consonants differing in voicing (/f/, /-v/, /s/, /-z/, /ʃ/, /-ʒ/ or /b/, /-p/, /d/, /-t/, /g/, /-k/). The first sound (X) was always a sample from the male speaker played in quiet; the subsequent sounds (A and B) were samples from each of the two female speakers played in noise. The interstimulus interval was 500 ms. The task was delivered in a computer game format, and responses were recorded using MATLAB on a touch-screen tablet. Each trial displayed one character at the top and two at the bottom, representing the three intervals (speakers). Participants selected which bottom character matched the top sound. They had unlimited time to respond and received feedback via a “smiley” or “frown” face. The noise level was adjusted using an adaptive 2-down 1-up procedure. The initial SNR was +20 dB, with the first step size being 5 dB, which was then reduced to 2 dB after two reversals. The run concluded after either six reversals or 32 trials. The stimuli were presented through the same headphones
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and sound card as the behavioral AM tasks. The SNR was modified by fixing the target level at 65 dB SPL and adjusting the masker level. Only one run was performed for each condition, if the adaptive scale obtained converged on a visual examination of the track. If not, a second estimate was performed. The outcome measure was the averaged SNR level of the last four reversals, considered as the consonant identification thresholds in noise.

## Data Analyses

The statistical analyses were performed using RStudio version 2022.12.0 (RStudio Team) and R version 4.2.1. First, normality was verified for each outcome measure using the Shapiro-Wilk test. Then, within each task, the effect of the testing condition was evaluated. Finally, we conducted correlation analyses using the Spearman test to evaluate the relationship between the variables. We examined the correlations between the SNR values of the AMFR obtained in each AM condition versus AM detection thresholds obtained in the Tone condition (reflecting AM sensitivity at 8 Hz), and AM masking effect values (reflecting AM selectivity at 8 Hz). We also explored the correlations between the AM measures obtained at 8 Hz (SNR-AMFR, AM sensitivity, and AM selectivity) with the averaged consonant identification thresholds in noise. Additional exploratory Spearman correlations were computed between AM processing metrics and identification thresholds obtained in each phonetic condition.

For the AM behavioral tasks, the detection thresholds were normally distributed (*P* > 0.319). A parametric paired *t* test was thus used to compare thresholds obtained between the two conditions (Tone versus Noise condition) to assess the presence of an AM masking effect. The SNR values obtained for the AMFR recorded at 8 or 40 Hz modulations were not normally distributed (*Ps* < 0.002). We use a Wilcoxon signed rank exact test to assess the difference in SNR values between the two conditions. For the consonant in noise task, the thresholds averaged over the six phonetic conditions were normally distributed (*P* = 0.09). A repeated analysis of variance (ANOVA) was used to evaluate the effect of phonetic condition on these identification thresholds in noise, to provide further information about the role of phonetic difference on those thresholds.

# RESULTS

## Behavioral Measures of AM Sensitivity/Masking and Neural Measures of AM Processing

Behavioral AM detection thresholds were compared between the two carrier conditions (see Fig. 1A) and results showed that worse (higher) thresholds were obtained in the Noise condition compared with the Tone condition [$M = -16.16$, SD = 2.35; $M = -23.22$, SD = 2.62, respectively; *t*(33) = -17.02, *P* < 2.2e-16]. The mean masking effect value was 7.06 (SD = 2.42) showing that participants' ability to detect AM at 8 Hz was affected by the stochastic envelope fluctuations of the noise carrier (greater masking indicates worse performance).

For the AMFR, the individual SNRs of the neural response are represented in Fig. 1B for each modulation rate (8 vs. 40 Hz). As observed in previous studies (24, 25), the SNR values of the AMFR were significantly higher at 40 Hz compared with 8 Hz ($z = 12$, *P* < 0.001).

## Consonant in Noise Identification

Figure 1C represents the identification thresholds for consonants in noise for the six phonetic conditions tested. The ANOVA showed that thresholds were significantly different between the conditions [$F(5,165) = 32.07$, *P* < 0.001], and post hoc tests with Bonferroni corrections revealed that some conditions differ significantly from each other but not all. The condition that led to the highest (worse) thresholds was the one with stops contrasting on place. The one that led to the lowest (best) was the fricatives contrasting on place ($M = -7.51$, SD = 4.00; $M = -18.40$, SD = 4.18, respectively, *P* = 0.008). The thresholds obtained with the fricative consonants contrasting on voicing were not statistically different than the unvoiced or voiced contrasting on manner of articulation. The latter was also not different from the thresholds obtained with stops contrasting on place of articulation.

## Relationship between Behavioral and Neural AM Processing Measures

First, we assessed the relationship between the AMFR (8 Hz and 40 Hz), AM detection thresholds in the Tone condition, and AM masking effect values obtained at 8 Hz. Table 1 summarizes these correlations. We used Bonferroni corrections

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**Figure 1.** A: violin and box plots representing amplitude modulation (AM) detection thresholds (in dB) for the Tone and Noise conditions. The lower the threshold, the better. B: signal-to-noise ratio of the AM following response (AMFR) (in dB SNR) recorded at 8 and 40 Hz. Each dot corresponds to one individual. C: identification thresholds (in dB SNR) of fricative consonants differing in place (F-P) or voicing (F-V); stops differing in place (S-P) or voicing (S-V); and unvoiced (U-M) or voiced (V-M) consonants differing in manner.
<figure><img src="image_13.png" /><figcaption>**Table 1.** Descriptive statistics (n, M, and SD) for each measure and Spearman correlations (r and P values) between the SNR of the AMFR obtained 8 or 40 Hz, AM detection thresholds obtained in the Tone condition and AM masking effect values (N = 34)</figcaption></figure>

<table><thead><tr><th>Variable</th><th>M</th><th>SD</th><th>AM Detection Thresholds (Tone)</th><th>AM Masking Effect</th></tr></thead><tbody><tr><td>1. SNR of AMFR-8 Hz</td><td>2.45</td><td>0.52</td><td>r = -0.11<br>P = 0.53</td><td>r = 0.49<br>P = 0.003</td></tr><tr><td>2. SNR of AMFR-40 Hz</td><td>6.81</td><td>4.20</td><td>r = -0.19<br>P = 0.29</td><td>r = 0.05<br>P = 0.75</td></tr><tr><td>3. AM detection thresholds (Tone)</td><td>-23.22</td><td>2.62</td><td>-</td><td>-</td></tr><tr><td>4. AM masking effect</td><td>7.06</td><td>2.42</td><td>-</td><td>-</td></tr></tbody></table>

Significant correlations appear in bold. AM, amplitude modulation; AMFR, AM following response; SNR, signal-to-noise ratio.

setting the new *P* value to α = 0.0125. Note that we did not correlate AM detection thresholds and masking scores together as they are inter-related, nor the SNR of the AMFR at 40 and 8 Hz as there is no theoretical or empirical basis to expect a link here. As different carrier frequencies were used between the behavioral and neural data, we run a paired *t* test to verify that the participants' hearing thresholds between 500 and 1,000 Hz were not significantly different (*P* = 0.424). As observed in Table 1 and Fig. 2, only AM masking effect values obtained at a rate of 8 Hz correlated with the strength of the AMFR measured at the same rate.

Relationship between AM Measures at 8 Hz and Consonant in Noise

We assessed the relationship between the AM processing measures (the SNR of AMFR at 8 Hz, AM detection thresholds at 8 Hz, and AM masking effect at 8 Hz) and the identification thresholds for consonants in noise averaged across the six phonetic conditions. This investigation specifically focused on the processing of slow 8 Hz AM, which has been suggested to relate to consonant identification in noise (3, 5). Table 2 summarizes these correlation analyses, showing that

<figure><img src="image_14.png" /><figcaption>**Figure 2.** Correlations between the signal-to-noise ratio (SNR) of the amplitude modulation (AM) following response (AMFR) obtained at 8 or 40 Hz, AM detection thresholds obtained in the Tone condition, and AM masking effect values of *n* = 34 participants.</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>**Table 2.** Spearman correlations (r and P values) between the mean consonant identification thresholds and the AM measures at 8 Hz</figcaption></figure>

<table><thead><tr><th>Variable</th><th>Mean Consonant Identification<br>Thresholds</th></tr></thead><tbody><tr><td>1. SNR of AMFR-8 Hz</td><td>r = -0.19<br>P = 0.28</td></tr><tr><td>2. AM detection thresholds (Tone)</td><td>r = 0.32<br>P = 0.06</td></tr><tr><td>3. AM masking effect</td><td>r = -0.38<br>P = 0.027</td></tr></tbody></table>

n = 34. AM, amplitude modulation; AMFR, AM following response; SNR, signal-to-noise ratio.

none of the AM measures correlated significantly with the mean consonant identification thresholds when applying Bonferroni corrections (α = 0.0167).

Further exploratory correlations were run to assess the relationship between AM processing and each phonetic condition tested. Results indicate no significant correlation (after Bonferroni corrections, α = 0.003) between the identification thresholds obtained in each phonetic condition and the AM measures. Supplemental Table S1 sums up this exploratory analysis and shows only trends in the correlations between the fricative place condition and the behavioral AM values (sensitivity and masking).

## DISCUSSION

The present study aimed to investigate, in young normally hearing adults: 1) the link between the AMFR, a steady-state neural-evoked response that follows the temporal envelope of a stimulating waveform, and behavioral psychoacoustic measures of AM processing (AM sensitivity and selectivity) for an 8 Hz modulation; 2) the possible associations between these measures of AM processing and participants' thresholds of consonant-in-noise identification.

### Dissociation between AM Sensitivity and Neural Encoding at 8 Hz

As a control, we also recorded the SNR of the AMFR measured at 40 Hz and results showed that it did not correlate with the behavioral measures of AM processing at 8 Hz (AM detection thresholds for a tone carrier and AM masking effect).

Unexpectedly, however, the SNR of the AMFR measured at 8 Hz did not correlate with the behavioral measure of AM detection thresholds for a tone carrier modulated in amplitude at 8 Hz. This result disagrees with two earlier studies reporting significant relations between AM detection thresholds and the AMFR. Specifically, Verhulst et al. (21) found a significant correlation between AM detection thresholds obtained with a 4-kHz tone modulated at 100 Hz and the AMFR for the same carrier, 100% modulated at 120 Hz, in a mixed group of normal-hearing and hearing-impaired listeners. Bharadwaj et al. (22) observed a significant correlation between AM detection thresholds measured with a broadband noise carrier modulated at 19 Hz and the AMFR recorded using a pure tone centered at 4 kHz and modulated at 100 Hz, in a cohort of young normally hearing adults. The discrepancy between former results and our results might be

explained by the different AM rates used (fast vs. slow in our study), or by the fact that we presented only 100% modulated sounds in the AMFR task. In particular, Bharadwaj et al. (22) recorded the AMFR at varying modulation depths, and found that the correlation between AMFR and AM detection thresholds was stronger when considering the slope of the AMFR (i.e., the slope of the linear function that relates the SNR of the AMFR to varying modulation depths, compared with the absolute SNR of the AMFR itself). In contrast, we used a fixed depth of 100% and could only look at the absolute SNR of the AMFR.

As we did not use the same audio frequencies for AM carriers in the behavioral and neural measures, this could have impacted the relationship between AM sensitivity and AMFR measures. However, electrophysiological data in animals have shown that AM information is encoded independently from the carrier (i.e., with different spectral properties), especially for low AM rates (≤8 Hz; cf. Ref. 27). Future studies in humans should systematically explore the effects of AM carriers on AM selectivity, combining both neural and psychoacoustic measures to investigate the link between cochlear and modulation filters, and perhaps to include more recording EEG electrodes to inform about the brain sources of the AMFR for slow versus fast modulation rates. Moreover, it will be important to record the AMFR at a modulation depth corresponding to the participants' detection thresholds at a specific AM rate, to fully understand the relationship between behavioral thresholds and neural responses for AM.

### Association between AM Selectivity and Neural Encoding at 8 Hz

Although we did not observe significant associations between AM sensitivity and the AMFR in the current study, we did find a significant correlation between the SNR of the AMFR at 8 Hz and AM masking effect measured at the same rate. However, contrary to our expectations, the higher AM masking effect, the higher the AMFR-SNR. We propose here possible interpretations of this relationship. Importantly, higher AM masking effects are supposed to reflect reduced selectivity of the AM filters (e.g., see Ref. 28). This is because broader filters are less capable of isolating specific modulation rates and are therefore more easily influenced by masker signals with similar fluctuation rates. Broader AM filters imply that neurons are tuned less sharply to specific modulation rates. In such cases, neurons do not respond selectively to a single target AM rate, but rather to a wider range of rates. This broader tuning might then result in an elevated neural response to the target AM rate, not because of increased selectivity, but due to the overlap in tuning across multiple AM rates, leading to a less specific yet stronger aggregate response. In addition, AM masking has been ascribed not only to “peripheral” limitations but also to higher-level factors, such as increased “internal noise.” The term internal noise is used to denote the accumulation of several sources of variability. Such sources include the stochastic nature of neuronal firing, the internal state of the observer organism, and fluctuations in attention (29–32). In an AM masking task, both external variability of the noise and internal variability of the listeners will thus be at play
<figure><img src="image_16.png" /></figure>

(33). In other words, the ability to process an 8 Hz AM target depends on two types of noise in the modulation domain: one internal and sensory, and the other external/acoustic and estimated by the variance of our AM mask (i.e., the carrier exhibiting random and intrinsically slow AM fluctuations). If we assume that the 8 Hz AM detection ability in the presence of a variable AM mask depends both on the selectivity of the AM filter tuned to 8 Hz and also on the ratio between the variances of these two noises (internal/external), then participants with higher level of internal noise versus the level of external noise would show higher values of AM masking. Higher AMFR-SNR values for slow AM rates could then also be related to a combination of both elevated internal noise and to broader AM filters. The absence of correlation between the AMFR-SNR values and the unmasked AM detection thresholds may then relate to the fact that the internal noise is partially multiplicative, i.e., it combines additive internal noise, independent of the signal and therefore of external noise, and multiplicative noise, dependent on the signal and therefore on external noise (34, 35). It will be important in future studies to measure the relationship between AMFR and behavioral estimates of internal noise levels. Our results also particularly encourage computational studies to implement cortical levels of AM processing to fully understand the nature of internal noise in AM processing from faster to slower rates.

## Lack of Association between AM Measures and Speech Perception in Noise

Finally, our study also aimed to explore the relationship between AM processing measures, neural and behavioral, and consonant in-noise identification. Overall, we did not observe such a relationship between identification thresholds and the AMFR. Upon examining the different phonetic conditions, we found a trend that behavioral AM measures at 8 Hz and the AMFR-8 Hz measure were associated with the identification thresholds for fricative consonants contrasting in place of articulation or voicing. However, these results did not survive correction for multiple comparisons. In the present study, we tested young adult listeners with normal hearing, which may partly explain why we did not observe a clear link between auditory measures and speech-in-noise perception, despite the considerable variability in the speech-in-noise test outcomes. In fact, normally hearing young adults can count on a constellation of auditory and linguistic skills and knowledge to perform speech-in-noise tasks, thus making correlations with specific abilities harder to observe (36).

In contrast with our results, previous studies reported significant associations between AM processing and speech-in-noise perception. In particular, neurofunctional studies on markers of auditory synaptopathy in listeners aged 18 to 63 yr have found significant relationships between neural responses to AM sounds (obtained using higher-frequency carriers and higher modulation rates compares with our study) and word identification in noise (whereas we used consonant identification in noise). Other behavioral studies, conducted cross-linguistically with normal-hearing children speakers of English and French, observed a specific relationship between the processing of 8-Hz AM and identification

thresholds of fricative consonants differing on place of articulation, as weakly observed in the present study (5, 25). Overall, previous findings seem to be connected to age-related factors or to subtle auditory deficits, which are absent in our young, normal-hearing cohort.

In conclusion, the current study showed that normal-hearing listeners who exhibited stronger AM masking effects at 8 Hz also exhibited a stronger neural following response for AM variations at the same rate. These results contribute to a better understanding of the relationship between behavioral psychophysical measures and neural auditory responses.

## DATA AVAILABILITY

The raw data will be available upon request to the corresponding author.

## SUPPLEMENTAL MATERIAL

Supplemental Table S1: https://doi.org/10.5281/zenodo.15494798.

## ACKNOWLEDGMENTS

The authors thank Pierre Labendzki and Marielle Hababou-Bernson for help with data collection. The authors also thank Christian Lorenzi for valuable discussions and insights regarding data interpretation. The authors also thank all the listeners who participated in this study.

## GRANTS

This work was supported by the Agence Nationale de la Recherche (ANR), France, under Grant No. ANR-17-CE28-0008 DESIN, and No. ANR-22-CE28-0003-01 and the Labex EFL Program under Grant No. ANR-10-LABX-0083 (to L.C.); by the European Union Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant No. 895484 INCODE (to I.L.).

## DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the authors.

## AUTHOR CONTRIBUTIONS

I.L. and L.C. conceived and designed research; C.B. and I.L. performed experiments; C.B. and L.C. analyzed data; I.L. and L.C. interpreted results of experiments; C.B. and L.C. prepared figures; C.B., I.L., and L.C. drafted manuscript; C.B., I.L., and L.C. edited and revised manuscript; C.B., I.L., and L.C. approved final version of manuscript.

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