Neural signatures of stream segregation from childhood to adulthood.

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Learn more: PMC Disclaimer | PMC Copyright Notice Sci Rep . 2026 Feb 14;16:16977. doi: 10.1038/s41598-026-38602-8 Neural signatures of stream segregation from childhood to adulthood Elena Benocci 1, ✉ , Claude Alain 2, 3 , Axelle Calcus 1 Author information Article notes Copyright and License information 1 Center for Research in Cognitive Neuroscience (CRCN), Université Libre de Bruxelles (ULB), Brussels, Belgium 2 Rotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst St, North York, ON M6A 2E1 Canada 3 Department of Psychology, University of Toronto, Toronto, Canada ✉ Corresponding author. Received 2025 Apr 28; Accepted 2026 Jan 30; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13230834  PMID: 41691053 Abstract When faced with noisy environments, listeners perform auditory scene analysis, which allows them to parse the auditory target from concurrent interferences. Stream segregation involves organizing similar sound waves into a coherent stream, while distinguishing dissimilar acoustic components and attributing them to distinct sources. Two event-related potential components have been identified as “neural signatures” of stream segregation: the Object-Related Negativity (ORN) and the P400. Our study aims to examine (i) the maturation of neural and behavioural correlates of stream segregation and (ii) the development of the relationship between stream segregation and speech perception in noise. ORN/P400 were recorded while 8–23 year-olds performed an active stream segregation task based on temporal coherence. Participants also performed speech identification in noise tasks (behaviourally). Behavioural results indicate an improvement in both stream segregation and speech perception in noise from childhood to adulthood. The amplitude of the ORN and latency of the P400 decreased throughout development. The individual probability of showing a P400 significantly increased from childhood to adulthood. When restricting the analysis to those individuals who showed a significant ORN/P400, amplitude and latency of both ORN and P400 decreased throughout development. Critically, P400 amplitude significantly predicts stream segregation performance. Our results suggest that the neural mechanisms underlying stream segregation follow a prolonged maturation trajectory, supporting the progressive maturation of auditory scene analysis and speech perception in noise. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-026-38602-8. Subject terms: Perception, Auditory system, Neuronal physiology Introduction From playgrounds to classrooms and to sports practice, children and adolescents routinely navigate complex auditory environments. In everyday environments, sound waves from various sources are mixed before reaching the ears. Listeners must disentangle individual sounds from the mixture, performing the auditory scene analysis (ASA 1 , 2 ). Adequate analysis of complex auditory scenes relies on listeners’ ability to parse acoustic events into different streams 3 . Newborns perform auditory stream segregation from birth, albeit less efficiently than adults 4 , 5 . How auditory streaming develops in the first decades of life remains poorly understood. Development of stream segregation Canonical studies of stream segregation use sequences of tones organized temporally in repeated A-B-A—A-B-A patterns, where A and B represent successive tones of different frequencies and—represents a silence 6 , 7 . Adult listeners are asked to report whether they heard one or two streams as a function of parametric changes of the sequences (e.g., A and B frequency distance, repetition rate, etc.). When listeners report hearing two streams, they are effectively experiencing stream segregation: they parse the sequential auditory events into distinct streams. At a given presentation rate, increasing the frequency distance between A and B typically increases the likelihood that listeners will report hearing distinct sound streams. Early developmental studies suggest that the frequency distance required for children to experience sequential stream segregation is much larger than that of adults 8 , 9 . It keeps decreasing from childhood to adolescence, until about 15 years of age 10 . However informative, this canonical paradigm overlooks an important aspect of auditory scene analysis: auditory objects tend to occur simultaneously (for a review 11 ). Another paradigm was thus developed to evaluate the segregation of concurrent sounds. Listeners were presented with harmonic complex sounds, of which one tonal element was mistuned 12 or delayed 13 —manipulations contributing to segregation into distinct auditory objects. The larger the mistuning/the delay, the more likely adults are to report hearing two auditory objects, thereby experiencing concurrent stream segregation. Again, children require larger changes in frequency to detect a mistuned harmonic than adults 14 . How concurrent stream segregation develops at adolescence remains unknown. Each paradigm has been associated with specific neurophysiological markers of stream segregation. The mismatch negativity (MMN) reflects neural responses to violations of regularity. As such, it has been used in paradigms that assess sequential stream segregation. The brain generates an MMN when it processes a difference between an unexpected auditory stimulus (the deviant) and the neural representation of an expected (standard) stimulus (for a review, see 15 ). In a clever adaptation of this paradigm, adults were presented with sequences of interleaved sounds of different frequencies. An infrequent (louder) deviant was placed in the sequence, but it would only be processed as such when the frequency difference between successive sounds was large enough (hence eliciting the perception of two streams). In sequences processed as containing two auditory streams, an MMN would thus appear in response to the infrequent tone 16 . Similar results were reported in newborns, revealing an innate ability to parse auditory scenes 17 . Children (7–10 years) also showed an MMN in response to infrequent tones placed in sequences that promote stream segregation 18 , 19 . The MMN amplitude is thought to be similar in children (9–12 years) and adults if they are actively engaged in detecting the infrequent target, but not if they passively listen to the sequences 9 . The object-related negativity (ORN) and P400 are event-related potentials (ERPs) that index listeners’ processing of two concurrent auditory objects 20 (for a review 21 ). The ORN is an early, pre-attentive, negative response with a fronto-central distribution 22 – 24 . When participants actively engage in a segregation task, the ORN is followed by a positive component (P400) that displays a centro-parietal distribution 20 , 25 . Both ORN and P400 are typically elicited by comparing the neurophysiological response evoked by an in-tune complex harmonic to that of a harmonic complex that contains a mistuned component. When presented with such a mistuned component, newborns and infants show an ORN, again supporting the hypothesis of an innate ability to process concurrent streams 26 – 28 . Surprisingly, when presented with the same stimuli, 8–13 year-old children displayed a larger ORN than adults 14 , but no significant P400 14 , 28 . Together, neurophysiological studies thus suggest that children (~ 8–12 years old) show immature neurophysiological responses in sequential or concurrent streaming 14 , 18 . How these responses develop during adolescence has remained unexplored so far. A major limitation of these early studies is their focus on either sequential or concurrent stimuli. In everyday communication, listeners seek to understand a given speaker’s speech stream in the presence of other interfering speakers. Concurrent speech signals are broadband, often highly correlated, and overlapping with one another. In such situations, temporal coherence between different acoustic cues of the auditory objects is crucial for auditory segregation 29 . The “stochastic figure-ground paradigm” (SFG) was developed to evaluate streaming abilities based on temporal coherence, hence requiring listeners to perform both sequential and concurrent streaming 30 , 31 . In this paradigm, listeners must detect a temporally coherent figure presented against a background of randomly varying tones. Adults are remarkably sensitive to the appearance of such figures in stochastic noise backgrounds; discrimination performance even improves as figure coherence/duration increase 31 . Both ORN and P400 have been recorded in adults during the SFG task 32 . Some studies found significant relationships between ORN/P400 amplitude and behavioural streaming performance 20 , 25 , 32 , 33 , but not all 34 , 35 . So far, the causal relationship between ORN/P400 amplitude and streaming performance remains a topic of debate. To sum up, behavioural and neurophysiological studies indicate that stream segregation remains immature until adolescence. Yet the exact age of maturation and the neural processes underlying maturation remain unknown. Therefore, the main aim of this study is to investigate the development of both behavioural and neurophysiological indices of stream segregation from childhood to adulthood. A second objective of this study is to investigate the relationship between neurophysiological indices and performance in a stream segregation task that relies upon temporal coherence. Development of speech perception in noise In everyday life, listeners are not presented with pure or complex tones, but rather with speech signals uttered in the presence of environmental noise or competing speech. Whereas the former is thought to interfere with the speech signal at the peripheral level ( energetic masking ), the latter typically affects auditory processing at the central level ( informational masking ; for a review 36 ). In adults, speech intelligibility is worse in a background of interfering speakers than in (spectrally identical) speech-shaped noise (SSN) 37 – 40 , likely due to the added cognitive contribution inherent to speech stimuli 41 . Note that in the presence of only one interfering speaker, syllable identification tends to be better than in the presence of a spectrally identical SSN 42 . This benefit likely stems from the possibility to glimpse in brief dips of a 1-talker masker to improve the identification of the speech target 43 . In children, the peripheral auditory system is functionally mature at birth 44 , but complex auditory processes such as speech-in-noise (SiN) perception follow a protracted developmental trajectory (for a review 45 ). The exact timing of maturation for SiN perception remains uncertain, but it appears to be dependent upon the nature of the interference induced by the background noise. In the presence of SSN, speech intelligibility seems to be mature between 7 and 11 years of age 46 – 49 , although adolescents still improve by about 0.2 dB SNR per year between 12 and 17 years of age 50 . In the presence of one or two interfering speakers, speech intelligibility gradually improves until 13 47 , 51 – 53 or even 16 years of age 51 , 54 , 55 . In the presence of multiple speakers (8- to 20-talker babble), speech intelligibility improves from childhood to late adolescence 56 – 59 . In short, speech intelligibility in the context of energetic masking appears to reach maturity by late childhood. Intelligibility in backgrounds that induce mostly informational masking appears to follow a protracted developmental trajectory, from childhood to (late) adolescence. Relationship between streaming and SiN perception during development Very few studies have investigated the relationship between streaming and SiN perception throughout development. In adults, behavioural and neuroimaging studies converge to support a relationship between stream segregation and SiN perception 60 , 61 . Our recent study examined the direction of this relationship throughout development 62 . Children, adolescents and adults were presented with both the SFG task and speech perception in noise tasks (in the presence of SSN and one interfering talker). Children had more difficulties than adolescents on the SFG task (adolescents’ performance did not significantly differ from adults’). The same developmental improvement was observed in a consonant identification task in the presence of SSN (children being poorer than adolescents, who did not differ from adults). In the presence of a single interfering talker, we observed a gradual improvement until adulthood. Using structural equation modelling, stream segregation predicted SiN perception (irrespective of the noise condition). Age significantly contributed to both stream segregation and SiN perception. Remarkably, musical abilities significantly contributed to SiN perception by mediating effect stream segregation. This finding was in line with the bulk of the literature indicating a musician advantage for SiN perception in adults 63 – 68 (for a meta-analysis 69 )—although some studies did not find such a musician advantage 70 – 74 . Similarly, adult musicians seem to exhibit better stream segregation abilities than nonmusicians 75 – 77 . In adolescents, music lessons in high school are thought to support neural processing of speech in quiet 78 . Two years of active musical training in elementary school even led to significant benefits for SiN perception 79 . Note that all the studies mentioned here compared musicians to non-musicians, using a dichotomous categorization that may not reflect the full range of musical abilities within the population 80 . Such a categorical approach is particularly problematic in developmental studies, where the duration of musical training (which is typically used as a criterion to distinguish between musicians and non-musicians) is necessarily limited in the younger participants. Therefore, measuring musical abilities on a continuous scale might offer a more accurate view of the relationship between music and speech processing 81 . The present study The objective of this study was twofold: to investigate (i) the development of, and (ii) the relationship between behavioural and neurophysiological indices related to stream segregation and SiN perception, from childhood to adulthood. Regarding the first objective, we predict a significant decrease in ORN amplitude from childhood to adulthood. The presence/morphology of a P400 in childhood/adolescence remains debated, and will be explored here. Regarding the second objective, adult studies show contradictory results regarding the relationship between ORN/P400 and streaming performance. To the best of our knowledge, no study has investigated the relationship between ORN/P400 and SiN perception. Therefore, we explore these relationships throughout development. A third, exploratory, hypothesis is that musical abilities could influence behavioural and neurophysiological indices related to stream segregation and SiN perception. Methods Participants Seventy-eight French natives were recruited for this study, including 24 children (aged 8–12 years, 12 males), 27 adolescents (aged 13–17 years, 14 males), and 27 adults (aged 18–22 years, 11 males). EEG data from 11 participants were excluded due to noisy recordings. The final sample thus included 23 children, 22 adolescents, and 24 adults. All participants had normal audiometric thresholds (≤ 20 dB HL) as measured at octave intervals from 0.25 to 8 kHz. Cognitive abilities were evaluated using the WISC-V (for children/adolescents) or the WAIS-IV (for adults). Specifically, working memory was assessed using the Working Memory Index, and fluid reasoning was assessed using the Matrix Reasoning. Participants were excluded if they showed cognitive scores three standard deviations above/below age norms. Two adolescents who did not exhibit age-appropriate working memory and reasoning abilities were excluded. All included participants were non-musicians, having received less than three years of formal musical training 65 , 67 , 82 . None of them reported any history of neurological, hearing, or developmental disorders. Two participants were left-handed, as confirmed by the Edinburgh Handedness Inventory 83 . Prior to the study, all participants (and their legal guardians, when needed) provided their written informed consent. The study protocol was approved by the ethical committee of Hôpital Universitaire des Enfants Reine Fabiola (CEH n°48/21). Behavioural testing Musical abilities: MICRO-PROMS The MICRO-PROMS battery is a standardized tool designed to evaluate musical perceptual abilities 84 . This streamlined version of the original PROMS battery 80 uses a three-interval forced-choice procedure to assess discrimination abilities across various musical dimensions: accent, melody, pitch, rhythm, tempo, tuning, and timbre. In each trial, participants heard two repetitions of a reference musical stimulus, followed by the target stimulus. Participants had to judge whether the reference stimulus and the target were similar or different. The target stimulus could only differ from the reference stimulus along one of the musical dimensions at a time. In total, 18 trials were presented to the participants. This task was presented through Sennheiser (HD300) headphones at an intensity of 73 dB SPL. Speech perception in noise The stimuli were the same as in Benocci and Calcus 62 . Participants were presented with 32 vowel-consonant–vowel (VCV) logatomes, which comprised two repetitions of 16 possible /aCa/ logatomes, spoken by a French female voice. The consonant was one of the following: /p, t, k, b, d, g, f, s, ∫, m, n, r, l, v, z, j/. Logatomes were presented in silence and in two different noisy backgrounds: an interfering speaker (1-talker) and SSN. Although a single interfering talker is not the most disruptive for speech perception 43 , it was selected because it is somewhat comparable to the stochastic figure-ground task of discriminating a coherent target figure (here: a logatome) from a similar-sounding background. This masker was composed of a male speaker reading French press articles. Pauses longer than one second, pronunciation errors, and proper nouns were edited out (without being noticeable by the listeners). SSN maskers were then generated from the 1-Talker condition using a fast Fourier transform. This process examined the power and phase spectrum of the original recording. A new signal was created with the same power spectrum but randomized phases, resulting in the SSN. Each target logatome was approximately 500 ms long, and both types of background noise lasted 2 s. The logatomes started randomly between 200 ms and one second after the start of the noise. All audio files were standardized to the same root-mean-square level. In noisy conditions, the signal-to-noise ratio (SNR) was set at − 5 dB. The target logatomes and the noise were played simultaneously to both ears. This task was implemented via the Gorilla online testing platform (Anwyl-Irvine et al., 2020). Sounds were presented through Sennheiser (HD300) at an intensity of 73 dB SPL. Neurophysiological testing: auditory stream segregation Stream segregation was evaluated using the stochastic figure-ground task, as described by Teki et al. (2013), with parameters that yielded the highest discrimination scores in Experiment 1 (see Fig. 1 ). Trials consisted of 264 2-s long sequences of 5 to 15 50-ms simultaneous pure tones (all of equal intensity) with random frequencies ranging from 179 to 7246 Hz (log-distribution), forming a background of inharmonic chords, with no inter-chord interval. Chords were gated on and off with 10-ms raised-cosine ramps. In 2/3 sequences, a subset of eight pure tones was repeated over seven chords, constituting a 350-ms figure that was added to the background. Figure onset was randomised (uniform distribution) in a 750–1000 ms interval after sequence onset. A third of the sequences did not contain any figure, consisting merely of the stochastic background (henceforth: control sequences). In order to circumvent potential confounds regarding temporary changes in density within the sequences, eight random, non-repeating pure tones were also added over seven chords in the control sequences (onset randomised between 750 and 1000 ms). All stimuli were created online, with new sequences generated for every participant and every trial. Stimuli were generated using MATLAB2020 software (The MathWorks) at a sampling rate of 44.1 kHz and 16-bit resolution. Sequences were presented binaurally at 75 dB SPL (ER-2, Etymotic Research). Fig. 1. Open in a new tab Schematic representation of the stochastic figure-ground stimuli. Sequences consisted of 50-ms chords containing a random set of pure tone components (left). In 2/3 sequences, a subset of pure tones was repeated over seven chords, resulting in the percept of a figure that popped out of the background noise (right, figure shown in the box). Participants were asked to indicate whether they heard a figure in each trial. They were first given examples of the figure alone and the background, followed by ten practice trials. Participants pressed a key with their right hand when a figure was present and another key with their left hand when it was absent. A response was required after each trial. Feedback was provided throughout the entire task. Performance was measured by calculating hit rates (correct detections of the figure when present) and false alarm rates (incorrect reports of a figure when only the background was present). These rates were then used to compute the d′ sensitivity index, determined by the difference between the z-scores of the hit rates and the false alarm rates, as described by Macmillan and Creelman 85 . Neuroelectric brain activity was continuously recorded during this task using a BioSemi Active Two system at a sampling rate of 2048 Hz, from 64 scalp locations, according to the international 10–20 system 86 . Additional electrodes were placed on each mastoid, and recordings were re-referenced offline to the average of activity at the mastoid electrodes. Electrode offsets were consistently < 50 mV. Preprocessing was carried out using MATLAB 2021b (MathWorks) with custom scripts based on EEGLAB 87 . The recorded data was band-pass filtered between 1 and 30 Hz (zero-phase, finite impulse response, − 6 dB/octave). Next, the Picard algorithm of Independent Component Analysis (ICA) was performed. ICA components corresponding to blink artifacts were removed via automatic detection of artifacts (ICLabel plug-in). Event-related potentials were obtained by creating 1000-ms long epochs that started 50 ms before the figure onset. In control sequences, epochs started 700 ms after sequence onset. All 264 epochs were baseline-corrected using the mean value from − 50 to 0 ms. Epochs exceeding ± 100 μV were rejected. A minimum of 50% figure and control clean epochs were included for all participants. This proportion did not differ across groups with respect to either figure or background epochs (both F s < 1, p > 0.05). Difference waveforms were obtained by subtracting the ERPs corresponding to control sequences from those of figure sequences, averaged across a subset of electrodes. The subset of electrodes was defined by visually inspecting the voltage maps of the responses (see Fig. 1 for group averages). For the ORN, we selected electrodes F1, F2, F3, F4, FC1, FC2, FC3, FC4, Fz, and FCz. For the P400, we selected electrodes CP1, CP2, P1, P2, Pz, CPz, and POz. The ORN and P400 were statistically assessed in three ways. First, we tested whether ORN/P400 were present at the group level. To do so, we used cluster-based permutation tests 88 , which control for multiple comparisons whilst maintaining sensitivity by accounting for the temporal dependency between neighbouring samples. First, the data were clustered by performing pairwise comparisons (t-tests) between each sample, in two conditions (figure and control epochs). Contiguous values with threshold p ≤ 0.05 were grouped into clusters, and we computed the sum of t-values within each cluster. Then, a null distribution assuming full interchangeability (i.e. no difference between figure and control epochs) was estimated by drawing 1000 random permutations of the observed data and computing the sum of t-tests for each randomization. Finally, the cluster-level statistics observed in the actual data were evaluated under this null distribution. This cluster-based permutation was applied separately to the subset of electrodes corresponding to the ORN within the time window 100–600 ms post-figure onset. It was applied to the subset of electrodes corresponding to the P400 within the 300–800 ms post-figure onset. Second, we aimed to compute the individual amplitude and latencies of ORN/P400 in each participant. Individual latencies corresponded to the timing of the minimum/maximum amplitude within the ORN/P400 (respectively) significant time window observed in the corresponding group. Whenever the ORN/P400 appeared to be ‘scattered’ (e.g., P400 in children), the time window was computed as ranging from the onset of the first significant cluster to the offset of the last significant cluster. Individual amplitudes were calculated as the average amplitude recorded within an 80-ms window centred on the individual peak. Finally, we sought to determine how many individuals showed significant ORN/P400 within each group. Indeed, differences in amplitude/latency could merely reflect the proportion of individuals showing each specific component within each group. Cluster-based permutations were performed again on individual figure versus control epochs (averaging responses from the same electrodes used to examine the grand-average ORN/P400); details by age group are available in Table 1 . Table 1. Descriptive statistics of ORN and P400, by age group. Children ( n = 23) Adolescents ( n = 22) Adults ( n = 24) ORN Individuals 17 11 15 Mean (μV) − 5.93 − 2.93 − 3.27 SD 3.81 2.38 2.11 Duration 100–448 457–486 143–376 136–365 P400 Individuals 8 12 16 Mean (μV) 2.59 2.68 3.43 SD 3.19 2.60 1.86 Duration 551–553 604–636 757–777 464–535 565–635 714–772 364–732 Open in a new tab Individuals indicate the number of participants showing a significant ORN/P400 at the individual level, for each age group. Duration represents the periods that contained a significant ORN (100–600 ms) or P400 (300–800 ms) for each age group, as determined by cluster-based permutations. General procedure All participants completed the tasks in the same order. They began with the MICRO-PROMS auditory task, followed by the speech perception task. Then, they participated in the auditory stream segregation task while their electroencephalogram (EEG) was recorded in a soundproof booth. The EEG recording session lasted approximately 90 min, including setup. All methods were carried out in accordance with relevant guidelines concerning audiological, auditory and/or neurophysiological data acquisition. Statistical analyses Statistical analyses were conducted using R Studio software (R Core Team, 2021). First, we assessed the normality of the distribution of behavioural data for each age cohort and dependent variable to guide the choice of statistical models. Data from the streaming task were normally distributed for all groups (Shapiro–Wilk test: p = 0.785 for children, p = 0.628 for adolescents, p = 0.216 for adults). Similarly, data from the SSN condition showed normal distribution for adults ( p = 0.454) and children ( p = 0.097), while adolescents showed a significant deviation ( p = 0.011). However, for the 1-talker condition, data did not meet the assumption of normality for adolescents and adults ( p < 0.05), but were normally distributed for children ( p = 0.273). No outliers were detected, as all values were within three standard deviations of the mean. We conducted one-sided t-tests to determine whether the performance was above chance/below ceiling in the behavioural tasks. In all three age groups, participants performed significantly above chance (d′ = 0) in the streaming task, and below ceiling (100%) in the speech perception in noise tasks (all ps < 0.05). Next, we assessed developmental effects on behavioural and neurophysiological measures in different ways. Throughout the analyses, we treated age as a continuous variable to better capture developmental changes. In the event of a significant interaction involving age, post-hoc analyses were performed with age treated categorically. With respect to the neurophysiological data, we first computed linear regressions to determine whether age, music abilities or their interaction predicted the amplitude of the neurophysiological responses. Then, logistic regressions (binomial) were performed on the individual presence/absence dependent variable (as computed with cluster-based permutations, see description in the “Neurophysiological testing” section above) to evaluate the proportion of participants who presented a significant ORN and P400 as a function of age. Linear regressions were performed again, on the subset of participants who did show a significant ORN/P400—see supplementary materials. In all models, the factor listener was used as a random intercept. Finally, we examined the relationship between the neurophysiological data and behavioural auditory performance (streaming and SiN perception). A linear regression analysis was conducted on stream segregation, with age, musical abilities, amplitude, and latency of both the ORN and P400 components, as well as all their interactions (all orders) as predictors. To identify the optimal model and the most relevant interactions, we conducted a LASSO (Least Absolute Shrinkage and Selection Operator) regression. LASSO regression identifies variables and coefficients that minimize prediction error by constraining model parameters. It simplifies high-dimensional models by shrinking regression coefficients toward zero, limiting their total sum. Variables with zero coefficients are excluded from the final model. This method reduces model complexity and improves its predictive capability 89 . A two-level Generalized Linear Mixed Model (GLMM) was used to investigate whether age, musical abilities, stream segregation performance, amplitude of the ORN, and P400 predicted speech perception in noise (each level of the GLMM represented a noise condition). Here again, LASSO regressions were applied to identify the most relevant interactions and simplify the final model. All statistically significant results are reported with their semi-partial R 2 statistic, an indication of effect size. Results Development of neural signatures of stream segregation Figure 2 shows the grand-average of the differential waveforms (figure trials—control trials) evoked in the fronto-central cluster and the centro-parietal cluster in each age group. In all three groups, an early negativity component appears in the fronto-central electrode cluster, representing the ORN. It is followed by a positive component with a centro-parietal distribution, reflecting the P400. Table 1 presents the periods of significant ORN/P400 for each age group. At the group level, children displayed longer ORN than adolescents/adults. The P400 in children and adolescents appears shorter and more discontinuous compared to adults. Fig. 2. Open in a new tab ( A ) Grand averages of the ORN (top, from fronto-central electrode cluster) and P400 (bottom, from parietal electrode cluster) by age group, including all participants. Shaded areas indicate clusters of significant ORN and P400, as a function of age group. ( B ) Voltage maps show the mean differential response (figure trials—control trials) during the 0–900 ms post-stimulus time window. ORN Throughout development, amplitude (but not latency) of the ORN decreased with age ( p = 0.005); see Table 2 and Fig. 3 . There was no significant effect of PROMS nor a significant age × PROMS interaction on ORN amplitude or latency (all p s > 0.10). To examine whether the age effect could be due to differences in the variability of the ORN peak latency throughout development, we conducted Levene’s tests for equality of variance. Children’s and adolescents’ ORN peak latencies were significantly more variable than adults’ (both p s < 0.05), but did not significantly differ from each other ( p > 0.10). Table 2. Results of the linear mixed-effect analyses for the amplitude and latency of ORN and P400. Component Effect Amplitude Latency F df p -val R 2 β F df p -val R 2 β ORN Age 8.39 1 0.005 0.011 2.18 1 0.144 0.018 PROMS 0.74 1 0.392 0.00 2.59 1 0.112 0.022 Age × PROMS 0.01 1 0.912 0.00 0.66 1 0.418 0.010 P400 Age 1.56 1 0.215 0.014 28.21 1 < 0.001 0.012 PROMS 0.97 1 0.328 0.035 0.01 1 0.899 0.002 Age × PROMS 1.75 1 0.190 0.026 0.17 1 0.675 0.003 Open in a new tab The best fitting models for each component’s amplitude and latency were all an acceptable fit (ORN amplitude [AIC = 353.14, R 2 = 0.123], latency [AIC = 775.9, R 2 = 0.077]; P400 amplitude [AIC = 331.7, R 2 = 0.062], latency [AIC = 831.2, R 2 = 0.304]). Effects that are significant are shown in boldface. Fig. 3. Open in a new tab Amplitude (µV) and latency (ms) of the ORN and P400 as a function of age. Individual (dots) and group (lines) data are shown in each panel. Shaded lines represent the 95% confidence interval. The proportion of participants showing significant ORN at the individual level ranged from 50% (in adolescents) to 63% (in adults) and 74% (in children), see Fig. 4 . Despite the surprisingly lower rate observed in adolescents, there was no significant linear or quadratic effect of age (treated as a continuous variable) on the participants’ probability to exhibit an ORN (both p s > 0.10). Linear regressions were performed on the amplitude/latency of the subset of individuals who did show a significant ORN. Here, both ORN amplitude and latency decreased with age (see supplementary Table 1 ). Fig. 4. Open in a new tab Probability of presence of ORN and P400 as a function of age. Individual (circles) data are shown for both components. At the individual level, components were either present (probability = 1) or absent (probability = 0). Lines result from a logistic (ORN) or linear (P400) regression, fitting a generalized model with age as a quadratic (ORN) or linear (P400) term and presence of the component as the outcome variable. P400 There was no significant effect of age, PROMS, or age × PROMS interaction on P400 amplitude (all p s > 0.10); see Table 2 and Fig. 2 . P400 latency significantly decreased with age ( p < 0.001). Variability in P400 latency was larger in children and adolescents than in adults, as indicated by Levene’s test for equality of variance (both p s < 0.05). There was no significant difference in the variance of P400 latency between children and adolescents ( p > 0.10). The proportion of participants showing a significant P400 at the individual level ranged from 35% (in children) to 55% (in adolescents) and 67% (in adults), as shown in Fig. 3 . There was a significant linear effect of age (treated as a continuous variable) on participants’ probability to exhibit a P400 ( p = 0.031). Considering only the subset of individuals who showed a P400, linear regressions indicate a significant decrease in both amplitude ( p = 0.050) and latency ( p = 0.009) of P400 with age (see supplementary Table 1 ). Predictors of stream segregation performance What predictors contribute to the performance in figure-ground segregation? To answer this question, we performed a least absolute shrinkage and selection operator (LASSO) regularized regression, which enforces sparse solutions. The initial selection indicated the age × PROMS × ORN amplitude × P400 amplitude as a potential interaction to investigate. A subsequent linear regression analysis was thus conducted with these four factors and all their 2-, 3- and 4-way interactions as predictors of the figure-ground performance. Results revealed three significant main effects, each illustrated in Fig. 5 (also see Supplementary Table 2 ). The main effect of age indicates that performance improves from childhood to adulthood ( p = 0.001). Notably, P400 (but not ORN) amplitude also predicts figure-ground segregation: larger P400 responses were associated with higher d′ ( p < 0.001). There was also a significant effect of PROMS: better musical abilities predict higher d′ on the segregation task ( p = 0.048). Note that there was no significant effect of age on PROMS ( p > 0.10). Fig. 5. Open in a new tab Predictors of the behavioural stream segregation (d′) performance. ( A ) Age (in years), ( B ) PROMS and ( C ) P400 amplitude (in µV). The dashed horizontal lines indicate chance level. Predictors of SiN performance LASSO regularized regression was also applied to identify the potential factors contributing to speech perception in noise performance. It indicated that the triple interaction noise condition × ORN latency × P400 latency, as well as the age × stream segregation performance, could be predictors of the SiN perception. A subsequent generalized logistic regression was conducted with these two interactions (as well as the main effects associated with each interaction) as predictors of SiN perception (for details, see Supplementary Table 3 ). Significant findings are illustrated in Fig. 6 . The main effect of the condition was significant: overall, listeners performed better in the presence of one interfering speaker than in SSN ( p < 0.001). Regarding the interactions, only the age × stream segregation came out significant ( p = 0.020). Post-hoc analyses indicate that in adults, better stream segregation predicted better SiN perception ( p < 0.001). This was not the case in either children or adolescents (both ps > 0.10). Fig. 6. Open in a new tab Predictors of the consonant identification in noise. ( A ) Noise condition, (B) Interaction between stream segregation (d′) and age. The interaction was decomposed and is illustrated by age band. The dashed lines indicate the chance level. Discussion Neurophysiological responses (ORN and P400) were recorded in children, adolescents and young adults, as they performed a stream segregation task based on temporal coherence. Amplitude of the ORN decreased between childhood and early adulthood. P400 latency also decreased throughout development. The individual probability of showing an ORN did not significantly change throughout development, but the probability of showing a P400 significantly increased from childhood to adulthood. When restricting the analysis to those individuals who showed a significant ORN/P400, amplitude and latency of both ORN and P400 decreased throughout development. Notably, P400 amplitude significantly predicted behavioural performance in the stream segregation task (together with age). In turn, stream segregation significantly predicted consonant identification in noise in adults, but not in children or adolescents. Neurophysiological measures At the group level, children, adolescents and adults all showed a significant ORN, followed by a significant (albeit ‘segmented’ in children and adolescents) P400. This contrasts with previous studies conducted in school-age children, showing a significant ORN but no P400 14 , 28 . This discrepancy is likely attributable to differences in the electrode cluster selected to analyse the P400: previous children’s studies focused on a cluster of fronto-central electrodes, whereas we analysed centro-parietal electrodes where the P400 is typically maximal in adults 32 . This finding suggests that children, like adolescents and adults, exhibit neural markers of both bottom-up (ORN) and top-down (P400) processing of stream segregation based on temporal coherence. When analysing the responses from the subset of individuals who showed significant ORN/P400, both amplitude and latency appeared to decrease from childhood to adulthood. Yet when including all participants, the developmental decrease in latency was only evident for the P400. Many developmental ERP studies report decreasing latencies throughout childhood and adolescence years 90 – 92 . Shorter latencies are generally interpreted as indicative of faster processing speed, which has been related to the myelination of the neural tracts linking generators of the different ERP components 93 , 94 . Axon myelination supports the maturation of the human connectome by increasing the speed and fidelity of neural signalling. In humans, myelination follows an exceptionally long developmental trajectory, extending beyond late adolescence 95 – 97 . This extended maturation of intracortical myelination likely explains the decrease in P400 (and ORN, if we focus solely on those individuals who showed significant responses) latency throughout adolescence. In parallel with shorter latencies, ORN amplitude gradually decreased from childhood to adulthood—in line with previous results collected in children 14 . Two (non-mutually exclusive) interpretations are possible. Cortical ERPs reflect the aggregate measure of post-synaptic potentials of pyramidal cells within a given population of neurons in response to a stimulus. In adults, both ORN and P400 are thought to reflect activity of generators located in the primary auditory and anterior cingulate cortices 32 , 98 . Additionally, the ORN has specific generators in the superior temporal gyrus and in the inferior parietal sulcus 32 , 99 . ORN generators thus appear mostly localised in the temporal and parietal areas of the brain. Coincidentally, synaptic pruning accelerates around puberty onset in temporal, frontal and parietal brain areas 100 , 101 . In fact, synaptic pruning is thought to relate to a decrease in the spectral composition of resting EEG below 8 Hz during the adolescent period 102 . A first explanation for the decreasing ORN amplitude throughout development might thus pertain to synaptic pruning at adolescence. Second, age differences in ORN amplitude might be attributable to structural changes to the generators of the response. The lower probability of adolescents showing a significant ORN at the individual level could support this interpretation (even though it did not reach significance). The EEG is thought to best detect activity from neurons that are oriented perpendicular to the cortical surface, forming current dipoles 103 . Structural changes could thus impact the orientation of the current dipoles, affecting the adolescent and adult surface ERPs, compared to those of children. We did not observe any obvious changes in the distribution of ERP amplitude across the scalp throughout development. Studies using magnetoencephalography are warranted to further our understanding of the developmental changes in generators of the ORN and their relationship to ORN amplitude. Behavioural measures We identified three significant predictors of stream segregation based on temporal coherence: age, musical abilities (as measured by PROMS) and P400 amplitude recorded during the segregation task. The effect of age indicates a gradual improvement in stream segregation, from childhood to adulthood. So far, behavioural results have indicated persistent immaturity in auditory streaming until at least 12 years of age, in paradigms that rely on either sequential 8 – 10 , 18 , 19 or concurrent stream segregation 14 (for a review 104 ). Our results confirm this protracted development of stream segregation until adulthood in complex auditory scenes where streaming must operate based on the temporal coherence across auditory filters 105 . Irrespective of the listeners’ age, perceptual musical abilities measured in non-musicians also predicted stream segregation. In adults, musical expertise has been linked to improved stream segregation 75 – 77 . Our results suggest that these observations may not be directly related to musical training per se, but to inherent musical abilities. Longitudinal studies are needed to better understand the transfer effect of musical expertise to auditory abilities. Last but not least, P400 amplitude strongly predicts performance on the streaming task in our sample of participants (irrespective of age). This concurs with the existing adult literature 20 , 32 , and extends the findings to children and adolescents. In other words, even though we did not observe a significant age effect on the P400 amplitude, this component appears to predict behavioural performance (which itself improves with age). The P400 thus appears to reflect attentional and decisional processes involved in stream segregation, which are already evident in childhood. Interestingly, stream segregation performance was shown to predict SiN (regardless of the condition), but only in adults; this relationship was not significant in either children or adolescents. This supports previous reports of a marginally significant relationship between figure detection in a stochastic background and speech-in-babble in adults 60 . In our recent study 62 , we used structural equation modelling to investigate the relationship between age (8–22 years), musical abilities, stream segregation and SiN perception—all tested with materials that were very similar to those used in the present study. The results indicated a significant effect of age on stream segregation, which itself contributed to SiN perception. In this paper, we did not report the results of the linear regression conducted on SiN perception with age and stream segregation as predictors. In the spirit of replication, we have now re-analysed this earlier dataset, and observe a significant relationship between stream segregation and SiN perception in adults ( p = 0.026). This relationship was marginally significant in adolescents ( p = 0.075) but not significant in children ( p = 0.667). Therefore, the relationship between stream segregation and SiN may only become apparent gradually throughout adolescence. Children and adolescents may not yet be able to rely on their immature stream segregation abilities to process SiN efficiently. Future work should aim to determine whether children/adolescents rely on other factors (auditory, linguistic or cognitive) to perform SiN, and/or whether the relationship between streaming and SiN emerges as a result of exposure to complex auditory backgrounds. To sum up, SiN perception is predicted by behavioural streaming performance in adults—but not in children or adolescents. Stream segregation itself improves gradually from childhood to adulthood, and is better in individuals who show larger P400 (irrespective of their age). Unusual as it may be, we would like to highlight the factors that do not appear to contribute to SiN perception in our study: none of the neurophysiological markers of stream segregation are significant SiN predictors. This observation appears to contradict a number of studies that found significant relationships between ERPs and SiN perception across the lifespan 65 , 106 – 109 . The absence of a predictive relationship between ORN/P400 and SiN may relate to task-specific factors. Unlike many adult studies measuring SiN using sentence material or adaptive speech reception thresholds, we used a consonant identification task at a fixed SNR. Such tasks may engage different cognitive (e.g., working memory) and linguistic processes, potentially reducing the contribution of early neural markers of stream segregation. Our data suggest that, in adults, the only significant predictor of SiN perception is stream segregation, as measured by a task that is closely related to SiN. Performance in non-linguistic auditory scene analysis may thus present a bottleneck to SiN difficulties in children and adolescents. Clinical relevance Children have poorer SiN perception than adults (for a review 45 ). Adding insult to injury, classrooms are often very noisy. While this double penalty is distressing for all children, some are disproportionately affected by background noise. In 2021, close to 100 million children and adolescents (< 20 years) were affected by hearing loss worldwide 110 . Even the milder degrees of hearing loss affect SiN perception 111 – 113 . Childhood hearing loss is known to affect the central processing of sounds, with some effects that only become apparent in adolescence 114 . Children with a hearing loss also show smaller/later ORN than age-matched children with typical hearing 28 . How a peripheral hearing loss affects the maturation of P400, stream segregation performance, and its late-emerging relationship with SiN remains an open question. Some children report having listening difficulties despite normal hearing (as measured by the audiogram), a condition sometimes referred to as ‘auditory processing disorder’ (APD, for a review 115 ). Children with APD experience pronounced difficulties with SiN and often exhibit impaired stream segregation abilities 116 – 118 . Whether their difficulties involve the bottom-up, automatic processing of streaming (ORN), and/or the top-down, attentional processing of the streams (P400)—and how these two components develop in the context of APD—has remained so far unexplored. In general, children with developmental disorders (e.g., ADHD, reading difficulties) also perform more poorly in SiN tasks than typically developing children 114 , 119 – 121 . Better characterizing SiN difficulties throughout development, and their relationship to stream segregation, could open new avenues for diagnosis and intervention. Limitations Our SiN task was designed to be quick and easy to administer to young participants, and to resemble the streaming task (with a similar duration and auditory signal to detect amidst the background noise). Therefore, we used a consonant identification task in noise, at a fixed SNR, in the presence of only one interfering talker and in SSN. This led to a very high (non-normally distributed) performance in the 1-talker condition, even though it was significantly lower than the ceiling. It may be that the relationship between the streaming task and SiN is different for different types of material and measures. Future studies would benefit from measuring speech reception thresholds and incorporating more ecological stimuli that vary in, for example, linguistic complexity, sound source spatialization, and the number of interfering talkers. Additionally, ERPs were only recorded in an active condition, as participants performed the stream segregation task. Including a passive listening condition could clarify the role of automatic versus controlled auditory processing on stream segregation. For example, children and adults are thought to have similar MMN amplitude when actively engaged in a streaming task, but not when listening passively to the sequences 9 . Finally, we explored the relationship between neurophysiological measures of streaming and SiN performance by means of correlations. Longitudinal studies are warranted to further clarify the causal relationship between auditory scene analysis and SiN throughout development. Conclusion In conclusion, our results reveal a progressive maturation of both automatic and attention-driven mechanisms underlying stream segregation, extending until adulthood. P400 amplitude emerges as a strong predictor of segregation performance, highlighting the critical role of attentional processes on auditory scene analysis across development. Notably, the relationship between stream segregation and SiN perception only manifests in adulthood, suggesting a late maturation of the integration between these processes. Processing of non-linguistic stream segregation may thus present a bottleneck to SiN difficulties in children and adolescents. Therefore, our findings have significant clinical implications, particularly for children with peripheral and/or central impairments along the auditory pathway. Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (22KB, docx) Acknowledgements We are very grateful for the time and efforts of the children who participated in this study and to their parents. We would like to thank Luna Leonardy, Hasnae El Arround, Jessy Angama-Gravier, Mélanie Rupaire, Aubrey Plume and Manon Couvignou for their assistance with recruitment and data collection. This work was supported by a grant from The Belgian Kids’ Fund for Pediatric Research awarded to Elena Benocci, and by a European Research Council Starting Grant “SensationaHL” (101076968) awarded to Axelle Calcus. Author contributions Elena Benocci and Axelle Calcus designed the experiment, analysed, interpreted the data and wrote the manuscript. 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