Individual differences in auditory scene analysis abilities in music and speech

Individual differences in auditory scene analysis abilities in music and speech | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Individual differences in auditory scene analysis abilities in music and speech Robin Hake, Daniel Müllensiefen, Kai Siedenburg This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6026003/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 7 You are reading this latest preprint version Abstract Auditory scene analysis (ASA) is the ability to organize complex auditory mixtures into meaningful events and streams and is fundamental for auditory perception of both music and speech. Individual differences in ASA are recognized in the literature, yet the factors driving this variability remain poorly understood. This study employs a novel music-based ASA task, the Musical Scene Analysis (MSA) test, alongside a speech-in-noise test, to examine the influence of hearing loss, age, working memory capacity (WMC), and musical training. Ninety-two participants were categorised into four groups: 31 older normal-hearing, 34 older hearing-impaired, 26 younger normal-hearing, and one younger hearing-impaired individual. Results reveal a moderate correlation between ASA performance in speech and music (r = − .5), suggesting shared underlying perceptual processes, yet the factors influencing individual differences varied across domains. A dual modelling approach using ridge regression and gradient-boosted decision trees identified hearing loss as the strongest predictor of speech-based ASA, with a weaker effect of age, while musical training and WMC had no impact. In contrast, musical training showed a substantial effect on musical ASA, alongside moderate effects of hearing loss and age, while WMC exhibited only a marginal, non-robust effect. These findings highlight both shared and domain-specific factors influencing ASA abilities in music and speech. Biological sciences/Psychology/Human behaviour Biological sciences/Neuroscience/Cognitive neuroscience/Perception Biological sciences/Neuroscience/Auditory system Biological sciences/Neuroscience/Cognitive ageing auditory scene analysis individual differences age working memory musical training hearing loss Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Auditory Scene Analysis (ASA) is the fundamental process by which the auditory system organizes complex acoustic environments into meaningful auditory objects and streams, enabling listeners to group, segregate, and track individual sound sources that overlap in time and frequency ⁠ 1 . In speech, ASA allows individuals to disentangle concurrent sounds, such as separating a single voice from background noise or interfering speakers (commonly referred to as the ‘cocktail party problem’). In the context of multi-source music, this ability - termed Musical Scene Analysis (MSA) - is essential for parsing individual instruments within a mixture (e.g., an oboe within an orchestra) and forms the basis for understanding the rich interplay of instruments, melodies, and harmonies. Individuals are known to vary in their ASA abilities, both in musical and speech dominated acoustic scenes ⁠ 2 – 4 ; these individual differences - defined as the measurable variability between individuals in perceptual, cognitive, and experiential traits, characteristics, or abilities ⁠ 5 - are, however, not yet fully understood. This study aims to identify the key contributors to individual differences in ASA abilities and determine their relative importance. The literature identifies several potential factors that might contribute to individual differences in ASA abilities. Among these, the influence of hearing impairments (such as the effect of elevated audiometric thresholds) has received considerable attention ⁠ 6 , 7 . In fact, the negative effects of hearing impairment on speech perception first tend to become obvious in complex acoustic scenes that place considerable demands on ASA such as having a conversation in a noisy restaurant. Specifically, hearing loss, even in mild forms, has been shown to disrupt the processing of acoustic cues that are essential for separating auditory objects within complex scenes. Such impairments are characterized by compromised frequency resolution, attenuated temporal processing, and diminished spatial hearing capabilities ⁠ 8 – 11 . These deficits impair auditory object formation 12 and the ability to segregate overlapping acoustic streams 13 , resulting in notable difficulties in speech-in-speech and speech-in-noise perception 13 , 14 . Compared to speech, the role of hearing loss in ASA for music has received less attention. However, hearing-impaired individuals has been shown to experience difficulties in tasks that are associated with successful MSA, such as timbre ⁠ 4 , 15 , 16 , pitch 17 , 18 , and melody perception ⁠ 4 , 19 . Siedenburg et al. 20 directly examined MSA abilities, demonstrating poorer performance among older hearing-impaired (HI) individuals compared to their younger normal-hearing (NH) counterparts in tracking individual instruments within complex musical excerpts. Survey studies further corroborate these findings, with HI individuals frequently reporting difficulties in discriminating instruments 21 , 22 . A notable limitation of this research, however, is the confounding of age and hearing loss, underscoring the need for studies that isolate the specific effects of hearing impairment on ASA abilities. Hearing difficulties and impaired speech perception often persist in the absence of overt hearing loss (i.e., elevated hearing thresholds) 14 , 23 . This indicates the importance of other factors beyond hearing thresholds in shaping auditory perception, with cognitive processes - particularly working memory capacity (WMC) - emerging as a potential contributor to the observed performance variability among individuals. Working memory, the cognitive system responsible for temporarily storing and manipulating information, allows to hold and process transient auditory details, a capability demonstrably associated with auditory tasks outcomes 24 . Indeed, research has consistently linked WMC to various aspects of auditory perception 25 – 28 . For example, individuals with better WMC often excel in speech-related tasks 29 – 31 and are better able to adapt to difficult listening situations 26 , 32 , 33 . Other research linked low-level concurrent sound segregation to attention and WMC 34 . Moreover, WMC is closely associated with listening effort, wherein cognitive resources are allocated to overcome auditory obstacles - a challenge that is significantly greater for individuals with lower WMC 35 , 36 . Yet, for ASA-related tasks, the link between WMC and performance were not consistent across all listening conditions. For instance, multitalker scenarios versus single speech streams may differentially engage WMC, and factors such as age and hearing status further modulate this relationship 24 , 37 , 38 . Furthermore, the extent to which variability in WMC accounts for MSA abilities remains unclear. Beyond fundamental cognitive abilities, refined listening abilities and knowledge of musical structure, like those acquired by deliberate musical practice may further shape individual perceptual abilities in complex auditory scenarios 39 – 42 . This reasoning aligns with research demonstrating superior performance of musicians in music-specific tasks like rhythm and beat perception, pitch discrimination, and timbre recognition ⁠ 4 , 43 – 47 . Furthermore, musical training has been linked to a cascade of cognitive benefits, including enhanced information processing speed 48 , enhanced attentional resolution 49 , and even overall improvements in fundamental cognitive regiments like memory 40 , 50 . This also tends to be associated with enhanced fundamental auditory abilities such as improved frequency discrimination, interaural time differences perception and attentive tracking 51 – 54 . Musicians also have been shown to outperform non-musicians in auditory stream segregation 55 – 60 , with some evidence suggesting that musical training may even offer some compensation against age-related decline in auditory sensitivity 61 – 65 . However, it's also worth noting that the influence of musical training on speech perception remains under debate 52 , 66 – 68 . Furthermore, its specific role in MSA processing also remains a subject of active research: While studies like Siedenburg et al. ⁠ 4 and Marozeau et al. 60 demonstrate an advantage for musically trained individuals in MSA tasks, other investigations failed to establish a link between musical expertise or musical training with MSA performance 69 , 70 , suggesting a more nuanced relationship or a relatively weak effect that is only observed with sufficient statistical power. While investigation of peripheral auditory and cognitive factors might offer a unique lens for understanding individual differences in ASA, previous research underscores ageing as a key contributor. Age has been consistently linked to destructive alterations along the auditory pathways and within the cochlea 33 , 71 , 72 , with age-related hearing loss emerging as the most prevalent sensory health condition among adults 73 , 74 . The effect of age extends beyond the auditory periphery, though. Age-related neural degeneration within the central auditory system also affects higher-level auditory processing, compromising timing information, gap detection, sound localisation, and spectral cue processing 26 , 75 – 78 – all of which are essential components for effective ASA ⁠ 2 , 79 . These age-related auditory alterations frequently manifest themselves as challenges in filtering out irrelevant competing auditory stimuli 80 and difficulties with understanding speech in complex listening environments, even when standard audiometric assessments fall within the normal range ⁠ 6 , 81 . Alongside these changes, age also brings a decline in cognitive domains crucial for auditory processing. Processing speed, working memory, and selective attention all diminish with age 33 , 82 , possibly with a negative impact on the ability to detect and track auditory information central to ASA. There is also some evidence that certain age-related auditory effects persist independently of hearing loss and cognitive decline. For example, Lentz et al. 26 showed that older adults performed worse on several auditory tasks involving temporal processing, even after accounting for hearing loss and working memory. Interestingly, their study also found that stream segregation – a key component of ASA – was the only task that could not be predicted by working memory, hearing loss, or age itself. Certainly, the combined burden of age-related hearing and cognitive changes poses a significant challenge for ASA and warrants careful investigation. Traditionally, research in auditory perception has primarily concentrated on a limited number of individual factors, often explored in isolation within experiments. However, no single factor can fully explain the variability in ASA performance observed across individuals. Instead, growing evidence highlights the complex interplay between hearing thresholds, cognitive abilities (e.g., working memory), musical training and expertise, and ageing 52 , 83 – 87 . To address the complexities inherent in ASA performance, a multifactorial approach is essential - one that examines the relative contributions of these interrelated factors. Such an approach can provide deeper insights into how individuals organise and interpret complex auditory scenes. Guided by previous literature, the present study investigates several candidate factors to examine their contribution to individual differences in ASA abilities within a single experimental framework: hearing thresholds, working memory capacity (WMC), musical training, and age. ASA abilities were assessed using two domain-specific task. The recently developed Musical Scene Analysis (MSA) test ⁠ 3 , was used to hear out a target instrument within realistic musical mixtures, while a standardised speech-in-noise task (the Oldenburger Satztest 88 ) was employed to evaluate ASA performance in the speech domain, as it requires auditory stream formation and tracking. These tasks were selected for their ecological validity and sensitivity to individual variability in real-world auditory contexts. While the study does not aim to exhaustively capture all potential sources of individual variability, it is based on a diverse sample and a well-defined set of predictors grounded in prior research. We therefore consider the current design well-suited to identifying meaningful individual differences in ASA performance. We hypothesise that younger individuals, those with better hearing sensitivity (i.e. lower hearing thresholds), stronger WMC, and greater musical training background will demonstrate superior ASA performance. Furthermore, we predict that the factors influencing musical ASA performance will similarly affect speech-in-noise perception, which could be indicative of shared processes for ASA across the music and speech domains. Results Ninety-two participants were classified into three groups based on age and hearing thresholds (see Fig. 1 ). Participants between 18 and 38 years were assigned to the ‘younger’ groups and participants with pure-tone average (PTA) thresholds of the better ear exceeding 20 dB HL (averaged across octave-spaced frequencies from .125 to 8 kHz) were classified as 'hearing-impaired' (HI). As the yHI group included only one participant, this individual was excluded from all group-based inferential statistics (e.g., ANOVA). Overall, yNH participants demonstrated significantly better performance compared to both older oNH and OHI individuals in ASA related tasks for both speech and music (see Fig. 2 ). In the speech domain, yNH participants achieved superior speech reception thresholds (SRT) as measured by the Oldenburg Sentence Test 88 , with a significant effect of group ( F (2, 85) = 20.13, p < .001, η ² = .32). Similarly, in the music domain, performance on the MSA test revealed a significant main group effect ( F (2, 85) = 11.77, p < .001, η ² = .22). Modelling individual differences Despite attempts to match age across the groups of oNH and oHI participants and hearing thresholds across the groups of yNH and oNH during recruiting, small but significant differences remained. Specifically, oHI were significantly older than oNH ( M difference = 5.56 years, p = .004). Additionally, yNH demonstrated significantly better hearing thresholds than the oNH group ( p < .001), with mean pure-tone average thresholds values of the better ear (PTA) of 3.58 ( SD = 3.79) and 11.39 ( SD = 5.30), respectively. Furthermore, all examined factors were significantly intercorrelated (see Table 1 ). These factor intercorrelations complicate isolating the unique contribution of each factor to ASA abilities. For example, the observed strong negative association between PTA and MSA scores ( r = .49, p < .001) might reflect a confound by age-related decline of both hearing and cognition that might influence MSA independently, instead of representing a direct effect of hearing loss. Table 1 The correlation matrix for the test battery Variable MSA SRT Age PTA Musical training WMC MSA - − .50 *** − .41 *** − .49 *** .44 *** .35 *** SRT 84 - .47 *** .64 *** − .18 − .27 * Age 88 90 - .63 *** − .23 * − .42 *** PTA 87 89 90 - − .28 ** − .42 *** Musical training 82 84 85 84 - .37 *** WMC 82 84 85 84 85 - Note . Above the diagonal are the Pearson correlations calculated from a complete pair of values, which are significant at * p < .05, ** p < .01, *** p < .001. Below the diagonal is the total N for the respective analysis (differences due to missing data). MSA = Musical Scene Analysis; SRT = Speech Reception Thresholds; PTA = Pure Tone Average; WMC = Working Memory Capacity (see Methods for more details). To account for intercorrelations among predictors, two complementary statistical approaches were employed: Ridge-penalized linear regression and a gradient-boosted decision tree model (’ XGBoost ’) 89 . By incorporating an L2-norm regularization penalty, ridge regression addresses intercorrelations by shrinking the coefficients of correlated predictors, resulting in more robust and stable model estimates 90 . However, given the plausibility of possible non-linear effects and complex interactions among factors, ‘ XGBoost ’ was employed in addition to the ridge-penalized linear regression model. Importantly, predictor interactions are implicitly considered in ‘ XGBoost ’ models through sequential splits on different factors, allowing the model to capture how the influence of one predictor can vary depending on the value of another. All participants with complete data, including the single yHI individual, were included in the modelling to support the estimation of independent and interacting effects of age, hearing loss, and other predictors. Both models were used to predict music-related MSA and speech-related SRT scores (see Methods for details on model construction). For MSA, the ridge regression model yielded a root mean square error (RMSE) of .30 and an R² of .36. The ‘ XGBoost ’ model demonstrated comparable performance, with an RMSE of .30 for the training set and an RMSE of .31 for the test set, resulting in R² values of .34 and .31, respectively. For SRT, ridge regression produced an RMSE of 1.4 and an R² of .41, while ‘ XGBoost ’ achieved an RMSE of 1.19 (MAE = .64) for the training set and 1.21 (MAE = .84) for the test set, with corresponding R² values of .58 and .55. The small differences in RMSE values between the training and test sets indicate that the ‘ XGBoost ’ models generalize appropriate to new data, indicating a relatively low risk of overfitting. Hearing loss In both statistical models, poorer hearing thresholds (higher PTA values) were associated with worse ASA performance in both music (MSA) and speech. Specifically, higher PTA correlated with higher (i.e., worse performance) SRT scores ( r = .64, p < .001, see also Fig. 3 ) and lower (worse) MSA scores ( r = − .49, p < .001). The ridge regression model identified PTA as the strongest predictor of ASA performance across both domains, yielding standardized coefficients of .09 ([.04, .13], p < .001) for MSA and .96 ([.68, 1.26], p < .001) for SRT. However, while the overall trend was linear, the strength of this relationship varied across groups; that is, PTA had little influence on ASA performance within the normal-hearing range (< 20 dB HL) but became increasingly pronounced as hearing thresholds deteriorated (Fig. 3 b, d). The SHAP analysis (SHapley Additive exPlanations) provided by the ‘ XGBoost ’ model corroborated these group-based findings by quantifying the predictor’s marginal contribution to ASA performance. SHAP values reflect the extent to which PTA influences model predictions, independent of other predictors, and are expressed directly in the test score scale (see Methods for details). The SHAP-based trajectory - captured by using a locally estimated scatterplot smoothing (LOESS) regression line - revealed a nonlinear but monotonic trend: PTA had minimal impact below 20 dB HL, followed by a steady increase in its effect up to ~ 40 dB HL, where it plateaued (Fig. 3 c, e). For instance, individuals with ~ 40 dB HL exhibited an expected SRT performance 1.6 dB worse than those with a PTA of ~ 23 dB HL, with the latter representing an SHAP score of 0. Working memory capacity Working memory capacity (WMC), assessed using the Backwards Digit Span task, correlated significantly with ASA performance in both speech (r = − .27, p < .05) and music (r = .35, p < .001, see also Fig. 4 ). However, these correlations were largely attributable to group membership, defined by age and PTA: Within-group analyses showed no significant correlations between WMC and either SRT or MSA (Fig. 4 b, d). The ridge regression model further indicated that WMC did not significantly predict ASA performance in either domain, yielding a coefficient of − .03 ([-.08, .01], p = .16) for MSA and .03 ([-.18, .17], p = .90) for SRT. Yet, SHAP-based feature analysis suggested a different interpretation: For SRT, SHAP values indicated no discernible effect of WMC, with a near-flat trajectory suggesting negligible predictive relevance across the observed WMC range (Fig. 4 e). In contrast, for MSA, WMC scores revealed a modest but consistent positive effect on performance (Fig. 4 c). Musical training Musical training, as measured by the Gold-MSI subscale 92 , exhibited distinct relationships with ASA performance in speech and music. For speech, no significant correlation was observed ( r = − .18, p = .11, see also Fig. 5 ). In contrast, for music, a moderate positive correlation emerged ( r = .44, p < .001), with these trends remaining generally consistent within groups (Fig. 5 b, d). The ridge regression model further substantiated these findings, indicating that musical training was a significant predictor of MSA performance while showing minimal influence on SRT. For MSA, musical training yielded a standardized coefficient of .08 ([.14, .03], p = .002), which was comparable in magnitude to PTA. In contrast, for SRT, musical training did not emerge as a significant predictor ( β = − .07 [-.30, .17], p = .53). SHAP-based feature analysis further supported these findings. For MSA, SHAP values for musical training exhibited a steady increase across training levels (Fig. 5 c). Conversely, in SRT, SHAP values remained near zero across the observed range, suggesting the absence of a meaningful contribution on ASA performance (Fig. 5 e). Age Age was significantly correlated with ASA performance in both speech ( r = .47, p < .001, see also Fig. 6 ) and music ( r = − .41, p < .001), suggesting a general decline with increasing age. However, within groups, which are categorised based on PTA and age itself, these correlations disappeared (Fig. 6 b, d). The only significant within-group correlation, observed for SRT in the yNH group ( n = 25; r = .48, p = .01; Fig. 6 b), was driven by a single extreme outlier. Excluding this participant eliminated the effect ( r = − .03, p = .87). Despite the absence of age effects within specific groups, SHAP-based analysis revealed a monotonic increase in its contributions across the full lifespan (Fig. 6 c, e) in both domains. Ridge regression results aligned with this pattern, showing a significant effect of age on MSA (𝛽 = .05, [.01, .10], p = .02), while the effect on SRT did not reach significance (𝛽 = .17, [-.02, .38], p = .08). This suggests that, while age effects are less evident within specific subgroups, they manifest across the entire age range. Overall factor strength The ‘ XGBoost ’ and ridge regression models largely align in identifying key predictors of ASA performance, though slight differences emerge in the relative magnitudes of associations, particularly for MSA. To quantify these relationships, the SHAP contribution range was evaluated in relation to the listeners performance range on the respective test score scale. Specifically, instead of considering the full observed range of MSA and SRT scores, the top and bottom 2.5% of performers were excluded to remove extreme values (i.e., retain only the central 95% of the distribution). This allowed for an estimate of each predictor’s relative contribution to ASA performance across domains. For instance, a SHAP value of .5 for SRT corresponded to approximately 8.3% of the performance range within this adjusted 95% distribution (Fig. 7 a, c). Applying this method, musical training accounted for the largest share of variance in MSA (12%), followed by age (10%) and hearing loss (9%), while WMC covered 7%. Ridge regression results showed a similar pattern, ranking hearing loss and musical training as the strongest predictors, with age exerting a slightly weaker effect. WMC showed a small but non-significant association. For speech, both models consistently identify hearing loss as the strongest predictor, with SHAP contributions covering 33% of the observed range. In contrast, neither model detected a meaningful association for WMC or musical training, both of which remained negligible across the observed data range. Age exhibited a weaker association with SRT compared to hearing loss, covering 19% of the SHAP contribution range, but did not reach significance in the ridge model (Fig. 7 b, d). Discussion This study investigated a diverse sample of listeners with varying hearing thresholds, WMC, musical training levels, and ages to evaluate the unique contributions of these factors to ASA performance in music and speech. Consistent with prior research, younger NH individuals outperformed all other groups in both ASA tasks, followed by older participants with NH, with older HI individuals performing the worst. Importantly, however, the observed and well-documented interrelationship among the examined factors complicate the interpretation of individual isolated factor contributions 25 , 86 , 87 . To address this, two approaches have been employed, one that implies linearity (ridge regression) and another one that does not (called ‘ XGBoost ’). Notably, individual ASA performance in speech and music are moderately correlated ( r = − .50), suggesting shared underlying mechanisms across both domains (in line with Bregman ⁠ 1 ). Conversely, listener-specific factors exhibited domain-dependent associations with ASA performance, aligning with earlier suggestions of additional and distinct mechanisms for speech and non-speech processing 93 . Effect of hearing loss We identified a strong relationship between hearing loss (PTA in dB HL) and poorer ASA performance in speech, alongside a moderate relationship with MSA. Both the ‘ XGBoost ’ model estimations and group-level analyses indicated that the adverse effects of hearing loss became apparent even in individuals with mild hearing loss (PTA > 20 dB HL), with ridge regression identifying it as the strongest predictor of ASA abilities in both domains. While some research has reported no significant relationship between hearing loss and stream segregation 26 , the present findings align with broader evidence highlighting the critical role of intact auditory pathways for effective ASA across both music and speech ⁠ 3 , 10 – 12 . Discrepancies in prior findings may be a consequence of differences in task design, as many studies have relied on artificial paradigms (e.g., pure-tone ‘ABA’ designs) rather than ecologically valid stimuli. Additionally, the extent to which high-frequency hearing loss is considered may be relevant, as ASA deficits may be more pronounced when thresholds above 4 kHz are taken into account 94 , which are often disregarded in conventional assessments. Interestingly, MSA appeared more robust to hearing loss compared to speech-based ASA, with the relative impact of hearing loss on speech being approximately three times greater than in music. This difference may be partially attributed to task demands. In the speech task, participants were required to recall exact words embedded in noise. In contrast, the MSA task allowed for the detection of a target instrument based on brief, salient cues. That is, listeners may have benefitted from short temporal gaps in the background mixture—so-called ‘listening in the dips’ opportunities—without needing to track the target continuously throughout the excerpt. If this ability remains at least partially preserved, hearing loss may exert a weaker influence on MSA. However, dip listening in musical scenarios may also be impaired in individuals with hearing loss ⁠ 4 , which could limit the effectiveness of this strategy. Effect of working memory capacities As listening conditions become more challenging (e.g., increased background noise or increasing the number of instruments in a mixture), auditory processing becomes more effortful, relying increasingly on attentional and WM resources 32 , 95 . Consequently, we hypothesized that individuals with greater WMC would be better equipped to manage the complexity of musical mixtures, facilitating the retention, rehearsal, and recall of individual instruments. Indeed, while an overall relationship between WMC and ASA performance in both speech and music domains was observed, this relationship was absent within individual groups. Both models further support this, identifying WMC as the least predictive factor of ASA performance overall. Specifically, WMC showed no effect on speech-based ASA. For MSA, model estimates diverged: while ‘ XGBoost ’ suggested a small positive association, the ridge regression model did not yield a statistically significant effect. These results suggest that any potential effect of WMC on ASA is likely minimal, and the current data do not offer strong support for its practical significance. Nonetheless, this finding is somewhat unexpected, given the well-documented role of WMC in auditory perception 25 , 27 and prior evidence linking WMC to stream segregation and speech-in-noise listening performance 96 , particularly among older hearing-impaired listeners 28 , 29 . Several factors may explain this discrepancy. One possible explanation is that previously reported associations between WMC and ASA may primarily reflect confounding influences of hearing impairment or aging, rather than a direct causal relationship. For instance, Golden et al. 97 found no significant differences in ASA abilities in music between Alzheimer’s patients and healthy age-matched controls, despite the former group exhibiting pronounced cognitive impairments, including deficits in WMC 98 . Other evidence suggests the WMC association with ASA to be age-dependent, with no effects being reported among NH individuals 38 - a pattern consistent with our group-based findings. Alternatively, task-specific factors may account for the weak or inconsistent findings. The speech-in-noise task employed in this study may impose minimal WMC demands compared to other ASA tasks requiring more demanding scenarios, such as multiple talkers embedded in background sounds. Similarly, the weak but visible trend for an effect of WMC on MSA scores may be attributed to the design of the MSA task, which involves a single-item recall paradigm with a short (1-second) delay, thereby relying to a small degree on a working memory mechanism. While the current study assessed WMC in terms of capacity (i.e., the measurable cognitive limit to store items), future work should consider a broader conceptualization of working memory, e.g. including attentional control, or broader working memory capabilities, as these factors may better explain variability in ASA performance across speech and music domains. Additionally, future studies could incorporate more demanding ASA paradigms, such as those requiring sustained stream tracking or complex auditory scene recall, to better evaluate the role of WMC in advanced ASA tasks. Effect of musical training Musical practice often involves the identification and discrimination of subtle acoustic details and advance integration of complex auditory streams on a regular basis. Thus, musical training cultivates a unique skill set, potentially offering musicians a distinct advantage within complex auditory scenes like those encountered in ASA tasks 55 . To investigate this, we abstained from a general cut-off score for musicianship, instead considering musical training as a continuum. Our findings revealed a moderate positive relationship between musical training and MSA abilities, independent of hearing loss or age. Specifically, the model suggests that the strength of association of having no musical training compared to extensive musical training on MSA performance is comparable to the impact of mild-to-moderate hearing loss or ageing by approximately 40 years. This finding aligns with research demonstrating the potential of musical training to mitigate age-related auditory decline or declines associated with reduced hearing sensitivity 61 – 65 . Notably, however, this effect was observed exclusively for musical and not for speech-based ASA. The lack of a clear transfer effect highlights the domain-specific nature of auditory processing, suggesting that skills cultivated through musical education and training may not readily generalise to the unique cognitive and perceptual demands of speech perception in noise. Our findings thus challenge the assumption that musical training universally enhances auditory abilities, raising the question of whether and how its benefits might extend to speech perception - a debate that remains unresolved and polarised in the current literature 52 , 66 – 68 , 99 . However, such associations may depend on task difficulty and listener characteristics, with transfer effects more likely to be of benefit under challenging listening conditions. Follow-up studies should explicitly account for these contextual dependencies 100 . Effect of ageing Age, with its associated changes in hearing sensitivity, neural degeneration, and general cognitive functioning ⁠ 7 often serves as a proxy for a multitude of underlying physiological and auditory influences. These factors, however, complicate the isolation of chronological age's distinct contribution to ASA. To disentangle these effects, we examined a broad age range while controlling for variations in hearing thresholds within groups. At the sample level, we observed a general decline in ASA performance across both speech and music, with increased age and hearing loss. However, within the three groups defined by age range and hearing loss severity, the age effect was largely diminished. Even among older participants (50s-80s), a period typically associated with hearing impairment onset, age was not a strong predictor of ASA performance when hearing loss was accounted for. This suggests that the initial correlations between ASA and age are primarily driven by variability in individuals' hearing thresholds rather than chronological age itself. Nonetheless, considering the full lifespan, aging manifests a practically relevant negative impact on ASA performance in both MSA and speech-based ASA. However, incorporating additional factors - such as attentional control or processing speed - as well as factors capturing hearing impairments beyond overt hearing loss may further account for the variance in performance that is currently attributed to aging. Limitations While the present study offers valuable insights into individual differences in ASA across music and speech domains, several limitations should be acknowledged. First, while the dataset covers a broad range of ages and hearing profiles, it includes only one younger HI adult. As a result, generalizations to younger HI populations are not feasible on the basis of the present dataset. Second, cognitive screening was limited to the WMC only, however, deficits in attention, processing speed, executive functioning or other undiagnosed cognitive impairments could impact task performance and may obscure true effects by introducing variance that cannot be account for in the model. Given that ageing is often accompanied by cognitive decline, the lack of screening limits our ability to rule out confounding effects of cognitive impairment, particularly among older participants. In addition, while both ASA tasks tap into the ability to parse acoustic scenes, they likely engage working memory and attentional resources to different extents. Correspondingly, the tasks differ in structure and perceptual demands, limiting the direct comparability of performance across domains. Future research should aim to better align task designs—for example, by evaluating MSA performance alongside multi-talker speech scenarios, or possibly by comparing speech-in-noise perception using the Oldenburger Sentence Test alongside the Music-In-Noise Task 101 —to more clearly dissociate domain-general from domain-specific mechanisms in ASA. It would also be important to incorporate cognitive screening measures and to consider controlling for both the onset and type of hearing loss (e.g., sensorineural, conductive), as well as specific musical perceptual deficits such as congenital amusia. Conclusion This study highlights the complex interplay of factors contributing to individual differences in ASA processes across speech and music domains. While performance in speech and music-related ASA tasks was generally correlated, distinct predictors emerged to be practically impactful for each domain. In speech, hearing loss and age were the most influential factors, whereas in music, musical training played a pivotal role in predicting MSA abilities. Aging and hearing loss had a less pronounced but still notable impact on the performance in the musical ASA task. Working memory capacity showed no effect on speech-based ASA performance and had only a limited influence on the musical ASA task. These findings highlight the critical role of hearing sensitivity in processing auditory scenes across domains, but particularly in speech-related ASA. Conversely, the observed influence of musical training suggests that extensive musical experience may partially mitigate the effects of hearing loss or aging in domain-specific (i.e. music-related) tasks, highlighting its compensatory potential. Materials and Methods Participants The experiment included a total of 92 participants (47 males and 45 females). Participants were divided into three groups: 31 older adults with NH (M = 62.3, SD = 7.3; 18 female), 34 older adults with HI (M = 67.8, SD = 7.7; 16 female), and 26 younger adults with NH (M = 25.3; SD = 4.1, 11 female). One additional younger adult with HI (yHI; age = 26, male) was excluded from group comparisons but retained in regression-based model analysis. A graphical illustration of individuals' pure-tone audiometric thresholds can be found in Figure A1 (in Supplementary Materials). If the average pure-tone audiometric measurement for the better ear in a quiet setting surpassed 20 dB HL, subjects were categorised as hearing-impaired (e.g., Humes, 2019). Test battery Musical Scene Analysis task (MSA) ⁠ 3 . The adaptive Musical Scene Analysis test (MSA) is a listening task developed to assess participants' ASA abilities in the context of realistic musical stimuli. The MSA adopts a ‘yes-no’ paradigm that resembles a 2-alternative-forced-choice task (2-AFC). In each trial, participants hear a two-second excerpt of a single instrument or voice (the target), a one-second silence, and a two-second multi-instrument excerpt (the mixture). Participants are then asked to decide whether the target was part of the mixture or not. The target instrument varies across trials and includes four different instrument types: bass, guitar, lead, and piano. Stimuli are sourced from an open-source music-database ("MedleyDB"), which consists of real-world multitrack music recordings representing a wide range of musical genres (e.g., pop, rock, world/folk, fusion, jazz, rap, classical). All stimuli were presented monaurally. Participant ability is estimated using weighted-likelihood estimation and is projected on a scale of approximately − 3 to + 3. A score of zero corresponds to the median performance of the reference population from the calibration experiment ⁠ 3 , which predominantly included NH and mild HI individuals. The study employed two test sets of the adaptive MSA (version 2.4) with 30 items each. Backwards Digit Span Memory Test ( WMC) 91 ). This established working memory assessment requires participants to recall digit sequences in reverse order (e.g., for the sequence 1 2 3 4, the correct response is 4 3 2 1). Digits are presented visually, one at a time. The test comprised sequences of varying lengths (2 trials each of 4-digit and 5-digit sequences; 4 trials each of 6-digit and 7-digit sequences). Performance is measured by the proportion of correctly recalled digits in each sequence. Average results over all sequences are reported. Oldenburger Satztest ( SRT) 88 . This adaptive speech-in-noise perception test assesses an individual's ability to comprehend spoken sentences within a background of speech-shaped white noise. Participants have to verbally repeat five-word sentences presented by a male speaker. These sentences were randomly generated from a predefined word class structure (name-verb-numeral-adjective-object), with each word class drawn from a pool of 10 possible word alternatives. The speech level is adaptively varied against a constant noise level to determine the individual's 50% speech reception threshold (SRT in dB HL). That is, speech level was adaptively adjusted based on word accuracy: decreased by 1.0 dB for five correctly repeated words, 0.5 dB (4 correct), unchanged (3 correct), or increased by 0.5–1.5 dB (2 to 0 correct). The composite score of two test lists of 20 sentences each were used. Lower scores indicate better performance. Pure-tone-average audiometry ( PTA ). Hearing thresholds were assessed using standard clinical procedures with an Interacoustics AD528 portable audiometer. Pure-tone thresholds were measured at .125, .25, .5, 1, 2, 4, and 8 kHz. Given the established impact of high-frequency hearing loss on complex auditory processing 102 , 103 , the PTA was calculated as an average across all measured frequencies. Participants with a PTA exceeding 20 dB HL in the better ear were classified as hearing-impaired 104 . A lower PTA indicates better hearing sensitivity. Musical training subscale of the Goldsmith Musical Sophistication Index ( GMSI) 92 . The GMSI is a self-report questionnaire that assesses several aspects of musical expertise and experience, including a subscale for musical training. Participants responded to questions such as “ At the peak of my interest, I practised my primary instrument for _ hours per day ” and rated their agreement with statements like “ I would not consider myself a musician ” (negatively coded). Responses were given either numerically or on a 7-point Likert scale, ranging from 1 (‘Completely Disagree’) to 7 (‘Completely Agree’). The final musical training score is calculated from a 7-item set, with scores ranging from 1 to 7, where higher scores indicate greater levels of musical training. Procedure Ethical approval for the study was obtained from the Ethics Committee at the Carl von Ossietzky University Oldenburg (Drs.EK/2019/092). All participants provided informed consent prior to participation and received compensation at a rate of €10 per hour. All methods used in this study were performed in accordance with relevant guidelines and regulations. Testing was conducted in two phases. The first phase took place in a controlled laboratory setting. Participants were seated in a soundproof booth and stimuli were presented via calibrated equipment consisting of a computer, an RME Babyface sound card, and Sennheiser HD650 headphones. Long-term sound level was set to 75 dB SPL (A), measured with a Norsonic Nor140 sound-level metre using music-shaped noise. Following a demographics questionnaire, participants completed a pure-tone audiometric test and the speech-in-noise assessment. Participants then undertook the first set of a 30-item adaptive MSA test, followed by additional tests, which will not be addressed in this manuscript. The first phase concluded with a second set of the adaptive MSA. The second testing phase was administered online at least 24 hours after the initial session among the same participants. This phase included the full GMSI self-report questionnaire and the ‘Backwards Digit Span’ test. Each of the employed tests incorporated a brief training session with practice items. Participants received immediate feedback and could repeat the training as needed. Participants wearing hearing aids were instructed to remove their devices for the duration of the study (N = 19). This decision was made to avoid unwanted acoustic interference when using headphones and to reduce listener-specific variability introduced by hearing aid processing. To ensure audibility, all participants underwent a loudness check not wearing any hearing aids during the training phase of each task. They were explicitly asked whether the sound level was comfortable and intelligible and were invited to adjust the presentation level if needed. Only two participants requested a higher volume (+ 4 and + 5 dB), while five opted to reduce the level. Note that previous research did not find marked effects of level on MSA in these small ranges that all fall within the comfortable listening range of participants ⁠ 4 . Further details on hearing aid usage history and individual presentation level adjustments are provided in Table A1 (in Supplementary Materials). Overall, the laboratory phase required approximately 80–120 minutes, while the online phase took 25–40 minutes. Data Analyses All analyses were executed in R (v2023.10.31) 105 . The significance level was set at p < .05 for all statistical tests. Pearson's correlation coefficients were calculated to examine the relationships between SRT and MSA scores and other auditory and cognitive tests, including Oldenburger Satztest, Backwards Digit Span Memory Test, and the subtest for musical training of the Goldsmith Musical Sophistication Index. Analysis of Variance (ANOVA) was conducted to compare the mean MSA and SRT scores across three different participant groups (excluding the one yHI). Post-hoc comparisons were performed using Bonferroni corrections to control for Type I error. Analyses were conducted on available data, with sample size variations explicitly reported. To predict SRT and MSA scores, both ridge regression and gradient boosted decision tree models were employed. Ridge regression applies L2 regularization to mitigate multicollinearity while assuming linear relationships among predictors. In contrast, the decision tree model accounts for both multicollinearity and complex non-linear interactions without assuming linearity. Predictors included the subtest of the GMSI for musical training, age, PTA, and WMC. Ridge Regression Model Construction Prior to analysis, all predictor variables were standardized, and only complete data were included (MSA: n = 82; SRT: n = 84). The optimal regularization parameter (L2) lambda parameter was determined via 10-fold cross-validation. The final ridge regression model was then fitted using this optimal lambda (~ .11 for SRT and ~ .16 for MSA). Bootstrap resampling (1,000 iterations) was applied to estimate the variability of the regression coefficients. Confidence intervals (95%) and two-sided bootstrapped p-values were computed for each coefficient, providing robust inferential statistics. This approach allowed for the assessment of the stability and significance of predictor effects across resampled datasets. The ridge regression model was applied to the full dataset, without holding out a separate test set for validation. Gradient Boosted Decision Tree Model (XGBoost) For the ‘ XGBoost ’ model (Chen & Guestrin, 2016), the ‘ gbtree ’ booster method was implemented using the ‘ xgboost ’ package in R (v1.7.7.1). Complete datasets from participants with available MSA (n = 89) and SRT (n = 91) scores were included. Model construction employed a random hyperparameter search, an efficient approach for exploring a broad hyperparameter space. Random search is often preferred for hyperparameter optimization as it tests diverse parameter combinations, increasing the likelihood of identifying configurations that minimize validation error 106 . A total of 2,000 random iterations for both MSA and SRT were conducted, with parameter bounds set to balance model complexity and mitigate the risk of overfitting. The specified bounds were as follows: learning rate ( eta : .05–.15), maximum tree depth ( max_depth : 3–5), number of boosting rounds ( nrounds : 50–100), data subsample ratio per boosting round ( subsample : .5–.9), feature subsample ratio per tree (colsample_bytree: .5–.8), minimum child weight ( min_child_weight : 1–20), L2 regularisation ( lambda : 0–1.5), L1 regularisation ( alpha : 0–1.5), and minimum loss reduction required to make a further split ( gamma : 0–1.5). The training and test set split was also optimised as part of the tuning process, with bounds set between .5 and .9. Model performance was evaluated using a combination of Root Mean Squared Error (RMSE) and differences in RMSE and R 2 between the training and test sets as an indicator of model fit and overfitting risk. To ensure robust RMSE estimation, repeated random sub-sampling validation (also known as Monte Carlo cross-validation) with 500 iterations was implemented, where each iteration involved a different random split of the data into training and test sets. To further ensure hyperparameter suitability, only models with an RMSE and R 2 difference between the training and test sets below .1 were considered. Instead of selecting a single best-performing model, the average of the top 1% (n = 20) of models with the lowest RMSE was then used for the analysis. This approach mitigates the influence of hyperparameter variability and enhances the robustness of the final model estimates. This procedure has been done predicting MSA and SRT scores separately. For MSA, the final hyperparameter estimates were as follows (mean ± 95% CI): learning rate (η = .079 [.069, .090]), maximum tree depth (3.7 [3.36, 4.04]), subsample ratio (.557 [.542, .573]), feature subsample ratio per tree (.684 [.660, .708]), minimum child weight (16.6 [15.7, 17.5]), L2 regularization (λ = .776 [.629, .923]), L1 regularization (α = .646 [.484, .807]), and minimum loss reduction (γ = .254 [.175, .334]). The average number of boosting rounds was 75.7 [71.0, 80.4], with a data split of 88.3% for training and 11.7% for testing. For SRT, the final hyperparameter estimates were: learning rate (η = .082 [.07, .093]), maximum tree depth (4.05 [3.63, 4.47]), subsample ratio (.746 [.7, .792]), feature subsample ratio per tree (.693 [.670, .717]), minimum child weight (14.5 [13.1, 15.9]), L2 regularization (λ = .926 [.745, 1.11]), L1 regularization (α = .831 [.615, 1.05]), and minimum loss reduction (γ = .803 [.611, .995]). The average number of boosting rounds was 72. [66.1, 78.], with 88.7% of data used for training and 11.3% for testing. SHAP-based model estimates for individual feature contribution Importantly, ‘ XGBoost ’ model allows the calculation of SHAP values (SHapley Additive exPlanations), a method for quantifying individual-level feature contributions to model predictions 107 . SHAP values assess each predictor’s marginal contribution by considering all possible combinations of features being present or absent, providing a nuanced interpretation of how individual differences influence model outcomes. Positive SHAP values indicate that a feature increases the predicted score, while negative values suggest a decreasing effect. Since SHAP values are presented on the original test scale, their interpretation follows the scoring direction of each measure. For SRT, lower values indicate better performance, meaning higher SHAP values correspond to poorer outcomes. In contrast, for MSA, higher values denote better performance, so higher SHAP values reflect improved outcomes. The sum of all SHAP values for a participant, combined with the model’s baseline prediction, determines the final predicted score (see Fig. 8 for an illustration). SHAP value estimates and their corresponding LOESS trajectory are constrained to the observed data range and should not be extrapolated beyond it. For instance, PTA values exceeding 50 dB HL fall outside the estimated range and cannot be reliably inferred from the model. Final SHAP estimates were derived from the top 1% of models (n = 20) to account for variability in hyperparameter configurations. Confidence intervals (99%) for these estimates were generated through 500 bootstrap iterations, enhancing the robustness of feature contribution interpretations. Declarations Acknowledgements. This work has been funded by the Freigeist Fellowship of the Volkswagen Foundation to KS. This study was also funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project ID 352015383 – SFB 1330 A6. Author contributions. RH, DM and KS designed the study and analysed the data. RH wrote the first manuscript draft. All co-authors edited and approved the final version of the manuscript. Data Availability Statement. The dataset, analysis scripts, and MSA test used in this study are available in the author’s GitHub repository (https://github.com/rhake14). Ethics approval. Ethical approval was provided by the Ethics Committee at University of Oldenburg (Drs.EK/2019/092). Consent to participate. Informed consent was obtained from all participants in this study, permitting the collection of anonymized data and its potential publication in journal articles. Conflict of interest. The authors declare that there is no conflict of interest. Copyright. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. References Bregman, A. S. Auditory Scene Analysis (The MIT Press, 1990). Alain, C. & Bernstein, L. J. Auditory Scene Analysis. Music Perception 33, 70–82 (2015). Hake, R. et al. Development of an adaptive test of musical scene analysis abilities for normal-hearing and hearing-impaired listeners. Behavior research methods, 5456–5481 (2024). Siedenburg, K., Röttges, S., Wagener, K. C. & Hohmann, V. Can You Hear Out the Melody? Testing Musical Scene Perception in Young Normal-Hearing and Older Hearing-Impaired Listeners. Trends in hearing 24, 2331216520945826 (2020). Williamson, J. M. in Teaching to Individual Differences in Science and Engineering Librarianship (Elsevier, 2018), pp. 1–10. Anderson, S. & Karawani, H. Objective evidence of temporal processing deficits in older adults. Hearing research 397, 108053 (2020). Auerbach, B. D. & Gritton, H. J. Hearing in Complex Environments: Auditory Gain Control, Attention, and Hearing Loss. Frontiers in neuroscience 16, 799787 (2022). Phillips, S. L., Gordon-Salant, S., Fitzgibbons, P. J. & Yeni-Komshian, G. Frequency and temporal resolution in elderly listeners with good and poor word recognition. Journal of speech, language, and hearing research : JSLHR 43, 217–228 (2000). Rimmele, J. M., Sussman, E. & Poeppel, D. The role of temporal structure in the investigation of sensory memory, auditory scene analysis, and speech perception: a healthy-aging perspective. International journal of psychophysiology : official journal of the International Organization of Psychophysiology 95, 175–183 (2015). Akeroyd, M. A., Gatehouse, S. & Blaschke, J. The detection of differences in the cues to distance by elderly hearing-impaired listeners. The Journal of the Acoustical Society of America 121, 1077–1089 (2007). Middlebrooks, J. C. in The Auditory System at the Cocktail Party, edited by J. C. Middlebrooks, J. Z. Simon, A. N. Popper & R. R. Fay (Springer International Publishing, 2017), pp. 137–168. Shinn-Cunningham, B. G. & Best, V. Selective attention in normal and impaired hearing. Trends in amplification 12, 283–299 (2008). Bayat, A. et al. A comparison of auditory perception in hearing-impaired and normal-hearing listeners: an auditory scene analysis study. Iranian Red Crescent medical journal 15, e9477 (2013). Johannesen, P. T., Pérez-González, P., Kalluri, S., Blanco, J. L. & Lopez-Poveda, E. A. The Influence of Cochlear Mechanical Dysfunction, Temporal Processing Deficits, and Age on the Intelligibility of Audible Speech in Noise for Hearing-Impaired Listeners. Trends in hearing 20 (2016). Choi, J. E. et al. Relationship between spectrotemporal modulation detection and music perception in normal-hearing, hearing-impaired, and cochlear implant listeners. Scientific reports 8, 800 (2018). Emiroglu, S. & Kollmeier, B. Timbre discrimination in normal-hearing and hearing-impaired listeners under different noise conditions. Brain research 1220, 199–207 (2008). Uys, M. & van Dijk, C. Development of a music perception test for adult hearing-aid users. The South African journal of communication disorders = Die Suid-Afrikaanse tydskrif vir Kommunikasieafwykings 58, 19–47 (2011). Oxenham, A. J. Pitch perception and auditory stream segregation: implications for hearing loss and cochlear implants. Trends in amplification 12, 316–331 (2008). Kirchberger, M. J. & Russo, F. A. Development of the adaptive music perception test. Ear and hearing 36, 217–228 (2015). Siedenburg, K., Goldmann, K. & van de Par, S. Tracking Musical Voices in Bach's The Art of the Fugue: Timbral Heterogeneity Differentially Affects Younger Normal-Hearing Listeners and Older Hearing-Aid Users. Frontiers in psychology 12, 608684 (2021). Madsen, S. M. K. & Moore, B. C. J. Music and hearing aids. Trends in hearing 18 (2014). Vaisberg, J. M., Martindale, A. T., Folkeard, P. & Benedict, C. A Qualitative Study of the Effects of Hearing Loss and Hearing Aid Use on Music Perception in Performing Musicians. Journal of the American Academy of Audiology 30, 856–870 (2019). Goossens, T., Vercammen, C., Wouters, J. & van Wieringen, A. Masked speech perception across the adult lifespan: Impact of age and hearing impairment. Hearing research 344, 109–124 (2017). Akeroyd, M. A. Are individual differences in speech reception related to individual differences in cognitive ability? A survey of twenty experimental studies with normal and hearing-impaired adults. International journal of audiology 47 Suppl 2, S53-71 (2008). Davidson, A. & Souza, P. Relationships Between Auditory Processing and Cognitive Abilities in Adults: A Systematic Review. Journal of speech, language, and hearing research : JSLHR 67, 296–345 (2024). Lentz, J. J., Humes, L. E. & Kidd, G. R. Differences in Auditory Perception Between Young and Older Adults When Controlling for Differences in Hearing Loss and Cognition. Trends in hearing 26, 23312165211066180 (2022). Humes, L. E. Factors Underlying Individual Differences in Speech-Recognition Threshold (SRT) in Noise Among Older Adults. Frontiers in aging neuroscience 13, 702739 (2021). Lo, C. Y. et al. On-beat rhythm and working memory are associated with better speech-in-noise perception for older adults with hearing loss. Quarterly journal of experimental psychology (2006), 17470218241311204 (2025). Dryden, A., Allen, H. A., Henshaw, H. & Heinrich, A. The Association Between Cognitive Performance and Speech-in-Noise Perception for Adult Listeners: A Systematic Literature Review and Meta-Analysis. Trends in hearing 21, 2331216517744675 (2017). Heinrich, A., Henshaw, H. & Ferguson, M. A. The relationship of speech intelligibility with hearing sensitivity, cognition, and perceived hearing difficulties varies for different speech perception tests. Frontiers in psychology 6, 782 (2015). Janse, E. & Andringa, S. J. The roles of cognitive abilities and hearing acuity in older adults’ recognition of words taken from fast and spectrally reduced speech. Applied Psycholinguistics 42, 763–790 (2021). Nitsan, G., Banai, K. & Ben-David, B. M. One Size Does Not Fit All: Examining the Effects of Working Memory Capacity on Spoken Word Recognition in Older Adults Using Eye Tracking. Frontiers in psychology 13, 841466 (2022). Windle, R., Dillon, H. & Heinrich, A. A review of auditory processing and cognitive change during normal ageing, and the implications for setting hearing aids for older adults. Frontiers in neurology 14, 1122420 (2023). Garami, L., Chow, R., Fakuade, A., Swaminathan, S. & Alain, C. Orienting Attention to Auditory and Visual Short-term Memory: The Roles of Age, Hearing Loss, and Cognitive Status. Experimental aging research 46, 22–38 (2020). Pichora-Fuller, M. K. et al. Hearing Impairment and Cognitive Energy: The Framework for Understanding Effortful Listening (FUEL). Ear and hearing 37 Suppl 1, 5S-27S (2016). Zekveld, A. A., Kramer, S. E., Kessens, J. M., Vlaming, M. S. M. G. & Houtgast, T. The influence of age, hearing, and working memory on the speech comprehension benefit derived from an automatic speech recognition system. Ear and hearing 30, 262–272 (2009). Schoof, T. & Rosen, S. The role of auditory and cognitive factors in understanding speech in noise by normal-hearing older listeners. Frontiers in aging neuroscience 6, 307 (2014). Füllgrabe, C. & Rosen, S. On The (Un)importance of Working Memory in Speech-in-Noise Processing for Listeners with Normal Hearing Thresholds. Frontiers in psychology 7, 1268 (2016). Deutsch, D. (ed.). The psychology of music (Academic Press, 2013). Herholz, S. C. & Zatorre, R. J. Musical training as a framework for brain plasticity: behavior, function, and structure. Neuron 76, 486–502 (2012). Conzelmann, K. & Süß, H.-M. Auditory intelligence: Theoretical considerations and empirical findings. Learning and Individual Differences 40, 27–40 (2015). Woods, K. J. P. & McDermott, J. H. Schema learning for the cocktail party problem. Proceedings of the National Academy of Sciences of the United States of America 115, E3313-E3322 (2018). Bianchi, F., Santurette, S., Wendt, D. & Dau, T. Pitch Discrimination in Musicians and Non-Musicians: Effects of Harmonic Resolvability and Processing Effort. Journal of the Association for Research in Otolaryngology : JARO 17, 69–79 (2016). Grahn, J. A. & Rowe, J. B. Feeling the beat: premotor and striatal interactions in musicians and nonmusicians during beat perception. The Journal of neuroscience : the official journal of the Society for Neuroscience 29, 7540–7548 (2009). Micheyl, C., Delhommeau, K., Perrot, X. & Oxenham, A. J. Influence of musical and psychoacoustical training on pitch discrimination. Hearing research 219, 36–47 (2006). Nave-Blodgett, J. E., Snyder, J. S. & Hannon, E. E. Hierarchical beat perception develops throughout childhood and adolescence and is enhanced in those with musical training. Journal of experimental psychology. General 150, 314–339 (2021). Whittom, A. et al. Music Perception in Older Adults With Hearing Loss: Protective Effect of Musical Experience. Ear and hearing ; 10.1097/AUD.0000000000001615 (2024). Bugos, J. A. et al. Jazz Piano Training Modulates Neural Oscillations and Executive Functions in Older Adults. Music Perception 41, 378–392 (2024). Nandi, B. et al. Musical Training Facilitates Exogenous Temporal Attention via Delta Phase Entrainment within a Sensorimotor Network. The Journal of neuroscience : the official journal of the Society for Neuroscience 43, 3365–3378 (2023). Talamini, F., Altoè, G., Carretti, B. & Grassi, M. Musicians have better memory than nonmusicians: A meta-analysis. PloS one 12, e0186773 (2017). Nikjeh, D. A., Lister, J. J. & Frisch, S. A. Preattentive cortical-evoked responses to pure tones, harmonic tones, and speech: influence of music training. Ear and hearing 30, 432–446 (2009). Madsen, S. M. K., Marschall, M., Dau, T. & Oxenham, A. J. Speech perception is similar for musicians and non-musicians across a wide range of conditions. Scientific reports 9, 10404 (2019). Roden, I., Grube, D., Bongard, S. & Kreutz, G. Does music training enhance working memory performance? Findings from a quasi-experimental longitudinal study. Psychology of Music 42, 284–298 (2014). Berg, M. von, Steffens, J., Weinzierl, S. & Müllensiefen, D. Assessing room acoustic listening expertise. The Journal of the Acoustical Society of America 150, 2539 (2021). Johnson, N., Shiju, A. M., Parmar, A. & Prabhu, P. Evaluation of Auditory Stream Segregation in Musicians and Nonmusicians. International archives of otorhinolaryngology 25, e77-e80 (2021). Sauvé, S. A., Marozeau, J. & Rich Zendel, B. The effects of aging and musicianship on the use of auditory streaming cues. PloS one 17, e0274631 (2022). Zendel, B. R. & Alain, C. Concurrent sound segregation is enhanced in musicians. Journal of cognitive neuroscience 21, 1488–1498 (2009). Benocci, E. & Calcus, A. Stream segregation, musical abilities, and the development of speech perception in noise. JASA express letters 4 (2024). Benocci, E. & Calcus, A. in Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023 (European Acoustics Association, 2024), pp. 3893–3899. Marozeau, J., Innes-Brown, H., Grayden, D. B., Burkitt, A. N. & Blamey, P. J. The effect of visual cues on auditory stream segregation in musicians and non-musicians. PloS one 5, e11297 (2010). Dubinsky, E., Wood, E. A., Nespoli, G. & Russo, F. A. Short-Term Choir Singing Supports Speech-in-Noise Perception and Neural Pitch Strength in Older Adults With Age-Related Hearing Loss. Frontiers in neuroscience 13, 1153 (2019). Parbery-Clark, A., Strait, D. L., Anderson, S., Hittner, E. & Kraus, N. Musical experience and the aging auditory system: implications for cognitive abilities and hearing speech in noise. PloS one 6, e18082 (2011). Parbery-Clark, A., Anderson, S., Hittner, E. & Kraus, N. Musical experience offsets age-related delays in neural timing. Neurobiology of aging 33, 1483.e1-4 (2012). Reuter-Lorenz, P. A. & Park, D. C. How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychology review 24, 355–370 (2014). Zendel, B. R. & Alain, C. Musicians experience less age-related decline in central auditory processing. Psychology and aging 27, 410–417 (2012). McKay, C. M. No Evidence That Music Training Benefits Speech Perception in Hearing-Impaired Listeners: A Systematic Review. Trends in hearing 25, 2331216520985678 (2021). Schellenberg, E. G. & Lima, C. F. Music Training and Nonmusical Abilities. Annual review of psychology 75, 87–128 (2024). Coffey, E. B. J., Mogilever, N. B. & Zatorre, R. J. Speech-in-noise perception in musicians: A review. Hearing research 352, 49–69 (2017). Bürgel, M., Picinali, L. & Siedenburg, K. Listening in the Mix: Lead Vocals Robustly Attract Auditory Attention in Popular Music. Frontiers in psychology 12, 769663 (2021). Bürgel, M. & Siedenburg, K. Salience of Frequency Micro-modulations in Popular Music. Music Perception: An Interdisciplinary Journal 41, 1–14 (2023). Liberman, M. C. Noise-induced and age-related hearing loss: new perspectives and potential therapies. F1000Research 6, 927 (2017). Cunningham, L. L. & Tucci, D. L. Hearing Loss in Adults. The New England journal of medicine 377, 2465–2473 (2017). Bowl, M. R. & Dawson, S. J. Age-Related Hearing Loss. Cold Spring Harbor perspectives in medicine 9 (2019). Yueh, B., Shapiro, N., MacLean, C. H. & Shekelle, P. G. Screening and management of adult hearing loss in primary care: scientific review. JAMA 289, 1976–1985 (2003). Chen, B., Zhong, Y., Peng, W., Sun, Y. & Kong, W.-J. Age-related changes in the central auditory system: comparison of D-galactose-induced aging rats and naturally aging rats. Brain research 1344, 43–53 (2010). Profant, O. et al. Functional Age-Related Changes Within the Human Auditory System Studied by Audiometric Examination. Frontiers in aging neuroscience 11, 26 (2019). Syka, J. in Aging and Hearing, edited by K. S. Helfer, E. L. Bartlett, A. N. Popper & R. R. Fay (Springer International Publishing, 2020), pp. 67–96. Walton, J. P. Timing is everything: temporal processing deficits in the aged auditory brainstem. Hearing research 264, 63–69 (2010). Gallun, F. J. & Best, V. in Aging and Hearing, edited by K. S. Helfer, E. L. Bartlett, A. N. Popper & R. R. Fay (Springer International Publishing, 2020), pp. 143–171. Tun, P. A., O'Kane, G. & Wingfield, A. Distraction by competing speech in young and older adult listeners. Psychology and aging 17, 453–467 (2002). Tremblay, K. L. et al. Self-Reported Hearing Difficulties Among Adults With Normal Audiograms: The Beaver Dam Offspring Study. Ear and hearing 36, e290-9 (2015). Park, D. C. & Bischof, G. N. The aging mind: neuroplasticity in response to cognitive training. Dialogues Clin Neurosci 15, 109–119 (2013). Lin, F. R. et al. Hearing loss and cognitive decline in older adults. JAMA internal medicine 173, 293–299 (2013). Loughrey, D. G., Kelly, M. E., Kelley, G. A., Brennan, S. & Lawlor, B. A. Association of Age-Related Hearing Loss With Cognitive Function, Cognitive Impairment, and Dementia: A Systematic Review and Meta-analysis. JAMA otolaryngology-- head & neck surgery 144, 115–126 (2018). Pelofi, C., Gardelle, V. de, Egré, P. & Pressnitzer, D. Interindividual variability in auditory scene analysis revealed by confidence judgements. Phil. Trans. R. Soc. B 372, 20160107 (2017). Jayakody, D. M. P., Friedland, P. L., Martins, R. N. & Sohrabi, H. R. Impact of Aging on the Auditory System and Related Cognitive Functions: A Narrative Review. Frontiers in neuroscience 12, 125 (2018). Fisher, G. G., Chacon, M. & Chaffee, D. S. in Work Across the Lifespan (Elsevier, 2019), pp. 17–45. Wagener, K. C. Entwicklung und Evaluation eines Satztests für die deutsche Sprache. Teil 1: Design des Oldenburger Satztests. Zeitschrift für Audiologie 38, 4–15 (1999). Chen, T. & Guestrin, C. XGBoost: A Scalable Tree Boosting System 11, 785–794 (2016). Gruber, M. Improving Efficiency by Shrinkage. The James--Stein and Ridge Regression Estimators. 1st ed. (CRC Press, 1998). Vock, M. & Holling, H. The measurement of visuo–spatial and verbal–numerical working memory: Development of IRT-based scales. Intelligence 36, 161–182 (2008). Müllensiefen, D., Gingras, B., Musil, J. & Stewart, L. The musicality of non-musicians: an index for assessing musical sophistication in the general population. PloS one 9, e89642 (2014). Bailey, T. Auditory pathways and processes: implications for neuropsychological assessment and diagnosis of children and adolescents. Child neuropsychology : a journal on normal and abnormal development in childhood and adolescence 16, 521–548 (2010). Mishra, S. K., Saxena, U. & Rodrigo, H. Extended High-frequency Hearing Impairment Despite a Normal Audiogram: Relation to Early Aging, Speech-in-noise Perception, Cochlear Function, and Routine Earphone Use. Ear and hearing 43, 822–835 (2022). Pichora-Fuller, M. K. Cognitive aging and auditory information processing. International journal of audiology 42 Suppl 2, 2S26-32 (2003). Lotfi, Y., Mehrkian, S., Moossavi, A., Zadeh, S. F. & Sadjedi, H. Relation between Working Memory Capacity and Auditory Stream Segregation in Children with Auditory Processing Disorder. Iranian Journal of Medical Sciences 41, 110–117 (2016). Golden, H. L. et al. Music Perception in Dementia. Journal of Alzheimer's disease : JAD 55, 933–949 (2017). Stopford, C. L., Thompson, J. C., Neary, D., Richardson, A. M. T. & Snowden, J. S. Working memory, attention, and executive function in Alzheimer's disease and frontotemporal dementia. Cortex; a journal devoted to the study of the nervous system and behavior 48, 429–446 (2012). Coffey, E. B. J., Chepesiuk, A. M. P., Herholz, S. C., Baillet, S. & Zatorre, R. J. Neural Correlates of Early Sound Encoding and their Relationship to Speech-in-Noise Perception. Frontiers in neuroscience 11, 479 (2017). Maillard, E., Joyal, M., Murray, M. M. & Tremblay, P. Are musical activities associated with enhanced speech perception in noise in adults? A systematic review and meta-analysis. Current research in neurobiology 4, 100083 (2023). Coffey, E. B. J., Arseneau-Bruneau, I., Zhang, X. & Zatorre, R. J. The Music-In-Noise Task (MINT): A Tool for Dissecting Complex Auditory Perception. Frontiers in neuroscience 13, 199 (2019). Moore, B. C. The importance of high-frequency hearing in a medico-legal context. The Journal of the Acoustical Society of America 148, 2542 (2020). Narne, V. K. et al. The effect of recreational noise exposure on amplitude-modulation detection, hearing sensitivity at frequencies above 8 kHz, and perception of speech in noise. The Journal of the Acoustical Society of America 153, 2562 (2023). Humes, L. E. The World Health Organization's hearing-impairment grading system: an evaluation for unaided communication in age-related hearing loss. International journal of audiology 58, 12–20 (2019). RStudio Team. RStudio: Integrated Development for R (2020). Bergstra, J. & Bengio, Y. Random search for hyper-parameter optimization. Journal of Machine Learning Research 13 (2012). Lundberg, S. & Lee, S.-I. A Unified Approach to Interpreting Model Predictions, 2017. Additional Declarations No competing interests reported. Supplementary Files 2025HakeASAindividualdifferencessupplementarymaterialV4.docx Cite Share Download PDF Status: Published Journal Publication published 05 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 02 Jul, 2025 Reviewers agreed at journal 26 May, 2025 Reviews received at journal 21 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviewers invited by journal 20 May, 2025 Submission checks completed at journal 15 May, 2025 First submitted to journal 12 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6026003","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":459309436,"identity":"b6558bbe-3898-40df-bac9-65fc97fe6451","order_by":0,"name":"Robin Hake","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYFACHoYDDEAkwcAMpA0YGPgYEhiYIVIJhLSwJYC1sBGjhQGihccAzCeoxby99+DhAoY78pLtPd+kCwoY5NjYk489Lmy7Z9fAnnwAmxaZM+cSDs9geGY4m+fsNukZBgzGbDzP0o1nthUnN/A8w2qNhESOwWEehsOM8yRyt0nzGPxPbJPIMZPmbUtIZgBK4dNiP0/+zTOgFgaglvxvUC35H/BpSZwtwcMG1ZLDBtJiB7QFq/cleIB+4TE4nDyzJ83YmgfiFzPpGecSEoAM7A5j7z38mafisO2M44cf3ub5wyDHz578TLqgLMEeyHiA1RowwGZaYhtu9TiAPck6RsEoGAWjYLgCACOVVw7M7d1cAAAAAElFTkSuQmCC","orcid":"","institution":"Carl von Ossietzky University of Oldenburg","correspondingAuthor":true,"prefix":"","firstName":"Robin","middleName":"","lastName":"Hake","suffix":""},{"id":459309437,"identity":"67b65a95-43b5-4f7a-a1a5-743f91e9db41","order_by":1,"name":"Daniel Müllensiefen","email":"","orcid":"","institution":"University of Hamburg","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Müllensiefen","suffix":""},{"id":459309438,"identity":"4ba2122f-2447-426e-b940-5076e3f004e8","order_by":2,"name":"Kai Siedenburg","email":"","orcid":"","institution":"Graz University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Siedenburg","suffix":""}],"badges":[],"createdAt":"2025-02-13 21:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6026003/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6026003/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-10263-z","type":"published","date":"2025-07-05T15:56:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83298596,"identity":"0a4c17cf-b9dd-40b0-aa6e-0a86f0cdf83d","added_by":"auto","created_at":"2025-05-22 14:34:59","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":362152,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticipant distribution by age and hearing thresholds.\u003c/strong\u003e Colours indicate thresholds for group categorization (age: 50 years; PTA: 20 dB HL). Scatterplots in each quadrant show group-specific correlations between age and PTA, with regression lines and 95% CIs shaded accordingly. Pearson's correlation and significance are noted in the lower right corner. Participant groups are as follows: older adults with normal hearing (oNH, yellow, n = 31), older adults with hearing impairment (oHI, grey, n = 34), younger adults with normal hearing (yNH, blue, n = 26). The red cross indicates the single young adult with hearing impairment (yHI, 26 years).\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6026003/v1/1e145d62dfcc537b732cd6e5.jpeg"},{"id":83299766,"identity":"e8ebfe61-da25-413f-85b4-daae43f27ad5","added_by":"auto","created_at":"2025-05-22 14:42:59","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1514978,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAuditory scene analysis scores for music and speech by groups.\u003c/strong\u003e (a) Musical scene analysis (MSA) scores and (b) Speech-reception thresholds (SRT) by groups: Younger normal-hearing (yNH, blue dots), older normal-hearing (oNH, yellow triangle), older hearing-impaired (oHI, grey squares). The red cross marks the single yHI participant. Individual scores are plotted as jittered dots on the left to the boxplots. Higher values indicate better performance for MSA, whereas lower values indicate better performance for SRT. To facilitate visual comparison between domains, the y-axis in (a) was inverted, ensuring that better performance is consistently represented by lower values across both panels. Note significance at *p \u0026lt; .05, **p \u0026lt; .01, ***p \u0026lt; .001\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6026003/v1/f2e5d8ad3b0bf7bdef628279.jpeg"},{"id":83299764,"identity":"67219646-1084-4a7e-9a21-e4e85a5b3d48","added_by":"auto","created_at":"2025-05-22 14:42:59","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3715055,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of hearing thresholds on ASA abilities in music and speech. (a) \u003c/strong\u003eDistribution of individual pure-tone average hearing thresholds (PTA) across groups. \u003cstrong\u003e(b, d) \u003c/strong\u003eASA scores for \u003cstrong\u003e(b) \u003c/strong\u003emusic and \u003cstrong\u003e(d)\u003c/strong\u003e speech plotted against PTA. Regression lines are fitted for each group, with shaded areas representing 95% CI. \u003cstrong\u003e(c, e)\u003c/strong\u003e SHAP feature contributions for \u003cstrong\u003e(c) \u003c/strong\u003emusic and \u003cstrong\u003e(e)\u003c/strong\u003e speech plotted against PTA, with positive and negative values indicating their respective impact on model predictions. The LOESS curve (span = .35) illustrates the trend, while shaded grey areas show the 95% (light), 80% (medium), and 50% (dark) ranges of LOESS trajectories from the top 1% of models. For interpretability, the y-axis in (b) and (c) is inverted so that better performance visually aligns across domains. Significance levels: *p \u0026lt; .05, **p \u0026lt; .01, ***p \u0026lt; .001.\u003c/p\u003e","description":"","filename":"Figure3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6026003/v1/488d0782c5d0820c715a5b3c.jpeg"},{"id":83298604,"identity":"0e15e2d4-5b03-4345-b4ec-8fdc447cbdf0","added_by":"auto","created_at":"2025-05-22 14:34:59","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4107670,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of working memory capacities (WMC) on ASA abilities in music and speech.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003eDistribution of WMC across groups. \u003cstrong\u003e(b, d)\u003c/strong\u003e ASA scores for \u003cstrong\u003e(b) \u003c/strong\u003emusic and \u003cstrong\u003e(d)\u003c/strong\u003e speech plotted against WMC. Regression lines are fitted for each group, with shaded areas representing 95% CI. \u003cstrong\u003e(c, e)\u003c/strong\u003e SHAP feature contributions for \u003cstrong\u003e(c) \u003c/strong\u003emusic and \u003cstrong\u003e(e)\u003c/strong\u003e speech plotted against WMC. The LOESS curve (span = .35) illustrates the trend, while shaded grey areas show the 95% (light), 80% (medium), and 50% (dark) ranges of LOESS trajectories from the top 1% of models. For interpretability, the y-axis in (b) and (c) is inverted so that better performance visually aligns across domains. Significance levels: *p \u0026lt; .05, **p \u0026lt; .01, ***p \u0026lt; .001.\u003c/p\u003e","description":"","filename":"Figure4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6026003/v1/50013bd2243292d2a560095a.jpeg"},{"id":83300084,"identity":"81460b32-ae20-4af8-91a7-e66719eb659d","added_by":"auto","created_at":"2025-05-22 14:50:59","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3989949,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of musical training (Gold-MSI subscale) on ASA abilities in music and speech.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003eDistribution of musical training scores across groups. \u003cstrong\u003e(b, d) \u003c/strong\u003eASA scores for \u003cstrong\u003e(b) \u003c/strong\u003emusic and \u003cstrong\u003e(d)\u003c/strong\u003e speech plotted against musical training. Regression lines are fitted for each group, with shaded areas representing 95% CI. \u003cstrong\u003e(c, e)\u003c/strong\u003e SHAP feature contributions for \u003cstrong\u003e(c) \u003c/strong\u003emusic and \u003cstrong\u003e(d)\u003c/strong\u003e speech plotted against musical training. The LOESS curve (span = .35) illustrates the trend, while shaded grey areas show the 95% (light), 80% (medium), and 50% (dark) ranges of LOESS trajectories from the top 1% of models. For interpretability, the y-axis in (b) and (c) is inverted so that better performance visually aligns across domains. Significance levels: *p \u0026lt; .05, **p \u0026lt; .01, ***p \u0026lt; .001.\u003c/p\u003e","description":"","filename":"Figure5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6026003/v1/ef7f67236f0e491a4e7bcdd9.jpeg"},{"id":83299772,"identity":"383efa68-1e36-4dd3-9542-cb8b7ec865a0","added_by":"auto","created_at":"2025-05-22 14:43:00","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3718673,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of age on ASA abilities in music and speech.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003e Distribution of age across groups (*p \u0026lt; .05, **p \u0026lt; .01, ***p \u0026lt; .001). \u003cstrong\u003e(b, d) \u003c/strong\u003eASA scores for \u003cstrong\u003e(b) \u003c/strong\u003emusic and \u003cstrong\u003e(d)\u003c/strong\u003e speech plotted against age. Regression lines are fitted for each group, with shaded areas representing 95% CI. (\u003cstrong\u003ec, e)\u003c/strong\u003e SHAP feature contributions for \u003cstrong\u003e(c)\u003c/strong\u003e music and \u003cstrong\u003e(d)\u003c/strong\u003e speech plotted against age. The LOESS curve (span = .35) illustrates the trend, while shaded grey areas show the 95% (light), 80% (medium), and 50% (dark) ranges of LOESS trajectories from the top 1% of models. For interpretability, the y-axis in (b) and (c) is inverted so that better performance visually aligns across domains. Significance levels: *p \u0026lt; .05, **p \u0026lt; .01, ***p \u0026lt; .001.\u003c/p\u003e","description":"","filename":"Figure6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6026003/v1/4d66a4efcb0bae37bbe04c43.jpeg"},{"id":83301232,"identity":"0245495f-77c2-4949-a8b8-f2e1efce2020","added_by":"auto","created_at":"2025-05-22 15:07:00","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3187461,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSHAP-based model predictions and ridge regression estimates for ASA abilities in music and speech.\u003c/strong\u003e \u003cstrong\u003e(a, b)\u003c/strong\u003eSHAP-based feature contributions from the XGBoost model for MSA \u003cstrong\u003e(a)\u003c/strong\u003e and SRT \u003cstrong\u003e(b)\u003c/strong\u003e, presented as a simplified (straightened) representation of each predictor’s association with ASA performance. The left axis denotes the SHAP contribution in the original test score scale, while the right axis represents the relative percentage contribution within the central 95% range of observed scores. \u003cstrong\u003e(c, d)\u003c/strong\u003e Ridge regression estimates for MSA \u003cstrong\u003e(c)\u003c/strong\u003e and SRT \u003cstrong\u003e(d)\u003c/strong\u003e, showing coefficient distributions, that are established by bootstrap resampling with 1000 iterations. Black markers indicate the 95% CI.\u003c/p\u003e","description":"","filename":"Figure7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6026003/v1/003683b5b82abed0ef268f60.jpeg"},{"id":83300992,"identity":"cda2b561-b564-422c-905b-380a59be54e4","added_by":"auto","created_at":"2025-05-22 14:59:00","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":881124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSHAP-based feature contributions for ASA abilities in music and speech.\u003c/strong\u003e Example SHAP-feature contributions for a single participant (Participant #42) for both SRT and MSA. Positive contributions (green) increase the prediction relative to the baseline value (overall mean), while negative contributions (red) decrease it. Lower scores indicate better performance for SRT, whereas higher scores reflect better performance for MSA.\u003c/p\u003e","description":"","filename":"Figure8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6026003/v1/149f4f0793aa2c66d9057184.jpeg"},{"id":86178884,"identity":"c22c8642-c739-4781-a1e5-0562de50b6b3","added_by":"auto","created_at":"2025-07-07 16:07:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":22956138,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6026003/v1/f44df087-0404-46c7-ae08-f7ae6a80beb1.pdf"},{"id":83298603,"identity":"6d64bda9-00a0-444e-869a-38295ba9a732","added_by":"auto","created_at":"2025-05-22 14:34:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":579256,"visible":true,"origin":"","legend":"","description":"","filename":"2025HakeASAindividualdifferencessupplementarymaterialV4.docx","url":"https://assets-eu.researchsquare.com/files/rs-6026003/v1/b704b6eac2d8387fcde88e46.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Individual differences in auditory scene analysis abilities in music and speech","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAuditory Scene Analysis (ASA) is the fundamental process by which the auditory system organizes complex acoustic environments into meaningful auditory objects and streams, enabling listeners to group, segregate, and track individual sound sources that overlap in time and frequency\u003csup\u003e⁠\u003c/sup\u003e\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In speech, ASA allows individuals to disentangle concurrent sounds, such as separating a single voice from background noise or interfering speakers (commonly referred to as the \u0026lsquo;cocktail party problem\u0026rsquo;). In the context of multi-source music, this ability - termed Musical Scene Analysis (MSA) - is essential for parsing individual instruments within a mixture (e.g., an oboe within an orchestra) and forms the basis for understanding the rich interplay of instruments, melodies, and harmonies. Individuals are known to vary in their ASA abilities, both in musical and speech dominated acoustic scenes\u003csup\u003e⁠\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e; these individual differences - defined as the measurable variability between individuals in perceptual, cognitive, and experiential traits, characteristics, or abilities\u003csup\u003e⁠\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e - are, however, not yet fully understood. This study aims to identify the key contributors to individual differences in ASA abilities and determine their relative importance.\u003c/p\u003e \u003cp\u003eThe literature identifies several potential factors that might contribute to individual differences in ASA abilities. Among these, the influence of hearing impairments (such as the effect of elevated audiometric thresholds) has received considerable attention\u003csup\u003e⁠\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In fact, the negative effects of hearing impairment on speech perception first tend to become obvious in complex acoustic scenes that place considerable demands on ASA such as having a conversation in a noisy restaurant. Specifically, hearing loss, even in mild forms, has been shown to disrupt the processing of acoustic cues that are essential for separating auditory objects within complex scenes. Such impairments are characterized by compromised frequency resolution, attenuated temporal processing, and diminished spatial hearing capabilities\u003csup\u003e⁠\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. These deficits impair auditory object formation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and the ability to segregate overlapping acoustic streams\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, resulting in notable difficulties in speech-in-speech and speech-in-noise perception\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Compared to speech, the role of hearing loss in ASA for music has received less attention. However, hearing-impaired individuals has been shown to experience difficulties in tasks that are associated with successful MSA, such as timbre\u003csup\u003e⁠\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, pitch\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and melody perception\u003csup\u003e⁠\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Siedenburg et al.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e directly examined MSA abilities, demonstrating poorer performance among older hearing-impaired (HI) individuals compared to their younger normal-hearing (NH) counterparts in tracking individual instruments within complex musical excerpts. Survey studies further corroborate these findings, with HI individuals frequently reporting difficulties in discriminating instruments\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. A notable limitation of this research, however, is the confounding of age and hearing loss, underscoring the need for studies that isolate the specific effects of hearing impairment on ASA abilities.\u003c/p\u003e \u003cp\u003eHearing difficulties and impaired speech perception often persist in the absence of overt hearing loss (i.e., elevated hearing thresholds)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This indicates the importance of other factors beyond hearing thresholds in shaping auditory perception, with cognitive processes - particularly working memory capacity (WMC) - emerging as a potential contributor to the observed performance variability among individuals. Working memory, the cognitive system responsible for temporarily storing and manipulating information, allows to hold and process transient auditory details, a capability demonstrably associated with auditory tasks outcomes\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Indeed, research has consistently linked WMC to various aspects of auditory perception\u003csup\u003e\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. For example, individuals with better WMC often excel in speech-related tasks\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and are better able to adapt to difficult listening situations\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Other research linked low-level concurrent sound segregation to attention and WMC\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Moreover, WMC is closely associated with listening effort, wherein cognitive resources are allocated to overcome auditory obstacles - a challenge that is significantly greater for individuals with lower WMC\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Yet, for ASA-related tasks, the link between WMC and performance were not consistent across all listening conditions. For instance, multitalker scenarios versus single speech streams may differentially engage WMC, and factors such as age and hearing status further modulate this relationship\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Furthermore, the extent to which variability in WMC accounts for MSA abilities remains unclear.\u003c/p\u003e \u003cp\u003eBeyond fundamental cognitive abilities, refined listening abilities and knowledge of musical structure, like those acquired by deliberate musical practice may further shape individual perceptual abilities in complex auditory scenarios\u003csup\u003e\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. This reasoning aligns with research demonstrating superior performance of musicians in music-specific tasks like rhythm and beat perception, pitch discrimination, and timbre recognition\u003csup\u003e⁠\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR44 CR45 CR46\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Furthermore, musical training has been linked to a cascade of cognitive benefits, including enhanced information processing speed\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, enhanced attentional resolution\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, and even overall improvements in fundamental cognitive regiments like memory\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. This also tends to be associated with enhanced fundamental auditory abilities such as improved frequency discrimination, interaural time differences perception and attentive tracking\u003csup\u003e\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Musicians also have been shown to outperform non-musicians in auditory stream segregation\u003csup\u003e\u003cspan additionalcitationids=\"CR56 CR57 CR58 CR59\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, with some evidence suggesting that musical training may even offer some compensation against age-related decline in auditory sensitivity\u003csup\u003e\u003cspan additionalcitationids=\"CR62 CR63 CR64\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. However, it's also worth noting that the influence of musical training on speech perception remains under debate\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Furthermore, its specific role in MSA processing also remains a subject of active research: While studies like Siedenburg et al.\u003csup\u003e⁠\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and Marozeau et al.\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e demonstrate an advantage for musically trained individuals in MSA tasks, other investigations failed to establish a link between musical expertise or musical training with MSA performance\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, suggesting a more nuanced relationship or a relatively weak effect that is only observed with sufficient statistical power.\u003c/p\u003e \u003cp\u003eWhile investigation of peripheral auditory and cognitive factors might offer a unique lens for understanding individual differences in ASA, previous research underscores ageing as a key contributor. Age has been consistently linked to destructive alterations along the auditory pathways and within the cochlea\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, with age-related hearing loss emerging as the most prevalent sensory health condition among adults\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. The effect of age extends beyond the auditory periphery, though. Age-related neural degeneration within the central auditory system also affects higher-level auditory processing, compromising timing information, gap detection, sound localisation, and spectral cue processing\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan additionalcitationids=\"CR76 CR77\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e \u0026ndash; all of which are essential components for effective ASA\u003csup\u003e⁠\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. These age-related auditory alterations frequently manifest themselves as challenges in filtering out irrelevant competing auditory stimuli\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e and difficulties with understanding speech in complex listening environments, even when standard audiometric assessments fall within the normal range\u003csup\u003e⁠\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Alongside these changes, age also brings a decline in cognitive domains crucial for auditory processing. Processing speed, working memory, and selective attention all diminish with age\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e, possibly with a negative impact on the ability to detect and track auditory information central to ASA. There is also some evidence that certain age-related auditory effects persist independently of hearing loss and cognitive decline. For example, Lentz et al.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e showed that older adults performed worse on several auditory tasks involving temporal processing, even after accounting for hearing loss and working memory. Interestingly, their study also found that stream segregation \u0026ndash; a key component of ASA \u0026ndash; was the only task that could not be predicted by working memory, hearing loss, or age itself. Certainly, the combined burden of age-related hearing and cognitive changes poses a significant challenge for ASA and warrants careful investigation.\u003c/p\u003e \u003cp\u003eTraditionally, research in auditory perception has primarily concentrated on a limited number of individual factors, often explored in isolation within experiments. However, no single factor can fully explain the variability in ASA performance observed across individuals. Instead, growing evidence highlights the complex interplay between hearing thresholds, cognitive abilities (e.g., working memory), musical training and expertise, and ageing\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan additionalcitationids=\"CR84 CR85 CR86\" citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. To address the complexities inherent in ASA performance, a multifactorial approach is essential - one that examines the relative contributions of these interrelated factors. Such an approach can provide deeper insights into how individuals organise and interpret complex auditory scenes.\u003c/p\u003e \u003cp\u003eGuided by previous literature, the present study investigates several candidate factors to examine their contribution to individual differences in ASA abilities within a single experimental framework: hearing thresholds, working memory capacity (WMC), musical training, and age. ASA abilities were assessed using two domain-specific task. The recently developed Musical Scene Analysis (MSA) test\u003csup\u003e⁠\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, was used to hear out a target instrument within realistic musical mixtures, while a standardised speech-in-noise task (the Oldenburger Satztest\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e) was employed to evaluate ASA performance in the speech domain, as it requires auditory stream formation and tracking. These tasks were selected for their ecological validity and sensitivity to individual variability in real-world auditory contexts. While the study does not aim to exhaustively capture all potential sources of individual variability, it is based on a diverse sample and a well-defined set of predictors grounded in prior research. We therefore consider the current design well-suited to identifying meaningful individual differences in ASA performance.\u003c/p\u003e \u003cp\u003eWe hypothesise that younger individuals, those with better hearing sensitivity (i.e. lower hearing thresholds), stronger WMC, and greater musical training background will demonstrate superior ASA performance. Furthermore, we predict that the factors influencing musical ASA performance will similarly affect speech-in-noise perception, which could be indicative of shared processes for ASA across the music and speech domains.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e Ninety-two participants were classified into three groups based on age and hearing thresholds (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants between 18 and 38 years were assigned to the \u0026lsquo;younger\u0026rsquo; groups and participants with pure-tone average (PTA) thresholds of the better ear exceeding 20 dB HL (averaged across octave-spaced frequencies from .125 to 8 kHz) were classified as 'hearing-impaired' (HI). As the yHI group included only one participant, this individual was excluded from all group-based inferential statistics (e.g., ANOVA).\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e Overall, yNH participants demonstrated significantly better performance compared to both older oNH and OHI individuals in ASA related tasks for both speech and music (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the speech domain, yNH participants achieved superior speech reception thresholds (SRT) as measured by the Oldenburg Sentence Test\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e, with a significant effect of group (\u003cem\u003eF\u003c/em\u003e(2, 85)\u0026thinsp;=\u0026thinsp;20.13, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eη\u003c/em\u003e\u0026sup2; = .32). Similarly, in the music domain, performance on the MSA test revealed a significant main group effect (\u003cem\u003eF\u003c/em\u003e(2, 85)\u0026thinsp;=\u0026thinsp;11.77, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eη\u003c/em\u003e\u0026sup2; = .22).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eModelling individual differences\u003c/h2\u003e \u003cp\u003e Despite attempts to match age across the groups of oNH and oHI participants and hearing thresholds across the groups of yNH and oNH during recruiting, small but significant differences remained. Specifically, oHI were significantly older than oNH (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003edifference\u003c/em\u003e\u003c/sub\u003e = 5.56 years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.004). Additionally, yNH demonstrated significantly better hearing thresholds than the oNH group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with mean pure-tone average thresholds values of the better ear (PTA) of 3.58 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.79) and 11.39 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.30), respectively. Furthermore, all examined factors were significantly intercorrelated (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These factor intercorrelations complicate isolating the unique contribution of each factor to ASA abilities. For example, the observed strong negative association between PTA and MSA scores (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) might reflect a confound by age-related decline of both hearing and cognition that might influence MSA independently, instead of representing a direct effect of hearing loss.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe correlation matrix for the test battery\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePTA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMusical\u003c/p\u003e \u003cp\u003etraining\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWMC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.50\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.41\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.49\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.35\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.64\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.27\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.63\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.23\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.28\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMusical training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.37\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e. Above the diagonal are the Pearson correlations calculated from a complete pair of values, which are significant at \u003csup\u003e*\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.05, \u003csup\u003e**\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.01, \u003csup\u003e***\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.001. Below the diagonal is the total N for the respective analysis (differences due to missing data). MSA\u0026thinsp;=\u0026thinsp;Musical Scene Analysis; SRT\u0026thinsp;=\u0026thinsp;Speech Reception Thresholds; PTA\u0026thinsp;=\u0026thinsp;Pure Tone Average; WMC\u0026thinsp;=\u0026thinsp;Working Memory Capacity (see Methods for more details).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo account for intercorrelations among predictors, two complementary statistical approaches were employed: Ridge-penalized linear regression and a gradient-boosted decision tree model (\u0026rsquo;\u003cem\u003eXGBoost\u003c/em\u003e\u0026rsquo;)\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. By incorporating an L2-norm regularization penalty, ridge regression addresses intercorrelations by shrinking the coefficients of correlated predictors, resulting in more robust and stable model estimates\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. However, given the plausibility of possible non-linear effects and complex interactions among factors, \u0026lsquo;\u003cem\u003eXGBoost\u003c/em\u003e\u0026rsquo; was employed in addition to the ridge-penalized linear regression model. Importantly, predictor interactions are implicitly considered in \u0026lsquo;\u003cem\u003eXGBoost\u003c/em\u003e\u0026rsquo; models through sequential splits on different factors, allowing the model to capture how the influence of one predictor can vary depending on the value of another.\u003c/p\u003e \u003cp\u003eAll participants with complete data, including the single yHI individual, were included in the modelling to support the estimation of independent and interacting effects of age, hearing loss, and other predictors. Both models were used to predict music-related MSA and speech-related SRT scores (see Methods for details on model construction). For MSA, the ridge regression model yielded a root mean square error (RMSE) of .30 and an R\u0026sup2; of .36. The \u0026lsquo;\u003cem\u003eXGBoost\u003c/em\u003e\u0026rsquo; model demonstrated comparable performance, with an RMSE of .30 for the training set and an RMSE of .31 for the test set, resulting in R\u0026sup2; values of .34 and .31, respectively. For SRT, ridge regression produced an RMSE of 1.4 and an R\u0026sup2; of .41, while \u0026lsquo;\u003cem\u003eXGBoost\u003c/em\u003e\u0026rsquo; achieved an RMSE of 1.19 (MAE\u0026thinsp;=\u0026thinsp;.64) for the training set and 1.21 (MAE\u0026thinsp;=\u0026thinsp;.84) for the test set, with corresponding R\u0026sup2; values of .58 and .55. The small differences in RMSE values between the training and test sets indicate that the \u0026lsquo;\u003cem\u003eXGBoost\u003c/em\u003e\u0026rsquo; models generalize appropriate to new data, indicating a relatively low risk of overfitting.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHearing loss\u003c/h3\u003e\n\u003cp\u003eIn both statistical models, poorer hearing thresholds (higher PTA values) were associated with worse ASA performance in both music (MSA) and speech. Specifically, higher PTA correlated with higher (i.e., worse performance) SRT scores (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, see also Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and lower (worse) MSA scores (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The ridge regression model identified PTA as the strongest predictor of ASA performance across both domains, yielding standardized coefficients of .09 ([.04, .13], p\u0026thinsp;\u0026lt;\u0026thinsp;.001) for MSA and .96 ([.68, 1.26], p\u0026thinsp;\u0026lt;\u0026thinsp;.001) for SRT. However, while the overall trend was linear, the strength of this relationship varied across groups; that is, PTA had little influence on ASA performance within the normal-hearing range (\u0026lt;\u0026thinsp;20 dB HL) but became increasingly pronounced as hearing thresholds deteriorated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, d). The SHAP analysis (SHapley Additive exPlanations) provided by the \u0026lsquo;\u003cem\u003eXGBoost\u003c/em\u003e\u0026rsquo; model corroborated these group-based findings by quantifying the predictor\u0026rsquo;s marginal contribution to ASA performance. SHAP values reflect the extent to which PTA influences model predictions, independent of other predictors, and are expressed directly in the test score scale (see Methods for details). The SHAP-based trajectory - captured by using a locally estimated scatterplot smoothing (LOESS) regression line - revealed a nonlinear but monotonic trend: PTA had minimal impact below 20 dB HL, followed by a steady increase in its effect up to ~\u0026thinsp;40 dB HL, where it plateaued (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, e). For instance, individuals with ~\u0026thinsp;40 dB HL exhibited an expected SRT performance 1.6 dB worse than those with a PTA of ~\u0026thinsp;23 dB HL, with the latter representing an SHAP score of 0.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eWorking memory capacity\u003c/h3\u003e\n\u003cp\u003eWorking memory capacity (WMC), assessed using the Backwards Digit Span task, correlated significantly with ASA performance in both speech (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.27, p\u0026thinsp;\u0026lt;\u0026thinsp;.05) and music (r\u0026thinsp;=\u0026thinsp;.35, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, see also Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, these correlations were largely attributable to group membership, defined by age and PTA: Within-group analyses showed no significant correlations between WMC and either SRT or MSA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, d). The ridge regression model further indicated that WMC did not significantly predict ASA performance in either domain, yielding a coefficient of \u0026minus;\u0026thinsp;.03 ([-.08, .01], p\u0026thinsp;=\u0026thinsp;.16) for MSA and .03 ([-.18, .17], p\u0026thinsp;=\u0026thinsp;.90) for SRT. Yet, SHAP-based feature analysis suggested a different interpretation: For SRT, SHAP values indicated no discernible effect of WMC, with a near-flat trajectory suggesting negligible predictive relevance across the observed WMC range (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). In contrast, for MSA, WMC scores revealed a modest but consistent positive effect on performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMusical training\u003c/h3\u003e\n\u003cp\u003eMusical training, as measured by the Gold-MSI subscale\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e, exhibited distinct relationships with ASA performance in speech and music. For speech, no significant correlation was observed (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.11, see also Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In contrast, for music, a moderate positive correlation emerged (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with these trends remaining generally consistent within groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, d). The ridge regression model further substantiated these findings, indicating that musical training was a significant predictor of MSA performance while showing minimal influence on SRT. For MSA, musical training yielded a standardized coefficient of .08 ([.14, .03], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002), which was comparable in magnitude to PTA. In contrast, for SRT, musical training did not emerge as a significant predictor (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;.07 [-.30, .17], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.53). SHAP-based feature analysis further supported these findings. For MSA, SHAP values for musical training exhibited a steady increase across training levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Conversely, in SRT, SHAP values remained near zero across the observed range, suggesting the absence of a meaningful contribution on ASA performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAge\u003c/h2\u003e \u003cp\u003eAge was significantly correlated with ASA performance in both speech (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, see also Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and music (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.41, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting a general decline with increasing age. However, within groups, which are categorised based on PTA and age itself, these correlations disappeared (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, d). The only significant within-group correlation, observed for SRT in the yNH group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), was driven by a single extreme outlier. Excluding this participant eliminated the effect (\u003cem\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u003c/em\u003e\u0026thinsp;.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.87). Despite the absence of age effects within specific groups, SHAP-based analysis revealed a monotonic increase in its contributions across the full lifespan (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec, e) in both domains. Ridge regression results aligned with this pattern, showing a significant effect of age on MSA (\u0026#120573; = .05, [.01, .10], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02), while the effect on SRT did not reach significance (\u0026#120573; = .17, [-.02, .38], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.08). This suggests that, while age effects are less evident within specific subgroups, they manifest across the entire age range.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOverall factor strength\u003c/h3\u003e\n\u003cp\u003eThe \u0026lsquo;\u003cem\u003eXGBoost\u003c/em\u003e\u0026rsquo; and ridge regression models largely align in identifying key predictors of ASA performance, though slight differences emerge in the relative magnitudes of associations, particularly for MSA. To quantify these relationships, the SHAP contribution range was evaluated in relation to the listeners performance range on the respective test score scale. Specifically, instead of considering the full observed range of MSA and SRT scores, the top and bottom 2.5% of performers were excluded to remove extreme values (i.e., retain only the central 95% of the distribution). This allowed for an estimate of each predictor\u0026rsquo;s relative contribution to ASA performance across domains. For instance, a SHAP value of .5 for SRT corresponded to approximately 8.3% of the performance range within this adjusted 95% distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, c). Applying this method, musical training accounted for the largest share of variance in MSA (12%), followed by age (10%) and hearing loss (9%), while WMC covered 7%. Ridge regression results showed a similar pattern, ranking hearing loss and musical training as the strongest predictors, with age exerting a slightly weaker effect. WMC showed a small but non-significant association. For speech, both models consistently identify hearing loss as the strongest predictor, with SHAP contributions covering 33% of the observed range. In contrast, neither model detected a meaningful association for WMC or musical training, both of which remained negligible across the observed data range. Age exhibited a weaker association with SRT compared to hearing loss, covering 19% of the SHAP contribution range, but did not reach significance in the ridge model (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb, d).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated a diverse sample of listeners with varying hearing thresholds, WMC, musical training levels, and ages to evaluate the unique contributions of these factors to ASA performance in music and speech. Consistent with prior research, younger NH individuals outperformed all other groups in both ASA tasks, followed by older participants with NH, with older HI individuals performing the worst. Importantly, however, the observed and well-documented interrelationship among the examined factors complicate the interpretation of individual isolated factor contributions\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e,\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. To address this, two approaches have been employed, one that implies linearity (ridge regression) and another one that does not (called \u0026lsquo;\u003cem\u003eXGBoost\u003c/em\u003e\u0026rsquo;). Notably, individual ASA performance in speech and music are moderately correlated (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.50), suggesting shared underlying mechanisms across both domains (in line with Bregman\u003csup\u003e⁠\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e). Conversely, listener-specific factors exhibited domain-dependent associations with ASA performance, aligning with earlier suggestions of additional and distinct mechanisms for speech and non-speech processing\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEffect of hearing loss\u003c/h2\u003e \u003cp\u003eWe identified a strong relationship between hearing loss (PTA in dB HL) and poorer ASA performance in speech, alongside a moderate relationship with MSA. Both the \u0026lsquo;\u003cem\u003eXGBoost\u003c/em\u003e\u0026rsquo; model estimations and group-level analyses indicated that the adverse effects of hearing loss became apparent even in individuals with mild hearing loss (PTA\u0026thinsp;\u0026gt;\u0026thinsp;20 dB HL), with ridge regression identifying it as the strongest predictor of ASA abilities in both domains. While some research has reported no significant relationship between hearing loss and stream segregation\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, the present findings align with broader evidence highlighting the critical role of intact auditory pathways for effective ASA across both music and speech\u003csup\u003e⁠\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Discrepancies in prior findings may be a consequence of differences in task design, as many studies have relied on artificial paradigms (e.g., pure-tone \u0026lsquo;ABA\u0026rsquo; designs) rather than ecologically valid stimuli. Additionally, the extent to which high-frequency hearing loss is considered may be relevant, as ASA deficits may be more pronounced when thresholds above 4 kHz are taken into account\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e, which are often disregarded in conventional assessments.\u003c/p\u003e \u003cp\u003e Interestingly, MSA appeared more robust to hearing loss compared to speech-based ASA, with the relative impact of hearing loss on speech being approximately three times greater than in music. This difference may be partially attributed to task demands. In the speech task, participants were required to recall exact words embedded in noise. In contrast, the MSA task allowed for the detection of a target instrument based on brief, salient cues. That is, listeners may have benefitted from short temporal gaps in the background mixture\u0026mdash;so-called \u0026lsquo;listening in the dips\u0026rsquo; opportunities\u0026mdash;without needing to track the target continuously throughout the excerpt. If this ability remains at least partially preserved, hearing loss may exert a weaker influence on MSA. However, dip listening in musical scenarios may also be impaired in individuals with hearing loss\u003csup\u003e⁠\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, which could limit the effectiveness of this strategy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEffect of working memory capacities\u003c/h2\u003e \u003cp\u003eAs listening conditions become more challenging (e.g., increased background noise or increasing the number of instruments in a mixture), auditory processing becomes more effortful, relying increasingly on attentional and WM resources\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. Consequently, we hypothesized that individuals with greater WMC would be better equipped to manage the complexity of musical mixtures, facilitating the retention, rehearsal, and recall of individual instruments. Indeed, while an overall relationship between WMC and ASA performance in both speech and music domains was observed, this relationship was absent within individual groups. Both models further support this, identifying WMC as the least predictive factor of ASA performance overall. Specifically, WMC showed no effect on speech-based ASA. For MSA, model estimates diverged: while \u0026lsquo;\u003cem\u003eXGBoost\u003c/em\u003e\u0026rsquo; suggested a small positive association, the ridge regression model did not yield a statistically significant effect. These results suggest that any potential effect of WMC on ASA is likely minimal, and the current data do not offer strong support for its practical significance.\u003c/p\u003e \u003cp\u003eNonetheless, this finding is somewhat unexpected, given the well-documented role of WMC in auditory perception\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and prior evidence linking WMC to stream segregation and speech-in-noise listening performance\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e, particularly among older hearing-impaired listeners\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Several factors may explain this discrepancy. One possible explanation is that previously reported associations between WMC and ASA may primarily reflect confounding influences of hearing impairment or aging, rather than a direct causal relationship. For instance, Golden et al.\u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e found no significant differences in ASA abilities in music between Alzheimer\u0026rsquo;s patients and healthy age-matched controls, despite the former group exhibiting pronounced cognitive impairments, including deficits in WMC\u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. Other evidence suggests the WMC association with ASA to be age-dependent, with no effects being reported among NH individuals\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e - a pattern consistent with our group-based findings. Alternatively, task-specific factors may account for the weak or inconsistent findings. The speech-in-noise task employed in this study may impose minimal WMC demands compared to other ASA tasks requiring more demanding scenarios, such as multiple talkers embedded in background sounds. Similarly, the weak but visible trend for an effect of WMC on MSA scores may be attributed to the design of the MSA task, which involves a single-item recall paradigm with a short (1-second) delay, thereby relying to a small degree on a working memory mechanism. While the current study assessed WMC in terms of capacity (i.e., the measurable cognitive limit to store items), future work should consider a broader conceptualization of working memory, e.g. including attentional control, or broader working memory capabilities, as these factors may better explain variability in ASA performance across speech and music domains. Additionally, future studies could incorporate more demanding ASA paradigms, such as those requiring sustained stream tracking or complex auditory scene recall, to better evaluate the role of WMC in advanced ASA tasks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEffect of musical training\u003c/h2\u003e \u003cp\u003eMusical practice often involves the identification and discrimination of subtle acoustic details and advance integration of complex auditory streams on a regular basis. Thus, musical training cultivates a unique skill set, potentially offering musicians a distinct advantage within complex auditory scenes like those encountered in ASA tasks\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. To investigate this, we abstained from a general cut-off score for musicianship, instead considering musical training as a continuum. Our findings revealed a moderate positive relationship between musical training and MSA abilities, independent of hearing loss or age. Specifically, the model suggests that the strength of association of having no musical training compared to extensive musical training on MSA performance is comparable to the impact of mild-to-moderate hearing loss or ageing by approximately 40 years. This finding aligns with research demonstrating the potential of musical training to mitigate age-related auditory decline or declines associated with reduced hearing sensitivity\u003csup\u003e\u003cspan additionalcitationids=\"CR62 CR63 CR64\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Notably, however, this effect was observed exclusively for musical and not for speech-based ASA. The lack of a clear transfer effect highlights the domain-specific nature of auditory processing, suggesting that skills cultivated through musical education and training may not readily generalise to the unique cognitive and perceptual demands of speech perception in noise. Our findings thus challenge the assumption that musical training universally enhances auditory abilities, raising the question of whether and how its benefits might extend to speech perception - a debate that remains unresolved and polarised in the current literature\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e. However, such associations may depend on task difficulty and listener characteristics, with transfer effects more likely to be of benefit under challenging listening conditions. Follow-up studies should explicitly account for these contextual dependencies\u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEffect of ageing\u003c/h2\u003e \u003cp\u003eAge, with its associated changes in hearing sensitivity, neural degeneration, and general cognitive functioning\u003csup\u003e⁠\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e often serves as a proxy for a multitude of underlying physiological and auditory influences. These factors, however, complicate the isolation of chronological age's distinct contribution to ASA. To disentangle these effects, we examined a broad age range while controlling for variations in hearing thresholds within groups. At the sample level, we observed a general decline in ASA performance across both speech and music, with increased age and hearing loss. However, within the three groups defined by age range and hearing loss severity, the age effect was largely diminished. Even among older participants (50s-80s), a period typically associated with hearing impairment onset, age was not a strong predictor of ASA performance when hearing loss was accounted for. This suggests that the initial correlations between ASA and age are primarily driven by variability in individuals' hearing thresholds rather than chronological age itself. Nonetheless, considering the full lifespan, aging manifests a practically relevant negative impact on ASA performance in both MSA and speech-based ASA. However, incorporating additional factors - such as attentional control or processing speed - as well as factors capturing hearing impairments beyond overt hearing loss may further account for the variance in performance that is currently attributed to aging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile the present study offers valuable insights into individual differences in ASA across music and speech domains, several limitations should be acknowledged. First, while the dataset covers a broad range of ages and hearing profiles, it includes only one younger HI adult. As a result, generalizations to younger HI populations are not feasible on the basis of the present dataset. Second, cognitive screening was limited to the WMC only, however, deficits in attention, processing speed, executive functioning or other undiagnosed cognitive impairments could impact task performance and may obscure true effects by introducing variance that cannot be account for in the model. Given that ageing is often accompanied by cognitive decline, the lack of screening limits our ability to rule out confounding effects of cognitive impairment, particularly among older participants.\u003c/p\u003e \u003cp\u003eIn addition, while both ASA tasks tap into the ability to parse acoustic scenes, they likely engage working memory and attentional resources to different extents. Correspondingly, the tasks differ in structure and perceptual demands, limiting the direct comparability of performance across domains. Future research should aim to better align task designs\u0026mdash;for example, by evaluating MSA performance alongside multi-talker speech scenarios, or possibly by comparing speech-in-noise perception using the Oldenburger Sentence Test alongside the Music-In-Noise Task\u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e\u0026mdash;to more clearly dissociate domain-general from domain-specific mechanisms in ASA. It would also be important to incorporate cognitive screening measures and to consider controlling for both the onset and type of hearing loss (e.g., sensorineural, conductive), as well as specific musical perceptual deficits such as congenital amusia.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the complex interplay of factors contributing to individual differences in ASA processes across speech and music domains. While performance in speech and music-related ASA tasks was generally correlated, distinct predictors emerged to be practically impactful for each domain. In speech, hearing loss and age were the most influential factors, whereas in music, musical training played a pivotal role in predicting MSA abilities. Aging and hearing loss had a less pronounced but still notable impact on the performance in the musical ASA task. Working memory capacity showed no effect on speech-based ASA performance and had only a limited influence on the musical ASA task. These findings highlight the critical role of hearing sensitivity in processing auditory scenes across domains, but particularly in speech-related ASA. Conversely, the observed influence of musical training suggests that extensive musical experience may partially mitigate the effects of hearing loss or aging in domain-specific (i.e. music-related) tasks, highlighting its compensatory potential.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe experiment included a total of 92 participants (47 males and 45 females). Participants were divided into three groups: 31 older adults with NH (M\u0026thinsp;=\u0026thinsp;62.3, SD\u0026thinsp;=\u0026thinsp;7.3; 18 female), 34 older adults with HI (M\u0026thinsp;=\u0026thinsp;67.8, SD\u0026thinsp;=\u0026thinsp;7.7; 16 female), and 26 younger adults with NH (M\u0026thinsp;=\u0026thinsp;25.3; SD\u0026thinsp;=\u0026thinsp;4.1, 11 female). One additional younger adult with HI (yHI; age\u0026thinsp;=\u0026thinsp;26, male) was excluded from group comparisons but retained in regression-based model analysis. A graphical illustration of individuals' pure-tone audiometric thresholds can be found in Figure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003eA1\u003c/span\u003e (in Supplementary Materials). If the average pure-tone audiometric measurement for the better ear in a quiet setting surpassed 20 dB HL, subjects were categorised as hearing-impaired (e.g., Humes, 2019).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eTest battery\u003c/h2\u003e \u003cp\u003e\u003cb\u003eMusical Scene Analysis task (MSA)\u003c/b\u003e\u003csup\u003e⁠\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The adaptive Musical Scene Analysis test (MSA) is a listening task developed to assess participants' ASA abilities in the context of realistic musical stimuli. The MSA adopts a \u0026lsquo;yes-no\u0026rsquo; paradigm that resembles a 2-alternative-forced-choice task (2-AFC). In each trial, participants hear a two-second excerpt of a single instrument or voice (the target), a one-second silence, and a two-second multi-instrument excerpt (the mixture). Participants are then asked to decide whether the target was part of the mixture or not. The target instrument varies across trials and includes four different instrument types: bass, guitar, lead, and piano. Stimuli are sourced from an open-source music-database (\"MedleyDB\"), which consists of real-world multitrack music recordings representing a wide range of musical genres (e.g., pop, rock, world/folk, fusion, jazz, rap, classical). All stimuli were presented monaurally. Participant ability is estimated using weighted-likelihood estimation and is projected on a scale of approximately \u0026minus;\u0026thinsp;3 to +\u0026thinsp;3. A score of zero corresponds to the median performance of the reference population from the calibration experiment\u003csup\u003e⁠\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, which predominantly included NH and mild HI individuals. The study employed two test sets of the adaptive MSA (version 2.4) with 30 items each.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBackwards Digit Span Memory Test\u003c/b\u003e (\u003cb\u003eWMC)\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e). This established working memory assessment requires participants to recall digit sequences in reverse order (e.g., for the sequence 1 2 3 4, the correct response is 4 3 2 1). Digits are presented visually, one at a time. The test comprised sequences of varying lengths (2 trials each of 4-digit and 5-digit sequences; 4 trials each of 6-digit and 7-digit sequences). Performance is measured by the proportion of correctly recalled digits in each sequence. Average results over all sequences are reported.\u003c/p\u003e \u003cp\u003e\u003cb\u003eOldenburger Satztest\u003c/b\u003e (\u003cb\u003eSRT)\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. This adaptive speech-in-noise perception test assesses an individual's ability to comprehend spoken sentences within a background of speech-shaped white noise. Participants have to verbally repeat five-word sentences presented by a male speaker. These sentences were randomly generated from a predefined word class structure (name-verb-numeral-adjective-object), with each word class drawn from a pool of 10 possible word alternatives. The speech level is adaptively varied against a constant noise level to determine the individual's 50% speech reception threshold (SRT in dB HL). That is, speech level was adaptively adjusted based on word accuracy: decreased by 1.0 dB for five correctly repeated words, 0.5 dB (4 correct), unchanged (3 correct), or increased by 0.5\u0026ndash;1.5 dB (2 to 0 correct). The composite score of two test lists of 20 sentences each were used. Lower scores indicate better performance.\u003c/p\u003e \u003cp\u003e\u003cb\u003ePure-tone-average audiometry\u003c/b\u003e (\u003cb\u003ePTA\u003c/b\u003e). Hearing thresholds were assessed using standard clinical procedures with an Interacoustics AD528 portable audiometer. Pure-tone thresholds were measured at .125, .25, .5, 1, 2, 4, and 8 kHz. Given the established impact of high-frequency hearing loss on complex auditory processing\u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e,\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e, the PTA was calculated as an average across all measured frequencies. Participants with a PTA exceeding 20 dB HL in the better ear were classified as hearing-impaired\u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. A lower PTA indicates better hearing sensitivity.\u003c/p\u003e \u003cp\u003e\u003cb\u003eMusical training subscale of the Goldsmith Musical Sophistication Index\u003c/b\u003e (\u003cb\u003eGMSI)\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. The GMSI is a self-report questionnaire that assesses several aspects of musical expertise and experience, including a subscale for musical training. Participants responded to questions such as \u0026ldquo;\u003cem\u003eAt the peak of my interest, I practised my primary instrument for _ hours per day\u003c/em\u003e\u0026rdquo; and rated their agreement with statements like \u0026ldquo;\u003cem\u003eI would not consider myself a musician\u003c/em\u003e\u0026rdquo; (negatively coded). Responses were given either numerically or on a 7-point Likert scale, ranging from 1 (\u0026lsquo;Completely Disagree\u0026rsquo;) to 7 (\u0026lsquo;Completely Agree\u0026rsquo;). The final musical training score is calculated from a 7-item set, with scores ranging from 1 to 7, where higher scores indicate greater levels of musical training.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003eEthical approval for the study was obtained from the Ethics Committee at the Carl von Ossietzky University Oldenburg (Drs.EK/2019/092). All participants provided informed consent prior to participation and received compensation at a rate of \u0026euro;10 per hour. All methods used in this study were performed in accordance with relevant guidelines and regulations. Testing was conducted in two phases. The first phase took place in a controlled laboratory setting. Participants were seated in a soundproof booth and stimuli were presented via calibrated equipment consisting of a computer, an RME Babyface sound card, and Sennheiser HD650 headphones. Long-term sound level was set to 75 dB SPL (A), measured with a Norsonic Nor140 sound-level metre using music-shaped noise. Following a demographics questionnaire, participants completed a pure-tone audiometric test and the speech-in-noise assessment. Participants then undertook the first set of a 30-item adaptive MSA test, followed by additional tests, which will not be addressed in this manuscript. The first phase concluded with a second set of the adaptive MSA. The second testing phase was administered online at least 24 hours after the initial session among the same participants. This phase included the full GMSI self-report questionnaire and the \u0026lsquo;Backwards Digit Span\u0026rsquo; test. Each of the employed tests incorporated a brief training session with practice items. Participants received immediate feedback and could repeat the training as needed. Participants wearing hearing aids were instructed to remove their devices for the duration of the study (N\u0026thinsp;=\u0026thinsp;19). This decision was made to avoid unwanted acoustic interference when using headphones and to reduce listener-specific variability introduced by hearing aid processing. To ensure audibility, all participants underwent a loudness check not wearing any hearing aids during the training phase of each task. They were explicitly asked whether the sound level was comfortable and intelligible and were invited to adjust the presentation level if needed. Only two participants requested a higher volume (+\u0026thinsp;4 and +\u0026thinsp;5 dB), while five opted to reduce the level. Note that previous research did not find marked effects of level on MSA in these small ranges that all fall within the comfortable listening range of participants\u003csup\u003e⁠\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Further details on hearing aid usage history and individual presentation level adjustments are provided in Table A1 (in Supplementary Materials). Overall, the laboratory phase required approximately 80\u0026ndash;120 minutes, while the online phase took 25\u0026ndash;40 minutes.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eData Analyses\u003c/h2\u003e \u003cp\u003eAll analyses were executed in R (v2023.10.31)\u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e. The significance level was set at p\u0026thinsp;\u0026lt;\u0026thinsp;.05 for all statistical tests. Pearson's correlation coefficients were calculated to examine the relationships between SRT and MSA scores and other auditory and cognitive tests, including Oldenburger Satztest, Backwards Digit Span Memory Test, and the subtest for musical training of the Goldsmith Musical Sophistication Index. Analysis of Variance (ANOVA) was conducted to compare the mean MSA and SRT scores across three different participant groups (excluding the one yHI). Post-hoc comparisons were performed using Bonferroni corrections to control for Type I error. Analyses were conducted on available data, with sample size variations explicitly reported. To predict SRT and MSA scores, both ridge regression and gradient boosted decision tree models were employed. Ridge regression applies L2 regularization to mitigate multicollinearity while assuming linear relationships among predictors. In contrast, the decision tree model accounts for both multicollinearity and complex non-linear interactions without assuming linearity. Predictors included the subtest of the GMSI for musical training, age, PTA, and WMC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eRidge Regression Model Construction\u003c/h2\u003e \u003cp\u003ePrior to analysis, all predictor variables were standardized, and only complete data were included (MSA: n\u0026thinsp;=\u0026thinsp;82; SRT: n\u0026thinsp;=\u0026thinsp;84). The optimal regularization parameter (L2) lambda parameter was determined via 10-fold cross-validation. The final ridge regression model was then fitted using this optimal lambda (~\u0026thinsp;.11 for SRT and ~\u0026thinsp;.16 for MSA). Bootstrap resampling (1,000 iterations) was applied to estimate the variability of the regression coefficients. Confidence intervals (95%) and two-sided bootstrapped p-values were computed for each coefficient, providing robust inferential statistics. This approach allowed for the assessment of the stability and significance of predictor effects across resampled datasets. The ridge regression model was applied to the full dataset, without holding out a separate test set for validation.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003eGradient Boosted Decision Tree Model (XGBoost)\u003c/h2\u003e \u003cp\u003eFor the \u0026lsquo;\u003cem\u003eXGBoost\u003c/em\u003e\u0026rsquo; model (Chen \u0026amp; Guestrin, 2016), the \u0026lsquo;\u003cem\u003egbtree\u003c/em\u003e\u0026rsquo; booster method was implemented using the \u0026lsquo;\u003cem\u003exgboost\u003c/em\u003e\u0026rsquo; package in \u003cem\u003eR\u003c/em\u003e (v1.7.7.1). Complete datasets from participants with available MSA (n\u0026thinsp;=\u0026thinsp;89) and SRT (n\u0026thinsp;=\u0026thinsp;91) scores were included. Model construction employed a random hyperparameter search, an efficient approach for exploring a broad hyperparameter space. Random search is often preferred for hyperparameter optimization as it tests diverse parameter combinations, increasing the likelihood of identifying configurations that minimize validation error\u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e. A total of 2,000 random iterations for both MSA and SRT were conducted, with parameter bounds set to balance model complexity and mitigate the risk of overfitting. The specified bounds were as follows: learning rate (\u003cem\u003eeta\u003c/em\u003e: .05\u0026ndash;.15), maximum tree depth (\u003cem\u003emax_depth\u003c/em\u003e: 3\u0026ndash;5), number of boosting rounds (\u003cem\u003enrounds\u003c/em\u003e: 50\u0026ndash;100), data subsample ratio per boosting round (\u003cem\u003esubsample\u003c/em\u003e: .5\u0026ndash;.9), feature subsample ratio per tree (colsample_bytree: .5\u0026ndash;.8), minimum child weight (\u003cem\u003emin_child_weight\u003c/em\u003e: 1\u0026ndash;20), L2 regularisation (\u003cem\u003elambda\u003c/em\u003e: 0\u0026ndash;1.5), L1 regularisation (\u003cem\u003ealpha\u003c/em\u003e: 0\u0026ndash;1.5), and minimum loss reduction required to make a further split (\u003cem\u003egamma\u003c/em\u003e: 0\u0026ndash;1.5). The training and test set split was also optimised as part of the tuning process, with bounds set between .5 and .9. Model performance was evaluated using a combination of Root Mean Squared Error (RMSE) and differences in RMSE and R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e between the training and test sets as an indicator of model fit and overfitting risk. To ensure robust RMSE estimation, repeated random sub-sampling validation (also known as Monte Carlo cross-validation) with 500 iterations was implemented, where each iteration involved a different random split of the data into training and test sets. To further ensure hyperparameter suitability, only models with an RMSE and R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e difference between the training and test sets below .1 were considered. Instead of selecting a single best-performing model, the average of the top 1% (n\u0026thinsp;=\u0026thinsp;20) of models with the lowest RMSE was then used for the analysis. This approach mitigates the influence of hyperparameter variability and enhances the robustness of the final model estimates. This procedure has been done predicting MSA and SRT scores separately.\u003c/p\u003e \u003cp\u003eFor MSA, the final hyperparameter estimates were as follows (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;95% CI): learning rate (η\u0026thinsp;=\u0026thinsp;.079 [.069, .090]), maximum tree depth (3.7 [3.36, 4.04]), subsample ratio (.557 [.542, .573]), feature subsample ratio per tree (.684 [.660, .708]), minimum child weight (16.6 [15.7, 17.5]), L2 regularization (λ\u0026thinsp;=\u0026thinsp;.776 [.629, .923]), L1 regularization (α\u0026thinsp;=\u0026thinsp;.646 [.484, .807]), and minimum loss reduction (γ\u0026thinsp;=\u0026thinsp;.254 [.175, .334]). The average number of boosting rounds was 75.7 [71.0, 80.4], with a data split of 88.3% for training and 11.7% for testing. For SRT, the final hyperparameter estimates were: learning rate (η\u0026thinsp;=\u0026thinsp;.082 [.07, .093]), maximum tree depth (4.05 [3.63, 4.47]), subsample ratio (.746 [.7, .792]), feature subsample ratio per tree (.693 [.670, .717]), minimum child weight (14.5 [13.1, 15.9]), L2 regularization (λ\u0026thinsp;=\u0026thinsp;.926 [.745, 1.11]), L1 regularization (α\u0026thinsp;=\u0026thinsp;.831 [.615, 1.05]), and minimum loss reduction (γ\u0026thinsp;=\u0026thinsp;.803 [.611, .995]). The average number of boosting rounds was 72. [66.1, 78.], with 88.7% of data used for training and 11.3% for testing.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eSHAP-based model estimates for individual feature contribution\u003c/h2\u003e \u003cp\u003eImportantly, \u0026lsquo;\u003cem\u003eXGBoost\u003c/em\u003e\u0026rsquo; model allows the calculation of SHAP values (SHapley Additive exPlanations), a method for quantifying individual-level feature contributions to model predictions\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e. SHAP values assess each predictor\u0026rsquo;s marginal contribution by considering all possible combinations of features being present or absent, providing a nuanced interpretation of how individual differences influence model outcomes. Positive SHAP values indicate that a feature increases the predicted score, while negative values suggest a decreasing effect. Since SHAP values are presented on the original test scale, their interpretation follows the scoring direction of each measure. For SRT, lower values indicate better performance, meaning higher SHAP values correspond to poorer outcomes. In contrast, for MSA, higher values denote better performance, so higher SHAP values reflect improved outcomes. The sum of all SHAP values for a participant, combined with the model\u0026rsquo;s baseline prediction, determines the final predicted score (see Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e for an illustration). SHAP value estimates and their corresponding LOESS trajectory are constrained to the observed data range and should not be extrapolated beyond it. For instance, PTA values exceeding 50 dB HL fall outside the estimated range and cannot be reliably inferred from the model. Final SHAP estimates were derived from the top 1% of models (n\u0026thinsp;=\u0026thinsp;20) to account for variability in hyperparameter configurations. Confidence intervals (99%) for these estimates were generated through 500 bootstrap iterations, enhancing the robustness of feature contribution interpretations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u003c/strong\u003e This work has been funded by the Freigeist Fellowship of the Volkswagen Foundation to KS. This study was also funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) \u0026ndash; Project ID 352015383 \u0026ndash; SFB 1330 A6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions.\u0026nbsp;\u003c/strong\u003eRH, DM and KS designed the study and analysed the data. RH wrote the first manuscript draft. All co-authors edited and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement.\u003c/strong\u003e The dataset, analysis scripts, and MSA test used in this study are available in the author\u0026rsquo;s GitHub repository (https://github.com/rhake14).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval.\u0026nbsp;\u003c/strong\u003eEthical approval was provided by the Ethics Committee at University of Oldenburg (Drs.EK/2019/092).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate.\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all participants in this study, permitting the collection of anonymized data and its potential publication in journal articles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest.\u003c/strong\u003e The authors declare that there is no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCopyright.\u003c/strong\u003e This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBregman, A. S. \u003cem\u003eAuditory Scene Analysis \u003c/em\u003e(The MIT Press, 1990).\u003c/li\u003e\n\u003cli\u003eAlain, C. \u0026amp; Bernstein, L. J. Auditory Scene Analysis. \u003cem\u003eMusic Perception \u003c/em\u003e\u003cstrong\u003e33, \u003c/strong\u003e70\u0026ndash;82 (2015).\u003c/li\u003e\n\u003cli\u003eHake, R.\u003cem\u003e et al. \u003c/em\u003eDevelopment of an adaptive test of musical scene analysis abilities for normal-hearing and hearing-impaired listeners. \u003cem\u003eBehavior research methods, \u003c/em\u003e5456\u0026ndash;5481 (2024).\u003c/li\u003e\n\u003cli\u003eSiedenburg, K., R\u0026ouml;ttges, S., Wagener, K. C. \u0026amp; Hohmann, V. Can You Hear Out the Melody? Testing Musical Scene Perception in Young Normal-Hearing and Older Hearing-Impaired Listeners. \u003cem\u003eTrends in hearing \u003c/em\u003e\u003cstrong\u003e24, \u003c/strong\u003e2331216520945826 (2020).\u003c/li\u003e\n\u003cli\u003eWilliamson, J. M. in \u003cem\u003eTeaching to Individual Differences in Science and Engineering Librarianship \u003c/em\u003e(Elsevier, 2018), pp. 1\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eAnderson, S. \u0026amp; Karawani, H. Objective evidence of temporal processing deficits in older adults. \u003cem\u003eHearing research \u003c/em\u003e\u003cstrong\u003e397, \u003c/strong\u003e108053 (2020).\u003c/li\u003e\n\u003cli\u003eAuerbach, B. D. \u0026amp; Gritton, H. J. Hearing in Complex Environments: Auditory Gain Control, Attention, and Hearing Loss. \u003cem\u003eFrontiers in neuroscience \u003c/em\u003e\u003cstrong\u003e16, \u003c/strong\u003e799787 (2022).\u003c/li\u003e\n\u003cli\u003ePhillips, S. L., Gordon-Salant, S., Fitzgibbons, P. J. \u0026amp; Yeni-Komshian, G. Frequency and temporal resolution in elderly listeners with good and poor word recognition. \u003cem\u003eJournal of speech, language, and hearing research : JSLHR \u003c/em\u003e\u003cstrong\u003e43, \u003c/strong\u003e217\u0026ndash;228 (2000).\u003c/li\u003e\n\u003cli\u003eRimmele, J. M., Sussman, E. \u0026amp; Poeppel, D. The role of temporal structure in the investigation of sensory memory, auditory scene analysis, and speech perception: a healthy-aging perspective. \u003cem\u003eInternational journal of psychophysiology : official journal of the International Organization of Psychophysiology \u003c/em\u003e\u003cstrong\u003e95, \u003c/strong\u003e175\u0026ndash;183 (2015).\u003c/li\u003e\n\u003cli\u003eAkeroyd, M. A., Gatehouse, S. \u0026amp; Blaschke, J. The detection of differences in the cues to distance by elderly hearing-impaired listeners. \u003cem\u003eThe Journal of the Acoustical Society of America \u003c/em\u003e\u003cstrong\u003e121, \u003c/strong\u003e1077\u0026ndash;1089 (2007).\u003c/li\u003e\n\u003cli\u003eMiddlebrooks, J. C. in \u003cem\u003eThe Auditory System at the Cocktail Party, \u003c/em\u003eedited by J. C. Middlebrooks, J. Z. Simon, A. N. Popper \u0026amp; R. R. Fay (Springer International Publishing, 2017), pp. 137\u0026ndash;168.\u003c/li\u003e\n\u003cli\u003eShinn-Cunningham, B. G. \u0026amp; Best, V. Selective attention in normal and impaired hearing. \u003cem\u003eTrends in amplification \u003c/em\u003e\u003cstrong\u003e12, \u003c/strong\u003e283\u0026ndash;299 (2008).\u003c/li\u003e\n\u003cli\u003eBayat, A.\u003cem\u003e et al. \u003c/em\u003eA comparison of auditory perception in hearing-impaired and normal-hearing listeners: an auditory scene analysis study. \u003cem\u003eIranian Red Crescent medical journal \u003c/em\u003e\u003cstrong\u003e15, \u003c/strong\u003ee9477 (2013).\u003c/li\u003e\n\u003cli\u003eJohannesen, P. T., P\u0026eacute;rez-Gonz\u0026aacute;lez, P., Kalluri, S., Blanco, J. L. \u0026amp; Lopez-Poveda, E. A. The Influence of Cochlear Mechanical Dysfunction, Temporal Processing Deficits, and Age on the Intelligibility of Audible Speech in Noise for Hearing-Impaired Listeners. \u003cem\u003eTrends in hearing \u003c/em\u003e\u003cstrong\u003e20 \u003c/strong\u003e(2016).\u003c/li\u003e\n\u003cli\u003eChoi, J. E.\u003cem\u003e et al. \u003c/em\u003eRelationship between spectrotemporal modulation detection and music perception in normal-hearing, hearing-impaired, and cochlear implant listeners. \u003cem\u003eScientific reports \u003c/em\u003e\u003cstrong\u003e8, \u003c/strong\u003e800 (2018).\u003c/li\u003e\n\u003cli\u003eEmiroglu, S. \u0026amp; Kollmeier, B. Timbre discrimination in normal-hearing and hearing-impaired listeners under different noise conditions. \u003cem\u003eBrain research \u003c/em\u003e\u003cstrong\u003e1220, \u003c/strong\u003e199\u0026ndash;207 (2008).\u003c/li\u003e\n\u003cli\u003eUys, M. \u0026amp; van Dijk, C. Development of a music perception test for adult hearing-aid users. \u003cem\u003eThe South African journal of communication disorders = Die Suid-Afrikaanse tydskrif vir Kommunikasieafwykings \u003c/em\u003e\u003cstrong\u003e58, \u003c/strong\u003e19\u0026ndash;47 (2011).\u003c/li\u003e\n\u003cli\u003eOxenham, A. J. Pitch perception and auditory stream segregation: implications for hearing loss and cochlear implants. \u003cem\u003eTrends in amplification \u003c/em\u003e\u003cstrong\u003e12, \u003c/strong\u003e316\u0026ndash;331 (2008).\u003c/li\u003e\n\u003cli\u003eKirchberger, M. J. \u0026amp; Russo, F. A. Development of the adaptive music perception test. \u003cem\u003eEar and hearing \u003c/em\u003e\u003cstrong\u003e36, \u003c/strong\u003e217\u0026ndash;228 (2015).\u003c/li\u003e\n\u003cli\u003eSiedenburg, K., Goldmann, K. \u0026amp; van de Par, S. Tracking Musical Voices in Bach\u0026apos;s The Art of the Fugue: Timbral Heterogeneity Differentially Affects Younger Normal-Hearing Listeners and Older Hearing-Aid Users. \u003cem\u003eFrontiers in psychology \u003c/em\u003e\u003cstrong\u003e12, \u003c/strong\u003e608684 (2021).\u003c/li\u003e\n\u003cli\u003eMadsen, S. M. K. \u0026amp; Moore, B. C. J. Music and hearing aids. \u003cem\u003eTrends in hearing \u003c/em\u003e\u003cstrong\u003e18 \u003c/strong\u003e(2014).\u003c/li\u003e\n\u003cli\u003eVaisberg, J. M., Martindale, A. T., Folkeard, P. \u0026amp; Benedict, C. A Qualitative Study of the Effects of Hearing Loss and Hearing Aid Use on Music Perception in Performing Musicians. \u003cem\u003eJournal of the American Academy of Audiology \u003c/em\u003e\u003cstrong\u003e30, \u003c/strong\u003e856\u0026ndash;870 (2019).\u003c/li\u003e\n\u003cli\u003eGoossens, T., Vercammen, C., Wouters, J. \u0026amp; van Wieringen, A. Masked speech perception across the adult lifespan: Impact of age and hearing impairment. \u003cem\u003eHearing research \u003c/em\u003e\u003cstrong\u003e344, \u003c/strong\u003e109\u0026ndash;124 (2017).\u003c/li\u003e\n\u003cli\u003eAkeroyd, M. A. Are individual differences in speech reception related to individual differences in cognitive ability? A survey of twenty experimental studies with normal and hearing-impaired adults. \u003cem\u003eInternational journal of audiology \u003c/em\u003e\u003cstrong\u003e47 Suppl 2, \u003c/strong\u003eS53-71 (2008).\u003c/li\u003e\n\u003cli\u003eDavidson, A. \u0026amp; Souza, P. Relationships Between Auditory Processing and Cognitive Abilities in Adults: A Systematic Review. \u003cem\u003eJournal of speech, language, and hearing research : JSLHR \u003c/em\u003e\u003cstrong\u003e67, \u003c/strong\u003e296\u0026ndash;345 (2024).\u003c/li\u003e\n\u003cli\u003eLentz, J. J., Humes, L. E. \u0026amp; Kidd, G. R. Differences in Auditory Perception Between Young and Older Adults When Controlling for Differences in Hearing Loss and Cognition. \u003cem\u003eTrends in hearing \u003c/em\u003e\u003cstrong\u003e26, \u003c/strong\u003e23312165211066180 (2022).\u003c/li\u003e\n\u003cli\u003eHumes, L. E. Factors Underlying Individual Differences in Speech-Recognition Threshold (SRT) in Noise Among Older Adults. \u003cem\u003eFrontiers in aging neuroscience \u003c/em\u003e\u003cstrong\u003e13, \u003c/strong\u003e702739 (2021).\u003c/li\u003e\n\u003cli\u003eLo, C. Y.\u003cem\u003e et al. \u003c/em\u003eOn-beat rhythm and working memory are associated with better speech-in-noise perception for older adults with hearing loss. \u003cem\u003eQuarterly journal of experimental psychology (2006), \u003c/em\u003e17470218241311204 (2025).\u003c/li\u003e\n\u003cli\u003eDryden, A., Allen, H. A., Henshaw, H. \u0026amp; Heinrich, A. The Association Between Cognitive Performance and Speech-in-Noise Perception for Adult Listeners: A Systematic Literature Review and Meta-Analysis. \u003cem\u003eTrends in hearing \u003c/em\u003e\u003cstrong\u003e21, \u003c/strong\u003e2331216517744675 (2017).\u003c/li\u003e\n\u003cli\u003eHeinrich, A., Henshaw, H. \u0026amp; Ferguson, M. A. The relationship of speech intelligibility with hearing sensitivity, cognition, and perceived hearing difficulties varies for different speech perception tests. \u003cem\u003eFrontiers in psychology \u003c/em\u003e\u003cstrong\u003e6, \u003c/strong\u003e782 (2015).\u003c/li\u003e\n\u003cli\u003eJanse, E. \u0026amp; Andringa, S. J. The roles of cognitive abilities and hearing acuity in older adults\u0026rsquo; recognition of words taken from fast and spectrally reduced speech. \u003cem\u003eApplied Psycholinguistics \u003c/em\u003e\u003cstrong\u003e42, \u003c/strong\u003e763\u0026ndash;790 (2021).\u003c/li\u003e\n\u003cli\u003eNitsan, G., Banai, K. \u0026amp; Ben-David, B. M. One Size Does Not Fit All: Examining the Effects of Working Memory Capacity on Spoken Word Recognition in Older Adults Using Eye Tracking. \u003cem\u003eFrontiers in psychology \u003c/em\u003e\u003cstrong\u003e13, \u003c/strong\u003e841466 (2022).\u003c/li\u003e\n\u003cli\u003eWindle, R., Dillon, H. \u0026amp; Heinrich, A. A review of auditory processing and cognitive change during normal ageing, and the implications for setting hearing aids for older adults. \u003cem\u003eFrontiers in neurology \u003c/em\u003e\u003cstrong\u003e14, \u003c/strong\u003e1122420 (2023).\u003c/li\u003e\n\u003cli\u003eGarami, L., Chow, R., Fakuade, A., Swaminathan, S. \u0026amp; Alain, C. Orienting Attention to Auditory and Visual Short-term Memory: The Roles of Age, Hearing Loss, and Cognitive Status. \u003cem\u003eExperimental aging research \u003c/em\u003e\u003cstrong\u003e46, \u003c/strong\u003e22\u0026ndash;38 (2020).\u003c/li\u003e\n\u003cli\u003ePichora-Fuller, M. K.\u003cem\u003e et al. \u003c/em\u003eHearing Impairment and Cognitive Energy: The Framework for Understanding Effortful Listening (FUEL). \u003cem\u003eEar and hearing \u003c/em\u003e\u003cstrong\u003e37 Suppl 1, \u003c/strong\u003e5S-27S (2016).\u003c/li\u003e\n\u003cli\u003eZekveld, A. A., Kramer, S. E., Kessens, J. M., Vlaming, M. S. M. G. \u0026amp; Houtgast, T. The influence of age, hearing, and working memory on the speech comprehension benefit derived from an automatic speech recognition system. \u003cem\u003eEar and hearing \u003c/em\u003e\u003cstrong\u003e30, \u003c/strong\u003e262\u0026ndash;272 (2009).\u003c/li\u003e\n\u003cli\u003eSchoof, T. \u0026amp; Rosen, S. The role of auditory and cognitive factors in understanding speech in noise by normal-hearing older listeners. \u003cem\u003eFrontiers in aging neuroscience \u003c/em\u003e\u003cstrong\u003e6, \u003c/strong\u003e307 (2014).\u003c/li\u003e\n\u003cli\u003eF\u0026uuml;llgrabe, C. \u0026amp; Rosen, S. On The (Un)importance of Working Memory in Speech-in-Noise Processing for Listeners with Normal Hearing Thresholds. \u003cem\u003eFrontiers in psychology \u003c/em\u003e\u003cstrong\u003e7, \u003c/strong\u003e1268 (2016).\u003c/li\u003e\n\u003cli\u003eDeutsch, D. (ed.). \u003cem\u003eThe psychology of music \u003c/em\u003e(Academic Press, 2013).\u003c/li\u003e\n\u003cli\u003eHerholz, S. C. \u0026amp; Zatorre, R. J. Musical training as a framework for brain plasticity: behavior, function, and structure. \u003cem\u003eNeuron \u003c/em\u003e\u003cstrong\u003e76, \u003c/strong\u003e486\u0026ndash;502 (2012).\u003c/li\u003e\n\u003cli\u003eConzelmann, K. \u0026amp; S\u0026uuml;\u0026szlig;, H.-M. Auditory intelligence: Theoretical considerations and empirical findings. \u003cem\u003eLearning and Individual Differences \u003c/em\u003e\u003cstrong\u003e40, \u003c/strong\u003e27\u0026ndash;40 (2015).\u003c/li\u003e\n\u003cli\u003eWoods, K. J. P. \u0026amp; McDermott, J. H. Schema learning for the cocktail party problem. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America \u003c/em\u003e\u003cstrong\u003e115, \u003c/strong\u003eE3313-E3322 (2018).\u003c/li\u003e\n\u003cli\u003eBianchi, F., Santurette, S., Wendt, D. \u0026amp; Dau, T. Pitch Discrimination in Musicians and Non-Musicians: Effects of Harmonic Resolvability and Processing Effort. \u003cem\u003eJournal of the Association for Research in Otolaryngology : JARO \u003c/em\u003e\u003cstrong\u003e17, \u003c/strong\u003e69\u0026ndash;79 (2016).\u003c/li\u003e\n\u003cli\u003eGrahn, J. A. \u0026amp; Rowe, J. B. Feeling the beat: premotor and striatal interactions in musicians and nonmusicians during beat perception. \u003cem\u003eThe Journal of neuroscience : the official journal of the Society for Neuroscience \u003c/em\u003e\u003cstrong\u003e29, \u003c/strong\u003e7540\u0026ndash;7548 (2009).\u003c/li\u003e\n\u003cli\u003eMicheyl, C., Delhommeau, K., Perrot, X. \u0026amp; Oxenham, A. J. Influence of musical and psychoacoustical training on pitch discrimination. \u003cem\u003eHearing research \u003c/em\u003e\u003cstrong\u003e219, \u003c/strong\u003e36\u0026ndash;47 (2006).\u003c/li\u003e\n\u003cli\u003eNave-Blodgett, J. E., Snyder, J. S. \u0026amp; Hannon, E. E. Hierarchical beat perception develops throughout childhood and adolescence and is enhanced in those with musical training. \u003cem\u003eJournal of experimental psychology. General \u003c/em\u003e\u003cstrong\u003e150, \u003c/strong\u003e314\u0026ndash;339 (2021).\u003c/li\u003e\n\u003cli\u003eWhittom, A.\u003cem\u003e et al. \u003c/em\u003eMusic Perception in Older Adults With Hearing Loss: Protective Effect of Musical Experience. \u003cem\u003eEar and hearing\u003c/em\u003e; 10.1097/AUD.0000000000001615 (2024).\u003c/li\u003e\n\u003cli\u003eBugos, J. A.\u003cem\u003e et al. \u003c/em\u003eJazz Piano Training Modulates Neural Oscillations and Executive Functions in Older Adults. \u003cem\u003eMusic Perception \u003c/em\u003e\u003cstrong\u003e41, \u003c/strong\u003e378\u0026ndash;392 (2024).\u003c/li\u003e\n\u003cli\u003eNandi, B.\u003cem\u003e et al. \u003c/em\u003eMusical Training Facilitates Exogenous Temporal Attention via Delta Phase Entrainment within a Sensorimotor Network. \u003cem\u003eThe Journal of neuroscience : the official journal of the Society for Neuroscience \u003c/em\u003e\u003cstrong\u003e43, \u003c/strong\u003e3365\u0026ndash;3378 (2023).\u003c/li\u003e\n\u003cli\u003eTalamini, F., Alto\u0026egrave;, G., Carretti, B. \u0026amp; Grassi, M. Musicians have better memory than nonmusicians: A meta-analysis. \u003cem\u003ePloS one \u003c/em\u003e\u003cstrong\u003e12, \u003c/strong\u003ee0186773 (2017).\u003c/li\u003e\n\u003cli\u003eNikjeh, D. A., Lister, J. J. \u0026amp; Frisch, S. A. Preattentive cortical-evoked responses to pure tones, harmonic tones, and speech: influence of music training. \u003cem\u003eEar and hearing \u003c/em\u003e\u003cstrong\u003e30, \u003c/strong\u003e432\u0026ndash;446 (2009).\u003c/li\u003e\n\u003cli\u003eMadsen, S. M. K., Marschall, M., Dau, T. \u0026amp; Oxenham, A. J. Speech perception is similar for musicians and non-musicians across a wide range of conditions. \u003cem\u003eScientific reports \u003c/em\u003e\u003cstrong\u003e9, \u003c/strong\u003e10404 (2019).\u003c/li\u003e\n\u003cli\u003eRoden, I., Grube, D., Bongard, S. \u0026amp; Kreutz, G. Does music training enhance working memory performance? Findings from a quasi-experimental longitudinal study. \u003cem\u003ePsychology of Music \u003c/em\u003e\u003cstrong\u003e42, \u003c/strong\u003e284\u0026ndash;298 (2014).\u003c/li\u003e\n\u003cli\u003eBerg, M. von, Steffens, J., Weinzierl, S. \u0026amp; M\u0026uuml;llensiefen, D. Assessing room acoustic listening expertise. \u003cem\u003eThe Journal of the Acoustical Society of America \u003c/em\u003e\u003cstrong\u003e150, \u003c/strong\u003e2539 (2021).\u003c/li\u003e\n\u003cli\u003eJohnson, N., Shiju, A. M., Parmar, A. \u0026amp; Prabhu, P. Evaluation of Auditory Stream Segregation in Musicians and Nonmusicians. \u003cem\u003eInternational archives of otorhinolaryngology \u003c/em\u003e\u003cstrong\u003e25, \u003c/strong\u003ee77-e80 (2021).\u003c/li\u003e\n\u003cli\u003eSauv\u0026eacute;, S. A., Marozeau, J. \u0026amp; Rich Zendel, B. The effects of aging and musicianship on the use of auditory streaming cues. \u003cem\u003ePloS one \u003c/em\u003e\u003cstrong\u003e17, \u003c/strong\u003ee0274631 (2022).\u003c/li\u003e\n\u003cli\u003eZendel, B. R. \u0026amp; Alain, C. Concurrent sound segregation is enhanced in musicians. \u003cem\u003eJournal of cognitive neuroscience \u003c/em\u003e\u003cstrong\u003e21, \u003c/strong\u003e1488\u0026ndash;1498 (2009).\u003c/li\u003e\n\u003cli\u003eBenocci, E. \u0026amp; Calcus, A. Stream segregation, musical abilities, and the development of speech perception in noise. \u003cem\u003eJASA express letters \u003c/em\u003e\u003cstrong\u003e4 \u003c/strong\u003e(2024).\u003c/li\u003e\n\u003cli\u003eBenocci, E. \u0026amp; Calcus, A. in \u003cem\u003eProceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023 \u003c/em\u003e(European Acoustics Association, 2024), pp. 3893\u0026ndash;3899.\u003c/li\u003e\n\u003cli\u003eMarozeau, J., Innes-Brown, H., Grayden, D. B., Burkitt, A. N. \u0026amp; Blamey, P. J. The effect of visual cues on auditory stream segregation in musicians and non-musicians. \u003cem\u003ePloS one \u003c/em\u003e\u003cstrong\u003e5, \u003c/strong\u003ee11297 (2010).\u003c/li\u003e\n\u003cli\u003eDubinsky, E., Wood, E. A., Nespoli, G. \u0026amp; Russo, F. A. Short-Term Choir Singing Supports Speech-in-Noise Perception and Neural Pitch Strength in Older Adults With Age-Related Hearing Loss. \u003cem\u003eFrontiers in neuroscience \u003c/em\u003e\u003cstrong\u003e13, \u003c/strong\u003e1153 (2019).\u003c/li\u003e\n\u003cli\u003eParbery-Clark, A., Strait, D. L., Anderson, S., Hittner, E. \u0026amp; Kraus, N. Musical experience and the aging auditory system: implications for cognitive abilities and hearing speech in noise. \u003cem\u003ePloS one \u003c/em\u003e\u003cstrong\u003e6, \u003c/strong\u003ee18082 (2011).\u003c/li\u003e\n\u003cli\u003eParbery-Clark, A., Anderson, S., Hittner, E. \u0026amp; Kraus, N. Musical experience offsets age-related delays in neural timing. \u003cem\u003eNeurobiology of aging \u003c/em\u003e\u003cstrong\u003e33, \u003c/strong\u003e1483.e1-4 (2012).\u003c/li\u003e\n\u003cli\u003eReuter-Lorenz, P. A. \u0026amp; Park, D. C. How does it STAC up? Revisiting the scaffolding theory of aging and cognition. \u003cem\u003eNeuropsychology review \u003c/em\u003e\u003cstrong\u003e24, \u003c/strong\u003e355\u0026ndash;370 (2014).\u003c/li\u003e\n\u003cli\u003eZendel, B. R. \u0026amp; Alain, C. Musicians experience less age-related decline in central auditory processing. \u003cem\u003ePsychology and aging \u003c/em\u003e\u003cstrong\u003e27, \u003c/strong\u003e410\u0026ndash;417 (2012).\u003c/li\u003e\n\u003cli\u003eMcKay, C. M. No Evidence That Music Training Benefits Speech Perception in Hearing-Impaired Listeners: A Systematic Review. \u003cem\u003eTrends in hearing \u003c/em\u003e\u003cstrong\u003e25, \u003c/strong\u003e2331216520985678 (2021).\u003c/li\u003e\n\u003cli\u003eSchellenberg, E. G. \u0026amp; Lima, C. F. Music Training and Nonmusical Abilities. \u003cem\u003eAnnual review of psychology \u003c/em\u003e\u003cstrong\u003e75, \u003c/strong\u003e87\u0026ndash;128 (2024).\u003c/li\u003e\n\u003cli\u003eCoffey, E. B. J., Mogilever, N. B. \u0026amp; Zatorre, R. J. Speech-in-noise perception in musicians: A review. \u003cem\u003eHearing research \u003c/em\u003e\u003cstrong\u003e352, \u003c/strong\u003e49\u0026ndash;69 (2017).\u003c/li\u003e\n\u003cli\u003eB\u0026uuml;rgel, M., Picinali, L. \u0026amp; Siedenburg, K. Listening in the Mix: Lead Vocals Robustly Attract Auditory Attention in Popular Music. \u003cem\u003eFrontiers in psychology \u003c/em\u003e\u003cstrong\u003e12, \u003c/strong\u003e769663 (2021).\u003c/li\u003e\n\u003cli\u003eB\u0026uuml;rgel, M. \u0026amp; Siedenburg, K. Salience of Frequency Micro-modulations in Popular Music. \u003cem\u003eMusic Perception: An Interdisciplinary Journal \u003c/em\u003e\u003cstrong\u003e41, \u003c/strong\u003e1\u0026ndash;14 (2023).\u003c/li\u003e\n\u003cli\u003eLiberman, M. C. Noise-induced and age-related hearing loss: new perspectives and potential therapies. \u003cem\u003eF1000Research \u003c/em\u003e\u003cstrong\u003e6, \u003c/strong\u003e927 (2017).\u003c/li\u003e\n\u003cli\u003eCunningham, L. L. \u0026amp; Tucci, D. L. Hearing Loss in Adults. \u003cem\u003eThe New England journal of medicine \u003c/em\u003e\u003cstrong\u003e377, \u003c/strong\u003e2465\u0026ndash;2473 (2017).\u003c/li\u003e\n\u003cli\u003eBowl, M. R. \u0026amp; Dawson, S. J. Age-Related Hearing Loss. \u003cem\u003eCold Spring Harbor perspectives in medicine \u003c/em\u003e\u003cstrong\u003e9 \u003c/strong\u003e(2019).\u003c/li\u003e\n\u003cli\u003eYueh, B., Shapiro, N., MacLean, C. H. \u0026amp; Shekelle, P. G. Screening and management of adult hearing loss in primary care: scientific review. \u003cem\u003eJAMA \u003c/em\u003e\u003cstrong\u003e289, \u003c/strong\u003e1976\u0026ndash;1985 (2003).\u003c/li\u003e\n\u003cli\u003eChen, B., Zhong, Y., Peng, W., Sun, Y. \u0026amp; Kong, W.-J. Age-related changes in the central auditory system: comparison of D-galactose-induced aging rats and naturally aging rats. \u003cem\u003eBrain research \u003c/em\u003e\u003cstrong\u003e1344, \u003c/strong\u003e43\u0026ndash;53 (2010).\u003c/li\u003e\n\u003cli\u003eProfant, O.\u003cem\u003e et al. \u003c/em\u003eFunctional Age-Related Changes Within the Human Auditory System Studied by Audiometric Examination. \u003cem\u003eFrontiers in aging neuroscience \u003c/em\u003e\u003cstrong\u003e11, \u003c/strong\u003e26 (2019).\u003c/li\u003e\n\u003cli\u003eSyka, J. in \u003cem\u003eAging and Hearing, \u003c/em\u003eedited by K. S. Helfer, E. L. Bartlett, A. N. Popper \u0026amp; R. R. Fay (Springer International Publishing, 2020), pp. 67\u0026ndash;96.\u003c/li\u003e\n\u003cli\u003eWalton, J. P. Timing is everything: temporal processing deficits in the aged auditory brainstem. \u003cem\u003eHearing research \u003c/em\u003e\u003cstrong\u003e264, \u003c/strong\u003e63\u0026ndash;69 (2010).\u003c/li\u003e\n\u003cli\u003eGallun, F. J. \u0026amp; Best, V. in \u003cem\u003eAging and Hearing, \u003c/em\u003eedited by K. S. Helfer, E. L. Bartlett, A. N. Popper \u0026amp; R. R. Fay (Springer International Publishing, 2020), pp. 143\u0026ndash;171.\u003c/li\u003e\n\u003cli\u003eTun, P. A., O\u0026apos;Kane, G. \u0026amp; Wingfield, A. Distraction by competing speech in young and older adult listeners. \u003cem\u003ePsychology and aging \u003c/em\u003e\u003cstrong\u003e17, \u003c/strong\u003e453\u0026ndash;467 (2002).\u003c/li\u003e\n\u003cli\u003eTremblay, K. L.\u003cem\u003e et al. \u003c/em\u003eSelf-Reported Hearing Difficulties Among Adults With Normal Audiograms: The Beaver Dam Offspring Study. \u003cem\u003eEar and hearing \u003c/em\u003e\u003cstrong\u003e36, \u003c/strong\u003ee290-9 (2015).\u003c/li\u003e\n\u003cli\u003ePark, D. C. \u0026amp; Bischof, G. N. The aging mind: neuroplasticity in response to cognitive training. \u003cem\u003eDialogues Clin Neurosci \u003c/em\u003e\u003cstrong\u003e15, \u003c/strong\u003e109\u0026ndash;119 (2013).\u003c/li\u003e\n\u003cli\u003eLin, F. R.\u003cem\u003e et al. \u003c/em\u003eHearing loss and cognitive decline in older adults. \u003cem\u003eJAMA internal medicine \u003c/em\u003e\u003cstrong\u003e173, \u003c/strong\u003e293\u0026ndash;299 (2013).\u003c/li\u003e\n\u003cli\u003eLoughrey, D. G., Kelly, M. E., Kelley, G. A., Brennan, S. \u0026amp; Lawlor, B. A. Association of Age-Related Hearing Loss With Cognitive Function, Cognitive Impairment, and Dementia: A Systematic Review and Meta-analysis. \u003cem\u003eJAMA otolaryngology-- head \u0026amp; neck surgery \u003c/em\u003e\u003cstrong\u003e144, \u003c/strong\u003e115\u0026ndash;126 (2018).\u003c/li\u003e\n\u003cli\u003ePelofi, C., Gardelle, V. de, Egr\u0026eacute;, P. \u0026amp; Pressnitzer, D. Interindividual variability in auditory scene analysis revealed by confidence judgements. \u003cem\u003ePhil. Trans. R. Soc. B \u003c/em\u003e\u003cstrong\u003e372, \u003c/strong\u003e20160107 (2017).\u003c/li\u003e\n\u003cli\u003eJayakody, D. M. P., Friedland, P. L., Martins, R. N. \u0026amp; Sohrabi, H. R. Impact of Aging on the Auditory System and Related Cognitive Functions: A Narrative Review. \u003cem\u003eFrontiers in neuroscience \u003c/em\u003e\u003cstrong\u003e12, \u003c/strong\u003e125 (2018).\u003c/li\u003e\n\u003cli\u003eFisher, G. G., Chacon, M. \u0026amp; Chaffee, D. S. in \u003cem\u003eWork Across the Lifespan \u003c/em\u003e(Elsevier, 2019), pp. 17\u0026ndash;45.\u003c/li\u003e\n\u003cli\u003eWagener, K. C. Entwicklung und Evaluation eines Satztests f\u0026uuml;r die deutsche Sprache. Teil 1: Design des Oldenburger Satztests. \u003cem\u003eZeitschrift f\u0026uuml;r Audiologie \u003c/em\u003e\u003cstrong\u003e38, \u003c/strong\u003e4\u0026ndash;15 (1999).\u003c/li\u003e\n\u003cli\u003eChen, T. \u0026amp; Guestrin, C. XGBoost: A Scalable Tree Boosting System \u003cstrong\u003e11, \u003c/strong\u003e785\u0026ndash;794 (2016).\u003c/li\u003e\n\u003cli\u003eGruber, M. \u003cem\u003eImproving Efficiency by Shrinkage. \u003c/em\u003e\u003cem\u003eThe James--Stein and Ridge Regression Estimators. \u003c/em\u003e1st ed. (CRC Press, 1998).\u003c/li\u003e\n\u003cli\u003eVock, M. \u0026amp; Holling, H. The measurement of visuo\u0026ndash;spatial and verbal\u0026ndash;numerical working memory: Development of IRT-based scales. \u003cem\u003eIntelligence \u003c/em\u003e\u003cstrong\u003e36, \u003c/strong\u003e161\u0026ndash;182 (2008).\u003c/li\u003e\n\u003cli\u003eM\u0026uuml;llensiefen, D., Gingras, B., Musil, J. \u0026amp; Stewart, L. The musicality of non-musicians: an index for assessing musical sophistication in the general population. \u003cem\u003ePloS one \u003c/em\u003e\u003cstrong\u003e9, \u003c/strong\u003ee89642 (2014).\u003c/li\u003e\n\u003cli\u003eBailey, T. Auditory pathways and processes: implications for neuropsychological assessment and diagnosis of children and adolescents. \u003cem\u003eChild neuropsychology : a journal on normal and abnormal development in childhood and adolescence \u003c/em\u003e\u003cstrong\u003e16, \u003c/strong\u003e521\u0026ndash;548 (2010).\u003c/li\u003e\n\u003cli\u003eMishra, S. K., Saxena, U. \u0026amp; Rodrigo, H. Extended High-frequency Hearing Impairment Despite a Normal Audiogram: Relation to Early Aging, Speech-in-noise Perception, Cochlear Function, and Routine Earphone Use. \u003cem\u003eEar and hearing \u003c/em\u003e\u003cstrong\u003e43, \u003c/strong\u003e822\u0026ndash;835 (2022).\u003c/li\u003e\n\u003cli\u003ePichora-Fuller, M. K. Cognitive aging and auditory information processing. \u003cem\u003eInternational journal of audiology \u003c/em\u003e\u003cstrong\u003e42 Suppl 2, \u003c/strong\u003e2S26-32 (2003).\u003c/li\u003e\n\u003cli\u003eLotfi, Y., Mehrkian, S., Moossavi, A., Zadeh, S. F. \u0026amp; Sadjedi, H. Relation between Working Memory Capacity and Auditory Stream Segregation in Children with Auditory Processing Disorder. \u003cem\u003eIranian Journal of Medical Sciences \u003c/em\u003e\u003cstrong\u003e41, \u003c/strong\u003e110\u0026ndash;117 (2016).\u003c/li\u003e\n\u003cli\u003eGolden, H. L.\u003cem\u003e et al. \u003c/em\u003eMusic Perception in Dementia. \u003cem\u003eJournal of Alzheimer\u0026apos;s disease : JAD \u003c/em\u003e\u003cstrong\u003e55, \u003c/strong\u003e933\u0026ndash;949 (2017).\u003c/li\u003e\n\u003cli\u003eStopford, C. L., Thompson, J. C., Neary, D., Richardson, A. M. T. \u0026amp; Snowden, J. S. Working memory, attention, and executive function in Alzheimer\u0026apos;s disease and frontotemporal dementia. \u003cem\u003eCortex; a journal devoted to the study of the nervous system and behavior \u003c/em\u003e\u003cstrong\u003e48, \u003c/strong\u003e429\u0026ndash;446 (2012).\u003c/li\u003e\n\u003cli\u003eCoffey, E. B. J., Chepesiuk, A. M. P., Herholz, S. C., Baillet, S. \u0026amp; Zatorre, R. J. Neural Correlates of Early Sound Encoding and their Relationship to Speech-in-Noise Perception. \u003cem\u003eFrontiers in neuroscience \u003c/em\u003e\u003cstrong\u003e11, \u003c/strong\u003e479 (2017).\u003c/li\u003e\n\u003cli\u003eMaillard, E., Joyal, M., Murray, M. M. \u0026amp; Tremblay, P. Are musical activities associated with enhanced speech perception in noise in adults? A systematic review and meta-analysis. \u003cem\u003eCurrent research in neurobiology \u003c/em\u003e\u003cstrong\u003e4, \u003c/strong\u003e100083 (2023).\u003c/li\u003e\n\u003cli\u003eCoffey, E. B. J., Arseneau-Bruneau, I., Zhang, X. \u0026amp; Zatorre, R. J. The Music-In-Noise Task (MINT): A Tool for Dissecting Complex Auditory Perception. \u003cem\u003eFrontiers in neuroscience \u003c/em\u003e\u003cstrong\u003e13, \u003c/strong\u003e199 (2019).\u003c/li\u003e\n\u003cli\u003eMoore, B. C. The importance of high-frequency hearing in a medico-legal context. \u003cem\u003eThe Journal of the Acoustical Society of America \u003c/em\u003e\u003cstrong\u003e148, \u003c/strong\u003e2542 (2020).\u003c/li\u003e\n\u003cli\u003eNarne, V. K.\u003cem\u003e et al. \u003c/em\u003eThe effect of recreational noise exposure on amplitude-modulation detection, hearing sensitivity at frequencies above 8 kHz, and perception of speech in noise. \u003cem\u003eThe Journal of the Acoustical Society of America \u003c/em\u003e\u003cstrong\u003e153, \u003c/strong\u003e2562 (2023).\u003c/li\u003e\n\u003cli\u003eHumes, L. E. The World Health Organization\u0026apos;s hearing-impairment grading system: an evaluation for unaided communication in age-related hearing loss. \u003cem\u003eInternational journal of audiology \u003c/em\u003e\u003cstrong\u003e58, \u003c/strong\u003e12\u0026ndash;20 (2019).\u003c/li\u003e\n\u003cli\u003eRStudio Team. \u003cem\u003eRStudio: Integrated Development for R \u003c/em\u003e(2020).\u003c/li\u003e\n\u003cli\u003eBergstra, J. \u0026amp; Bengio, Y. Random search for hyper-parameter optimization. \u003cem\u003eJournal of Machine Learning Research \u003c/em\u003e\u003cstrong\u003e13 \u003c/strong\u003e(2012).\u003c/li\u003e\n\u003cli\u003eLundberg, S. \u0026amp; Lee, S.-I. A Unified Approach to Interpreting Model Predictions, 2017.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"auditory scene analysis, individual differences, age, working memory, musical training, hearing loss","lastPublishedDoi":"10.21203/rs.3.rs-6026003/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6026003/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAuditory scene analysis (ASA) is the ability to organize complex auditory mixtures into meaningful events and streams and is fundamental for auditory perception of both music and speech. Individual differences in ASA are recognized in the literature, yet the factors driving this variability remain poorly understood. This study employs a novel music-based ASA task, the Musical Scene Analysis (MSA) test, alongside a speech-in-noise test, to examine the influence of hearing loss, age, working memory capacity (WMC), and musical training. Ninety-two participants were categorised into four groups: 31 older normal-hearing, 34 older hearing-impaired, 26 younger normal-hearing, and one younger hearing-impaired individual. Results reveal a moderate correlation between ASA performance in speech and music (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.5), suggesting shared underlying perceptual processes, yet the factors influencing individual differences varied across domains. A dual modelling approach using ridge regression and gradient-boosted decision trees identified hearing loss as the strongest predictor of speech-based ASA, with a weaker effect of age, while musical training and WMC had no impact. In contrast, musical training showed a substantial effect on musical ASA, alongside moderate effects of hearing loss and age, while WMC exhibited only a marginal, non-robust effect. These findings highlight both shared and domain-specific factors influencing ASA abilities in music and speech.\u003c/p\u003e","manuscriptTitle":"Individual differences in auditory scene analysis abilities in music and speech","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-22 14:34:54","doi":"10.21203/rs.3.rs-6026003/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-07-02T10:40:59+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"259549788039298803024213391953832125219","date":"2025-05-26T08:24:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-21T08:13:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161015749772126853443447920114035236791","date":"2025-05-21T07:09:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-20T11:56:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-15T17:22:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-12T19:30:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8519fa74-ec45-4bd1-b9eb-552c1cfe5c6c","owner":[],"postedDate":"May 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48792733,"name":"Biological sciences/Psychology/Human behaviour"},{"id":48792734,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Perception"},{"id":48792735,"name":"Biological sciences/Neuroscience/Auditory system"},{"id":48792736,"name":"Biological sciences/Neuroscience/Cognitive ageing"}],"tags":[],"updatedAt":"2025-07-07T15:59:37+00:00","versionOfRecord":{"articleIdentity":"rs-6026003","link":"https://doi.org/10.1038/s41598-025-10263-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-05 15:56:59","publishedOnDateReadable":"July 5th, 2025"},"versionCreatedAt":"2025-05-22 14:34:54","video":"","vorDoi":"10.1038/s41598-025-10263-z","vorDoiUrl":"https://doi.org/10.1038/s41598-025-10263-z","workflowStages":[]},"version":"v1","identity":"rs-6026003","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6026003","identity":"rs-6026003","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}