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Frontiers in Audiology and Otology ·

Volume 4 - 2026 | https://doi.org/10.3389/fauot.2026.1913765

The pleasurable urge to move to music is maintained in cochlear implant users

  • Alberte B. Seeberg1*corresponding author
  • Tomas E. Matthews1
  • Andreas Højlund2
  • Peter Vuust1
  • Bjørn Petersen1
  1. 1Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
  2. 2Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus, Denmark
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Abstract

Normally hearing (NH) listeners experience the strongest pleasurable urge to move to music (PLUMM) when listening to rhythmic patterns of medium complexity compared to high and low complexity, reflected by an inverted U-shaped relationship. This relation is strongest when these patterns consist of more rhythmic layers (e.g., snare and bass drum vs. snare only). In cochlear implant (CI) users, the sensation of PLUMM remains unexplored. In this study, we investigated CI users' perception of rhythm, and their PLUMM, compared to NH listeners. Given some of the technical limitations of the implant, we hypothesized that CI users would prefer rhythms consisting of fewer rhythmic layers. An online study (29 CI users and 31 NH listeners) was conducted, asking participants to rate “wanting to move” and “experienced pleasure” while listening to drum patterns that varied both in terms of degree of syncopation (i.e., rhythmic complexity) and the number of rhythmic layers (snare only, SN; snare and hi-hat, SN+HH; snare and bass drum, SN+BD; snare, bass drum and hi-hat, SN+BD+HH). Both CI users and NH listeners preferred rhythmic patterns of medium complexity in terms of degree of syncopation. Further, both groups showed preferences toward rhythmic patterns consisting of more rhythmic layers, with the highest ratings and most prominent inverted U-shaped relationships for the versions containing all three layers (SN+BD+HH), and the single-layer versions showing almost no effect of syncopation (SN). There were no significant differences in PLUMM ratings between the two groups except for a significantly bigger difference between SN only and SN+HH in CI users compared to NH listeners, reflected by a flatter curve for SN only in the CI users. Our findings indicate that CI users can still appreciate and respond to some musical elements, despite the technical limitations in pitch and timbre perception, with the underlying perceptual mechanisms for PLUMM remaining intact.

Keywords

Introduction

The cochlear implant (CI) functions by bypassing the outer and middle ear and directly stimulating the auditory nerve end fibers. Although CIs in many cases successfully restore speech perception (Bodmer et al., 2007; Dillon et al., 2013), they fall short in conveying music, leading to dissatisfaction among many CI users with their musical experience post-implantation (Fuller et al., 2021; Gfeller et al., 2000; Mirza et al., 2003). The electrode array, which is inserted into the cochlea, comprises a maximum of 22 electrodes and aims to replicate the function of approximately 3,500 hair cells in the inner ear. This considerable reduction in the number of stimulation sites poses, alongside current spread and poor encoding and transmission of temporal fine structure information (Limb and Roy, 2014), challenges in transmitting features like pitch and timbre, both of which are crucial for music perception (Vuust et al., 2022; Wessel, 1989). Furthermore, the limited dynamic range and obligatory compression applied in the CI's processing of incoming signals reduce the variations in loudness and may lead to distortion and degraded music appreciation (Gilbert et al., 2019; Halliwell et al., 2015). Nevertheless, CI users typically perform at the same level as normally hearing (NH) listeners in tasks with simple rhythmic stimuli (Limb and Roy, 2014; Jiam et al., 2017; Seeberg et al., 2023). However, their processing of more complex musical rhythms, constructed of several rhythmic layers presented simultaneously may be challenged both by the limited transmission of spectrotemporal fine structure information (Jiam and Limb, 2019) and the reduced dynamic range.

Yet, being able to perceive more complex real-world musical rhythm is important for the experience of the pleasurable urge to move to music (PLUMM), often referred to as groove. PLUMM is a key component of the musical experience, and missing out on this aspect can have implications for CI users' wellbeing, as the phenomenon not only induces pleasure (Vuust et al., 2022) but also facilitates social bonding (Dotov et al., 2021; Engel et al., 2022). Thus, it is important to understand whether the experience of PLUMM is altered for CI users, as many social and cultural events center around music and moving to music. Additionally, understanding whether complex rhythmic perception is compromised in CI users is crucial, as musical rhythm perception and production are closely linked to language processing skills, affecting phonological awareness and literacy development (Huss et al., 2011). Similar to musical rhythm, linguistic rhythm organizes events in time, and research has shown that children with developmental dyslexia perform poorly in tasks that require temporal processing and rhythm perception (Flaugnacco et al., 2014).

Previous studies have consistently shown that NH listeners provide higher PLUMM ratings for rhythmic patterns of medium complexity compared to those of high or low complexity, suggesting a “sweet spot” for the strongest pleasurable urge to move (Matthews et al., 2019, 2022; Sioros et al., 2014; Spiech et al., 2022, 2024; Stupacher et al., 2022a; Witek et al., 2014a,b; Stupacher et al., 2022b). In a study by Hidalgo et al., congenitally/prelingually deafened children with CIs performed poorer in sensorimotor synchronization tasks than NH peers, with the biggest difference between groups when listening to complex stimuli compared to simple stimuli (Hidalgo et al., 2021). Yet, in a study by Phillips-Silver et al., CI users were found to be able to find the beat and move their bodies in time to unpitched drum tones, though still not as well as NH listeners (Phillips-Silver et al., 2015). Nevertheless, the sensation of PLUMM in CI users remains largely unexplored.

In previous literature, rhythmic complexity has often been operationalized as the degree of syncopation (Witek et al., 2014a; Longuet-Higgins and Lee, 1982), where syncopation denotes a note on a relatively weak beat preceding a rest on a relatively strong beat (Longuet-Higgins and Lee, 1982; Fitch and Rosenfeld, 2007) (see Figure 1), thus violating rhythmic expectations. However, another dimension of rhythmic complexity is the number of rhythmic layers constructing the rhythm and the way these layers interact to reinforce or violate rhythmic expectations. In Western music, drum rhythms often consist of a bass drum, a snare drum and a hi-hat layer. The bass drum often falls on strong beats, accentuating the main pulse and alternating with the snare drum which creates a dynamic push-and-pull effect, driving the rhythm forward. The hi-hat often ties the other layers together by maintaining a steady pulse at a faster metrical level, thus establishing a metrical grid (or scaffold) for the slower metrical levels delineated by the bass and snare drums.

Examples of syncopation. **(A)** 4/4 bar illustrating syncopation in an 1/8-th note subdivision. The second and the third note occur on the relatively weak beats preceding rests on the relatively strong beats two and three. **(B)** 4/4 bar illustrating the syncopated bass drum part of a medium complex drum pattern used in the experiment. The second and the third note occur on the relatively weak beats preceding rests on the relatively strong beats two and three but here by a (faster) 1/16-th note. Black arrows indicate the four downbeats of the bar. Blue arrows indicate syncopated notes.
Figure 1. Examples of syncopation. (A) 4/4 bar illustrating syncopation in an 1/8-th note subdivision. The second and the third note occur on the relatively weak beats preceding rests on the relatively strong beats two and three. (B) 4/4 bar illustrating the syncopated bass drum part of a medium complex drum pattern used in the experiment. The second and the third note occur on the relatively weak beats preceding rests on the relatively strong beats two and three but here by a (faster) 1/16-th note. Black arrows indicate the four downbeats of the bar. Blue arrows indicate syncopated notes.

In a recent study, we investigated the role of these rhythmic layers and how their interaction with syncopation affected PLUMM (Seeberg et al., 2025). Our findings revealed that NH listeners showed the strongest inverted U-shaped relationship between ratings and the degree of syncopation for full three-layer versions, containing both bass drum, snare and hi-hat. In contrast, the single-layer versions (snare only) showed a near-flat pattern, indicating little-to-no effect of syncopation (Seeberg et al., 2025). Previous studies consistently show that the bass drum plays a central role in driving PLUMM for NH listeners (Seeberg et al., 2025; Hove et al., 2020; Lustig and Tan, 2020; Stupacher et al., 2016).

However, CI users typically experience reduced low-frequency stimulation due to varying low-frequency cutoffs based on both individual mappings as well as brand and processor (most common manufacturers set default cut-off settings between 100–250 Hz) (Gifford et al., 2022). This reduced stimulation results in diminished transmission of bass sound properties, and in a study by Roy et al. (2012) they show that CI users do not perceive differences in sound quality between musical stimuli missing up to 400Hz of bass frequency information. Therefore, rhythmic patterns containing more low-frequency energy may not lead to increased PLUMM in CI users to the same extent that it does in NH listeners.

Further, given the abovementioned technical limitations of the implant, it may be harder to parse the many elements in more complex rhythms, particularly as the number of rhythmic layers increases, because musical events from different layers might occur in close temporal proximity or even overlap. This may be especially relevant given previous findings showing reduced sensorimotor synchronization in CI users, particularly for more complex rhythmic stimuli (Hidalgo et al., 2021; Phillips-Silver et al., 2015), suggesting that beat perception may be challenged under conditions of increased rhythmic complexity. Since beat perception and rhythmic predictability are closely linked to PLUMM (Stupacher et al., 2022a), such difficulties could plausibly alter the relationship between rhythmic complexity, number of layers, and affective responses to rhythm.

Additionally, dissatisfaction with the overall music quality experienced through the implant could further negatively affect PLUMM. In this online study, we investigated CI users' PLUMM ratings when presented with drum rhythms that varied in complexity and in the number of rhythmic layers with a reference to NH listeners. We hypothesized that the number of rhythmic layers would modulate the preference patterns of CI users differently from NH listeners. Specifically, we expected that CI users would show a reduced layer-related enhancement of PLUMM and a shift toward lower degrees of syncopation as the number of rhythmic layers increased, reflecting potential difficulties in perceptually parsing overlapping or temporally adjacent events from different layers. Additionally, we hypothesized that CI users' PLUMM ratings would generally be lower than those of the NH listeners.

Methods

We conducted an online study, distributed through various online fora (e.g., Facebook, Reddit, etc.) and mailing lists. The study was created using the JsPsych JavaScript library (Leeuw et al., 2023).

and hosted on a local university server. To be included in the study, participants had to be fluent in English. Both CI users with unilateral and bilateral CIs could participate. NH participants were required to have no hearing impairments. Participation was anonymous and completely voluntary, and participants were not compensated monetarily. Participants provided digital informed consent by clicking a button, confirming that they had read the participant information. The study was approved by the Institutional Review Board of the Danish Neuroscience Center at Aarhus University (DNC-IRB-2022-006). This study used a subset of the stimuli, the same paradigm, and a similar analysis pipeline as our previously mentioned paper investigating the effect of rhythmic layers on PLUMM (Seeberg et al., 2025).

Stimuli

We selected a subset of nine rhythms from a stimulus set consisting of 12 rhythms, which varied in their degree of syncopation (see Supplementary material 1, Appendix A for a validation that quadratic fits are still a viable approach with this subset). The syncopation index was calculated using the method from Witek et al. (2014a) which adapted the method from Longuet-Higgins and Lee (1982) for polyphonic rhythms. In this method each metric position is given a weight and the index for each syncopation is calculated as the difference in weights between the syncopated note on the subsequent rest, with the polyphonic version further including instrument-specific weights [e.g., the bass drum is more strongly weighted than the snare; see Witek et al. (2014a) for details].

We used nine instead of 12 rhythms to shorten the completion time of the study. The original set of 12 rhythms was used in a previous study to investigate the role of rhythmic layers in PLUMM in NH listeners (Seeberg et al., 2025). In that study, the 12 rhythms were broken down into four different layer versions: snare only (SN), snare and hi-hat (SN+HH), snare and bass drum (SN+BD) and the original version containing snare, bass drum and hi-hat (SN+BD+HH). The nine rhythms selected for the current study similarly varied in the degree of syncopation and again the different layer versions as described above were utilized, resulting in a total of 36 distinct stimuli [see Supplementary material 1, Appendix B for more details on the stimuli and Seeberg et al. (2025), for more details on and sources of the original stimuli]. The drumkit sounds used for the stimuli were taken from the “Passion and Respect” preset found in the Groove Agent SE5 virtual drum machine, an integral part of Cubase 10.5. These sounds were chosen to ensure they were within the perceptible ranges of CI users. This was confirmed by creating electrodograms for each drum sound (see Supplementary material 1, Appendix C) and by having a CI user identify how many different drum sounds were present and which ones they were. Each of the eight-second rhythmic patterns had the same average number of onsets across the degrees of syncopation. The number of onsets (aside from the hi-hat) was consistent between the various versions of each rhythmic pattern, as the bass drum onsets from the original versions were converted into snare onsets for the versions without the bass drum (SN and SN+HH; see Figure 2 for an example of the stimuli). Maintaining an equal number of onsets across the versions was important to isolate the effects of the layers and syncopation manipulations as event density is a strong predictor of ratings of wanting to move (Madison et al., 2011) and since musicians intentionally increase event density in performances when seeking increased urge to move in listeners (Madison and Sioros, 2014).

Example of rhythmic patterns. **(A)** Examples of rhythmic patterns with the original instrumentation varying in degrees of syncopation. Low, medium and high complexity refers to the relative levels of syncopation (here low complexity = syncopation index 13, medium complexity = syncopation index 35, high complexity = syncopation index 69). **(B)** Examples of the different rhythmic-layer-versions of the medium complexity rhythm, snare only (SN), snare and hi-hat (SN+HH), snare and bass drum (SN+BD) and snare, bass drum and hi-hat (SN+BD+HH). For SN and SN+HH versions, the bass drum onsets were converted into snare onsets. Adapted from Seeberg et al. (2025).
Figure 2. Example of rhythmic patterns. (A) Examples of rhythmic patterns with the original instrumentation varying in degrees of syncopation. Low, medium and high complexity refers to the relative levels of syncopation (here low complexity = syncopation index 13, medium complexity = syncopation index 35, high complexity = syncopation index 69). (B) Examples of the different rhythmic-layer-versions of the medium complexity rhythm, snare only (SN), snare and hi-hat (SN+HH), snare and bass drum (SN+BD) and snare, bass drum and hi-hat (SN+BD+HH). For SN and SN+HH versions, the bass drum onsets were converted into snare onsets. Adapted from Seeberg et al. (2025).

Participants

In this study, 29 CI users (mean age: 52; range: 19–78; 15 women; see Table 1 for clinical characteristics) and 31 NH participants (mean age: 38; range: 18–68; 12 women) were included. Participants reported their countries of origin from five different continents, with the majority being from Europe and North America (see Supplementary material 1, Appendix D for details). Participants were asked how long they had received formal training on a musical instrument (including voice) over the course of their lives. Both groups included participants with little-to-no formal training as well as some with a significant amount of formal training (see Supplementary material 1, Appendix D for details). Of the 29 CI users, 15 were bilaterally implanted and 14 were unilaterally implanted. Of the 14 unilaterally implanted CI users, nine had a contralateral hearing aid. Additionally, CI users were asked about their listening setup while completing the experiment (i.e., speakers, headphones or direct streaming to CI). Of the 29 CI users, 20 streamed the sound directly to the implant(s), while four out of nine CI users listening through speakers or headphones reported having no residual hearing (four said yes with their HA and one NA). For further details on the CI users and their listening setups, see Table 1.

Table 1. Clinical characteristics and listening setups of CI users.

IDCIsHABrandProcessorCI exp.Dur. deaf.CI satisfactionResidual hearingTinnitusListening
CI0110MED-ELSonnet16.00NA9NoYesHeadphones
CI0220CochlearN80.750.179No (2 CIs)YesStreaming
CI0311CochlearN73.007.007NAYesSpeakers
CI0420CochlearN718.001.5010No (2 CIs)YesSpeakers
CI0520CochlearN717.004.009No (2 CIs)NoSpeakers
CI0620CochlearN68.005.009No (2 CIs)YesStreaming
CI0720CochlearN72.002.008No (2 CIs)YesStreaming
CI0820CochlearN73.004.007No (2 CIs)NoStreaming
CI0910CochlearN719.000.008NoYesStreaming
CI1020CochlearN712.000.337No (2 CIs)YesStreaming
CI1111ABMarvel0.178.009Yes with HAYesHeadphones
CI1220ABMarvel8.000.4210No (2 CIs)NoStreaming
CI1320CochlearN79.000.009No (2 CIs)NoStreaming
CI1411OMNeuro22.000.507NANoStreaming
CI1520CochlearN719.000.177No (2 CIs)NoStreaming
CI1610CochlearN726.001.509NANoStreaming
CI1710CochlearN72.5015.0010NoYesSpeakers
CI1820ABMarvel5.000.589No (2 CIs)YesStreaming
CI1911ABMarvel17.000.0010Yes with HANoSpeakers
CI2011CochlearN80.500.4210NAYesStreaming
CI2110CochlearNA0.332.008YesYesStreaming
CI2220ABMarvel15.0010.0010No (2 CIs)NoStreaming
CI2320MED-ELRondo3, Sonnet22.501.009No (2 CIs)NoStreaming
CI2420MED-ELSonnet210.0025.0010No (2 CIs)NoStreaming
CI2520CochlearN720.0025.009No (2 CIs)YesStreaming
CI2611CochlearN71.670.429Yes with HAYesSpeakers
CI2711CochlearKanso20.752.008Yes with HAYesSpeakers
CI2811ABMarvel0.422.0010Yes with HAYesStreaming
CI2911CochlearNA0.171.0010Yes with HANoStreaming
Mean8.584.258.83

CI satisfaction was rated on a scale from 0–10. Listening refers to how they listened to the stimuli while completing the experiment. Streaming is direct streaming to their implant(s). CI experience (CI exp) and duration of deafness (dur. deaf.) is reported in years. AB, Advanced Bionics; OM, Oticon Medical; HA, Hearing Aid.

Procedure

Participants were presented with written instructions, asking them to listen to each rhythm and rate both how much each rhythm made them want to move and how much pleasure they experienced while listening to each rhythm. Continuous slider scales with values ranging from 0 (none/not at all) to 100 (very much) were used for both ratings. The study took approximately 15 min to complete.

Analyses

RStudio (Posit team, 2023), and IDE for R, was used for the analyses. The correlation coefficient between the ratings of wanting to move and experienced pleasure was calculated using the “rmcorr” package (Bakdash and Marusich, 2023) in order to take repeated measures into account, revealing high correlations between wanting to move and experienced pleasure for both CI users [r(1,014) = 0.90, p < 0.001] and NH listeners [r(1,084) = 0.83, p < 0.001]. Therefore, these two ratings were averaged into a single PLUMM rating, so each participant only had one combined rating for each version of each rhythm in the subsequent analyses (for transparency, separate plots for “wanting to move” and “experienced pleasure” are provided in Supplementary material 1, Appendix E. These show highly similar response profiles across conditions in both groups). For the analyses, we used the syncopation index of the original version for all versions of the same rhythmic pattern to compare directly between versions of the same rhythmic patterns. This approach was chosen, as the difference in syncopation index between versions, especially at higher levels of syncopation, was larger for the subset of the currently used nine rhythmic patterns than in our previous study with 12 rhythmic patterns (Seeberg et al., 2025). For ease of comparison, we also performed the analysis based on the calculated syncopation index for each version, mimicking the approach of our previous study. Results from this analysis is available in Supplementary material 1, Appendix F.

We employed linear mixed-effects analyses, using the lmer function from the “lme4” package (Bates et al., 2015), to statistically assess both linear and quadratic effects of syncopation on PLUMM ratings across all rhythm versions. For all models, individual differences were accounted for by including by-participant random intercepts and slopes for both the linear and quadratic effects of syncopation as well as version. Starting from an intercept-only model, we progressively added independent variables as both within-subject and between-subject factors. These variables were comprised of the linear and quadratic effects of syncopation, the rhythm version (SN, SN+HH, SN+BD, SN+BD+HH), group (CI, NH) and their interactions. We included linear and quadratic fits in the models in order to be able to capture the commonly observed quadratic nature of the PLUMM ratings commonly seen (i.e., the so-called inverted U-shape). The linear fits thus reflect any linear trends across syncopation levels (e.g., higher ratings for rhythms of low syncopation than for rhythms of high syncopation), whereas the quadratic fits reflect any quadratic effects across syncopation levels (e.g., higher ratings for medium syncopation compared to both low and high syncopation). All models were fit using maximum likelihood, and improvements in model fit were evaluated through likelihood ratio tests.

Following significant differences in model evidence, we planned to explore any main or interaction effect involving the group term via pairwise contrasts testing for differences between groups in the estimated means from the linear and quadratic fits of different rhythm versions at specific syncopation levels, namely 0, 20, 40, 60, and 80, as representative points along the fitted line or quadratic curve (rather than discrete syncopation categories derived from the stimuli). Furthermore, since the focus of the current study is on CI users' experience of PLUMM, and the group of NH listeners is mainly included as a reference group, we planned to test any significant two-way interaction effect between rhythm version and linear and quadratic fits within each group, allowing to test the robustness of this interaction within each group. This was thus done as previously described, only without the group term, and thus only with the two within-subject terms (rhythm version and linear and quadratic effects) and their interaction in the models.

Contrasts were run in a consecutive order based on the level of complexity according to the rhythmic layers, i.e., SN+BD+HH was only contrasted with SN+BD, SN+BD was further contrasted with SN+HH, and SN+HH was further contrasted with SN. Contrasts were performed using the “emmeans” package (Lenth et al., 2021). P-values and confidence intervals were computed using Kenward–Roger approximated degrees of freedom, with Holm correction applied to adjust for multiple comparisons as per the default in the “emmeans” package. For the linear mixed effects analyses, residual plots of the final model were examined to check for violations of normality and homoscedasticity. No violations were found.

In addition, for CI users, we investigated whether there were any relations between ratings and CI satisfaction scores, CI experience or duration of deafness using both correlations with within-participant averages as well as linear mixed-effects regression models. We found no significant relations.

Visualizations of the data were made using the “ggplot2” package (Wickham, 2016).

Results

The combined linear and quadratic fit of the PLUMM ratings for all versions in each group is visualized in Figure 3. Fixed effects from the best-performing models from both between- and within-group analyses can be found in Supplementary material 1, Appendix G. Results from post-hoc contrasts are in Table 2. The alpha level was set to 0.05.

PLUMM ratings as a function of syncopation and rhythm version. Mean PLUMM ratings of different versions across CI users **(left)** and NH listeners **(right)**. Versions include SN = snare only; SN+HH = snare + hi-hat; SN+BD = snare + bass drum; and SN+BD+HH = snare + bass drum + hi-hat. The black lines at the bottom indicate the syncopation indices of the nine rhythms that were used for the study.
Figure 3. PLUMM ratings as a function of syncopation and rhythm version. Mean PLUMM ratings of different versions across CI users (left) and NH listeners (right). Versions include SN = snare only; SN+HH = snare + hi-hat; SN+BD = snare + bass drum; and SN+BD+HH = snare + bass drum + hi-hat. The black lines at the bottom indicate the syncopation indices of the nine rhythms that were used for the study.

Table 2. Contrasts between versions at different syncopation indices (SI) for CI users and NH listeners.

SIVersion contrastCINH
β EST.SEDoFSIGN.β EST.SEDoFSIGN.
0SN+BD+HH–SN+BD7.314.2974213.413.85759******
SN+SN+BD–SN+HH4.654.743261.140.29666
SN+SN+H–SN+HH4.915.342277.494.00577
20SN+BD+HH–SN+BD6.372.01114******11.241.89122*******
SN+SN+BD–SN+HH9.752.8552******10.352.0286*******
SN+H–SN13.033.7661******8.382.1880*******
40SN+BD+HH–SN+BD5.692.36204*****9.732.19207*******
SN+SN+BD–SN+HH11.753.1273******13.062.30142*******
SN+SN+HH–SN+HH14.733.9675******8.392.44124******
60SN+BD+HH–SN+BD5.282.21163*****8.872.06168*******
SN+SN+BD–SN+HH10.633.0063******9.272.18116*******
SN+HH–SN10.013.8868*****7.502.33103******
80SN+BD+HH–SN+BD5.132.843598.682.60354******
SN+SN+BD–SN+HH6.413.49114−1.012.70251
SN+SN+HH–SN+HH−1.134.271005.722.82209

SI, syncopation index; CI, cochlear implant; NH, normally hearing; Est., estimate; SE, standard error; DoF, degrees of freedom; Sign., significance; SN, snare; BD, bass drum; HH, hi-hat.

*******p < 0.001,

******p < 0.01,

*****p < 0.05.

Between groups

The best-performing model included a two-way interaction between the linear + quadratic effects of syncopation and rhythm version (see Table 3 for model evaluation metrics). Since there was no effect of group, we did not explore this interaction further across groups. Instead, we tested whether this two-way interaction was robust within each group.

Table 3. Model evaluation metrics (between groups).

ModellogLikχ2DoFPr (>χ2)SIGN.
Null model−9,536
+ Group−9,5360.0710.79
+ Version−9,446181.553< 0.001*******
+ Linear and quadratic fit of syncopation−9,40287.212< 0.001*******
+ Group: version−9,4004.0930.25
+ Group: linear and quadratic fit of syncopation−9,3992.3120.31
+ Version: Linear and quadratic fit of syncopation−9,35882.156< 0.001*******
+ Group * version * linear and quadratic fit of syncopation−9,3547.8560.25

logLik, log likelihood; χ2, Chi square; DoF, degrees of freedom; Pr, probability.

***p < 0.001.

Within groups

The largest improvement in model evidence in the between group analysis was seen when including the interaction term between rhythm version and syncopation [χ2(6) = 137.13, p < 0.001]. Since this contrast most directly addresses our primary aim with this study when not considering the group contrast, namely the effect of rhythmic layers on the experience of PLUMM, we wanted to test whether it was robust within each group separately.

Indeed, the best-performing model within both groups included the linear + quadratic effects of syncopation, the rhythm version and the interaction between the two (see Supplementary material 1, Appendix H for model evaluation metrics).

CI users

Based on the likelihood ratio tests, the model fit significantly improved by adding the linear + quadratic effects of syncopation [χ2(2) = 38.10, p < 0.001] and the rhythm version [χ2(3) = 61.50, p < 0.001]. The best-fitting model further included the interaction between these two independent variables [χ2(6) = 43.40, p < 0.001].

When looking at the fixed effects of the best-performing model alongside Figure 3, the quadratic fit of the SN versions was significantly flatter than the quadratic fit of the SN+HH versions [t(847.54) = −3.17, p < 0.01]. The quadratic fit of the SN+HH did not differ significantly from the fit of the SN+BD versions. Similarly, the fit of the SN+BD versions did not differ significantly from the fit of the original versions (SN+BD+HH; see Supplementary material 1, Appendix G for fixed effects). Looking at Figure 3, the versions containing two or more rhythmic layers show more prominent inverted U-shaped curves while the SN curve is almost flat, indicating no effect of syncopation for rhythms in the SN versions.

Based on the best-performing model, the post-hoc contrasts between the estimated marginal means from the quadratic fits revealed significant differences between all consecutive versions at syncopation levels 20, 40 and 60. At syncopation levels 0 and 80, there were no significant differences between any of the versions (see Table 2).

NH listeners

Based on the likelihood ratio tests, the model fit significantly improved by adding the linear + quadratic effects of syncopation [χ2(2) = 52.80, p < 0.001] and the rhythm version [χ2(3) = 133.80, p < 0.001]. Again, the best-fitting model further included the interaction between these two independent variables [χ2(6) = 46.70, p < 0.001].

When looking at the fixed effects of the best-performing model, the quadratic fits between the SN and SN+HH versions and between the SN+BD and original versions were not significantly different. However, the quadratic fit between the SN+HH versions and SN+BD versions was significantly different [t(932.05) = −3.66, p < 0.001]. Looking at Figure 3, the curves for the original versions and the SN+BD versions are more pronounced inverted-Us compared to the curves of the other two versions.

Based on the best-performing model, the post-hoc contrasts between the estimated marginal means from the quadratic fits again revealed significant differences between all consecutive versions at syncopation levels 20, 40 and 60. At syncopation levels 0 and 80, there were significant differences between the SN+BD+HH and SN+BD versions, though not between the SN+BD and SN+HH versions nor between SN+HH and SN versions (see Table 2).

Discussion

This study investigated CI users' preferences for rhythmic complexity depending on the number of rhythmic layers. We hypothesized that the number of rhythmic layers would modulate the preference patterns of CI users differently from NH listeners, pushing the CI users' preferences toward simpler rhythms when adding more rhythmic layers. Additionally, we hypothesized that CI users' preferences would generally be lower than those of the NH listeners. Contrary to our hypotheses, we found that CI users have a preference for medium rhythmic complexity similar to that of NH listeners when listening to drum patterns with varying levels of syncopation. Additionally, we found a strong effect of the number of rhythmic layers on PLUMM in both CI users and NH listeners, with the highest ratings for the original versions with three rhythmic layers and the lowest ratings for the single-layer versions. Furthermore, the effect of rhythmic layers interacted with syncopation in both groups, showing more pronounced inverted U-shaped relationships for rhythms containing two or more layers, particularly in the SN+BD and SN+BD+HH versions. There was little to no effect of syncopation in the SN versions. These rating patterns replicate our findings in NH listeners from our previous study (Seeberg et al., 2025). Thus, we did not find any support for our hypothesized overall lower appreciation of the rhythms for the CI users compared to the NH listeners.

Our findings reveal that CI users exhibit similar preferences in rhythmic complexity for PLUMM as NH listeners, particularly in their preference for versions with more instruments, including the bass drum. This suggests that despite the known limitations in pitch and timbre perception with CIs, users can still appreciate and respond to some musical elements similar to NH listeners, with the perceptual mechanisms underlying the experience of PLUMM (at least elicited from drum rhythms) remaining intact. The observed similarities in preferences between CI users and NH individuals indicate that PLUMM is primarily driven by rhythmic and temporal patterns, which are effectively transmitted by CIs.

However, even though the rhythms used for this study contain several rhythmic layers, the overlap of events from different streams is still relatively limited, making it easier to extract information from each layer as they are temporally spaced apart. In naturalistic music, the drum kit (e.g., snare drum, bass drum and hi-hat) is often accompanied by other instruments, providing distinct variations in pitch and timbre. In a previous study, Gfeller et al. (2003) investigated the effects of prior song familiarity and perceived complexity on music appraisal. They found that while normal-hearing listeners tended to prefer songs with moderate or slightly higher complexity, CI users showed a strong preference for music they perceived as simpler. This was evident in CI users' ratings of classical music, which they found significantly more complex—and less likable—than pop and country styles, in contrast to the preferences of normal-hearing listeners. Importantly, whereas the current study focused on polyphonic complexity using just three distinct percussion sounds, the “real-world” music excerpts used in the Gfeller study featured combinations of melody, harmony, rhythm, and timbre, making direct comparisons difficult. Nonetheless, the findings clearly indicate that the limitations of cochlear implants become more apparent as musical complexity increases. In future studies, it would be interesting to investigate whether there is a “saturation in differentiation” where CI users, compared to NH listeners, experience less pleasure and wanting to move when listening to music with full orchestration compared to simpler arrangements (i.e., fewer instruments or rhythmic layers). Furthermore, it might be valuable to explore how the number and types of layers might differentially affect rhythmic appreciation.

CI users' preference for versions with more rhythmic layers, including the bass drum, suggests that they can perceive and enjoy rhythmic complexity and the low-frequency components in the rhythms. This implies that the rhythmic structure and the presence of bass elements are perceptible enough to influence their musical enjoyment. However, as previously mentioned, the bass drum sound used for this study was also chosen to be within CI users' perceptible ranges. And thus, much other music may contain lower bass frequencies that are not perceptible with CIs, which could substantially affect the CI users' experience of PLUMM. While the authors are aware of the many challenges in maintaining low-frequency representation post-implantation (e.g., electrode array insertion depth, number of available channels, frequency compression and the emphasis on restoring speech in general), these insights can guide future enhancements in CI technology and CI mappings. Specifically, advancing bass frequency representation through technological development, or personal maps for music listening, could further enrich the musical experiences of CI users, as it appears to play a crucial role in driving PLUMM. Alternatively, it could be beneficial to look at different modalities to assist CI users in representing these lower frequencies (e.g., through vibrotactile stimulation) (Fletcher, 2021).

Additionally, targeted rhythm training in hearing-impaired listeners have been found to affect language skills (Torppa and Huotilainen, 2019), but it may also have implications for CI users' musical experiences, especially if such rhythm training includes a large range of rhythmic complexities (Petersen et al., 2012). Furthermore, investigating the impact of different musical genres and rhythms on CI users' enjoyment and movement could provide further insights into optimizing musical experiences for this population.

Limitations

Firstly, while most participants listened through direct streaming to their implant(s), other participants listened through headphones or speakers, and some of these participants also had a hearing aid. These participants could benefit from having preserved acoustic hearing, especially in lower frequencies, affecting their PLUMM ratings. To check whether this affected the observed patterns, we visualized the inverted U-shapes for each version excluding these CI users. This showed a similar pattern to the one observed with all the CI users (see Supplementary material 1, Appendix I), indicating that this potential confounding factor did not significantly affect the results. To further support this notion, we visualized the fitted ratings for each individual participant (see Supplementary material 1, Appendix J), which showed very similar inverted U-shaped patterns across participants and rhythmic layers (except for the SN only condition).

Secondly, our sample included both unilateral and bilateral CI users, which may affect how they adapt to the CI sounds and thereby their music perception through the CI. We visualized the inverted U-shapes for these groups separately (see Supplementary material 1, Appendix K). This suggests a potential difference between the two subgroups, with larger distance between rhythm versions in the unilateral group. However, due to the small sample size and associated variance, further data would be required to test this statistically.

Finally, the groups differed descriptively in some background variables, including a larger proportion of participants with more years of musical training in the NH group. Musical training may influence rhythm perception and music-related affective responses, and future studies with larger samples could examine its potential role more directly. However, as we did not observe significant differences in the overall PLUMM ratings between CI users and NH listeners, and as any potential effect of musical training would be expected to favor the NH group, this difference is unlikely to explain the main pattern of preserved PLUMM responses in CI users reported here.

To capture more subtle group differences, future studies could compare PLUMM ratings to ratings of complexity to assess within-subject relations between affective responses to subjective complexity. However, individuals and groups may differ in terms of how they use the rating scales. Therefore, it would be interesting to test the paradigm in single-sided deafened CI users to have a within-participant design and thereby a more direct comparison of listening with and without a CI, as the internal scale for the electric ear and acoustic ear would be the same for each individual.

Conclusion

This study explored the perception of complex musical rhythms and the experience of PLUMM in CI users compared to NH listeners. Our findings demonstrated that CI users exhibit similar preferences in rhythmic complexity for PLUMM compared to NH listeners, particularly favoring rhythms with more rhythmic layers, including the bass drum. This suggests that despite the limitations in pitch and timbre perception with CIs, users can still appreciate and respond to some musical elements, with the underlying perceptual mechanisms for PLUMM remaining intact. Our study contributes to understanding how CI users perceive and enjoy complex musical rhythms, revealing potential areas for technological improvements and therapeutic interventions to enhance their musical experiences.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fauot.2026.1913765/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Jennifer A. Bugos, University of South Florida, United States

Reviewed by: Rory Kirk, The University of Sheffield, United Kingdom

Marco Carner, Integrated University Hospital Verona, Italy

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was funded by the William Demant Foundation (case number: 21-0462). The Center for Music in the Brain is funded by the Danish National Research Foundation (DNRF117).

Data availability

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: the code for the online experiment and the statistical analyses are available on GitHub (https://github.com/alberteseeberg/The-pleasurable-urge-to-move-to-music-in-cochlear-implant-users). Data is available on OSF (https://osf.io/yu7vr/?view_only=2a4196726a8e4c1da8cdeba8c5573ddc).

Ethics approval

The studies involving humans were approved by the Institutional Review Board of the Danish Neuroscience Center at Aarhus University. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants' legal guardians/next of kin because participation was anonymous. All participants provided electronic informed consent to participate in the study and publish their data by clicking a button prior to participation. The questionnaires were anonymized, and participants were free to opt out of participation in the study at any time until completion, without any data being stored.

Author contributions

AS: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Visualization, Writing – original draft, Writing – review & editing, Project administration. TM: Methodology, Investigation, Data curation, Writing – review & editing. AH: Resources, Formal analysis, Supervision, Writing – review & editing. PV: Conceptualization, Writing – review & editing, Funding acquisition. BP: Conceptualization, Supervision, Writing – review & editing, Funding acquisition.

Acknowledgements

The authors wish to sincerely thank Kathleen Faulkner, David Landsberger and Samantha O'Connell for their help in distributing the study. Further, the authors wish to thank all the participants for their voluntary participation in this study.

References

  1. Bakdash J. Z., Marusich L. R. (2023). rmcorr: Repeated Measures Correlation. Available from: (Accessed March 12, 2024).
  2. Bates D., Maechler M., Bolker B., Walker S. (2015). Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1 – 48. doi: doi:10.18637/jss.v067.i01
  3. Bodmer D., Shipp D. B., Ostroff J. M., Ng A. H. C., Stewart S., Chen J. M.. (2007). A comparison of postcochlear implantation speech scores in an adult population. Laryngoscope. 117, 1408 – 11. doi: doi:10.1097/MLG.0b013e318068b57e
  4. Dillon M. T., Buss E., Adunka M. C., King E. R., Pillsbury H. C. >III, Adunka O. F.. (2013). Long-term speech perception in elderly cochlear implant users. JAMA Otolaryngol. Head Neck Surgery. 139, 279 – 83. doi: doi:10.1001/jamaoto.2013.1814
  5. Dotov D., Bosnyak D., Trainor L. J. (2021). Collective music listening: Movement energy is enhanced by groove and visual social cues. Quar. J. Exper. Psychol. 74, 1037 – 53. doi: doi:10.1177/1747021821991793
  6. Engel A., Hoefle S., Monteiro M. C., Moll J., Keller P. E. (2022). Neural correlates of listening to varying synchrony between beats in samba percussion and relations to feeling the groove. Front Neurosci. 16: 779964. doi: doi:10.3389/fnins.2022.779964
  7. Fitch W. T., Rosenfeld A. J. (2007). Perception and production of syncopated rhythms. Music Perception. 25, 43 – 58. doi: doi:10.1525/mp.2007.25.1.43
  8. Flaugnacco E., Lopez L., Terribili C., Zoia S., Buda S., Tilli S.. (2014). Rhythm perception and production predict reading abilities in developmental dyslexia. Front. Hum. Neurosci. 8: 392. doi: doi:10.3389/fnhum.2014.00392
  9. Fletcher M. D. (2021). Can haptic stimulation enhance music perception in hearing-impaired listeners? Front. Neurosci. 15: 723877. doi: doi:10.3389/fnins.2021.723877
  10. Fuller C., Free R., Maat B., Başkent D. (2021). Self-reported music perception is related to quality of life and self-reported hearing abilities in cochlear implant users. Cochlear Implants Int. 23: 1 – 10. doi: doi:10.1080/14670100.2021.1948716
  11. Gfeller K., Christ A., John K., Witt S., Mehr M. (2003). The effects of familiarity and complexity on appraisal of complex songs by cochlear implant recipients and normal hearing adults. J. Music Therapy. 40, 78 – 112. doi: doi:10.1093/jmt/40.2.78
  12. Gfeller K., Christ A., Knutson J. F., Witt S., Murray K. T., Tyler R. S.. (2000). Musical backgrounds, listening habits, and aesthetic enjoyment of adult cochlear implant recipients. J. Am. Acad. Audiol. 11, 390 – 406. doi: doi:10.1055/s-0042-1748126
  13. Gifford R. H., Sunderhaus L. W., Dawant B. M., Labadie R. F., Noble J. H. (2022). Cochlear implant spectral bandwidth for optimizing electric and acoustic stimulation (EAS). Hear Res. 426, 108584. doi: doi:10.1016/j.heares.2022.108584
  14. Gilbert M., Jiradejvong P., Limb C. (2019). Effect of compression on musical sound quality in cochlear implant users. Ear Hear. 40, 1368 – 75. doi: doi:10.1097/AUD.0000000000000715
  15. Halliwell E. R., Jones L. L., Fraser M., Lockley M., Hill-Feltham P., McKay C. M.. (2015). Effect of input compression and input frequency response on music perception in cochlear implant users. Int J Audiol. 54, 401 – 7. doi: doi:10.3109/14992027.2014.986689
  16. Hidalgo C., Zécri A., Pesnot-Lerousseau J., Truy E., Roman S., Falk S.. (2021). Rhythmic abilities of children with hearing loss. Ear Hearing. 42: 364. doi: doi:10.1097/AUD.0000000000000926
  17. Hove M. J., Martinez S. A., Stupacher J. (2020). Feel the bass: Music presented to tactile and auditory modalities increases aesthetic appreciation and body movement. J. Exp. Psychol.: General. 149: 1137 – 47. doi: doi:10.1037/xge0000708
  18. Huss M., Verney J. P., Fosker T., Mead N., Goswami U. (2011). Music, rhythm, rise time perception and developmental dyslexia: perception of musical meter predicts reading and phonology. Cortex. 47, 674 – 89. doi: doi:10.1016/j.cortex.2010.07.010
  19. Jiam N. T., Caldwell M. T., Limb C. J. (2017). What does music sound like for a cochlear implant user? Otol Neurotol. 38, e240 – 7. doi: doi:10.1097/MAO.0000000000001448
  20. Jiam N. T., Limb C. J. (2019). Rhythm processing in cochlear implant–mediated music perception. Ann. New York Acad. Sci. 1453, 22 – 8. doi: doi:10.1111/nyas.14130
  21. Leeuw J. R. d. e., Gilbert R. A., Luchterhandt B. (2023). jsPsych: enabling an open-source collaborative ecosystem of behavioral experiments. J. Open Source Softw. 8, 5351. doi: doi:10.21105/joss.05351
  22. Lenth R. V., Buerkner P., Herve M., Love J., Riebl H., Singmann H.. (2021). emmeans: Estimated Marginal Means, aka Least-Squares Means. Available online at: (Accessed October 26, 2021).
  23. Limb C. J., Roy A. T. (2014). Technological, biological, and acoustical constraints to music perception in cochlear implant users. Hear Res. 308, 13 – 26. doi: doi:10.1016/j.heares.2013.04.009
  24. Longuet-Higgins H. C., Lee C. S. (1982). The perception of musical rhythms. perception. 11, 115 – 28. doi: doi:10.1068/p110115
  25. Lustig E., Tan I. (2020). All about that bass: Audio filters on basslines determine groove and liking in electronic dance music. Psychol. Music. 48, 861 – 75. doi: doi:10.1177/0305735619836275
  26. Madison G., Gouyon F., Ullén F., Hörnström K. (2011). Modeling the tendency for music to induce movement in humans: First correlations with low-level audio descriptors across music genres. J. Exp. Psychol.: Hum. Perc. Perf. 37, 1578 – 94. doi: doi:10.1037/a0024323
  27. Madison G., Sioros G. (2014). What musicians do to induce the sensation of groove in simple and complex melodies, and how listeners perceive it. Front Psychol. 5, 894. doi: doi:10.3389/fpsyg.2014.00894
  28. Matthews T. E., Witek M. A. G., Heggli O. A., Penhune V. B., Vuust P. (2019). The sensation of groove is affected by the interaction of rhythmic and harmonic complexity. PLoS ONE. 14, e0204539. doi: doi:10.1371/journal.pone.0204539
  29. Matthews T. E., Witek M. A. G., Thibodeau J. L. N., Vuust P., Penhune V. B. (2022). Perceived motor synchrony with the beat is more strongly related to groove than measured synchrony. Music Perception. 39, 423 – 42. doi: doi:10.1525/mp.2022.39.5.423
  30. Mirza S., Douglas S. A., Lindsey P., Hildreth T., Hawthorne M. (2003). Appreciation of music in adult patients with cochlear implants: a patient questionnaire. Cochlear Implants Int. 4, 85 – 95. doi: doi:10.1179/cim.2003.4.2.85
  31. Petersen B., Mortensen M. V., Hansen M., Vuust P. (2012). Singing in the key of life: A study on effects of musical ear training after cochlear implantation. Psychomusicol.: Music, Mind, and Brain. 22, 134 – 51. doi: doi:10.1037/a0031140
  32. Phillips-Silver J., Toiviainen P., Gosselin N., Turgeon C., Lepore F., Peretz I.. (2015). Cochlear implant users move in time to the beat of drum music. Hearing Res. 321, 25 – 34. doi: doi:10.1016/j.heares.2014.12.007
  33. Posit team (2023). RStudio: Integrated Development for R. Boston: Posit Software, PBC. Available online at: (Accessed June 15, 2023).
  34. Roy A. T., Jiradejvong P., Carver C., Limb C. J. (2012). Assessment of sound quality perception in cochlear implant users during music listening. Otol Neurotol. 33, 319 – 27. doi: doi:10.1097/MAO.0b013e31824296a9
  35. Seeberg A. B., Haumann N. T., Højlund A., Andersen A. S. F., Faulkner K. F., Brattico E.. (2023). Adapting to the sound of music—development of music discrimination skills in recently implanted ci users. trends in hearing. 27, 23312165221148035. doi: doi:10.1177/23312165221148035
  36. Seeberg A. B., Matthews T. E., Højlund A., Vuust P., Petersen B. (2025). Beyond syncopation: The number of rhythmic layers shapes the pleasurable urge to move to music. Cognition. 262: 106178. doi: doi:10.1016/j.cognition.2025.106178
  37. Sioros G., Miron M., Davies M., Gouyon F., Madison G. (2014). Syncopation creates the sensation of groove in synthesized music examples. Front. Psychol. 5: 1036. doi: doi:10.3389/fpsyg.2014.01036
  38. Spiech C., Danielsen A., Laeng B., Endestad T. (2024). Oscillatory attention in groove. Cortex. 174, 137 – 48. doi: doi:10.1016/j.cortex.2024.02.013
  39. Spiech C., Sioros G., Endestad T., Danielsen A., Laeng B. (2022). Pupil drift rate indexes groove ratings. Sci Rep. 12, 11620. doi: doi:10.1038/s41598-022-15763-w
  40. Stupacher J., Hove M. J., Janata P. (2016). Audio features underlying perceived groove and sensorimotor synchronization in music. Music Perception. 33, 571 – 89. doi: doi:10.1525/mp.2016.33.5.571
  41. Stupacher J., Matthews T. E., Pando-Naude V., Foster Vander Elst O., Vuust P. (2022a). The sweet spot between predictability and surprise: musical groove in brain, body, and social interactions. Front. Psychol. 13: 906190 doi: doi:10.3389/fpsyg.2022.906190
  42. Stupacher J., Wrede M., Vuust P. (2022b). A Brief and efficient stimulus set to create the inverted U-shaped relationship between rhythmic complexity and the sensation of groove. PLoS ONE. 17, e0266902. doi: doi:10.1371/journal.pone.0266902
  43. Torppa R., Huotilainen M. (2019). Why and how music can be used to rehabilitate and develop speech and language skills in hearing-impaired children. Hear Res. 380, 108 – 22. doi: doi:10.1016/j.heares.2019.06.003
  44. Vuust P., Heggli O. A., Friston K. J., Kringelbach M. L. (2022). Music in the brain. Nat Rev Neurosci. 23, 5. doi: doi:10.1038/s41583-022-00578-5
  45. Wessel D. (1989). Timbre and the perceptual organization of musical patterns. J. Acoust. Society Am. 86, S58 – 9. doi: doi:10.1121/1.2027569
  46. Wickham H. (2016). ggplot2: Elegant Graphics for Data Analysis. New York, NY: Springer, Verlag. Available online at: (Accessed March 6, 2024).
  47. Witek M. A. G., Clarke E. F., Kringelbach M. L., Vuust P. (2014a). Effects of polyphonic context, instrumentation, and metrical location on syncopation in music. Music Perception. 32, 201 – 17. doi: doi:10.1525/mp.2014.32.2.201
  48. Witek M. A. G., Clarke E. F., Wallentin M., Kringelbach M. L., Vuust P. (2014b). Syncopation, body-movement and pleasure in groove music. PLoS ONE. 9: e94446. doi: doi:10.1371/journal.pone.0094446

Citation

Seeberg, A. B., et al. (2026). The pleasurable urge to move to music is maintained in cochlear implant users. Frontiers in Audiology and Otology, Volume 4. https://doi.org/
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4 - 2026

Copyright

Copyright © 2026 Seeberg, Matthews, Højlund, Vuust and Petersen.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Alberte B. Seeberg

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