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Abstract

Research has explored how musical timing, tempo, and complexity influence experiences of groove in listeners. Musical complexity and tempo have also been associated with distortions in time perception, likely due to their demand on attentional resources within working memory. Individual factors such as arousal or age typically shape time perception in addition. The present study aimed at investigating how both groove and time perception are modulated by varying levels of complexity and tempo. Using short percussion patterns rooted in the funk tradition and performed by a professional drummer, a pilot study tested three levels of complexity – operationalized as degrees of syncopation – at two tempi (110 and 130 BPM). In the main study, a total of 192 participants rated the stimuli with the Experience of Groove Questionnaire (EGQ), and judged both the duration and subjective passage of time (PoT) during listening. Results indicate that low and medium complexity versions were experienced higher in the groove dimensions of Urge to Move and Pleasure, compared with the highly complex music. Medium complexity caused time to pass more quickly compared with the low or high complexity versions. Higher tempo resulted in time passing more quickly, as well as in higher groove ratings in terms of Urge to Move. Multiple linear regression analysis revealed that PoT and participants’ arousal predicted Urge to Move, and PoT also predicted the Pleasure dimension of groove. No significant effects were observed for age, musical training, or duration estimation. Taken together, this study demonstrates that only high levels of rhythmic complexity diminish groove experiences, contrasting with previous research proposing an inverted U-shaped relationship. Given that the perception of time often accelerates during pleasurable experiences, the current results provide additional support for the link between groove and affective enjoyment.

Keywords

syncopation urge to move pleasure arousal passage of time duration estimation music listening funk percussion

Introduction

Perceiving groove in music is widely defined as an urge to move one’s body, giving rise to pleasure (Janata et al., 2012; Madison, 2006; Senn et al., 2018). The term groove is also employed for popular music genres such as funk or rock, to express preferences for a piece of music, and in musicians’ descriptions of their play (Berliner, 1994). Similar notions exist across various musical cultures and genres, such as ‘swing’ in traditional jazz (Pfleiderer, 2006). Musicians may experience parallels with the concept of flow regarding the positive affect, perceived automaticity, or low attentional effort (Janata et al., 2012; Stupacher, 2019). Experiences of being in the groove, and in particular moving to music while feeling pleasure and even flow, are all related to temporal dimensions. The sense of time, in turn, may change in music and dance in relation to tempo, complexity, or individual attention (Wöllner, 2023), yet less is known about the role of groove in these changes. The current study aims at investigating both the perception of groove and time by analysing the impact of musical complexity and tempo, using human-performed percussion patterns in the funk tradition.

While there is general agreement on the main components in the perception of groove, the musical parameters and methods for studying it typically differ. An often-used approach is the Experience of Groove-Questionnaire (EGQ), developed by Senn et al. (2020), that includes the components ‘Urge to Move’ and ‘Pleasure’. There is less agreement on what actually causes sensations of groove. Studies investigated musical and individual factors including micro-timing (Câmara et al., 2020; Frühauf et al., 2013; Senn et al., 2016), rhythmic and harmonic complexity (Matthews et al., 2019; Senn et al., 2024; Sioros et al., 2014; Witek et al., 2014), and tempo (e.g., Etani et al., 2018). At the individual level, for example, familiarity with the piece as well as style preferences (Senn et al., 2021), and general preference for dance have been investigated as predictors for groove (O’Connell et al., 2022; Witek et al., 2014). In the following, musical complexity and tempo will be further discussed, as they were shown to both have an impact on groove experiences as well as on perceived changes in time.

Complexity is typically associated with a high density of rhythmic events, the number of sound sources or instruments, and the micro-timing in a musical interpretation. Complexity may refer to melodic, harmonic, rhythmic dimensions, or the instrumentation and timbre of a piece of music (Matthews et al., 2019; Senn et al., 2024). The experience of complexity in groove research is often varied by the level of rhythmic syncopation (Hoesl & Senn, 2018; Pfleiderer, 2006; Witek et al., 2014), which is a key expressive component in musical genres such as jazz, funk, or rock. In the current study, we adapted this definition and varied complexity by the degree of syncopation, that is the number of rhythmic events that are not located on the expected positions within the metrical structure. When following the metrical structure, listeners anticipate rhythmic events at certain positions. If the anticipated events are absent or occur at slightly different positions, their expectations are violated, typically creating tension, and in turn potentially rendering the music more interesting (London, 2012; Sioros et al., 2014; Witek et al., 2014). In this way, syncopation may thus distort or disguise the actual pulse or metrical structure of a piece, and a high degree of syncopation may even give rise to the illusion of a different pulse that deviates from the actual pulse. Syncopations are indeed highly related to listeners’ perceived complexity in drum patterns of popular music (Hoesl & Senn, 2018). While higher levels of syncopation may decrease experiences of groove (cf. Sioros et al., 2014) since the patterns could be too complex and do not afford entrainment and an urge to move, other studies suggested an inverted-U relationship between syncopation and groove, such that intermediate levels of syncopation achieve the highest ratings in Urge to Move (Matthews et al., 2019; Witek et al., 2014; but see Senn et al., 2024). A potential explanation may lie in the fact that higher syncopation places more difficulty on entrainment and synchronization due to increased complexity, resulting in a decrease in groove sensation as a pleasurable urge to move (Senn et al., 2018).

Comparable to experiences of groove, the perception of time can be modulated by factors in the individual such as emotional arousal, age, or listening context, and factors in the music. Models of internal clocks provide explanations as to how humans process time and make judgements of durations as well as how they experience the passage of time in the moment (Block & Zakay, 1997; Grondin, 2010; Löckenhoff & Rutt, 2015), which are often prone to subjective distortions. Music can cause distortions in the perception of time (Wang & Wöllner, 2020), and some pieces seem to last longer than others despite having the same clock-time duration. Factors such as emotions, tempo, timbre (Droit-Volet et al., 2013), degree of complexity (e.g., Bueno et al., 2002; Yeager, 1969 but see Wöllner, 2023), and attention to metrical levels (Hammerschmidt & Wöllner, 2020; Wöllner & Hammerschmidt, 2021) have been shown to influence time judgements. An increase in the level of complexity should lead to higher demands on the attentional resources in working memory for processing the stimulus (Grondin, 2010; Wang & Wöllner, 2020). Thus, altered resource allocation in working memory caused by higher percussive complexity should result in subjective time being longer in duration.

The impact of tempo on time perception, as can be expected (lat. ‘tempus’ simply means ‘time’), is large. While time seems to pass more quickly at the moment if the musical tempo is fast, afterwards, rather paradoxically, listeners tend to judge the duration to have been longer compared with slower tempi (Droit-Volet & Martinelli, 2023; Wöllner, 2023). This leads to the distinction between two measures: duration estimates after the stimulus that can be indicated in seconds, and passage of time (PoT) felt during an interval of time that can be indicated on a rating scale. As stated above, in music, tempo affects time perception such that faster music yields longer estimates of durations than slower pieces, whereas time is perceived as passing more quickly during the process of listening to faster music (e.g., Droit-Volet et al., 2013; Hammerschmidt & Wöllner, 2020; Oakes, 2003). One explanation for these effects lies in the relationship between tempo and physiological arousal, since higher tempi may raise the body’s arousal level. Another explanation refers to stimulus properties including event density. According to psychological inner clock models, higher arousal or a higher number of perceived events facilitate the emission of more pulses by a pacemaker that are, in turn, accumulated by a putative counter system. In comparison with the same time period at a lower tempo, the higher number of pulses are then experienced as having been derived from a longer period of time (e.g., Block & Zakay, 1997; Grondin, 2010). Hence duration is judged to be longer for highly arousing and complex stimuli with a high number of events (e.g., Block & Zakay, 1997; Droit-Volet et al., 2013; Grondin, 2010; Wang & Wöllner, 2020).

Tempo also plays an important role in the context of entrainment and synchronization, and is often associated with the perception of groove (Janata et al., 2012). Various findings on preferred tempo for synchronization and groove can be found in the literature, with most observations ranging between 100 and 140 BPM. In an analysis of songs from popular and electronic dance music, Moelants (2003) found a predominant number of pieces in the tempo range between 120 and 130 BPM, suggesting a particular preference for synchronization with music in this range. Etani et al. (2018) identified an optimal tempo for entrainment between 100 and 120 BPM with drum patterns. These results are consistent with research on spontaneous motor tempo (SMT) for human locomotion, showing that average locomotion frequency approximates 2 Hz or 120 BPM for short distances (MacDougall & Moore, 2005), which could be an explanation for preferred musical tempi, since synchronization is particularly comfortable at this speed (McAuley et al., 2006). However, recent findings suggest a lower speed in SMT (Hammerschmidt & Wöllner, 2023), and Senn et al. (2018) observed only a small effect of tempo on groove ratings compared with other musical or person-related variables associated with groove.

Taken together, previous research found effects of complexity on time estimates (Bueno et al., 2002) as well as on groove ratings, for which complexity was typically operationalised as the degree of syncopation (e.g., Matthews et al., 2019; Senn et al., 2018; Witek et al., 2014). In addition, tempo influences time perception (e.g., Droit-Volet et al., 2013; Hammerschmidt & Wöllner, 2020; Oakes, 2003), though its effects on groove perception have been less pronounced (Senn et al., 2018). Based on these findings, we hypothesize the following:

Rhythmic complexity, operationalised as level of syncopation, should influence both time perception and groove. Higher complexity is expected to lead to longer time estimates, faster passage of time judgments, and lower groove ratings.

Tempo should affect both time and groove experiences, so that faster tempo results in longer time estimates as well as time passing more quickly. While previous research has shown inconsistent tempo effects on groove, we expect that higher tempo may interact with complexity in groove ratings, potentially increasing the effects for the high-complexity patterns.

Finally, we propose that perceived time and individual factors such as arousal, musical training, and age, will influence groove experiences. Given that the relationship between time perception and groove, to our knowledge, has not been addressed before, this part of the analysis is exploratory.

Methods

Participants

In an online experiment, a total of 200 individuals took part, recruited via online forums and word of mouth in lectures and personal networks. Of these 200 participants, eight individuals were excluded from the analysis due to incomplete data or overly long processing times, so 192 valid data sets were entered into the analysis. Of the 192 participants, 123 identified themselves as female, 67 as male, and 2 as non-binary. The mean age was 27.76 years (SD = 8.72). Ninety individuals (46.9%) reported playing one or more musical instruments, with a mean of 6.01 years (SD = 9.77) of playing experience, and a mean of 2.49 years (SD = 3.63) of formal lessons taken. There was an opportunity to take part in a prize draw for nine Spotify vouchers, each worth €10, after the survey was completed. For this purpose, the option to leave an e-mail address was given at the end of the questionnaire. To ensure the anonymity of the data, the e-mail addresses were stored separately. The study was conducted in accordance with the guidelines of the Ethics Committee at the Faculty of Humanities, University of Hamburg, and participants provided informed consent prior to taking part.

Stimuli and pilot study testing

A total of ten short musical sequences were composed and recorded. They consisted of three types of chord patterns of two tempi, and four ‘catch trials’ as explained below. The goal was to provide varying degrees of percussive complexity, including no syncopation, moderate, and high levels of syncopation (Figure 1).

Figure 1.

Drum patterns of low (top), medium, and high complexity, including bass drum, snare, and hi-hat.

The first sequence is based on quarter notes and contains no syncopation, with the bass drum on beats 1 and 3, and the snare on 2 and 4, while the hi-hat plays a continuous accompaniment. This sequence is a typical standard pattern in many styles such as pop, rock, or funk, including a clearly recognizable pulse. In the medium complexity sequence, the bass drum hits are slightly syncopated, on the beats of 1, 2 +, and 3 +, with a ghost note on each of the 2 + e and 3e in the snare pattern. The pulse is still easily recognizable due to the continuous eighth notes in the hi-hat, and the pattern can still be considered to be common. The third sequence includes bass drum and snare hits at unusual beat positions. The hi-hat pattern is no longer continuous and strongly syncopated, leading to a less recognizable pulse compared with the other patterns.

The drum sequences were recorded with a drum kit consisting of a 13" Turkish hi-hat, a Pearl 20x14" bass drum, and a 14x5.5" Ludwig snare drum. The recording was done with three microphones (Samson CO2, Samson Q Kick, Shure SM57) and the DAW Reaper. The drum tracks were not quantized and only minimally processed with an equalizer to give the sequences as natural a sound as possible. To construct musically meaningful stimuli, the drum sequences were embedded in a musical context including short chord progressions with an electronic organ sound and a bass guitar. Chords and the accompanying bass riff were programmed as MIDI sequences and remained the same for all complexity levels. The microtiming of the beats was slightly adjusted for consistency reasons across the three levels, and in order to keep the music from sounding too static. The stimuli can be found here: https://doi.org/10.5281/zenodo.18507530.

To analyse the impact of tempo differences on groove and time experiences, two tempi (110 and 130 BPM) were used for all stimuli. All experimental sequences had a total duration of 10 seconds with a short fadeout of one second. The 110 BPM version consisted of five bars, and the 130 BPM version of six bars. In addition, four ‘catch-trial’ sequences of 5 and 17 seconds in duration, at medium and high complexity levels, served as control stimuli to indicate if participants listened attentively and estimated duration appropriately. Taken together, a total of ten stimuli were used.

A pilot study was conducted to verify whether the three intended levels of complexity would be perceived. Using an online questionnaire (SoSci Survey; Leiner, 2019), five male and one female participants (M = 34.50 years, SD = 16.51) rated the sequences. They were all musically trained with at least six years of instrumental playing experience (M = 22.67 years, SD = 20.78), including bass, guitar, piano, and drums. Five of the six pilot participants had experience in more than one instrument, and two named drums as a secondary instrument. Participants were presented with six of the stimuli (excluding the catch trials) in randomized order (three complexity levels at both 110 and 130 BPM) and rated the complexity of the patterns using a slider on a 101-point scale. As expected, the low-complexity stimuli received the lowest complexity ratings, M_110BPM = 3.83, SD = 4.92, M_130BPM = 4.67, SD = 4.80. The medium-complexity stimuli received ratings in the middle, M_110BPM = 22.50, SD = 17.06, M_130BPM = 32.83, SD = 26.93, and the high-complexity stimuli received the highest complexity ratings, M_110BPM = 55.83, SD = 26.01, M_130BPM = 60.67, SD = 23.73. Mann–Whitney U tests revealed that the ratings of low and medium complexity showed a significant difference, U = 14.5, p < .001, r = .799. A significant difference was also found between medium and high complexity, U = 26.0, p = .008, r = .639, as well as between low and high complexity, U = 2.00, p < .001, r = .972. Thus, the stimuli sufficiently displayed the three levels of complexity in accordance with the aims of the main study.

Procedure and duration estimation control

Data was collected with SoSci Survey (Leiner, 2019), and participants were asked to use high-quality audio devices. After providing their informed consent, they checked their audio equipment and volume settings with two additional sound clips that were not part of the experiment, and they were subsequently instructed not to change the settings. In addition to age and gender, data about their musical training was collected (‘Do you play an instrument?’, ‘How long have you played an instrument?’, ‘How many years of formal instrumental lessons have you had?’).

The 10 stimuli were presented in individually randomized order. Four of these sound clips served as control stimuli (catch trials, two with a duration of 5 seconds and two with a duration of 17 seconds) to check if participants would carefully listen to the examples and distinguish between the durations. After each stimulus, participants provided duration estimates (in seconds) and passage of time judgements (5-point scale from ‘very slow’ to ‘very fast’). For the groove ratings, the German version of the Experience of Groove Questionnaire was used that consists of two scales with three items each, capturing the Urge to Move and the perceived Pleasure derived from the music on 7-point scales (Düvel et al., 2021; Senn et al., 2020). In addition, arousal was assessed using the Physiological Arousal Questionnaire (PAQ; Kallen, 2002, adapted from Dieleman et al., 2010).

To evaluate if participants indeed distinguished between the different durations (experimental stimuli and catch trials) in their estimations, a repeated-measures ANOVA was conducted. The analysis revealed a significant difference between the three durations, F(2, 380) = 450.66, p < .001, η²_p = .70, with post hoc comparisons (Bonferroni) all different at p < .001. While participants mostly estimated the 5-second stimuli adequately (M = 5.26 s, SD = 3.16), as well as the 10-second experimental stimuli (M = 9.59 s, SD = 4.60); the 17 second stimuli were underestimated in duration (M = 14.34 s, SD = 6.55).

Results

Time perception: Passage of time and duration estimation

To test for differences in rhythmic complexity (three syncopation levels) and tempo (110 and 130 BPM) on Passage of Time judgements (PoT), a 3 × 2 repeated-measures ANOVA was calculated (Figure 2, left panel). A small main effect of complexity was found, F(2, 382) = 5.00, p = .007, η²_p = .03. Stimuli of medium complexity caused time to pass more quickly compared with low (p = .011) and high complexity (p = .044), as analysed with pair-wise post hoc comparisons (Bonferroni). Tempo showed a large main effect on passage of time, F(1, 191) = 78.86, p < .001, η²_p = .29. Stimuli at a tempo of 110 BPM caused time to pass more slowly than stimuli at a faster tempo of 130 BPM. There was no significant interaction between complexity and tempo (p = .194, η²_p = .01).

Figure 2.

Passage of time (PoT) and durations estimates (M, 95% CI). Apart from complexity levels, tempo yielded significant differences in PoT judgements.

A 3 x 2 repeated-measures ANOVA was calculated for duration estimation. No significant main effects were found for complexity, F(2, 380¹) = 1.10, p = .335, η²_p = .01, or tempo, F(1, 190) = .275, p = .600, η²_p = .001, and no interaction effect occurred.

Groove: Urge to move und pleasure

To investigate the effects of complexity and tempo on Urge to Move, a 3 x 2 repeated-measures ANOVA was conducted (Figure 3, left panel). A large main effect of complexity was found, F(2, 382) = 38.24, p < .001, η²_p = .17. While low and medium complexity levels did not differ regarding Urge to Move in post hoc comparisons (p = 1.00), the high complexity version was lower in Urge to Move than the two other versions (both p < .001). A large main effect of Tempo on Urge to Move was also observed, F(1, 191) = 42.34, p < .001, η²_p = .18. The faster 130 BPM yielded higher Urge to Move ratings than the 110 BPM versions. Complexity and tempo interacted with each other, F(2, 382) = 7.56, p < .001, η²_p = .04. In order to follow up the interaction, pairwise comparisons (Bonferroni corrected) within each complexity level revealed that higher tempo led to greater urge to move in the low and medium complexity versions (both p < .001), whereas no tempo effect was found when complexity was high (p = .156).

Figure 3.

Groove ratings (M, 95% CI). Apart from complexity levels, tempo yielded significant differences in Urge to Move ratings.

A 3 x 2 repeated measures ANOVA was conducted on the effect of complexity and tempo on Pleasure (Figure 3, right panel). A significant effect of complexity was observed, F(2, 382) = 37.41, p < .001, η²_p = .16. Post hoc analyses revealed that low and medium complexity versions were rated similarly (p = .509), and high complexity versions received lower Pleasure ratings (both p < .001). While Tempo did not lead to an overall significant difference, F(1, 191) = 2.51, p = .115, η²_p = .01, complexity and tempo interacted with each other, F(2, 382) = 11.66, p < .001, η²_p = .06. Pairwise comparisons, separate for each complexity level, revealed that Pleasure was higher at 130 BPM for low (p < .009) and medium complexity levels (p < .042), whereas the slower tempo of 110 BPM was more pleasurable for the high complexity versions (p < .042).

Predictors for groove experiences

To analyse the influence of potential predictors on participants’ overall groove experience, results for Urge to Move and Pleasure were averaged across complexity levels and tempi per participant. Two multiple linear regression analyses were calculated for each of the two groove components, taking into account age, years of musical training, arousal (PAQ, Kallen, 2002), overall passage of time judgements, and duration estimates.

The first regression model explained 16.9% of the variance for Urge to Move, R = 0.41 (BIC = 590), F(5, 185) = 7.54, p < .001. PoT and arousal were significant predictors (Table 1), suggesting that the faster participants perceived the passage of time during listening, and the higher their self-assessed arousal was, the more they felt an urge to move.

Table 1.

Predictors for the groove components urge to move and pleasure (multiple linear regression, significant effects highlighted by asterisks).

Predictors	Urge to move			Pleasure
Predictors	ß	t	p	ß	t	p
Intercept	0.454	0.87	.386	0.381	0.68	.498
PoT***	0.783	5.47	<.001	0.925	6.04	<.001
Arousal*	0.109	2.07	.040	0.068	1.20	.231
Training	0.033	1.58	.115	0.032	1.41	.159
Duration Est	0.001	0.08	.937	0.019	0.86	.389
Age	−0.002	−0.23	.816	−0.006	−0.64	.523

The second regression explained 19.1% of the variance for Pleasure, R = 0.44 (BIC = 615), F(5, 185) = 8.72, p < .001. PoT was the only significant predictor. In both models, age, years of training, or duration estimation did not significantly predict any of the groove components. Figure 4 displays the linear impact of PoT on groove experience.

Figure 4.

Effects of PoT on groove experience.

Discussion

This study investigated whether groove experiences and the perception of time share central underlying dimensions by testing the influence of rhythmic complexity and tempo on both. Using human-performed percussion patterns with three levels of syncopation in order to vary complexity, and two different tempi, it was found that low and medium complexity as well as higher tempo led to higher groove ratings, and medium complexity and higher tempo caused time to pass more quickly. While there were no effects for duration estimation, multiple regression showed that passage of time and participants’ arousal predicted groove experience. As will be discussed below, these results are in line with several previous findings in the two domains of groove and time research. To the best of our knowledge, we believe that both have been investigated for the first time together in this study, which permits drawing conclusions on a key phenomenon of music listening as well as on theories of time perception.

Rhythmic complexity

Comparable to previous approaches (Hoesl & Senn, 2018; Witek et al., 2014), rhythmic complexity was controlled by using percussion patterns with different degrees of syncopation. It should be noted that the number of rhythmic events was lower in the low-complexity condition (n = 8 per measure), whereas there were similar numbers of events (n = 13, not taking into account the ghost notes) for complexity levels 2 and 3. To ensure that stimuli would resemble real music in the funk tradition, simple harmonic chord progressions and a bass line were added for enhancing the listening experience, while not drawing away the listeners’ attention from the percussion instruments. These chords and the bass remained the same across all conditions. The pilot study showed that complexity was successfully differentiated between the three levels of percussive syncopation.

The first hypothesis was confirmed for three of the four variables in question. The degree of complexity in the percussion patterns had a strong influence on participants’ groove perception. On both the Urge to Move and Pleasure scales of the EGQ (Düvel et al., 2021; Senn et al., 2020), low and medium complexity were rated highest. The interaction effect between complexity and tempo points to slightly different perceptions for the high-complexity condition. These interactions were followed up in separate ANOVAs for each of the two tempo conditions and yielded similar results as for the overall ANOVA, hence high levels of complexity resulted in significantly less Urge to Move and Pleasure. Our findings are consistent with previous research (e.g., Senn et al., 2018; Witek et al., 2014), yet the underlying mechanisms are not entirely understood. A potential explanation for the effect of complexity is provided by Witek et al. (2014), suggesting that with increasing syncopation, the information of a stimulus becomes increasingly difficult to process, which in turn places more difficulty on sensorimotor synchronization. This could lead to lower ratings for Urge to Move, since following the music’s pulse becomes more and more challenging. There has been some debate as to whether musical stimuli could also be too simple, e.g. by possessing too little syncopation, in order to afford Urge to Move and Pleasure (Witek et al., 2014), or from the perspective of ‘New Experimental Aesthetics’ (Berlyne, 1974), raise sufficient interestingness to be aesthetically pleasing. In this regard, an inverted-U relationship between complexity and groove had been suggested, with potentially the highest ratings for medium complexity. The most recent other study (Senn et al., 2024), using a total of forty pre-recorded drum patterns, did not confirm the inverted-U model and resulted in no effects of complexity on Urge to Move. However, a structural equation model indicated that higher complexity decreased perceived regularity in the music, which in turn also decreased Urge to Move. Higher complexity, on the other hand, positively affected both interestingness and pleasure. Hence even relatively simple musical patterns may evoke an urge to move and to dance.

Regarding participants’ experiences of time, a small main effect was found for one measure, suggesting indeed an inverted U-relationship between complexity and passage of time (PoT). While previous research assumed that higher rhythmic complexity increases the demands on working memory and in turn leads to longer duration estimates (Bueno et al., 2002), these results are not consistent across time experiments (Wöllner, 2023). Furthermore, in the current study, duration estimates were not influenced by level of complexity, hence duration estimations and PoT point to different experiences and judgement strategies of participants. A potential reason may lie in the construction of the stimuli such that participants may have followed the quarter and eighth notes provided by the bass guitar and the electronic organ chords, and focused less on the different complexity levels of the percussion patterns when judging the overall duration of the musical examples. The effects for PoT, on the other hand, indicating how quickly subjective time had passed (Droit-Volet & Martinelli, 2023), were affected by complexity, and also predicted Urge to Move and Pleasure in the regression model. In other words, time was subjectively going faster when participants enjoyed the musical examples and wanted to move along. For that reason, the dimension of time passing may add relevant information to individual experiences of groove.

Tempo, passage of time, and arousal

In accordance with the second hypothesis, higher musical tempo (130 BPM vs. 110 BPM) resulted in a strong effect on PoT. Faster examples were perceived as subjectively passing faster (cf. Droit-Volet & Martinelli, 2023; Wöllner & Hammerschmidt, 2021). Comparable to the factor complexity, perceived duration was not affected by tempo, even though participants correctly estimated duration differently for the ‘catch trials’ that were shorter or longer than the experimental trials. According to theories of time perception (Grondin, 2010; Wang & Wöllner, 2020), higher tempo should have increased the number of events perceived, and potentially increased participants’ arousal, leading to a higher number of pulses emitted in the supposed pacemaker-counter system of an inner clock (Block & Zakay, 1997 ; Droit-Volet et al., 2013). Compared with durations filled with a smaller number of events, then, participants should have perceived the fast-tempo conditions to last longer. In the current study, a tempo difference of 20 BPM was chosen so as to still lie in comfortable ranges of synchronization such as in popular dance music (see Moelants, 2003). Yet the difference may have been too small to influence duration judgements, and previous studies employed larger tempo differences for prospective duration estimates (Droit-Volet et al., 2013; Hammerschmidt & Wöllner, 2020; Oakes, 2003). Thus, the current ‘comfortable tempi’ may not have affected participants’ physiological arousal or the number of pulses emitted by the central pacemaker as strongly as predicted. If a difference of 20 BPM had been employed at more extreme tempi, for instance 50–70 BPM or 180–200 BPM, a jump of 20 BPM might have been sufficient for affecting time estimates.

Regarding groove, faster tempo strongly affected Urge to Move ratings. These results are partially in line with previous findings (Janata et al., 2012; Jerjen et al., 2024; Senn et al., 2018). It should be repeated that both tempi used in the current study were still in a comfortable locomotion and synchronization range (MacDougall & Moore, 2005), and the aim was not to test for extreme tempo ranges. Pleasure ratings were not significantly affected by tempo. Yet the significant interactions between tempo and complexity, for both Urge to Move and Pleasure, indicate that higher tempo caused stronger groove experiences only for low and medium rhythmic complexity. For the high complexity pattern, in contrast, there was no tempo effect on urge to move, and a converse effect for pleasure. In other words, with the highly complex stimulus, individuals found the slower tempo more pleasurable. It seems reasonable to assume that higher tempo even increases the information density in more complex music, unless listeners focus their attention on other structural elements in the musical metre (Wöllner & Hammerschmidt, 2021).

Results of the regression revealed relationships with PoT, as mentioned before, on both groove dimensions, and of arousal on Urge to Move. In this regard, arousal may reflect an individual’s stronger response to the musical stimulus, and may in turn demonstrate an increased readiness to move or act. Since arousal has for long been considered a key component in models of inner clocks as well (Block & Zakay, 1997; Droit-Volet et al., 2013; Grondin, 2010; Wang & Wöllner, 2020), studying arousal in musical groove experiences offers insights into both individual- and stimulus-related dimensions. Musical training or age did not significantly predict groove, and could be systematically varied in further studies (cf. Senn et al., 2021).

Limitations and suggestions for future work

As in many studies, we aimed at finding a reasonable balance between experimental control and validity of the musical material. The examples were designed to sound as natural as possible and were only minimally processed. While Pattern 1 was a relatively simple standard one, Pattern 2 was more complex but still standard, whereas in Pattern 3, the hi-hat showed unusual syncopations in addition to the bass syncopations at a 16th note level. The total number of rhythmic events was slightly different across patterns, yet when not taking into account the ghost notes in Pattern 2, the number of events was similar in Patterns 2 and 3. Given that event density may influence the internal pacemaker (Grondin, 2010), this could have affected results. Yet the nonsignificant outcome for tempo differences on duration judgements does not point in this direction: While the absolute number of events was highest at 130 BPM for Patterns 2 and 3, this was not reflected in time perception. Complexity and tempo could have been even more extreme, and it should be highlighted again that only the medium syncopation level was played with ghost notes on the snare, which may affect perceptions of groove. Since low-syncopation examples were perceived to be less complex in the pilot study, and also achieved high groove ratings that were comparable to those of the medium syncopation level, the ghost notes appears to be less of an issue in the current study, but could be studied further (Düvel, 2024).

Second, while there were effects of syncopation levels in the percussion patterns on groove, the relationship between syncopation in the melodic, harmonic, and percussive dimensions of music should be further investigated. Because of the large set of stimuli in different studies, comparisons across research findings are often less straightforward. Existing music could be used (Senn et al., 2024), or, employing an analysis-by-synthesis approach, different stimuli could be created in which beats are systematically syncopated at different levels. This could provide further insight into the perception of harmony, melody, and percussion instruments across different musical genres. In this regard, it should also be noted that contemporary popular music is typically strongly post-processed and based on high-quality synthesized instruments or sounds. While it would have been possible to process the funk-genre stimuli of this study even more in order to strengthen ‘naturalness’ from a production point of view, it was believed that the professional percussionist’s timing should remain untouched.

Conclusions

The perception of temporal experience and the sensation of groove are modulated by musical characteristics, with emerging evidence suggesting a bidirectional relationship between them. Consequently, psychological models of time research may be effectively applied within the context of groove research, allowing for the systematic examination of factors such as perceived and felt arousal in experimental settings. Similarly, the influence of intrinsic musical features, alongside participant-specific factors, warrants further investigation (Janata et al., 2012; Senn et al., 2024). This line of research may provide useful knowledge for musicians as to how certain elements and stylistic devices could enhance listener engagement and the danceability of the music, while also contributing to theoretical frameworks related to time perception and flow states.

Footnotes

Acknowledgements

We are grateful to Gerd Vierkötter for his collaboration in creating the percussion patterns, and to Olivier Senn for comments on a previous version of this manuscript.

ORCID iD

Clemens Wöllner

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The dataset and the musical stimuli are available at .

Notes

References

Berliner

P. A.

(1994). Thinking in Jazz: The infinite art of improvisation. University of Chicago Press.

Berlyne

D. E.

(Ed.). (1974). Studies in the new experimental aesthetics: Steps toward an objective psychology of aesthetic appreciation. Hemisphere.

Block

R. A.

Zakay

(1997). Prospective and retrospective duration judgments: A meta-analytic review. Psychonomic Bulletin & Review, 4(2), 184–197. https://doi.org/10.3758/BF03209393

Bueno

J. L.

Firmino

E. A.

Engelman

(2002). Influence of generalized complexity of a musical event on subjective time estimation. Perceptual and Motor Skills, 94(2), 541–547. https://doi.org/10.2466/pms.2002.94.2.541

Câmara

G. S.

Nymoen

Lartillot

Danielsen

(2020). Timing is everything. . . or is t? Effects of instructed timing style, reference and pattern on drum kit sound in groove-based performance. Music Perception, 38(1), 1–26. https://doi.org/10.1525/mp.2020.38.1.1

Dieleman

G. C.

van der Ende

Verhulst

F. C.

Huizink

A. C.

(2010). Perceived and physiological arousal during a stress task: Can they differentiate between anxiety and depression? Psychoneuroendocrinology, 35(8), 1223–1234. https://doi.org/10.1016/j.psyneuen.2010.02.012

Droit-Volet

Martinelli

(2023). The psychological underpinnings of feelings of the passage of time. In Wöllner

London

(Eds.), Performing time. Synchrony and temporal flow in music and dance (pp. 67–80). Oxford University Press.

Droit-Volet

Ramos

Bueno

J. L. O.

Bigand

(2013). Music, emotion, and time perception: The influence of subjective emotional valence and arousal? Frontiers in Psychology, 4, Article 417. https://doi.org/10.3389/fpsyg.2013.00417

Düvel

(2024). Den leisen Schlägen auf der Spur: Ghostnotes und Groove in Schlagzeug-Patterns der populären Musik. Tectum.

10.

Düvel

Labonde

Bechtold

Senn

Kopiez

(2021). Experience of Groove Questionnaire. Music Perception, 39(1), 83–99. https://doi.org/10.1525/mp.2021.39.1.83

11.

Etani

Marui

Kawase

Keller

P. E.

(2018). Optimal tempo for groove: Its relation to directions of body movement and Japanese nori. Frontiers in Psychology, 9, Article 462. https://doi.org/10.3389/fpsyg.2018.00462

12.

Frühauf

Kopiez

Platz

(2013). Music on the timing grid: The influence of microtiming on the perceived groove quality of a simple drum pattern performance. Musicae Scientiae, 17(2), 246–260. https://doi.org/10.1177/1029864913486793

13.

Grondin

(2010). Timing and time perception: A review of recent behavioral and neuroscience findings and theoretical directions. Attention, Perception & Psychophysics, 72(3), 561–582. https://doi.org/10.3758/APP.72.3.561

14.

Hammerschmidt

Wöllner

(2020). Sensorimotor synchronization with higher metrical levels in music shortens perceived time. Music Perception, 37(4), 263–277. https://doi.org/10.1525/mp.2020.37.4.263

15.

Hammerschmidt

Wöllner

(2023). Spontaneous motor tempo over the course of a week: The role of the time of the day, chronotype, and arousal. Psychological Research, 87(1), 327–338. https://doi.org/10.1007/s00426-022-01646-2

16.

Hoesl

Senn

(2018). Modelling perceived syncopation in popular music drum patterns. A preliminary study. Music & Science, 1. https://doi.org/10.1177/2059204318791464

17.

Janata

Tomic

S. T.

Haberman

J. M.

(2012). Sensorimotor coupling in music and the psychology of the groove. Journal of Experimental Psychology. General, 141(1), 54–75. https://doi.org/10.1037/a0024208

18.

Jerjen

Bechtold

Etani

Kilchenmann

Hoesl

Kawase

Duman

Senn

(2024). The effect of tempo manipulation on the urge to move. OSF Preprints. https://doi.org/10.31219/osf.io/6vnuy

19.

Leiner

D. J.

(2019). SoSci survey 3106. https://www.soscisurvey.de

20.

Löckenhoff

C. E.

Rutt

J. L.

(2015). Age differences in time perception and their implications for decision making across the life span. In Hess

T. M.

Strough

Löckenhoff

C. E

. (Eds.), Aging and decision making (pp. 213–233). Academic Press.

21.

London

(2012). Hearing in time: Psychological aspects of musical meter. Oxford University Press.

22.

MacDougall

H. G.

Moore

S. T.

(2005). Marching to the beat of the same drummer: The spontaneous tempo of human locomotion. Journal of Applied Physiology, 99(3), 1164–1173. https://doi.org/10.1152/japplphysiol.00138.2005

23.

Madison

(2006). Experiencing groove induced by music: Consistency and phenomenology. Music Perception: An Interdisciplinary Journal, 24(2), 201–208. https://doi.org/10.1525/mp.2006.24.2.201

24.

Matthews

T. E.

Witek

M. A. G.

Heggli

O. A.

Penhune

V. B.

Vuust

(2019). The sensation of groove is affected by the interaction of rhythmic and harmonic complexity. PLOS ONE, 14(1), Article e0204539. https://doi.org/10.1371/journal.pone.0204539

25.

McAuley

J. D.

Jones

M. R.

Holub

Johnston

H. M.

Miller

N. S.

(2006). The time of our lives: Life span development of timing and event tracking. Journal of Experimental Psychology: General, 135(3), 348–367. https://doi.org/10.1037/0096-3445.135.3.348

26.

Moelants

(2003). Dance music, movement and tempo preferences. In Kopiez

(Hrsg.), Proceedings of the 5th triennial conference of the European Society for the Cognitive Sciences of Music (ESCOM): Hanover University of Music and Drama, September 8–13, 2003 (pp. 649–652). http://hdl.handle.net/1854/LU-213437

27.

Oakes

(2003). Musical tempo and waiting perceptions. Psychology and Marketing, 20(8), 685–705. https://doi.org/10.1002/mar.10092

28.

O’Connell

S. R.

Nave-Blodgett

J. E.

Wilson

G. E.

Hannon

E. E.

Snyder

J. S.

(2022). Elements of musical and dance sophistication predict musical groove perception. Frontiers in Psychology, 13, Article 998321. https://doi.org/10.3389/fpsyg.2022.998321

29.

Pfleiderer

(2006). Rhythmus: Psychologische, theoretische und stilanalytische Aspekte populärer Musik. Transcript.

30.

Senn

Bechtold

Rose

Câmara

G. S.

Düvel

Jerjen

Kilchenmann

Hoesl

Baldassarre

Alessandri

(2020). Experience of Groove Questionnaire. Music Perception, 38(1), 46–65. https://doi.org/10.1525/mp.2020.38.1.46

31.

Senn

Bechtold

T. A.

Hoesl

Kilchenmann

(2021). Taste and familiarity affect the experience of groove in popular music. Musicae Scientiae, 25(1), 45–66. https://doi.org/10.1177/1029864919839172

32.

Senn

Hoesl

Bechtold

T. A.

Kilchenmann

Jerjen

Witek

(2024). Null effect of perceived drum pattern complexity on the experience of groove. PLOS ONE, 19(11), Article e031187. https://doi.org/10.1371/journal.pone.0311877

33.

Senn

Kilchenmann

Bechtold

Hoesl

(2018). Groove in drum patterns as a function of both rhythmic properties and listeners’ attitudes. PLOS ONE, 13(6), Article e0199604. https://doi.org/10.1371/journal.pone.0199604

34.

Senn

Kilchenmann

Georgi

von Bullerjahn

(2016). The effect of expert performance microtiming on listeners’ experience of Groove in swing or funk music. Frontiers in Psychology, 7, Article 1487. https://doi.org/10.3389/fpsyg.2016.01487

35.

Sioros

Miron

Davies

Gouyon

Madison

(2014). Syncopation creates the sensation of groove in synthesized music examples. Frontiers in Psychology, 5, Article 1036. https://doi.org/10.3389/fpsyg.2014.01036

36.

Stupacher

(2019). The experience of flow during sensorimotor synchronization to musical rhythms. Musicae Scientiae, 23(3), 348–361. https://doi.org/10.1177/1029864919836720

37.

Wang

Wöllner

(2020). Time as the ink that music is written with: A review of internal clock models and their explanatory power in audiovisual perception. Jahrbuch Musikpsychologie, 29, Article e67. https://doi.org/10.5964/jbdgm.2019v29.67

38.

Witek

M. A. G.

Clarke

E. F.

Wallentin

Kringelbach

M. L.

Vuust

(2014). Syncopation, body-movement and pleasure in groove music. PLOS ONE, 9(4), Article e94446. https://doi.org/10.1371/journal.pone.0094446

39.

Wöllner

(2023). Changes in psychological time when attending to different temporal structures in music. In Wöllner

London

(Eds.), Performing time: Synchrony and temporal flow in music and dance (pp. 251–268). Oxford University Press.

40.

Wöllner

Hammerschmidt

(2021). Tapping to hip-hop: Effects of cognitive load, arousal, and musical meter on time experiences. Attention, Perception & Psychophysics, 83(4), 1552–1561. https://doi.org/10.3758/s13414-020-02227-4

41.

Yeager

(1969). Absolute time estimates as a function of complexity and interruption of melodies. Psychonomic Science, 15(4), 177–178. https://doi.org/10.3758/BF03336267

Groove and time perception are influenced by rhythmic complexity and tempo

Abstract

Keywords

Introduction

Methods

Participants

Stimuli and pilot study testing

Procedure and duration estimation control

Results

Time perception: Passage of time and duration estimation

Groove: Urge to move und pleasure

Predictors for groove experiences

Discussion

Rhythmic complexity

Tempo, passage of time, and arousal

Limitations and suggestions for future work

Conclusions

Footnotes

Acknowledgements

ORCID iD

Funding

Declaration of conflicting interests

Data availability statement

Notes

References