Which factors modulate spontaneous motor tempo? A systematic review of the literature

Abstract

Intentionally or not, humans produce rhythmic behaviors (e.g., walking, speaking, and clapping). In 1974, Paul Fraisse defined rhythmic behavior as a periodic movement that obeys a temporal program specific to the subject and that depends less on the conditions of the action (p. 47). Among spontaneous rhythms, the spontaneous motor tempo (SMT) corresponds to the tempo at which someone produces movements in the absence of external stimuli, at the most regular, natural, and pleasant rhythm for him/her. However, intra- and inter-individual differences exist in the SMT values. Even if several factors have been suggested to influence the SMT (e.g., the age of participants), we do not yet know which factors actually modulate the value of the SMT. In this context, the objectives of the present systematic review are (1) to characterize the range of SMT values found in the literature in healthy human adults and (2) to identify all the factors modulating the SMT values in humans. Our results highlight that (1) the reference value of SMT is far from being a common value of 600 ms in healthy human adults, but a range of SMT values exists, and (2) many factors modulate the SMT values. We discuss our results in terms of intrinsic factors (in relation to personal characteristics) and extrinsic factors (in relation to environmental characteristics). Recommendations are proposed to assess the SMT in future research and in rehabilitative, educative, and sport interventions involving rhythmic behaviors.

1. Introduction

Rhythm is an essential human component. “Rhythm is defined as the pattern of time intervals in a stimulus sequence” (Grahn, 2012, p. 586), and the tempo is the rate of the stimuli's onset within a regular sequence (Grahn, 2012). Early in life, rhythm is present in a large number of activities of daily life, such as walking, speaking, chewing, doing leisure activities (dancing, swimming, pedaling, playing a musical instrument, singing, clapping, etc.), or school activities (writing and reading). Some activities require producing a rhythm with a spontaneous tempo (e.g., writing, reading, chewing, walking, speaking, etc.), and some others require synchronizing with a rhythm produced by an external event (e.g., playing a musical instrument, singing, clapping, dancing, etc.). Those activities can have different rhythmic components. For example, speech generally shows a non-isochronous rhythmic structure, but other language skills, such as reading, may also show beat-based patterns (i.e., isochronous patterns based on equal time intervals; see Ozernov-Palchik and Patel, 2018). Writing seems to be linked to isochronous rhythmic production (Lê et al., 2020b), even if it is not yet well-known whether writing shows more beat- or non-beat-based processing. Other activities, such as tapping or clapping, are well-known to show isochronous patterns.

Rhythmic abilities are deficient in various populations, and nowadays, rehabilitative interventions based on rhythmic synchronization are used to improve motor control. This is the case for populations with neurological diseases (e.g., Parkinson's disease, stroke, and cerebral palsy; see Braun Janzen et al., 2021), rare diseases or conditions (Launay et al., 2014; Bégel et al., 2017, 2022a; Tranchant and Peretz, 2020), or neurodevelopmental disorders (e.g., dyslexia, developmental coordination disorder, and attention deficit and hyperactivity disorder; Puyjarinet et al., 2017; Bégel et al., 2018, 2022b; Lê et al., 2020a; Blais et al., 2021; Daigmorte et al., 2022). In this context, participants are required to synchronize their movements to an external rhythm, usually with an auditory metronome, to regulate the speed of their gait or manual or verbal responses. The ability to synchronize with an external rhythm is particularly studied during sensorimotor synchronization tasks that consist of the “coordination of a rhythmic movement with an external rhythm” (Repp and Su, 2013, p. 1). The tempo and the sensory modality of the external rhythmic stimuli can modulate the performance of sensorimotor synchronization (see Repp, 2005; Repp and Su, 2013 for extensive reviews of the literature). Sensorimotor synchronization is less accurate and stable when the tempo is slower (Drewing et al., 2006; Repp and Su, 2013) and slower than the spontaneous motor tempo (SMT; Varlet et al., 2012). SMT is the rhythm at which a person produces movements in the absence of stimuli at his/her own most regular, natural, and pleasant rate. Hence, the tempo of the external rhythm has to be adapted to the actual tempo of the participants. Recent studies individualize the parameters of the intervention by adapting the tempo of the metronome to be synchronized (Benoit et al., 2014; Dalla Bella et al., 2017; Cochen De Cock et al., 2021; Frey et al., 2022). This is done by measuring the individual's SMT before an intervention. Rehabilitation is then performed with music at either ±10% of this tempo. Therefore, it seems interesting to evaluate rhythmic abilities, especially spontaneous motor tempo (SMT), to individualize learning and rehabilitation.

It is usually admitted in the pioneering work of Paul Fraisse that the most representative reference value of the spontaneous motor tempo (SMT) is 600 ms in healthy human adults (Fraisse, 1974). However, a growing body of literature about SMT suggests that this value is not universal. Fraisse himself pointed out that, even if the SMT is supposed to be relatively stable in one individual, inter-individual differences are more important and could be related to the instructions, the material of measurement, the body position, the chronological and intellectual development, and the sensory deficits (Fraisse, 1974). Even if these factors have been tested in a few studies, to our knowledge, no updated review of the literature has been made to provide complete and recent knowledge on the range of SMT values in healthy human adults and the factors influencing them. For example, recent studies suggest that age is a major factor modulating the value of SMT. The review by Provasi et al. (2014a) focuses on the spontaneous (and induced) rhythmic behaviors during the perinatal period, with a special emphasis on the spontaneous rhythm of sucking, crying, and arm movements in newborns. The authors indicate that the SMT evolves from newborns to the elderly. Fast rhythmical movements of the arms have been identified in fetuses with a tempo of 3 or 4 movements per second (250–333 ms; Kuno et al., 2001), whereas a tempo of 450 ms has been found during drumming (Drake et al., 2000) or tapping (McAuley et al., 2006) in children around 4 years old and more. The value of the SMT is relatively fixed around 400 ms between 5 and 8 years, even if the variability of the SMT tends to decrease with age (Monier and Droit-Volet, 2019). The SMT is supposed to increase to achieve 600 ms in adulthood (Fraisse, 1974) and to slow down further with age to achieve 700–800 ms in the elderly (Vanneste et al., 2001). In the case of tempo produced with the mouth, the SMT of non-nutritive sucking is around 450 ms in neonates (Bobin-Bègue et al., 2006), whereas the spontaneous crying frequency is between 1,100 and 2,400 ms in newborns (Brennan and Kirkland, 1982). All these results suggest that the relationship between SMT and age is not general and linear. The effector producing the SMT could be a potential factor affecting the relationship between SMT and age.

Some studies focus on the SMT produced with the mouth in a quasi-rhythmic pattern during speech production and in an isochronous repetitive pattern during syllable rate production. The review of Poeppel and Assaneo (2020) reports that the temporal structure of speech “is remarkably stable across languages, with a preferred range of rhythmicity of 2–8 Hz” (125–500 ms; Poeppel and Assaneo, 2020, p. 322). One could suggest that this rhythm is faster than the rhythm supposed to be found in rhythmical movements of the arms (600 ms in adulthood, Fraisse, 1974). However, in the broader context of speech production, we cannot neglect the communicative aspect of speech. The audience for the speech could also influence the SMT (Leong et al., 2017). Thus, it is possible that, in addition to the age previously mentioned, not only the effector but also the communicative goal of the activity may influence the SMT.

Moreover, environmental factors are supposed to influence SMT values. In the review of Van Wassenhove (2022), it is suggested that the manipulation of external landmarks, such as the time of day, can modulate the endogenous temporal representation of time and, as a consequence, the SMT (Van Wassenhove, 2022).

In this context, the objectives of the systematic review are (1) to characterize the range of SMT values found in the literature in healthy human adults and (2) to identify all the factors modulating the SMT values in humans.

2. Materials and methods

We conducted a systematic review according to PRISMA recommendations (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; Page et al., 2021).

2.1. Information sources and search strategy

Studies were identified by searching in the PubMed, Science Direct, and Web of Science databases. These databases were selected because they represent a broad spectrum of disciplines related to motor behavior. The final search was performed on 4 July 2022. There was no restriction on the year of publication; all articles present in the databases at this time point were searched. The search was first conducted in all languages, and then only English and French studies were selected for screening. As the term “spontaneous motor tempo” is not exclusively used, we searched a broad spectrum of synonyms for this term. Filters were also used to identify relevant research depending on the database (Table 1).

2.2. Selection of studies and eligibility criteria

We only selected articles and reviews before screening by excluding congress papers, chapters, books, and theses. Reviews identified in databases were just used to find missing original articles about SMT, and they have not been included in the systematic review (reviews not included: Provasi et al., 2014a; Poeppel and Assaneo, 2020; Van Wassenhove, 2022).

For greater specificity in the selection of the studies, inclusion criteria were based on the PICO (population, intervention, comparator, and outcome) strategy (Table 2). For this, we selected studies carried out on human samples producing rhythmic tasks. A control factor or control group was identified as a comparator. Spontaneous motor tempo was identified as the Outcome. Moreover, we selected other exclusion criteria: (1) studies that did not present experimental data; (2) studies that did not present a SMT task (i.e., focusing only on sensorimotor synchronization or on perception of rhythmic stimuli); (3) studies that did not report data on SMT (a SMT task is produced by the participants, but variables studied assess, for example, brain data or relative phases); (4) studies that did not focus on intentional SMT (studies on cardiorespiratory rhythms like breath or heart rate); and (5) studies that focus on walking with displacement (locomotion). We excluded studies on locomotion because locomotion involves spatiotemporal regulation; however, we retained studies on walking on a treadmill because walking on a treadmill involves mainly temporal regulation.

All titles and abstracts were screened by one researcher (AD), and if the articles fit the review criteria, they were read in full. The full-text eligibility assessment was conducted by two independent reviewers (AD and JT). Disagreements were resolved by a discussion according to the PICO strategy with a third researcher (EM).

2.3. Data collection process

For tabulation and extraction of data referring to the selected studies, Excel^® software spreadsheets were used. After screening the selected studies, we classified them into two categories, i.e., those measuring the SMT values (in general, as a prerequisite for a subsequent rhythmic sensorimotor synchronization task) and those examining the effect of factor(s) on the SMT values.

For studies measuring the SMT values, we extracted study characteristics, demographic variables, methodological variables, and outcome indicators from each study. The extracted characteristics included the authors, the year of publication, and the sample size. Demographic variables included sex, age, and laterality. Methodological variables included the instruction, the task, the effector(s), and the measurement recording. Outcome indicators included SMT values and their units. We finally convert all of the SMT values to milliseconds to be comparable and to provide a range of SMT values.

For studies about factor(s) modulating SMT values, we extracted study characteristics (first author and year of publication), methodological variables (task and effector(s)), and outcome indicators (factor(s) effects, their significance, and their direction on SMT values, i.e., on the mean or median and/or the standard deviation or coefficient of variation). Sometimes, we also extracted other information (e.g., subgroups and specific statistical analyses) to understand and interpret the results.

3. Results

A total of 3,179 studies were identified via databases. Before screening, 357 duplicates and 159 studies were removed (e.g., language, chapters and books, congress papers, or theses). According to the exclusion criteria, 2,349 studies were excluded based on the title or the abstract. After verifying the records left in full, according to the pre-established eligibility criteria, 93 studies from databases were included in the systematic review. Moreover, 14 out of 25 studies identified via citation searching were included. Finally, a total of 107 studies were included in the systematic review. Results from the process for selecting the included articles (following the recommendations of Page et al., 2021) are described in the flowchart (Figure 1).

Figure 1

In total, 13 studies provide a SMT value or a range of SMT values in healthy adults (Table 3). Our results reveal that the range of SMT values is from 333 to 3,160 ms. Notably, 94 studies measure the effect of the factor(s) on the SMT values (Table 4). We classified studies according to the type of factors modulating the SMT values: intrinsic factors, in relation to personal characteristics, and extrinsic factors, in relation to environmental characteristics. Concerning intrinsic factors, we have found studies investigating the effects of a pathology (N = 27), age (N = 16), the effector or the side (N = 7), the expertise or a predisposition (N = 7), and the genotype (N = 2). Concerning extrinsic factors, we have found studies investigating the effects of physical training (N = 10), external constraints (N = 7), observation training (N = 5), the time of testing (N = 4), the internal state (N = 3), the type of task (N = 5), and a dual task (N = 2).

The number of studies exploring the SMT across years is presented in Figure 2.

Figure 2

4. Discussion

The present systematic review aimed to (1) characterize the range of SMT values found in the literature in healthy human adults and (2) identify all the factors modulating the SMT values in humans.

First, it is interesting to note that the global number of studies has grown since the early 1970's (Figure 2). The increase in studies about SMT actually started in the mid-1990's and has grown non-linearly to reach a peak in 2020. Thus, interest in SMT is old but has recently increased.

Second, our results highlight that (1) the reference value of SMT is far from being a common value of 600 ms in healthy human adults, but a range of SMT values exists and (2) many factors modulate the SMT values. We discuss these factors according to a classification as intrinsic factors, in relation to personal characteristics, and extrinsic factors, in relation to environmental characteristics. We also provide recommendations to measure, report, and use the SMT values for future studies on rhythmic production and perception.

4.1. Range of SMT values in healthy human adults

Regarding the range of SMT values, we have selected the studies that propose an SMT task as a baseline, followed by a second task that is usually a sensorimotor synchronization task, without comparison between factors or conditions (Table 3). However, no value of SMT is reported in some studies (N = 2/13). Hence, it is important to measure the SMT as a baseline before any rhythmic task and to report the SMT values in order to interpret the results with regard to this baseline.

The number of studies measuring the SMT as a baseline for a rhythmic task (to adjust the tempo of the rhythmic task) is rather low (Table 3), compared to those testing the effects of variables on the SMT values (Table 4). This may be due to the fact that the terminology used to designate the spontaneous motor tempo is heterogeneous. Although the SMT was clearly defined by Fraisse (1974) as the speed that the subject considers most natural and pleasant (p. 50), this terminology is not unanimous. Although some authors use the term “spontaneous motor tempo” (Drake et al., 2000; McPherson et al., 2018; Amrani and Golumbic, 2020), others use different terms, such as “preferred motor tempo” (Michaelis et al., 2014), “preferred rate” (McCombe Waller and Whitall, 2004), “preferred frequency” (Volman et al., 2006; Bouvet et al., 2020), “internal clock” (Yahalom et al., 2004), “spontaneous production rate” (Wright and Palmer, 2020), “motor spontaneous tempo” (Dosseville and LaRue, 2002; Moussay et al., 2002), “spontaneous movement tempo” (Avanzino et al., 2015; Bisio et al., 2015), “freely chosen cadence” (Sidhu and Lauber, 2020; Hansen et al., 2021), or “personal tempo” (Tajima and Choshi, 1999). In the same vein, the term “self-paced” is not used with a consensual definition. Sometimes, this term relates to an intentional spontaneous motor behavior without a rhythmic component, even if authors use the term “self-paced tapping” (e.g., Bichsel et al., 2018, not included in the present review), and sometimes it relates to an intentional spontaneous rhythmic motor behavior when “self-paced” is followed by “tempo” (Serrien, 2009; Hattori et al., 2015). For future studies measuring the SMT, we recommend using the terminology “spontaneous motor tempo” when the participant is invited to produce a rhythmic motor task not induced by external stimuli specifying a required tempo. The term “spontaneous motor tempo” should be preferred to the term “self-paced” to define the task. To increase the visibility of studies implying SMT, the term “spontaneous motor tempo” and its acronym “SMT” should appear in the title or keywords of the articles.

The tasks used to measure the SMT are also very heterogeneous. Even if Fraisse (1974) declared that SMT is commonly measured during a manual task (Fraisse, 1974), our results reveal that studies exploring SMT also measure other effectors apart from manual ones. Some studies use self-paced tapping with one or two effectors; others use drumming, hopping, pointing, cycling, swaying, and producing syllables; and another uses jaw opening-closing and chewing (Table 3). Regarding the SMT values, participants seem to be slower when the whole body or the jaw is required, compared to manual responses. Thus, the heterogeneity of effectors (finger, arm, leg, whole body, mouth/lips, and jaw) used to produce the SMT could explain the heterogeneity of results. This hypothesis could be in accordance with the results of Sakamoto et al. (2007), highlighting that the SMT is effector-dependent (Sakamoto et al., 2007), but we recommend to carry out further studies to test the impact of effectors on SMT.

The range of SMT values (from 333 to 3,160 ms) is far from being a common value of 600 ms, as first reported by Fraisse (1974). More specifically, it is important to note that studies reporting the slowest SMT values involve cyclical movements compared to the discrete isochronous movements of tapping or clapping. Regarding finger tapping, SMT appears to be faster (from 333 to 931 ms). Bouvet et al. (2020), who investigate the effect of accents and subdivisions in synchronization, performed a measurement of SMT during finger-tapping with a large number of taps in several trials. They also find a faster value around 650 ms. The heterogeneity of results can be explained by the heterogeneity in the paradigm applied to measure the SMT in the studies. We provide such examples in the following paragraphs.

First, the characteristics of participants are not homogeneously reported, particularly their level of musical experience. In some studies listed in Table 3, authors report that participants have no musical training. Note that some studies mix musicians and non-musicians in their samples (e.g., Michaelis et al., 2014; De Pretto et al., 2018). However, three studies reported in Table 4 show an effect of music expertise (Drake et al., 2000; Slater et al., 2018; Hammerschmidt et al., 2021). Information about musical expertise is particularly important, including the expertise of listening to music, given that it is possible that participants could present amusia or a deficit in rhythm production or perception (Stewart et al., 2006; Clark et al., 2015; Peretz, 2016; Sarasso et al., 2022). To have a better overview of the range of SMT values in healthy adults without musical expertise, we recommend reporting a general level of musical experience, that is, both the level of expertise in music/rhythm production and music/rhythm exposure.

Second, the characteristics of participants are also heterogeneous across studies in terms of age, sex, and laterality. Regarding the age, participants are from 18 to 45 years old (Table 1). Despite the fact that the age range is representative of healthy young adults, the range of SMT values varies in five studies about manual responses from 333 to 1,100 ms (Michaelis et al., 2014; De Pretto et al., 2018; McPherson et al., 2018; Zhao et al., 2020). Regarding the sex repartition, only two studies recruit an equal number of women and men (Michaelis et al., 2014; De Pretto et al., 2018); the others recruit either more women or more men. As reported in Table 4, the effect of sex on SMT has not been extensively studied, given that only one study addresses this question and reports no significant results (Suzuki and Ando, 2018). Regarding the laterality, the majority of studies do not report the laterality of participants (Table 3, N = 8/13). The other studies generally recruit right-handed participants (Table 3, N = 3/5). Some studies include one or two left-handed participants (Table 3, N = 2/5). In Table 4, no study investigates the effect of laterality on the SMT values. In the absence of clear results about laterality, we recommend specifying the laterality of the participants by means of a laterality questionnaire (e.g., Oldfield, 1971) in the case of a SMT task performed with a lateralized effector (hand or leg). More globally, to have a better overview of the range of SMT values in healthy adults, we recommend reporting the age, sex, and laterality of participants and specifying, if possible, whether the SMT differs according to these variables.

Third, how the SMT is measured is not consistent across studies (Table 3). As specified in Table 3, SMT paradigms differ according to the number of trials and their duration, as well as to the instructions provided to the participants. The number and duration of trials vary across studies. Globally, the number of trials is from 1 to 10, and the duration of each trial can be expressed as a range of time (seconds or minutes), a number of responses, or a number of inter-response intervals (Table 3). Two studies do not report any information about trials (Ruspantini et al., 2012; Malcolm et al., 2018). Regarding the instructions, it is important to note that the instructions are not reported in three out of 13 studies (Eriksson et al., 2000; Hattori et al., 2015; Malcolm et al., 2018). When reported, the instructions contain the terms “natural,” “comfortable,” “most comfortable,” “naturally comfortable,” “preferred,” “steady,” “freely chosen,” “own self-selected,” “spontaneously,” “without mental effort,” “do not require much awareness,” “without fatigue,” and “could be performed all day if necessary,” to characterize the manner to produce the SMT (Table 3). Moreover, the tempo itself is characterized as “tempo,” “pace,” “cadence,” “speed,” “rate,” and “frequency.” Even if these terms are supposed to represent the same instruction, we would like to emphasize that the semantics is not a detail. The instruction can modify the participant's behavior depending on the interpretation he/she makes of it. For example, the term “speed” can be interpreted by participants as an instruction to go fast. Thus, to have a better overview of the range of SMT values in healthy adults, we recommend reporting exactly and exhaustively the standardized instructions given to participants. More precisely, we recommend giving priority to the notions of “preferred,” “spontaneous,” and “comfortable tempo,” in the instructions given to the participant. It seems important to avoid the notion of “speed” in order not to induce the idea of performing the task as quickly as possible.

Fourth, how SMT is recorded and computed is not consistent. Regarding the measurement recordings, authors report the inter-response interval, frequency, number of movement cycles during the total duration of the trial, rate, cycle time, speed, or cadence. If reported, the values also have different units (milliseconds, seconds, beats per minute, Hertz, repetitions per minute, or kilometers per hour). Furthermore, the authors usually report the range of SMT values, the SMT mean and/or median, its standard deviation, and/or the coefficient of variation (Table 3). These discrepancies are probably due to the type of task used. Only two studies recording SMT do not report any value for SMT (LaGasse, 2013; Zhao et al., 2017). On this basis, we recommend reporting the SMT values when recorded and homogenizing the measurement recording, the variables, and their units (in milliseconds or Hz). It is, therefore, necessary to report, at least, the SMT values with the median and the range of SMT values with a box plot representing individual values to get access to the distribution of data with the minimal and maximal values. It is also important to specify the methodology to compute the SMT, in particular to report excluded data, for example, the first responses that were performed by the participants, which can be considered warm-up.

4.2. Intrinsic and extrinsic factors modulating SMT values

Table 4 summarizes the results of studies about factors that could modulate the SMT values. We classified these factors as intrinsic and extrinsic ones, i.e., factors that could explain inter- and intra-individual variability in SMT values. Figure 3 presents the repartition of studies about the factors modulating the SMT values according to the intrinsic factors (N = 59) and the extrinsic factors (N = 36).

Figure 3

Regarding the intrinsic factors, our results reveal that the SMT is affected by several factors such as pathology, age, effector, expertise, or genotype (see Table 4). First, our results reveal that several pathologies modify the SMT values. Studies investigate brain lesions (six on Parkinson's, four on stroke, one on Huntington disease, one on Alzheimer's disease, one on Whiplash, and two on cerebellar lesions), neurodevelopmental disorders (two on attention deficit and hyperactivity disorder, two on developmental coordination disorder, one on developmental intellectual deficit, one on stuttering, and one on minor neurological dysfunction), and mental disorders (two on schizophrenia). Two studies test the effects of a deficit in music perception (beat deafness, i.e., difficulties in tracking or moving to a beat), and only one study examines the effect of an amputation. Globally, our results show that the most studied pathologies are brain lesions. Results indicate quasi-unanimously that SMT is affected by brain lesions (Table 4, N = 12/15). Studies report that either the frequency or the stability of the SMT differs in brain-injured patients compared to controls. In brain lesions, neurodegenerative disorders are the most studied, such as Parkinson's and Huntington's diseases (both implying a lesion of the basal ganglia) or Alzheimer's disease. Studies on Parkinson's disease report quasi-consistently that SMT is significantly affected in patients compared to healthy elderly individuals (Table 4, N = 5/6), and the study on Huntington's disease reports the same effect (Martínez Pueyo et al., 2016). The only study on Alzheimer's disease does not report any difference between patients and healthy elderly individuals (Martin et al., 2017). Moreover, most of the studies report that SMT is significantly affected in patients with stroke compared to healthy adults (Table 4, N = 3/4). In contrast, results are less consistent for neurodevelopmental and mental disorders. Attention deficit and hyperactivity disorder seems to affect the SMT (Table 4, N = 2/2), as does developmental coordination disorder (Table 4, N = 2/2). Only two studies report the effects of beat deafness with no consistent results (Phillips-Silver et al., 2011; Palmer et al., 2014). Based on these results, it is interesting to note that the SMT is affected regardless of the location of the lesion (motor cortex, language areas, basal ganglia, or cerebellum) and regardless of the physiopathology (neurodegenerative vs. neurological vs. neurodevelopmental). Although it seems more likely that focal lesions affect the SMT, future studies are required to better understand if and how the SMT is affected by neurodevelopmental, mental, and sensory disorders.

A second factor modulating the SMT is age. Studies investigate mostly infants (Table 4, N = 14/16). Only three studies investigate the elderly (Vanneste et al., 2001; Baudouin et al., 2004; McAuley et al., 2006). Our results reveal that age modifies the value of the SMT in the majority of studies (Table 4, N = 11/14). In fact, only three out of 14 studies do not find an effect of age in infants or children (Droit et al., 1996; Fitzpatrick et al., 1996; Yu and Myowa, 2021). It is interesting to note that only two studies test the possible effects of age on the SMT in individuals between 18 and 60 years old (McAuley et al., 2006; Hammerschmidt et al., 2021). Anyway, our results suggest that future studies about the SMT should take into account the effect of age bands or include the age of participants as a covariate, especially if participants are infants or elderly individuals.

A third intrinsic factor modulating the SMT is the effector/side used to produce the task. Results are very contradictory, with one study revealing an effect of the effector (Sakamoto et al., 2007) and two studies failing to reveal this effect (Tomyta and Seki, 2020; Rose et al., 2021). It seems that there is no effect of the side of the hand producing the SMT (Kay et al., 1987; Byblow and Goodman, 1994; Whitall et al., 1999). Moreover, it is also possible that SMT differs when it is produced with arms and legs (Sakamoto et al., 2007). Finally, the study of Getchell et al. (2001) reveals a correlation between SMT produced by different effectors. This result suggests that individuals have a general ability to produce their own SMT regardless of the type and number of effectors used (Getchell et al., 2001). Given that only one study reports this finding, further studies are required to confirm this effect.

As previously discussed above, expertise in music seems to modify the SMT. Musicians seem to have a more stable SMT than non-musicians (Scheurich et al., 2018; Slater et al., 2018; Bégel et al., 2022c). Moreover, two studies suggest that a predisposition to high or low synchronization (i.e., good or poor synchronization skills in rhythmic synchronization tasks) alters the SMT (Tranchant et al., 2016; Assaneo et al., 2021). Even if long-lasting intensive training could modify the SMT in certain conditions, it seems that intrinsic predispositions could be important. This result is in accordance with the last intrinsic factor identified in the current literature review, namely, the genotype. Two studies focus on this factor (Wiener et al., 2011; Suzuki and Ando, 2018). The first study finds a significant correlation between the tempo level in monozygotic twins but not in dizygotic twins, thereby suggesting that the genetic code could have a role in the SMT values (Suzuki and Ando, 2018). However, no difference between women and men is found, thereby preventing the possible role of sex on the SMT values (Suzuki and Ando, 2018). The second study reveals a significant effect of a polymorphism (Wiener et al., 2011). If we consider that one polymorphism (A1+) seems implied in the regulation of the density of receptors in the striatum (see Wiener et al., 2011), this result is in accordance with the results of studies showing an effect of Parkinson's disease, which affects the striatum, on the SMT (Konczak et al., 1997; Byblow et al., 2002; Flasskamp et al., 2012; Rose et al., 2020; Horin et al., 2021). Even if further studies are required to confirm this hypothesis, there is evidence that the genotype plays a role in the SMT values.

Regarding the extrinsic factors, our results highlight that the SMT is affected by several factors such as physical training, external constraints, observation training, time of testing, type of task, or dual tasking (see Table 4).

In total, 10 studies report results about the effects of physical training on the SMT. Six studies reveal a significant effect of cycling, strength training, synchronization, or physical exercise on the SMT values measured before and after training (Table 4). This result suggests that all studies about SMT should report the activity preceding the measurement of the SMT, especially physical activity.

In the same vein, all the studies (N = 5) testing the effects of the observation of a rhythmic action on the SMT found a significant effect (see Table 4). This result indicates that observing a rhythmic action without moving or synchronizing with it induces a spontaneous change in the SMT. This result is in accordance with the results of studies about the effects of physical training with rhythmic stimuli (Byblow et al., 1994; Carson et al., 1999; Hansen et al., 2021; Rocha et al., 2021). They are also in accordance with results about the effect of external constraints that show a significant effect of producing SMT while listening to a rhythmic metronome without synchronizing (Bouvet et al., 2019). The effect of observation or listening could be related to the implication of the Mirror System that is activated during observation, listening, and action (Kohler et al., 2002; Rizzolatti and Craighero, 2004). More precisely, it is possible that observing/listening a rhythm activates the same cerebral areas (i.e., the fronto-parietal system) as synchronizing to rhythmic stimuli (Konoike and Nakamura, 2020), hence modifying the SMT values according to the observed/listened tempo.

Regarding the effect of a dual task on the SMT, only one of the two studies reports a significant difference in the SMT during a single vs. dual task (Serrien, 2009). In the other study (Aubin et al., 2021), participants were instructed to swing their legs at their preferred frequency while performing a secondary task (reaction times), but no significant effect of the dual task was found. The discrepancy of results between the two studies could be explained by the fact that the secondary task is not rhythmic in Aubin et al. (2021), whereas the secondary task implies a rhythmic component in Serrien (2009). This hypothesis is in accordance with the results of studies examining the effects of rhythmic external constraints (Bouvet et al., 2019, 2020). We could deduce that the SMT is robust to a general cognitive load but can be impacted by external rhythmic stimulation. Hence, we can recommend not to perform a rhythmic task before or during the production of a task assessing SMT because it can change the SMT values.

Regarding the external constraints, most studies (N = 5/7) report consistent results about the significant effects of external constraints, such as a noisy environment, the presence of fingertip contacts, or a varying spring constraint on the SMT values (Table 4). However, the effect of loading is not consistent (Hatsopoulos and Warren, 1996; Wagener and Colebatch, 1997; Hansen and Ohnstad, 2008).

The type of task seems to quasi-consistently modulate the SMT values in four out of five studies (Table 4). Specifically, results indicate that the SMT is affected by in-phase or anti-phase bimanual tapping, polyrhythmic or single rhythmic tapping, and by tapping, drawing, playing a melody, or reciting a sentence (Tajima and Choshi, 1999; Forrester and Whitall, 2000; Zelaznik et al., 2000; Pfordresher et al., 2021).

The internal state seems to modulate the SMT values as well (Table 4). Three out of 3 studies report an effect of the internal state, such as apnea, mental stress, and gravity on the SMT values (Murata et al., 1999; Dosseville and LaRue, 2002; Boulanger et al., 2020). Once again, these results indicate that the SMT is not robust and that intra-individual variability exists. In the same vein, the time of testing seems to have an effect on the SMT values (Table 4). More precisely, studies unanimously report an effect of the time of day on the SMT values (Oléron et al., 1970; Dosseville et al., 2002; Moussay et al., 2002; Wright and Palmer, 2020). It seems that the SMT values vary in the course of the day, being slower in the morning than in the evening (Moussay et al., 2002; Wright and Palmer, 2020). As for the effect of internal state mentioned above, this effect may be related to the circadian variations of internal physiological and psychological factors, such as hormones or fatigue. Anyway, it is important to interpret this result in relation to the results of many studies that have shown an effect of trial measurement (Collyer et al., 1994; Drake et al., 2000; Scheurich et al., 2018, 2020; Bouvet et al., 2019).

5. Conclusion and perspectives

All in all, our systematic review highlights large intra- and inter-individual variability in the SMT values. According to the internal clock model (Treisman, 1963), individuals have an internal clock that is a reference generating time information, used to perceive information, and to produce and reproduce behaviors. Each individual has his/her own internal clock, leading to strong intra-individual consistency, but individual preferences exist in the production and perception of rhythms. Moreover, the internal clock can be affected by many intrinsic and extrinsic factors. We hope that the current review will lead to a better choice of reference values for SMT. We have proposed specific recommendations and points of vigilance to assess the SMT in future research.

Our results could also be transferred to applied contexts related to rehabilitative, educative, and sport interventions involving rhythmic sensorimotor synchronization. For example, dance can be viewed as a rhythmic activity in which individuals have to learn a choreography in synchrony with rhythmic stimuli provided by music and partners. Irrespective of the context (e.g., rehabilitation, education, and sport), current studies recommend individualizing music-based rhythmic cueing to induce motor improvement (Dalla Bella et al., 2018). Given that performance in synchronization-continuation tasks is improved when the tempo of stimuli is closest to the SMT (Delevoye-Turrell et al., 2014) and that the SMT seems to predict performance in externally paced tasks such as sensorimotor synchronization (McPherson et al., 2018), the choice of the tempo of the music should be carefully determined to correspond to the SMT. However, our systematic review highlights that the SMT is not a fixed and universal value but rather a range of values, so it should be measured just before intervention to provide a reference at the time of the intervention, considering the effectors used to produce the task and the current conditions. Accordingly, the measurement of SMT should be explicitly and exhaustively described to interpret the value obtained (including the instructions provided to measure the SMT). To consider the large intra-individual variability of the SMT, we advise performing more than a single trial per participant to measure the SMT. In line with the recommendation of Amrani and Golumbic (2020), SMT consistency should be measured within a trial, within a session, and across sessions (Amrani and Golumbic, 2020). Finally, it could be interesting to conduct a similar systematic review on the preferred perceived tempo (PPT), which can be measured either as the chosen tempo among several tempi (Baruch et al., 2004; Bauer et al., 2015) or from a dynamic tempo adjustment (speed up or slow down) of a rhythmic metronome until individuals reach their preferred tempo (e.g., Amrani and Golumbic, 2020; Hine et al., 2022). Given the possible relationship between the SMT and the preferred music tempo (e.g., Hine et al., 2022), it is possible that a common tempo for motor and perceived preferences exists. In the case of a common internal clock, we could expect that similar factors affect the SMT and the PPT.

Interdisciplinary implications extend to the field of rehabilitative, educative, and sport interventions involving rhythmic sensorimotor synchronization. Indeed, studies have highlighted the strong role of rhythm in engagement, motivation, and pleasure in performing physical activities. In the context of sport performance, music—through its intrinsic qualities, such as rhythm and particularly its tempo—is known to promote engagement and involvement in a physical activity or sport (Karageorghis et al., 2021). For example, synchronization with music during endurance-based activities (treadmill running tasks) allows for increased time spent practicing (Terry et al., 2012). More globally, results from a meta-analytic review support “the use of music listening across a range of physical activities to promote more positive affective valence, enhance physical performance (i.e., ergogenic effect), reduce perceived exertion, and improve physiological efficiency” (Terry et al., 2020, p. 91).

As a conclusion, the present review provides new elements to understand the inter- and intra-variability of the SMT, and we hope that our recommendations will be taken into account in future studies investigating performance in rhythmic production and perception tasks.

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