# How does background music affect drivers' behaviours, emotions and mood behind the wheel?

Jordan Navarro, Vivien Gaujoux, Marie Claude Ouimet, Laura Ferreri,
Emanuelle Reynaud

► To cite this version:

Jordan Navarro, Vivien Gaujoux, Marie Claude Ouimet, Laura Ferreri, Emanuelle Reynaud. How does background music affect drivers' behaviours, emotions and mood behind the wheel?. Transportation Research Part F: Traffic Psychology and Behaviour, 2023, 98, pp.47-60. 10.1016/j.trf.2023.08.003. hal-04991171

HAL Id: hal-04991171

https://hal.univ-lyon2.fr/hal-04991171v1

Submitted on 1 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
How does background music affect drivers' behaviours, emotions and mood behind the wheel?

Running title: Music and Driving

Jordan Navarro<sup>a,b</sup>, Vivien Gaujoux<sup>a</sup>, Marie Claude Ouimet<sup>c</sup>, Laura Ferreri<sup>a,d</sup> & Emanuelle Reynaud<sup>a</sup>

<sup>a</sup> Laboratoire d'Etude des Mécanismes Cognitifs (EA 3082), Université Lyon 2, France

<sup>b</sup> Institut Universitaire de France, Paris, France

<sup>c</sup> Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Canada

<sup>d</sup> Department of Brain & Behavioural Science, University of Pavia, Italy

Email: jordan.navarro@univ-lyon2.fr

Corresponding author: Jordan Navarro
Abstract

The purpose of this study was to better understand how music listening impacts driving behaviours, two common activities. Driving behaviours were categorized into three levels of cognitive control (operational, tactical, and strategical) while the selection of the musical background experienced during simulated driving was left to participants. The following characteristics were considered as candidates to investigate driving behaviours changes: individual characteristics in terms of driving experience and music listener (i.e., individual sensitivity to music reward and frequency of music listening), music parameters (i.e., tempo, mode), and mood and emotional changes induced by music. Ninety-two drivers' behaviours were analysed in a variety of simulated driving situations. The findings support the rationale that musical background has the potential to impact driving behaviours. Driving behaviours requiring an “operational” level of cognitive control were found to be the most influenced by the musical stimuli. Music listening was also found to generate mood and emotion changes, which can in turn influence driving behaviours, especially when the induced emotional valence is negative. However, background music's influence on driving behaviours, either direct or mediated by emotional or mood changes, seemed rather limited in terms of effect size.

Keywords

Background music, tempo, driving behaviours, car driving, mood, emotion
# 1 Introduction

Music listening and car driving are common everyday activities for many of us. From a scientific perspective, those activities have often been investigated separately. Beyond anecdotal evidence that drivers often engage in music listening behind the wheel, a survey carried out on 1780 British adults found that about two-thirds listened to recorded music and music radio when driving. Listening to music while driving was reported as the preferred in-vehicle activity for most drivers (Dibben & Williamson, 2007). The nature of the interactions between music listening and driving is thus a research question of interest to a large audience of drivers.

Music is a universal and ubiquitous human trait. Individuals have been playing and listening to music all around the globe for at least hundreds of generations. Music listening engages a variety of cognitive processes related to auditory perception, attention, and emotions among others, to such an extent that music may even have a notable place in the evolution of our species (Pearce & Rohrmeier, 2012). Music is therefore a large and complex research topic.

The human appetence for music listening may be supported by the associated musical pleasure known to be biologically rewarding for individuals (Ferreri et al., 2019). When considering the effect of music listening on cognition through the scientific literature, it seems impossible not to find contradicting results. Even the famous Mozart effect is far from being systematically observed. If listening to Mozart's sonata for two pianos (K448) was initially observed to improve subsequent spatial reasoning (Rauscher et al., 1993), several early attempts failed to replicate the effect (Newman et al., 1995; Steele, 2000). If the fascinating replicability question itself is out of the scope (Feest, 2019), we must acknowledge that music's effects on cognitive processes are highly sensitive to the context of observation. For the Mozart effect, conflicting results are abundant in the tens of experiments devoted to that effect (Pietschnig et al., 2010). More generally, background music has been shown to influence cognitive performance in opposite ways, representing a facilitator during some tasks (e.g., memory or learning, Ferreri et al., 2015; Kang, H. J., & Williamson, 2014) but also a source of interference in others (see Cheah et al., 2022 for a review).

Among the variety of experimental data available dealing with the influence of music on the different cognitive functions, the most evident constant is the systematic existence of
conflicting observations. Music's impact on cognitive functions is thus highly dependent on the experimental factors to assess the cognitive functions, as well as the musical stimuli itself such as the musical complexity for instance (Ferreri & Verga, 2016). Furthermore, the impact of background music on cognition can vary according to individual profiles, such as participants' musical expertise (Patston & Tippett, 2011), personality traits (Küssner, 2017), or sensitivity to music reward (Cardona et al., 2020).

When focusing on musical stimuli, and disentangling music complexity, researchers may create musical backgrounds or modify existing musical tracks. In the reported experiment, an alternative approach has been used. Participants were asked to select the musical background they wished to listen to, without any experimental restriction. This is slightly different from using individual preferred music as one does not necessarily want to listen to its preferred music at any time, and different music genres might be associated with different tasks (Goltz & Sadakata, 2021). On top of being an expression of social relatedness, the possibility to select any music may improve participants' ability to regulate their arousal and mood and promote self-awareness, the two major meta-dimensions of the big three of music listening (Schäfer et al., 2013). These dimensions correspond to the music listening benefits declared by drivers. Of the 1780 British surveyed drivers, 62% stated that music made them feel calmer and more relaxed and about 25% that music aided their concentration (Dibben & Williamson, 2007).

When it comes to describing and understanding the driving activity, a naïve approach would be to consider “driving” as an activity engaging a unitary cognitive process. In reality, “the driving task” is composed of a large variety of subtasks (McKnight & Adams, 1970a, 1970b). Those subtasks depend upon the specific driving situation considered. For instance, a reverse parking manoeuvre (Douissembekov, Michael, et al., 2015; e.g., Douissembekov, Navarro, et al., 2015) requires different cognitive processes than driving on a highway (Navarro et al., 2017; e.g., Navarro, Deniel, et al., 2019) or a bendy road (e.g., Mars & Navarro, 2012; Navarro et al., 2020), or following a vehicle that keeps on changing its speed (Navarro, Osiurak, Ovigue, et al., 2019; e.g., Navarro et al., 2021).

Different attempts to model the driving activity in its various dimensions and/or based on different indicators have been undertaken (e.g., de Winter & Happee, 2012; Engström et al., 2017; Lappi & Mole, 2018; Ranney, 1994; Salvucci, 2006). In the past few years, a neuro-ergonomics approach has been used to combine the psychological modelling of driving (Michon, 1985) with neuroscientific measures and models (Koechlin et al., 2003;
Koechlin & Summerfield, 2007). Based on a quantitative meta-analysis of the neural correlates of driving, the Driver NeuroErgonomical Cascade Model (DNECM) was proposed to describe driving behaviours, from drivers' intentions (e.g., route planning) to sensory-motor control (e.g., moving hands to steer the vehicle). In the DNECM, it is assumed that drivers' intentions can modulate all driving behaviours in a cascade fashion. The strategical level is engaged at first, then the tactical level, and finally the operational level (Navarro, Reynaud, et al., 2018). For instance, when drivers are entering a highway, they choose to drive at, above, or below the speed limit based on strategical processes (e.g., wish to speed up to catch a train, or, in contrast, to speed down because feeling tired). At the tactical processes level, drivers may adjust their plan according to unpredicted events (e.g., traffic congestion or roadworks, leading to reduce speed for a moment or to speed for overtaking a vehicle that swerved on the road). At the operational processes level, drivers adjust their vehicle speed by pressing or releasing the gas and brake pedals (and eventually changing gears when using a manual gearbox), which is a matter of sensory-motor control. Observable driving behaviours, defined as drivers' actions on the steering wheel and pedals themselves, and their outcome on vehicle trajectory (in our example, vehicle current speed and its variation) are thus the product of the three nested levels of cognitive control detailed above. Consequently, it is hardly possible to investigate one of the three levels of cognitive control in isolation from the other two. Indeed, in practice, drivers' behaviours may be the result of one or several cognitive processes described in the model. In the reported experiment, a set of driving situations and associated driving metrics have been implemented to investigate a variety of driving behaviours supported in various proportions by strategical, tactical, and operational levels of cognitive control that mediate driver intention to vehicle trajectory. Table 1 presents the different driving behaviours measures performed in the reported experiment and how they engage the different levels of cognitive control as defined by Navarro et al. (2018). The driving behaviours measures have been grouped into four classes of metrics: (i) car-following behaviour, (ii) speed control, (iii) drivers' actions on the steering wheel and pedals, and (iv) drivers' responses to unexpected events (see method section below for a detailed description of the measures). The driving tasks and situations have been created and implemented to collect these four classes of measures as they were expected to require different levels of cognitive control. More strategical control was expected for car-following metrics, more tactical control for
the speed control class and more operational control for the drivers' action and event-related classes were also expected.

<table><caption>Table 1. Driving behaviours measures along cognitive levels of control.</caption><thead><tr><th rowspan="2">Driving behaviours measures</th><th colspan="4">Level of cognitive control</th></tr><tr><th>Strategical</th><th>Tactical</th><th>Operational</th><th>Driving situations</th></tr></thead><tbody><tr><td>Car-following</td><td>+++</td><td>++</td><td>++</td><td>Car-following task</td></tr><tr><td>Speed control</td><td>+</td><td>+++</td><td>++</td><td>Urban and highway drive</td></tr><tr><td>Drivers' actions</td><td>-</td><td>-</td><td>+++</td><td>Urban drive</td></tr><tr><td>Event related</td><td>-</td><td>-</td><td>+++</td><td>Brief response to an event</td></tr></tbody></table>

In a meta-analysis investigating the influence of background music on a large variety of activities, no overall effect was observed. However, when the different activities were classified into three independent categories (i.e., mundane behaviour, cognition and emotion), a slightly significant positive effect of background music was reported for motor behaviours and emotional experience (Kämpfe et al., 2011). The same observation was made in a meta-analysis focusing on background music's influence on driving behaviours: music listening was reported as detrimental to speed control and the number of collisions, but no significant difference related to music tempo was noted (Millet et al., 2019). On top of the specific driving task considered, music peculiarities may also have a major impact on concomitant driving behaviours. A myriad of musical characteristics, and potential interactions between those characteristics, could be considered. In the context of driving, the most commonly described music parameters are tempo, volume, instrumentation and musical style (Millet et al., 2019).

The influence of musical background tempo on driving behaviours has attracted researchers' interest for a long time, probably because it is easy to measure objectively and does not require an in-depth understanding of music itself. In a pioneer study, fast music was found to increase the number of actions on the steering wheel and pedals whereas slow music was reducing those same actions. Moreover, both slow and fast musical backgrounds resulted in increased driving speed compared to no music background (Konz & Mcdougal, 1968). Fast music tempo was also reported to increase both the speed and the frequency of traffic violations and collisions using a video game as a driving simulator (Brodsky, 2001). On the reverse, other experiments described no effects of tempo on
driving behaviours (Cassidy & MacDonald, 2009; Navarro, Osiurak, et al., 2018; Wang et al., 2015). A meta-analysis of the influence of music tempo on driving behaviours supports those latter observations (Millet et al., 2019). Apart from the commonly investigated music tempo dimension, an important body of literature also supports the potential benefits of musical background on driving behaviours to (i) avoid boredom and improve emotional and affective states (e.g., Brodsky, 2021; Karageorghis, Kuan, et al., 2021; Pêcher et al., 2009; van der Zwaag et al., 2012), (ii) reach an appropriate level of activation, especially during long-lasting drives (e.g., Dibben & Williamson, 2007; Ünal et al., 2012; Ünal, de Waard, et al., 2013; Ünal, Platteel, et al., 2013), and (iii) improve safety in case of soft, easy-listening music (e.g., Brodsky & Kizner, 2012; Karageorghis, Payre, et al., 2021; Oron-Gilad et al., 2008; Ünal, de Waard, et al., 2013).

Besides, just as drinking the same amount of caffeine may result in different physiological and psychological effects on different individuals (e.g., Smith, 2002), the same musical background may affect differently singular individuals. Not only the links between music preferences and personality have been established (Nave et al., 2018), but the idiosyncratic nature of musical experience can explain the variance of the impact of music listening from one individual to another (Mas-Herrero et al., 2013).

The purpose of this study was to better understand how music listening impacts technology-mediated mobility, two activities many people are engaged in daily. A strong originality of the current work was to decompose driving behaviours along three levels of cognitive control and associated cognitive processes (Navarro, Reynaud, et al., 2018), to disentangle the influence of background music depending on the specific driving task requirements. Original network analyses were used as a method to model the interactions between the many driving behaviours variables recorded. The method offered a direct estimate of the relation between all the driving variables with and without musical background. Moreover, just as musical background influences differently diverse cognitive tasks, it was hypothesized that the impact of musical background on driving behaviours would differ as a function of the driving subtask, and more precisely that it would depend on the level of cognitive control engaged to perform the driving subtasks (see Table 1). As background music was reported to trigger positive effects on motor behaviours (e.g., sports) and negative effects on higher-level cognitive memory performances (Kämpfe et al., 2011), it was hypothesized driving tasks engaging operational control would be positively impacted by music whereas driving tasks engaging strategical control would be
negatively impacted by music. A data-driven approach was adopted with no experimental control over the musical backgrounds, freely selected by participants. The following characteristics were considered as candidates to investigate driving behaviours changes: individual characteristics in terms of driving experience and music listener (i.e., individual sensitivity to music reward and frequency of music listening), music parameters [i.e., tempo and mode, two parameters easily available for all musical tracks and also shown to impact driving behaviours (Brodsky, 2001; Karageorghis et al., 2022; Navarro, Osiurak, et al., 2018)], and mood and emotional changes induced by music.

# 2 Method

## 2.1 Participants

Ninety-two participants (64.1% females), aged between 19 and 57 years old (26 on average), volunteered to take part in this experiment. Participants' age, number of years since licensure, number of kilometres driven per year and number of hours of music listening per week are presented in Table 3. All were licenced drivers for at least one year, had normal or corrected-to-normal vision and had a normal audition. None experienced motion sickness. The study protocol was approved by the ethics committee of Université Lyon 2, and participants provided written informed consent.

## 2.2 Driving simulator

The experiment was carried out on a low-cost fixed-base simulator composed of three screens (Dell P2219H monitors) providing a horizontal field of view of about 145° (at a 3\*1920\*1080 resolution), with a refresh rate of 60 Hz (see Figure 1). The driving simulation software was developed with C++ and Ogre 3D rendering engine by the University of Sherbrooke (see Navarro, Osiurak, Gaujoux, et al., 2019 for more details). Data was logged at a sampling rate of 60 Hz. Physically, the driving simulator included an adjustable seat (JCL Sim Racing), a steering wheel with force feedback, as well as an accelerator and a brake pedal (Logitech G27). Gears were automatically shifted thanks to the automatic gearbox used. A speedometer was permanently displayed at the bottom right of the central screen during the drives.
<figure><img src="image_2.png" /><figcaption>Figure 1. Driving simulator setup.</figcaption></figure>

## 2.3 Experiment

### 2.3.1 Simulated driving and auditory backgrounds

Participants were first asked to adjust the simulator seat and get familiar with the simulator in a 10-minute practice drive. During practice, participants were asked to follow a leading vehicle, drive in an urban area and through various curves. During the experiment, participants performed the same drive twice: with a musical background, i.e., Music (M) and without musical background, i.e., No Music (NM). The presentation order of the drives was counterbalanced between participants. There was a five-minute break between the two experimental drives. Participants were instructed to drive as they would in real life.

In the M condition, the musical background consisted of a music track freely chosen by each participant from their list played at 70 dB for the complete duration of the drive via headphones (Sennheiser HD 457). For both NM and M drives, the car engine sound effects (±25 dB) were also present.

Each experimental drive lasted about 15 minutes and could be divided into three consecutive sections of similar length: a car-following task, driving in an urban and a highway setting. The car-following task took place on a two-lane main road with no traffic. The leading vehicle was stopped in the driving lane and started moving when participants started to drive. The leading vehicle speed varied between 50 and 80 km/h in a sine wave fashion. Participants were instructed to follow the leading vehicle as if they were following friends without knowing the destination. In the urban setting (with a 50 km/h speed limit), drivers had to follow route instructions given via road signs, opposite traffic was present and participants had to turn at several intersections. During this section, two events (one per experimental drive) were generated, and drivers had to react appropriately. The events
and associated measures are detailed below (driving behaviours section). Finally, drivers
had to drive on a single-direction two-lane highway section with a 110 km/h speed limit.

2.3.2 Driving behaviours

Driving behaviours have been investigated in a variety of situations. Driving-related variables have been grouped into four classes: (i) variables related to the car-following task for which drivers must adjust their vehicle speed (i.e., following vehicle) to the leading vehicle speed, (ii) variables associated with speed control, (iii) variables describing the driver's actions on the pedals (both gas and brake pedals) and on the steering wheel, and (iv) variables related to the reaction to a brief event occurring during the drive.

2.3.2.1 Variables associated with the car-following task

1- Mean IVT (s): Inter-Vehicular Time was computed as the time required for the following vehicle to reach the leading vehicle's current location at the following vehicle's current speed.

2- IVT SD (s): Inter-Vehicular Time Standard Deviation was computed as the standard deviation of the IVT during the car-following task.

3- COH: COHerence measured the similarity in the frequency domain of the spectral densities of the two signals (here, the speeds of the leading and the following vehicles) representing the amount of linear coupling between signals. It was computed by dividing the cross-spectral densities of the signals by the product of the individual power spectral densities. This indicator ranged from 0 (no relationship at all) to 1 (linear relationship).

4- GAIN measured, at the frequency that gave the maximal coherence, the degree to which the first signal amplitude was found in the second signal. A value of 1 indicated a perfect speed adjustment, a value greater than 1 an over-speed adjustment and a value smaller than 1 an under-speed adjustment.

5 - DELAY (s) measured, at the frequency that gave the maximal coherence, the temporal lead or lag of one signal concerning the other. The shorter the delay was, the faster the driver's reaction to the leading vehicle speed variations was.

COH, DELAY and GAIN were computed by adapting the CARSPAN software (Mulder, 1992) to the car-following task (see Brookhuis et al., 1994 for more details).
### 2.3.2.2 Variables associated with speed control

6. MS over 10 (km/h): Mean Speed over 10 km/h during the urban section of the drive. Used as an indicator of the average speed when the vehicle was not stopped.

7. MT over 50 (min): Mean Time over 50 km/h during the urban section of the drive. Used as an indicator of the average time spent over the speed limit in the urban setting.

8. MSH (km/h): Mean Speed on the Highway section of the drive.

9. MSH SD (km/h): Mean Speed Highway Standard Deviation.

### 2.3.2.3 Variables associated with drivers' actions

10. GPR (count): Number of Gas Pedal Reversals (GPR) during the urban section of the drive. The number of times the driver initiated a push or a release of the gas pedal.

11. SR (count): Number of Steering Reversals (SR) during the urban section of the drive. The number of times the driver turned the steering wheel from left to right or from right to left.

12. BPP (count): Number of Brake Pedal Pushes (BPP) during the urban section of the drive. The number of times the driver initiated a brake.

### 2.3.2.4 Event-related variables

13. WT (s): Waiting Time, as the driving lane was impassable due to works on the road and opposite traffic present, participants had to stop on their driving lane. When the participant stopped, the opposite traffic was also programmed to stop. The opposite traffic lane was thus free to avoid the roadworks area. However, oncoming vehicles were programmed to remain still so they were also stopped, but could potentially start again, generating a conflict with the driven vehicle. Road traffic regulations rules indicate that in such a situation, opposite vehicles have priority as they remain in their driving lane. WT was the time participants waited before entering the opposite driving lane to avoid the work area.

14. BRT (s): Brake Reaction Time in case of an emergency. Because of the surprising nature of the event, the same situation could not be used in both No Music (NM) and Music (M) drives, thus two different situations were used. Half of the participants experienced a given event in the NM drive and the other half in the M drive. Both events occurred at a road crossing with traffic lights located at different places in the simulated drive. In the first case scenario, the traffic light turned from green to red when the driver's time-to-contact with the traffic light longitudinal position reached 2.5 s. In the second case
scenario, the traffic light remained green but a vehicle crossing the intersection from the left to the right of the driven vehicle appeared in such a way the driver also had 2.5 s to stop.

### 2.3.3 Musical background

Because of the diversity of music tracks hand-picked by each participant, a restricted number of variables associated with the music played during the experiment were used in the analyses. Only the music track mode (i.e., major or minor) and tempo (Beats Per Minute, see Table 3) were considered.

### 2.3.4 Subjective assessments

Before driving, participants completed the French version of the Barcelona Music Reward Questionnaire (BMRQ, Mas-Herrero et al., 2013; Saliba et al., 2016). This questionnaire provided a general music reward score and five factors: musical seeking, emotion evocation, mood regulation, social reward, and sensory-motor dimensions. These factors of musical reward experience were useful to investigate the emotion and pleasure associated with music listening experienced by each individual.

After each drive participants were required to complete the Brief Mood Introspection Scale (BMIS, Mayer & Gaschke, 2001) and an adapted version of the Geneva Emotion Wheel (GEW, Scherer, 2005; Scherer et al., 2013). Pleasant-unpleasant and arousal-calm dimensions of the BMIS (Mayer & Gaschke, 2001) were used as an estimate of mood induction triggered by music. Based on the Geneva Emotion Wheel (GEW, Scherer, 2005; Scherer et al., 2013), four emotional dimensions were computed: (i) high control and positive valence (i.e., interest, amusement, pride dimensions), (ii) high control and negative valence (i.e., fear, disgust, anger), (iii) low control and positive valence (i.e., pleasure, contentment, relief), and (iv) low control and negative valence (i.e., sadness, regret, disappointment).

### 2.3.5 Data analysis

First, driving behaviours assessed through the 14 driving variables detailed above were examined using network analysis (Epskamp et al., 2018). This analysis offered a model of the driving behaviours network under No Music (NM) and Music (M) conditions.

Second, the difference between the M and NM conditions was computed for each of the 14 driving variables as well as for the subjective measures (M-NM). A stepwise linear
regression was undertaken for each of the driving variables with the six mood and emotion
subjective measures as covariates (BMIS pleasant-unpleasant, BMIS arousal-calm, GEW
high control and positive valence, GEW high control and negative valence, GEW low
control and positive valence and GEW low control and negative valence).

Third, to investigate individual differences in music reward experiences, a linear regression was undertaken for each of the driving variables, collected in the M condition, with the BMRQ scores as covariates (general reward, musical seeking, emotion evocation, mood regulation, social reward, and sensory-motor). To investigate individual differences related to the drivers and music characteristics listened to during M condition, another linear regression was undertaken for each of the driving variables with age, number of years since licensure, number of kilometres driven per year, number of hours of music listening per week, and music tempo as covariates as well as with sex (male or female) and listened to music mode (minor or major) as factors.

All analyses were carried out using JASP Version 0.16.3 (JASP Team, 2022). Least-square method was used for linear regressions.

3 Results

3.1.1 Driving behaviours network without and with music

Figure 2 represents the driving behaviours networks in the Music and No Music conditions, and thus represents the structure of the relationships between the different driving behaviours observed. In the network analysis, nodes represent observed driving behaviours connected by edges indicating a statistical relationship between them. The thickness of the edge represents the strength of the relationship between two nodes, the larger the edge the stronger the relationship. The colour of the edges indicates if the relationship is positive (green) or negative (red). The four classes of driving variables were coloured manually and were not used as prior information for modelling the networks.
<figure><img src="image_3.png" /><figcaption>Figure 2: Driving behaviours networks in Music (left) and No Music (right) conditions.</figcaption></figure>

Abbreviations: Mean IVT: Mean Inter-Vehicular Time, IVT SD: Inter-Vehicular Time Standard Deviation, COH: COHerence, MS over 10 (kph): Mean Speed over 10 kph during the urban section, MT over 50 (min): Mean Time over 50 kph during the urban section, MSH: Mean Speed on the Highway section, MSH SD: Mean Speed Highway Standard Deviation, GPR: number of Gas Pedal Reversals, SR: number of Steering Reversals, BPP: number of Brake Pedal Pushes, WT: Waiting Time, BRT: Brake Reaction Time.

Descriptively, the driving behaviours networks modelled were not severely different in NM and M conditions. The four driving behaviours classes appeared to be quite consistent by being graphically close, with the notable exception of the event-related class.

Considering the M network, and only the relationships between the different variables of the same class, strong positive direct connections between all variables of the speed control class appeared (variables number 6 to 9). This is coherent as the different variables were all measures of speed and its variability. Within the car-following class, Mean IVT and DELAY were directly connected negatively to COH and GAIN. Moreover, COH and GAIN on the one hand and Mean IVT and DELAY on the other hand were directly connected positively. This pattern is thus very consistent. The IVT SD variable appeared further apart from the other variables of the car-following class and was only directly connected to Mean IVT (positively) and COH (negatively). Within the drivers' actions class, SR was directly connected to BPP negatively and to GPR positively. For the event-related class, no direct connection between WT and BRT was observed. Regarding interclasses direct connections, several speed control class variables were found connected to car-following variables. MS over 10 and MT over 50 were negatively connected to Mean IVT and positively connected to COH and GAIN. MSH was positively connected to IVT SD and GAIN, and MSH SD to IVT SD only. Besides BRT was found positively
connected to COH, GAIN, MS over 10, MS over 50, MSH and GPR. WT was not found to be directly connected to other nodes. Altogether, interclasses variable direct connections appeared restricted (see supplementary material for all details).

The comparison between the M network and the NM network was computed to qualify how the M network differed from a no-music background. Again, the general driving behaviours network was not dramatically modified depending on the musical background. Looking at the relationships between variables of the same class, the relationships within the speed control class were unchanged suggesting that musical background did not impact the nature of the connections between variables for speed control. Within the car-following class, the general pattern was similar to the one observed in NM, apart from the connections between Mean IVT and DELAY, and between IVT SD and COH. These few differences suggest that musical background slightly modifies the direct connections between car-following variables. Within the drivers' actions class, more important differences were found. Contrary to the NM network, direct connections were observed between GPR, SR and BPP in the M network. Regarding interclasses direct connections, the M background revealed new connections and also made connections observed in the NM network disappear. In the M network, WT was no longer negatively connected to COH, MS over 10 and MS over 50, and SR to MT over 50 and MSH SD, whereas BPP connections were restricted to the speed control class (see supplementary material for all details).

To facilitate the interpretation of the networks, two centrality indices were computed (Figure 3). The degree was defined as the sum of the absolute input weights of each node and betweenness was defined as the number of shortest paths that pass through each node. The shortest path was the minimum number of edges needed to get from one node to another one (Epskamp et al., 2018).

Both degree and betweenness indices pointed out that several nodes were more central to the observed networks than others. For the degree, differences between NM and M networks were small. The most central variables were COH, GAIN, MS over 10, MT over 50 (degree >1) and to a lesser extent Mean IVT and MSH (0<degree<1). Regarding betweenness, COH, GAIN, MS over 10 and MT over 50 also appeared as central to the NM and M networks. However, the centrality of several variables was under the influence of the musical background. Mean IVT, COH and MT over 50 nodes were more central (i.e., the number of shortest paths that passed through the node of interest) to the NM
network than to the M network. In contrast, MS over 10, GPR, BPP and BRT nodes were
more central to the M network than to the NM network (see Figure 3).

<figure><img src="image_4.png" /><figcaption>Figure 3: Centrality indices of No Music and Music networks. Degree on the left and betweenness on the right.</figcaption></figure>

3.1.2 *Changes in driving behaviours depending on mood and emotional changes*

3.1.2.1 Driving variables changes explained by mood and emotion changes

Table 2 presents an overview of the significant results. In detail, in the car-following class of variables, GEW low control and negative, GEW high control and negative and BMIS pleasant-unpleasant significantly contributed to explaining Mean IVT [F (3, 88) = 5.39; *p* < .01; with 15.5% of variance explained; 4.5%, 6.2% and 4.8% respectively], and only GEW low control and negative as well as GEW high control and negative significantly contributed to explaining IVT SD [F (2, 89) = 5.40; *p* < .01; with 10.8% of variance explained; 4.6% and 8.8% respectively]. No significant results were found for COH, GAIN, and DELAY.

In the speed control class, MS over 10 and MT over 50 were observed to be significantly
explained by GEW low control and negative [respectively, F (1, 90) = 5.09; *p* < .05; with
6.2% of variance explained and F (1, 90) = 6.55; *p* < .05; with 6.8% of variance explained].
In the drivers' action class, all three variables were observed to be significantly explained
by GEW high control and negative [GPR: F (1, 90) = 4.41; *p* < .05; with 4.7% of variance
explained, SR: F (1, 90) = 5.89; *p* < .05; with 6.1% of variance explained, BPP: F (1, 90) =
4.45; *p* < .05; with 4.7% of variance explained].

For the event-related class, only the BMIS pleasant-unpleasant dimension significantly
contributed to explaining BRT [F (3, 88) = 9.14; *p* < .01; with 9.2% of variance explained].
No significant results were found for WT.
<table><caption>Table 2. Significant stepwise linear regression undertaken for each of the driving variables with the six</caption><thead><tr><th></th><th></th><th>BMIS<br>pleasant-<br>unpleasant</th><th>BMIS<br>arousal-<br>calm</th><th>GEW<br>high<br>control<br>and<br>positive<br>valence</th><th>GEW<br>high<br>control<br>and<br>negative<br>valence</th><th>GEW<br>low<br>control<br>and<br>positive<br>valence</th><th>GEW<br>low<br>control<br>and<br>negative<br>valence</th></tr></thead><tbody><tr><td rowspan="2">Car-<br>following</td><td>Mean IVT</td><td>4.8%</td><td></td><td></td><td>6.2%</td><td></td><td>4.5%</td></tr><tr><td>IVT SD</td><td></td><td></td><td></td><td>8.8%</td><td></td><td>4.6%</td></tr><tr><td rowspan="2">Speed<br>control</td><td>MS over<br>10</td><td></td><td></td><td></td><td></td><td></td><td>6.2%</td></tr><tr><td>MT over<br>50</td><td></td><td></td><td></td><td></td><td></td><td>6.8%</td></tr><tr><td rowspan="3">Drivers'<br>action</td><td>GPR</td><td></td><td></td><td></td><td>4.7%</td><td></td><td></td></tr><tr><td>SR</td><td></td><td></td><td></td><td>6.1%</td><td></td><td></td></tr><tr><td>BPP</td><td></td><td></td><td></td><td>4.7%</td><td></td><td></td></tr><tr><td>Event<br>related</td><td>BRT</td><td>9.2%</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

mood and emotion subjective measures as covariates with the percentage of variance explained.

3.1.2.2 Nature of the links between mood and emotion changes induced by music and the driving variables measured.

In the following lines, the nature of the relationships between driving variables changes between the M and NM conditions on the one hand, and mood and emotion changes between the M and NM conditions on the other hand, flagged as significant in the previous regression analyses are detailed.

GEW low control and the negative increase were negatively correlated to Mean IVT and IVT SD and positively correlated to MS over 10 and MS over 50. This indicates that the more participants felt and expressed sadness, regret, and disappointment, in the M condition compared to the NM condition, the faster they drove and the shorter the safety margin with the followed vehicle.

GEW high control and the negative increase were negatively correlated to BPP and positively correlated to Mean IVT, IVT SD, GPR and SR. This indicates that the more
participants felt and expressed fear, disgust, and anger, in the M condition compared to the NM condition, the less often they pressed the braking pedal, but the more often they pressed and released the gas pedal and changed the direction of rotation of the steering wheel. They also drove faster in that context.

*GEW high control and the positive* increase were correlated positively to MSH and negatively to MSH SD, indicating that the more participants felt and expressed interest, amusement, and pride, in the M condition compared to the NM condition, the faster they drove and the less they changed their vehicle speed.

*BMIS pleasant-unpleasant* dimension increase was correlated positively to Mean IVT and negatively to BRT. This indicates that the more participants felt and expressed unpleasantness, in the M condition compared to the NM condition, the faster they drove and the later they reacted in case of an emergency.

### 3.1.3 Driving with music

#### 3.1.3.1 Inter-individual differences based on general music reward experiences

The regressions revealed significant differences only for two-speed control variables MS over 10 and MSH, no significant effect was found for the other 12 driving variables collected. All six BMRQ scores significantly contributed to explaining MS over 10 and MSH [respectively: F (6, 74) = 5.26; *p* < .01; with 20.9% of variance explained and F (6, 74) = 2.43; *p* < .05; with 16.4% of variance explained].

#### 3.1.3.2 Inter-individual differences based on drivers and music characteristics

Drivers' general characteristics (i.e., age, sex), driving experience (i.e., number of years since licensure, number of kilometres driven per year) as well as the frequency of music listening (i.e., declared number of hours of music listening per week) and information about the music track listened to while driving in the experiment (i.e., mode -minor/major- and tempo) were collected. The distributions of the two binary variables were of 33 males and 59 females (36% and 64% of the distribution respectively), and 52 music tracks listened to were in minor mode and 40 in major mode (56.5% and 43.5% of the distribution respectively). The distributions related to the other variables are presented in Table 3.

<table><caption>Table 3: Descriptive statistics of drivers' and music characteristics distributions.</caption><thead><tr><th></th><th>Mean</th><th>SD</th><th>Min</th><th>Max</th><th>25th</th><th>50th</th><th>75th</th></tr></thead><tbody><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><tr><td>Age</td><td>25.8</td><td>7.2</td><td>19.4</td><td>56.8</td><td>21.4</td><td>23.4</td><td>26.0</td></tr><tr><td>Number of years since licensure</td><td>6.8</td><td>7.1</td><td>1.2</td><td>31.7</td><td>2.7</td><td>4.1</td><td>7.1</td></tr><tr><td>Number kilometres driven per year</td><td>8467.4</td><td>8523.8</td><td>0</td><td>40000</td><td>1000</td><td>5000</td><td>10750</td></tr><tr><td>Number of hours of music listening per week</td><td>15.4</td><td>12.7</td><td>0</td><td>50</td><td>7</td><td>10</td><td>21</td></tr><tr><td>Music tempo (BPM)</td><td>113.4</td><td>23.1</td><td>60.0</td><td>159.0</td><td>93.8</td><td>113.5</td><td>130.0</td></tr></table>

Abbreviations: Standard Deviation, Minimum, Maximum, 25th, 50th and 75th percentile.

The regressions carried out for the M condition used age, number of kilometres driven per year, number of years since licensure, declared number of hours of music listening per week, and music tempo as covariates as well as sex (male or female) and the mode of the listened music (minor or major) as factors. These regressions revealed significant differences only for four driving behaviours variables out of the 14 computed. Two belonged to the car-following class (Mean IVT and IVT SD), one to the speed control class (MS over 10) and one to the drivers' actions class (BPP).

In the car-following class, Mean IVT and IVT SD [respectively, F (7, 84) = 2.44; *p* < .03 and F (7, 84) = 2.81; *p* < .02] were significantly explained only by the declared number of hours of music listening per week. The more drivers listened to music in their everyday lives, the higher the Mean IVT and the IVT SD. Besides, MS over 10 and BPP [respectively, F (7, 84) = 3.34; *p* < .01 and F (7, 84) = 2.32; *p* < .04] were both significantly explained by drivers' sex along with music mode for BPP. Female drivers were driving slower and pressed the brake pedal less often than male drivers. While listening to music in the minor mode, drivers also pressed the brake pedal less often than with music in the major mode.

## 4 Discussion

Qualifying the influence of music listening on driving behaviours is a complex and challenging quest. Both music listening and driving behaviours are complex activities that can hardly be reduced to a single manifestation. Consequently, investigating musical background influence on driving behaviours requires an approach that can embrace these complexities without being case-specific. To disentangle those relationships, we used a broad-spectrum experimental approach where drivers freely chose the music to be listened to while driving in a variety of driving tasks requiring different levels of cognitive control. In the reported experiment, a variety of driving behaviours categorized along three levels
of cognitive control has been considered while participants selected the musical background. The data, collected on a large sample of drivers, indicate a restricted impact of background music on driving behaviours although driving behaviours requiring an “operational” level of control were found to be the most influenced by the musical stimuli. Music listening was also found to generate mood and emotion changes, which can in turn influence driving behaviours, especially when the induced emotional valence is negative.

The network analysis conducted on the 14 driving variables computed during the experiment revealed a network structure extremely consistent with the proposed three levels of cognitive control driving variables classification (see Figure 2). This analysis validates the classes of driving metrics grouped based on previous knowledge, support the neuroergonomics model in three level of cognitive control and extend its validity to a large variety of driving behaviours (Navarro, Reynaud, et al., 2018). The network analysis grouped the variables of the same classes with the notable exception of the two event-related variables assumed to be under the operational level of control. If WT (variable 13), was close to the driver’s action class (also assumed to rely on the operational level of control), BRT (variable 14) was closer to the speed control than to the driver’s action class. Although requiring further investigations, this result might suggest that braking in case of an emergency event would rely on both the tactical and the operational levels of cognitive control. A plausible interpretation is that the tactical level serves to cope with the emergency event, whereas the operational level would be engaged to perform the required action (here braking). Besides, the different classes of driving behaviours variables were relatively independent of one another. Interclasses’ direct connections were limited.

Interestingly the presence of a musical background did not deeply modify the structure of the driving behaviours variables network. This result not only reinforces the reliability of the driving behaviours network structure but also suggests that the impact of music listening on the driving behaviours network is not overwhelming. If very few changes were observed for the strategical and tactical levels of cognitive control, operational control was found to be more sensitive to the musical background. Direct connection changes between driving variables were observed, when comparing the M network to the NM network, for all driving behaviours handled at the operational control either of the driver’s actions class (GPR, SR and BPP), or the event-related class (WT and BRT). This result points toward an impact of musical background on motor action implementation, in line with previous data including psychomotor speed (Bottiroli et al., 2014), walking speed increase (Metcalfe,
2016), and sport performances in general (and see Kämpfe et al., 2011 for a meta-analysis), along with strong neural evidence (Chen et al., 2008). In the car-driving domain, a recent study reported no impact of musical background on situation awareness and hypothesized that music might influence response selection and/or response execution (Brodsky & Borowsky, 2022). The reported data both support and refine that hypothesis. Response execution, in other words, operational control, indeed appeared to be more sensitive to music than the other levels of cognitive control. Additionally, GPR, BPP and BRT were more central to the NM network than to the M network reinforcing this interpretation of a stronger influence of music on action implementation.

When investigating drivers’ mood and emotional changes between the no music drive and the music drive, as well as associated driving behaviours, music listening was found to explain a few changes in driving behaviours through mood and emotion changes. Music is known for its strong emotional power able to efficiently change mood and arousal states (see Juslin, 2016). Out of the six mood and emotion subjective measures considered, two (BMIS arousal-calm and GEW low control and positive valence) did not explain any driving behaviour change between the NM and M conditions. The GEW high control and negative valence were found to explain all three variables of the drivers’ actions class. This was also the only subjective measure that explains drivers’ actions. This result completes the previous interpretation of the impact of music on behaviours undertaken at the operational level of cognitive control. High control and negative valence emotions (i.e., fear, disgust and anger) induced by music listening while driving tend to increase the frequency of gas pedal and steering reversals and reduce the number of brake pedal pushes. This pattern of behaviours may indicate a more energetic and jittery trajectory control. A parallel can be drawn with the common anger-while-driving situation, experienced at least once every two weeks by 85% of the drivers (Underwood et al., 1999) and able to induce more steering variability (Steinhauser et al., 2018).

The negative valence of emotions induced by music listening seemed essential to describe the dataset, in addition to the three variables mentioned above, GEW low or high control with negative valence explained four other driving variables: Mean IVT and IVT SD in the car-following class, MS over 10 and MT over 50 in the speed control class. These observations also fit with previous data pointing at negative valence impact on car-following performances (Zhang et al., 2016) and driving speed (Pêcher et al., 2009).
Individual characteristics as a car driver (i.e., driving experience), and as a music listener (i.e., individual sensitivity to music reward and frequency of music listening), along with music characteristics (i.e., tempo, mode) were then considered as candidates to investigate driving behaviour. The data indicated a very sparse influence of these individual and music dimensions on the 14 driving variables analysed. The driving experience was not found to impact any driving behaviours: music reward was only associated with MS over 10 and MSH out of the 14 variables considered, while Mean IVT and IVT SD were related to the frequency of music listening. When considering the big picture, it must be acknowledged that participants’ characteristics collected as car drivers and music listeners, as well as the music track tempo and mode, have a very restricted impact on the recorded driving behaviours.

This overall statement does not perfectly align with previously reported results. Indeed, the fast-music tempo was recently observed to increase risk-taking behaviours (Karageorghis et al., 2022) and physiologically stimulate drivers (Li et al., 2019). A possible explanation is that tempo isolated from other musical components is a source of dissimilar outcomes. Another source of explanation is the definition of fast-music tempo, that often differs from studies. An option to avoid those limitations would be to investigate the effect of music through the listener, for instance in terms of arousal and valence (e.g., Karageorghis et al., 2022; Navarro, Osiurak, et al., 2018; Ünal, de Waard, et al., 2013). If music tempo was not observed to impact the mean driving speed, extraversion was found to interact with music tempo (Karageorghis, Mouchlianitis, et al., 2021). This suggests that individual differences could be seen as a mediator between music characteristics and their consequences on driving behaviours. However, in doing so the impact of the music itself is mediated by the listener and thus prevents direct recommendations related to music listening while driving. Moreover, a large variety of individual characteristics could be envisioned including driving style (Taubman-Ben-Ari & Skvirsky, 2016), known to be related to driving experience (Lajunen et al., 2022) among many others.

## 5 Limitations and future work

Considering the complexity of the investigated issue, the experiment suffers from several limitations. Apart from the external validity limits associated with the use of a driving simulator, a major limitation is related to the investigation of individual characteristics and music characteristics. A single experiment seems unsuited to investigate these issues in
depth. A much larger set of participants that distribute nicely along the targeted
characteristics would be more suited to conclude firmly.

Listening to music while driving seems of critical importance as it could induce a variety
of different effects. It may come at a cognitive processing cost and may be considered a
secondary task interfering with driving, the primary task. Music listening as a secondary
task might distort the strict influence of music on behaviours. In any case, music selection
is key. Here, an ecological approach was used, each participant selected a musical track to
listen to while driving. The downside of this methodology is the absence of control over
the musical stimulus. The influence of this methodological choice remains to be clarified.

Another critical point is the influence of music over time. Say for instance that a given
music track triggers a powerful happiness emotion for a given driver, that may not last for
the duration of the experimental drive. Moreover, happiness may not be felt during the
complete duration of the musical track. Consequently, measures of mood and emotion
collected immediately after the drives may not be able to capture the dynamics of music
induction.

The debate about music background on driving behaviours in the scientific literature may
not be representative of the real effects. The publication bias likely affects the pattern of
results, as music's influence on driving has been repeatedly observed as limited (e.g.,
Brodsky & Borowsky, 2022; Navarro, Osiurak, et al., 2018). As the current scientific trend
is to publish significant results, several data collections investigating musical background
influence on driving behaviours have likely remained unpublished due to a lack of
significant results. Music listening influence on driving could be even weaker than
suggested by the current scientific literature (and see Millet et al., 2019 for a meta-
analysis).

The description of the driving activity in three levels of cognitive control although
supported by behavioural and neuroscientific evidence (Michon, 1985; Navarro, Reynaud,
et al., 2018), does not reveal in detail the nature and variety of the cognitive processes
engaged in the different driving task. Refinements of driving task classification could thus
be imagined.

Considering those various limitations, subjective assessment of driving performances may
be a highly relevant way to explore individual experience. Ideally, a standard subjective
scale could be used to facilitate comparison between studies.
## 6- Conclusion

The current experimental study, based on a large sample of participants (ninety-two drivers), supports the rationale that musical background has the potential to impact driving behaviours, but the influence either direct or mediated by emotional or mood changes appears rather restricted. Indeed, the effect size of behavioural changes induced by the musical background is small. Additionally, music listening was observed to impact driving tasks engaging the operational level of cognitive control the most, indicating that the level of cognitive control engaged to undertake different driving subtasks are not sensitive to a musical background in a similar fashion.

Understanding the nature of the musical background and driving behaviours relationships is thus a challenging issue as the influence of musical background on driving behaviours is likely to be impacted by many different variables, associated with (i) the driver's characteristics, (ii) the characteristics of the music heard and as evidenced in the current study, (iii) the level of cognitive control engaged in the driving situation considered. Future investigations aiming to investigate how listening to music influences our driving behaviour should therefore consider the level of cognitive control engaged as a significant variable.

## 6 Authors contributions

J.N. and E.R. developed the research question. J.N. and E.R. designed the experimental task, the protocol and the driving simulation using the driving simulator developed by M.C.O. V.G. experimented. J.N. and E.R. analysed the data. J.N. wrote the manuscript with input from E.R., M.C.O., L.F. and V.G.

The authors have no competing interests to declare.

## 7 References

* Bottiroli, S., Rosi, A., Russo, R., Vecchi, T., & Cavallini, E. (2014). The cognitive effects of listening to background music on older adults: processing speed improves with upbeat music, while memory seems to benefit from both upbeat and downbeat music.
  *Frontiers in Aging Neuroscience*, *6*, 284.
Brodsky, W. (2001). The effects of music tempo on simulated driving performance and vehicular control. *Transportation Research Part F: Traffic Psychology and Behaviour, 4*(4), 219–241. https://doi.org/10.1016/S1369-8478(01)00025-0

Brodsky, W. (2021). An exploratory study of in-cabin music engagement among young-adult drivers. *Psychomusicology: Music, Mind, and Brain, 31*(1), 35–48. https://doi.org/10.1037/pmu0000273

Brodsky, W., & Borowsky, A. (2022). How (where) Does Music Background Hamper Driver Behaviour? *Human Factors: The Journal of the Human Factors and Ergonomics Society, 64*(2), 1177–1199. https://doi.org/10.1177/00187208221127939

Brodsky, W., & Kizner, M. (2012). Exploring an alternative in-car music background designed for driver safety. *Transportation Research Part F: Traffic Psychology and Behaviour, 15*(2), 162–173.

Brookhuis, K., Waard, D. de, & Mulder, B. (1994). Measuring driving performance by car-following in traffic. *Ergonomics, 37*(3), 427–434.

Cardona, G., Rodriguez-Fornells, A., Nye, H., Rifà-Ros, X., & Ferreri, L. (2020). The impact of musical pleasure and musical hedonia on verbal episodic memory. *Scientific Reports, 10*(1), 1–13.

Cassidy, G., & MacDonald, R. (2009). The effects of music choice on task performance: A study of the impact of self-selected and experimenter-selected music on driving game performance and experience. *Musicae Scientiae, 13*(2), 357–386.

Cheah, Y., Wong, H. K., Spitzer, M., & Coutinho, E. (2022). Background Music and Cognitive Task Performance: A Systematic Review of Task, Music, and Population Impact. *Music & Science, 5*, 20592043221134390.

Chen, J. L., Penhune, V. B., & Zatorre, R. J. (2008). Listening to Musical Rhythms Recruits Motor Regions of the Brain. *Cerebral Cortex, 18*(12), 2844–2854. https://doi.org/10.1093/cercor/bhn042

de Winter, J. C. F., & Happee, R. (2012). Modelling driver behaviour: a rationale for multivariate statistics. *Theoretical Issues in Ergonomics Science, 13*(5), 528–545. http://www.tandfonline.com/doi/abs/10.1080/1463922X.2010.546437

Dibben, N., & Williamson, V. J. (2007). An exploratory survey of in-vehicle music listening. *Psychology of Music, 35*(4), 571–589.

Douissembekov, E., Michael, G. A., Rogé, J., Bonhoure, P., Gabaude, C., & Navarro, J. (2015). Effects of shrinkage of the visual field through ageing on parking
performance: a parametric manipulation of salience and relevance of contextual
components. *Ergonomics*, 58(5), 698–711.
https://doi.org/10.1080/00140139.2014.987699

Douissembekov, E., Navarro, J., Michael, G. A., Bonhoure, P., Gabaude, C., & Rogé, J. (2015). Parking Manoeuvres Differ among Drivers with Narrower and Wider Field of View in the Presence of a Spatial Reference. *Applied Cognitive Psychology, 29*(2), 309–313. https://doi.org/10.1002/acp.3110

Engström, J., Markkula, G., Victor, T., & Merat, N. (2017). Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis. *Human Factors, 59*(5), 734–764. https://doi.org/10.1177/0018720817690639

Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. *Behavior Research Methods, 50*(1), 195–212. https://doi.org/10.3758/s13428-017-0862-1

Feest, U. (2019). Why Replication Is Overrated. *Philosophy of Science, 86*(5), 895–905.
https://doi.org/10.1086/705451

Ferreri, L., Bigand, E., & Bugaiska, A. (2015). The positive effect of music on source memory. *Musicae Scientiae, 19*(4), 402–411.

Ferreri, L., Mas-Herrero, E., Zatorre, R. J., Ripollés, P., Gomez-Andres, A., Alicart, H., Olivé, G., Marco-Pallarés, J., Antonijoan, R. M., Valle, M., Riba, J., & Rodriguez-Fornells, A. (2019). Dopamine modulates the reward experiences elicited by music. *Proceedings of the National Academy of Sciences, 116*(9), 3793–3798.
https://doi.org/10.1073/pnas.1811878116

Ferreri, L., & Verga, L. (2016). Benefits of Music on Verbal Learning and Memory. *Music Perception, 34*(2), 167–182. https://doi.org/10.1525/mp.2016.34.2.167

Goltz, F., & Sadakata, M. (2021). Do you listen to music while studying? A portrait of how people use music to optimize their cognitive performance. *Acta Psychologica, 220*, 103417.

Juslin, P. N. (2016). Emotional reactions to music. *The Oxford Handbook of Music Psychology, 197–213*.

Kämpfe, J., Sedlmeier, P., & Renkewitz, F. (2011). The impact of background music on adult listeners: A meta-analysis. *Psychology of Music, 39*(4), 424–448.
https://doi.org/10.1177/0305735610376261

Kang, H. J., & Williamson, V. J. (2014). Background music can aid second language
learning. *Psychology of Music*, 42(5), 728-747.

Karageorghis, C. I., Kuan, G., Mouchlianitis, E., Payre, W., Howard, L. W., Reed, N., & Parkes, A. M. (2022). Interactive effects of task load and music tempo on psychological, psychophysiological, and behavioural outcomes during simulated driving. *Ergonomics*, 65(7), 915-932.
https://doi.org/10.1080/00140139.2021.2003872

Karageorghis, C. I., Kuan, G., Payre, W., Mouchlianitis, E., Howard, L. W., Reed, N., & Parkes, A. M. (2021). Psychological and psychophysiological effects of music intensity and lyrics on simulated urban driving. *Transportation Research Part F: Traffic Psychology and Behaviour*, 81(July), 329-341.
https://doi.org/10.1016/j.trf.2021.05.022

Karageorghis, C. I., Mouchlianitis, E., Payre, W., Kuan, G., Howard, L. W., Reed, N., & Parkes, A. M. (2021). Psychological, psychophysiological and behavioural effects of participant-selected vs. researcher-selected music in simulated urban driving. *Applied Ergonomics*, 96(June), 103436. https://doi.org/10.1016/j.apergo.2021.103436

Karageorghis, C. I., Payre, W., Howard, L. W., Kuan, G., Mouchlianitis, E., Reed, N., & Parkes, A. M. (2021). Influence of music on driver psychology and safety-relevant behaviours: a multi-study inductive content analysis. *Theoretical Issues in Ergonomics* *Science*, 23(6), 643-662.
https://doi.org/10.1080/1463922X.2021.2009933

Koechlin, E., Ody, C., & Kouneiher, F. (2003). The architecture of cognitive control in the human prefrontal cortex. *Science*, 302(November), 1181-1185.
https://doi.org/10.1126/science.1088545

Koechlin, E., & Summerfield, C. (2007). An information theoretical approach to prefrontal executive function. *Trends in Cognitive Sciences*, 11(6), 229-235.
https://doi.org/10.1016/j.tics.2007.04.005

Konz, S., & Mcdougal, D. (1968). The effect of background music on the control activity of an automobile driver. *Human Factors: The Journal of the Human Factors and Ergonomics Society*, 10(3), 233-243.

Küssner, M. B. (2017). Eysenck's theory of personality and the role of background music in cognitive task performance: a mini-review of conflicting findings and a new perspective. *Frontiers in Psychology*, 8, 1991.

Lajunen, T., Sullman, M. J. M., & Gaygısız, E. (2022). Self-Assessed Driving Skills and
Risky Driver Behaviour Among Young Drivers: A Cross-Sectional Study. *Frontiers in Psychology*, 13(April). https://doi.org/10.3389/fpsyg.2022.840269

Lappi, O., & Mole, C. (2018). Visuomotor control, eye movements, and steering: A unified approach for incorporating feedback, feedforward, and internal models. *Psychological Bulletin*, 144(10), 981–1001. https://doi.org/10.1037/bul0000150

Li, R., Chen, Y. V., & Zhang, L. (2019). Effect of Music Tempo on Long-Distance Driving: Which Tempo Is the Most Effective at Reducing Fatigue? *I-Perception*, 10(4), 204166951986198. https://doi.org/10.1177/2041669519861982

Mars, F., & Navarro, J. (2012). Where we look when we drive with or without active steering wheel control. *PloS One*, 7(8), e43858. https://doi.org/10.1371/journal.pone.0043858

Mas-Herrero, E., Marco-Pallares, J., Lorenzo-Seva, U., Zatorre, R. J., & Rodriguez-Fornells, A. (2013). Individual Differences in Music Reward Experiences. *Music Perception*, 31(2), 118–138. https://doi.org/10.1525/mp.2013.31.2.118

Mayer, J. D., & Gaschke, Y. N. (2001). Brief Mood Introspection Scale (BMIS). *Psychology*, 19(3), 1995–1995. https://doi.org/10.1037/0022-3514.55.1.102

McKnight, A. J., & Adams, B. B. (1970a). *Driver education task analysis. Vol I: Task descriptions.*

McKnight, A. J., & Adams, B. B. (1970b). *Driver Education Task Analysis. Volume II: Task Analysis Methods. Final Report*. ERIC.

Metcalfe, T. (2016). The contribution of different musical variables to the effect of background music on motor behavior. *Psychomusicology: Music, Mind, and Brain*, 26(2), 132.

Michon, J. A. (1985). *A critical view of driver behavior models: what do we know, what should we do?* (L. Evans & R. Schwing (eds.)). Human behavior and traffic safety. Plenum press, New York.

Millet, B., Ahn, S., & Chattah, J. (2019). The impact of music on vehicular performance: A meta-analysis. *Transportation Research Part F: Traffic Psychology and Behaviour*, 60, 743–760. https://doi.org/10.1016/j.trf.2018.10.007

Mulder, L. J. M. (1992). Measurement and Analysis-Methods of Heart-Rate and Respiration for Use in Applied Environments. *Biological Psychology*, 34(2–3), 205–236. https://doi.org/10.1016/0301-0511(92)90016-N

Navarro, J., Deniel, J., Yousfi, E., Jallais, C., Bueno, M., & Fort, A. (2017). Influence of
lane departure warnings onset and reliability on car drivers' behaviors. *Applied Ergonomics*, 59, 123-131. https://doi.org/10.1016/j.apergo.2016.08.010

Navarro, J., Deniel, J., Yousfi, E., Jallais, C., Bueno, M., & Fort, A. (2019). Does False and Missed Lane Departure Warnings Impact Driving Performances Differently? *International Journal of Human-Computer Interaction*, 35(14), 1292-1302.
https://doi.org/10.1080/10447318.2018.1519166

Navarro, J., Hernout, E., Osiurak, F., & Reynaud, E. (2020). On the nature of eye-hand coordination in natural steering behavior. *PLOS ONE*, 15(11), e0242818.
https://doi.org/10.1371/journal.pone.0242818

Navarro, J., Lappi, O., Osiurak, F., Hernout, E., Gabaude, C., & Reynaud, E. (2021). Dynamic scan paths investigations under manual and highly automated driving. *Scientific Reports*, 11(1), 3776. https://doi.org/10.1038/s41598-021-83336-4

Navarro, J., Osiurak, F., Gaujoux, V., Ouimet, M. C., & Reynaud, E. (2019). Driving Under the Influence: How Music Listening Affects Driving Behaviors. *Journal of Visualized Experiments*, 145, e58342. https://doi.org/10.3791/58342

Navarro, J., Osiurak, F., Ovigue, M., Charrier, L., & Reynaud, E. (2019). Highly Automated Driving Impact on Drivers’ Gaze Behaviors during a Car-Following Task. *International Journal of Human-Computer Interaction*, 35(11), 1008-1017.
https://doi.org/10.1080/10447318.2018.1561788

Navarro, J., Osiurak, F., & Reynaud, E. (2018). Does the Tempo of Music Impact Human Behavior Behind the Wheel? *Human Factors*, 60(4), 556-574.
https://doi.org/10.1177/0018720818760901

Navarro, J., Reynaud, E., & Osiurak, F. (2018). Neuroergonomics of car driving: A critical meta-analysis of neuroimaging data on the human brain behind the wheel. *Neuroscience & Biobehavioral Reviews*, 95, 464-479.
https://doi.org/10.1016/j.neubiorev.2018.10.016

Nave, G., Minxha, J., Greenberg, D. M., Kosinski, M., Stillwell, D., & Rentfrow, J. (2018). Musical Preferences Predict Personality: Evidence From Active Listening and Facebook Likes. *Psychological Science*. https://doi.org/10.1177/0956797618761659

Newman, J., Rosenbach, J. H., Burns, K. L., Latimer, B. C., Matocha, H. R., & Vogt, E. R. (1995). An experimental test of “the Mozart effect”: does listening to his music improve spatial ability? *Perceptual and Motor Skills*, 81(3_suppl), 1379-1387.

Oron-Gilad, T., Ronen, A., & Shinar, D. (2008). Alertness maintaining tasks (AMTs)
while driving. *Accident Analysis & Prevention*, 40(3), 851-860.

Patston, L. L. M., & Tippett, L. J. (2011). The effect of background music on cognitive performance in musicians and nonmusicians. *Music Perception*, 29(2), 173-183.

Pearce, M., & Rohrmeier, M. (2012). Music Cognition and the Cognitive Sciences. *Topics in Cognitive Science*, 4(4), 468-484. https://doi.org/10.1111/j.1756-8765.2012.01226.x

Pêcher, C., Lemercier, C., & Cellier, J. (2009). Emotions drive attention: Effects on driver's behaviour. *Safety Science*, 47(9), 1254-1259.
https://doi.org/10.1016/j.ssci.2009.03.011

Pietschnig, J., Voracek, M., & Formann, A. K. (2010). Mozart effect–Shmozart effect: A meta-analysis. *Intelligence*, 38(3), 314–323.
https://doi.org/10.1016/j.intell.2010.03.001

Ranney, T. A. (1994). Models of driving behavior: a review of their evolution. *Accident Analysis & Prevention*, 26(6), 733-750.

Rauscher, F. H., Shaw, G. L., & Ky, C. N. (1993). Music and spatial task performance. *Nature*, 365(6447), 611–611. https://doi.org/10.1038/365611a0

Saliba, J., Lorenzo-Seva, U., Marco-Pallares, J., Tillmann, B., Zeitouni, A., & Lehmann, A. (2016). French validation of the Barcelona Music Reward Questionnaire. *PeerJ*, 2016(3), 1–12. https://doi.org/10.7717/peerj.1760

Salvucci, D. D. (2006). Modeling Driver Behavior in a Cognitive Architecture. *Human Factors: The Journal of the Human Factors and Ergonomics Society*, 48(2), 362-380.
https://doi.org/10.1518/001872006777724417

Schäfer, T., Sedlmeier, P., Städtler, C., & Huron, D. (2013). The psychological functions of music listening. *Frontiers in Psychology*, 4(AUG).
https://doi.org/10.3389/fpsyg.2013.00511

Scherer, K. R. (2005). What are emotions? And how can they be measured? *Social Science Information*, 44(4), 695-729.

Scherer, K. R., Shuman, V., Fontaine, J., & Soriano, C. (2013). The GRID meets the Wheel: Assessing emotional feeling via self-report. *Components of Emotional Meaning: A Sourcebook*.

Smith, A. (2002). Effects of caffeine on human behavior. *Food and Chemical Toxicology*, 40(9), 1243-1255.

Steele, K. M. (2000). Arousal and mood factors in the “Mozart effect.” *Perceptual and*
*Motor Skills, 91*(1), 188–190.

Steinhauser, K., Leist, F., Maier, K., Michel, V., Pärsch, N., Rigley, P., Wurm, F., & Steinhauser, M. (2018). Effects of emotions on driving behavior. *Transportation Research Part F: Traffic Psychology and Behaviour, 59*, 150–163.

Taubman-Ben-Ari, O., & Skvirsky, V. (2016). The multidimensional driving style inventory a decade later: Review of the literature and re-evaluation of the scale. *Accident Analysis and Prevention, 93*, 179–188.
https://doi.org/10.1016/j.aap.2016.04.038

Ünal, A. B., de Waard, D., Epstude, K., & Steg, L. (2013). Driving with music: Effects on arousal and performance. *Transportation Research Part F: Traffic Psychology and Behaviour, 21*, 52–65. https://doi.org/10.1016/j.trf.2013.09.004

Ünal, A. B., Platteel, S., Steg, L., & Epstude, K. (2013). Blocking-out auditory distractors while driving: A cognitive strategy to reduce task-demands on the road. *Accident Analysis & Prevention, 50*, 934–942.

Ünal, A. B., Steg, L., & Epstude, K. (2012). The influence of music on mental effort and driving performance. *Accident; Analysis and Prevention, 48*, 271–278.
https://doi.org/10.1016/j.aap.2012.01.022

Underwood, G., Chapman, P., Wright, S., & Crundall, D. (1999). Anger while driving. *Transportation Research Part F: Traffic Psychology and Behaviour, 2*(1), 55–68.

van der Zwaag, M., Dijksterhuis, C., de Waard, D., Mulder, B. L. J. M., Westerink, J. H. D. M., & Brookhuis, K. a. (2012). The influence of music on mood and performance while driving. *Ergonomics, 55*(1), 12–22.
https://doi.org/10.1080/00140139.2011.638403

Wang, D.-Y. D., Jimison, Z., Richard, D., & Chuan, C.-H. (2015). Effect of listening to music as a function of driving complexity: A simulator study on the differing effects of music on different driving tasks. *Driving Assessment Conference, 8*(2015).

Zhang, T., Chan, A. H. S., Ba, Y., & Zhang, W. (2016). Situational driving anger, driving performance and allocation of visual attention. *Transportation Research Part F: Traffic Psychology and Behaviour, 42*, 376–388.
8 Supplementary material

Weight Matrix for the No Music and Music networks.