This study was supported by the LABEX CORTEX (ANR-11-LABX-0042) of Universit&#233; de Lyon, within the program &#8216;&#8216;Investissements d&#8217;Avenir&#8217;&#8217; (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR).

All methods described here have been approved by the Ethics Committee of the Department of Psychology of University Lyon 2 and informed consent was obtained from all participants.
NOTE: Participants were recruited by means of notices posted in the local community and at the University.
1. Participants
<ol>
	<li>Ensure that participants have a driving license and at least two years of driving experience.</li>
	<li>Ensure that participants have normal or corrected to normal vision and audition.</li>
	<li>Ensure that participants do not suffer from a mental illness (e.g., schizophrenia).</li>
	<li>Recruit a similar number of females and males.</li>
	<li>Equip participants with a heart rate belt linked to a watch to monitor heart rate.</li>
	<li>Ask participants to choose freely their own preferred music track (examples of participants choice: Uptown Funk by Mark Ronson featuring Bruno Mars or Cheerleader by OMI). Ask participants to provide that music track on a USB stick. Use this track to create different musical backgrounds for the experiment (see section 3).</li>
</ol>
2. Driving simulation
NOTE: The following steps are described based on a new driving simulator, a BB_Sim developed at the Universit&#233; de Sherbrooke, which includes one computer, three screens, as well as a steering wheel and acceleration and braking pedals for driving controls. The initial study was conducted with a different driving simulator using the open-source OpenSD2S software18.
<ol>
	<li>Set the driving simulation driving noise (i.e., engine of the driven vehicle) at &#177; 25dB.</li>
	<li>Seat the participant in front of the simulator, approximately 60 cm from the screens (three screens located in front of the participant,&#160;48 cm x 30 cm each,&#160;covering a total of&#160;137.52&#176; of the visual angle on the horizontal axis and 28.65&#176; of the visual angle on the vertical axis), in a modified car seat.</li>
	<li>Let the participant adjust the distance between the seat and the pedals with the handle underneath the seat.</li>
	<li>Once the participant is comfortably positioned, provide instructions on using the features of the simulator (i.e., how to interact with the acceleration and braking pedals and the steering wheel).</li>
	<li>Inform the participant about simulation sickness that can sometimes occur, and let the participant know that the simulation will be stopped at any time if necessary.</li>
	<li>Use a rural roadway with one traffic lane per direction with five left bends and five right bends and without any traffic.</li>
</ol>
3. Musical background
<ol>
	<li>Use software to modify the tempo of the music, without any pitch modification.</li>
	<li>Create four auditory backgrounds for each participant: (1) No Music, with no additional music played; (2) Music, each participant preferred music track without modification; (3) Music +10, each participant preferred music track with an increased tempo. The tempo is increased by 10 % of the regular tempo; (4) Music-10, each participant preferred music track with a decreased tempo. The tempo is decreased by 10 % of the regular tempo.</li>
	<li>Control the intensity of the music. Music intensity is known to modify driving performances19. Set the intensity to 75 dB for all auditory backgrounds but the No Music condition.</li>
	<li>Use a software to play one of the four musical backgrounds for the entire duration of each drive. Play the music on two lateral powered monitor speakers located on the right and on the left of the participant.</li>
</ol>
4. Simulated car-following task
<ol>
	<li>Provide instructions concerning the task: &#34;Drive as you would do in a real-life situation. Your goal is to follow the vehicle in front of you at a close but safe constant distance, as if following a friend on an unknown route.&#34;&#160;Inform the participant that there is no traffic or obstacles on the path.</li>
	<li>Start the simulation with a training phase in order to familiarize the participant with the driving simulator, the simulated environment, the vehicle controls, and the car-following task. When the participant feels comfortable, stop the training phase.</li>
	<li>Proceed with the experimental drives. Launch the simulated car-following task and one of the four musical backgrounds.</li>
	<li>Start recording the heart rate data at the beginning of each simulated car-following task and end it at the end of that driving task.</li>
	<li>For the simulated car driving task, have the driver first drive for 50 m before stopping at a &#34;Stop&#34;&#160;sign. Once the participant&#8217;s vehicle has stopped, a leading vehicle appears on the road at the left of the intersection. Instruct the participant to follow the vehicle. After an initial phase in which the speed of the leading vehicle is stationary and set at 20 kph allowing the driven vehicle to catch up, its speed then varies sinusoidally between 45 kph and 70 kph within each period of 60 s for a total of 3 min. Then, ask the participant to stop driving.</li>
	<li>For the car-following task, use a two-lane road with opposite traffic directions. In order to provide a realistic driving environment, use a road section with a balanced number of left (n = 5) and right bends (n = 5) with various radii of curvature from 45 m to 300 m. Additionally, add visual elements on the edges of the road section such as trees, barriers, fields, and landform.</li>
	<li>Repeat the car-following task for each of the four musical backgrounds. The car-following task takes about 4 min (3 min of actual car-following plus the beginning and ending of the driving simulation). 
	NOTE: The duration of the car-following task can be extended if required.</li>
	<li>Take a 5 min break between each car-following task to reduce carryover effects.</li>
	<li>Counterbalance the order of presentation of the four musical backgrounds between participants using a Latin square design.</li>
</ol>
5. Data collection
<ol>
	<li>Collect the participants&#39;&#160;subjective mood after each condition using the Brief Mood Introspection Scale (BMIS)20 validated in French21,22. This questionnaire provides data on participants&#39;&#160;mood on four mood dimensions: pleasant/unpleasant, arousal/calm, positive/tired, and negative/relaxed.</li>
	<li>Collect physiological measures. Compute the mean heart rate and heart rate variability over the entire drive for each experimental condition using the data recorded by the heart rate monitoring watch at a sample per second. In practice, compute mean heart rate by averaging all the data collected during each experimental condition and heart rate variability by calculating the standard deviation on the same data.</li>
	<li>Measure objective driving behaviors through the mean inter-vehicular time and the inter-vehicular time variability. Record both driven and leading vehicle position and speed at each time step at a sample rate of 60 Hz.
	<ol>
		<li>At each time step, compute the inter-vehicular time as the time required for the driven vehicle to reach the position of the lead vehicle if the position of the lead vehicle was frozen and the speed of the driven vehicle was constant.</li>
		<li>Average all the values collected for a drive to obtain the mean inter-vehicular time and compute the standard deviation on those values to obtain the inter-vehicular time variability. 
		NOTE: Several variables can be computed to qualify driving behaviors during a car-following task. The inter-vehicular time is particularly well-suited as it offers an indication of the safety margin between the driven vehicle and the lead vehicle chosen by the driver.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

The proposed method is well suited for cognitive ergonomic investigations as it offers an excellent compromise between experimental control and ecological vailidity16. If a strong experimental control is required to ensure that the collected results are related to the experimental manipulations, no results are of interest if restricted to the experimental conditions. Indeed, scientific results are of interest if transferable to real life situations. These assertions do not mean that only real-life experiments are valuable, but rather stress that the ecological validity of a method depends on the objectives of the research16. The reported method should be favored by researchers that are interested in human cognition and behaviors in real life situations (i.e., car driving) and that are in need of a good experimental control. Of course, the reported method is not reproducing real life driving situations, where a variety of sounds/noises can interfere with the impact of music listening on driving behaviors. In real driving situations, as drivers&#8217; own safety is engaged, this could also impact on the results. However, with the method proposed here, the driving task is not broken down and drivers can drive as they usually do in a simulated environment where the events (i.e., car to follow speed changes) can be reproduced accurately from a condition to the other and from a participant to the other. The proposed method also has limitations. Driving is a complex activity and the car-following task reproduced here is just one driving task among many other possible driving tasks. In the car-following task, the focus is set on speed control, but lateral control, as well as tactical and strategical tasks, which engage different levels of cognitive control and neural circuits are not investigated23. In addition, the brief duration of the protocol is not sufficient to investigate fatigue, distraction or mind wandering issues for instance.
Fortunately, the proposed method can be adjusted depending on the experimental needs. Here, and in line with previous studies on the impact of driving with music24,25, only the well-known car-following task26 has been used. In the future studies, other driving situations implying different visual explorations strategies27 could also be considered and added to the experimental protocol.
Irrespective of these possible adaptations of the protocol, a familiarization period with the driving simulator is required to ensure that participants are familiar with the vehicle dynamics and properly installed. In the same vein, the quality and intensity of music tracks, as well as loudspeaker quality are critical to unsure the method reproducibility.
Human emotions are classically considered as a combination of two orthogonal dimensions28,29,30,31: valence (from negative affect to positive affect) and arousal (from deactivation to activation). For instance, sadness is associated with a highly negative valence (i.e., unpleasant) but is neutral in terms of activation31. A future research direction would be to further investigate these two dimensions of emotions using music listening. Besides, music is a complex stimulus that could be described based on its own properties (e.g., intensity, tempo, rhythm, mode). Music can also be described based on its effects on the listeners. As such a given music track can have a different impact on each individual listener. Here we investigated the influence of listening to the favorite music track, along with a tempo manipulation, on driving performances. In the future, other music dimensions could be investigated along with differential impact depending on the listener.
Future applications of the method could also be made beyond music listening investigations. As such, research topics such as texting while driving32, mind-wandering behind the wheel33, and driving automation34,35,36,37 could benefit from the proposed method.

Car driving activity has increased quickly over the last decades and is now a daily activity for many individuals in modern societies. In line with this growth, car-driving activity has become a hot topic of investigation for the human factor community1.
There is an extremely limited number of major cultural activities that typically define humankind regardless of the population and the period of history considered2. Music is one of these activities along with tool use and symbolic reasoning underpinning language abilities3. Playing and listening to music is therefore a crucial individual and social activity. Based on an exhaustive literature review, Sch&#228;fer et al.4 found about 130 different non-redundant functions associated to music listening and three music listening meta-functions were then identified: (1) arousal and mood regulation, (2) self-awareness achievement, and (3) social relatedness expression. As a consequence, people are frequently listening to music in a variety of situations and locations5. Among those situations, listening to music while driving is extremely common with drivers reporting listening to music during about three quarters of their driving time6.
Music listening is known to impact the listener&#8217;s emotional state7 and thus to induce mood changes in a variety of situations and research areas8. According to the mood congruency theory, a person&#8217;s behavior is related to his/her mood9 with evidence gained from both neuroimaging10 and behavioral experiments11. Following this rationale, music listening can change drivers&#8217; mood, which in turn can change driving behaviors.
Musically-induced mood changes while driving were found to result in performance improvements under certain conditions or impairments under other conditions. On the one hand, in complex and highly demanding driving environments, calm music was found to mitigate affective states:&#160; it has a softening effect on negative emotions, reduces the level of stress, and improves driver&#8217;s relaxation and calm12. This relaxing effect was reported to be more efficient when using abrupt music changes compared to gradual music changes13. On the other hand, people were slower to detect hazards in driving scenes when anger was previously induced through music and guided imagery compared with neutral mood performances14. Happy music listening while driving was also found to be detrimental to lateral control efficiency, while sad music had no significant effect on lateral control of the vehicle15. In brief, drivers&#8217; mood can impact driving performances in opposite ways depending on the music and associated mood induction and on the driving situation considered.
The purpose of the method reported here is to offer an experimentally well-controlled driving situation to investigate the impact of music listening on driving behaviors. To ensure the reproducibility of the driving situation, a method based on driving simulation has been implemented. At the first glance, a driving simulation might be considered as a degraded version of real driving investigations. However, the reality is more complex, and a given experimental setup cannot be considered as the best experimental solution in absolute terms. Rather the best experimental setup is the one that suits the most accurately the needs of the investigation concerned16. If real car-driving is the experimental setup that best reproduces the everyday life driving situations, it also comes with some drawbacks: driver safety in case of experimental manipulations, potential impairments in terms of driving performances, and difficulties in the control of the driving environment, including traffic, weather conditions, light conditions, level of ambient noise, etc. Conversely, if driving simulators are not as realistic as real vehicles, experimental conditions and manipulations can be strictly controlled and replicated17. As a result, different participants can be exposed to the exact same experimental conditions. In addition, experimental manipulations potentially impairing driving performances are made possible as only virtual safety (and not real safety) is engaged. Altogether, a driving simulation offers an excellent compromise between the need to conserve the driving activity natural (i.e., external validity) and the need for a strong experimental control (i.e., internal validity).

<table><tbody><tr><td>Audacity software</td><td>Audacity</td><td>Open source</td><td>https://www.audacityteam.org</td></tr><tr><td>Driving simulator</td><td>Universit&amp;eacute; de Sherbrooke</td><td>BB sim</td><td></td></tr><tr><td>Polar watch with heart rate monitor</td><td>Polar</td><td>RC3</td><td>https://www.polar.com/fr</td></tr><tr><td>Speakers</td><td>Yamaha</td><td>MSP3</td><td></td></tr><tr><td>Steering wheel and pedals</td><td>Logitech</td><td>G27</td><td></td></tr></tbody></table>

driving under the influence: how music listening affects driving behaviors

Car driving is a daily activity for many individuals in modern societies. Drivers often listen to music while driving. The method presented here investigates how listening to music influences driving behaviors. A driving simulation was selected because it offers both a well-controlled environment and a good level of ecological validity. Driving behaviors were assessed through a car-following task. In practice, participants were instructed to follow a lead vehicle as they would do in real life. The lead vehicle speed changed over time requiring constant speed adjustments for the participants. The inter-vehicular time was used to assess driving behaviors. To complement the driving behaviors, the subjective mood and physiological level of arousal were also collected. As such, the results collected using this method offer insights on both the human internal state (i.e., subjective mood and physiological arousal) and driving behaviors in the car following task.

The main comparisons are based on the following experimental conditions. The first experimental condition is No Music versus Music, a comparison between the No Music background and the individual preferred Music background using a pairwise t-test. These analyses were meant to assess the influence of listening to preferred music compared to a control condition without music. The second experimental condition is four different musical backgrounds, a comparison between No Music, Music, Music +10 and Music -10 corresponding to a music tempo manipulation compared to a control condition without music, using repeated measures ANOVAs.
As compared with No Music, Music had a significant effect on subjective mood as observed on three of the four dimensions of the Brief Mood Introspection Scale (Figure 1). Significant differences were found for the pleasant-unpleasant (t(23) = -2.75; p &#60; 0.01), arousal-calm (t(23) = -2.67; p &#60; 0.01), and positive-tired (t(23) = 3.54; p &#60; 0.001) dimensions. No significant differences were observed for the negative-relaxed dimension (t(23) = 1.05; p = 0.153).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58342/58342fig1.jpg" /> 
Figure 1: Subjective mood assessed by the BMIS under no music and music background conditions. (A) Pleasant-unpleasant level from 16 (most unpleasant situation) to 64 (most pleasant situation). (B) Arousal-calm level from 12 (minimal activation level) to 48 (maximum arousal level). (C) Positive-tired level from 7 (most tired level) to 28 (most positive level). (D) Negative-relaxed level from 6 (most negative level) to 24 (most relaxed level). Error bars represent standard errors. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58342/58342fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Considering all four auditory backgrounds, a significant effect on subjective mood was observed on the four dimensions of the BMIS (Figure 2). Significant differences were found for the pleasant-unpleasant (F(3,69) = 2.75; p &#60; 0.05), arousal-calm (F(3,69) = 7.74; p &#60; 0.001), positive-tired (F(3,69) = 7.36; p &#60; 0.001), and negative-relaxed (F(3,69) = 3.24; p &#60; 0.03) dimensions.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58342/58342fig2.jpg" /> 
Figure 2: Subjective mood assessed by the BMIS under the four different auditory backgrounds conditions. (A) Pleasant-unpleasant level from 16 (most unpleasant situation) to 64 (most pleasant situation). (B) Arousal level from 12 (minimal activation level) to 48 (maximum arousal level). (C) Positive-tired level from 7 (most tired level) to 28 (most positive level). (D) Negative-relaxed level from 6 (most negative level) to 24 (most relaxed level). Error bars represent standard errors. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58342/58342fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The mean heart rate was significantly different for the No Music and Music backgrounds (t(18) = -5.05; p &#60; 0.001; Figure 3). The heart rate variability was not significantly impacted by the background condition (t(18) = -1.58; p = 0.07; Figure 3).
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58342/58342fig3.jpg" /> 
Figure 3: Mean heart rate and heart rate variability under no music and music background conditions. (A) Mean heart rate. (B) Mean heart rate variability. Error bars represent standard errors. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58342/58342fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The mean heart rate was significantly different under the four auditory background conditions (F(3,51) = 4.25; p &#60; 0.01; Figure 4). The heart rate variability was not significantly impacted by the background condition (F(3,51) = 0.94; p = 0.43; Figure 4).
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58342/58342fig4.jpg" /> 
Figure 4: Mean heart rate and heart rate variability under the four different auditory backgrounds conditions. (A) Mean heart rate. (B) Mean heart rate variability. Error bars represent standard errors. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58342/58342fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The mean inter-vehicular time was significantly different for the No Music and Music backgrounds (t(23) = 2.53; p &#60; 0.01;&#160; Figure 5). The standard deviation of the inter-vehicular time was not significantly impacted by the background condition (t(23) = -0.11; p = 0.55; Figure 5).
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/58342/58342fig5.jpg" /> 
Figure 5: Mean inter-vehicular time and inter-vehicular time standard deviation under no music and music background conditions. (A) Mean inter-vehicular time. &#160;(B) Mean inter-vehicular time standard deviation. Error bars represent standard errors. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58342/58342fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The mean inter-vehicular time was not significantly different under the four auditory background conditions (F(3,69) = 1.88; p = 0.14; Figure 6). Similarly, the standard deviation of the inter-vehicular time was not significantly impacted by the background condition (F(3,69) = 1.57; p = 0.20; Figure 6).
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/58342/58342fig6.jpg" /> 
Figure 6: Mean inter-vehicular time and inter-vehicular time standard deviation under the four different auditory backgrounds conditions. (A) Mean inter-vehicular time. (B) Mean inter-vehicular time standard deviation. Error bars represent standard errors. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58342/58342fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Listening to music or not (No Music versus Music conditions) while driving was found to have a significant effect on subjective mood, physiological level of arousal, and driving performances. In more detail, listening to favorite music behind the wheel was found to impact positively on the mood, leading to a higher level of pleasantness, arousal, and a more positive mood (Figure 1). The comparison between no music and music backgrounds revealed a significant influence of music listening on drivers&#8217; mean heart rate. Music increased the mean heart rate. In line with subjective data, this increase can be interpreted as an increase in arousal induced by music listening (Figure 3). It is assumed that the increase of arousal produced by music listening translates into driving behavior modifications. In the car-following task, drivers had a smaller inter-vehicular time while listening to music compared with the control condition without music to listen (Figure 5). This can be interpreted as a reduction of drivers&#8217; safety margin with the vehicle to follow in an external assessment perspective (i.e., absolute safety margin). However, from the drivers&#8217; perspective, the inter-vehicular time reduction is most probably adjusted depending on their own perceived abilities. With a higher level of arousal, faster detections and responses to follow the speed changes of the leading vehicle can be made. Still from drivers&#8217; perspective, a reduction of the inter-vehicular time is required to maintain the same safety margin (i.e., relative safety margin). In sum, the experimental protocol reported here was found to be sensitive enough to reveal mood changes, physiological changes, and driving behavior changes for the comparison between no music and music conditions. The manipulation of the music tempo (i.e., comparison of four different auditory backgrounds) did not translate into the same pattern of results. If all four dimensions of subjective mood and physiologically assessed arousal were found to be significantly influenced by the auditory background, no clear impact of the music tempo was found. Indeed, music, music +10 and music -10 conditions had an insignificantly different effect on drivers&#8217; subjective mood and physiological arousal (Figure 2 and Figure 4). Additionally, those four conditions considered together did not change driving behaviors significantly as revealed by the inter-vehicular time (Figure 6).

Watch this Scientific Journal Video about Driving Under the Influence: How Music Listening Affects Driving Behaviors at JoVE.com

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Here, we present a protocol to assess driving behaviors while following a vehicle in a simulated driving environment. The presented protocol is used to compare the impact of different auditory backgrounds on driving behaviors.

Car driving is a daily activity for many individuals in modern societies. Drivers often listen to music while driving. The method presented here ...

driving-under-influence-how-music-listening-affects-driving

Nanyang Technological University

Research

JoVE Journal

Behavior

12.3K Views.  University Lyon 2. Here, we present a protocol to assess driving behaviors while following a vehicle in a simulated driving environment. The presented protocol is used to compare the impact of different auditory backgrounds on driving behaviors.

Video: Driving Under the Influence: How Music Listening Affects Driving Behaviors

Making Sense of Listening: The IMAP Test Battery

The ability to hear is only the first step towards making sense of the range of information contained in an auditory signal. Of equal importance are the abilities to extract and use the information encoded in the auditory signal. We refer to these as listening skills (or auditory processing AP). Deficits in these skills are associated with delayed language and literacy development, though the nature of the relevant deficits and their causal connection with these delays is hotly debated.
When a child is referred to a health professional with normal hearing and unexplained difficulties in listening, or associated delays in language or literacy development, they should ideally be assessed with a combination of psychoacoustic (AP) tests, suitable for children and for use in a clinic, together with cognitive tests to measure attention, working memory, IQ, and language skills. Such a detailed examination needs to be relatively short and within the technical capability of any suitably qualified professional. Current tests for the presence of AP deficits tend to be poorly constructed and inadequately validated within the normal population. They have little or no reference to the presenting symptoms of the child, and typically include a linguistic component. Poor performance may thus reflect problems with language rather than with AP. To assist in the assessment of children with listening difficulties, pediatric audiologists need a single, standardized child-appropriate test battery based on the use of language-free stimuli.
We present the IMAP test battery which was developed at the MRC Institute of Hearing Research to supplement tests currently used to investigate cases of suspected AP deficits. IMAP assesses a range of relevant auditory and cognitive skills and takes about one hour to complete. It has been standardized in 1500 normally-hearing children from across the UK, aged 6-11 years. Since its development, it has been successfully used in a number of large scale studies both in the UK and the USA. IMAP provides measures for separating out sensory from cognitive contributions to hearing. It further limits confounds due to procedural effects by presenting tests in a child-friendly game-format. Stimulus-generation, management of test protocols and control of test presentation is mediated by the IHR-STAR software platform. This provides a standardized methodology for a range of applications and ensures replicable procedures across testers. IHR-STAR provides a flexible, user-programmable environment that currently has additional applications for hearing screening, mapping cochlear implant electrodes, and academic research or teaching.

A test battery (IMAP) for performing an in-depth assessment of auditory and cognitive abilities contributing to listening skills is described. It is quick to administer, child-friendly and free from linguistic confounds. Stimulus generation and protocol management are controlled via a software platform (IHR-STAR) to ensure replicable procedures.

The ability to hear is only the first step towards making sense of the range of information contained in an auditory signal. Of equal importance are the ...

Medicine

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Attention control is the ability to selectively attend to some sensory signals while ignoring others. This ability is thought to involve two processes: enhancement of sensory signals that are to be attended and the attenuation of sensory signals that are to be ignored. The overall strength of attentional modulation is often measured by comparing the amplitude of a sensory neural response to an external input when attended versus when ignored. This method is robust for detecting attentional modulation, but precludes the ability to assess the separate dynamics of attending and ignoring processes. Here, we describe methodology to measure independently the neurophysiological signals of attending and ignoring using the intermodal attention task (IMAT). This task, when combined with electroencephalography, isolates neurophysiological sensory responses in auditory and visual modalities, when either attending or ignoring, with respect to a passive control. As a result, independent dynamics of attending and of a ignoring can be assessed in either modality. Our results using this task indicate that the timing and cortical sources of attending and ignoring effects differ, as do their contributions to the attention modulation effect, pointing to unique neural trajectories and demonstrating sample utility of measuring them separately.

Attention control comprises enhancement of target signals and attenuation of distractor signals. We describe an approach to measure separately but concurrently, the neurophysiology of attending and ignoring in sustained intermodal attention, utilizing a passive control condition during which neither process is continuously engaged.

Attention control is the ability to selectively attend to some sensory signals while ignoring others. This ability is thought to involve two processes: ...

Assessing the Effects of Music Listening on Psychobiological Stress in Daily Life

Music listening is associated with stress-reducing effects. However, most of the results on music listening and stress were gathered in experimental settings. As music listening is a popular activity of daily life, it is of utmost importance to study the effects of music listening on psychobiological stress in an everyday, daily-life setting. Here, a study protocol is presented that allows the assessment of associations between music listening and psychobiological stress in daily life by noninvasively measuring salivary cortisol (as a marker of the Hypothalamic-Pituitary-Adrenal (HPA) axis) and salivary alpha-amylase (as a marker of the Autonomic Nervous System (ANS)). The protocol includes advice on the study design (e.g., sampling protocol), the materials and methods (e.g., the assessment of psychobiological stress in daily life, the assessment of music listening, and the manual), the selection of participants (e.g., the approval of the institutional review board and inclusion criteria), and the statistical analyses (e.g., the multilevel approach). The representative results provide evidence for a stress-reducing effect of music listening in daily life. Particularly, specific reasons for listening to music (especially relaxation), as well as the presence of others while doing so, increase this stress-reducing effect. At the same time, music listening in daily life differentially affects the HPA axis and ANS functioning, thus emphasizing the need for a multi-dimensional assessment of stress in daily life.

A study protocol is presented on how to assess associations between music listening and psychobiological stress (as measured by subjective stress levels, salivary cortisol, and salivary alpha-amylase) in daily life. Advice on study design, materials, methods, selection of participants, and statistical handling is provided. Representative results are presented and discussed.

Music listening is associated with stress-reducing effects. However, most of the results on music listening and stress were gathered in experimental ...

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Patients suffering from homonymous hemianopia after infarction of the posterior cerebral artery (PCA) report different degrees of constraint in daily life, despite similar visual deficits. We assume this could be due to variable development of compensatory strategies such as altered visual scanning behavior. Scanning compensatory therapy (SCT) is studied as part of the visual training after infarction next to vision restoration therapy. SCT consists of learning to make larger eye movements into the blind field enlarging the visual field of search, which has been proven to be the most useful strategy1, not only in natural search tasks but also in mastering daily life activities2. Nevertheless, in clinical routine it is difficult to identify individual levels and training effects of compensatory behavior, since it requires measurement of eye movements in a head unrestrained condition. Studies demonstrated that unrestrained head movements alter the visual exploratory behavior compared to a head-restrained laboratory condition3. Martin et al.4 and Hayhoe et al.5 showed that behavior demonstrated in a laboratory setting cannot be assigned easily to a natural condition. Hence, our goal was to develop a study set-up which uncovers different compensatory oculomotor strategies quickly in a realistic testing situation: Patients are tested in the clinical environment in a driving simulator. SILAB software (Wuerzburg Institute for Traffic Sciences GmbH (WIVW)) was used to program driving scenarios of varying complexity and recording the driver's performance. The software was combined with a head mounted infrared video pupil tracker, recording head- and eye-movements (EyeSeeCam, University of Munich Hospital, Clinical Neurosciences).
The positioning of the patient in the driving simulator and the positioning, adjustment and calibration of the camera is demonstrated. Typical performances of a patient with and without compensatory strategy and a healthy control are illustrated in this pilot study. Different oculomotor behaviors (frequency and amplitude of eye- and head-movements) are evaluated very quickly during the drive itself by dynamic overlay pictures indicating where the subjects gaze is located on the screen, and by analyzing the data. Compensatory gaze behavior in a patient leads to a driving performance comparable to a healthy control, while the performance of a patient without compensatory behavior is significantly worse. The data of eye- and head-movement-behavior as well as driving performance are discussed with respect to different oculomotor strategies and in a broader context with respect to possible training effects throughout the testing session and implications on rehabilitation potential.

Patients with visual deficits after stroke report about different constraints in daily life most likely due to variable compensatory strategies, which are difficult to differentiate in clinical routine. We present a clinical set-up which allows measurement of different compensatory head- and eye-movement-strategies and evaluating their effects on driving performance.

Patients suffering from homonymous hemianopia after infarction of the posterior cerebral artery (PCA) report different degrees of constraint in daily ...

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

We have developed a fully automated system for operant behavior testing and neuronal activity recording by which multiple cognitive brain functions can be investigated in a single task sequence. The unique feature of this system is a custom-made, acoustically transparent chamber that eliminates many of the issues associated with auditory cue control in most commercially available chambers. The ease with which operant devices can be added or replaced makes this system quite versatile, allowing for the implementation of a variety of auditory, visual, and olfactory behavioral tasks. Automation of the system allows fine temporal (10 ms) control and precise time-stamping of each event in a predesigned behavioral sequence. When combined with a multi-channel electrophysiology recording system, multiple cognitive brain functions, such as motivation, attention, decision-making, patience, and rewards, can be examined sequentially or independently.

In this report, we present a fully automated and highly versatile system capable of simultaneously testing multi-cognitive behaviors and recording neuronal activities for rodents.

We have developed a fully automated system for operant behavior testing and neuronal activity recording by which multiple cognitive brain functions can be ...

Neuroscience

Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization

Alcohol use disorder (AUD) remains a serious problem in our society. To develop effective interventions for addiction, it is important to understand the underlying neurobiological mechanisms, for which diverse experimental approaches and model systems are needed. The main ingredient of alcoholic beverages is ethanol, which causes adaptive changes in the central nervous system and behavior upon chronic intake. Behavioral sensitization (i.e., escalated responses) in particular represents a key adaptive change underlying addiction. Most ethanol-induced behavioral sensitization studies in animal models have been conducted on the locomotor activating effect of ethanol. A prominent effect of ethanol is behavioral disinhibition. Behavioral sensitization to the disinhibition effect of ethanol, however, is underrepresented. To address this issue, we developed the Flypub assay that allows measuring the escalated increase in disinhibited courtship activities upon recurring ethanol exposure in Drosophila melanogaster.&#160;Here, we report the step-by-step Flypub assay including assembly of ethanol exposure chambers, setup of the assay station, criteria for fly care and collection, ethanol delivery, quantification of disinhibited courtship activities, data processing and statistical analysis. Also provided are how to troubleshoot critical steps, overcome limitations and expand its utility to assess additional ethanol-induced behaviors. The Flypub assay in combination with powerful genetic tools in Drosophila melanogaster will facilitate the task of discovering the mechanism underlying ethanol-induced behavioral sensitization.

The Flypub assay measures the behaviors that the fruit fly Drosophila melanogaster displays under the influence of ethanol. The assay can be readily mastered by experimenters at all levels and applied to various vaporized stimuli, facilitating substance abuse and addiction studies.

Alcohol use disorder (AUD) remains a serious problem in our society. To develop effective interventions for addiction, it is important to understand the ...

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Collision warning system plays a key role in the prevention of driving distractions and drowsy driving. Previous studies have proven the advantages of tactile warnings in reducing driver&#8217;s brake response time. At the same time, tactile warnings have been proved effective in take-over request (TOR) for partially autonomous vehicles.
How the performance of tactile warnings can be optimized is an ongoing hot research topic in this field. Thus, the presented low-cost driving simulation software and methods are introduced to attract more researchers to take part in the investigation. The presented protocol has been divided into five sections: 1) participants, 2) driving simulation software configuration, 3) driving simulator preparation, 4) vibrating toolkit configuration and preparation, and 5) conducting the experiment.
In the exemplar study, participants wore the tactile vibrating toolkit and performed an established car-following task using the customized driving simulation software. The front vehicle braked intermittently, and vibrating warnings were delivered whenever the front vehicle was braking. Participants were instructed to respond as quickly as possible to the sudden brakes of the front vehicle. Driving dynamics, such as the brake response time and brake response rate, were recorded by the simulation software for data analysis.
The presented protocol offers insight into the exploration of the effectiveness of tactile warnings on different body locations. In addition to the car-following task that is demonstrated in the exemplar experiment, this protocol also provides options&#160;to apply other paradigms to the driving simulation studies by making simple software configuration without any code development. However, it is important to note that due to its affordable price, the driving simulation software and hardware introduced here may not be able to fully compete with other high-fidelity commercial driving simulators. Nevertheless, this protocol can act as an affordable and user-friendly alternative to the general high-fidelity commercial driving simulators.

This protocol describes a driving simulation platform and a tactile vibrating toolkit for the investigation of driving-related research. An exemplar experiment exploring the effectiveness of tactile warnings is also presented.&#160;

Collision warning system plays a key role in the prevention of driving distractions and drowsy driving. Previous studies have proven the advantages of ...

Disruption of Frontal Lobe Neural Synchrony During Cognitive Control by Alcohol Intoxication

Decision making relies on dynamic interactions of distributed, primarily frontal brain regions. Extensive evidence from functional magnetic resonance imaging (fMRI) studies indicates that the anterior cingulate (ACC) and the lateral prefrontal cortices (latPFC) are essential nodes subserving cognitive control. However, because of its limited temporal resolution, fMRI cannot accurately reflect the timing and nature of their presumed interplay. The present study combines distributed source modeling of the temporally precise magnetoencephalography (MEG) signal with structural MRI in the form of &#34;brain movies&#34; to: (1) estimate the cortical areas involved in cognitive control (&#34;where&#34;), (2) characterize their temporal sequence (&#34;when&#34;), and (3) quantify the oscillatory dynamics of their neural interactions in real time. Stroop interference was associated with greater event-related theta (4 - 7 Hz) power in the ACC during conflict detection followed by sustained sensitivity to cognitive demands in the ACC and latPFC during integration and response preparation. A phase-locking analysis revealed co-oscillatory interactions between these areas indicating their increased neural synchrony in theta band during conflict-inducing incongruous trials. These results confirm that theta oscillations are fundamental to long-range synchronization needed for integrating top-down influences during cognitive control. MEG reflects neural activity directly, which makes it suitable for pharmacological manipulations in contrast to fMRI that is sensitive to vasoactive confounds. In the present study, healthy social drinkers were given a moderate alcohol dose and placebo in a within-subject design. Acute intoxication attenuated theta power to Stroop conflict and dysregulated co-oscillations between the ACC and latPFC, confirming that alcohol is detrimental to neural synchrony subserving cognitive control. It interferes with goal-directed behavior that may result in deficient self-control, contributing to compulsive drinking. In sum, this method can provide insight into real-time interactions during cognitive processing and can characterize the selective sensitivity to pharmacological challenge across relevant neural networks.

This experiment uses an anatomically-constrained magnetoencephalography (aMEG) method to examine brain oscillatory dynamics and long-range functional synchrony during engagement of cognitive control as a function of acute alcohol intoxication.

Decision making relies on dynamic interactions of distributed, primarily frontal brain regions. Extensive evidence from functional magnetic resonance ...

Clinical Practice Protocol of Creative Music Therapy for Preterm Infants and Their Parents in the Neonatal Intensive Care Unit

Creative music therapy for preterm infants and their parents (CMT) has emerged as a promising family-integrated early intervention involving communicative musicality to improve infant development, parental well-being, and bonding. It aims at relaxing and nurturing the infant as well as promoting safety and social interaction for the parent-infant dyad. A music therapist specially trained in CMT hums or sings in an infant-directed, improvised, lullaby style continually adjusting to the individual needs, expressions, and breathing pattern of the preterm infant. Based on the principles of family-integrated care, the family is incorporated individually in the therapeutic process, namely by delivering CMT during kangaroo care (KC) and by motivating and facilitating parental vocal interaction with their infant to strengthen the parent-infant bonding. CMT aims at relaxing, stimulating, and coregulating premature infants at a time when many other interventions are still risky and can overwhelm the vulnerable patient group. CMT may be advantageous not by educating and teaching parents, but rather by uncovering the intuitive capacities of parenting that are often overshadowed by the traumatic experience of preterm birth. However, CMT can only be provided when the infants are clinically stable. CMT with parental integration is feasible when parents are available and receptive to participate. This paper presents a detailed protocol on how to use CMT to empower preterm infants and their families.

Creative music therapy for preterm infants and their parents has emerged as a promising family-integrated early intervention. We present a detailed protocol on how to use vocal interaction, humming, or singing to empower preterm infants and their families.

Creative music therapy for preterm infants and their parents (CMT) has emerged as a promising family-integrated early intervention involving communicative ...

Dichotic Listening

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University

It is a well-known fact that the human ability to process incoming stimuli is limited. Nonetheless, the world is complicated, and there are always many things going on at once. Selective attention is the mechanism that allows humans and other animals to control which stimuli get processed and which become ignored. Think of a cocktail party: a person couldn&#8217;t possibly attend to all of the conversations taking place at once. However, everyone has the ability to selectively listen to one conversation, leading all the rest to become unattended to and nothing more than background noise. In order to study how people do this, researchers simulate a more controlled cocktail party environment by playing sounds to participants dichotically, i.e., by playing different sounds simultaneously to each ear. This is called a dichotic listening paradigm.
This experiment demonstrates standard procedures for investigating selective auditory attention with a paradigm called dichotic listening.

Dichotic Listening: Investigating Selective Auditory Attention

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University

It is a well-known fact that the human ability to process incoming stimuli is ...