The influence of music tempo on driving behaviour - Essay Example

A study on ‘The influence of music tempo on driving behaviour’ AL AFFILIATION A study on ‘The influence of music tempo on driving behaviour’ Abstract The purpose of this study is to determine the effects of music tempo and the genre of music on the driving behaviour in a driving simulation. Participants were subjected to a simulation and their responses recorded using questionnaires. The study was conducted on 72 persons from varied age groups, fields of work and tastes in music. The participants consisted of thirty-six females and thirty-six males. The subjects each took a five minute driving test on a laptop and were divided into three groups. The responses to the questionnaires were used as statistical data and their behaviours tabulated. Two one-way ANOVA’s were done, the first for Speed and the next for the tendency to overtake. The first ANOVA showed no significant difference (F=1.533, alpha=.05) for the three different tempo levels. The second ANOVA also showed no major variance (F= 1.801, alpha=.05) for the three tempo levels. The study concluded that none of these three tempo levels had any apparent effect on driving behaviour. One possible reason for this conclusion could be the varied age group considered for the study. A study on ‘The influence of music tempo on driving behaviour’ Introduction How does listening to music while driving affect the quality of driving? Does it make you drive rashly, carefully, or does it make no difference? Does hearing to music stress you out or reduces your stress level? These are some questions that psychologists are trying to answer. This study conducted some tests to ascertain the real relationship between music and driving. Some theories suggest that music can be distractive to the driver’s concentration; some say that it relaxes the driver; some do not find a relation between the two. The nature of this relationship will help psychology devise methods to either encourage or discourage the usage of music while driving. If it can be determined with proof that music has a positive influence on drivers (Furnham & Allass, 1999), then, for example, truck drivers could be asked to hear music while driving to prevent highway accidents due to the stress of long hours of driving. Many studies have already been performed to ascertain this. Previous researches concentrated on a particular age group (Furnham & Allass, 1999) which does not support drawing a generic conclusion. The simulation has also been made more sophisticated and more like the real life situations due to improved technology. Hopefully this study benefits the society and the psychologists to understand better the connection between music and driving. In this study, three levels of tempo were considered, fast, slow and none. The ‘No Music’ level was used as a control level. Tempo level was the only independent variable considered. Subjects were divided into groups prior to the test depending on their choice of tempo. The genre of music chosen depended totally on the subject. The dependent variables measured were their speed levels and tendency to overtake. The main focus of this study is to determine the role of music in the mishaps or accidents that occur. It tries to find a relation to the type of music, tempo level and the speed levels and possibility of a desire to overtake. The conclusion may make us realize the amount of distraction or diversion music creates while driving. The general consensus is that listening to music while driving affects the driver’s reaction time and attention span. It is said to impair the motor, visual and cognitive capability (Furnham & Allass, 1999). More accidents are said to be a result of distraction on the road due to changing DVDs, CDs or radio channels while driving (Jancke, Musial & Vogt & Kalveram, 1994). Cell phones are said to be equally disturbing (ScienceDaily Magazine, 2000). The car is currently believed to be the most common place people listen to music (Brodsky, 2002). Music might be a very likely reason for distraction but it also provides many benefits by facilitating reduction in stress levels and drawing away attention from stress, anger and other dangerous emotions (Wiesenthal, Hennessy, and Totten, 2003). Wiesenthal, Hennessy, and Totten also found that music had a greater influence on driver aggression in the more demanding conditions in high congestion traffic. High congestion traffic can lead to dangerous frustration levels and is very easy to vent it out on passersby and co-travellers. Listening to music is considered beneficial when it does not require concentration and focus (ScienceDaily Magazine, 2000). Mild driver aggression has been defined as any intentionally harmful action within the driving context, including horn honking, swearing, and using hand gestures (Hennessy & Wiesenthal & Totten, 1999). Compared to more extreme acts of roadway violence (e.g. fighting, chasing, and shooting) mild driver aggression is a fairly common response to frustration, irritation, and hassles (Hennessy & Wiesenthal & Totten, 2000). Recent research has found that traffic congestion can be a powerful precursor to roadway aggression, largely due to increased stress, arousal, and time urgency (Hennessy & Wiesenthal & Totten, 2003). As traffic congestion increases, other drivers are viewed as an obstacle to reaching a destination in a desired time frame, leading to stress induced anger and frustration (Frith 2002) Music has been found to reduce stress, anger, agitation, and arousal due, in part, to distraction (Wiesenthal, Hennessy, & Totten, 2000b; Wostratzky). As the number of demands for attention increase, the amount of resources available to any single source is decreased and music acts as a distracter in that it assumes a portion of cognitive or attention resources that might otherwise be directed toward a negative or demanding stimulus (Jancke, Musial& Vogt& Kalveram, 1994). As attention to music increases, cognitive resources are diverted from negative stimuli, thus reducing negative affect and behaviour. Within the driving environment, frustration and provocation from other drivers represent common negative events that have been found to increase the likelihood of mild aggression (Wiesenthal, Hennessy, & Totten, 2003). By redirecting attention from such conditions, music should consequently minimize the motivation, justification, and objective behind mild driver aggression. Music may also influence negative behaviour through relaxation (Kroener, Diergarten, Diergarten, & Seeger, 1988). Under demanding or undesirable conditions, heart rate, blood pressure, general arousal and negative affect have been found to increase (Frith, 2002). These in turn have been linked to increased aggressive behaviours in a variety of settings, such as crowding, excessive noise, exercise, and traffic congestion (Wiesenthal, Hennessy, & Totten, 2003). Fast background music during PC based simulations increases the heart rate (indicating higher levels of physiological arousal), decrease simulated lap time (indicating accelerated speeds), and increase the amount of virtual traffic violations (indicating reckless behaviours), in comparison to usage of slow-paced, medium-paced or no music in the background (Brodsky, 2002). Furthermore the extent to which the tempo of music heard during vehicular driving contributes to information processing failures (Brodsky, 2002). However, music has been found to reduce both physiological and psychological stress and arousal, particularly when it is preferred or self selected musical varieties (North & Hargreaves, 1999). The ability of music to reduce these processes in the driving environment should also lead to decreased driver aggression. Research has shown that music does require mental effort to listen to, more than most other sounds (Brodsky, 2002). This study was started with the following hypotheses. It was assumed that music affects the mood or behaviour of the driver. Slow music has a calming effect on the driver and increases his/her concentration. Whereas fast, loud or no music has a negative impact wherein the effect of no music was considered to be more subtle than the violent or dangerous possibilities of fast music. This study attempts to provide either proof to support or refute the above assumptions. While the study exclusively highlights PC-controlled simulated driving, it is clear that the on-the-road inaccuracies involving perceptions of velocity could have serious implications. Materials and Methods There were two experiments conducted Task A and B, in as conducive environments as possible to put the varied age group of participants at ease. There were prerequisites that the subjects had to meet before the start of the experiment. All the subjects signed a form confirming their willingness to participate and also confirming that they hold a valid driver’s license. None of the participants were below the age of twenty. Participants were then handed over two questionnaires (The Eysenck personality questionnaire and a general questionnaire). The Eysenck questionnaire was used for an analysis about ‘extraversion-introversion’ and the general questionnaire for personal details about age range, gender, profession, preferences in genre of music while driving and generally, years of driving experience, preference of tempo, their opinion about their driving behaviour, frequency of their driving mishaps and the reason for their use of music while driving. These data were tabulated and their means found out for statistical calculations. Table 1: Subject Factors - Gender They were then divided into three groups depending upon their preference of tempo in music as Slow Music, No Music and Fast Music. The ‘No music’ group is the control. Then each of the subjects was given a choice to choose from a few choices of Genre that he/she preferred. Table 2: Subject factors - Genre After he/she chooses his genre they are given a set of instructions that they have to follow as they are directed towards their respective places to undergo a simulated PC based driving test. Their reactions, decisions to on-screen questions, reflexes, reaction timings, driving mistakes made etc, are recorded as two different experiments. Each subject was given a one-minute test run without any music for him/her to get adjusted to the controls and equipment. This test run data was not used in any form for the results of this study. Music was played belonging to the genre chosen and the tempo set according to the subject’s choice on an Aiwa stereo with two speakers directed towards the subject. Analysis The first experiment is related to the speed maintained by the subject during the simulation, and his tendency to accelerate etc. This is also called Task A. The duration of the drive is about five minutes. In such a case that the game ends prior to the stipulated time due to some errors committed by the subject, the task is restarted until the five minutes are fully utilized. The second experiment or Task B involved a similar simulated drive but with on-screen prompts to determine the tendency of the subject to drive rashly by overtaking. The duration is five minutes and the subject has to repeatedly perform the test until he utilizes the whole period of five minutes. All the simulations and music works were preloaded into the laptops used by the subjects. The data was collected and stored in the SPSS software compatible with the Windows platform. After the subject undergoes both the tasks he/ she return to the adjoining room to leave the surroundings. The results of the study were forwarded to the subject if he or she willing asked for them at the forwarding address left with us. The recorded data was later analysed and compared to obtain relations between various factors like Gender/Tempo; Genre/Tempo. The results are tabulated as below. Two One- Way ANOVA’s were taken for Task A and Task B with the tempo of the music chosen. The ANOVA was performed using the SPSS tool. The output table is divided into between group effects (effects due to the experiment) and within the group effects (this is the unsystematic variation in the data). The sum of squares and mean squares represent the experimental effect. The row labelled within group gives details of the unsystematic variation within the data. The table tells us how much unsystematic variation exists (the residual sum of squares, SSR). It then gives the average amount of unsystematic variation, the mean squares (MS). The test of whether the group means are the same is represented by the F-ratio for the between-group effect. Finally SPSS tells whether this value is likely to have happened by chance. The final column labelled sig. indicates how likely it is that an F-ratio of that size would have occurred by chance. Social scientists use a cut of point of 0.05 as their criterion for statistical significance. However, at this stage we still do not know exactly what the effect was because we do not know the groups that differed. Experiment 1: Task A: From the above data we can notice that since the sig. value is greater than .05 (.082) it means that there was no significant effect on speed. If the value was less than .05 then we could have assumed a significant effect and gone on for a trend analysis to determine which groups differed. Experiment 2: Task B: From the above data we can notice that since the sig. value is greater than .05 (.222) it means that there was no significant effect on overtake. If the value was less than .05 then we could have assumed a significant effect and gone on for a trend analysis to determine which groups differed. The basic GLM output includes two tables. The first table is a list of the between subjects factors shown above a stable 1 and 2 and the ns for each level of those factors. Paste the descriptive statistics table here. The second table, the descriptive statistics gives the means, standard deviations, and ns for each between-subjects cell in the design. As you go across the table the first variable displayed is the type of genre/gender options available, the second variable displayed is the tempo level of music. Because of this organization the means for each level of tempo are shown within each of two genre/gender options. The means displayed in the total summary at the bottom of the table are the main effect means for genre/ gender option. This order was determined when the factors were initially selected in the dialog box. In this instance the genre/ gender factor was listed first. The main effect means can be found in the rows identified as Total. The third table contains the results of the analysis of variance. It includes the sums of squares, F values, and significance levels. Paste the tests of between subjects factors here. The intercept term in this ANOVA is a test of whether the grand mean is different from zero. Because all the dependent variable scores are positive the grand mean is different from zero. Therefore the test of the intercept is not of interest to us. The corrected model, with 5 df, is the overall model. It includes the variance due to the two main effects and the interaction, hence the 5 degrees of freedom. When the design is balanced, that is when there are equal ns in each cell and there are no missing cells, the sums of squares for the corrected model will be sum of the sums of squares for the each of the main effects and interactions. In this case SS corrected model = SSdrive + SSreward + SSdrive*tempo The sum of squares for the corrected total is the sum of the sums of squares for the corrected model and the error terms. In psychology it is unusual to report statistics for the corrected model. We are typically interested in the main effect and interaction effects rather than the model as a whole. The sum of squares for the total is the sum of the sums of squares for the intercept, the main effects, the interaction, and the error term. The statistics for the within cells error term is reported in the Error row.  This mean square error is used to test the main effects and the interaction. The Estimated marginal means option was used to display the means and the profile plots draw a graph with these estimated marginal means to depict the pattern followed by these means. In addition to the means, the table also displays the standard error and the 95% confidence intervals for each mean. Four Estimated Marginal Means were done and their graphs drawn. The first Task A (speed), the tempo of music, and the gender of the subjects. The second was Task A (speed), the tempo of music, and the genre chosen by the subjects. The third was for Task B (Overtake), the tempo of music and the gender of the subjects. The final was for Task B (Overtake), the tempo of music and the genre chosen by the subjects. The data is tabulated as follows. The graphs are also shown. Paste all the profile plots and Estimated Marginal means tables here. The means can be displayed graphically using the Profile Plots. The vertical axis of a profile plot will always be the dependent variable. The horizontal axis of the plot can be chosen as anything. Once a significant F-value is obtained it provides a scope for some significant effect to have been there. The numbers have to be observed even more carefully to say exactly if there was any significant difference or not. Hence we used post-hoc tests. The most widely used post hoc test in Psychology and the behavioral sciences is Tukey’s Honestly Significant Difference or HSD test. Each group of subjects were compared with all the remaining groups. For each pair of groups the difference between group means is displayed, the standard error of that difference, the significance level of that difference and a 95% confidence interval. Paste the table here: Multiple Comparisons with gender and task A and task B (2 tables) From the above data we can notice that the sig. value is greater than .05 when compared with any of the groups which means that there was no significant effect. If the value was less than .05 then we could have assumed a significant effect. Paste the table here: Multiple Comparisons with genre and task A and task B (2 tables) From the above data we notice that there the sig. value is less than .05 in only one case, when ‘easy listening’ group is compared with the ‘top 40’ group and in the rest it is greater than .05. This result goes against the hypotheses we started with. There has not been much significant difference reflected till now. Thus we can see that since there was no significant difference to be found with any of the methods we reach a conclusion of a ‘null hypothesis’. Thus none of our hypotheses were proved true. Music does not seem to have any effect on the behavior of the driver. Discussion and Conclusions The purpose of this study is to determine the effects of music tempo on speed and the tendency to overtake in a PC-based driving simulation. When the results were analyzed in a one-way ANOVA there appeared to be no significant difference between the groups or interaction between the factors. The music had no apparent effect on drivers’ performance. This is probably because of the subject’s age variance and the huge range of ability which masked any influence from the independent variables. Because there were no differences between any of the groups, the research hypothesis was not supported. The findings of this study disagree with Brodsky’s (2002) findings that an increased tempo increases the speed and number of collisions of the driver, because the music apparently had no effect. The differences in findings are most likely a result of the limitations of the study. The fact that subjects knew that they were being judged might have changed their attitude and usual habits, but the greatest limitation was that the subjects varied so much in ability, which completely masked any possible effects of the music. A suggestion for a future study would be one testing the same conditions of music but instead let the subjects each take a much longer test, during which they will be exposed to all of the different conditions in random order. By finding the level of performance for each subject at under all conditions, perhaps a trend would be more likely apparent. It would eliminate some of the variability resulting from the huge gaps in driving competence between different individuals. Observing the subjects, it appeared that they were also hindered by their lack of familiarity and limited learning time for the simulation. As long as the conditions were in random order, a study more like Brodsky’s would give each subject more time with the program. Acknowledgements This manuscript is based, in part, on a study conducted by a group of seven students as partial completion of the Bachelors degree requirements of York University. The research was supervised by Professor David Wiesenthal and ssupported by RW (provided a DVD driving simulation) / ND (provided a personal questionnaire, helped the selection of music of “juke-box”), DM (Technical supports of making DVDs & CDs). References Brodsky, W. (2002). The effect of music tempo on simulated driving performance and vehicular control, Transportation Research Part F, 4, 219-241. Frith, Simon.(2002). Music and everyday life. Critical Quarterly, 44(1), 35-48. Furnham, A & Allass, K. (1999). The influence of musical distraction of varying complexity on the cognitive performance of extraverts and introverts, European Journal of Personality, 27-38. Horswill, M.S., & McKenna, F.P. (1999). The effect of interference on dynamic risk-taking judgments. British Journal of Psychology, 90, 189-199. Jancke, L., Musial, F., & Vogt, J. & Kalveram, K.T. (1994). Monitoring radio programmes and time of day affect simulated car-driving performance, Perceptual and Motor Skills, 79(1), 484.. Kroener, H. B., Diergarten, D., Diergarten, D., & Seeger, S. R. (1988). Psycho-physiological reactivity of migraine sufferers in conditions of stress and relaxation. Journal of Psychosomatic Research, 32, 483–492. North, A. C., & Hargreaves, D. J. (1999). Music and driving game performance. Scandinavian Journal of Psychology, 40(4), 285-292. ScienceDaily Magazine (2000). New study shows for the first time how thinking can impair driving [Online]. Wiesenthal, D. L., Hennessy, D. A. & Totten, B. (2000). The influence of music on driver stress, Journal of Applied Social Psychology, 30, 1709-1719. Wiesenthal, D.L., Hennessy, D.A., & Totten, B. (2003). The influence of music on mild driver aggression, Transportation Research Part F 6(2), 125-134. Read More