You Are the Music: How Music Reveals What it Means to be Human (14 page)

Psycho-physiological arousal

Arousal is an umbrella concept that describes your level of physical and mental alertness at any one moment. The most cited scientific model that relates everyday task performance to psycho-physiological arousal is the Yerkes–Dodson (Y–D) curve from 1908.
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This model and accompanying theory postulate that each person’s ‘base’ level of psycho-physiological arousal is somewhere on this upside down U-shaped curve. Some people have a naturally high level and some people have a much lower level; the peak of the curve is the optimum where we are all able to achieve our best performance.

Music is an agent that can increase (push us towards an optimum) or lower (push us over the top) our position on the Y–D curve depending on the characteristics of the music, such as its complexity, volume and familiarity.

The effect of music on psycho-physiological arousal is one mechanism proposed to explain the so-called Mozart effect, whereby listening to music can temporarily improve performance on a spatial task. In studies of performance, the Y–D curve is often cited as an important factor that explains why, for example, fast and loud music can have a detrimental effect on reading comprehension compared to slow, quiet music:
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the fast and loud music is more likely to push you over your optimum level of arousal and performance will consequently fall down into the area of weaker output.

Cognitive engagement

Cognitive engagement describes the level of attention that we are able to devote to a task. An optimal state of cognitive engagement is a ‘flow state’ where motivation is focused
on the job at hand and task performance is optimised.
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One recognisable characteristic of such a state is that we tend to lose track of time.

Music has the potential to contribute to a flow state partly in relation to psycho-physiological arousal (which is tied to attention) but also by the nature of our limited attention system. In theory we have a certain amount of attention that we can devote to tasks; if we try to do too much then we experience anxiety and if we use too little then there is the danger that we drift off into boredom. In effect, music can soak up that extra unused attention during a simple task that may otherwise contribute to boredom and a subsequent sudden need to surf the net or call a friend.

Mood

The effects of music on mood are well recognised (as seen in Chapter 3, and as we shall see again in Chapter 6). In relation to studies of task performance, music mood associations have also been called upon to explain findings such as the Mozart effect, as upbeat music that improves mood results in better task performance compared to slow, sad music that depresses mood.
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Mood and arousal are often seen to go together but it is possible for them to diverge; for example, you can be in a positive mood and either very relaxed or very alert. Therefore it is important to consider both arousal and mood as independent agents that can be influenced by background music.

These three agents, arousal, engagement and mood, all play a role in our response to background music individually and in combination. ‘Smart music listening’ is a balancing act that requires a person to gauge their own level on each of these factors and then to assess the demands of the task at hand. Smart music listening can then add a positive contribution to a task when it is used to adjust any or all of the factors towards an optimum for performance.

Even when we take into consideration general influences of arousal and mood, music does not impact in the same way on everyone. We need to consider the influences of personality, preference and choice.

Personality

The last thing I did as part of my honours degree, back in 2003, was to design my very first experiment. I wanted to understand why we choose to listen to music in certain situations; the problem was that I had no idea where to start. One evening I came home from university to find my boyfriend of the time engrossed in a computer racing game. I slumped down on the sofa and watched his progress impassively for a while, glad of the distraction from my thesis dilemma.

Then an idea struck. Before each race, my other half was taking time to choose the music he wanted to hear. I asked him why. He said that the automatic music selection within the game software was ‘rubbish’ so he always had to select something more engaging. Was it because he didn’t like some of the music? No, it was all pretty good, but some of it was more ‘effective’ than others. He believed that modulating the music level would improve his performance. I decided then and there – that was my thesis.

I think my professor was rather bemused the next day when I announced that I needed a room decked out with a sofa and a computer game console for my first experiment. Nevertheless we managed to obtain a TV from the home studies team and my other half kindly parted with his PS2 for four weeks.

The next step was to get some music. I decided to remain as true to the genre as possible and contacted computer game music composers. Many were kind enough to donate tracks for my experiment and I gathered in people to help me pre-rate the music for levels of excitement, complexity,
engagement and variety. My aim was to select some low, medium and complex music in order to see if these different levels really had an effect on the way that people raced during a computer game.

On top of this I planned to have two upper levels where I added first the lyrics and then all of the game sound effects on to the most complex musical condition. Including the silence condition at the bottom, I now had six increasing levels of musical sound complexity in my computer game experiment.

During this design phase I came across an intriguing finding that changed one vital aspect of my experiment. Apparently, people with different personalities responded in a different way to background music. In particular I was struck by an experiment conducted by Adrian Furnham and Anna Bradley on the effects of background music.
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They were interested in the influence of one personality trait: extraversion.

In 1967 Hans Eysenck
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stated that extraverts and introverts differ in their level of cortical arousal, with extraverts having a lower baseline level than introverts. Think of this baseline as being like an internal source of stimulation or ‘psychophysiological arousal’, as I described in the last section.

You will remember that we all have an optimal level of arousal (the peak of the Y–D curve) where we function at our best, and because of this difference in baseline levels, introverts and extraverts differ in how much more they need to get to that optimum. Extraverts are naturally lower down on the left-hand side of the Y–D curve and introverts are comparatively higher up.

As a result of their higher level of internal arousal, introverts seek out less stimulation from the external world, preferring quieter and more familiar environments. They are already closer to their optimum point so do not need too much more before they are pushed over the edge into the area of poorer performance. Extraverts on the other hand, with
their lower level of internal excitation, seek out stimulation, such as daredevil activities. They have a longer way to go to reach their optimum. Furnham and Bradley drew a clear prediction for their study of background music: extraverts should benefit from more musical stimulation whereas introverts probably won’t.

The participants in the Furnham and Bradley study listened to three upbeat pop songs (‘Sowing the Seeds of Love’, Tears for Fears; ‘New Sensation’, INXS; and ‘Strange Brew’, Cream) while completing memory and reading tasks. As expected, the introverts had poorer memory for the things they had observed when music was playing compared to the extraverts. The introverts also had a lower score on the reading task when it was carried out in music compared to silence. Finally, the introverts reported that they were less likely to work with the radio on at home, listened to the radio less in general and found it more distracting during the experiment.

When I read this finding as an undergraduate I rethought one aspect to my computer game experiment. Maybe my partner was an extravert. It made sense on an intuitive level: he was certainly much more comfortable in a crowd or at a party compared to me, and to my horror he had recently begun to talk about skydiving. Maybe his desire to pump up the level of the music in his computer games at home was being driven partly by his personality. So I opted to test the impacts of game music on groups of introverts and extraverts.

My experimental findings replicated the Furnham and Bradley study result nicely: my introverts did worse in the most complex music conditions compared to the extraverts. Not only this but my extraverts showed a clear improvement as I pumped up the musical complexity. Introverts on the other hand got better up to about the middle level of complexity and then their performance started to go down as the level increased.

My experiment and that of Furnham and Bradley show clearly that background music complexity has a different effect on people’s task performance depending on their personality.

There is, of course, another explanation. Because extraverts tend to listen more to music in their everyday lives we can’t rule out the possibility that all we did in these studies was create a more familiar environment for them.

Whether you buy the psycho-physiological response theory or another explanation, these studies and others conducted since
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have shown that personality can influence task performance in the presence of background music.
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Going back to our initial workplace question, the implication of these results is that unless you can guarantee that all the people in a work space have a similar personality then you are unlikely to be able to select music that has a positive effect on everyone.

Preference and choice

In the early factory studies of workplace music there is often no description of the music heard other than a broad category such as ‘dance hall’. And a fair few modern studies do not compare different kinds of music, preferring instead to simply compare the difference between music and silence. We know from discussions in earlier chapters that this type of research design is limited as it tells us nothing about music in particular – it only informs us about the effect of doing something other than nothing. For all we know you may get a similar effect if you pipe in an audio book, the shipping forecast or animal noises.

If we are going to work out
how
music impacts on productivity and performance at work as opposed to other types of sounds then we need to explore what
kinds
of music have an influence on our psyche at work and why. In fact, personal preference and choice over music selection may be the most important influences of all.

A lack of choice and control over the music that we hear is frequently cited as one of the main reasons for a negative appraisal of that same music.
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Simon Frith likened this adverse reaction to a social response to territory marking. In this sense, background music is an overt display that someone else has control of the space where you find yourself; this display can really irritate us.

Back in the lab, Joseph Parente found that people completed an attention-demanding test better in the presence of their preferred music than in the presence of their least preferred music.
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Equally, more recent studies of music preferences and the effects of self-selected music have re-emphasised the importance of music preference and familiarity in everyday listening.
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So why don’t scientists take more care with personal preferences when selecting music for experiments? Simply put, if researchers allowed people to bring in their own music to play during a test then people would likely bring in so many different varieties (slow, fast, loud, soft, all manner of genres) that it would be impossible to control the situation in the way that science demands.

The reality is that even if I did a huge study and found the most effective piece of background music for work then it would still have little positive influence on task performance if you hated it. So ‘smart music listening’ must take into account individual preferences and, ultimately, give an individual as much perceived control over the selection as possible.

The bad news

The studies above make it clear that background music can help some people, some of the time, when we apply ‘smart music listening’. We must not overlook the fact, however, that sometimes our performance does not benefit from music, whether the task is a driving game
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or one that draws on vigilance,
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memory
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or writing.
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How do we reconcile these positive and negative results? In 2011 Juliane Kämpfe and colleagues carried out a large overview of studies into background music.
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She found nearly 100 suitable studies that were classified into a positive or a negative effect of background music on performance. The studies were also classified based on the type of performance they measured: ‘mundane behaviour’ (eating, driving), ‘cognition’ (reading, maths), or ‘emotion’.