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The present study pragmatically examines the relationship between arousal and cognitive attention. Motivation has become paradigmatic for much research and scholarship, but the intricacies surrounding motivational factors arousal are yet to be fully understood. This is an obviously broad area, and it is not within the scope of this paper to focus on every single motivational theory. Instead, the present paper explores Arousal Theory and the Inverted U hypotheses. These seem to complement each other very well and provide a useful framework for both analysis and interpretation of present findings.

Understanding Human Motivation

The impetus to understand human motivation is a topic which can be traced back to ancient Greece through the work of Plato (Cooper, 1984). In this respect, in his Republic writing, Plato proposes that personality is made of three key components including reason, spirit, and appetite. In his views, it was the latter aspect of personality (i.e. appetite) which played a key role on individuals' motivation (Cooper, 1984). In turn, motivation is often defined as the enthusiasm, incentive and interest which drive a person towards adopting a given type of behaviour or action (Madsen, K. B. (1974; Steel & Konig, 2006). In this way, it should be evident that motivation is a human attribute that contributes towards the individual degree of commitment to a task or behaviour (Stoke, 1999). It is also worth mentioning that motivation can be classified into two types including; (1) intrinsic motivation, and (2) extrinsic motivation. Thus, the former type of motivation refers to when people engage in a any given activity for personal pleasure or desire, whereas the latter refers to when external factors lead the individuals act in a certain way (Lionel, 1994). It follows that, motivation as a concept has led to development of a wide range of theories of motivation and factors associated with it. These are discussed in more detail in the subsequent paragraphs.

Arousal Theory

For instance, among theories of motivation one of the most prominent is known as Arousal Theory. This theory holds that individuals engage on a particular form of behaviour in order to increase or decrease their levels of arousal (Weiner, 2013). Thus, one of the basic assumptions of Arousal Theory is the fact that environmental factors may influence the brain's level of arousal (Peters, 2015). A considerable amount of research and scholarship has demonstrated that Arousal Theory is a useful framework to explaining the ways in which individuals undergo different levels of arousal brought about through a variety of experiences (Deckers, 2015). It follows that, when levels of arousal are low, individuals may feel bored and thus engage in activities which will increase their arousal levels (Strombach, Strang, Park & Kenning, 2016). Similarly, when arousal levels are too high, such as in the case of being very anxious and overstressed, one my resort to engaging in relaxation methods (this may involve meditating, reading a book, or getting a massage [Deckers, 2015]). It has been postulated that maintenance of an optimal level of arousal is the key for providing the main motivation for the individual (Corbett, Swain, Newsom, Wang, Song & Edgerton, 2014). However, one should note that optimal levels of arousal may vary from one individual to another as some people may be natural thrill seekers and would require intense emotional, physical, and intellectual activities to make them feel happy (Strombach, Strang, Park & Kenning, 2016). In contrast, other people may prefer low levels of arousal and engage in activities such as taking an afternoon nap, watching TV, or reading a book (Peters, 2015). In this way, Arousal Theory proposes that, ultimately, individuals seek to achieve an optimum level of arousal in order to perform to our best capacity (Peters, 2015).

Inverted U Theory

Another prominent theory of arousal is commonly known as inverted U theory, and was developed by Yerkes and Dodson in 1908 (Kerr, 2014). Similar to Arousal Theory, this theory also holds that optimal performance occurs when the performer reaches an optimal level of arousal (Kerr, 2014). More specifically, it would appear that individuals will improve their performance as arousal increases until it suitably reaches a point in which optimum performance has been reached, whereby arousal is at its optimal level (Kerr, 2014). In addition, one should also note that when arousal increases beyond the point of optimum level, performance begins to deteriorate (Shih & Lin, 2016). However, this theory attracted a number of criticisms from some scholars who argued that this theory fits into observations from sports performers, but in fact it may be too simplistic to adapt to the understanding of whether or not U theory applies equally to expert performers and beginners (Shih & Lin, 2016). Furthermore, one should also note that levels of arousal may also vary according to the skill set required to perform a given task (Shih & Lin, 2016). For instance, in sports, performers who engage in activities which incorporate major muscle groups may benefit from higher levels of arousal (Sapolsky, 2015). In contrast, performers that involved in activities which are low physical demanding, and only involve finer skills (e.g. snooker, darts), may benefit from lower levels of arousal (Sapolsky, 2015).

The present study examines the ways in which levels of arousal can be affected by white noise. More specifically, it has been hypothesised that white noise levels have an impact upon physiological measures of heart rate and galvanic skin response. This experiment was focused on a non-clinical sample, and aims to gain useful insights into understanding the relationship between optimal within university student settings.



A sample of participants (N=37, male= 6, female= 31) was drawn from a population of university students. Participants belonged to a non-clinical sample and had normal or corrected to normal vision.


The present study adopted a repeated measures experimental design. Hypothesis:

Independent Variable (IV): White Noise Levels

Dependent Variable (DV): Measures of Heart Rate and Galvanic Skin Responses

The present study articulated the following hypothesis:

[H.sub.1]--White Noise levels have an impact on measures of Heart Rate and Galvanic Skin Responses.

Statistical Analysis

Data was analysed with IBM SPSS statistical software package (version 23). Statistical analysis was divided into two key parts including (a) descriptive statistics, and (b) inferential statistics. In addition, prior to carrying the inferential statistics normality tests were conducted as means of ensuring that required assumptions for carrying out normality tests were not violated. This has also helped determining which type of test would be more appropriate (i.e. parametric or non-parametric tests) for analysing current data. Moreover, Criterion a (alpha) level for statistical significance of relevant tests was set at p[less than or equal to] 0.05 for ANOVA tests.


Descriptive Statistics

It should be noted that, as prescribed by Kim (2013), when the values of skewness and kurtosis are between -1.96 and 1.96 the assumption of normality can be accepted. Initial tests (i.e. Skewness and Kurtosis) were carried out in order to find out if average response times, accuracy, and confidence scores satisfied prescribed assumptions required for parametric tests. The above table shows that satisfied this assumption as skewness and kurtosis values are between -1.96 and 1.96 (see Table 1).

Inferential Statistics

Measure for white noise - GSR

Mauchly's test indicated that the assumption of sphericity had been violated, [X.sup.2](2) = 37.83, p <.001, therefore Greenhouse-Geisser corrected tests are reported. The results show that the level of white noise did not have a significant effect on Galvanic Skin Response., F (1.20, 43.36) = .75, p = .41.

Measure of white noise--HR (heart rate)

A further analysis was made using ANOVA, to measure the level of heart rate: Mauchly's test indicated that the assumption of sphericity had been violated, [X.sup.2](2) = 13.97, p = .001, therefore Greenhouse-Geisser corrected tests are reported. The results show that the level of white noise did not have a significant effect on Heart Rate, F(1.51, 54.18) = 1.20, p = .30.


The present study examined the effects of heightened levels of white noise upon galvanic skin response and heart rate. Results from ANOVA tests indicate that there was no relationship between white noise levels and heart rate, and galvanic skin responses. Thus, the hypothesis that white noise levels have an impact on measures of Heart Rate and Galvanic Skin Responses (H1) was rejected in favour of the null hypothesis (H0). Previous studies (see Strombach, Strang, Park & Kenning, 2016) have found that individual and group differences may explain distinct levels of stimulation required for reaching optimal levels of arousal. In this way, although counterintuitive, the present results seem to suggest that the student population may require a much higher level of stimulation in order to achieve optimal arousal.

However, one must acknowledge some limitations with the present study. For instance, participants were only trained how to use the equipment a week before. Participants were not familiar with the equipment and therefore results may have been partially distorted as they do not know how to use it properly.


The present findings may seem to indicate group and individual differences matter when it comes to understanding optimal arousal. In this respect, one should notice that some people may need substantially more stimulation to feel aroused than others. In particular, students may require much heightened levels of stimulation in order to feel aroused to optimal levels. These findings may have important implications for policy making and development of university programmes which foster engagement, and stimulates motivation, and optimal academic performance.


Cooper, J. M. (1984) Plato's Theory of Human Motivation. History of Philosophy Quarterly. 1,(1), pp. 3-21.

Corbett, B. A., Swain, D. M., Newsom, C., Wang, L., Song, Y., & Edgerton, D. (2014). Biobehavioral profiles of arousal and social motivation in autism spectrum disorders. Journal of Child Psychology and Psychiatry, 55(8), 924-934.

Deckers, L. (2015). Motivation: Biological, psychological, and environmental. Psychology Press.

Kerr, J. H. (2014). Motivation and emotion in sport: Reversal theory. Psychology Press.

Madsen, K. B. (1974). Modern theories of motivation: A comparative metascientific study. Oxford: John Wiley & Sons.

Peters, R. S. (2015). The concept of motivation. Routledge.

Sapolsky, R. M. (2015). Stress and the brain: individual variability and the inverted-U. Nature Neuroscience, 18(10), 1344-1346.

Shih, H. H., & Lin, M. J. (2016). Does Anxiety Affect Adolescent Academic Performance? The Inverted-U Hypothesis Revisited. Journal of Labor Research, 1-37.

Steel, P., & Konig, C. J. (2006). Integrating theories of motivation. Academy of management Review, 31(4), 889-913.

Strombach, T., Strang, S., Park, S. Q., & Kenning, P. (2016). Common and distinctive approaches to motivation in different disciplines. Progress in Brain Research, 229, 3-23.

Weiner, B. (2013). Human motivation. Psychology Press.
Table 1--Participant Demographics

               Women           Men
               (N=31)          (N=6)
               M (SD)          M (SD)

Age            23.13 (5.92)    25 (11.35)
Silent GSR      4.44 (6.76)        18.77 (18.46)
Low GSR         5.21 (5.60)        22.95 (21.58)
High GSR        5.47 (7.23)        11.27 (6.19)
Silent HR      79.70 (10.47)       83.97 (13.75)
Low HR         84.06 (19.79)       84.32 (14.67)
High HR        85.09 (20.63)       72.49 (35.57)
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Article Details
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Author:Mueller, Mathias
Publication:Journal of Social and Psychological Sciences
Date:Jan 1, 2015

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