The vulnerabilities of the human mind have deep ties to the realm of human hearing and shed light on the inner workings of the human subconscious. Behavior is a multifaceted concept that can be divided into three core parts – emotion, decision-making, and performance. Although closely related, these three aspects lie on separate parts of the reaction spectrum, falling within external physical reactions and internal emotional reactions. Emotional influences have an almost complete lack of physical manifestations and are the most difficult behavioral changes to track. Human interpretations of emotion are necessarily subjective, and efforts to quantify data about emotions relies on the thorough and accurate introspection of the subjects in question. Alternatively, decision making draws from both emotional reactions and the resulting physical actions. Arbitrated by mood, this branch of behavior has emotional components that can be determined through an analysis of the sonic environment before the decision and the very nature of a decision made within that environment. At the physical extreme of the reaction spectrum sits performance, focusing on accuracy and precision as quantitative functions of a sonic environment. As a retrospective measurement of emotional and decision-making effects, performance evaluations offer concrete data about sound impacts during cognitively intensive tasks. In essence, the three stages of behavior follow each other in a natural progression, dealing with subconscious influences of sound, conscious choices made under those influences, and the physical consequences brought on by those choices.
The emotional effect of sounds plays a crucial role in the understanding of human reaction, yet for the scope of a research project the term “emotion” must be comprehensible and mathematically natural. Researchers like Didier Grandjean have simplified the complex task of researching emotions by employing a survey of descriptive words for data gathering. He believed that “some emotions may be more felt than acted on, and these emotions may not have obvious behavioral, expressive, or physiological manifestations,” so feelings “thus identified include emotions with behavioral or physiological manifestations, without excluding those emotional states that may not have these overt expressions but still represent highly characteristic reactions” (Grandjean 2008). Given the elusive and at times indefinable nature of emotion, qualitative studies of emotional reactions can be concrete only to the extent defined by researchers like Grandjean and often contain data supplied subjectively by test subjects. The genres of music offer a fundamental introduction to categorizing emotional influences of sound. An experiment by Krumhansl published in 1997 details an experiments where college students listened to classical music selections that expressed sadness, happiness, or fear. During this study, “all music, regardless of which discrete emotion was represented, resulted in decreased heart rate and increased breathing rate and blood pressure” as well as “an aggregated decrease in sympathetic system measures (i.e., skin conductance level and finger temperature)… because of increases in comfort, familiarity, and even boredom” (Carpentier 2007). The lack of negative emotions when listening to music remains consistent among experiments; many indicate that “various kinds of positive emotions can be both aroused by music and perceived by music,” and “music rated as fearful or sad still tends to produce positive affect” (Grandjean 2008). The surge of positive emotions experienced when listening to music of negative emotions may seem counterintuitive, but the relationship is grounded on the ability of the listener to relate to the music being played. A study on why people listen to sad music when feeling stressed led to the term “self-identified sad music” and it can be inferred that the positivity of a song has more to do with how a person relates to the music than the genre of the music itself (Edwards 2011). If a listener in his or her saddened state cannot relate to happy experiences relayed by a song, the effect of the song is lost. However, a feeling of connection to a sad song that relates to how the listener is feeling can add a sense of belonging and establish an emotional connection to the message of the song itself. The study by Jane Edwards expands on this hypothesis with a model of song type and effect as seen below:
Interestingly enough, the overt human tendency to seek comfort through a relationship to negativity as opposed to trying to change through positivity speaks to the human mind’s focus on itself over its environment. Regulation of emotions is strongest when the personal needs of belonging are met and a connection is felt. Further studies on this subject will likely indicate that humans get progressively less pleasure from positive things in the environment if they have an initial state of sadness, but get more pleasure when the environment’s events mimic their own emotions. Since finding the right emotion for a listener has such a strong impact on their experience, many music companies including Spotify and iTunes have adopted models for categorizing media. One prominent example is the Tellegen-Watson-Clark model (seen below), which has found widespread acceptance in the classification of emotions in not only music but also television shows, movies, and other kinds of media.
Decision-making has a close correlation to mood and performance but stands at the cross-roads of physical action and emotionally-driven thought. The independent assessment of decision-making as a function of sound exposure is therefore a crucial component to understanding behavior and the role of sound within it. A study by Cohen (1984) tried to understand the effects of sonic environments on human cooperation and state of mind. During the span of the study, researchers generated car horn and construction noises and then acted in scenarios where they dropped parcels, requested interviews, and asked for spare change. As the environment became more crowded with urban noises, people were shown to decrease their cooperation while walking along a street. These results were confirmed in a similar study involving the request for participation in a survey, where participants not only displayed less cooperation but also had increased walking paces in crowded areas (Boles 1978). Alternatively, strangers in rural, relatively quiet environments partook more willingly in interviews, alerted strangers to objects they dropped, and looked for spare change to donate (Cohen 1984). In general, increased urban sounds lead to higher levels of noncommittal behavior in subjects.
The difference in cooperation between soundscapes was most likely due to the rate and intensity of high- and mid-tier frequencies. As explained by researcher Flindell (2014), low frequencies are “felt,” but the middle and high frequency ranges are truly processed by the brain and can have powerful psychological impacts. One key impact is subconscious stress, which the body can express with the common feeling of annoyance. In fact, the relationship between high frequencies and mental stresses has natural links that could be explored further. The brain stimulates the body for energy-intensive activities through high frequency signals as seen in the Brain Waves Graph.
External sounds with high frequencies could force the brain to process such sounds with a higher level of activity or energy, leading to stress, annoyance, and consequential desire to leave the sound environment. Such conclusion coincides with the result of the UK RANCH study and its follow-ups, where children growing up in high-intensity airport noise environments experienced uncommon levels of annoyance towards noise and even showed lower academic performance (Clark 2013). The intensity of a soundscape also plays a key factor in decisions and could similarly induce higher stress levels as it increases. Intent on studying the effects of sound on behavior, some researchers gave shocks to random subjects in urban environments with two noise levels: 55dB and 95dB . When given the option to retaliate, subjects in the louder environment expressed greater aggression by giving a greater number of shocks at higher intensities (Cohen 1984). The recurring link between high-energy noises and noncommittal behavior generates the following profound behavioral chain: An external high-energy noise induces stress in the brain, the body experiences a negative mood shift, and the listener experiences a desire to avoid any external, unexpected, and cognitively demanding task within that environment.
As an evaluation of emotional stability and decision-making efficacy, performance stands out as the most concrete, physical manifestation of behavior. The idea that background sounds can either impair or promote engagement in a task has been historically accepted, but the degree of engagement that sound can provide has garnered strong scientific interest. From developing more immersive games to improving working environments, work-environment quality depends on the control of disruptive noises and the promotion of helpful sounds. The audial engagement and learning performance for players in a progressive, task-oriented game sees significant improvement when the in-game characters are given voice-over recordings (Byun 2014). Though sound can induce an improvement in cognition through assistive audio, noise can distract at both conscious and unconscious levels of human thought processes. As a task becomes more cognitively demanding, the vulnerability of the task performer to external noise interruption grows. Naturally then, the performance of thirty three undergraduate students “did not differ significantly” when detecting spelling errors but did have differing results in identifying grammatical errors (Weinstein 1974).
With an understanding that noisy environments impair physical performance, I internalized the process of behavioral analysis by developing my own case study. Following up on research in cognitive function measurements, I decided to focus on memory capacity as a function of noise. With a twenty-four person sample, I expected to lay the ground work for a larger and more comprehensive study. Through research, I learned that noise impairs cognitive function and that mid-range to high-range frequencies have the greatest impact on behavioral thought. My hypothesis thus developed as follows: memory function will be incrementally impaired as the intensity of a high-frequency noise interrupts a cognitively-intensive task.
Randomly assigning my sample of twenty four subjects a noise intensity – Zero, Medium, High – I used a personal audio recording of the Duke University fire alarm, accessible below.
This type of audio perfectly aligns with the extreme case of a high frequency noise environment, best indicating if such an environment affects cognitive abilities. The intensities were standard 0dB, 10dB, and 20dB increments from the natural sonic environment (about 50 dB) and measured using the Decibel 10th meter application.
Once assigned an intensity, the subjects watched a short video of twenty objects, from which I asked them to only focus on the first ten as they watch the whole video. The purpose of this was to test their long term, processed memory as opposed to short-term, regurgitated memory. The video is only a little over a minute in length:
A bar graph for the results of the experiment is available below, colorized based on sonic environment. As shown in the data, the subjects were able to memorize a minimum of four objects and a maximum of seven objects, but the distinction between performances is not readily apparent.
The influence of intensity on memory becomes clear when the data is further assimilated into average values based on intensity, as seen on the right:
Clearly, there is a negative correlation between noise intensity and cognitive performance, indicating that high frequency noises as intense as the fire alarm do impair some brain functionality. However, to test my hypothesis and remain within the time and resource constraints of my project, my sample contains convenience, voluntary response, and undercoverage bias. These result from the easy accessibility of Duke students and locals of the Durham area and the unwillingness of certain people from participating. The university environment is not representative of other regions from an intellectual and emotional perspective which, if added to the study, may have led to different results.
In addition, the sample size is too low to give strong statistical significance and ignore large variability. The emphasis and importance of this case study lie in the foundational contribution to further research and the educational observation that experiments can contribute tremendously to a deeper understanding of a scientific field.
The state of the human mind has an inextricable link to the state of its surrounding sound environment and, as seen, can be subjected to the synergistic effects of emotional connection or the deleterious result of disorienting, high frequency noise. Although a tangible method of regulating such sounds and noises in the environment has yet to make a surge in the market, the question of how sounds affect humans now has a variety of answers. Whether it be prolonged or short exposure to noise, unexpected and distracting sonic environments can put a large strain on the human brain and force a person to perform poorly or even sink into emotional extremes, as seen in many of the urban environment cases. A truly thorough examination of human responses to sound would involve a series of neurological, psychological, and physiological studies in different sonic environments, but as the field of acoustics research expands, so will the understanding of the true power and influence of soundscapes.
- Boles, W.E., and S.C. Hayward. 1978. “The Effects of Urban Noise and Sidewalk Density upon Pedestrian Cooperation and Tempo.” Journal of Social Psychology. Volume 104: 29-35.
- Byun, JaeHwan. 2014. “Audial Engagement: Effects of Game Sound on Learner Engagement in Digital Game-Based Learning Environments.” Computers in Human Behavior Volume 46: 129-138.
- Carpentier, Francesca R.D. , and Robert Potter. 2007. “Effects of Music on Physiological Arousal: Explorations into Tempo and Genre.” Media Psychology. Volume 10: 339-363.
- Cohen, Sheldon; Spacapan, Shirlynn. 1984. Noise and Society. 221-243. John Wiley & Sons Ltd.
- Clark, Charlotte. September 2013. “Longitudinal Effects of Aircraft Noise Exposure on Children’s Health and Cognition: A Six-Year Follow-Up of the UK RANCH Cohort.” Journal of Environmental Psychology Volume 35: 1-9.
- Edwards, Jane, and Annemiek Van den Tole. 2015. “Listening to Sad Music in Adverse Situations: How Music Selection Strategies Relate to Self-Regulatory Goals, Listening Effects, and Mood Enhancement.” Psychology of Music. Vol 43: 473–494.
- Flindell, Ian, and Antonio Torjia. 2014. “Differences in Subjective Loudness and Annoyance Depending on the Road Traffic Noise Spectrum.” Journal of the Acoustical Society of America Volume 135 (1): 1-4.
- Grandjean, Didier; Klaus R. Scherer; Marcel Zentner. 2008. “Emotions Evoked by the Sound of Music: Characterization, Classification, and Measurement.” Emotion Volume 8: 494-521.
- Krumhansl, Carol. 1997. “An Exploratory Study of Musical Emotions and Psychophysiology.” Canadian Journal of Experimental Psychology. Volume 51 (4): 336-352.
- Weinstein, Neil. Oct 1974. “Effect of noise on intellectual performance.” Journal of Applied Psychology, Vol 59: 548-554.