Running Head: SOUND PROCESSING AND ATTENTION 1library.emmanuel.edu/archive/sites/default/files/Sorice_B_Thesiso.pdf · a condition for constant ringing in the ears. Tinnitus, over

Running Head: SOUND PROCESSING AND ATTENTION

The Effects of Sound Processing on Attention

Brittany L. Sorice

Emmanuel College

© Brittany Sorice

1

SOUND PROCESSING AND ATTENTION 2

Abstract

Research has shown that if two frequencies differing in 30Hz or less are presented in each ear,

the brain will process these frequencies as an entirely separate third tone. This third frequency, or

binaural beat, can stimulate production of brain waves if created in the correct frequency range.

This study recruited 38 participants to determine if binaural beats in the beta range can increase

attention task performance. The participants were asked to alternate between listening to the

binaural beat stimulus and performing two attention tasks. The findings suggest that beta wave

stimulation through binaural beats may require additional factors paired with the auditory

stimulus to increase attention.


The Effects of Sound Processing on Attention

When two auditory tones differing in frequency are heard in each ear, the brain will

create a third tone that will be perceived as a combination of the two (Oster, 1973). The

differences between the two frequencies will become the new frequency of the perceived tone.

This tone is referred to as a binaural beat (Oster, 1973). For example, if one auditory tone with a

frequency of 120Hz is presented in the right ear and another auditory tone is presented in the left

ear at 130Hz, the perceived tone, or binaural beat, would have a frequency of 10Hz (Kennel,

Taylor, Lyon, & Bourguignon, 2010). Gerald Oster (1973) compared these binaural beats to the

"cocktail party effect," when someone is able to filter out many conversations around them and

focus in on just one.

In order for this effect to occur, each of the tones must be presented in a frequency that is

less than 1,000Hz, preferably around 440Hz, and the difference between the two must be less

than 30Hz. If either of these requirements are not met the tones will be interpreted separately in

the brain, and the binaural beat will not be created (Oster, 1973). If the binaural beat is greater

than 30Hz it will become difficult to hear. The further above 30Hz it goes, the less likely a

person is to experience the beat. The point where the beat can no longer be detected is called the

cutoff point, and it is varies for different people (Licklider, Webster, & Hedlun, 1950).

In contrast to binaural beats, monaural beats are what is heard when the same auditory

frequency is presented into both ears. The brain will perceive the tone the exact way it enters the

ear. Monaural beats can be heard with just one ear; however, binaural beats require the

perception of one stimulus in each ear at the same time (Oster, 1973).

Research suggests that binaural beats can increase brainwave activity and enhance the

functions of these brainwaves (Lane, Kasian, Owens, & Marsh, 1997). The goal of the current


study is to test whether binaural beats improve performance on attention related tasks. The

sections that follow will review the literature on where these binaural beats are created, as well

as how they affect the four types of waves that naturally occur in the brain. The following

sections will also discuss how the use of binaural beats can increase production of these

brainwaves and the implications that these effects may have.

Neuroanatomy and Binaural Beats

There are many inconsistencies in the research that seeks to locate the exact area of the

brain where binaural beats are created and interpreted. Jiang (1996) suggests that the area in the

brain responsible for creating the binaural beat is the cochlear nucleus. This is where sounds that

enter into each ear are processed so that outside stimuli can be interpreted in a way that the brain

can understand. He argued that when a different auditory signal in a similar frequency range is

perceived in each ear, the cochlear nucleus meshes them together to create the third tone, the

binaural beat. Conversely, some researchers believe that the source of the binaural beats could

stem from the superior olivary nucleus and inferior colliculus neurons (David, Naftali, Katz,

2010). The superior olivary nucleus is a part of the brain stem that plays a number of different

roles in the auditory process, and inferior colliculus neurons are sound sensitive neurons. In some

experimental manipulations, it seems as though the binaural beats are coming from somewhere

in the center of the brain (Pratt, 2009), while in other experimental manipulations research has

pointed to the left temporal lobe as the area associated with these beats (Pratt, 2009). These

inconsistencies suggest the need for further research in this area in order to correctly locate

where these tones are produced.

Research has shown that at low frequencies, the binaural beat can be heard in the brain as

an oscillating sound that travels from right to left in the brain. Wavelengths with lower


frequencies have a larger diameter than the human head, so when they are perceived in the brain

they appear to bounce back and forth. As a result of this, many participants often report

perceiving the beat as a sound that travels from side to side (Rosenzweig, 1961).

Pratt et al. (2009) studied the difference between using higher frequency binaural beats

created with both higher and lower base frequencies, and lower frequency binaural beats created

with both high and low base frequencies. Participants were exposed to binaural beats with

frequencies of 3Hz and 6Hz, and each one was created from base frequencies around 250Hz

compared with base frequencies around 1000Hz. In total, 4 different stimuli were used in the

experiment. The main findings from this study suggested that binaural beats were heard in all

four sound conditions, but that the amplitudes were higher when the beat frequency and base

frequency were lower. In other words, the 3Hz binaural beat with a base frequency of 250Hz had

higher amplitude than all of the other sound conditions, and the 6Hz binaural beat created from

frequencies around 1,000Hz produced the lowest amplitudes. Current research on binaural beat

stimulation has focused on using these binaural beats to enhance performance and relaxation

through stimulation of the four naturally occurring brainwaves (Brady & Stevens, 2000).

The Four Natural Brainwaves and Binaural Beat Entrainment

There are four naturally occurring brain waves that present themselves during various

times of the day: delta, theta, beta, and alpha. Delta and theta waves are known to be associated

with sleep and deep relaxation states, while alpha waves area associated with wakeful relaxation,

and beta waves are associated with increased attention and alert states (Brady & Stevens, 2000).

Previous research has suggested that these brain waves can be stimulated by the presence of

binaural beats in the same frequency range (Brady & Stevens, 2000). Because researchers are

able to stimulate these naturally occurring brain waves, in theory, producing beta waves should


cause the participant to come to a more alert, attentive, state of mind. Research suggests that

creating binaural beats in the beta range can increase attentive states and increase performance

(Lane et aI., 1997; Kennel et aI., 2010).

For example, Lane et al. (1997) examined the effects of binaural beats on attention tasks

by simulating three of the four naturally occurring brain waves: delta, theta, and beta. The

researchers played two auditory tones that created a binaural beat to match brain waves in the

delta (I-4Hz), theta (4-8Hz), and beta (l6-24Hz) ranges, and measured the brain activity during

each stimulus. Participants were given headphones containing binaural beats either in the delta,

theta, or beta range and were asked to focus on a monitor and indicate when visual stimuli

appeared on the screen. Reaction times for each stimulus were recorded to determine if any shifts

in attention occurred. Results from this experiment suggest performance data associated with the

stimulation of beta waves resulted in higher accuracy scores and lower reaction times when

compared to performance data associated with the stimulation of theta and delta waves.

Participants indicated less confusion and more of an ability to concentrate with the beta binaural

beats than with the delta/beats, indicating that binaural beats can affect mood as well as behavior

(Lane et aI., 1997).

Binaural Beats as an Option for Therapy

David et al. (2010) investigated the effects of binaural beat training on Tinnitus, which is

a condition for constant ringing in the ears. Tinnitus, over a long period of time, can lead to other

problems such as anxiety, stress, depression, and insomnia, due to the annoyance of the constant

ringing (David et aI., 2010). To reduce the ringing associated with Tinnitus, these researchers

created a therapy method that they called "Tinntrain". Tinntrain is an auditory stimulus that

combines binaural beats, guided imagery, and calming nature sounds. Binaural beat stimulation


in Tinntrain is either in the beta range, or the delta/theta range. This research suggests that

combinations of binaural beats and other factors used in the Tinntrain therapy reduce the long

term health problems caused by Tinnitus (David et aI., 2010).

Binaural Beats and Relaxation

The uses and potential benefits for stimulating naturally occurring brain waves are

exponential. Research has investigated, for example, the effects binaural beats can have on

relaxation. Brady and Stevens (2000) tested the effects binaural beats in the theta frequency

range had on hypnotic susceptibility. Participants were asked to listen to two different sound

conditions. The first condition masked binaural beats in the theta range with background music,

while the second, acting as the control condition, played the same background music without the

binaural beats. An initial measurement of theta wave activity was taken as a baseline to compare

the experimental data. Participants were exposed to one of the two stimuli for three-20 minute

sessions over the course of a week, at which time theta wave activity was measured again. Five

of the six participants ended the experiment with higher levels of theta waves when compared

with the baseline measurements. This supports the idea that binaural beat stimulation in the theta

range can increase theta wave production in the brain (Brady & Stevens, 2000).

Relaxation through binaural beat training can also be used to reduce pre-operative anxiety

(Padmanabhan, Hildreth, & Laws, 2005). The researchers suggest that the root of pre-operative

anxiety often stems from a feeling of having no control. Patients may also experience pre

operative anxiety for the potential of death or other major side effects that could occur during the

operation. Typically, to reduce this anxiety, patients will be given headphones with relaxing

music before they go into the operating room (Wang et. ai, 2002), but this was not reducing

anxiety enough for these patients. Based on the results from previous research conducted on the


effect binaural beats can have on increasing relaxation (Brady & Stevens, 2000), this study

examined the effects of combining this music therapy with binaural auditory training. The study

consisted of three groups: one group was exposed to music, another was exposed to the

music/binaural beat combination, and the third group acted as the control and was not exposed to

either stimulus. The listening period lasted for about 30 minutes. The data from these patients

show that embedding binaural beats and music can reduce the level of pre-operative anxiety by

50%.

Binaural Beats and Attention

Previous research suggests binaural beat stimulation can increase production of

delta/theta waves, which can lead to an increase in relaxation among participants (Brady, 2000;

Padmanabhan, 2005). In addition to this, research has shown that binaural beat stimulation can

also increase production of beta waves, which can lead to an increase in attention (Kennel et aI.,

2010; Kennerly, 1996; Lane et aI., 1997).

Research by Kennerly (1996) examined how binaural beat training in the beta range

would affect performance on word recall tasks. Participants in this study were exposed to either

pure instrumental music, or music combined with beta binaural beats. All participants were given

four word recall tasks as a measure of attention. Each of the four tasks completed by the

participants yielded higher mean recall scores for the binaural beat group than those from the

pure music group. These results suggest that the stimulation of beta waves through binaural beats

combined with music can increase performance on attention tasks (Kennerly, 1996).

Kennel et al. (2010) examined the immediate and long-term effects of binaural beats on

attention in children and adolescents diagnosed with ADHD. A sample size of 20 children and

adolescents diagnosed with ADHD were given compact disks with containing either BAB's


(binaural auditory beat stimulation) in the beta range masked by music, or music without the

binaural beats. They were instructed to listen to the music as they completed their homework

assignments (Kennel et aI., 2010). The parents were asked to report any changes in attention to

their homework over the three week trial. There were no significant differences in either group

when they were given a single exposure to the tones, but parents reported noticing a difference in

their child's ability to complete their assignments after the three-week exposure. Based on the

data recorded in this study, there is more significance if the brain is exposed to beta binaural

beats over a period of time than if they are just given a one time exposure (Kennel et aI., 2010).

Potential Directions with Binaural Beats

The data gathered from previous research on binaural beat stimulation points to

substantial evidence for the possibility of binaural beat training to increase both relaxation and

attentive states. However, many of the studies published to date have very small sample sizes.

This can affect the validity of the data by not creating an accurate representation of the

population. Brady (2000) found significant results to support entrainment of theta waves in

hypnosis, but the sample size consisted of only six participants. Wahbeh et al. (2007) published a

paper on binaural beat entrainment on a physiological level, but found no significant results from

the four participants in the study. Future research should include gathering larger sample sizes to

accurately determine the effects binaural beats have on the population.

Brady (2000) also mentions that future research should examine the long term effects of

the binaural beat therapy through increased exposure time. Another area open to further research

should examine the comparison between masked binaural beats and pure binaural beats to further

increase stimulation of brain waves (Brady, 2000).


So far, research on binaural beat stimulation has yielded very inconsistent findings.

Kennerly (1996) suggests that if research begins to produce consistent data supporting beta

binaural beats and increasing attention rates, future applications can include uses for these beats

in the classroom. Children who struggle in school and need extra help paying attention could

benefit from the use of binaural beats. If the use of beta binaural beats becomes a therapy option

supported by significant research, this method has the potential to become a more cost effective

alternative to medication. With more research, this method has the potential to provide an

effective therapy for children suffering with ADD and ADHD as well as other attention related

disorders (Kennerly, 1996).

This study will attempt to solve this problem by gathering data to support increased

attention through beta binaural beat stimulation. This study will also aim to support combining

binaural beats with music to further increase performance through attention. Based on the

findings from previous research, the present study will incorporate both masked and pure

binaural beats, and increased exposure time to test the effects of binaural beat stimulation in the

beta range on attention.

Methods

Setting and Participants

A total of 40 students participated in this study (32 females, 8 males). Participants were

between 18 and 24 years of age (mean age ~ 19.4). 16 participants were majoring in psychology,

and 20 participants had some history of musical training. 8 participants were familiar with either

the Stroop task or Simon task, and 2 participants were familiar with the concept of binaural beats

to some extent. Participants were recruited on-line and in person. Participants signed up for a


time slot to participate in the study through SONA Systems, an online research participation

tracking system. Participants received course credit for their participation.

Measures

Stroop Task. An electronic version of the Stroop task was used to measure reaction time

for each participant in this experiment. Two trials of this task were created using a programming

tool called DMDX, developed at the University of Arizona by K.I. Forster and lC. Forster. The

task showed color words (i.e. the words blue, red and green), written in different color ink. For

example, the word blue appeared on the screen in blue, red, or green ink. Participants were asked

to respond to these stimuli by indicating, using a computer keyboard, the color the word was

written in. If the word blue was written in red, the correct response for the participant was red.

Three keys on the keyboard were used for this task. The key "r" indicated red, "g" indicated

green, and "b" indicated blue. Fifteen congruent stimuli (i.e. the word blue written in blue) and

fifteen incongruent stimuli (i.e. the word blue written in red or green) were used in this version

of the Stroop task. Both trials consisted of the same number of stimuli in a randomized order.

The computerized Stroop task recorded the response time of participants in milliseconds, as well

as the number of correct and incorrect responses for each trial.

Simon Task. An electronic version of the Simon task was created for this experiment

using the programming tool DMDX. The Simon task is an attention task that shows visual

stimuli and focuses on the orientation of the stimulus on the screen. In the current study, the

Simon task showed blocks of color (purple and orange) either on the left or right side of the

screen. The participants were asked to indicate which color appeared, regardless of its orientation

on the screen. Participants were asked to press the right directional key if the color orange was

shown, and to press the left directional key if the color purple was shown. Fifteen congruent


(purple appearing on the left side/orange appearing on the right side) and fifteen incongruent

(purple appearing on the right side/orange appearing on the left side) stimuli were shown in a

random order with each trial randomized differently. The computerized Simon task measured

response time in milliseconds and kept track of correct and incorrect responses for each stimulus.

Attention as a Measure of Performance. For the purpose of data anal ysis in this

experiment, performance will be defined as the difference in reaction times between congruent

and incongruent stimuli from each attention task, and not simply just each mean reaction time.

These differences will be referred to as the difference scores. Comparing the reaction times alone

does not indicate an increase in performance in terms of attention. Previous studies have reported

using this method as a valid way measure changes in performance (Bialystok et. aI, 2004; Van

der Lubbe & Verleger, 2002). Bialystok et. al (2004) refers to this difference as the Simon effect.

If the Simon effect is larger, the attention rate is lower, and if the Stroop effect is smaller, the

attention rate is higher.

Procedure

After providing consent to participate in the study, participants were randomly assigned

to one of two experimental groups. 21 participants (17 female, 4 male) were assigned to group 1,

and 19 participants (15 female, 4 male) were assigned to group 2. Each participant placed

themselves into a group by choosing a slip of paper from a pile. The slips were folded and placed

on the table in front of the participant. 50% of the slips had a "1" written inside the fold and 50%

had a "2" written inside. Group 1 was exposed to a binaural beat stimulus with background white

noise played through headphones attached to an iPod, while group 2 was exposed to a binaural

beat in the same beta wave range with new age style music in the background through

headphones attached to an iPod (see Figure 1). The purpose of the background noise (either


white noise or music) was to hide the binaural beats. Both auditory stimuli were provided by the

Monroe Institute in Faber, Virginia.

Both groups completed identical attention tasks. Before exposure to the binaural beat,

participants in both groups completed a practice test containing ten Stroop examples (five

congruent and five incongruent) and ten Simon examples (five congruent and five incongruent)

to ensure that they understood how to correctly respond for both. Participants in group I were

then asked to put on their headphones and listen to the binaural beat stimulus with white noise

for seven minutes, and participants in group 2 were asked to put on their headphones and listen

to the binaural beat stimulus with background music for seven minutes.

Following the initial seven minute listening period, participants were asked to complete

the first trial of the Stroop task while continuing to listen to the stimulus assigned to them. After

completion of the Stroop task, participants were asked to sit quietly for another seven minute

period while still listening to the binaural beat stimulus. After this listening period, participants

completed the first trial of the Simon task. Participants were asked to again sit quietly for seven

minutes and listen to the stimulus, and then complete the second trial of the Stroop task. This

was followed by another seven minute listening period, and then completion of the second trial

of the Simon task. After completion of all tasks, participants completed a basic demographic

survey (see Figure 2).

Results Participant Exclusions

Data from two participants was removed from the analysis. One participant's data was

excluded because he/she had turned the volume for the auditory stimulus off, and as a result the

binaural beat would not have been created. Another participant's data was excluded because


he/she completed one of the tasks incorrectly, resulting in all incorrect responses. Data from 38

participants was used in the final analysis.

2x2x2ANOVA

Two separate 2 (Group) x 2 (Task order) x 2 (Congruency) mixed analysis of variance

(ANOVA's) were performed to examine the reaction times from the first and second Stroop and

Simon task respectively. The first ANOV A analyzed the reaction times from the first and second

Stroop task, while the second ANOV A analyzed the reaction times from the first and second

Simon task. Here, the between-subjects factor was Group (Group 1: Binaural Beat + White Noise

vs. Group 2: Binaural Beat + Music). Task order (first vs. second attention task) and Congruency

(Congruent vs. Incongruent stimuli) served as the two within-subject factors. All significant

statistical results were reported at an alpha level of .05.

Stroop Task. Significant main effects indicated that on average reaction time decreased

by 64.75ms from the first (M ~ 845.56ms) and second Stroop tasks (M ~ 780.81ms), F (1, 36) ~

12.18, P ~ .001. There was also a significant main effect for reaction times between groups, F (1,

36) ~ 4.26, P ~ .046. On average, reaction times were 113.03ms higher in group 2 (M ~

869.70ms) than group 1 (M ~ 756.67ms) for the Stroop task (see Figure 3). Another significant

main effect indicated a difference in congruent and incongruent reaction times for the Stroop task,

F (1, 36) ~ 63.56, P < .001. Specifically, on average congruent stimuli yielded faster reaction

times (M ~ 736.40ms) than incongruent stimuli (M ~ 889.98ms, i.e. an overall Stroop effect).

In addition, there was a significant two-way interaction effect between task order and

congruency, F (1, 36) ~ 63.56, P ~ .021. Specifically, when considering the two groups together,

the Stroop effect shown in the first task (78 1. 54ms for congruent stimuli vs. 909.59ms for

incongruent stimuli) was smaller than that shown in the second task (69 1. 26ms vs. 870.37ms).


This interaction implies that participant's performance decreased in terms of attention as the

experiment progressed. There were no significant effects found from the difference scores.

Simon Task. Significant main effects indicated that average reaction time decreased by

63.62ms from the first (M ~ 570.23ms) to the second Simon task (M ~ 506.65), F (1, 36) ~ 25.07,

P < .001. There was also a significant main effect for reaction times in between groups, F (1,36)

~ 8.19, P ~ .01. On average, reaction times were 79.67ms higher in group 2 (M ~ 578.28ms) than

group 1 (M ~ 498.60ms) for the Simon task (see Figure 5). There were no significant effects

found for the two-way interaction between task order and congruency, or from the difference

scores.

2x2ANOVA

Two 2 (Group) x2 (Task order) mixed analysis of variance (ANOVA's) were performed

to examine the difference scores from the first and second Stroop tasks respectively. This first

ANOV A analyzed the reaction times from the first and second Simon task. Here, the between

subjects factor was Group (Group 1: Binaural Beat + White Noise vs. Group 2: Binaural Beat +

Music). Task order (first vs. second attention task) served as the within-subject factors. The

difference scores were used as reaction times for the attention tasks. No significant effects were

determined from the difference scores, so no statistics were reported except for graphs to further

clarify the performance results (see Figures 4 and 6).

Discussion

Previous research has shown that binaural beats in the beta range can stimulate beta

brainwaves and increase performance on attention related tasks (Kennel et. aI, 2010; Kennerly,

1996; Lane et. aI, 1997). Other studies have shown that binaural beats masked with music can

increase the stimulation of brainwaves more than just listening to the binaural beat by itself


(Brady and Stevens, 2000; Kennel et. ai, 2010). This study aimed to reinforce both these findings

by exposing participants to a beta binaural beat stimulus and recording reaction times in the

Stroop task and the Simon task. This study also aimed to determine if beta binaural beats masked

with music would increase performance more than beta binaural beats masked with white noise.

It is important to note that no causation can be determined from this data, as this study

was not set up to be able to determine causality. In order to determine if binaural beats actually

had some impact in either direction on these reaction times a causational study would need to be

set up to track stimulation of brain waves. Without this causation, the best conclusions that can

be drawn on this data are based on the correlations between binaural beat exposure length and

performance on these attention tasks.

Hypothesis 1: Exposure to beta binaural beats will increase performance on attention tasks

The results from the reaction time ANOVA's indicate that performance for both groups

increased significantly from the first to second attention tasks. Reaction times decreased

significantly between the first and second Stroop (see Figure 3) and Simon tasks (see Figure 5).

Performance on the Stroop tasks had, on average, slower reaction times than performance on the

Simon task, indicating that the Stroop task was more difficult for participants. Although the

reaction times increased significantly as the length of the exposure time increased for both

groups, this measure of performance does not accurately support or reject the hypothesis by

saying that attention increased. These results may have been caused from the appearance of a

practice effect, indicating that as the participants became more familiar with each task they were

able to respond more quickly to each stimuli.

The Performance based on the difference scores is what determines the overall change in

attention through the study and because there were no significant results from these difference


scores, attention did not increase with length of binaural beat exposure. This data does not

support the hypothesis that binaural beats in the beta range increase performance on attention

tasks. In terms of the difference scores, there were no significant results showing that binaural

beats had any effect on attention in either direction (see Figures 4 & 6). This could suggest that

the exposure length was not long enough for the binaural beats to be able to increase stimulation

of beta wave activity, but that cannot be determined from this study because there was no

indication that beta waves were stimulated. Further studies would need to be performed in order

to support causality.

Hypothesis 2: Music combined with beta binaural beats will increase performance on

attention tasks more than beta binaural beats combined with white noise.

The results from the reaction time ANOV A indicate that there were significant effects

between reaction times on the first and second attention tasks for both groups. Group 1 (BB +

WN) had, on average; lower reaction times on all Stroop task performance (see Figure 3.A) than

Group 2 (BB + M) (see Figure 3.B). Group 1 (BB + WN) had, on average; lower reaction times

on all Simon task performance (see Figure S.A) than Group 2 (see Figure S.B). For the sake of

determining performance based on an increase in attention, these results will not determine

whether the hypothesis was supported or not supported. To determine this, the results from the

difference scores will be examined as the measure of attention.

The results from the difference scores ANOV A indicate that there were no significant

effects between the difference scores and the first and second attention tasks for both groups.

Group 1 (BB + WN) had, on average, lower difference scores on the Stroop task than group 2

(BB + M), indicating that Group 1 performed better overall on this task (see Figure 4). Both

groups performed worse on the second task than on the first task as their difference scores


increased from the first to second tasks. Group 1 had a slightly larger gap in difference scores

over group 2, indicating that group l's decrease in performance is correlated negatively with the

exposure length of the binaural beat stimulus.

Group 2 (BB + M) had, on average, lower difference scores on the Simon task than group

1 (BB + WN), indicating that Group 2 performed better overall on this task (see Figure 6). Group

1 performed worse on the second task than on the first task as the difference scores increased

from the first to second tasks. Group 2 performed slightly better on the second attention task, as

the difference scores decreased from the first to second tasks. Group 1 had a larger gap in

difference scores over group 2, indicating that group l's decrease in performance is correlated

negatively with the exposure length of the binaural beat stimulus.

Using the difference scores as a measure of attention, the results show that neither group

1 nor group 2 performed significantly better than the other one on either attention task. These

results do not support the hypothesis that binaural beats combined with music will increase

attention more so than binaural beats combined with white noise.

Study Limitations

There are three potential factors that could have provided significant error in this study.

The first being the length of the binaural beat listening period. Previous studies have used longer

listening periods for their binaural beat exposure that were as long as 30 minutes in some cases,

before participants were asked to complete any attention tasks (Kennel, 2010; Kennerly, 1996;

Wahbeh, 2007). Although this current study had a total binaural beat exposure length of 28

minutes (four-7 minute listening periods), each attention task was performed after each seven

minute listening interval. This means that the reason the data showed a decrease in average

reaction time between the first and second trails of each attention task could have been because


the binaural beats needed time to stimulate the production of the beta waves before any effect in

performance could be observed.

Another factor that could have limited the results of this study is using the Simon task as

a measure of performance. The Simon task is a fairly easy task. Participants had much faster

reaction times in all trials of the Simon task over the reaction times for the Stroop task. Because

the task was fairly easy to begin with, participants might have experienced a ceiling effect during

their performance on these tasks. Having an attention task that doesn't prove to be much of a

challenge does not leave much room for improvement when the participants have to repeat the

task a second time. The Simon task has not been used before in previous studies on binaural

beats, and may not prove to be the best attention task for these types of studies.

A third factor to note is that the sound stimulus for group 2 (BB + M) was only 35

minutes in length, and the entire experiment last for approximately 40 minutes. The iPod was set

to repeat the stimulus once it had finished, but there were a few seconds between when the song

finished and when it started back up again where there was no sound. This period of silence

might have disrupted the stimulation of beta waves because there was a break in the binaural beat

exposure. To determine that beta wave stimulation ceased during that moment when the song

started over, further studies would need to be conducted to measure the beta wave activity during

the time of the binaural beat exposure.

Directions for Future Research

The purpose of this study was to examine the effectiveness of beta binaural beats in

increasing performance on attention related tasks. Research to this point has provided conflicting

data as to whether or not beta binaural beats can increase attention. The results from this study

exposure to beta binaural beats did not have a significant effect on attention performance, and


combining the binaural beats with music did not increase performance any more than exposure to

the binaural beats with white noise did. Neither one of the hypotheses for this experiment were

supported. Future studies should combine methods of confirming stimulation of beta waves

through electroencephalograph (EEG) measurements (Brady, 2000; Jiang, 1996; Pratt, 2009;

Stevens, L., Haga, Z., Brady, B., Adams, D., Gilbert, J., et. ai, 2003; Wahbeh, 2007), along with

completion of attention tasks during the entire binaural beat exposure (Kennel, 2010; Lane, 1997)

instead of in between.

A solution to this gap in research could be taken care of by performing the same

procedure of alternating listening periods with attention tasks, but have the participants actively

engaging in a task during the listening period. This way the participants are not sitting idly and

listening to the stimulus. One option for this approach could be to use a word recall task as the

measure of attention, and have participants memorize words from a list during the listening

period, and then recall as many as they can in between listening (Kennerly, 1996).

It also could be worth comparing different lengths of exposure to the beta binaural beats

in order to determine what length will provide optimal results in terms of performance. Another

interesting study could involve comparing the effectiveness of different binaural beat frequencies

within the beta wave range. For example, if the beta wave range is between 16Hz and 24Hz, is

the binaural beat more effective at stimulation brain waves at 20Hz in the middle of the

frequency range, or around 16Hz or 24Hz at the edge of the frequency range (Pratt, 2009).

Conclusion

Binaural beats in the beta wave range did not result in increased performance on attention

related tasks. Instead, the difference scores increased, indicating that the performance decreased

as the binaural beat exposure increased. Individual reaction times yielded an increase in


perfonnance, but these reaction times are not good indicators of an increase in attention based on

the definition of attention given in this study. The results from this experiment also show that

combining music with beta binaural beats did not increase attention more than combining

binaural beats with white noise in either task, indicating that binaural beats combined with music

do not increase attention better than when the binaural beats are paired with a white noise

background.


Figure 1

Group Auditory Stimuli Exposures. This figure shows the difference between the two groups in

terms of their assigned auditory stimuli.

Group 1

Binaural Beats + White Noise

(BB+WN)

Participants

n =38

Group 2

Binaural Beats + Music (BB +M)


Figure 2

Procedure for Groups 1 and 2. This figure provides a pictorial representation of both groups'

order or attention tasks and listening periods.

Practice Test

7-minute listening period

(Group 1- SS + WN? Group 2- SB + M)

Stroop Task 1


(Group 1- 88 + WN, Group 2- 88 + M)

Simon Task 1


(Group 1- SB + WN, Group 2- 88 + M)

Stroop Task 2


(Group 1- SB + WN, Group 2- 88 + M)

Simon Task 2


Figure 3

Results from Stroop 2x2x2 Mixed ANOV A This figure shows the change in mean reaction

times for participants in groups 1 and 2 for the first and second Stroop tasks. Reaction times for

congruent (solid lines) and incongruent stimuli (dashed lines) are displayed in tlie figure .

A Stroop T3~k Ructioa Timu fur GrQDp I

1050(l(Xl-

! ~= • ~ •

~ . . -. .- . ._. .~ € 850 ,()()(t

~ • " • <

750.co:.l"

.. ~ ~ Stroop Tasks

Conlneon

- , - ,

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B

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750.0CI0"

650,0Cl0"

Stroop Task RuctWa Times for Group 2

"-. .-. --._,

St""0ll Tas .....

Conlneon

SOUND PROCESSING AND AT1ENTION 25

Figure 4

Results from Stroop 2x2 Mixed ANOVA Using Difference Scores. This figure shows the results

from the 2x2 mixed ANOVA in comparing the difference scores (difference between average

congruent and incongruent stimuli) in each Stroop task. for group 1 (solid line) and group 2

(dashed line).

-. .! •

' 00

~ 100

.~ ~ 170.CXXl

o

" • "" 160

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Groups OD. t he Stroop T ask

./

Str .... p Task

Group - , -,


Figure 5

Results from Simon 2x2x2 Mixed ANOVA This figure shows the change in mean reaction

times for participants in groups 1 and 2 for the first and second Simon tasks. Reaction times for

congruent (solid lines) and incongruent stimuli (dashed lines) are displayed in the figure.

Simon Task Reaction Time. for Group I B Simon Task Reaction Time. for Group 2

1 1 • • ., , . ~ .. . ~ "- .~

'. , ~ , ~ 0 0

" ~ , : • , .'. ::=. .. ••

Simon Task. Simon Tas .....


Figure 6

Results from Simon 2x2 Mixed ANOVA using Difference Scores. This figure shows the

difference scores (difference between average congruent and incongruent stimuli) in each Simon

task for group 1 (solid line) and group 2 (dashed line).

'" ~ • ~ .~ ~)(XXl • " ~ o

" • ~ 20

10'(XXl

Coagruealllacoagrueaf Differeace. Bern-en

Groups aD tbe Simoa Task

Simoa Task


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