The Cognitive Effects of Music: Working Memory Is Enhanced

The Cognitive Effects of Music: Working Memory Is Enhanced in Healthy Older Adults

After Listening to Music

A thesis submitted to the University of Arizona College of Medicine -- Phoenix

in partial fulfillment of the requirements for the Degree of Doctor of Medicine

Alan Wang

Class of 2013

Mentor: Natalie L. Denburg, PhD

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Acknowledgement

Thank you to the following institutions for providing me with a fantastic academic opportunity:

University of Iowa Department of Neurology, Doris Duke Clinical Research Foundation,

Special thanks to

…my mentor, Dr. Natalie Denburg, for her support in my uncharted topic of music and neuroscience,

…Dr. Daniel Tranel for his extensive knowledge, wisdom, and basketball challenges,

…Dr. Peg Nopoulos for her fiery unwavering encouragement,

…Shannon Christensen for being the rock and glue to everything I attempted, accomplished, and

experienced in Iowa City.

Lastly, a lifelong gratitude to my parents, Tair and Lih Wang, for starting my musical endeavors. Dad,

here is some proof why your musical skills will continue to enhance your engineering brain.

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Abstract

Music is ubiquitous in all media, and, in the last decade, has become a potential tool for

enhancing cognition. This study aimed to investigate the facilitating effect of music on working

memory performance in a healthy older adult cohort. Sixty-three healthy, community-dwelling

older adults who had previously undergone comprehensive neuropsychological testing were

enrolled in the study. Participants were randomized into one of two groups, and were

presented with a series of positive and negative musical clips. Following listening, working

memory performance was tested using Wechsler Digit Span and a computerized Spatial Span

task. For each task, a total score consisting of number of correct forward and backward

sequences was calculated. A significant improvement in Digit Span scores was found after

listening to music as compared to Digit Span scores collected ~5 years ago. Contrary to our

hypothesis, this facilitative effect of music on working memory held for both positive and

negative musical stimuli. It has been shown that negative music can illicit the same pleasurable

feelings as positive music, and, given West’s frontal lobe hypothesis, can therefore produce the

same effects on working memory as positive music.

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Table of Contents

Introduction………………………………………………………………………………………………………………………………5

Methods……………………………………………………………………………………………………………………………………8

Results…………………………………………………………………………………………………………………………………… 15

Discussion/Future Directions……………………………………………………………………………………………………19

References……………………………………………………………………………………………………………………………….23

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Introduction

Working memory refers to the innate ability to retain information over short delays in

order to perform more complex tasks, and the dorsolateral prefrontal cortex plays a key role in

this cognitive skill (Sawaguchi & Goldman-Rakic, 1991). One theory, referred to as the “frontal

lobe hypothesis”, proposes that some older people have disproportionate age-related changes

of frontal lobe brain structures, including the dorsolateral prefrontal cortex, and of the

associated cognitive abilities (West, 1996). To illustrate, a behavioral study comparing 20 older

adults (64 to 80 years of age) to 20 younger adults (18 to 28 years of age) showed an age-

related deficit on a working memory maintenance task in which participants made brightness

judgments (Mikels et al., 1995). Furthermore, a neuroimaging study by Charlton and colleagues

demonstrated that working memory has a more profound age-related decline than other

domains of cognition. This study used diffusion tensor imaging (DTI) and neuropsychological

evaluation to prospectively study older adults. After two years, age-related white matter

changes were demonstrated with a single corresponding cognitive deficit in working memory

(Charlton et al., 2010).

Multiple studies have demonstrated that working memory performance can be

modulated by mood. Negative mood has generally been found to adversely impact working

memory performance (but see Phillips, Smith, & Gilhooly, 2002, for an example of contradictory

data), while positive mood has been found to more consistently enhance working memory

performance, often to the point of attenuating the aforementioned age-related deficit in

working memory ability (Brose, 2011; Perlstein, 2002). The emotional stimuli used across these

experiments have typically been the well-validated set of still pictures, referred to as the IAPS

(International Affective Picture System; Lang et al., 1998).

Music has also been found to be a particularly strong mood inducer (Lima & Castro,

2011; Vieillard et al., 2007). Its use in virtually every form of mass media is a striking example of

music’s ability to evoke emotion. From a neuroscience perspective, music’s induction of a

variety of emotion spans many parts of the cortex (Green et al., 2012; Khalfa et al., 2005).

However, the frontal lobe of the brain is known to be the control center for mood and emotion.

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It is well documented that dopamine’s effects in the frontal lobe influences our mood and

emotion. For example, a deficiency of dopamine can lead to depression, while a surplus can

lead to euphoria (Dackis et al., 1985). Thus, it follows logically that mood processes can

influence frontal lobe activation, as positive mood states increase dopaminergic transmission to

the frontal lobes. Ashby and colleagues suggest the mechanism by which mood influences

cognition may be one of mental flexibility, in which executive functions, specifically, are

enhanced during positive mood induction (Ashby, Isen, & Turken 1999). Given the finding that

dopamine receptors and transporters decrease with age (de Keyser et al., 1990; Volkow et al.,

1995; Wong et al., 1984), music’s strong ability to induce a positive mood would provide an

opportunity for an improvement in certain cognitive abilities among older adults.

A large literature in the neuropsychology of music stems from the “Mozart effect”,

which originally suggested that listening to Mozart’s Sonata for Two Pianos in D Major, K. 448

increased an individual’s performance on visuospatial tasks (Rauscher et al., 1993). In this study,

36 college students were given three sets of spatial IQ tests from the Stanford-Binet intelligence

scale. Preceding each set, participants were presented with one of three 10-minute listening

conditions involving Mozart’s sonata, a relaxation tape, or silence. Spatial IQ testing revealed

that participants in the Mozart condition scored the highest, suggesting that listening to Mozart

enhanced cognitive performance temporarily.

Since the “Mozart effect” finding, it has been widely agreed upon that the mechanism

behind the increased cognitive performance is secondary to improved mood and not the music

per se (Chabris, 1999; Roth & Smith, 2008; Thompson et al., 2001). Many of the studies that

have attempted to replicate the finding have utilized different cognitive outcome tasks (Stough,

et al., 1994). A meta-analysis performed by Chabris analyzed 20 published Mozart-to-silence

comparisons and eight Mozart-to-auditory relaxation instructions. Chabris concluded that the

“Mozart effect” may be due to a small, positive “enjoyment arousal” effect, and that the

improvement would not be seen in any person who does not find the particular stimuli

enjoyable or arousing. Several years later, Thompson et al. (2001) attempted to replicate

Rauscher’s study, this time by comparing Mozart’s music to a slow, “sad”, adagio by Albinoni to

examine it’s impact on visuospatial task performance. Thompson’s conclusion agreed with the

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majority opinion of the underlying mechanism of the Mozart effect: enjoyable stimuli induced a

positive affect, which lead to improvements in visuospatial task performance. This effect was

termed the “arousal and mood hypothesis”.

As outlined above, the “Mozart effect” has largely been examined with visuospatial

tasks, which tax right parietal cortex. To date, little research has focused on musical stimuli’s

ability to impact working memory in particular. One study, focusing on younger adults,

demonstrated particularly interesting findings. Schellenberg et al. (2007) investigated whether

listening to Mozart’s music versus a slower song, Albinoni’s adagio, improved working memory

in young adults. Mood was rated before and after music stimuli presentation to assess for

affective changes. The study concluded that, regardless of the type of music, an improvement

in working memory correlated with an increase in mood, confirming Thompson’s arousal and

mood hypothesis.

Mammarella et al. (2007) attempted to elicit such an effect in an elderly population by

comparing the popular composer Vivaldi’s “Spring” versus white-noise as the auditory stimuli,

and using the Digit Span task from the Wechsler Adult Intelligence Scale (WAIS) to assess for

working memory. Taking a departure from most music and cognition studies, Mammarata and

colleagues played the music/white-noise during the tasks. The study concluded that, in a small

sample (N = 24), Vivaldi’s “Spring” was able to elevate mood and thus enhance working

memory (while white-noise was not beneficial for enhancing working memory). This finding,

again, supports the arousal and mood hypothesis.

In the present study, we continue this line of inquiry in a larger sample of older adults

(range 57-86 years), by asking whether positive or negative musical stimuli (arguably the most

ubiquitous and enjoyed form of media) can influence cognitive performance in a healthy, older

population. Moreover, we seek to examine the impact of both positive and negative affect

music (i.e., the Mozart effect) on a cognitive ability that declines in the context of normal aging,

namely, working memory. We hypothesize that healthy older adults will demonstrate enhanced

working memory following novel, positive music stimuli relative to negative music stimuli, and

that this enhancing effect will be further augmented among participants with musical expertise.

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Methods

Participants

Sixty-three older adults (Mean age = 74.29, SD = 6.37, range = 57-86 years; Mean

education = 15.86, SD = 3.07, range 11-20 years; 54% female) participated in the present study,

having been recruited from an ongoing study investigating real-world decision-making.

Participants were financially compensated for their involvement. A structured interview

screening procedure was used to determine that all persons enrolled in the study were

neurologically and psychiatrically healthy, using a method described previously (Tranel, Benton,

& Olson, 1997). During each participant’s initial evaluation an average of five years ago, the

Digit Span task was assessed without music.

In addition, participants’ auditory perception was screened with a short hearing test.

The highest and lowest pitches of all songs were played to ensure that the participant was able

to hear every song in its entirety. Demographic and neuropsychological characterization of the

participants can be found in Table 1. For the present study, the author administered all memory

tests.

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Table 1: Descriptive Statistics

Characteristica Nb Minimumc Maximumd Meane Std. Deviationf

MMSE score 63 26 30 29.3 0.978 AVLT 30min delay 63 2 15 9.63 2.991 Rey-O copy score 62 20 36 31.984 3.3437 Rey-O 30min delay score 62 7 32 16.863 6.3544 WASI Verbal IQ 58 97 139 118.78 11.009 WASI Performance IQ 62 86 144 116.24 13.944 WASI Full Scale IQ 57 91 144 119.91 11.535 Trailmaking A time (sec) 63 18 69 34 9.879 Trailmaking B time (sec) 63 36 180 77.86 28.303 WRAT Reading: total raw score

63 40 64 51.33 4.639

WRAT Reading: age-corrected standard score

63 86 120 109.68 7.744

BDI total score 62 0 19 4.05 3.48 BVRT Errors 53 0 11 4.02 2.508 Digit Span 63 7 28 18.5 4.567

a Shown are raw scores provided for each of the neuropsychological variables. Listed are Wechsler Adult Intelligence Scale-third edition (WAIS-III) Digit Span; Wide Range Achievement Test-revision 3 (WRAT-3) reading subtest; Rey Auditory–Verbal Learning Test 30 min delayed recall (AVLT 30 min delay); Benton Visual Retention Test number of errors (BVRT errors); and Wisconsin Card Sorting Test (WCST number of perseverative errors committed and categories achieved). b N = number of participants c Minimum raw score for each test, out of all participants d Maximum raw score for each test, out of all participants e Mean raw score for each test f Standard deviation given for each test

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Musical Stimuli

The musical stimuli were created by the primary author (ACW). Using GarageBand

software running on Mac OSX and an M-Audio Keystation 88es keyboard, novel classical songs

were composed, using some songs from Vieillard et al.’s (2007) previously piloted music as a

foundation for melody. Positive songs were considered to be faster tempo and in a major key,

while negative songs were slower and in a minor key. All songs involved a piano, cello, and flute.

The duration of positive music was 3 minutes and the duration of negative music was 3 minutes

and 30 seconds.

Working Memory Tasks

Digit Span. For auditory-verbal working memory, we utilized the Digit Span subtest of

the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III; Wechsler, 1997). This task had

been previously administered to participants approximately five years ago (at entry into the

ongoing study of real-world decision-making) and again during the current study. In this task,

participants are asked to repeat strings of numbers in both a forward and backward order.

Standard practice trials and discontinuation rules were employed. Each participant completed a

total of two Digit Span tests.

Spatial Span. Spatial Span, a visual-spatial working memory task, was obtained from the

Psychology Experiment Building Language (PEBL) website (Mueller, 2012). Spatial Span was

administered on a computer with a touch screen. Unlike Digit Span, this task was novel to all

the participants. In this test, nine squares are depicted on the computer monitor and they light

up one square at a time. Like Digit Span, participants were asked to watch and recall how the

squares light up in a forward order (increasing in span with successive trials) and in a backward

order (again, increasing in span with successive trials), so that they can touch the screen in the

designated order. Standard practice trials and discontinuation rules were employed. Each

participant completed a total of two spatial span tests.

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Expertise and Musical Preference Survey

Participants were asked to complete a paper-and-pencil musical survey examining their

experience with playing a musical instrument as well as their musical preference. The purpose

was to characterize the musical expertise of the cohort for further analysis. The complete

survey is provided in the Appendix.

Rating Scales

Participants were asked to rate their mood and arousal immediately before and after

listening to the musical stimuli. A Likert scale was used (Figure 1), and it featured two 0 to 9

scales, one for mood and one for arousal. For mood, 0 represented sad, 5 represented neutral,

and 9 represented happy. For arousal, 0 represented lethargy, 5 represented neutral, and 9

represented very awake and alert. Instructions were given to listen closely to the music and to

use the scale to rate any emotion, if any, the music made the participant feel. A total of eight

rating scales were completed by each participant (i.e., four during the positive music and four

during the negative music).

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Figure 1:

Fig 1: Likert scale. Participants were asked to rate emotion and arousal before and after listening to music.

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Procedure

Participants were tested on a single day for approximately 60 minutes duration.

Participants were tested in a comfortable room with padded chairs and a writing-height table.

Each participant was randomized to one of two groups, each consisting of two blocks of stimuli.

The first group listened to the positive music block first, followed by the negative music block,

while the second group listened to the negative music block first, followed by the positive

music block. Instructions were explained to each participant before beginning. See Figure 2 for

a schematic of the study’s procedures. Each block began with the mood/arousal rating scales

before listening to music. Immediately after the music ended, the mood/arousal rating scales

were obtained again. Next, Digit Span was administered. Following Digit Span, the rating scales

were again obtained. Finally, spatial span was administered, which concluded the block.

Procedures for the second block were identical to the first block, save for the type of musical

stimuli played. Each block was approximately 20 minutes in duration.

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Figure 2:

Fig 2: Schematic for experiment procedure. Half of the cohort listened to positive music first and then negative music. The order was counterbalanced as shown on the bottom half of the schematic.

Rate baseline valence/arousal

Preparation and

explanation

• 1 min/song x 3 • Ask

valence/arousal • Digit Span • Ask

valence/arousal • Spatial Span

Positive affect music




Negative affect music

Rate baseline valence/arousal

Preparation and

explanation




Negative affect music




Positive affect music

First: hearing test for range of pitches in music

50% Older cohort

50% Older cohort

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Results

As can be seen in Table 1, the participants were cognitively and psychologically intact.

More specifically, emotional status (Beck Depression Inventory; Beck, Steer, & Brown, 1996),

brief mental status testing (MMSE; Folstein, Folstein, & McHugh, 1975), reading ability (Wide

Range Achievement Test Revision 3 reading subtest; Wilkinson, 1993), overall intelligence

(Wechsler Abbreviated Scale of Intelligence Full-Scale Intelligent Quotient; Wechsler, 1999),

visuoperception (Benton Facial Recognition Test; Benton, Sivan, Hamsher, Varney, & Spreen,

1994), and anterograde memory (Rey Auditory-Verbal Learning Test 30-minute Delay (RAVLT;

Rey, 1964) were entirely within normal limits.

Order Effects and Rating Scales

The order of administration of the blocks (i.e., positive music followed by negative music

and negative music followed by positive music) was examined to see if a particular order

influenced our working memory measures. Repeated measures ANOVAs were used to examine

this possibility and the non-significant results indicated that order of administration did not

affect working memory performance, either for Digit Span [F(1,61) = .069, p = .79] or spatial

span [F(1,49) = .088, p = .77].

The rating scale data for mood and arousal were analyzed to examine whether the

musical stimuli induced the intended reaction. Ratings for positive music changed from an

average of 5.87 to 7.52, and ratings for negative music changed from an average of 5.97 to 6.23

(Figure 3). Thus, both sets of ratings indicate that for positive and negative music, self-rated

mood was more positive after music than before (in spite of our expectation that negative

music would induce a negative mood). Putting these numbers to a paired samples t-test

contrasting mood ratings for before and after positive music yielded a significant difference

(t(62) = -9.24, p < .0001). Likewise, a paired sample t-test contrasting mood ratings for before

and after negative music also yielded a significant difference, (t(62) = -2.85, p = .006). By

contrast, arousal ratings before and after music for both positive and negative stimuli were all

very comparable (ranging from a low of 5.87 to a high of 6.13). The associated contrasts were

non-significant (p = .97 and p = .35 for before and after positive music and before and after

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negative music, respectively), suggesting similar arousal for both forms of musical stimuli

(Figure 3).

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Figure 3: Mood Ratings

Fig 3: The changes in average mood ratings before and after listening to music for both working memory trials. The y-axis represents the Likert scale as seen in Figure 1, 0 = sad, 5 = neutral, and 9 = happy

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

Mood Before Music Mood After Music

Positive Music

Negative Music

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Digit Span

Using a paired samples t-test, we contrasted Digit Span performance following positive

music to Digit Span performance following negative music. This analysis was non-significant,

t(62) = .40, p > .05. Because we had available Digit Span data from each participant’s baseline

evaluation (conducted, on average, five years ago; Mean = 18.5, SD = 4.57), we conducted a

second analysis, this time comparing Digit Span data at baseline to Digit Span data collected

during the present study. Those paired samples t-test analyses were significant, for both

positive music (t(62) = 2.68, p = .01) and negative music (t(62) = 2.54, p = .01), indicating that

performance on Digit Span after both forms of music was improved.

Spatial Span

The spatial span data from 12 participants were omitted secondary to technical issues

with a touch monitor, which left 51 participants in the final sample for data analysis. Using a

paired samples t-test, we contrasted spatial span performance following positive music to

spatial span performance following negative music. This analysis was non-significant, t(50) =

1.08, p > .05.

Musical Expertise

Digit Span and Spatial Span performances were further analyzed according to musical

expertise. As mentioned above, a survey was administered to participants at the start of the

study to evaluate musical background and the extent of such experience. A cutoff of five years

was used to designate whether or not a participant would have “musical expertise” or not. The

expert and non-expert groups were contrasted using independent samples t-tests and the

results were non-significant (all p > .05) for the working memory tasks.

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Discussion

In this study, we found evidence that listening to music has a facilitative effect on

auditory-verbal working memory. This is noteworthy given the irrefutable fact that working

memory declines with increasing age, and there currently is no solution to halting or slowing

this process. Moreover, these data extend prior findings to suggest that music’s facilitative

effects go beyond visual-spatial cognitive tasks and into the auditory-verbal domain. Contrary

to our hypothesis, our data indicated that both positive and negative music improved auditory-

verbal working memory performance (i.e., Wechsler Digit Span) among healthy, older adults.

More specifically, Digit Span scores increased after listening to music when compared to Digit

Span data obtained approximately 5 years ago. By contrast, listening to music did not have a

facilitative effect on Spatial Span, a visual-spatial working memory task. However, it is essential

to note that we could not test for musically-driven improvement in spatial span in the same

manner in which we tested for improvement in Digit Span because spatial span was not a part

of the neuropsychological battery administered years previously. This leaves open the question

as to whether spatial span, too, would have shown musically-driven improvement if compared

to previously obtained data.

The finding that negative music facilitated Digit Span performance in the same manner

as positive music was unexpected. However, our self-rating data sheds some light on this

surprising finding. First, participants rated their mood as improving (i.e., becoming more

positive) from before musical stimuli to after musical stimuli for both positive and negative

stimuli. Second, self-rated arousal revealed notably similar ratings suggesting that arousal was

quite comparable for both the positive and negative music. Taken together, the mood and

arousal ratings for positive and negative stimuli suggest that the negative music was perceived

in a very similar fashion to the positive music.

The rating data find some traction in a well-replicated concept in lifespan development

entitled socioemotional selectivity theory (SST; Carstensen et al., 1992). SST postulates that,

secondary to an understanding of constraints on life longevity, older adults alter their strategies

for emotional regulation and focus on positive emotions. In other words, this phenomenon can

be termed a positivity bias/effect, and refers to the fact that older adults focus on and

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demonstrate a bias towards positively-valenced material and sometimes avoidance or non-

recognition toward negatively-valenced material (Carstensen, Isaacowitz, & Charles, 1999).

Thus, the music that was intended to be negative was perceived as positive, perhaps as a result

of SST, thereby negating (or greatly reducing) our mood manipulation (once again, see Figure 3).

This phenomenon has been demonstrated physiologically, in a functional magnetic resonance

imaging (fMRI) brain study. Participants were subjected to both positive and negative music

(versus dissonant chords) and the researchers found that the same brain regions of interest

were activated, suggesting that both positive and negative music were perceived in a

comparable fashion (Koelsch, 2006).

Behavioral (Sattler, 1982), neuroscientific (deKayser et al., 1990), and large-scale

standardization sample data (Wechsler, 1997) all converge on the same finding that working

memory abilities decline with age. In light of this fact, it is quite impressive that Digit Span data

gathered approximately 5 years ago was inferior to data gathered years later in an elderly

population. In fact, a recent meta-analysis on practice effects of neuropsychological

instruments found that increased age was associated with decreases in estimated scores after a

neuropsychological test was given more than once (Calamia, et al., 2012). An equation that the

study developed is able to predict the expected increase or decrease in specific

neuropsychological tests after participants were retested. Applying that equation to our cohort

with an average age of 74 and an average test-retest interval of 5 years, there would be a

predicted score decrease in Digit Span of approximately 1 point.

A proposed mechanism for our findings is consistent with Ashby’s hypothesis of

increased dopamine flow to the prefrontal cortex to modulate mood as well as enhance

working memory (1999). The feeling of happiness, or positive mood, is secondary to increased

dopaminergic flow to the prefrontal cortex, which is also responsible for working memory.

Ashby’s hypothesis states that this increased dopamine can simultaneously enhance working

memory. In the context of using music as a stimulus, our conclusion also supports Thompson’s

mood and arousal hypothesis (2001), that pleasurable music can enhance cognition through

mood modulation. As stated before, the negative music was also found to be pleasurable by

participants, and thus increased working memory using this proposed mechanism. Since it is

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well known that increased moods are mediated by increased dopaminergic flow to the

prefrontal cortex, it can safely be assumed that this is also the mechanism for enhanced

working memory.

Our data were also analyzed to examine participant’s musical expertise and genre

preferences in relation to working memory performance. The musical expertise analysis was of

particular interest given a myriad of research interest in the long-term benefits of musical

training. Many studies have investigated the influence of musical lessons on potential non-

musical cognitive benefits in children (Chan et al., 1998; Hassler et al., 1985; Schellenberg et al.,

2003). The outcomes of these studies have shown that there is no consensus given the

impossibility of comparing the same participant with and without musical training. However,

given our participant cohort, we felt that it would be interesting to compare musicians that

have had life-long training to those with little to none musical training. In addition, musical

genre preference was also of interest due to the possibility of preference affecting mood and

arousal ratings of the music samples. Unfortunately, we did not find any associations between

the expertise variable and working memory. However, an explanation for the lack of an effect

may lie in the characteristics of this cohort of elders. Some of the participants, while being

active members of the Iowa City Band, did not receive much formal training. Most learned on

their own or via group lessons, and thus few had private lessons or attended music school. It

may be that there is a particular skill level that is required to be met before any effects are seen.

Several neuroimaging studies have shown differences in the cortical structures between

musicians and non-musicians, but all the individuals in these studies were either professional

musicians or academicians in music (Chen et al., 2008). Regardless, it is still noteworthy that the

level of musical training that this cohort had did not produce any effect on working memory

after music, should it exist. An explanation for the lack of an effect of genre preference on

working memory performance may be that participants can have more than one preference.

Participants were only allowed to choose one genre on the musical survey given, and it is highly

likely that they have more than one favorite type of music.

Our study had several limitations. It should be noted that the subjective mood rating

scale filled out by the participants was rather dichotomous. Participants were asked to rate

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their mood as either happy or sad, and the combination or “neither” was not well represented.

An additional limitation involves the medium by which participants completed the spatial span

task. To reiterate, this was a computerized test that demanded a touch screen response. The

touch screen raised several concerns, including novelty, fine motor response, and calibration of

the touch screen. There were occasions where participants had difficulty grasping the tactile

response of the screen, resulting in faulty data collection that could not be corrected. As such,

this placed a restraint on the accuracy of some spatial span data that were not analyzed. In

hindsight, a tabletop version of this task may have been more advantageous.

The results of this study have important social and clinical implications, and have the

potential to be beneficial for all older adults in facilitating cognitive processes. This finding does

not represent a cure for the natural decline of different types of memory with age. Rather, it

supports the notion that there are inexpensive, non-invasive methods for enhancing day-to-day

tasks, such as paying bills or grocery lists. Such tasks are still crucial to the daily activities of

older adults. As the older adult segment of our population grows, inexpensive, non-invasive

methods of maintaining memory with age will become a very important topic. Music is

ubiquitous in all media, and enjoying a task while potentially providing some mental benefits

would be a great future direction to explore.

A possible future direction would be to examine working memory when comparing

music versus dissonant sound. Such a comparison may be more definitive for investigating

cognitive benefits from music and also would allow participants more freedom in exploring

their moods instead of being relegated to “happy” or “sad”. Another interesting next step

would be to incorporate functional imaging (e.g., fMRI) into the study, which would be essential

to obtaining neural data as to whether or not the prefrontal cortex demonstrates more activity

after listening to music. This would help alleviate concerns of subjective mood ratings and also

open the door for additional investigations of why music simply and elegantly captivates our

attention as well as, perhaps, our cognition.

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The Cognitive Effects of Music: Working Memory Is Enhanced