Does Music Listening Affect Attention? A Literature Review

Abstract

This review focused on knowledge about the effects of music on attention. The revision was performed in compliance with the PRISMA protocol, being registered at Prospero under number CRD42020172933. Across reviewed studies, the music improved performance on attention tasks, either by listening or using it within a procedure to modulate mood and motivation. It is still difficult to generalize and compare the results because of methodology and study design diversity. Further studies are needed to increase knowledge about the effect of music effect, especially to evaluate if it might have any potential clinical use.

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Does Music Listening Affect Attention? A Literature
Review
Camila Guimarães Mendes, Luiza Araújo Diniz & Débora Marques Miranda
To cite this article: Camila Guimarães Mendes, Luiza Araújo Diniz & Débora Marques
Miranda (2021): Does Music Listening Affect Attention? A Literature Review, Developmental
Neuropsychology, DOI: 10.1080/87565641.2021.1905816
To link to this article: https://doi.org/10.1080/87565641.2021.1905816
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Does Music Listening Aect Attention? A Literature Review
Camila Guimarães Mendes
a
, Luiza Araújo Diniz
b
, and Débora Marques Miranda
a,c
a
Graduate Program in Children and Adolescent Health, Federal University of Minas Gerais (UFMG), Belo Horizonte,
Brazil;
b
School of Medicine, Federal University of Viçosa (UFV), Belo Horizonte, Brazil;
c
Department of Pediatrics,
Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
ABSTRACT
This review focused on knowledge about the eects of music on attention.
The revision was performed in compliance with the PRISMA protocol, being
registered at Prospero under number CRD42020172933. Across reviewed
studies, the music improved performance on attention tasks, either by
listening or using it within a procedure to modulate mood and motivation.
It is still dicult to generalize and compare the results because of methodol-
ogy and study design diversity. Further studies are needed to increase
knowledge about the eect of music eect, especially to evaluate if it
might have any potential clinical use.
ARTICLE HISTORY
Received 11 August 2020
Revised 4 February 2021
Accepted 15 March 2021
Introduction
Attention facilitates selecting relevant information and allocating cognitive resources or processes
necessary during everyday life (Cohen, 2014). This skill is evident from birth and continues to develop
into young adulthood (e.g., 20-years-old) (Cohen, 2014). However, attention refers to a broad class of
cognitive processes and a wide range of behavioral phenomena (Cohen, 2014). Individual resources
(i.e., cognitive) influence both attention capacity and environmental factors (i.e., presence of reward or
stimulation). In this sense, music has been investigated in the literature as an external factor capable of
affecting cognitive tasks’ performance (Dalton & Behm, 2007; Schwartz, Ayres, & Douglas, 2017).
Specific regions of the brain are implicated by music. Music affects processes related to memory,
motor control, timing, language, emotion, and reward circuits, suggesting a possible involvement of
dopaminergic pathways (Demarin, Bedekovic, Puretic, & Pasic, 2016; Salimpoor, Benovoy, Larcher,
Dagher, Zatorre, & 2011). For example, when the listener listens to a piece of music, the brain’s right
and left hemispheres work together to process the melody and analyze the other musical elements (e.g.,
rhythm, pitch, and timbre) while the limbic system activates an emotional response (Hampton, 2007).
Such has been growing evidence that music improves attention since music enhances arousal and
increases motivation, potentially benefiting the learning process through emotional processes (Dalton
& Behm, 2007; Husain, Thompson, & Schellenberg, 2002).
Listening to music was investigated as a potential help for children with learning difficulties and
sustaining attention, such as children with ADHD and children with autism (Abikoff, Courtney,
Szeibel, & Koplewicz, 1996; Greenop & Kann, 2007; Hallam, Price, & Katsarou, 2002; Lanovaz,
Sladeczek, & Rapp, 2011). While in some studies, music was reported as a distractor that may deter
the performance of specific tasks such as reading comprehension, memory processes (i.e., memorizing
advertisements, specific memory tasks, and remembering texts read before) and driving performance
(Etaugh & Michaels, 1975; Kämpfe, Sedlmeier, & Renkewitz, 2010; Treisman, 2006).
Previous reviews gathered a series of studies about the effect of music listening procedures on an
individual’s performance in general tasks (Dalton & Behm, 2007; Schwartz et al., 2017). However, yet
CONTACT Camila Guimarães Mendes camilagmbh@gmail.com Universidade Federal de Minas Gerais, Avenida Prof. Alfredo
Balena, 190 Santa Efigênia, Belo Horizonte 30130-100, Brazil.
DEVELOPMENTAL NEUROPSYCHOLOGY
https://doi.org/10.1080/87565641.2021.1905816
© 2021 Taylor & Francis Group, LLC

the results were inconclusive and did not allow comparisons due to methodological and outcome
differences. Thus, this study focused on understanding music’s effects on a single outcome: attention.
Method
This study is registered at PROSPERO, under number CRD42020172933.
Search criteria
A systematic review was conducted in February 2020 with a guiding question: What is music’s effect
on the attention? For this, were used the following keywords: “music,” “background music,” and
“attention,” for search articles related to the topic. Mesh descriptors and keywords were used in
English and were combined with operators AND and OR. Databases were PubMed, PsycINFO, Eric,
and Scopus. The search strategies for each database are detailed (see Table 1). This review was
conducted according to the model of the Preferred Reporting Items for Systematic Reviews and Meta-
Analyzes – PRISMA (Shamseer et al., 2015).
The inclusion criteria were: (1) studies if they involved any music-listening procedure (i.e the
manipulation of background music or listening to music); (2) at least one dependent variable assessed
the participants’ attention; (3) in English. There was no delimitation of a year of publication. Studies
that addressed interventions using music, such as music therapy or art therapy were excluded, as well
as the studies in which participants actively manipulated musical instruments (e.g., musical training
studies). Because the term “attention” refers to many different behavioral and cognitive phenomena,
was considered a valid effect on attention since some measure was used to assess the ability to focus,
divide, or sustain mental effort during task performance (Zillmer, Spiers, & Culbertson, 2007). The
term “music” was considered as an organized sound that includes general elements that govern
melody and harmony, rhythm, dynamics (i.e., loudness and softness), and the sonic qualities of timbre
and texture (Burton, 2015).
Screening procedure
The screening of studies was performed in pairs, including initial screening of abstracts and titles from
the search independently to identify potential trials according to the inclusion and exclusion criteria.
After selecting the articles eligible for full reading, the two authors discussed the results and reached
a consensus of articles included to review. If no agreement was reached, a third author decided.
A unified neuropsychological model of attention (Cohen, 2014) was used to help group the
measurement tools mentioned by the articles. The categories were as follows: (1) selective attention
(when the task is to give preference to certain stimuli over others); (2) focused attention (when the task
requires the selection abilities of the individual); and (3) sustained attention (when the task requires
attentional persistence over a period of time) (Cohen, 2014). When a measure could group under
multiple categories, it was placed under the domain that best describes its function.
Table 1. Search strategies.
Data Base Search strategies
PubMed Title/abstract: music OR background music AND attention
PsycINFO Abstract: “music” OR Abstract: background music AND Abstract: attention AND Document Type: Journal Article
Eric music OR background music AND attention
Scopus ABS (“music” OR “background music” AND “attention”) AND DOCTYPE (ar) AND (LIMIT-TO (EXACTKEYWORD, “Music”)
OR LIMIT-TO (EXACTKEYWORD, “Attention”)) AND (LIMIT-TO (LANGUAGE, “English”) OR LIMIT-TO (LANGUAGE,
“Portuguese”))
2C. G. MENDES ET AL.

Data extraction
Data extracted followed a standard form, including first author, year, country, study design, objectives,
music listening procedures, study subject, measurement of attention, and main findings.
Risk of bias (quality) assessment
The same review authors assessed the risk of bias in each included study using the Cochrane
Collaboration’s risk of bias tool (Higgins et al., 2011).
Results
A total of 2940 articles were identified from the databases using the search strategy. Two additional
references were hand-searched and included in this review. Two hundred and twenty-four duplicates
were removed, and 2400 articles were assessed by title and abstract. Of these, 2296 reports were
excluded because they did not fulfill the predetermined criteria. Hence, 104 papers were included and
their full-text analyzed, of which 18 met inclusion criteria and one more hand-searched after full-text
reading.Nineteen (N = 19) studies were included in the review. A flow chart (Figure 1) of the process
was made following the PRISMA statement (Shamseer et al., 2015).
The participants and music listening procedures varied, so the focus remained on qualitative
synthesis in this review. When the studies presented secondary outcomes that were not directly related
to music and attention, the focus remained on presenting and discussing only the main findings
according to this review’s purpose.
Identification
Screening
Records after duplicates removed
(n =2400)
Additional records identified
through other sources
(n = 2)
Records identified through
database searching
(n = 2940)
Eligibility
Included
Records screened
(n =2400)
Records excluded
(n =2296)
Full-text articles excluded,
with reasons (n =86)
-Music as Therapy= 10
-Didn’t measured
attention=27
-Music training=16
-Non English=2
-Out of theme=33
Full-text articles assessed for eligibility
(n = 104)
Studies included in qualitative
synthesis
(n = 19)
Additional records identified through
full-text reading
(n = 1)
(n = 1)
Figure 1. Flow diagram.
DEVELOPMENTAL NEUROPSYCHOLOGY 3

Table 2. Descriptive characteristics of included studies according the model of attention.
First
author Year Country Study design Objectives Music Listening Procedures Study subject
Measurement of
attention Main findings
SELECTIVE ATTENTION
1 Cristy Ho 2007 UK Quasi
experimental
study
Investigate what influence, if
any, the ‘Mozart effect’
would have on temporal
attention using the visual AB
task
The participants performed the
Attentional Blink Task under
three conditions: – Silence
(no music) – Mozart forward
condition (listening Mozart’s
Sonata, normally) – Mozart
backward condition:
listening Mozart in reverse.
N = 34
Female = 21
Male = 13
Age: 18–23
Attentional Blink (AB)
Task
Accuracy to T2 on AB
task: Mozart
forward > Mozart
backward >
silence
2 Jefferies 2008 Canada Quasi
experimental
study
Examine how the emotion-
attention relationship is
influenced by chances in
mood valence and arousal
state
Participants performed the AB
task during the induction
mood Sad (negative affect,
low arousal) Calm (positive
affect, low arousal) Anxious
(negative affect, high
arousal) Happy (positive
affect, high arousal)
Neutral (no induction
procedure)
Induction mood: listening to
music and to generate
thoughts consistent with
induced mood, except in the
neutral condition
N = 100 Attentional Blink (AB)
Task
T2 accuracy on AB
task:
Sadness: highest
levels
Anxiety: lowest
levels
Calm and happy
states (low and
high arousal
combined with
positive affect):
intermediate
3 Xie 2012 China Quasi
experimental
study
To investigate the existence of
the temporal component of
Mozart effect, the influence
of arousal or mood changing
to AB when listening to
Mozart Sonata
The participants performed the
test listening Mozart Sonata
for Two Pianos in D Major,
K.448
Experiment 1:
- Baseline: in silence (no
music) – Mozart normal
speed – Mozart fast speed
Experiment 2: – Baseline: in
silence (no music) – Mozart
Major played in normal
speed – Mozart Minor
played in fast speed
EXP. 1: N = 26
Female = 12
Male = 14 Age:
21–27 EXP. 2:
N = 29 Female = 13
Male = 16
Age: 21–24
Attentional Blink (AB)
Task
Experiment 1:
Accuracy of T2:
Mozart Normal >
silence
Mozart fast <
Mozart Normal
Experiment 2:
Accuracy of T2:
Mozart Major and
Mozart Minor <
silence.
Not significant.
(Continued)
4C. G. MENDES ET AL.

Table 2. (Continued).
First
author Year Country Study design Objectives Music Listening Procedures Study subject
Measurement of
attention Main findings
4 Jiang 2011 China Quasi
experimental
study
The present experiment aimed
at determining the influence
of inducing a specific mood
on attentional networks
ANT before and after mood
induction (listen to music
and generate thoughts
consistent with induced
mood).
Positive: listen to a Bach’s
Brandenburg Concerto No. 3
Negative: listen to
Prokofiev’s “Alexander
Nevsky
Neutral: reading a collection
of facts about China
N = 36 Female = 25
Male = 11
Age = 18–24
Attention Network
Test (ANT)
Alerting scores:
negative mood >
positive mood >
neutral
RT:
negative >
positive and
neutral (under no
cue and double
cue condition)
negative <
positive and
neutral mood
under all cue
conditions except
the double-cue
condition.
5 Mc
Connell
2011 Canada Randomized
Controlled
Trial (RCT)
To examine the combined
effects of mood and arousal
on a variety of attention
measures.
STUDY 1:
Answered the mood-arousal
measures before and after
listen to music for 10 min.
Music: Mozart’s sonata
K. 448 varied in both tempo
and mode
STUDY 2: Perform the ANT
test after listen the music for
10 min.
Fast-major (n = 16);
Slow-major (n = 16)
Fast-minor (n = 17); Slow-
minor (n = 15)
STUDY 1: N = 24
Female = 16
Male = 8
Age = M.19.7
STUDY 2:
N = 66
Age = M.19.3
Female = 50
Male = 16
Attention Network
Test (ANT)
Alerting scores:
negative >
positive (not
significant)
Flanker
congruency
effects (executive
control):
Arousal high:
positive >
negative.
Arousal low:
Emotional
Valence did not
influence the
magnitude of the
flanker effect.
(Continued)
DEVELOPMENTAL NEUROPSYCHOLOGY 5

Table 2. (Continued).
First
author Year Country Study design Objectives Music Listening Procedures Study subject
Measurement of
attention Main findings
6 Darrow 2006 USA Quasi
experimental
study
To determine if music
compromises one’s selective
attention, and if music,
affects music majors and
non-music majors
differently.
Participants took the d2 test
both with the background
music and without. The
order of conditions was
counterbalanced between
subjects.
Groups: music majors and
nonmusic majors
The music was specific to
the individual participant.
N = 87 d2 Test Music majors music
first < non music
condition
Instrumental >
with vocal
More items under
the music
condition.
Music majors >
nonmusic majors
under all
conditions.
Errors:
Music major <
Non music
Items correctly:
Music major >
Non music
Concentration:
Music > non
music
7 Rowe 2006 Canadá Quasi
experimental
study
To examine if the increased
cognitive flexibility and
creative thinking associated
with positive mood reflects
a change in selective
attention
All participants performed the
tasks during each of induced
affective states.
Happy mood induction:
listen to a Bach’s
Brandenburg Concerto 3
Sad mood induction: listen
to Prokofiev’s “Alexander
Nevsky: Russia Under the
Mongolian Yoke”
Neutral: reading a collection
of facts about Canada
N = 24
Female = 12
Male = 12
Flanker Task Positive moods
resulted in greater
flanker
interference
relative to both
sad and neutral
moods
FOCUSED ATTENTION
(Continued)
6C. G. MENDES ET AL.

Table 2. (Continued).
First
author Year Country Study design Objectives Music Listening Procedures Study subject
Measurement of
attention Main findings
8 Lake 2011 EUA Quasi
experimental
study
Examination the effect of
listening music on attention
in groups of older adults
(mild cognitive impairtment
and normal)
Perform to test of attention
after listen to or not music
for 10 min
Group control (n = 12)
Group patients (n = 12)
Music: “Spring” movement
of Four Seasons by Vivaldi
(1990)
N = 12 amnestic mild
cognitive impairment
Female = 3
Male = 9
Age = M.74.3
N = 12 cognitively
intact
Female = 8
Male = 4
Age = M.66,1
Digit Span
Coding
Digit Span
Music x silence: no
difference found.
Music order
(Music then
Silence x Silence
then Music): no
difference found
Test order
(Version A then
B x Version B then
A)
Coding
Controls >
patients
Music x silence: no
difference found.
Music order
(Music then
Silence x Silence
then Music): no
difference found
Test order
(Version A then
B x Version B then
A)
(Continued)
DEVELOPMENTAL NEUROPSYCHOLOGY 7

Table 2. (Continued).
First
author Year Country Study design Objectives Music Listening Procedures Study subject
Measurement of
attention Main findings
9 Herlekar 2019 Indian Randomized
Control Trial
To assess the effect of classical
instrumental, background
music on successive divided
attention tests
- Form A – pre test, without
music (all students)
- Form B – during exposure
to music/control.
- Form C – during post test
in all subjects, without music
Indian/Malaysian: Music
group (n = 30)
Control group (n = 30): no
music
N = 60
Indian
Female = 15
Male = 15
Malaysian
Female = 15
Male = 15
Age: 18–20
Symbol digit modality
testing (SDMT)
Posttest (total and
correct scores):
Music group >
Control group
During music/rest:
“Correct”:
Malaysian music >
control
Malaysian music >
Indian music
“Total”: Malaysian
music > control
Malaysian music >
Indian music
Malaysian control:
highest errors
POSTTEST
“Correct”:
Malaysian music >
control
Malaysian music >
Indian music
“Total”: Malaysian
music > control
Malaysian music >
Indian music
(Continued)
8C. G. MENDES ET AL.

Table 2. (Continued).
First
author Year Country Study design Objectives Music Listening Procedures Study subject
Measurement of
attention Main findings
10 Begum 2019 Bangladesh Cross-Sectional To examine the impact of soft,
stimulating, and depressing
songs on the attention of
students.
Group1(Control): without any
songs
Group 2 (Soft): That’s My
Name (Akcent);
Group 3 (Stimulating): Rain
Over Me(Pitbull featuring
Marc Anthony);
Group 4 (Depressing):
Broken Angel (Arash
featuring Helena).
280 students
Female = 125
Male = 155
Age: 18–25
Numeral Finding Test
(NF test)
Typo Revealing Test
(TR test)
NF TEST and TR TEST:
% attention
Group 2 (soft
song) > Group 1
(control)
Group 3
(stimulating song)
> Group 1
(control).
Group 4
(depressing) <
Group 1 (control).
Among all groups
lowest attention
was reported by
Group 4 and
highest by Group
3.
All results were
statistic
significant.
SUSTAINED ATTENTION
(Continued)
DEVELOPMENTAL NEUROPSYCHOLOGY 9

Table 2. (Continued).
First
author Year Country Study design Objectives Music Listening Procedures Study subject
Measurement of
attention Main findings
11 Baldwin 2016 USA Quasi
experimental
study
To establish a database of
popular music varying along
the dimensions of tempo
and valence and to examine
the impact of music varying
along these dimensions on
restoring attentional
resources following
performance of a sustained
attention to response task
(SART) vigil.
Experiment 1:
80 popular songs (“top hits”
lists) were sorted by the
researchers to participants
listen and evaluate the
emotions inspired by music
Experiment 2:
7 minute SART block, + an
intervention + a second
7 minute SART block.
Intervention group: positive
fast/slow songs,, negative
fast/slow songs,
Control groups: no music
7 min. SART block, + 7 min.
break (silence)/no break
+ second SART block.
Experiment 1: N = 69
Female:48
Male: 21
Age: M.21.59
Experiment 2: N = 89
Female = 65
Male = 24
Age = M. 20.73
Sustained Attention to
Response Task
(SART)
Misses after the
intervention:
Positive slow
songs condition
showed
significant
reduction
Negative
conditions or no
break conditions
showed
significant
increases
Music preference:
Like the music
less = tended to
have increased
misses no matter
which group they
were
Moderately like
the music = the
misses were more
similar based on
their group.
12 Lejeune 2018 Switzerland Randomized
Controlled
Trial (RCT)
To evaluate long-term effects
of music listening on
cognitive and emotional
development in preterm
children by comparing them
to a preterm control group
with no previous music
exposure and to a full-term
group at 12 and 24 months.
Preterm-music (n = 23):
listened to music during
8 min with headphones,
from gestational age of
33 weeks until hospital
discharge or term-
equivalent age.
5 times/week agree with the
state of wakefulness (e.g
helping the baby to wake
up).
Preterm-control (n = 17):
without music
Full-term (n = 17): no
previous music exposure
N = 44 (17 full-term and
27 preterm).
Age = 12–24 months
Laboratory
Temperament
Assessment Battery
(Lab-TAB)
No difference found
for sustained
attention
(Continued)
10 C. G. MENDES ET AL.

Table 2. (Continued).
First
author Year Country Study design Objectives Music Listening Procedures Study subject
Measurement of
attention Main findings
13 Sarkamo 2007 Finland Randomized
Controlled
Trial (RCT)
To determine whether regular
selfdirected music listening
during the first months after
middle cerebral artery (MCA)
stroke can enhance the
recovery of cognitive
functions and mood.
Listen to music or narrated
audio books (minimum 1 h
per day) for 2 months while
still in the hospital or at
home.
Music group (n = 18): their
own favorite music in any
musical genre
Language group (n = 19):
narrated audio books on
cassette selected by the
patients from a collection of
the Finnish Celia library for
the visually impaired.
Control group (n = 17):
without listening material
N = 54
Music group:
M.56.1 years
Language group:
M.59.3 years
Control:
M.61.5 years
Clinical
neuropsychological
assessment
Focused attention
and verbal
memory:
Music group >
Language group
and Control Group
Depressed,
confuse mood:
Music group <
control group
DEVELOPMENTAL NEUROPSYCHOLOGY 11

Table 3. Descriptive characteristics of included studies that used the same test to measure the selective attention.
First
author Year Country Study design Objectives
Music Listening
Procedures
Study
subject
Measurement
of attention Main findings
1 Shih 2009 China Randomized
Controlled
Trial (RCT)
To determine whether background music
played during or preceding a task
requiring attention/concentration
influences performance.
Group 1 (n = 11): listened classical
compositions of Canon while
completed the test
Group 2 (n = 11): completed the
test in silence
Group 3 (n = 10): listened to
Canon music for 10 minutes prior
to the experiment, and took the
test in silence.
N = 32
Female = 14
Male = 18
Age: 20–27
Chu
Attention
Test
Total score of attention:
Group 3 (music prior to test) >
Group 2 (no music) > Group 1
(music during test).
Variation in performance:
group 2 > group 1 > group 3
Standard of error:
Group 3: the highest
Total number of questions
answered:
Group 3 > Group 2 > Group 1
2 Huang 2011 China Randomized
Controlled
Trial (RCT)
To understand how background music
and listener fondness for types of
music affects worker concentration
Listen to music or not while
perfoming test.
Group 1 (n = 23): quiet
environment
Group 2 (n = 22): popular music
Group 3 (n = 20): classical light
music Group 4 (n = 24):
traditional Chinese music
N = 89
Female = 52
Male = 37
Age: 19–28
Chu
Attention
Test
Test takers exposed to background
music < without music. But this
difference was not significant.
Liking for the background music:
Group 1 (no music) x Group
(background music): strongly
liked or strongly disliked = lower
attention test scores.
3 Shih 2012 China Randomized
Controlled
Trial (RCT)
To compare how music with and without
lyrics affects attention of workers
Baseline: Group 1 (n = 49) and
Group 2 (n = 53) performed the
test in a quiet environment
Experiment (3 weeks later):
listened music while performing
the test
Group 1 (music with lyrics)
Group 2 (music without lyrics)
N = 102
Female = 46
Male = 56
Age: 20–24
Chu
Attention
Test
Group 1 baseline x Group 2
baseline: no difference
Group 1 baseline > Group 1
(music with lyrics)
Group 2 baseline > Group 2
(without lyrics): not significant
Group 1 (with lyrics) < Group 2
(without lyrics): not significant
12 C. G. MENDES ET AL.

Table 2 summarizes the included studies grouped according to the measures of attention used for
this study. Since the same measurement of selective attention was found in six of the included studies,
the data was pooled together (Table 3). Thus, 13 studies grouped as outlined in what follows:
Selective attention (N = 7): included studies that used the paradigm of attention blink, that consists
in the task of rapid serial visual presentation (Ho, Mason, & Spence, 2007; Jefferies, Smilek, Eich, &
Enns, 2008; Xie, Miao, Zhang, & Tang, 2010). This task involves participants identifying two target
digits T1 and T2 (in their correct order of presentation) presented among a stream of distractor letters
(Cohen, 2014). Also, some studies used Flanker tasks, such as the Attention Network Test (ANT)
(designed to measure individual differences in alerting, orienting, and executive attention) (Jiang,
Scolaro, Bailey, & Chen, 2011; McConnell & Shore, 2010; Rowe, Hirsh, & Anderson, 2006). The ANT
is similar to that a single flanker task combined with cueing conditions and of dissociation of effects
associated with cueing and flanker conditions. The other flanker task used included one based on
Eriksen and Eriksen’s classic flanker task. Finally, one single study used the d2 Test (a cancellation test
measures accuracy and speed in differentiating stimuli varying in visual detail) (Darrow, Johnson,
Agnew, Fuller, & Uchisaka, 2006).
Focused attention (N = 3): included studies used tests that require working memory, and coding,
mental arithmetic, and other effortful cognitive operations. The tests were: Digit Span (requires
participants to repeat, in the same order, a string of numbers read to them) and Coding (requires to
rapidly fill in empty boxes as with digits corresponding to each unique geometric shape are given to
them) (Lake & Goldstein, 2011); Symbol digit modality testing (SDMT) (participants were asked to
scan the coding key which consists of 9 symbols each paired with a number from 1 to 9 and write the
number corresponding to each symbol) (Herlekar, Siddangoudra, & Professor, 2019); and Numeral
Finding Test (finding of the object, elimination of unwanted object, and calculation of desired object
figures within a given time frame) and Typo Revealing Test (memorizing power, retrieval capacity,
and finding of desired object figures) (Begum et al., 2019).
Sustained attention (N = 3): included studies which involved tests that measured sustained atten-
tion. A study used the Sustained Attention to Response Task (SART) (involves the rapid presentation
of black numbers, 0–9, on a white background and requires the participants to response with a mouse
click to every number except for the number 3) (Baldwin & Lewis, 2017). Another study used the
Laboratory Temperament Assessment Battery (Lab-TAB) to measure the attention of children. The
battery includes an episode task where the child should play freely with decorated cubes for 3 min
(divided into 6-time intervals of 10s). Each time interval is scored for intensity of the facial interest,
duration of observation, and manipulation. All scores were averaged to compute a composite score of
Sustained attention (Lejeune et al., 2019). A study also used a single clinical neuropsychological
assessment that assessed focused attention and sustained attention (Särkämö et al., 2008).
Table 3 summarizes six studies that used the same test to examine the attention. The Chu Attention
Test is a standard evaluation tool frequently used in occupational therapy as a predictor of attention
level in community services. The participants are asked to look at a series of scrambled codes and
search for the symbol, “*,” randomly distributed among 00 to 99 items within 10 minutes (Huang &
Shih, 2011; Shih, Chen, Chiang, & Liu, 2015; Shih, Chien, & Chiang, 2016; Shih, Huang, & Chiang,
2009, 2012; Wu & Shih, 2019).
Study design
All included studies were experimental, with nine randomized controlled trials (RCTs) (N = 9), eight
quasi-experimental (N = 8), and two Cross-Sectional studies (N = 2). The majority of included RCTs
(N = 5) were performed in China, two in Canada, and one in India, Finland, and Switzerland. Three
quasi-experimental studies were performed in China, three in EUA, and one in Canada and in UK.
The cross-sectional study was performed in Bangladesh. Thus, studies were conducted mostly in Asia,
followed by Europe and North America. The studies were published the year 2006 to 2019 (see
Table 2).
DEVELOPMENTAL NEUROPSYCHOLOGY 13

Risk of bias
‘Risk of bias’ summaries are reported in Figures 2 and 3, with details about each ‘Risk of bias’ item for
each included study. Most studies reported insufficient detail to allow accurate assessment of domains
of blinding and allocation concealment. With music listening, it is difficult to conceal the intervention
from the participant. Thus, participant blinding was not used in any of the studies included, only to the
researcher or caregiver, when appropriate.
Participant characteristics
Most studies (N = 16) enrolled healthy adults, workers, students (18–28-years-old), and typically
developing children (12–24-months-old). One study enrolled patients who suffered a stroke (56–61-
years-old), with chronic schizophrenia (29–63-years-old), and diagnosed with amnestic mild cognitive
impairment (mean of 74.3). Considering the samples, the number of subjects was more than 1,000
healthy people (mean of 80 participants per study; women and men), followed by 54 patients after
stroke, 49 patients with chronic schizophrenia, and 12 individuals with mild cognitive amnestic
impairment.
Music listening procedure
Different types of music listening procedures were identified: listening to music prior to or
followed by attention performance for a short time (Baldwin & Lewis, 2017; Lake & Goldstein,
2011; Shih et al., 2009), as background (listening to music while performing the task) (Begum et al.,
2019; Herlekar et al., 2019; Ho et al., 2007; Huang & Shih, 2011; Darrow et al., 2006; Shih et al.,
2015, 2016, 2012; Wu & Shih, 2019; Xie et al., 2010) or as an induction procedure (i.e music with
positive or negative valence to promote a particular mood) (Jefferies et al., 2008; Jiang et al., 2011;
McConnell & Shore, 2010; Rowe et al., 2006). The mood induction studies used listener self-
assessments as a measurement. Also, two studies evaluated the music as a long-term intervention
and before the test: patients listened to music by themselves daily (minimum 1 h per day) for 2
months while still in the hospital or at home (Särkämö et al., 2008); and the preterm-music group
listened to music for 8 min for 5 weeks and once in a full-term group at the first days of life
(Lejeune et al., 2019).
Most of the studies had music selected by the researchers, and the most used genre was classical
music (i.e., classical compositions of Bach and Mozart). Two studies did not describe the criteria for
choosing music (Shih et al., 2016, 2012), and three others chose music based on the participants’
Figure 2. Risk of bias graph: review authors’ judgments about each risk of bias item presented as percentages across all included
studies.
14 C. G. MENDES ET AL.

preferences (Begum et al., 2019; Darrow et al., 2006; Särkämö et al., 2008). The methodology to
evaluate the effects of attention yet varied.
Effects of music on selective attention
Three studies used the same task to measure attention. They examined the effect of music on
Attentional Blink Task (Ho et al., 2007; Jefferies et al., 2008; Xie et al., 2010). Two studies compared
Figure 3. Risk of bias summary: review authors’ judgments about each risk of bias item for each included study.
DEVELOPMENTAL NEUROPSYCHOLOGY 15

participants’ attention performance under conditions with background music (Mozart Sonata K.448)
and in silence (Ho et al., 2007; Xie et al., 2010). The results showed a significant improvement in the
accuracy of T2 identification in the AB task while the participants who listened to Mozart typically
played (i.e., forward) versus Mozart played in reverse (i.e., backward) or the silence condition (Ho
et al., 2007). In contrast, the performances of detecting the second target T2 were slightly reduced
under Mozart major and Mozart Minor when compare to the silence condition, though not signifi-
cantly (Xie et al., 2010).
To investigate the effects on attention by changes in both mood valence (negative vs. positive) and
arousal (low vs. high), Jefferies et al. (2008) created induction mood groups according to their ratings.
These ratings included sad (negative affect, low arousal), calm (positive affect, low arousal), anxious
(negative affect, high arousal), happy (positive affect, high arousal), happy (positive affect, low
arousal), anxious (negative affect, high arousal), happy (positive affect, high arousal), or neutral (no
induction procedure). The mood-induction procedure consisted of listening to music and to generate
thoughts consistent with induced mood, except in the neutral condition. This study observed sadness
(low arousal with negative affect) produced the highest levels of performance, anxiety (high arousal
with negative affect) led to the lowest levels of performance, and calm and happy states (low and high
arousal combined with positive affect) were associated with intermediate performance. These results
were specific to one measure of attention, the accuracy of T2 identification.
Darrow et al. (2006) examined the attention with and without previous musical training experience
using the D
2
Test. The study reported the music majors (participants with previous musical training
experience) achieved higher scores under the music condition than the no-music condition. Music
majors processed more items on a test than non-music majors under all conditions, made fewer errors,
processed more items correctly (number processed minus errors), and had better performance scores
than non-music majors. When they heard the music first, the study also reported these participants
completed significantly fewer total items in the following non-music condition, and those who listened
to instrumental music completed more total items than those who listened to music with vocals
(Darrow et al., 2006).
Rowe et al. (2006) and Jiang et al. (2011) used the same background music: a jazzed-up version of
Concerto No.3 of Bach Brandenberg for happy mood induction, the Alexander Nevsky of Prokofiev
(played at half speed) for sad mood, and a collection of basic facts about their own country (i.e.,
population size, gross national product) to read for neutral mood. Rowe et al. (2006) used a Flanker
Task based on Eriksen and Eriksen’s (Eriksen & Eriksen, 1974) to measure the attention and found
positive moods resulted in greater flanker interference relative to both sad and neutral moods.
Additionally, Jiang et al. (2011) revealed that the ANT test’s alerting scores were significantly more
harmful to mood conditions than positive and neutral moods. The same test was used by McConnell
and Shore (2010) during the background music of Mozart Sonata K.448, which varied in both tempo
and mode (Fast-major x Slow major x Fast-minor x Slow minor) as induction procedure for valence
and arousal state. Findings showed that when arousal levels were high, participants who experienced
positive valence showed larger flanker effects relative to participants who experienced negative
valence. However, when arousal levels were low, emotional valence did not influence flanker con-
gruency effects.
Separately, studies of a Chinese group that used the Chu Attention test reported significant music
effects on attention performance. In Shih et al. (2009) the total score of attention and number of
questions answered were greater in workers who listened to music before the test than those who
received the silence condition and then listened to music during the test. Additionally, the group that
listened to music prior to the test had the highest standard of error ratio reflecting the actual quality of
the work accomplished.
Other studies demonstrated that background music might impair performance attention when the
music had lyrics, compared to baseline (without music) (Shih et al., 2016, 2012). None of them
provided the effect size regarding differences between groups; therefore, the current study calculated
Cohen’s D from published data and found a moderate to large effect size in the comparison of the
16 C. G. MENDES ET AL.

control group (no music) and lyric music group (d = 0.41; d = 0.56) (Shih et al., 2016, 2012). These
results demonstrated a potential moderate to enormous harm in using background music with lyric in
the tested scenarios. Attention performance was also lower when the background music had lyrics
compared to when the music did not have lyrics, but the difference was not statistically significant
(Shih et al., 2012). Besides, Shih et al. (2016) found an association between feelings self-assessed by
listeners about the background music with lyrics. Listeners who self-reported feeling “loved” while
music played showed higher scores on their attention test. Whereas, listeners who self-report feeling
“sadness” while music played showed lower scores on attention.
Another study compared the attention performance of musicians and non-musicians and found
higher scores under the music condition than the no-music condition for both groups. Finally, one
study-tested persons with schizophrenia and found that the background music increased the
attention test scores under both types of music: popular and classical light music (Shih et al.,
2015).
Effects of music on focused attention
The posttest scores of Indian and Malaysian’s participants in attention performance in the Symbol
Digit Modality Test (SDMT) were higher in the music group than the control group (no music).
Malaysian participants in the music group were better on the attention test than the other groups
(Herlekar et al., 2019).
Different types of preferred music (divided into soft, stimulating, and depressing songs) showed
effects on two attention tests (Numeral Finding Test and Typo Revealing Test). The attention was
more significant in the stimulating song group than the control group, followed by a soft song group.
The lowest rate was reported for the depressing song group (Begum et al., 2019).
Lake and Goldstein (2011) examined the attention performance of Digit Span and Coding subtests
under music and no music conditions. No difference was found.
Effects on sustained attention
Sustained Attention to Response Task (SART) was performed sometimes with music (positive fast
songs, positive slow songs, negative fast songs, negative slow songs) between its blocks, sometimes
without music and with a break and sometimes without a break according to the groups (Baldwin &
Lewis, 2017). The valence (positive or negative) was used to perceived emotion and was classified by
researchers. The results showed only listening to positive music mitigated misses, while negative
music tended to increase misses compared to the no-music condition, though not above the no
break condition. Additionally, those who described liking the music less, tended to have increased
“misses” independent of the group they were in. Those who moderately liked the music displayed
the same amount of misses as their group did. Individuals after-stroke who listened to their favorite
music for two months had a more remarkable improvement in focused attention and verbal
memory than patients who listened to audiobooks or had no listening activity (Särkämö et al.,
2008).
Discussion
Music listening improved attention performance in healthy people and groups of individuals with
schizophrenia and stroke. Specifically, when the music listening occurs before or during the task,
performance in an attention test was better than in silence (Shih et al., 2016, 2009, 2012; Wu & Shih,
2019). Still, the effect of music on attention performance was influenced by the mood and arousal state
of participants (Baldwin & Lewis, 2017; Jefferies et al., 2008; Jiang et al., 2011; McConnell & Shore,
2010; Rowe et al., 2006) and by characteristics of music such as the presence of lyrics or not (Shih et al.,
2016, 2012).
Most studies showed the positive effects of music on attention, but it is impossible to say what kind
of music or the best time to listen to music (before the task or during the task).A preview meta-analysis
DEVELOPMENTAL NEUROPSYCHOLOGY 17

identified two moderate music factors to explain the heterogeneity on the effect of background music
in performance tasks: tempo and loudness (Kämpfe et al., 2010). The authors concluded that the
tempo with which music is played affects the behavior’s tempo (i.e., speed to eat or to drive). For
example, high-intensity music impaired performance during simple vigilance or simulated driving
tasks (Dalton & Behm, 2007). The first music effects on task performance described in literature were
with music listening before the task (Rauscher, Shaw, & Ky, 1993; Rauscher, Shaw, Levine, Ky, &
Wright, 1994). These studies found participants performed better on spatial-temporal tasks after
listening to a Mozart Sonata than those who did not listen to music (Rauscher et al., 1993, 1994).
One explanation for this result is the listening of preferred or enjoyable music increases pleasant mood
and arousal levels, benefitting cognitive tasks (Schellenberg & Hallam, 2005). However, background
music listening also might be concurrent with task performance (Perham & Sykora, 2012). During the
task, the music causes a narrowing of attention, allowing the performer to block out irrelevant cues
(O’Malley & Poplawsky, 1971). Music might also promote an attentional conflict (i.e., when a task
requires several attentional resources, making it difficult to divide the task’s attention and distractor).
Thus, music’s effect on performance will depend on the music, the task, and the performer (Gonzalez
& Aiello, 2019).
The presence of lyrics, the valence (i.e., negative and positive), mood state and degree of liking self-
reported (i.e., if happy or sad, if like or dislike) or other categories based on the feelings of the listener
(i.e., stimulant, depressing) are other factors that explain the differences found. Though still con-
troversial, findings seem to indicate that feelings such as happiness or calmness seem to improve the
performance of selective attention tasks more than elicited feelings of anxiety or sadness (Shih et al.,
2016, 2012). Feelings of happiness and calmness contributed to poor performance on the attentional
blink task (Jefferies et al., 2008). Rather than depressing songs, music without lyrics or stimulant or
soft music may be more useful to improve work performance (Begum et al., 2019). Previous
experiences with music also changed the effects found in attention. Since musicians and individuals
with music training performed significantly better than nonmusicians, may be because of experiences
with studying music and attending to details in the music. Therefore, they may be more adept at detail-
oriented tests and less distracted by background music (Darrow et al., 2006; Wu & Shih, 2019).
This review’s findings were consistent with previous studies about music’s effect in people with
schizophrenia or post-stroke (Na & Yang, 2009; Soto et al., 2009; Thaut, 1997). Based on previous
studies, music may be beneficial for people who experienced a stroke to improve focused attention and
for people with schizophrenia to improve selective attention. None of the studies in this review
examined other clinical conditions; therefore, the applicability of these conclusions to other clinical
conditions is minimal.
This is the first known systematic review that brought together studies investigating the effect of
music on a single dependent variable, the attention. Six studies from the Chinese population applied
to occupational activities and showed an improvement related to the effect of background music on
work performance. There were nine randomized controlled trials evaluating music used to improve
attention, with moderate to large effect size, but they are not comparable because they use different
attention tasks and stimuli, showing a need for more standardized research on the field. Given these
findings, it seems that music can improve attention, especially listener preferred music or music
without lyrics. Music listening also seems useful as a background (during the performance of a task.
However, more comparative studies are needed for generalization to other contexts and
populations.
Limitations
The first limitation is regarding the heterogeneity of methods for evaluating attention and for music
listening procedures implemented in the studies. These differences made it difficult to compare results
and draw broad conclusions. Secondly, the variety of culture backgrounds also made it challenging to
generalize to other cultural backgrounds different than those examined (Lee & Hu, 2014). While there
18 C. G. MENDES ET AL.

is strong evidence to support the effect of music on attention, it is unknown as to what extent these
results can be generalized to other populations and cultural contexts without further testing.
Lastly, most of the studies were conducted in a laboratory setting. Therefore, it was difficult to infer
the success of music listening in ecological contexts, such as home, school, or workplace.
Conclusion
Attention is a cognitive function essential to the performance of activities in daily life. This systematic
review contributes to synthesizing the current literature related to music listening in changing
attention. Does music listening affect attention? Yes, it does. Music, especially music without lyrics,
tends to help people pay more attention to tasks that require concentration. It is a noninvasive
intervention mostly studied in healthy individuals. More data is needed to determine music’s potential
applications to other clinical conditions that affect attention. If music does not have side effects, it
could at times be distracting, such as when the music has lyrics.
Declaration of interest statement
The authors report no conflict of interest.
Funding
This work was supported by the CNPq [2018]; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior [2019].
ORCID
Débora Marques Miranda http://orcid.org/0000-0002-7081-8401
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