A Comparison of the Effects of Soothing and Cacophonous Music
on Implicit Learning and Transfer
Victor Vuchic – vuchic@stanford.edu
Brandon Suzuki – bsuzuki@stanford.edu
1
Abstract
In the research for this paper we made two hypotheses. The first hypothesis was
that the presence of musical consistency during learning and transfer assessment would
improve implicit transfer results. We believed that subjects who both learned and
attempted transfer while listening to the same music would perform better than those
listening to different music during those tasks. The second hypothesis was that soothing
classical music would help implicit learning and transfer to a greater extent than
cacophonous music. We believed that soothing music would prevent the conscious mind
from interfering with implicit learning without overly taxing cognitive load. We
supposed that cacophonous music, which also served to distract conscious thought, would
overly tax cognitive load resulting in decreased implicit learning transfer performance.
Using Markov chains in the manner of Reber (1967) we trained subjects on examples of a
particular grammar in the guise of a memory test while having them listen to either a
soothing classical or cacophonous jazz music. Subjects were then informed of the
grammatical nature of the training sentences and given the task of identifying
grammatical words from a list while listening to one of the two music pieces used during
the learning stage. Due to what we believed to be flaws in our post test method, the
results did not show the expected implicit learning and transfer effect; therefore, we were
unable to draw conclusions directly addressing our hypotheses.
Introduction
Studies have shown that environmental consistency aids transfer. In the case of
music, a student who studies while listening to music is more likely to do better on the
test of the material if listening to the same music during the test. Our study looks at
whether implicit learning transfer works similarly to conscious learning transfer with
respect to auditory environmental consistency. We also examined whether the type of
auditory context provided had an effect on implicit learning transfer. At a more general
level, findings in our study could have implications for the situative perspective relative
to implicit learning both in terms of the impact of auditory environmental consistency
and the effect of different types of auditory environments. The situative perspective
considers the environment of the learner to be tightly linked to learning. In this study we
are looking at a small subset of the environment, the auditory domain. Specifically we
are examining two kinds of music. A finding of increased implicit learning transfer
would show that the environment does affect implicit learning and therefore support the
situative perspective.
We used Markov chains based on Reber (1967) to train and test for implicit
transfer. During the learning process subjects would implicitly learn the underlying
grammar (same one used by Reber 1967) under the guise of a memory recall test. In
order to test transfer, subjects would be informed of the grammatical consistency of the
words they had just memorized and asked to identify grammatical sentences from a list of
words containing equal amounts of previously memorized grammatical words, new
2
grammatical words, and subtly ungrammatical words. We intended to use the learning
time and results of the post test as the basis for drawing conclusions relative to our
hypotheses.
General Description of Experiment
Our independent variable was the audio accompaniment during the learning and
post test stages. We chose audio accompaniment because it is something that we believe
is relatively common in learning situations. People often have the radio on in the
background or music playing while they do other activities such as read, study, drive, or
cook. The audio accompaniment consisted of two musical pieces: one soothing and one
cacophonous. We also included a control condition where no audio accompaniment was
used in either stage as a baseline. In total we ran subjects in five conditions:
Condition
Number of
Subjects
1
4
2
4
3
4
4
5
5
1
Table 1 – Study Structure
Learning Stage
Post Test Stage
Soothing
Soothing
Cacophonous
Cacophonous
None
Soothing
Cacophonous
Soothing
Cacophonous
None
Our dependent variables include time to learn, repetitions of sentence sets during
learning, and recognition of the three types of items in the post test: seen grammatical,
new grammatical, and non-grammatical sentences. The time to learn and repetitions were
chosen because they provide information about how the subject learned the implicit
grammar (if there was an effect). The results of recognition of the three types of
sentences in the post test are important because the “seen grammatical” items indicate
how well students remembered sentences, the “new grammatical” items indicate the
extent of implicit learning transfer, and the “non-grammatical” items provides
information regarding the amount of negative-transfer.
If subjects who have the same music for both stages (table 1: conditions 1 and 4)
perform better on the post test this would imply that audio consistency in the environment
improves implicit learning transfer. This finding would support our first hypothesis
regarding consistent music and its positive effect on implicit learning transfer. On the
other hand, if subjects in conditions with different audio accompaniment in each stage
(table 1: conditions 2 and 3) did just as well or better, that would disprove our first
hypothesis.
If subjects listening to soothing music in both stages outperform subjects listening
to cacophonous music in both stages, this would imply that soothing music increases
implicit learning transfer relative to cacophonous music. This finding would support our
3
second hypothesis of soothing music increasing implicit learning transfer relative to
cacophonous music. It would also suggest that it may be worth while to take care in
choosing the kind of music a learner listens to while learning.
Comparing the average post test results on new grammar sentences between
conditions sharing the same learning stage audio accompaniment (table 1: conditions 1
and 2, 3 and 4), we can infer a particular audio accompaniment aided implicit learning if
its average is higher than that of the other. Otherwise, if both results are the same, it
would imply that the kind of audio accompaniment does not affect implicit learning.
Comparing the average post test results on new grammar sentences between
conditions sharing the same post test stage audio accompaniment (table 1: conditions 1
and 3, 2 and 4), we can infer a particular audio accompaniment aided transfer of implicit
learning if its average is higher than that of the other. Otherwise, if both results are the
same it would imply that the kind of audio accompaniment does not affect transfer of
implicit learning.
If all audio accompaniment conditions (table 1: conditions 1 – 4) have worse
results in the post test than the control condition (table 1: condition 5), this would imply
that audio accompaniment hinders implicit learning and transfer.
Method
The method for our study closely follows the method used by Reber (1967).
Participants
The study had 18 subjects. Participants were adults between the ages of 22 to 69, and
were chosen based on availability and convenience.
Materials
The materials for the study included the creation of the 16 strings for the memorization
exercises as well as an additional 16 strings for the post test. These strings are based on
the Markov chains used in Reber’s study (see Fig 1). See appendix for actual strings
used for memorization.
4
Figure 1. Markovian grammatical structure
The following methods were used for generating the grammatical and non-grammatical
strings:
Grammatical The grammatical sentences were generated by randomly generating grammatical strings
and adding any unique ones to an array until the maximum number possible was
generated. We believe a transition tracking tree traversal method could also have been
used which would not generate duplicates, but since the number of grammatical
sentences we needed was small we went with the quicker implementation.
Non-grammatical Aside from 4 random sentences, Reber (1967) used subtly different sentences for his nongrammatical post test entries. In an attempt to do the same we placed 3 constraints on the
non-grammatical entries.
1. Same range of lengths as the grammatical items
2. The first and last letters are always consistent with the grammar
3. Only letters that belonged to the grammar were used
In addition, for the soothing music we cut a 6 minute music clip of the Adante
movement from Franz Schubert’s The Trout and looped it. For the cacophonous music
we cut a 6 minute non-vocal section of John Coltrane’s OM and looped it. We then
created the post-test which consisted of 39 strings divided equally between strings the
subjects had memorized in the learning phase, strings that the subject had not seen but fit
within the grammar, and strings that were not grammatically compliant. Finally, we
developed a flash based program that would run on any PC to take the subject through the
learning and post-test process and a PHP/HTML based framework to handle structure and
file input/output.
Design
The design of our study was a classic 2 x 2 with a single control subject.
5
Learn Strings w/Classical
Learn Strings w/Jazz
Table 2 Study Design
Post-Test w/Classical
Condition 1
Condition 3
Post-Test w/Jazz
Condition 2
Condition 4
We used the 2x2 (see Table 2) to attempt to isolate the causality of 2 potential
phenomena in the results. Condition 1 (4 subjects) and Condition 4 (5 subjects) listened
to the same music for both the learning phase and the post-test phase, while Condition 2
(4 subjects) and Condition 3 (4 subjects) changed music for each section. This allowed
us to counter-balance the music the subjects listened to and isolate its effect on implicit
learning.
Our dependant measures were the number of repetitions per memorization
exercise, total time per memorization exercise, and the measured implicit learning effect
judged by the subject’s ability to correctly evaluate the strings on the post-test.
Procedure
Our procedure was for the subject to go through the study on a PC using a Flash
based program we developed. The subject was told they are going to go through four sets
of memorization exercises while listening to music. The subjects sat down at the PC, put
on the headset, adjusted the music to a comfortable volume and pushed the start button.
They were then shown 4 strings for 5 seconds apiece followed by a prompt to type in the
strings that they just saw. They would then be told which were correct and which were
not correct and if they made a mistake they would go through seeing all 4 strings again
and reenter them. The subject would repeat this until they could accurately reproduce the
4 strings, and then they would continue to the next set of four strings. Once the subject
finished the last set of strings, they were told that all of the strings they had memorized
followed a grammatical structure and were formed based on a rule. At this point subjects
in conditions 2 and 3 had the music change from classical to jazz or jazz to classical. For
conditions 1 and 4 we simply restarted the song they were listening to. The subject then
continued to the post test and was presented with 39 strings. They were asked to identify
any of the strings they thought followed the rule. Once they finished selecting all of the
strings, they clicked submit and the program terminated. We then followed up with a
post study interview with the following three questions:
1. Were they looking for a grammatical structure to the strings in the learning phase
and if so, did they see any?
2. How did they approach selecting the strings in the post test?
3. Did they know the music they were listening to?
Coding
The data we captured focused on two areas. First we captured how much time it
took them to go through each section of the memorization phase and how many
repetitions the subject had to go through for each memorization set. This gave us a
6
picture of any possible effects of the music on the memorization phase. The second area
we coded was for the post test. We tracked how many strings the subject correctly
identified that they had seen in the memorization phase, how many grammatically correct
strings they identified correctly that they had not seen, and finally how many strings they
correctly did not select that did not fit the rule (false positives). We combined these three
categories into a score for total correct identifications out of 39 strings.
Results
The results did not provide any conclusive evidence to support or negate our
hypothesis due to the fact that we did not get any clear implicit learning effect. The
average scores (out of a possible 13) by categories for the post test were 6.7 (52%)
correct identifications of strings seen in the memorization process, 2.8 (22%) correct
identifications of new grammatically correct strings, and 12 (92%) correct identifications
(by not selecting) of non-grammatical strings.
The results of the total correct identifications showed that none of the categories
performed as well as the control subject. Total correct identifications includes the
number correctly selected as fitting the rule for both strings the subjects had seen and not
seen, plus the number of non-grammatical strings that were correctly not selected as
fitting the rule. There was a trend in that on average the conditions that changed music
for the post test did better (22.5 out of 39) than the conditions that had the same music
during the post test (20.3 out of 39) (see Fig 2).
Total Correct IDs by Music Context for Learning and Post Test
30
25
20
15
Total Correct IDs
10
5
0
None-None
Same Music
Different Music
Fig 2 Total Correct IDs by Musical Context
The results did show some trends on how the music impacted the way people
learned the material even though the results did not show any implicit learning effect
from the post test. None of the subjects were able to identify grammatically correct
strings that were not in the memorization tasks at a greater rate than chance. We did see
7
some effect on the learning patterns as well as a trend in false positives. The data shows
us that on average, the subjects listening to classical music required less repetitions to
memorize the strings than the control subject did. We also see that subjects listening to
the cacophonous music during learning required more repetitions (see Fig 3) through the
4 strings to learn them than the classical music or the control subject.
Average Repetitions by Music Category
25
20
15
Avg Repetitions
10
5
0
No Music
Classical
Cacophonous
Figure 3. Average Repetitions by Music Category
Even though the classical music listeners required fewer repetitions to memorize the
strings, there was no difference or correlation in the amount of time it took any of the
subjects to learn the material.
Another area that showed an interesting dynamic was in the occurrence of false
positives during the post test. The subjects that listened to cacophonous music during
learning as well as the post test had a higher rate of false positives in that on average they
had the highest rate of selecting strings that did not fit the grammatical structure.
There were some more qualitative patterns that we found from interviewing our
subjects afterwards. Four of them used grouping identification as a strategy in the
memorization, for instance they would look for groups of letters that recur in the
beginnings or ends of the strings. Also, subjects that listened to the cacophonous music
during the learning stage tended to develop headaches by the end of the study.
Discussion
The below random results from our post test indicates a lack of an implicit
learning transfer effect. We believe this finding is a result of a flawed method. By
including seen grammatical items in the post test and showing all post test items on a
single screen there was not enough incentive for subjects to risk selecting items beyond
8
the ones they explicitly remembered. Interviewing subjects after the post test many
indicated they only specified as grammatical the sentences they explicitly remembered
from during the learning and did not feel the need to take more risk. If subjects only
selected those they explicitly remembered we would expect 100% for the nongrammatical, 0% for the transfer, and some middling percentage representing how well
people remembered sentences from the learning stage for the seen grammatical sentences.
This trend is visible in the data when looking at the average results for each post test
sentence type. On average subjects got 92% of the non-grammatical items, 52% of the
memorized items, and only 22% of the new grammatical items correct.
We suspect the results showing that repetition count for subjects learning with
soothing music was less than for the control and cacophonous music learning conditions
is indicative of higher mental load associated with listening to cacophonous music than
soothing music. We observed more frequent and stronger verbal expressions of
frustration during the learning stage and interviewed subjects often spoke of the irritation
and distraction caused by the cacophonous music.
We propose the higher rate of negative-transfer by subjects in the cacophonouscacophonous condition (5) is caused by the higher mental load associated with the
cacophonous music. Subjects in this condition indicated that by the end of the learning
stage their head hurt and they just wanted to get done with the post test. We hypothesize
that by the time they reached the post test they did not want to exert more mental effort
and essentially didn’t care as much as other subjects so were willing to take more
chances. This being the case we would expect a higher average result on new
grammatical items as well which is the case in the data.
An alternative interpretation for the lack of implicit transfer effect could be that
implicit learning is hurt by soothing or cacophonous audio accompaniment. This is
somewhat supported by the data since the single control subject got 31% of the new
grammatical sentences right while the average for all audio accompanied subjects was
21%. However, since there was only one subject run in the control group and she was
significantly older than the other subjects and had formal training in linguistics this data
point is fairly weak. The significantly longer time taken by the subject during the post
test and the interview also indicated that she had attempted a systematic identification of
the grammar once alerted to it during the post test based on the items recalled from the
learning stage, something no other subject reported doing.
If we were to believe that the audio accompaniment caused the below random
results for implicit learning rather than a flaw in the method as we suspect and mentioned
above, it is possible to draw some other conclusions from the data.
The finding that conditions with the same music during the learning and post test
stages (table 1: conditions 1 and 4) did worse than those with differing music indicates
that having musical consistency during the implicit learning and transfer of implicit
learning stages may actually be detrimental. This goes directly against our first
hypothesis. Given the fact that subjects performed below random, it is a story of less
9
detrimental as opposed to more beneficial. One possible, though we feel unlikely,
explanation would be that the music interfered with implicit learning causing subjects to
learn incorrect rules.
Since the average post test results for new grammar sentences between conditions
sharing the same post test stage music (table 1: 1 and 3, 2 and 4) are not statistically
different, it suggests that the music type does not affect transfer of implicit learning. This
is the opposite of our second hypothesis. However, we believe the previously mentioned
method flaws are a more likely explanation.
It could be argued that due to floor effects, post test new grammar item result
comparisons are not valid. Since there have been many other tests which found implicit
learning effects such as Reber (1967), we believe once the method has been fixed implicit
learning transfer results would be high enough to avoid floor effect problems.
Repetition count may not be a good measure of learning since it doesn’t account
for learning while confirming memorized sentences and studying those that were right
and wrong. As an example, a strategy might be used in which users don’t enter anything
the first few repetitions of a set until they feel they have a better chance of getting them
all right which may make repetitions seem higher than the average. A measure taking
into account both time of each repetition and repetition count may be a more accurate
measure of learning.
General Discussion
The two issues we chose to explore in this study are the effect of musical context
on implicit learning transfer and the impact of the kind of music someone listens to on
their ability to transfer implicit learning. The results from our study suggest that
maintaining a consistent auditory context actually has a worse effect on implicit learning
transfer than does listening to different kinds of music. This was demonstrated by the
higher performance on the post test by the groups that listened to different music relative
to the groups that listened to the same music. In relation to the second issue, we found
that the kind of music one listens to does not have an effect on their ability to transfer.
This was shown by the similar performance of all groups on the post test and their
inability to transfer much of what they learned.
While these results show some interesting dynamics, we think that they were
influenced by flaws in the design of our study. Analyzing the information gathered in our
post-study interviews, we believe that the redesign of some of the core components of the
study could yield different results. The two areas that would benefit from design
modifications are the control group selection and the post test exercise. We recommend
using a larger and more carefully selected control group in order to provide more accurate
baseline data to better assess the overall impact of music on implicit learning transfer. As
for the post test, we recommend showing the subjects one string at a time and forcing
them to say whether the string fits the grammar rule or not. Showing the words one at a
time would make the subject explicitly address each string rather than just selecting a few
10
that they know and moving on. This was especially the case for subjects listening to
cacophonous music during the post test, they simply wanted to finish the study as quickly
as possible.
We think that our study showed some interesting dynamics in relation to the
impact of any type of music on implicit learning. The data does not show much of an
effect on what kind of music you listen to, but it does show a potentially significant
negative effect on how listening to music has a detrimental effect on implicit learning
transfer. We think that this may be an interesting area to explore further by performing a
similar study with people listening to music or not. A study such as this could impact the
way educators manage sound and noise in the classroom. If any sound takes a
significantly heavier cognitive load, then there should be more care taken on the auditory
design of learning environments. In addition, parents could then substantiate telling their
kids to turn there Ipods off when they study!
References
Reber, A. S. Implicit Learning of Artificial Grammers, Journal of Verbal Learning and
Verbal Behavior 6 1967 Pp. 857-863
11
Appendix of Materials
Appendix 1
Learned Grammar List for Pre-training
Set 1 (20)
VVS
VXVPXVS
TTXXVS
VXVS
Set 2 (22)
TPPPTS
TTS
TTXVPXVS
TPPTS
Set 3 (25)
TPTS
VVPXXXVS
VVPXVS
TPPTXVS
Set 4 (24)
VXXVPS
TTXXVPS
VXXVS
TPPPTXVS
12
Appendix 2
Post test results
Post test
results
Control
Condition 1 –
Class, Class
Condition 1
Condition 1
Condition 1
Condition 2 –
Class, Jazz
Condition 2
Condition 2
Condition 2
Condition 3 –
Jazz, Class
Condition 3
Condition 3
Condition 3
Condition 4 –
Jazz, Jazz
Condition 4
Condition 4
Condition 4
Condition 4
Average by
condition
Seengrammatical
Grammatical
Nongrammatical
Variance
by
condition
Seengrammatical
Grammatical
Nongrammatical
Seengrammatical
Grammatical
Radmila
Vuchic
9
4
13
26
cory lee
david
Jason
Nicole
9
2
9
7
1
0
4
5
13
13
9
13
23
15
22
25
Bruce
Jennifer
Fan
Laura 2
Lili Gill
5
4
13
22
4
8
8
4
5
1
13
12
13
21
25
22
Aneto
grant suzuki
Greg
Warman
Yeong Haur
Kok
7
6
1
2
13
13
21
21
7
5
12
24
7
4
12
23
Debbie
Dolores
Richard
Sava Saheli
Singh
Sherwin
Average
8
7
6
5
0
0
5
13
13
18
20
19
6
5
6.666666667
4
2
2.833333333
11
12
12
21
19
21.5
Control
ClassicalClassical
Nongrammatical
Total
ClassicalCacophonous
CacophonousClassical
CacophonousCacophonus
Overall
9
4
6.75
2.5
6.25
3.5
6.75
3
6.4
2.2
6.66667
2.83333
13
12
12.75
12.5
10.8
12
ClassicalCacophonous
CacophonousClassical
CacophonousCacophonus
Control
ClassicalClassical
Overall
0
0
10.91666667
5.666666667
4.25
3
0.25
3.333333333
6.4
5.2
6.47917
3.06771
0
4
0.25
0.333333333
11.2
11.0469
13
Learning Timings (in seconds)
Condition
0
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
4
Avg Learn
Times
1
334
300
291
407
447
106
356
89
105
546
151
109
141
204
249
333
743
99
Radmila
Cory
David
Jason
Nicole
Bruce
Jennifer
Laura
Lili
Aneto
Grant
Greg
Yeong
Debbie
Sherwyn
Dolores
Richard
Sava
2
476
169
417
304
285
313
212
275
188
226
443
338
184
311
342
413
181
288
3
479
349
422
292
372
267
270
438
198
146
361
353
268
269
214
485
235
391
4
762
301
415
115
277
255
610
400
400
230
921
517
227
509
269
344
346
500
Total Learning
2051
1119
1545
1118
1381
941
1448
1202
891
1148
1876
1317
820
1293
1074
1575
1505
1278
1
2
3
4
5
2051
1290.75
1120.5
1290.25
1345
Post
Test
1079
240
93
186
155
46
90
185
183
119
345
147
439
106
56
88
129
61
14
Total
3130
1359
1638
1304
1536
987
1538
1387
1074
1267
2221
1464
1259
1399
1130
1663
1634
1339
Repetitions
Section
Radmila
1
4
2
5
3
5
4
8
Total repetitions
22
Cory
David
Jason
Nicole
Bruce
Jennifer
Laura
Lili
Aneto
Grant
Greg
Yeong
Debbie
Sherwyn
Dolores
Richard
Sava
4
3
6
6
2
7
1
2
9
2
2
2
3
4
6
13
2
3
3
5
3
5
3
3
2
3
6
4
2
3
5
5
3
5
6
5
5
6
5
5
5
3
3
5
5
4
4
4
9
5
8
5
5
3
4
5
11
6
7
5
19
7
5
8
6
7
8
10
18
16
19
19
17
26
15
14
20
32
18
13
18
19
27
29
25
No
Music
Average
Repetitions
22
Classical
Cacophonous
18
22.33333333
15