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EXAMINING THE EFFECT OF ORAL TRANSMISSION ON FOLKSONGS
DANIEL SHANAHAN
Ohio State University
JOSHUA ALBRECHT
University of Mary-Hardin Baylor
S
OCIOLINGUISTS FREQUENTLY EXAMINE THE
nature of gradual, internal shifts in languages and dia-
lects over time, arguing for both cognitive and cultural
factors, as well as those that might be somehow internal
to the language itself. Similarly, musicologists have often
argued that musical genres and even specific songs can
be examined through gradual diachronic shifts, which
seem to be especially accelerated in traditions that rely
on oral transmission. For example, Spitzer (1994) exam-
ined the stemma of ‘‘Oh!Susanna’’ and noticed that it
tended to become more pentatonicized at cadence
points by dropping scale degree seven, and suggested
that this might be true with folk songs in general. To test
this, we employed both experimental and corpus-based
paradigms. The experimental approach attempted to
simulate oral transmission in a compressed timeframe
by involving singers who heard and replicated short
musical excerpts, and then would teach a colleague, who
in turn passed it on to another participant. Similarly, we
conducted a corpus analysis that examined the preva-
lence of descending stepwise endings in styles of music
primarily transmitted orally compared with those trans-
mitted primarily through notation. The experimental
results suggest that cadence points in Western folk
music are more likely to lose scale degree seven through
the act of oral transmission, and the corpus study sug-
gests that, although stylistic constraints play a large role
in folk music, there might also be a relationship between
transmission and physical affordances.
Received: April 13, 2017, accepted August 2, 2018.
Key words: oral transmission, folksongs, affordances,
social learning, cadences
T
HE CLASSIC CHILDREN
’
S GAME
‘‘
TELEPHONE
’’
consists of a simple premise: an utterance passed
from one observer to the next is likely to undergo
some sort of transformation through the simple act of
transmission, and a humorous disparity between the
original and final statements will result. The study of
how these statements change, and the constraints that
might inform these changes, is crucial not only to our
understanding of musical style, but also of musical com-
munication (and communication, in general).
We expect such changes with orally transmitted
ideas—we are less likely to have an authoritative refer-
ence point—but often a similar situation occurs even
with the presence of an authoritative text. In early
18th century New England, for example, the musically
literate elite realized that they were unable to preserve
much of the melodic materials in compiled editions of
music, as the general population began to sing their own
variations (see Brooks, 1998, p. 35). Their solution was
to educate the population on musical notation, a notion
that succeeded in its goal to maintain the authority of
the approved text, and to minimize excessive variations.
The effects of ‘‘musical telephone,’’ however, persist.
Spitzer (1994) examined the effects of transmission on
Stephen Foster’s ‘‘Oh!Susanna,’’ and found that as the
notated composition began to be transmitted orally,
a number of changes began to occur: rhythmic ideas
would gradually align more closely with the beat, the
harmonies were changed so that the melodic notes were
the root of the chords being used, sections that included
varied repeats transformed into literal repeats, and
cadence points became somewhat ‘‘more pentatonic’’
(Spitzer, 1994, p. 116); more specifically,b
7-
b
1motionoften
transformed into b
2-
b
1 motion.
1
When divorced from an authoritative text, it seems
that musical change follows a similar trajectory to lan-
guage, in that it constantly undergoes some sort of
transformation. To paraphrase Labov (2001, p. 9), the
fact that change occurs over time (in either language or
music) is one of the few safe assumptions one can make.
We might therefore ask, what can these changes tell us
about communication, human learning, and music?
What exactly are the implications of the leading tones
of a melody being consistently replaced by descending
stepwise motion at cadence points?
1
It could be argued that a melody becoming ‘‘more pentatonic’’ would
include the lowering of the leading tone by a semitone (
b
7 becoming b
7), but
Spitzer is quite clearly referring to a replacement of ‘‘ti’’ with ‘‘re.’’
Music Perception,VOLUME 36, ISSUE 3, PP. 273–288, ISSN 0730-7829, ELECTRONIC ISSN 1533-8312. ©2019 BY THE REGENTS OF THE UNIVERSITY OF CALIFORNIA ALL
RIGHTS RESERVED.PLEASE DIRECT ALL REQUESTS FOR PERMISSION TO PHOTOCOPY OR REPRODUCE ARTICLE CONTENT THROUGH THE UNIVERSITY OF CALIFORNIA PRESS’S
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Examining the Effect of Oral Transmission on Folksongs 273
This question has been studied in various capacities in
music for quite a while. Sharp (1907) argued for a three-
stage process of musical transmission, consisting of
continuity, variation, and selection. Continuity was ‘‘a
passive rather than an active agent; a condition, not
a cause’’ (Sharp, 1907, p. 29), whereas variation was the
creative element that led to further musical develop-
ment. For Sharp, continuity was closely related to the
simple passage of time—a perpetuation of an idea
already in motion. His analyses of singer Henry Lar-
combe’s improvisations, on the other hand, emphasized
the role of variation. The hour-long performance con-
sisted of no exact repetitions, despite the fact that many
points were deemed to be identical by the performer.
Finally, Sharp’s notion of selection protected against an
over-spawning of musical ideas in favor of a controlled
directed shift in musical ideas. There are obvious Dar-
winian aspects to this line of thinking, perhaps most
notably when he writes that, as ‘‘in the animal and veg-
etable worlds, those variations will be preserved, which
are of advantage to their possessors in the competition
for existence ...in the evolution of folk-tunes ...the
corresponding principle of selection is the taste of
the community. Those tune-variations which appeal to
the community will be perpetuated as against those
which attract the individual only.’’ (Sharp, 1907, p. 29,
as cited in Bronson, 1954, p. 5) Why would some tune-
variations appeal to the community whereas others
wouldn’t? Musical choices are rarely up for a vote, and
there must be a reason for certain musical events to be
more readily used than others.
Bronson (1954) would later use Sharp’s ideas on
musical selection as a foundation for his own analysis
of folk ballads, invoking what might be best described
as a corpus study to examine the similarity of musical
ideas amongst folksongs. He found that English and
Scottish folksongs differed in their usage of pentatonic
and hexatonic collections—Scottish tunes contained
far more pentatonic melodies (which Bronson often
refers to as ‘‘gapped’’ melodies), with two-thirds of
Scottish folk songs being pentatonic and only one-
fifth of the English folksongs. He also found that Appa-
lachian folksongs tended to align more closely with
Scottish than English melodic styles, perhaps provid-
ing some interesting fodder for migration corpus stud-
ies in the future. Bronson also discusses a melodic
‘‘core of identity,’’ and argues that ‘‘plagal melodies’’
were more ‘‘vulnerable to influence’’ (Bronson, 1954,
p. 12) than ‘‘authentic melodies,’’ suggesting that musi-
cal change is not uniform, and that the selection pro-
cess not affect all melodies equally. Although he never
suggests a possible cause for this, we might argue that
plagal melodies are likely more difficult to sing; the
fourth scale degree—a defining aspect of a plagal
melody—is often among the most difficult pitches for
novice singers to sing in tune.
The question of transmission—how things are trans-
mitted and why certain ideas might be more easily lost
in transmission than others—is the locus of the entire
research area known as social learning, defined as
‘‘learning that is influenced by observation of, or inter-
action with, another animal ...’’ (Hoppitt & Laland,
2013). The study of the transmission of ideas could be
examined in a controlled setting with many of the
methodologies pioneered by the social learning field.
For example, Payne and Payne (1985) found that male
humpback whales in a certain population all sang a song
that would gradually change through the season as var-
ious changes were gradually introduced, and Garland
et al. (2011) found that migration of humpback whales
created a change in song types that followed the migra-
tory patterns.
Perhaps one of the most influential social learning
studies that focused on humans was conducted by Bar-
tlett (1932) and involved participants being presented
with either text or a picture, recalling it, and having the
result of that recall passed to the next participant, who in
turn produced their own version of their recollection to
another participant. Bartlett concluded that there are
always some features (‘‘dominant features’’) that are
more easily maintained than others throughout trans-
mission.
2
This type of methodology is known as a ‘‘trans-
mission chain.’’ The simplest form of transmission chain
is a linear transmission chain, in which one person
directly demonstrates to another.
3
This is also commonly
referred to as an ‘‘iterated learning paradigm’’ (see Smith,
Brighton, & Kirby, 2003, for an extensive discussion of
this paradigm to analyze the evolution of language), and
has been used extensively in recent experimental work
addressing the impact of transmission on musical struc-
ture (Lumaca & Baggio, 2017; MacCallum, Mauch,
Burta, & Leroia, 2012; Ravignani, Delgado, & Kirby,
2016). Recent work by Janssen (2018) has examined the
role of phrase length, rehearsal, and expectancy on trans-
mission using pattern-discovery methods. Studies exam-
ining melodic memory, however, have tended to rely
more on recognition, rather than production, paradigms.
(see Halpern & Barlett, 2010; Halpern & Bower, 1982;
Halpern, Barlett, & Dowling, 1998).
2
Kleeman (1985, p. 5) draws the obvious comparison between
Bartlett’s dominant features and Schenker’s notion of an Urlinie.
3
For more on the history and background of such studies see Hoppitt
and Laland (2013).
274 Daniel Shanahan & Joshua Albrecht
The transmission chain methodology seems particu-
larly appropriate for examining how a musical signal like
a melody changes over time, but as with all methodolo-
gies it comes with its own set of strengths and weak-
nesses. A disadvantage to this method is that, as the
chain is linear, a single distorted signal can make signif-
icant changes. A common approach to this is to run
many transmission chains, as we have here, or to employ
a diffusion method, as is discussed in the final section of
this paper. Additionally, it’s not as reflective of musical
practice as other methods (such as diffusion) might be.
One rarely learns a melody from a single individual.
Instead they are presented with multiple versions of the
melody from multiple sources.
One benefit of the linear transmission chain approach
is the ability to conduct an experiment in a controlled
laboratory environment. Although orally transmitted
folk melodies change gradually over years through
many different forces, a linear chain methodology
provides a microcosm of these events within a short and
controlled setting.
Another advantage comes from the ability to detect
transmission biases. In other words, by carefully con-
trolling the stimuli, particular types of changes might be
witnessed that can cast light on cognitive or social biases
that may play a role in the broader culture. For example,
Mesoudi and Whiten (2004) found that participants
passed along information with increasing generality,
losing more of the specifics throughout the transmis-
sion. They argued that information might be processed
hierarchically, and that lower-level details are more
susceptible to loss and change than the higher-level
aspects of information.
Here, we examine the role of the oral transmission of
melodies through this linear transmission chain para-
digm, attempting to isolate the points of musical change
through the process of a series of musical communica-
tions. Specifically, we attempt to investigate Spitzer’s
theory of increasingly pentatonic cadential points. For
many reasons, however, we will abstain from looking
specifically at pentatonicism. Firstly, pentatonicism is
quite broadly defined (as discussed below), and can
mean either any collection of only five pitches, or a spe-
cific collection of pitches (for example,b
1, b
2, b
3, b
5, b
6). Van
Khe (1977) argues that the pentatonic scale is, to some
extent, universal, but each collection discussed is quite
different: some include semitones, some include only
whole steps, etc. It would therefore seem that a more
manageable way of analyzing Spitzer’s hypothesis would
be to first examine the treatment of the melodic
cadence, without making any broad generalizations of
pentatonicism.
Experiment 1: In Search of Changing
Penultimate Notes
METHOD
In order to examine whether or not there are predictable
patterns for how penultimate notes change throughout
the course of oral transmission, we invoked a linear
transmission chain in which participants were presented
with a melody (which either contained a b
7-
b
1orab
2-
b
1
cadence), and we asked them to sing it to another mem-
ber of the group (each group had four participants). We
hypothesized that b
7-
b
1 cadences were more likely to
transform into b
2-
b
1 cadences than the reverse.
Participants. Sixty participants were split into groups
of four. All participants were music majors, with train-
ing in music theory and aural skills. There is a danger
that using such participants might bias the sample when
looking at the transmission of musical ideas: music has
been transmitted for centuries by amateurs, and any
‘‘loss of the signal’’ that might be typically seen in the
field might be minimized by training in aural skills and
melodic dictation. Also, recall that the solution in early
18th-century New England to the spawning of many
melodic ideas through transmission was to provide
training in musical notation. It is therefore possible that
a population trained in the fundamentals of music
might be less likely than the general population to stray
from the given melody. On the other hand, signal deg-
radation might be too extreme if participants who had
no musical inclination were used—it is likely that, his-
torically, those responsible for oral transmission of mel-
odiesweremorelikelytobemusicians,evenifnot
formally trained. In the worst case scenario, a participant
who is unable to match pitch would break the transmis-
sion chain and no meaningful conclusions could be
made from the results. We therefore opted for a subject
pool consisting of musically-trained individuals. To
examine the effect of sophistication on transmission
accuracy within this trained population, each partici-
pant was asked to complete the Goldsmiths Musical
Sophistication Index survey for musical sophistication
(Mullensiefen, Gingras, Musil, & Stewart, 2014).
The participants were all music majors at the Univer-
sity of Mary Hardin-Baylor, enrolled in Music Theory 2,
3, or 4. They were first and second year university stu-
dents (32 F, 28 M; mean age ¼19.5; SD ¼1.7), and 25
were vocal majors. The mean Gold-MSI scores for gen-
eral musical sophistication was 81.1 (SD ¼6.2) out of
a possible score of 126. The Gold-MSI self-report scores
for singing ability were 30.4 (SD ¼4.0), out of a possible
score of 49. The participants reported an average of
1.8 years of formal training in music theory (SD ¼1.2).
Examining the Effect of Oral Transmission on Folksongs 275
Stimuli. A primary motivation in this experiment
was to mirror the process of oral transmission as closely
as possible. We therefore chose to select existing songs
from an oral tradition, in order to ensure that musical
materials were as amenable to oral transmission as pos-
sible. However, participant familiarity with selected
songs could pose a difficulty in distinguishing the pure
effects of oral transmission from the effects of memory.
To mitigate this issue as much as possible, we elected to
choose from an orally transmitted repertoire that would
be unfamiliar to our participants. Also, because we are
examining oral transmission, we chose to present par-
ticipants with a recording of each melody, and it was
furthermore important to use recordings of a singer
who was unaware of our hypothesis, so as not to uncon-
sciously bias the recordings. Finally, because we wanted
to isolate the effects of oral transmission on melodic
structure, we wanted recordings in which the songs
were sung on a neutral syllable.
A suitable corpus that fulfills all of these criteria can be
found in Weiss, Trehub, and Schellenberg (2012). This
experiment involved ‘‘unfamiliar folk melodies from the
United Kingdom and Ireland [that] ...conformed to
Western tonality’’ sung by ‘‘an amateur female (alto)
singer without lyrics (i.e., ‘‘la’’ for each note) in an every-
day (non-operatic) manner’’ (p. 1075). The melody
pitches were altered using Melodyne’s (2014) pitch cen-
ter and pitch drift functions, and some note timings were
manually adjusted for temporal accuracy. Weiss et al.
(2012) found that these altered melodies were rated as
sounding more ‘‘natural’’ than the original recordings,
likely as a result of correcting rhythms that were sung
out of time (Weiss, personal communication, 2014).
Post-experiment interviews in the present experiment
confirmed that no participants knew (or were able to
name) any of the songs used from this corpus.
Recall that our hypothesis has been constrained to
examine only the motion from the penultimate to the
final note of each song, and to specifically contrast b
7-
b
1
and b
2-
b
1 motion. More precisely, our hypothesis is that
b
7-
b
1 motion will be replaced by b
2-
b
1 motion more often
than the reverse. For this reason, it was important to
balance our sample of folk melodies such that we used
equal numbers of final cadence types. From the Weiss
et al. (2012) database of 64 folk melodies, 10 were
selected. Five of these melodies ended in b
7-b
1 motion
and the other five ended in b
2-
b
1 motion.
Despite our focus on only the final two notes of each
melody, it is important to observe that these notes are
not isolated from their context. In other words, it is
entirely possible that the melodic structure of earlier
moments in each melody afford one of the two cadence
types over the other. This extra-cadence musical mate-
rial may exert an influence over the cadence, such that
musically trained participants may be inclined by the
structure of the melody to use one cadence motion over
another. In order to isolate the effect of penultimate
approach from its greater melodic context, we created
alternate versions of each melody. Specifically, for
each melody that ended with b
7-
b
1 motion, we created
an alternate version that was exactly the same except
for the penultimate note, and vice versa. These alternate
versions were created manually using Melodyne’s
pitch correction tools. However, some of the alterations
produced note transitions into the penultimate note that
melodies that might be difficult to sing, such as those
containing melodic dissonances (augmented and dimin-
ished intervals, as well as intervals such as a 7th; see the
altered melody in Figure 1). We therefore introduced the
final exclusionary criterion that the interval approaching
the penultimate note would be a consonant interval in
the traditional music-theoretical sense (such as a third,
fifth, sixth, or octave), so altered melodies that featured
difficult to sing leaps (such as b
4-
b
7-
b
1) were excluded.
The final collection of excerpts consisted of 16 melo-
dies: eight unaltered melodies, consisting of 4 that ended
with b
7-
b
1motionand4thatendedwith
b
2-
b
1 motion; and
a parallel set of eight altered melodies that were identical
except for the penultimate note. All 16 melodies used in
this experiment are provided in Appendix A.
Pilot studies revealed that the entire melody was pro-
hibitively difficult to memorize in a short time. Trying
several permutations, one phrase (consisting of four
measures) seemed to be an ideal length for each excerpt;
it was short enough to permit encoding into short-term
memory, but long enough that participants were unable
to store every detail of the melody in short-term memory
exactly. This mirrors many of the studies in the melodic
recognition literature. For example, McAuley, Stevens,
and Humphreys (2004) examined the role of familiarity
strength on melodic memory, using novel melodies that
averaged about 12 notes in length, and familiar melodies
that averagedabout 16 notes. Our phrases also fell within
the 12–16 note range (see also Halpern & Bartlett, 2010,
for a discussion of this). Similarly, Dowling and Bartlett
(1981) used short melodic sequences of 5 seconds in
a melodic recognition paradigm, which is roughly sim-
ilar to our own melodic stimuli.
The final phrase of each excerpt was used in the
experiment, shortened using Pro Tools 10 (2012) and
with a 250-ms fade-in added to avoid pops or clicks in
the recording. Figure 1 shows a sample melody with the
penultimate note altered.
276 Daniel Shanahan & Joshua Albrecht
Procedure. Participants signed up in groups of four.
Upon arrival they were each given instructions (see
Appendix B). Participants were told ahead of time that
the experiment would involve singing and being
recorded, and were told that they could opt out if they
felt uncomfortable. After participants read the instruc-
tions, they filled out the Goldsmiths Musical Sophisti-
cation Index self-report questionnaire (Mu
¨llensiefen
et al., 2014). In order to ensure they put forward as
much effort as possible, participants were told that the
most accurate singer would receive an award.
Because the results of this experiment may be affected
by the skill-level of each participant, it was important to
balance the design such that each participant would
sing first, second, third, and fourth on different melo-
dies. We used a 4 x 4 Latin square for each grouping,
in which each iteration of participants was assigned
a random order.
For each melody, Participant A remained in the
experimenter’s office while the other three participants
were escorted to a practice room. Participant A then
heard the stimulus melody three times, with a pause
of 5 seconds in between each hearing. The participant
then had the option to practice singing the melody, and
then heard the melody a fourth time. While we were
careful that participants were not either over-practicing
or making too many mistakes for the integrity of the
melody to be transmitted, pilot studies indicated that
there were floor effects, and that the task was too diffi-
cult without practicing at all. We therefore found that
a single practice round was one of the most effective
means of helping the participants solidify the melody.
After the final hearing, Participant A sang back the
melody while the experimenter recorded the perfor-
mance. Finally, Participant A was escorted to a separate
practice room and Participant B was escorted back into
the experimenter’s office. Participant B then heard Par-
ticipant A’s recording three times, pausing 5 seconds
each time, practiced once, then heard it a fourth time,
and finally sang back the melody while it was recorded.
This continued until Participant D sang the melody back
the final time. Participants that had already sung were
kept in a separate waiting room from those that had not
yet sung. Finally, all four participants were gathered so
they could compare the original melody to the final mel-
ody. This was primarily done as a way of keeping parti-
cipants interested, as they were able tohear the mutations
that occurred through transmission. See Figure 2 below
for a diagram of this transmission chain. This process
was repeated for each of the four melodies, using a differ-
ent order each time. Participants were then brought
together to fill out a brief post-experiment questionnaire.
The transcription process was not straightforward.
Participants would occasionally sing with hesitancy, and
intonation was sometimes a problem. We employed
commercial software called ScoreCloud (www.
scorecloud.com), which performs monophonic tran-
scription of audio recordings fairly accurately. The
recordings were automatically transcribed first, includ-
ing an estimation of key and meter. These transcriptions
Original
Altered
FIGURE 1. The folk melody, “Can Ye Sew Cushions?” is presented in its original form with a b
2-
b
1 ending (above), and in its altered form with ab
7-
b
1 ending
(below). The altered form approaches the penultimate with a diminished fifth from 4-7, which is less common and harder to sing, so this melody was
excluded.
Participant A Participant B Participant C Participant D
FIGURE 2. Diagram of Linear Transmission Chain.
Examining the Effect of Oral Transmission on Folksongs 277
were then corrected by the authors. The number of total
notes in the presented melody were counted, which then
became the denominator of a ratio. Every note from this
presented melody that was sung in the proper order was
added to the numerator, regardless of whether there
were intervening notes. Any new notes added to the
melody were added to the denominator as well. Put
succinctly, a numerator represented the total number
of notes a participant got correct, whereas the denom-
inator represented the total number notes the partici-
pant could have gotten correct.
Results
Figure 3 demonstrates a typical round in which a melody
is provided in an original recording, followed by the four
participants. The third participant in this case alters
the b
7-
b
1, introducing a b
2-
b
1 ending. The mean accuracy
of singers who did not change the penultimate note from
the presented stimulus was 78%(SD ¼20.5%,n¼211)
whereas the mean accuracy of singers who did change the
penultimate note was 59.4%(SD ¼27%,n¼29). Pre-
dictably, this difference is significant, t(32.605) ¼3.53,
p< .001: the changing of notes (i.e., accuracy) is corre-
lated with the changing of the penultimate note. None of
the other covariates in the model were found to be
significant. We therefore conclude that neither singer
accuracy, individual melody, singer order, nor group accu-
racy were predictive of a change in the penultimate note.
In order to test the effects of the penultimate note on
changes in melody, we employed a mixed-effects logis-
tic regression, in which we examined the specific type of
melodic cadence as the factor of interest (i.e., b
7-
b
1 versus
b
2-b
1 endings). Additionally, we included four covariates:
singer accuracy, overall group accuracy, and specific mel-
ody as random effects, as well as singer order as a fixed
effect. In other words, these covariates were taken into
account in determining the significance of the indepen-
dent variable. The results from our regression analysis
indicate that the penultimate note presented in a melody
was significantly predictive of whether or not the pen-
ultimate note would be altered from the recording that
the participant heard ß ¼.23, df ¼1, N¼240, CI ¼
1.95–10.58, p< .001. As beta values are usually only
assigned to fixed effects, this coefficient is an average
of each slope in the model. We would argue that this
estimate might be a bit conservative. Interestingly,
although singer accuracy was correlated with the chang-
ing of the penultimate note, there was a disparity in how
frequently each type of cadence was altered. Out of the
29 recordings in which a penultimate note was changed,
20 of them were originally b
7-
b
1 progressions. These
results therefore suggest that, conservatively, melodies
that end withb
7-
b
1 motion are 1.09-1.44 times more likely
to be transformed intob
2-
b
1 than vice-versa.
Discussion
The results of our experiment seem to suggest that there
is an effect of ‘‘b
7-
b
1’’ endings transforming over time into
‘‘b
2-
b
1’’ endings. The next logical question might therefore
be: why is this the case? It could be the case that singing
a descending line is physically easier than singing an
ascending line. Physiological affordances can definitely
contribute to shifts in musical style. Much like statistical
learning, physiological affordances can serve to
}7-1 becomes
2-1
FIGURE 3. An example of the transmission of a melody through four participants. The dotted arrows indicate a point at which the melody signal
changed.
278 Daniel Shanahan & Joshua Albrecht
minimize elements that deviate from norms. For exam-
ple, Labov (2001) has studied how North American
accents tend to minimize vowel sounds that approach
the limits of certain physical abilities, and the outliers
become less prevalent over time. Among other reasons
for linguistic change, exuding a minimum level of effort
and difficulty is one of the most prominent reasons for
such shifts (for more, see the Discussion section). We
might therefore argue that, if physical affordances lead
to more frequent descending cadences, we would be
more likely to see such endings in vocal music than in
instrumental music. To examine this further, we
employed a corpus study approach.
Experiment 2: A Post hoc Corpus-Informed
Analysis of the Penultimate Note
Although Spitzer’s original claim focused on the role of
increasing pentatonicism (Spitzer, 1994), studying
pentatonicism as a consequence of transmission is far
from straightforward. As previously mentioned, Van
Khe (1977) has discussed the prevalence of the scale
across cultures, and has pointed out that five-tone scales
in general are by far the most common. It is, however,
difficult to understand exactly why the scale is so pro-
minent. Jackendoff (1977) has pointed out that the
tones, in fact, do not overlap with the natural overtone
series terribly well, a point similarly made by Schenker
(1906) regarding the diatonic scale. Day-O’Connell
(2007) has actually pointed to melodies becoming less
pentatonic over time, a theory at odds with Spitzer’s.
For example, the Lutheran hymn ‘‘Ein feste Burg ist
unser Gott’’ succumbed to the pressures of tonality to
the point that, by the time Bach set the melody, b
4 and b
7
were commonly included as passing tones.
A corpus-based approach to defining pentatonicism is
therefore quite difficult. As Van Khe (1977) points out,
although scales involving five pitches are quite common,
TABLE 1.
A Mixed-effects Logistic Regression Model was Fitted to Examine Predictors of Melodic Change
Source df AIC BIC Log likelihood
2
Chi df p
Change *Original
Melody þSinger
Accuracy þSinger
Order þPenultimate
Note (Group
Accuracy excluded)
36 181.66 306.96 -54.83 9.21 14 .81
Change *Group
Accuracy þSinger
Accuracy þSinger
Order þPenultimate
Note (Original
Melody excluded)
21 162.36 235.45 -60.179 10.696 15 .77
Change *Group
Accuracy þOriginal
Melody þSinger
Order þPenultimate
Note (Singer
accuracy excluded)
33 176.62 291.48 -55.31 .96 3 .81
Change *Original
Melody þGroup
Accuracy þSinger
Change þ
Penultimate Note
(Singer Order
excluded)
35 180.38 302.20 -55.19 .715 1 .40
Change *Original
Melody þGroup
Accuracy þSinger
Change (Penultimate
note excluded)
6 152.70 173.59 -70.35 6.69 1 .009**
Note: The model with group accuracy, original melody, singer accuracy and singer order was not found to be significantly different than whether or not the melody simply
contained a b
7-
b
1 cadential motion. This table examines models with each effect removed, to examine any possible significant predictors. It would therefore appear that the
majority of the variance in predicting whether the melody would undergo change in the cadence is accounted for by whether the melody has ab
7-
b
1orab
2-
b
1 ending.
Examining the Effect of Oral Transmission on Folksongs 279
the specific pitches employed in these scales permit
many variations. For example, the Japanese scale
‘‘Hirajoshi’’ consists of 1-2-3-5-6, but the ‘‘Ritsu’’ scale
consists of ‘‘1-2-4-5-6’’ (Van Khe, 1977). Therefore,
searching by scale degree would likely yield spurious
results. Similarly, it’s difficult to search by the number
of discrete pitch classes, as melodies that we might con-
sider pentatonic often employ non-diatonic pitches,
such as passing and neighbor tones. Some pieces even
modulate between multiple pentatonic scales, making
an algorithmic searching of pitch-classes or pitch-class
counts problematic.
An example of some of the difficulties inherent in
reifying the pentatonic scale can be seen inTable 2. Given
the assumption that orally transmitted musical traditions
would be more likely to be limited to pentatonic collec-
tions, we would predict that orally transmitted music
would contain more works consisting of five or fewer
discrete pitch-classes. In comparing amongst four cor-
pora (Table 2, details of each corpus are discussed below),
it is clear that there is a predominance of melodies
constrained to more than five pitch-classes. However,
melodies containing only five notes are not restricted
to folksong databases. In fact, as can be seen in Table 2,
the art song dataset (the Barlow & Morgenstern collec-
tion, 1948) contains a higher percentage of melodies
containing only five pitches than the folksong database
(the Essen Folksong collection; Schaffrath, 1995).
Rather than trying to operationalize the notion of
pentatonicism, it might make more sense (and would
be more in line with the experiment) to simply examine
final melodic gestures. We hypothesized that there
might be a correlation between b
2-
b
1 endings and vocal
melodies, as physical affordances would suggest that
these would be easier to sing. Although a corpus
method can only provide correlational evidence, we
would argue that it would be supportive of the overall
argument regarding the changing penultimate note as
a result of oral transmission. We therefore decided to
examine the prevalence of b
2-
b
1 endings compared to b
7-
b
1
endings with matched vocal and instrumental corpora
with a corpus-based approach, specifically with the
Meertens Dutch Folksong collection (Van Kranenberg,
de Bruin, Grijp, & Wiering, 2014). The commonly-used
Essen Folksong Collection (Schaffrath, 1995) contains
primarily vocal melodies transcribed from 19th-century
sources, and it can be difficult to discern instrumental
from vocal melodies. Similarly, Barlow and Morgen-
stern’s A Dictionary of Musical Themes (1948) is
entirely instrumental, and while there are currently
efforts to encode the companion vocal set, these themes
present melodic ideas based on their salience, and often
lack final melodic gestures or cadences. The Meertens
collection contains both instrumental and vocal folk-
songs, making it an ideal dataset for our analysis. In
particular, the vocal and instrumental songs are of the
same style and represent the same geographic and
(roughly) temporal location.
We might then ask, of the instrumental and vocal
melodies in the Meerten’s Folksong collection, are we
more likely to see final gestures ending with a b
2-b
1
motion or a b
7-
b
1 motion, and would the prevalence of
these gestures change depending on whether it was
instrumental or vocal music? Of the Dutch folksongs
labeled as vocal music ending with stepwise motion tob
1,
b
2-
b
1 melodic cadences were far more common than b
7-
b
1
cadences,
2
(1, N¼1439) ¼375.42, p< .001 (see Table
3). Conversely, the folksongs labeled as instrumental
music contained far more b
7-
b
1cadencesthan
b
2-
b
1
cadences,
2
(1, N¼790) ¼13.41, p< .001. We also
performed a chi-square test on the 2 x 2 contingency
table (Table 3) to examine not only the effect between
melodic endings but also between corpora (vocal and
instrumental). After correcting for multiple tests, this,
too, demonstrates a significant difference between cor-
pora and ending types,
2
(1) ¼302.65, p< .001. We
additionally examined the Essen folksong collection
(Schaffrath, 1995), which consists primarily of vocal
TABLE 2.
Percentage of Tunes in Each Database with Five or Fewer
Pitches
Number
of Scale
Degrees
B&M
(N¼6,210)
Essen
(N¼6,214)
Meertens
Ins.
(N¼1,960)
Meertens
Vocal
(N¼4,040)
7-12 78.6%89.1%89.4%92.5%
5 14.2%8.6%7.5%5.4%
45.4%1.8%2.8%1.3%
31.4%0.5%2.6%0.7%
20.2%0%0%0.04%
1 0.06%0%0%0%
Note: The Folksong databases actually contain a higher percentage of pieces with
more than five pitches, and the art song dataset (Barlow & Morgenstern, 1948)
contains a higher percentage of melodies containing only five pitches.
TABLE 3.
A Comparison of Stepwise Endings in the Meertens Vocal,
Meertens Instrumental, and Essen Folksong Collection
Collection b
2-b
1 endings b
7-
b
1 endings
Meerten’s Vocal 1087 (75.5%) 352(24.5%)
Meerten’s Instrumental 301 (38.2%) 489 (61.8%)
Essen Folksong Collection 636 (80.3%) 156 (19.7%)
Note: For both vocal groups, b
2-
b
1 was significantly more common than b
7-
b
1, but in
the instrumental collection, it was reversed.
280 Daniel Shanahan & Joshua Albrecht
works, and found that b
2-
b
1 endings were again far more
common than b
7-
b
1 endings (see Table 4).
It seems that much of these results might best be
explained by the principle of least effort (or the ‘‘Motor
Constraint Hypothesis’’ presented by Tierney, Russo, &
Patel, 2011). Descending melodic endings might always
simply be preferred to ascending melodic endings. To
further explore this in a more cross-cultural context, we
examined the Densmore Corpus of Native American
Music (Shanahan & Shanahan, 2014). After removing
all repeated perfect unisons (a very common ending,
occurring in more than 75%of melodies), we find that
the final melodic interval is more likely to be descending
than to be ascending (1146 descending/429 ascending),
and this difference is once again significant,
2
(1,
N¼1575) ¼326.4, p< .0001.
An analysis of multiple corpora would seem to indi-
cate that descending motion is far more common than
ascending motion at the point of a melodic cadence (see
Figure 4). This aligns closely with what others have
written about the cross-cultural aspects of melodic
shape and contour (see Huron, 2006; Savage, Tierney,
& Patel, 2017). It therefore comes as no surprise that
final resting points are far more likely to be approached
through b
2-
b
1 motion than b
7-
b
1motion.Interestingly,
when physiological constraints are less of a factor (as
in the case of instrumental folksongs) the tendency
seems to be reversed, and b
7-
b
1 seems to be more com-
mon. We briefly tested this further by looking at the
percentage of descending two-note relationships in the
final segments of a song. We examined the final 2–10
notes from the Meertens collection of both instrumental
(N¼4830) and vocal (N¼4120) Dutch folksongs. As
can be seen in Figure 5, vocal melodies do contain
a higher percentage of descending intervals than instru-
mental folksongs. The broader trend of increasing usage
of descending intervals toward the end of the phrase
supports the notion of an ‘‘arch-shape’’ melodic proto-
type (Huron, 1996), but the increased disparity in the
final cadence points might point to a cause that is per-
haps more stylistically informed.
Conclusion
In his 1989 work Style and Music, Leonard Meyer defines
musical style as ‘‘a replication of patterning, whether in
human behavior or in the artifacts produced by human
behavior, that results from a series of choices made within
some set of constraints’’ (p. 3). These can be stylistic
constraints, which are fairly easily learned and adaptable
(see Castellano, Bharucha, & Krumhansl, 1984; Krum-
hansl, 2000; Loui, Wessel, & Kam, 2010), as well as phys-
ical constraints, which are more immutable, and can
inform musical choices based on ‘‘the path of least resis-
tance’’ (see Huron, 1996; Savage et al., 2017; Shanahan &
Huron, 2011). The arch-shape mentioned as a result of
physical affordances (see Huron, 1996) might cause des-
cending pitch contours (such asb
2-b
1) to be more likely to
fall within an individual’s vocal range than a b
7-
b
1motion.
Future work will explicate the effect of overtly arch-
shaped melodies and the changing of a penultimate note.
Much of this conforms to what linguists often refer to
as the ‘‘principle of least effort’’ (Bloomfield, 1933;
Jespersen, 1921/2013). Labov defines this principle by
stating that, ‘‘[w]e speak with the least effort that is
required to be understood by our addressees, but with
sufficient effort to ensure that we are understood’’
(Labov, 2001, p. 17). One of the best examples of the
‘‘principle of least effort’’ might be seen in the nearly-
ubiquitous tendency of pitch to decline over the course
of an utterance. As a phrase progresses, speakers lose air
from the lungs, and the subglottal pressure decreases,
leading to an overall declination in pitch (see Collier,
1975; Cohen, Collier, & t’Hart, 1990). While it has been
argued that this phenomenon is more common in pre-
pared speech than spontaneous speech, and is less fre-
quent in interrogative statements (see Umeda, 1982, and
Thorsen, 1980, respectively), Hauser and Fowler (1992)
found that nonhuman primates also exhibit a declina-
tion in pitch over the course of utterances, further
pointing to a physiological origin. There is, however,
a chicken-and-egg problem with this argument: are des-
cending lines easier to sing because they’re more
TABLE 4.
A Comparison of Motion tob1 Between the Meerten’s Vocal and Instrumental Collections, as Well as the Essen Folksong Collection
Approach to 1 1 22 33 4 5 6 677
Meertens (vocal) 22.5%
(757)
0 46.3%
(1558)
0.6%
(19)
6.2%
(208)
0.1%
(4)
9.6%
(324)
0.06%
(2)
0.03%
(1)
0.4%
(14)
14.2%
(479)
Meertens (inst) 49%
(806)
0 18.2%
(300)
0.9%
(15)
3.6%
(59)
0 2.6%
(43)
0 0 0.1%
(2)
25.5%
(419)
Essen 7.6%
(346)
.3%
(13)
56.4%
(2579)
2.3%
(37)
4.6%
(210)
0.2%
(7)
4.6%
(208)
0.2%
(9)
0 0.5%
(25)
24.9%
(1135)
Examining the Effect of Oral Transmission on Folksongs 281
common, or are they more common because they’re
easier to sing? This question is beyond the scope of the
current experiment, but we hope that, by addressing the
nature of affordances throughout transmission, future
work might be able to employ experimental paradigms
that can disentangle the directions of causality.
When examining gradual changes to a musical signal
that are introduced through oral transmission, therefore,
it seems appropriate to consider the role that both
implicit grammars and physical affordances may play
in transforming a signal, and the way these two forces
may interact with one another. In the first instance, oral
transmission relies on the memory of performers, which
is mediated by their understanding of the musical gram-
mar of that music. It therefore seems likely that changes
would be introduced that reflect the statistical properties
of that music. In other words, it could be argued that
whatever deviations are introduced to musical signals
through oral transmission are likely to converge on those
musical gestures that are most commonly representative
of that style. Gradually, musical choices that are less
common or that do not conform to predictable or sty-
listically common tendencies will tend to be replaced
with more ‘‘syntactically-correct’’ choices. It is even pos-
sible that stylistic syntax may influence the physical ease
of producing particular musical licks in an analogous
way that particular sequences of muscle movements
become learned by athletes as they practice and become
easier and more automatic for them to perform.
While musical style might be contingent upon implic-
itly learned grammars, the embodied physicality of mak-
ing music also constrains what is possible or common in
a musical style. Physiological affordances can contribute
to the shifts in musical style in a similar process to that of
implicit enculturation. Much like statistical learning,
physiological elements can serve to minimize aspects
of a musical style that deviate from norms. For example,
Labov has studied how North American accents tend to
−m2 −M2 −m3 −M3 −m6 −M6 −m7 −M7 −P4 −P5 +m2 +M2 +m3 +M3 +m6 +M6 +m7 +P4 +P5
barlow
essen
meeri
meerv
0 500 1000 1500 2000 2500
FIGURE 4. Count data of ending intervals by corpus. Note that the Meerten’s Vocal collection has far more descending Major 2nd endings than the
Meerten’s Instrumental collection, but that this is reversed for ascending minor seconds.
282 Daniel Shanahan & Joshua Albrecht
minimize vowel sounds that approach the limits of cer-
tain physical abilities, and the outliers become less prev-
alent over time (2001, p. 488). Among other reasons for
linguistic change, exuding a minimum level of effort and
difficulty is one of the most prominent reasons for such
shifts. It can therefore be very difficult to disentangle the
way that the two forces of statistical learning and phys-
ical affordances individually contribute to musical style
due to the feedback loop in which they commonly rein-
force one another. Although our experiment was not
designed as a critical test of these two theories, we intend
to follow up this question in future work.
Our experiment attempted a small-scale replication of
the chain of events that might lead to such deviations in
musical gestures. As participants would make mistakes,
replacing b
7-
b
1 final endings with b
2-
b
1 was far more com-
mon than the converse, and it seems that vocal music is
far more likely to contain b
2-
b
1 endings than b
7-
b
1 endings,
whereas when the physical constraint is different (in
instrumental music), melodies are far more likely to end
with a b
7-
b
1 ending. This seems to suggest that, although
stylistic constraints play a large role in folk music, and
can inform how transmission occurs, physical constraints
are also likely to inform deviations in a musical idea.
Future work will examine how the transmission chain
differs depending on instrumental as opposed to vocal
music, and will attempt to isolate more specific physical
affordances. Additionally, it might be worth examining
how feasible more complex transmission chains might
be in an experimental setting. For example, Experiment
1 used a linear transmission chain, in which one dem-
onstrator conveyed information to one observer, but it is
possible that a model that allows for multiple demon-
strators to convey the same information to an observer,
such as a replacement transmission chain, or a diffusion
model (see, Hoppitt & Laland, 2013) could be more
0.60
0.65
0.70
0.75
0.80
246810
notes
mean
melody.type
Instrumental
Vocal
FIGURE 5. The average number of descending intervals in the final notes of a song, ranging from the last 10 notes of a song to the last 2 notes, taken
from the Meertens Dutch Folksong collection of vocal (
N
¼4120) and instrumental tunes (
N
¼4830). Note that the vocal songs contain significantly
more descending intervals than ascending intervals, supporting the notion that affordances play a role in the intervallic content. The broader trendof
increasing usage of descending intervals toward the end of the phrase supports the notion of an “arch-shape” melodic prototype (Huron, 1996), but the
increased disparity in the final cadence points might point to something more stylistically informed.
Examining the Effect of Oral Transmission on Folksongs 283
ecologically valid. Nevertheless, Spitzer’s claim that mel-
odies would increasingly contain b
2-b
1 cadence points
over time seems to make sense. Listeners are more likely
to sing notes that are easier to sing, and this cadential
formula is likely to provide just that.
Author Note
The authors would like to thank Eleanor Selfridge-Field
for pointing us to Spitzer’s work, Claire Arthur, and Nat
Condit-Schultz for assistance with mixed-effects mod-
els, as well as Niels Chr. Hansen and two anonymous
reviewers for their thoughtful comments and feedback
on previous versions of this article. We would also like
to thank Michael Weiss for his generosity in sharing
stimuli from previous research with us.
Correspondence concerning this article should be
addressed to Daniel Shanahan, Hughes Hall, 1899 Col-
lege Road North, Ohio State University, Columbus, OH
43210. E-mail: shanahan.37@osu.edu
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Appendix A
Sixteen Melodies Used in Experiment 1
ORIGINAL b
2-
b1 MELODIES
1, original
1, altered
2, original
2, altered
3, original
3, altered
286 Daniel Shanahan & Joshua Albrecht
4, original
4, altered
ORIGINAL b
7-
b1 MELODIES
5, original
5, altered
6, original
6, altered
7, original
7, altered
Examining the Effect of Oral Transmission on Folksongs 287
8, original
8, altered
Appendix B
Instructions
The purpose of this study is to gather information about
how music is transmitted orally. At the end of the exper-
iment, I’ll say more about my specific goals.
In this study, we will be playing a game of ‘‘musical
telephone.’’ First, we will randomly select orders for
you for the game. In other words, one of you will
randomly be chosen to go first, one randomly chosen
to go second, etc.
After selecting your order, the first person will stay
with me while the rest go to a practice room down the
hall. The first person will hear a recording of a short
musical excerpt three times, practice singing it once,
hear it a fourth time, and then sing it back to me once
while I record the performance. I will then accompany
the first person to a different practice room and then
retrieve the second person from the other practice
room. Once back in my office, the second person will
listen to a recording of the first person three times,
practice singing it, hear it once more, and then sing it
back to me once while I record again.
This pattern will continue until the last person sings the
melody while I record. At that point, the other three people
will be brought back into my office. The original recording
will then be played followed by the final recording, soyou
can all hear how the melody has been modified, if at all.
For this study, please do not sing any of the melodies
outside of my office.
New random orders will be assigned to each person,
and the process will be done for a second melody. There
will be a total of four melodies.
Throughout this process, I will be recording every
melody sung by every person, so I can better track how
the melodies change over time.
If singing in this kind of experiment makes you
uncomfortable, please let me know now and we can find
a different experiment for you to participate in.
288 Daniel Shanahan & Joshua Albrecht
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