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Isaac Hayes 1

Acknowledgments

Despite being the sole researcher on this project, I was far from the only mind

contributing to its development and completion. This thesis represents not only the

culmination of my tenure in the psychology department at the University of Arkansas, but

indeed the potent and manifold influences of three separate departments on my academic

and personal development. Without guidance from the university's many patient and

insightful musicians, philosophers and, yes, psychologists this research could not have

come to fruition, nor could I have such a clear vision of my future in the world of

academic study. In addition, I would like to extend thanks to the SURF grant foundation

for their generous investment in this research.


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Isaac Hayes 2

Table of Contents

1. Abstract

2. Introduction

3. Method

4. Data

5. Analysis

6. Conclusion

7. Bibliography

8. Index


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Isaac Hayes 3

Abstract

Despite the growing prevalence of research into the neurocognitive correlates of musical

training, little research investigates the effects of different paradigms of musical training.

This project seeks to take a first step into this investigation by comparing jazz-trained and

classically-trained musicians' ability to detect changes in expressive microtiming in

musical phrases.

Nineteen musicians divided into classically-trained and jazz-trained groups were

presented with 32 short musical passages that had potentially undergone micro-rhythmic

alteration to one note or chord on the order of 20-60 milliseconds. Participants were

instructed to indicate whether they had or had not detected micro-rhythmic alteration in

the passage. Hit rate and false alarm rate were recorded for each participant and a d'

value for each was calculated. It was hypothesized that jazz-trained musicians would

demonstrate a greater sensitivity to changes in expressive microtiming across all types of

musical stimuli. The difference measured between the two groups was found to be not

statistically significant and thus the data failed to support a rejection of the null

hypothesis. Future research will undoubtedly expand upon the results of this study by

greatly increasing the number of participants and developing rigorous criteria for the

categorization and quantization of participants' musical training, as well as developing

criteria for categorization and quantization of different types of expressive microtiming.


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Isaac Hayes 4

Introduction

Clearly, musical training is not a simple path from novice to expert along a single

axis of learned skills. Rather, there exist multiple paradigms of musical training, each

with their own origin, purpose and skill set. What we would call musical expertise is in

fact a set of heterogeneous, multifaceted traits, able to be attained via a variety of

avenues. Different schools of training may be differentiated not only by the types of

music that give rise to them, but by the different ways their practitioners come to think

about and execute their skills. Stemming from these clear differences, it should be

inferred that in addition, differences in musical training might have profound effects on

the way music is perceived in the trained listener. Authors Paul Berliner, Derek Bailey

and George Lewis among others have documented these distinctive properties of musical

training and pedagogy; in these cases, specifically the pedagogy of improvisation.

Further, multiple studies have demonstrated that musical training has numerous effects on

cognitive and perceptual abilities, including most notably Aaron Berkowitz and Daniel

Ansari's research into the neurological correlates of training in improvisation, which

investigated the ways musicians' motor corticies generate novel motor sequences during

improvisation. (Berkowitz, 2008) (Berkowitz, 2010)

This research paper seeks to fill a gap in the existing literature by investigating

differences in one such facet of perception and cognition, namely the ability to detect

differences in expressive microtiming in musical phrases. Of primary interest here is

whether there is a main effect of type of music training on ability to discriminate between

musical phrases which have undergone changes in expressive microtiming from those


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which have not. This particular variable was chosen for several reasons. First, it is a

musical trait present in any genre of music performed by humans. While it would be

possible to have a computer replicate a performance wherein each note was placed

exactly in its mathematically ideal location, human performances invariably alter

rhythmic placements of notes throughout. Second, it is a variable easily manipulable via

MIDI data and easily quantifiable. Given a few pieces of software it is possible to adjust

microtiming on a note or group of notes by a given number of milliseconds, even in

music which has been translated from human performance and already features

expressive microtiming.

In addition, there is a firm foundation of research into microtiming, including its

detection, as in Eric Clarke's 1989 study which determined the threshold for microtiming

detection as well as investigated factors that influenced that threshold. Along similar

lines, Bruno Repp found in his 1998 study that detection of microtiming depended greatly

on whether or not it was used congruently with typical usage in a given style of music.

These findings were in line with Edward Large and Caroline Palmer's 2002 paper which

found that expectation plays a large role in the ability to detect microtiming, and therefore

that microtiming typical of a given music's genre should be more readily detectible that

atypical microtiming. Perhaps most pertinent to this study, Henkjan Honing and Olivia

Ladinig found in 2009 that mere exposure to music was enough to influence microtiming

detection ability, namely, that instead of musical expertise being the primary determinant,

exposure to certain musical idioms made the most difference in participants' ability to

detect changes in microtiming. All things considered, microtiming is only a relatively


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Isaac Hayes 6

recent topic in the literature of music cognition. These further steps into research in this

area will advance currently lacking knowledge on the topic.

The first section of the paper will outline the widely acknowledged varieties of

musical training as represented in contemporary literature as well as prior researched

effects of musical training on aspects of cognition and perception, musical and otherwise.

This discussion will aim to contextualize the hypothesized effects mentioned later on by

illustrating the extent to which these main varieties of musical training differ. Finally,

armed with a greater understanding of these differences the hypothesized effect of

differences in musical training on microtiming detection will be outlined.

Classical Training

In common usage of the term and indeed in much of the literature on musical

training, it is taken for granted that a given musician's training and expertise are in what

is known as classical music (also referred to as western art music or European

music, though here as classical). As the more thoroughly researched style of music

training and performance practice in the literature, classical music training will largely be

defined in the context of this research as music training that does not feature

improvisation as the dominant performance practice, but that instead focuses on recitation

of fully written-out music, or recitation of music that allows for some variation in

dynamic level, tempo and timbre, but only very rarely pitch, rhythm, meter or structure.

Overall, it seems fair to make the generalization that classical music affords

musicians flexibility over the subtler facets of music performancespecifically over


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tempo, dynamics and microtiming, while jazz music affords many more freedoms,

especially during the solo portion of the performancespecifically pitch, rhythm, meter,

and structure, in addition to tempo, dynamics and microtiming. Though perhaps

involving no less creativity than jazz music, classical music relegates artistic expression

to these subtler elements due to the fact that, by its very nature, classical music relies on

recitation from a primary written source (which is then read or recited from rote) rather

than an ad-libbed performance. It is to be expected, then, that the skill set emphasized in

classical music pedagogy would reflect the skills necessary in classical music

performance, and would be in many ways very different from training in jazz and other

primarily improvisatory music.

While historically, composers and performers of what we now call classical music

trained in and made prominent use of improvisation in performance practice, this skill has

for the most part fallen out of classical performers' repertoire in the 20th and 21st

centuries. Commonly used texts and teaching methods for the learning of classical music

fail to make mention of the skill of improvisation at all, much less outline the pedagogy

necessary for instructing new musicians in improvisation. This trend has been noted by

authors such as Ken Prouty (2012). He claims that [t]echniques of improvisation are

found infrequently within the Western art music curriculum, and classical music's legacy

of imrpovisation is often a mystery to novice musicians, and thus, that [j]azz began

its academic life with a fundamentally different identity within the academy, at odds with

academic music culture (pg. 70).


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Improvisatory/Jazz Training

It is important to note before delving into a particular instance of a musical

paradigm that though mainstream jazz education is the most prevalent of

institutionalized improvisatory music training encountered at the middle school, high

school or university level, it is by no means the only form of improvisatory music taught.

Because of its prevalence, however, and the availability of its pedagogical material, it will

be taken as the focal point for discussion of improvisatory music in this research. It is

worth noting, though, that at base level all forms of music emphasizing improvisation as

the dominant performance practice share a few defining characteristics that differentiate

them from music that emphasizes solo or group recitation of fully-composed music.

From students' first forays into improvisatory music, their studies include lessons in at

least these three crucial elements: generativity, inter-musician communication and

expression. In general, musical generativity is present in all forms of improvisatory

music and rarely found in classical music performance with the exception of some

modern pieces unlikely to become the focal point of classical pedagogy until college

level. Further, while inter-musician communication is prominent in both genres of music

training, it takes highly differentiated forms in each. Where classical musicians might

use a conductor's gestures or subtle body language to achieve musical synchrony or a

certain group dynamic level or tempo, jazz musicians commonly use gesture during a

performance to alter the piece's structure by adding another chorus of soloing, to indicate

the next soloist, to move to double-time, in addition to altering dynamic and tempo.

While both classical and jazz musicians might make use of deliberate expressive


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microtiming to modify the feel of a piece of music, jazz musicians are more likely to

instigate these changes mid-stream, for an expressive effect or to indicate to other

musicians a desired change in feel.

Lastly, the qualities of artistic expression vary greatly between classical and jazz

performance practice. While undoubtedly a classical performance is made or broken by

the artist's choices regarding the piece's tempo, dynamic, timbre, microtiming, etc.,

artistic expression in jazz takes another form entirelythe performer essentially

composes cogent melodic lines and/or chordal accompaniment on the spot. In addition,

while jazz musicians are expected to train in this type of expression even in their first

lessons, classical musicians may not be expected to emphasize the development of these

skills until well into their musical career (advanced high school or college level)1.

To expand on these differences mentioned above, it helps to turn to salient

literature on jazz training and pedagogy. Paul Berliner in his 1994 Thinking in Jazz

describes the methods of musical training unique to jazz, confirming that these skills are

emphasized early and often. Though perhaps it is obvious that successful musical

improvisation involves musical generativity insofar as the corpus of any given

performance involves a significant length of spontaneously generated music (quite unlike

classical music) it is not as obvious that it requires great skills in inter-musician

communicativity. Berliner writes that in a jazz setting, improvisers are free of the

constraints that commercial engagements place upon repertory, length of performance,

and the freedom to take artistic risks. (Berliner, 42). These unplanned, spontaneous

1 This is with the exception, perhaps, of students of the Suzuki method who a0s early as three years old

are encouraged early on to perform imitatively or by ear. However, though the method emphasizes

playing music as though one were speaking, recitation, not expression or generativity, is still the main

goal (Kendall, 1985).


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perceptive/cognitive faculties. Indeed, much research has been conducted into the variety

of ways musical training writ large effects cognitive development in children and adults.

Wong, et al. (2007) demonstrated that participants who had undergone musical training

had more robustly encoded linguistic pitch patterns at the neurological level. Further,

research was conducted into nonmusical cognitive effects of musical training when

Sylvain Moreno et al. (2008) showed that after only six months of musical training, eight-

year-old children showed enhanced reading and pitch discrimination abilities in speech.

Similarly, a few studies have investigated cognitive and neurological effects of training in

musical improvisation. For example, Berkowitz and Ansari (2008) showed that

musicians with training in improvisatory music temporarily deactivated the right

temporoparietal junction (rTPJ) during melodic improvisation, while nonmusicians

showed no change in activity in this region, indicating that improvisatory music training

has a not insignificant effect on neural structures and therefore, we might assume,

cognitive structures as well. Unfortunately, with few noteworthy exceptions, little effort

has been made toward research into the variety of cognitive effects of multiple training

paradigms. Thus, it is not possible to directly consult literature in order to predict these

effectsinstead it is necessary to consult several disparate sources in order to form a

cogent hypothesis.

Hypothesis

The usage of the term expressive microtiming has varied somewhat in past

literature. The seminal 2002 paper by Vijay Iyer titled Embodied Mind, Situated


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Cognition, and Expressive Microtiming in African-American Music deals largely with

the prevalence of what he dubs microrhythmic techniques in jazz. Iyer argues that even

rhythmic phenomena that occur on the order of 10-60 Hz enter into our cognition of

music, despite being much shorter, and therefore less noticeable than, for example, an

average quarter, eighth or sixteenth note. His argument hinges on the notion that because

we regularly undertake or (undergo) body motions that occur on this timeframe (he gives

the examples of the production of phonemes or rapid flam[s] between fingers or

limbs) we are therefore well equipped to detect phenomena of this same time frame in

music, and that indeed we respond to them through embodied cognition. Iyer's paper

largely references groove specificallyfor example, as it occurs in James Brown's

music (Iyer 388). Expressive microtiming in this context makes up the groove of the

music, and provides dynamic and interest to otherwise static music.

For the sake of experimental manipulation in the context of this research,

expressive microtiming will be more simply defined as the extent to which a note or

group of notes deviate rhythmically from an ideal rhythmic placement, either specified

(i.e., written, as in classical music) or unspecified (i.e., implied, as in much of improvised

music). This proves to be a more useful definition in the context of this research as it

lends itself to easy measurement and manipulation. Using MIDI files, it is possible to

alter the placement of any note or group of notes by as little as one millisecond (well

below the measured detection threshold for rhythmic changes). For simplicity's sake,

each instance of microtiming in this experiment was only altered with respect to one

rhythmic sub-unit, that is, one note or vertically-stacked chord. This created a single


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locus of rhythmic difference in each musical passage that the participant would then

either detect or fail to detect.

Armed with this prior research and these experimental tools, it was possible to

make predictions regarding the experimental outcome. It was predicted first that

participants with more musical training would be able to more accurately detect changes

in microtiming than would participants with less training, due simply to the greater

exposure to scenarios that would necessitate cultivating these skills. Henkjan Honing and

Olivia Ladinig (2009) found that mere exposure to certain types of music improved

participants' ability to detect expressive timing in musical phrases within that type of

music. Thus, it was further predicted that participants with more experience in a

particular training paradigm would be able to more accurately detect changes between

stimuli in that same paradigm. That is, that classically trained musicians would be more

able to detect microrhythmic changes in classical stimuli and that jazz trained musicians

would be more able to detect them in jazz stimuli. This could feasibly be predicted due

to the presence of a broad-level familiarity bias with the type of music with which one is

most familiar2.

Last, and perhaps most contentiously, it was predicted that participants with jazz

training would be more able to detect microrhythmic changes overall versus participants

with an equal amount of classical training. Though the disparity here would likely be

smaller than between, say, a participant with two years of training and one with forty, it

might still be argued that due to jazz music and jazz training prominently featuring the

2 Ultimately, to keep research concise and timely, these predictions were left untested. Instead, energy

was expended testing the more interesting claim.


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skills mentioned abovegenerativity, communicativity, expressionmore so than

classical music and training, and due to the fact that each of these three skills develop

skills in microtiming detection, jazz training would, year-for-year improve skills in

microtiming detection more than would classical training.

For experimental purposes, the null hypothesis held that there would not be any

discernible difference between the group of classically-trained musicians and that of jazz-

trained musicians, and further, that if any difference was detected and was found to be

statistically significant that it would not be found to be due to the differences in their

training backgrounds.

Method

Stimuli and presentation

In preparation for the experiment, 32 short segments of music were chosen, each

on the order of four to eight measures at a medium tempo. 16 were designated

classical examples and 16 designated jazz. Classical examples were extracted

from a number of pieces of music written by well-known composers, but from less well-

known sections of their catalog in order to generally avoid a familiarity bias. Jazz

examples were a mixture of musical passages extracted from transcribed solos of well-

known jazz musicians (again, from less-well-known works of theirs) and solos

procedurally generated by the educational music software Band in a Box3. The latter

was used despite not having an analogous method for classical stimuli due to the

comparative lack of jazz MIDI files available from the sources consulted. Within each of

3 For a full list of composers/compositions, please see index.


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the two categories, further criteria were added in order to maintain diversity of stimuli.

Half of all stimuli were classed as sparse stimuli and half as dense. A sparse

segment of feature one or at most two musical voices playing at any given time, and

would have fewer notes and a less complex texture overall, while a dense segment

could feature up to five voices and have a busier musical texture. In addition, half of all

stimuli were to feature lead-type microrhythmic alteration and the other half lag-type

alteration. In segments designated lead, the note or group of notes to be altered would

be moved to occur earlier relative to their default positions, while segments designated

lag featured notes moved to occur later relative to their default positions. These

designations were added to more accurately represent the diverse varieties of

microrhythmic alteration that might be encountered in everyday performance practice.

Each main subset of classical and jazz examples featured equal numbers16 each

of sparse, dense, lead, and lag-classed stimuli4.

Within each segment of music, a note or chord was selected that would undergo

microrhythmic alteration for a duration of 20-60 milliseconds, according to the detection

threshold determined by factors such as sample tempo, instrument, density, musical

foreground vs. background, etc. Based on Clarke's 1989 study that found a baseline

threshold of 20ms for micro-rhythmic alteration, each specific alteration detection

threshold itself was arrived at through extensive if informal pre-screening during the

stimuli creation phase. In addition, Clarke found that the detection threshold was

influenced by sequential position and pitch structure of the musical phrase. Thus, a

4 Originally, statistics were to be compiled on each participant's performance within each group of

musical stimuli. Unfortunately, this was scrapped due to the lack of participation that came to affect all

descriptive statistics within the study.


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note or chord was selected if it was neither pivotal to the musical phrase nor completely

insignificant, on the assumption that too obvious a note would lead to a ceiling effect for

detection and too subtle a note would lead to an analogous floor effect. Similarly, the

time of alteration was selected to provide a just-noticeable difference, again in order to

avoid either ceiling or floor effects during testinga measure that proved successful.

It is important to note here that each musical example was created such that it

would already include expressive microtiming in order to better approximate actual

performance practice. In the case of the classical stimuli and non-generative jazz stimuli,

each example was based on a specific performance and had had expressive timing

applied accordingly by its author. In the case of the generative jazz stimuli, Band in a

Box's algorithm includes expressive timing in its phrases. Thus each alteration of timing

conducted for the experiment was a manipulation of already-existing expressive timing.

Care was taken to only apply expressive timing in the same direction as was already

present. That is, to further lag already-lagged notes or further lead already-lead notes. Of

course, timing was also altered on neutrally timed notes.

Using Logic Pro, each set of midi data was assigned an instrument or number of

instruments (though no more than three) depending on whether the musical segment

featured a solo instrument or multiple instruments. Once an instrument was assigned and

playback demonstrated a natural sounding5 excerpt, a second copy of each excerpt was

created with a single altered note or vertically-stacked chord, a single rhythmic sub-unit.

Logic Pro allowed the specification of a specific number of milliseconds by which the

5 According to the researcher and panel of pre-testers. It happened that more often the synthesized nylon

guitar instrument built into Logic Pro provided more clarity and a more life-like simulation than did the

default piano, saxophone or trumpet settings. Thus, some jazz examples had single-note piano,

saxophone or trumpet lines replaced with a guitar.


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note or chord was displaced. Each pair of examples underwent extensive pre-testing

using a number of informal participants in order to verify that the rhythmic displacement

was neither too easy nor too difficult to detect. Once an acceptable duration of alteration

was found, the amount of displacement was recorded along with each stimulus' type.

Once all 32 stimuli had been created, the creation of an appropriate presentation could

begin.

Using stimuli presentation software Superlab 4.5 two sets of stimuli were

selected for presentation and placed into groups Test A and Test B. In Test A, 16

stimuli were chosen to be presented as altered and 16 as unaltered. Test B featured the

opposite sixteen stimuli altered and unaltered. Ten participants received Test A and nine

Test B. In both tests the stimuli were presented in random order.

Experimental Procedure

Each participant was provided with a lab station and headphones. After pressing a

key to begin the experiment, participants were instructed to listen to the stimulus

presented first. After a brief pause (5) the stimulus was presented again either altered or

unaltered. The participant was instructed to press the Y key on the keyboard if he or

she detected a difference between the first presentation and the second, or the N key if

no difference was detected. After each pair of stimuli, a pause screen prompted the

participant to press a key to continue the experiment. After all 32 stimuli had been

presented, a screen was presented informing the participant that the experiment had

finished. Only one participant was given the experiment at a time and was allowed to


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proceed through the stimuli at his/her own leisure. At the end of each trial, data was

exported into a text file with the response to each stimulus pair in the order in which it

was presented. Each session lasted approximately 15-20 minutes.

Questionnaire6

In an effort to rigorously group participants into their respective classical or

jazz groups, a questionnaire was used before testing to ascertain quantity of training

and performance experience in either classical (written) or jazz (improvised) music.

Some candidates for participation had identical duration of training in both categories

and were therefore rejected. Degree of training was measured in years of formal and/or

informal instruction in either classical or jazz music. Participants were admitted to the

study as long as they had undergone at least two years of formal or informal study in

either musical paradigm, though most had at least five. The 19 participants demonstrated

a wide range of experience, from amateurs with the minimum of experience to seasoned

professionals with decades of training.

Unfortunately, given the small participant pool it was infeasible to attempt to

perform the more desirable statistical analysis and demonstrate a correlation between

years of training experience and microtiming discrimination despite the available data.

The distribution of training was skewed toward classical musicians due to the prevalence

of classically trained musicians in the University of Arkansas music department and the

comparative scarcity of jazz trained musicians. 14 musicians were placed in the

classical category and five in the jazz category. This would come to make deriving

6 Please see attached questionnaire for a complete catalog of information collected from participants.


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inferences from the data difficult due to very high standard deviations within the groups.

Data was also collected regarding participants' years of formal vs. informal

training, professional vs nonprofessional experience, aural training, music theory,

instrument used, age and sex. Though the use of this data ended up outside the purview

of this research, further research may well make use of it in order to make further

correlations with microtiming detection abilities.

Data/Analysis

The key dependent variables measured for this study were the d' values calculated

using each participant's hit and false alarm rate for jazz examples, classical examples and

overall. Rather than simply use the number of correct responses to the test, d' was

selected as the dependent variable due to its ability to take into consideration false alarm

rates as well as correct hits, making for a more sensitive measure of the participant's

ability to detect small changes in rhythm.

For each subject a jazz d', classical d', and overall d' were recorded.

Averaging the overall d' scores for each participant within the groups allowed the main

hypothesis to be tested, that is, that jazz-trained participants would demonstrate a greater

sensitivity to altered microtiming in musical phrases. Thus, an average d' for jazz-

trained participants and an average d' for classically-trained participants were

compared. The results are reproduced in Figure 1:


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Fig. 1 (above): Raw number of hits and false alarms per category

Fig. 2 (below): Hit and false alarm rate per category

d''=Z(hit rate) -Z(false alarm rate)

Fig. 3 (above): Formula used to arrive at d' for each participant

Part.# Jazz Hits Jazz FA Classical Hits Classical FA Total Hits Total FA

1 4 5 2 5 6 10

2 6 3 2 6 8 9

3 3 4 3 4 6 8

4 6 2 5 4 11 6

5 6 2 5 3 11 5

6 5 3 4 4 9 7

7 7 1 2 5 9 6

8 6 3 7 2 13 5

9 2 3 2 6 4 9

10 6 2 4 3 10 5

11 5 4 6 5 11 9

12 3 5 5 3 8 8

13 5 3 3 4 8 7

14 5 2 5 5 10 8

15 4 3 2 6 6 9

16 4 4 3 7 7 11

17 3 5 4 4 7 9

18 5 3 4 4 9 7

19 5 1 2 5 7 6

Part. # Jazz Hit Rate Jazz FA Rate Cl. Hit Rate Cl. FA Rate Total Hit Rate Total FA Rate

1 0.5 0.625 0.25 0.625 0.375 0.625

2 0.75 0.375 0.25 0.75 0.5 0.563

3 0.375 0.5 0.375 0.5 0.375 0.5

4 0.75 0.25 0.625 0.5 0.688 0.375

5 0.75 0.25 0.625 0.375 0.688 0.313

6 0.625 0.375 0.5 0.5 0.563 0.438

7 0.875 0.125 0.25 0.625 0.563 0.375

8 0.75 0.375 0.875 0.25 0.813 0.313

9 0.25 0.375 0.25 0.75 0.25 0.563

10 0.75 0.25 0.5 0.375 0.625 0.313

11 0.625 0.5 0.75 0.625 0.688 0.563

12 0.375 0.625 0.625 0.375 0.5 0.5

13 0.625 0.375 0.375 0.5 0.5 0.438

14 0.625 0.25 0.625 0.625 0.625 0.5

15 0.5 0.375 0.25 0.75 0.375 0.563

16 0.5 0.5 0.375 0.875 0.438 0.688

17 0.375 0.625 0.5 0.5 0.438 0.563

18 0.625 0.375 0.5 0.5 0.563 0.438

19 0.625 0.125 0.25 0.625 0.438 0.375


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Fig. 4 (above): Participant d' value per category

Group Jazz Classical

Mean 0.32472798358 0.074065495807

SD 0.571303933823 0.61374847251

SEM 0.2554948863682

5

0.16403117898488

N 5 14

[p-value = 0.4367]

Fig 5. (above):two tailed t-test results for the two groups

The main null hypothesis tested claimed that there would be no statistically

significant difference in microtiming discrimination ability between the two groups and

that if there were any difference, it would not be attributable to musical training

differences. Though the jazz group did display a greater overall mean d' and thus a

greater sensitivity to microtiming detection, this was not a statistically significant

difference, with the p-value of the two-tailed t-test at 0.4367. Thus the data collected

Participant # Jazz d' C lassical d' Total d' Affi l iation

1 - 0. 318639364 - 0. 9931291142 - 0. 6372787279 Classical

2 0 . 99 3 1 2 9 1 14 2 - 1 .3 4 8 9 7 9 50 0 4 - 0 .1 5 7 3 1 06 8 4 6 Ja zz

3 - 0. 31 8 63 9 36 4 - 0 .3 1 8 63 9 36 4 - 0. 31 8 63 9 3 64 Ja zz

4 1 . 34 8 9 7 9 5 00 4 0 . 3 1 86 3 9 3 6 4 0 . 8 07 4 1 5 7 7 51 C la ssi ca l5 1 .3 4 89 7 95 0 04 0 .6 3 72 7 8 72 7 9 0 .9 7 75 5 28 2 2 2 Ja zz

6 0.637 2787279 0 0.31462136 92 Classical

7 2 . 30 0 6 9 8 7 60 8 - 0 .9 9 3 1 2 9 11 4 2 0 . 4 75 9 5 0 0 4 86 C la ssi ca l

8 0 . 99 3 1 2 9 1 14 2 1 . 8 24 8 3 9 1 30 6 1 . 3 75 9 2 2 9 7 01 C la ssi ca l

9 - 0. 3558503862 - 1. 3489795004 - 0. 8318004348 Classical

10 1 .3 4 89 7 95 0 04 0 .3 1 8 63 9 36 4 0 .8 0 74 1 57 7 5 1 Ja zz

11 0 . 31 8 6 3 9 3 64 0 . 3 55 8 5 0 3 86 2 0 . 3 31 4 6 5 7 2 65 C la ssi ca l

12 -0.6372787279 0.63727 8727 9 0 Classical

13 0 . 63 7 2 7 8 7 27 9 - 0 .3 1 8 6 3 93 6 4 0 . 1 57 3 1 0 6 8 46 C la ssi ca l

14 0.993 1291142 0 0 .31863 9364 Classical

15 0. 318639364 - 1. 3489795004 - 0. 4759500486 Classical

16 0 - 1 .4 6 8 9 8 8 74 4 3 - 0 .6 4 6 0 8 70 9 5 7 C la ssi ca l

17 -0.6372787279 0 -0.314 6213692 Classical

18 0.637 2787279 0 0.31462136 92 Jazz

19 1 . 46 8 9 8 8 7 44 3 - 0 .9 9 3 1 2 9 11 4 2 0 . 1 61 3 2 8 6 7 94 C la ssi ca l


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failed to support a rejection of the null hypothesis at any tolerable significance level when

compared to the generally accepted value required for statistical significance p = 0.05.

Conclusion

The small sample size of the study yielded a much lower chance of detecting a

valid effect due to a very wide confidence interval. Though, numerically participants in

the jazz group were shown on average to be more sensitive to changes in microtiming,

this wide confidence interval demonstrates that repeat experiments with similar

parameters would be unlikely to demonstrate the same effect. Beside that, the fact that

the standard deviations of the two groups are a good deal higher than the difference

between the means indicates non-statistically significant data.

The ratio of musicians in the jazz-trained group to those in the classically-trained

group was unacceptably unbalanced (5 to 14). While the experimental design was sound

and was easily expandable to accommodate many more musicians, the resources were not

available at the time of experimentation to expand testing to include more participants.

For repeat experiments, at least 50 participants would be recommended for more robust

statistical analysis.

Though data were collected on participants' age, sex, training in other musical

arenas, etc, comparisons along these axes were not included in the research after it was

found that no statistically significant result was reached using the main axis of

comparison. Again, in order for this data to be meaningfully incorporated into the

experimental statistics, it would be necessary to include many more participants in the


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study.

That the data failed to support a rejection of the null hypothesis should not be

taken as an indication that the research was somehow unsuccessful. The benefits of these

first steps into a new sub-field of music cognition research are manifold. First, despite

the fact that no significant correlation was found as of yet between type of musical

training and microtiming detection ability, a novel axis of musical cognitive faculty was

tested that as of the date of publication of this research has not been tested in any other

literature. Second, a novel methodology was created to facilitate the creation of altered

musical stimuli by allowing for easy manipulation of a note or group of notes by a given

number of milliseconds. This experimental method could easily be replicated given only

access to a few pieces of software and a minimum of only one computer. The MIDI data

obtained for the study was all from public domain sources or created from scratch using

only the chord progressions of copyrighted material.

Discussion

The overall experimental design was simple and effective. The method of

creating and altering musical stimuli had a very shallow learning curve and was easily

adaptable to an unlimited variety of musical stimuli and many types and durations of

expressive microtiming. The selection of stimuli was unfortunately limited to selections

of music for which MIDI files were available. MIDI files for canonical jazz music are

unfortunately rare, thus experimentation relied mostly on procedurally-generated jazz

music based on the built-in generative algorithms in software Band in a Box. This


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dichotomy between canonical classical music and procedurally-generated jazz music was

not optimal. However, if alterable algorithms were used for the generation of both

classical and jazz music, finer control could be exercised over the music's duration,

tempo, key, and most importantly rhythm. Investigations into advanced music-generating

algorithms could thus be profitable for this research.

The questionnaire provided to participants was thorough, but more rigorous

criteria could be developed for classification of participants into classical and jazz

groups. Indeed, even more desirable would be finding statistical correlation between

performance on the test and extent of training in each paradigm measured in years.

Though the questionnaire included these measurements, many more data points would be

necessary in order to indicate a correlative effect, strong or weak, between the dependent

and independent variables with any statistical significance.

As mentioned above, this experimental design is effective because it can be easily

expanded to accommodate many more participants via multiple testing consoles being

utilized at one time. Further, the method of music alteration involving the combination of

softwares Band in a Box andLogic Pro worked quickly and easily and could potentially

be used to create stimuli that fit into many other paradigms of read and improvisatory

music. In this way, testing would not be limited only to musics from the two paradigms

of musical training discussed here.

Lastly, the method of determining the detection threshold for changes in

expressive microtiming in the stimuli could be improved. During pre-testing, it was

noted that the threshold for detection changed depending on several variables in the


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music, including tempo, sparse/dense texture, pitch of note altered. Because of this, each

stimulus was then altered not according to a specific rubric, but according to the

minimum detectable change for that particular stimulus. After each approximate

detection threshold was found, it was noted that for classical examples, the mean

detection threshold was 33.75 milliseconds, while for jazz examples, the mean was 40

milliseconds. Pragmatically, this method could have been improved by having a larger

panel of pretesters for whom each stimulus was demonstrated initially. Ideally though,

there would be no significant difference in the average time of alteration between types of

stimuli. The easiest means of accomplishing this would be to determine a fixed amount

of alteration for each stimulus which would eliminate any systematic difference between

the two types. However, due to the highly variegated nature of the stimuli (differing

density of sound, tempo, instrumentation, etc.) this change would run the risk of leaving

some alterations nigh undetectable while some would be glaringly obvious, leading to

skewed data for those particular stimuli. The less confounding though more time

consuming method would involve pre-testing more stimuli than were to be used for the

experiment, then ensuring that stimuli were finally chosen for the experiment such that

the average mean across both types was equal or very nearly so. Ultimately, due to the

growing prevalence of research into expressive microtiming, making formal

investigations into the smallest detectable difference between microtiming values for

varying musical parameters could be very valuable to future research.

There are many avenues by which an experiment along similar lines could be

improved or altered in order to test for different cognitive faculties. For example, an ideal


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experiment would account for the variety of training methods within the paradigms of

read music and improvisatory music. For example, participants might be admitted to

the experiment with some years of experience in specifically rock or blues improvisation

or something as unfamiliar as Hindustani classical music improvisation. In addition,

classical musicians could be classified according to their years of experience in reading

sub-genres of western art music, such as early music, baroque music, as each of these

types of music performance involve quite different aural and physical skills which might

well have an impact on the cognitive faculties under scrutiny.

On the subject of cognitive faculties, this same sort of experimental design might

be used to illustrate an effect of musical training paradigm on other faculties beside

microtiming detection. For example, detection of alteration of pitch (via vibrato, for

example), tempo accelerando or ritardando, subtle phrase alteration, relative or absolute

pitch alteration on the phrasal level, and many more. In the event that a correlation was

noted between duration of training and one of these cognitive faculties, an independent

measure of musical skill and development would be newly discovered and testable.

Most relevant to the research conducted, though, would be an improvement in the

type of micro-rhythmic alteration used to modify the musical stimuli in the study. In

actual performance practice, expressive microtiming hardly takes the form of a single

delayed or anticipated note. Rather, expressive microtiming more often occurs at the

phrasal level via playing ahead of or behind the beat. This approach to micro-

rhythmic alteration was rejected in favor of the much simpler single-note alteration for

the sake of limited time and resources. If a novel method of altering entire phrases were


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developed, it would allow for experimental conditions that would much more closely

resemble actual performance conditions.

Bibliography

Berkowitz, A. L., & Ansari, D. (2008). Generation of novel motor sequences: The neural

correlates of musical improvisation. NeuroImage, 41(2), 535543.

doi:10.1016/j.neuroimage.2008.02.028

Berkowitz, A. L., & Ansari, D. (2010). Expertise-related deactivation of the right

temporoparietal junction during musical improvisation.NeuroImage, 49(1), 712

719. doi:10.1016/j.neuroimage.2009.08.042

Berliner, P. F. (2009). Thinking in Jazz: The Infinite Art of Improvisation: The Infinite Art

of Improvisation. University of Chicago Press.

Clarke, E. F. (1989). The perception of expressive timing in music. Psychological

Research, 51(1), 29. doi:10.1007/BF00309269

Honing, H., & Ladinig, O. (2009). Exposure influences expressive timing judgments in

music. Journal of Experimental Psychology: Human Perception and

Performance, 35(1), 281288. doi:10.1037/a0012732

Iyer, V. (2002). Embodied Mind, Situated Cognition, and Expressive Microtiming in

African-American Music.Music Perception, 19(3), 387414.

doi:10.1525/mp.2002.19.3.387

Kendall, J. D. (1985). The Suzuki Violin Method in American Music Education: A Suzuki

Method Symposium. Alfred Music Publishing.


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Lampinen, Dr. James. (2008). SuperLab (Version 4.5) [software]. San Pedro, CA: Cedrus

Corporation.

Large, E. W., & Palmer, C. (2002). Perceiving temporal regularity in music. Cognitive

Science, 26(1), 137. doi:10.1016/S0364-0213(01)00057-X

Lewis, G. E. (2008).A Power Stronger Than Itself: The AACM and American

Experimental Music. University of Chicago Press.

Moreno, S., Bialystok, E., Barac, R., Schellenberg, E. G., Cepeda, N. J., & Chau, T.

(2011). Short-Term Music Training Enhances Verbal Intelligence and Executive

Function.Psychological Science, 22(11), 14251433.

doi:10.1177/0956797611416999

Prouty, K. (2012).Knowing Jazz: Community, Pedagogy, and Canon in the Information

Age. Univ. Press of Mississippi.

Sloboda, J. A. (1985). Expressive skill in two pianists: Metrical communication in real

and simulated performances. Canadian Journal of Psychology/Revue canadienne

de psychologie, 39(2), 273293. doi:10.1037/h0080062

University of Arkansas Student Technology Center. (2012). Band in a Box (Version 12)

[software]. Victoria BC: PG Music Inc.

University of Arkansas Student Technology Center. (2012). Logic Pro (Version 9)

[software]. Cupertino, CA: Apple Inc.

Wong, P. C. M., Skoe, E., Russo, N. M., Dees, T., & Kraus, N. (2007). Musical

experience shapes human brainstem encoding of linguistic pitch patterns.Nature

Neuroscience, 10(4), 420422. doi:10.1038/nn1872


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Fig. 7 (above): Extent of micro-rhythmic alteration for each musical phrase.

Participant # __________

Number Name Source Time Key

1 CSL1 JC Bach Piano Sonata 541 30ms C = Classical

2 CSL2 Bach (BWV 1005) 30ms J = Jazz

3 CSL3 Bach (BWV 995-4) 30ms

4 CSL4 Bach (BWV 1008) 30ms S = Sparse

5 CSA1 30ms D = Dense

6 CSA2 Bach Solo Lute (BWV 998) 20ms

7 CSA3 Bach Solo Cello (1010) 30ms L = Lead

8 CSA4 30ms A = Lag

9 CDL1 30ms

10 CDL2 Schubert 968a 40ms

11 CDL3 Beethoven SQ #3 Op 18 #2 30ms

12 CDL4 Brahms SQ 51-1 C min 30ms

13 CDA1 Mozart Piano Sonata 310 #2 50ms14 CDA2 30ms

15 CDA3 Brahms Op 114 40ms

16 CDA4 Franck FWV 24 #3 for organ 60ms

17 JSL1 40ms

18 JSL2 50ms

19 JSL3 40ms

20 JSL4 40ms

21 JSA1 50ms

22 JSA2 50ms

23 JSA3 40ms24 JSA4 40ms

25 JDL1 50ms

26 JDL2 40ms

27 JDL3 40ms

28 JDL4 30ms

29 JDA1 30ms

30 JDA2 30ms

31 JDA3 30ms

JC Bach Sonata in G maj 2

Sor Etude in B min

Debussy Golliwog's Cakewalk

Haydn Cantata Hob XXVIb 3

Polka Dots and Moonbeams (BiB)

Django (BiB)

Darn That Dream (BiB)

Days of Wine and Roses (thejazzpa

Polka Dots and Moonbeams (BiB)

Ornithology (BiB)

Blue Bossa (BiB)East of the Sun (thejazzpage.de)

Stella by Starlight (thejazzpage.de)

Daahoud (BiB)

Black Narcissus (thejazzpage.de)

There Will Never Be Another You (Bi

Wave (thejazzpage.de)

Autumn Leaves (thejazzpage.de)

There Will Never Be Another You (Bi


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Isaac Hayes 30

name ___________________________________________

email ___________________________________________

age _________

gender (female / male / other)

years of training in classical music _________

years of formal training _________

(lessons, ensembles, etc)

years of informal training _________(self-teaching, etc)

years of training in improvisatory music _________

(training will be considered improvisatory so long as not all

aspects of the music are decided upon before performance.)

years of formal training _________

(lessons, ensembles, etc)

years of informal training _________(self-teaching, etc)

years of training in aural perception skills _________

years of training in music theory _________

total years musical training _________

primary instrument[s] ________________________________

do you make your primary living playing music? ( yes / no )

if yes: ( mostly classical / mostly jazz / equal time in both / other ) ?

Published results will be anonymous.

Fig. 8 (above): Participant questionnaire

thesis draft 1111

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