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RUNNING HEAD: TEMPORAL EXPECTANCY IN TIMBRE AND LOUDNESS

Temporal Expectancy in Music Perception: Generalising Dynamic Attending Theory

Through Timbre and Loudness Tasks

By

Joshua P. Carroll

Bachelor of Science (Honours)

Murdoch University

This thesis is presented in partial fulfilment of the requirements for the degree of

Bachelor of Sciences (Honours), Murdoch University, 2016.

TEMPORAL EXPECTANCY IN TIMBRE AND LOUDNESS 2

I declare that this thesis is my own account of my research and contains as its

main content work that has not previously been submitted for a degree at any

tertiary educational institution.

…………………………

Joshua Carroll


Table of Contents

Table of Contents..............................................................................................................3

Acknowledgements...........................................................................................................4

Abstract..............................................................................................................................5

Introduction.......................................................................................................................6

Experiment 1: Timbre......................................................................................................15

Method.........................................................................................................................15

Results..........................................................................................................................18

Discussion....................................................................................................................26

Experiment 2: Loudness..................................................................................................29

Method.........................................................................................................................29

Results..........................................................................................................................31

Discussion....................................................................................................................38

General Discussion..........................................................................................................40

References.......................................................................................................................48

Appendices......................................................................................................................54

Appendix A..................................................................................................................54

Appendix B..................................................................................................................64

Appendix C..................................................................................................................67

Appendix D..................................................................................................................69

Appendix E..................................................................................................................70

Appendix F..................................................................................................................73

Appendix G..................................................................................................................74

Summary of Project.........................................................................................................75

Word Count: 9960


Acknowledgements

I owe extreme gratitude to my supervisor and unit coordinator Doctor Jon

Prince, who designed the framework of this project and throughout the year offered me

guidance and support in creating my thesis (despite an extremely busy schedule). I

would additionally like to thank PhD student Tom Scott, who dedicated his own time in

assisting me with the preparation and conduction of experiments when Jon was unable

to. Furthermore I thank my parents for their support in my overall education. Finally, I

would like to thank all my peers and professors at Murdoch University for helping me

trudge through the stresses of the Psychology Honours Course and making this year a

valuable learning experience.


ABSTRACT

Dynamic Attending Theory (DAT) dictates that temporal expectancies are formed when

attention becomes entrained to a rhythm of events, which influences performance on

stimulus-based tasks. Attention, and subsequently performance, peaks when stimulus

onset occurs at an expected point of time, and declines at more unexpected points of

stimulus onset, creating a temporal expectancy profile (TEP). Evidence for TEPs exist

in time-judgement tasks, but are limited in non-temporally based tasks. If findings of

TEPs extended to non-temporal tasks, it acts as evidence for general attention to stimuli

to be an overall dynamic process. The present study examined whether TEPs existed in

standard-comparison tasks with judgements on the timbre (N=54) and loudness (N=24)

of sounds. Results suggested no overall effect of temporal expectancy in timbre or

loudness judgements. Findings adhere with previous research (Bauer et al., 2015) and

suggest the standard-comparison task is not suitable for demonstrating temporal

expectancy effects. Future research is encouraged to investigate temporal expectancy

using different types of tasks, and to use caution with interference and

interconnectedness between auditory dimensions.


Temporal Expectancy in Music Perception: Generalising Dynamic

Attending Theory Through Timbre and Loudness Tasks

How does one attend to and perceive musical sounds compared to non-musical

sounds? The answer to this may lie in time structure of sounds, where temporal

expectancies are formed. Large & Jones (1999) described temporal expectancy as the

result of internal oscillations of attention that become entrained to a rhythmic

occurrence of events. In auditory perception, when listening to a sequence of sounds

with an established rhythmicity, attention would oscillate to focus on points of time

where each sound is expected to occur. This creates temporal entrainment of attention to

these expected points in time.

The premise of temporal expectancy stems from the Dynamic Attending Theory

(DAT). Jones (1976) suggested the incorporation of a time dimension alongside the

other auditory dimensions, such as pitch and volume. After reviewing previous studies,

Jones explained that an established pattern of sounds, or rhythm, resulted from the

uniformity of time periods between the sounds. Accordingly, changes to those time

periods would disrupt the rhythm. This knowledge was applied to attention, memory

and perception.

Several studies have demonstrated evidence for DAT by inducing temporal

expectancies to create performance benefits. Jones & Boltz (1989) used time duration

judgement tasks on rhythmical sequences. Time intervals between the sounds of each

sequence, or inter-onset intervals, were conditioned to be either consistent or variable,

allowing for a predictable or disrupted pattern respectively. Participants were instructed


to compare the total duration of one melody to a subsequent comparison melody. All

melodies spanned for the same duration of time, however results showed that

judgements of the comparison melodies were perceived as shorter or longer when

rhythm was disrupted. The study offered evidence suggesting perception of time was

influenced by the degree to which the ending of melodic events would either confirm or

violate expectancy.

Temporal expectancies were often demonstrated in studies that employed

isochronous musical sequences. This means that the inter-onset intervals (IOIs) in

sequences were always equal. For example McAuley & Kidd (1998) analysed tempo

discrimination tasks, where a standard sequence was compared to a comparison

sequence. The standard sequence established temporal expectancies so that the onset of

the comparison sequence would be expected to occur at a specific point of time. The

study found that having this onset occur earlier or later than expected influenced the

tempo judgements of the comparison sequence. Results reflected Jones & Boltz (1989)

and supported an oscillation model of attention towards time. Large & Jones (1999) also

demonstrated this oscillation model with time-duration judgements. As seen in Figure

1a, oscillating attention peaks at temporally expected points in the stimulus rhythm.

This results in the highest possible performance for a stimulus-based task, depicted in

Figure 1b. Large & Jones (1999) described DAT as an internal attentional rhythm that

forms in line with a stimulus rhythm.


a)

b)

Figure 1. a) Entrainment of Attention. Before the final tones in an isochronous sequence, attention falls in line with the inter-onset intervals to create a cyclical function of temporal expectancy. Illustration based on model by Jones, Moynihan, MacKenzie & Puente (2002).b) A Temporal Expectancy Profile. The profile of attention corresponds to accuracy in task performance (bold). Accuracy peaks at tones occurring on time with expectancy and falls at tones occurring earlier or later than expected.


Barnes & Jones (2000) proposed the premise of temporal expectancy profiles.

They examined the effect temporal expectancy had on judgements on the duration of the

final IOI of a sequence. The context sequence of tones that preceded it was isochronous,

so all tones had the same duration and spacing. This involuntarily aligned the listeners’

attention to the rhythm. The second-last IOI of the sequence, the Standard IOI, either

preserved or changed this timing, respectively confirming or violating expectancy.

Participants judged the subsequent Comparison IOI as either longer, shorter or the same

duration as the Standard IOI. The degree to which the Standard IOI was changed was

analysed to determine effects on the accuracy of the duration judgements on the

Comparison. Overall a negative quadratic function, or upside-down U shape, was

observed in relation to expectancy manipulations. Accuracy was highest when the

Standard IOI was consistent with the context sequence, however when the Standard IOI

disrupted the rhythm, accuracy declined. This decline was apparent when the Standard

IOI ended both earlier and later. The same expectancy pattern was observed by Large &

Jones (1999), and would later also be observed in a similar time-judgement study by

McAuley & Jones (2003). These quadratic patterns closely resembled the pattern of

attention dictated by DAT itself (see Figure 1b), acting as evidence for the theory.

Barnes & Jones (2000) named this pattern a temporal expectancy profile (TEP). TEPs

thusly dictate that, parallel with attention, performance on stimulus-based events peaks

when the event occurs at an expected point of time relative to a rhythm, and falls at

more unexpected points of time.

Jones, Moynihan, MacKenzie & Puente (2002) sought to examine whether

temporal expectancy could also reflect attending to tasks that were not temporally

based. This would offer the idea of attending on a whole to be a dynamically based

process dictated by DAT. Jones et al. (2002) designed experiments that had participants


perform a standard-comparison task on judging the pitch of tones in a temporal

sequence. An isochronous context sequence of 8 tones was placed between a Standard

tone and Comparison tone. As with previous studies (Barnes & Jones, 2000; Large &

Jones, 1999) the IOI preceding the Comparison either remained consistent with the

sequence or was manipulated to be slightly longer or shorter to disrupt rhythm. Thus the

comparison tone would respectively occur on time, later or earlier than expected.

Results demonstrated that the pitch for Comparison tones were judged less accurately

when the tone occurred unexpectedly as opposed to occurring on time, demonstrating

the shape of a TEP. Removal of the regularity of the context sequence eliminated the

quadratic pattern, producing a much more flat profile. Participants had additionally been

instructed to actively ignore the context sequence tones as they were considered

distractors and irrelevant to the task. Regardless of this instruction a TEP was still

found, reinforcing the idea that temporal expectancy is involuntarily formed and drives

attention to stimuli in a rhythmic pattern.

Plenty of research offers evidence for DAT. These included performance

benefits of temporal expectancy in sound detection tasks (Klein & Jones, 1996), simple

response tasks (Correa, Lupiáñez, & Tudela, 2005), artificial language studies (Hoch,

Tyler & Tillmann, 2012) and oddball paradigms (McAuley & Fromboluti, 2014).

Several studies that examined effects on task performance also incorporated pitch

(Bausenhart, Rolke & Ulrich, 2007; Boltz, 1993; Ellis & Jones, 2010; Jones, Johnston

& Puente, 2006; Penel & Jones, 2005; Selchenkova et al., 2014; Selchenkova, Jones &

Tillmann, 2014). Lange (2009) and Schwartze, Rothermich, Schmitt-Kassow & Kotz

(2011) found evidence for temporal expectancy by conducting event-related potential

(ERP) studies. Werner, Parrish & Holmer (2009) observed that effects of temporal

expectancy were evident even in infant perception.


Despite all the evidence for DAT, there are some studies that cast doubt on the

theory. Barnes & Johnston (2010) examined pitch discrimination tasks and found the

opposite of a TEP, instead observing that temporally unexpected events drew more

attention and had better performance than events that occurred as expected. Prince,

Schmuckler & Thompson (2009) examined pitch perception in another standard-

comparison task with temporal expectancy manipulations much like Jones et al. (2002).

They used two types of context sequences. When context sequences were musically

based, closely resembling the style of music found in contemporary Western music (in

other words, they were tonal), no TEP for accuracy scores in pitch judgements was

present. However when sequences were less musically based and atonal, that is they

consisted of more random notes with no apparent tune, a TEP was observed. They

argued that more musically based sequences bias attention towards the dimension of

pitch and away from temporal perception, eliminating temporal effects. Bauer et al.

(2015) directly examined the study by Jones et al. (2002) and attempted to replicate its

findings though a series of five experiments. They criticised a key methodological issue

in Jones et al. (2002), where the standard tone was always repeated as the final

distractor tone that preceded the comparison tone. This was done to decrease task

difficulty, but also made it redundant for the need to pay attention to the actual standard

tone or any of the intervening distractors other than the final one. Participants of Jones

et al. (2002) who reported recognising this repetition and using the final distractor tone

as a task-specific tone were excluded from analysis. However it was not clear whether

the remaining participants of the study may have implicitly used this technique. Thusly

one of the experiments by Bauer et al. (2015) was a direct replication of Jones et al.

(2002) whilst the other four experiments had small methodological variations such as

repeating the standard tone at different points of the context sequence, and slightly


altering the duration of the standard and comparison tones themselves. Across all five

experiments only 40 of the 140 total participants had demonstrated a TEP. Of these,

only two participants were from the direct replication experiment. These results casted

serious doubt on the effect of temporal manipulations and the ability of standard-

comparison pitch tasks to demonstrate TEPs.

Clearly DAT is a widely researched and evidence-based theory, with TEPs and

effects of expectancy evident across several studies, especially for time judgement

tasks. Despite this, evidence for TEPs specifically in pitch tasks are inconsistent across

studies. As a result the extent to which attention may be a dynamic process remains

unclear. The premise of Jones et al. (2002) still stands that examining temporal

expectancy in non-temporal based tasks could provide evidence for general attention to

be a dynamic process. The next step in exploring this notion may be to seek evidence

for TEPs from an alternate view other than pitch. There are other non-temporal

properties of musical notes that can be examined for this. Timbre and intensity are two

such properties, and both have not been widely explored with regards to temporal

expectancy. Intensity is the strength or amplitude of sound waves, measured in decibels.

It gives the perception of loudness in sounds, which can be more subjectively measured

on a single continuum from “soft” to “loud”. Timbre, which gives the “quality” of

sound, is a much more complex property. It is most simply understood as the sound

difference between two different musical instruments playing the same note at the same

loudness.

One way to manipulate timbre involves harmonics. Sounds are energy waves at

certain levels of amplitude and frequency, where there is always an underlying “pure”

tone, known as the fundamental frequency. However beyond this fundamental

frequency, energy is also distributed to other harmonic “partials” of the energy wave.


Each successive partial after the fundamental frequency is double the frequency of the

one previous to it. The distance between each of these partials is referred to as an

octave. A given note will have energy at numerous octaves, each with varying

amplitudes and temporal distribution. Altering this distribution artificially provides a

mechanism of timbre manipulation, so sounds are perceived as “duller” or “brighter”.

Timbre tasks have been incorporated in prior studies examining expectancies in

non-temporal domains. Margulis & Levine (2006) found that identifying the timbre of

sounds was done better when the pitch of the target stimuli fell in line with the

expectations of pitch formed by the melody. Russo & Thompson (2005) observed

effects on melodic interval judgements when the timbre of notes were congruent with

the melody and fell in line with expectations as opposed to when it did not. Tillmann &

Bigand (2010) used timbre discrimination tasks to investigate schematic expectations in

music. Other studies used timbre discrimination tasks to investigate tonal expectations

induced from musical priming and relatedness (Marmel & Tillmann, 2009; Marmel,

Tillmann & Delbe´, 2010; Tillmann, Bigand, Escoffier & Lalitte, 2006). Despite these

studies, none have directly incorporated temporal expectancy with the dimension of

timbre.

Some studies have suggested effects of expectancy on loudness. Lawrance,

Harper, Cooke & Schnupp (2014) investigated the effect of temporal entrainment on

target noise bursts at an isochronous rate. Results found that target bursts which fell in

line with the established rate of previous bursts were more easily detected than those

deviating from it. They suggested that the regularity of sequences could aid in detection

of low intensity sounds. Geiser, Notter & Gabrieli (2012) had participants perform an

intensity discrimination task while listening to sequences of tones that were either

isochronous or irregular. Overall performance was improved when listening to the


isochronous sequences, and larger intensity differences were perceived significantly

better than small differences. Although temporal expectancy has benefits on

performance in these loudness tasks, an actual TEP for loudness judgements is yet to be

examined.

The aim of the present study is to seek evidence for DAT and generalise it

through non-temporal tasks. Because of mixed findings on pitch tasks, the present study

moves away from the dimension of pitch and instead investigates whether temporal

expectancy profiles exist for timbre and loudness tasks. Two separate experiments

incorporating the standard-comparison task similar to Jones et al. (2002) are used,

replacing pitch judgements with timbre and loudness judgements. Given the similarity

in methodology to Jones et al. (2002), it is hypothesised that both tasks will reveal a

temporal expectancy profile in the form of a negative quadratic function for accuracy

against the degree of temporal expectancy manipulation. That is, accuracy will peak

when there is no manipulation of the comparison tone onset, but will fall when the

comparison tone is manipulated to occur earlier or later than expected. Alternatively the

null hypothesis of no quadratic function is possible given the recent implications of

Bauer et al. (2015).


Experiment 1: Timbre

Method

Participants

54 adult students (43 females) from Murdoch University participated in the

study as part of their Undergraduate Psychology course. Ages ranged from 18 to 53 (M

= 25.09, SD = 8.70). Participants were recruited by signing up for experiments via the

Sona System, where they received a credit point towards their course in return for their

participation. Musical experience of participants varied between 0 and 16 years (M =

3.56, SD = 4.07).

Stimuli

Stimuli were generated through MATLAB software. Sequences consisted of 10

notes per trial, which did not establish any musical key (i.e. they were atonal). The 1st

and 10th tones were the standard and comparison tones respectively, with the 8 tones in

between being distractor tones. Each note was randomly generated with frequencies

within normal hearing range from 196Hz to 1319Hz. There was up to a 3 semitone

difference between frequencies of notes per trial. The 1st, 2nd, 9th and 10th tones always

shared the same pitch per sequence. The starting and ending pitch was always one of

four tones: #A4 (233Hz), B4 (247Hz), C5 (262Hz) or #C5 (277Hz). Standard and

comparison tones played for 400ms whilst distractor tones played for 200ms. Unless the

onset of the comparison tone was manipulated, IOIs were always 500ms, as this is

theorised to be the preferred oscillation period of attention (McAuley & Fromboluti,

2014). Only two forms of timbre were used, one “bright” and one “dull”. The bright


timbre, or Timbre A, had 60% of energy allocated to the fundamental frequency, 20%

allocated to the second harmonic and 20% to the fourth harmonic. The dull timbre,

Timbre B, had 80% of energy allocated to the fundamental frequency, 15% allocated to

the second harmonic and 5% to the fourth harmonic. The timbres of the standard note,

comparison note and distractor sequence as a whole were manipulated as shown in

Table 1, giving 8 different conditions that were presented equally and randomly. The

onset of the comparison tone was temporally manipulated to occur very early (-100ms),

early (-50ms), on time (±0ms), late (+50ms) or very late (+100ms). For the main

experimental task there was a total of 160 trials within 4 blocks, allowing for 8 trials for

each temporal expectancy manipulation per block. The duration of each trial was

approximately 5 seconds for the sequence plus the time taken for response.

Combination Standard Distractors Comparison Correct

Answer

1 A A A Same

2 A A B Different

3 A B A Same

4 A B B Different

5 B B A Different

6 B B B Same

7 B A A Different

8 B A B Same

Table 1. Timbre Combinations and Respective Correct Judgements


Procedure

After reading an information sheet and signing consent (see Appendix A),

participants were verbally briefed by the experimenter on their task and given the

opportunity to ask any questions. They then filled out the Musical Experience

questionnaire (see Appendix A) before completing a practice version of the experiment

on a desktop computer, supervised by the experimenter. Sennheiser HD280 Pro

headphones were comfortably placed on and the master volume was appropriately

adjusted so that musical sequences were heard at comfortable volumes. The first part of

the practice played an example of the “dull” and “bright” timbre, which were the two

timbres they would be hearing throughout the experiment. Participants were given the

option to repeat these two tones until they were able to distinguish between the two.

This was followed by 16 practice trials of the 10-tone sequences. For each sequence

participants were instructed to compare the timbre of the first tone with the timbre of the

last tone, and to ignore all other tones in between. As each sequence ended participants

were prompted on-screen to indicate whether they perceived the timbre of the first and

last tone as “the same” or “different”. They responded by pressing the “P” or “Q” keys

on the computer keyboard; whichever response the keys indicated were reversed for

each participant to provide counterbalancing. Response times were measured, defined as

the time period between offset of the comparison tone and the participant indicating

their answer. Participants were instructed both on-screen and verbally to respond as fast

as they can, but not to sacrifice accuracy. Upon giving a response the trial ended with an

on-screen indication of whether the participant responded correctly or incorrectly with a

“ding” or “buzz” noise respectively. An on-screen instruction would say to progress to

the next trial by pushing the “space bar”, which the participant did in their own time.

Participants were instructed not to rush answers and only press the space bar when


absolutely ready. After the practice experiment was over performance accuracy as a

percentage was shown on-screen. Those with an unsatisfactory score (< 50%) were

given extra coaching and explanation of the task and were asked to carry out the

practice experiment again to improve their score. After this, and after any further

questions were answered, the real experiment was loaded by the experimenter. The

experimental trials were the same as the practice trials, but with the addition of temporal

expectancy manipulations. Participants were also no longer given any feedback for their

responses. At no point in time were the participants told about the theory behind TEPs

or the temporal expectancy manipulations within the experiment, until after they

completed the experiment and were debriefed. They were also not told of the specific

timbre combinations as shown in Table 1, other than the fact that only a “bright” and

“dull” timbre existed across sequences.

Results

All participants scored above 50% in the practice trials. Some participants were

required to repeat practice until getting an adequate score, however none repeated the

practice experiment more than twice. Accuracy for each variable was determined by the

proportion of correct answers across trials. Outliers in data likely attributed to guessing

were excluded, determined by response times being less than 200ms or over 3000ms.

This range was chosen since response times below 200ms may have implied rushed

answers, and times above 3000ms may have implied lack of attention to the sequence.

Two participants’ data were not used due to their highly variable response times, thus

much of their data were considered outliers resulting in average accuracy to fall below

50%. The remaining 52 participants (42 female) had a mean age of 24.94 (SD = 8.82)


and mean musical experience of 3.6 years (SD = 4.1). All data was determined to be

normally distributed for parametric analysis (skew < |2.0|, kurtosis < |9.0|). Detailed

output for all conditions can be found in Appendix B.

The accuracy of all participants individually across expectancy levels were

briefly assessed (see Appendix C). No individual participant conveyed a perfect

quadratic function, and only a select few demonstrated a peak in accuracy at the On

Time level. Thus, no individual participant demonstrated a TEP.

Figure 2. Mean Proportion Correct (0-1) Across Timbre and Expectancy Conditions

Figure 2 depicts mean accuracy in all conditions averaged across participants.

Numerically there is no quadratic pattern. To statistically test whether temporal

expectancy manipulations influenced accuracy, a 5 (Expectancy; very early, early, on

time, late, very late) by 8 (Timbre; AAA, AAB, ABA, ABB, BBA, BBB, BAA, BAB)

repeated measures ANOVA was conducted. Mauchly’s Test for Sphericity was satisfied


for Expectancy, χ2(9) = 8.22, p = .512. However it was not satisfied for Timbre

conditions, χ2(27) = 98.38, p < .001, or the combination of Expectancy by Timbre,

χ2(405) = 529.07, p < .001. The variance of differences between these variables were

therefore unequal. To reduce Type-I error rate a Huynh-Feldt correction was applied.

There was no statistically significant main effect for expectancy, F(4, 204) = 1.92, p

= .108, η2partial = .036, indeed with no significant effect for a quadratic trend, F(1, 51) =

0.12, ns, η2partial = .049. After correction, a statistically significant main effect was found

for Timbre, F(4.91, 250.19) = 11.77, p < .001, η2partial = .188, suggesting a difference

between means lay within the timbre conditions.

A statistically significant interaction between Expectancy and Timbre was also

observed after a Huynh-Feldt correction, F(24.07, 1227.63) = 1.59, p = .04, η2partial

= .035. This meant that performance at one level of a given variable (Timbre or

Expectancy) was influenced by the other variable. Simple main effects analysis was

conducted by isolating Expectancy at each level of Timbre, but still found no significant

main effects. Figure 2 suggests accuracy for the BBA timbre combination was lowest

across all levels of Expectancy. To test this a simple main effects analysis was

conducted by isolating Timbre at each level of Expectancy. A statistically significant

main effect of Timbre was found across all levels of Expectancy. For most of these

levels the BBA combination was significantly lower than other Timbre combinations

(see Appendix D).

Effects of temporal expectancy were then re-evaluated by conducting a 5

(Expectancy; very early, early, on time, late, very late) by 7 (Timbre; AAA, AAB,

ABA, ABB, BBB, BAA, BAB) repeated measures ANOVA, omitting the BBA Timbre

combination from analysis. This removed the presence of an interaction between

Expectancy and Timbre. The assumption of sphericity was met for Expectancy, χ2(9) =


12.16, p = .205 and yielded a statistically significant main effect, F(4, 204) = 3.22, p

= .01, η2partial = .059. However there was still no significant quadratic effect, F(1, 51) =

0.61, ns, η2partial = .012. Figure 3 depicts accuracy averaged across the remaining 7 timbre

conditions. The only significant difference in means was between the Very Early and

Very Late conditions, t(51) = -2.96, p = .004. A TEP would predict accuracy for these

two conditions to be identical.

Figure 3. Mean Proportion Correct (0-1) Averaged Across 7 Timbre Levels. The BBA timbre is omitted.

A 5 (Expectancy; very early, early, on time, late, very late) by 8 (Timbre; AAA,

AAB, ABA, ABB, BBA, BBB, BAA, BAB) repeated measures ANOVA was

conducted for response times for all correct answers. If a participant had less than 1

correct answer within any given condition, they were excluded from analysis. This left

29 participants available for analysis. Mean response times across conditions are shown


in Figure 4. Once again there is no numerical quadratic function for expectancy. The

BBA combination shows the longest response times, in accordance with its low

accuracy. Sphericity was violated for Expectancy, χ2(9) = 19.61, p = .02, and

Expectancy by Timbre, χ2(405) = 508.86, p = .003. However Timbre met the

assumption of sphericity, χ2(27) = 40.30, p = .05, which had a statistically significant

main effect, F(7, 196) = 2.61, p = .01, η2partial = .085. A Huynh-Feldt correction revealed

no significant main effect for Expectancy, F(3.49, 97.74) = 0.61, ns, η2partial = .021.

Figure 4. Mean Response Times (in seconds) Across Expectancy and Timbre Conditions

A Huynh-Feldt correction did reveal a statistically significant interaction

between Expectancy and Timbre, F(23.28, 68.07) = 1.76, p = .02, η2partial = .059. Simple

main effects analysis isolating Expectancy at each Timbre combination revealed a

statistically significant effect for the timbre combinations AAB and BAB. Only the

BAB combination demonstrated a significant quadratic effect for expectancy, F(1, 50) =


12.07, p < .001, η2partial = .194. However the only differences in means that reflected a

TEP pattern was the difference between On Time and Very Early, t(50) = -3.38, p

< .001, and between Early and Very Early, t(50) = -3.20, p = .002. This suggested that

response time worsened only when the comparison tone occurred much earlier than

expected. Note that this difference is only apparent in one direction, occurred only for

this timbre condition, and it is not a direct measure of accuracy. Simple main effect

analysis isolating Timbre at each Expectancy level was performed to statistically test

whether the BBA timbre it was significantly different to other Timbre conditions as it

was in accuracy. Results lacked significant differences in means between the BBA

timbre and other combinations (see Appendix D), and thusly not did not reflect its

significantly lower accuracy scores.

Performance based on musical experience was assessed to examine any

differences in temporal expectancy patterns. Musical experience was defined as the

years spent taking lessons to play an instrument. A small negative Pearson correlation

between average accuracy and musical experience across participants was found,

however this was not statistically significant (r = -.10, p = .48). Participants were split

between having 5 or less years of musical experience (N = 38) and more than 5 years (N

= 14). Means across all conditions are displayed in Figure 5. With musical experience

as a between-subjects factor, a 5 (Expectancy; very early, early, on time, late, very late)

by 8 (Timbre; AAA, AAB, ABA, ABB, BBA, BBB, BAA, BAB) by 2 (Musical

Experience; 5 years or less experience, over 5 years experience) mixed design ANOVA

was conducted.


a)

b)

Figure 5. Mean Proportion Correct (0-1) Across Conditions for a) Musically Inexperienced Participants, and b) Musically Experienced Participants


Homogeneity of variance was tested via Levene’s Test of Equality of Error

Variances and was satisfied for all conditions except the ABA timbre at the Very Early

level, F(1, 50) = 4.46, p = .04, indicating that variances were not equal across both

groups for this condition. Sphericity was met for Expectancy, χ2(9) = 10.62, p = .30, but

violated for Timbre, χ2(27) = 100.05, p < .001, and Expectancy by Timbre, χ2(405) =

527.58, p < .001, whereby a Huynh-Feldt correction was used. No statistically

significant main effect was found for Musical Experience, F(1, 50) = 0.41, ns, η2partial

= .008. After correction there was no interaction between all three variables, F(24.54,

1226.77) = 1.04, p = .41, η2partial = .020. There was however a statistically significant

interaction between Expectancy and Musical Experience, F(4, 200) = 2.49, p = .04,

η2partial = .047. This interaction is depicted in Figure 6, where performance on the Very

Early and Early conditions are dependent on the level of musical experience. This

difference was significant for both musically inexperienced persons, t(37) = -3.35, p

= .002, and musically experienced persons, t(13) = 2.29, p = .01. However, both

Musical Experience groups overall failed to demonstrate a TEP, making further

analyses on this trivial. After correction there was no interaction between Timbre and

Musical Experience, F(4.9, 244.99) = 0.95, ns, η2partial = .019. Therefore performance

across timbre conditions did not statistically vary as a function of the musical

experience of participants.


Figure 6. Mean Proportion Correct (0-1) Between Musically Experienced and Inexperienced Participants Averaged Across Timbre

Discussion

Results in this experiment show unpredictable patterns of accuracy across levels

of expectancy which do not resemble a TEP. Thus the findings fail to reject the null

hypothesis. Numerically, accuracy sometimes decreased with stronger manipulations in

temporal expectancy. However the overall directions and magnitudes of accuracy

changes were random, and differences temporal expectancy conditions still did not

statistically convey a TEP. The inability for the standard-comparison task in this

experiment to demonstrate a TEP accords with the findings of Bauer et al. (2015) who

found no TEPs in their standard-comparison pitch task. This experiment alone suggests

little or no effect of temporal expectancy on performance in timbre tasks. However

given it is the first to examine this relationship, the implication is too early to be


definite. Instead, combining the findings of this experiment and Bauer et al. (2015)

should cast more doubt on the ability of a standard-comparison task to provide TEPs.

There was no apparent effect of timbre on performance. Most of the timbre

conditions were clustered together, except in the unusual case of the BBA timbre, which

had statistically lower accuracy than the other timbres. There is no theoretical

foundation for why this specific combination had reduced performance, so implications

cannot be made by this. Theoretically one would expect inverse timbre combinations to

display similar patterns of accuracy. Thusly, the AAB timbre should reflect the accuracy

scores in the BBA timbre. For the most part this is not the case. Omitting the BBA

condition removed the interaction effect between Timbre and Expectancy, and all

remaining timbre combinations had similar accuracy scores. These findings suggest no

effect of timbre on performance or temporal expectancy.

There were also no effects of musical ability. The degree of music experience

was not correlated with overall accuracy. In comparing Figures 5a and 5b with Figure 2

a change in overall patterns of accuracy is evident, but these changes are not systematic

or statistically significant. Performance on the Very Early and Early levels in particular

varied as a function of musical experience. For musically inexperienced persons the

performance on the Very Early level was statistically lower than the Early level, thus

providing one small step towards a TEP pattern. However the overall profiles for both

groups did not indicate a TEP. Figure 5b numerically showed performance of the BBA

timbre to merge closer with other timbre conditions, unlike in Figure 5a where it

remained lower than all other conditions. However the lack of an interaction between

timbre and musical experience suggested this difference between groups was not

significant. The more dispersed variability in patterns for those with more musical


experience may simply be a result of imbalanced sample sizes across the musical

experience groups.

This experiment has one main limitation. Previous standard-comparison studies

that examined pitch (Bauer et al., 2015; Jones et al., 2002) only manipulated stimuli

based on the timing and pitch of tones. Thus, only the dimensions of time and pitch

were observed. Studies that demonstrated TEPs in time judgement tasks would only

directly manipulate the time dimension (Barnes & Jones, 2000; Large & Jones, 1999).

In the cases of these studies, all musical dimensions that were not of interest were held

constant during experiments. In the current experiment, the only dimensions of interest

were timbre and time. However, rather than keep the dimension of pitch constant, it was

randomised within distractor sequences across trials. Theoretically, since pitch was held

constant for the standard and comparison tones, and participants were instructed to

ignore distractors, this should not have caused interference with task-relevant timbre

judgements. Despite this, it is possible that given the complexity of timbre perception,

many participants may have confused their perception of timbre as perception of pitch,

instead making inferences on this and having judgement confounded by the randomised

pitches of the distractor sequence. For example, recall the findings by Margulis &

Levine (2006) where the dimension of pitch influenced performance in a timbre

identification task. More recently, Mercer & McKeown (2010) demonstrated that

having distractor sequences at novel pitches interfered with short-term auditory memory

and decreased performance in a standard-comparison memory task on timbre.

To tackle this potential problem of interference, the following experiment sought

to control for task irrelevant dimensions of sound. Since the timbre task did not produce

a TEP, the next logical step was to move away from both pitch and timbre, and instead

explore the dimension of loudness.

Experiment 2: Loudness

Method

Participants

24 adult students (20 females) from Murdoch University participated in the

study as part of their Undergraduate Psychology course. Ages ranged from 18 to 51 (M

= 31.5, SD = 11.6). Participants were recruited by signing up for experiments via the

Sona System, where they received a credit point towards their course in return for their

participation. Musical experience of participants varied between 0 and 11 years (M =

2.0, SD = 3.2).

Stimuli

Stimuli were generated though MATLAB software. Sequences consisted of 10

auditory stimuli per trial. The 1st and 10th stimuli were the standard and comparison

tones respectively, both with identical pitches per trial. Unlike Experiment 1, the 8

distractor stimuli between the standard and comparison now consisted of drum beats,

which served as isochronous clicks with no apparent pitch. Standard and comparison

tones were 400ms long. For each trial the pitch of both the standard and comparison

note was randomised between 262Hz and 494Hz. Loudness conditions were generated

through manipulating velocity, which were measures of intensity coded as Musical

Instrument Digital Interface (MIDI) files and synthesised as a wave form. Velocity

ranged from 0-127 possible values, which were multiplied by ratios to give a total of six

standard-comparison loudness conditions. Comparison tone could be softer than the

standard tone by having approximately 67%, 71% or 77% of the standard tone’s

velocity. Conversely, the comparison tone could be louder than the standard tone by

having 130%, 140% or 150% of the standard tone’s velocity. These six loudness

conditions resulted in different degrees of loudness differences, summarised in Table 2.

Unless the onset of the comparison tone was manipulated, IOIs were always 500ms.

Difference in Loudness Velocity of Comparison Relative to Standard

Softer Louder

Substantial 67% 150%

Moderate 71% 140%

Slight 77% 130%

Table 2. Loudness Difference for each Loudness Condition

Procedure

After reading the information sheet and signing consent (see Appendix A), participants

were verbally briefed by the experimenter on their task and given the option to ask any

questions. They then filled out the Musical Experience questionnaire (see Appendix A)

before completing a practice version of the experiment supervised by the experimenter.

Sennheiser HD280 Pro headphones were comfortably placed on and the master volume

was appropriately adjusted so that musical sequences were heard at comfortable

volumes. Practice consisted of 12 trials, where 6 of these had the comparison tone

Substantially Louder than the standard and the other 6 trials had the comparison

Substantially Softer, in randomised order. For each sequence participants were

instructed to compare the loudness of the first tone with the loudness of the last tone,

and to ignore all other sounds in between. As each sequence ended participants were

prompted on-screen to indicate whether they perceived the loudness of the last tone as

“softer” or “louder” than the first tone. Some participants responded using the “P” and

“Q” keys, while others used the “S” and “L” keys, to indicate whether the comparison

was softer or louder respectively. This provided counterbalancing across participants.

Response times were measured, defined as the time period between offset of the

comparison tone and the participant indicating their answer. Unlike Experiment 1,

participants were not explicitly instructed to respond as fast as they could. Upon giving

a response the participant received feedback onscreen indicating whether they answered

correctly or incorrectly, accompanied respectively by a “ding” or “buzz” sound. When

the trial ended an onscreen instruction would say to progress to the next trial by pushing

the “space bar”, which the participant did in their own time. Once the practice

experiment was completed, if performance was unsatisfactory (< 60%) their accuracy

score as a percentage was shown onscreen with instructions to repeat the practice

experiment until obtaining an adequate score. Once performance became satisfactory,

and any further questions were answered, the real experiment was loaded by the

experimenter. The experiment was the same as the practice trials, but with the addition

of temporal expectancy manipulations. At no point in time were the participants told

about the theory behind TEPs or the temporal expectancy manipulations until they

completed the experiment and were debriefed. Participants were warned of varying

degrees of difficulty in loudness differences between the standard and comparison, but

were not provided details of the exact conditions as seen in Table 2.

Results

None of the 24 participants that completed the loudness experiment were

excluded from analysis. All participants scored above the cut off score (60%) in the

practice trials. Some were required to repeat practice to get an adequate score. No

participants repeated the practice more than twice. All data was found to be normally

distributed for parametric analysis (skew < |2.0|, kurtosis < |9.0|). Detailed output for all

conditions can be found in Appendix E.

Mean accuracy across expectancy levels for each individual participant was

briefly examined (see Appendix F). Only one participant conveyed a perfect TEP shape,

while a select few had accuracy peak at the On Time level. Overall there is not much

indication of TEP patterns across participants.

Figure 7. Mean Proportion Correct (0-1) Across Expectancy and Loudness Conditions

Figure 7 illustrates mean accuracy across all conditions. Numerically it can be

seen that only the Moderately Loud condition depicts a negative quadratic function. To

statistically test the hypothesis of whether temporal expectancy manipulations

influenced accuracy in the form of a negatively quadratic function, a 5 (Expectancy;

Very Early, Early, On Time, Late, Very Late) by 6 (Loudness; Substantially Softer,

Moderately Softer, Slightly Softer, Slightly Louder, Moderately Louder, Substantially

Louder) repeated measures ANOVA was conducted. Mauchly’s test of sphericity

demonstrated that sphericity was met for Expectancy, χ2(9) = 14, p = .12, and for

Expectancy by Loudness, χ2(209) = 227.82, p = .43, but was violated for Loudness,

χ2(14) = 54.97, p < .001. To reduce Type-I error associated with having unequal

variances in differences of Loudness, a Huynh-Feldt correction was applied. There was

no statistically significant main effect for Expectancy, F(4, 92) = 1.17, p = .33, η2partial

= .048, and no statistically significant quadratic pattern for Expectancy, F(1, 23) = 0.03,

ns, η2partial = .001. A main effect for Loudness was observed, F(2.3, 52.97) = 10.52, p

< .001, η2partial = .314, because the Substantially Softer and Substantially Louder

conditions were easiest in difficulty (see Figure 8).

Figure 8. Mean Proportion Correct (0-1) for Loudness Conditions Averaged Across Expectancy Levels

There was also a statistically significant interaction effect between Expectancy

and Loudness, F(20, 460) = 2.04, p = .005, η2partial = .081. Simple main effects analysis

was conducted and isolated Expectancy at each level of Loudness. Significant effects

were only found in the Moderately Louder and Slightly Louder conditions. For the

Moderately Louder condition, Expectancy met the assumption of sphericity χ2(9) =

8.43, p = .49, and a main effect was found, F(4, 92) = 2.48, p = .05, η2partial = .097. This

effect was statistically significantly quadratic, F(1, 23) = 9.91, p = .005, η2partial = .301,

fitting the shape of a TEP. A significant difference in mean performance was present

between the On Time and Very Late condition, t(23) = 3.17, p = .004 as well as the On

Time and Very Early condition, t(23) = 2.11, p = .046. This demonstrates that, for the

Moderately Louder profile, the accuracy at the On Time level was higher than the Very

Late and Very Early levels, providing evidence for a TEP. Note that this profile was

only observed in the Moderately Louder condition. Expectancy isolated at the Slightly

Louder condition violated sphericity, χ2(9) = 17.26, p = .045. After a Huynh-Feldt

correction a significant main effect for Expectancy was found, F(3.17, 72.82) = 3.12, p

= .03, η2partial = .119. However the only significant difference in means was observed

between the On Time and Very Early condition, t(23) = -3.62, p < .001, indicating that

accuracy was higher when the comparison tone occurred earlier than expected, contrary

to what a TEP would predict.

Simple main effect analysis isolating Loudness at each Expectancy condition

was also conducted. Numerically in Figure 7 it can be seen how the profiles of

Loudness conditions are well spaced out at some points. Indeed, a statistically

significant main effect of Loudness was found at all levels of Expectancy. However this

had no theoretical significance to the hypothesis, so differences were not analysed

further. However, the profile at each Expectancy level had a statistically significant

quadratic effect on Loudness. This function averaged across all levels of Expectancy is

depicted in Figure 8, where mean performance systematically declined from large to

small differences in loudness.

To determine the overall effect of loudness on performance, a one-way

(Loudness; Substantially Softer, Moderately Softer, Slightly Softer, Slightly Louder,

Moderately Louder, Substantially Louder) repeated measures ANOVA was performed

averaging across expectancy levels. Mauchly’s Test showed that sphericity was

violated, χ2(14) = 54.97, p < .001. A main effect was observed after Huynh-Feldt

correction, F(2.3, 52.97) = 10.52, p < .001, η2partial = .314, which was significantly

quadratic, F(1, 23) = 83.89, p < .001, η2partial = .785, as evident in Figure 8. Thus overall,

larger differences in loudness led to higher performance. The mean differences in

performance between Loudness conditions can be observed in Appendix G, where most

differences were statistically significant, except for between two matching pairs of

Loudness conditions (Moderately Louder with Moderately Softer and Slightly Louder

with Slightly Softer). However there was also a statistically significant difference

between the Substantially Louder and Substantially Softer conditions.

Figure 9 depicts mean response times across conditions. Numerically they do

not reflect accuracy scores. This is likely due to lack of instruction to respond as quickly

as possible. Therefore further analyses were not conducted.

Figure 9. Mean Response Times (in seconds) Across Expectancy and Loudness Conditions

Performance based on musical experience was assessed to see if this

demonstrated any differences in temporal expectancy patterns. Musical experience was

defined as the years spent taking lessons to play an instrument. There was no significant

correlation between years of music experience and average accuracy (r = -.20, p = .35).

Participants were sorted between those with 5 or less years of musical experience (N =

20) and those with over 5 years (N = 4). Figure 10 depicts mean performance across all

conditions in both groups. Numerically the quadratic function of the Moderately Louder

condition is much less robust for untrained listeners (Figure 10a) compared to all

listeners (Figure 8). To determine differences between musical experience, a 5

(Expectancy; very early, early, on time, late, very late) by 6 (Loudness; Substantially

Softer, Moderately Softer, Slightly Softer, Slightly Louder, Moderately Louder,

Substantially Louder) by 2 (musical experience; 5 years or less experience, over 5 years

experience) mixed design ANOVA was conducted.

a)

b)

Figure 8. Mean Accuracy for a) Musically Inexperienced Participants, and b) Musically Experienced Participants

Homogeneity of variance was tested via Levene’s test and was satisfied for all

conditions except at the Very Early level for both the Substantially Softer condition,

F(1, 22) = 8.16, p = .01, and Moderately Softer condition, F(1, 22) = 5.87, p = .02.

Sphericity was met for Expectancy, χ2(9) = 15.06, p = .09, and Expectancy by

Loudness, χ2(209) = 229.28, p = .43. Sphericity was violated for Loudness, χ2(14) =

57.28, p < .001, whereby a Huynh-Feldt correction was used. No statistically significant

main effect was observed for Musical Experience, F(1, 22) = 0.61, ns, η2partial = .027.

There was no statistically significant interaction across all three variables, F(20, 440) =

0.41, ns, η2partial = .018. There was also no statistically significant interaction between

Expectancy and Musical Experience, F(4, 88) = 0.88, ns, η2partial = .038, or between

Loudness and Musical Experience after correction, F(2.29, 50.36) = 0.66, ns, η2partial

= .029. This suggested no effect of musical experience on any variables.

Discussion

This experiment did not obtain a TEP except in one condition (Moderately

Louder). This condition had lower accuracy for both the Very Early and Very Late

expectancy points compared to the On Time point, thus providing strong evidence for a

TEP. Regardless, this pattern was only observed in the one loudness condition. By

contrast, the trend in the Slightly Louder condition had increases in accuracy with

greater temporal expectancy violations in having the comparison tone occur earlier than

expected, contrary to what a TEP would indicate. With random patterns across all other

conditions, an overall TEP for performance is not evident. This falls in line with the

findings of Bauer et al. (2015) in conveying lack of TEPs in their standard-comparison

task.

It is not to say that lack of influence of temporal expectancy in this experiment

implies no effect of temporal expectancy on performance in loudness tasks in general.

Recall that benefits of temporal expectancy have been demonstrated for performance on

other loudness based tasks (Geiser et al., 2012; Lawrance et al., 2014). Reasons for lack

of expectancy effects in this experiment cannot be attributed to confounding effects by

other dimensions of sound, as all of these were controlled and kept constant per trial.

The sample size, though smaller than Experiment 1, should still have been adequate to

display some interpretable pattern; it was not too different from those in the experiments

by Jones et al. (2002), and did manage to offer a significant loudness effect despite none

for expectancy. Task difficulty cannot attribute to lack of interpretable patters either, as

performance was not at floor or ceiling levels, and was in fact slightly more variable

than results from Experiment 1. Instead, also given the findings of Bauer et al. (2015), it

may be the standard-comparison task itself that prevents observable expectancy effects.

This experiment did demonstrate an effect of loudness on performance.

Specifically, the larger the loudness difference between the standard and comparison

tone was, the higher the performance of the task. This reflects the findings of Geiser et

al. (2012) in their intensity discrimination tasks, where larger differences in intensity led

to better performance. Additionally, accuracy in this experiment improved almost

regardless of whether the comparison was louder or softer; each difference was

generated by similar ratios to create slight, moderate or substantial differences. As with

Experiment 1, conditions came in pairs that were the inverse of each other, thusly

displaying similar accuracy scores. This is intuitive, as larger differences would be

much easier to detect and would reduce difficulty and uncertainty in answers.

Regardless of these findings, no pair of loudness conditions appeared to demonstrate

similar patterns across temporal expectancy. It is worth noting that accuracy for when

the comparison was Substantially Louder was higher than when it was Substantially

Softer. This indicates a potential effect of large loudness differences on accuracy based

on whether the comparison tone is louder or softer. Overall the effects of these loudness

differences on performance were not relevant to the hypothesis, however they do

demonstrate that the task is still valid in producing significant effects, which may open

ideas to future research.

Although this experiment had an adequate sample size, it was limited by

inability to convey potential differences in musical experience on temporal expectancy.

Only 4 persons were reported as musically experienced. Because of this, isolating this

group revealed no effects of musical experience. A larger number of musically

experienced participants in this experiment may have offered some potential examinable

differences between variables and profiles.

General Discussion

The present study aimed to explore temporal expectancy profiles (TEP) in

standard-comparison tasks that employed timbre and loudness judgements. A TEP in

the form of a negative quadratic function for accuracy across time was hypothesised for

both timbre and loudness tasks. However, neither the timbre nor loudness standard-

comparison tasks offered a TEP. No trace of a quadratic function for accuracy was

observed in the Timbre Experiment. The Loudness Experiment displayed a hint of a

quadratic function in favour of TEP, but only within one of the loudness conditions.

Additionally, another loudness condition displayed a higher accuracy for when the

comparison occurred earlier than expected, contrary to what TEP dictates. Since these

findings were limited only to single loudness conditions, results for this experiment also

provided no robust evidence of a TEP. Overall, the present study failed to refute the null

hypotheses, but could still be prone to Type II error.

Results for both experiments reflect the findings of Bauer et al. (2015), who

could not produce a temporal expectancy profile in standard-comparison pitch

judgement tasks despite a direct replication of Jones et al. (2002). Similarly, Prince et al.

(2009) did not observe TEPs in their standard-comparison pitch task, at least for when

distractors were more musically based. The lack in findings of a TEP in the pitch

domain, and now additionally in the timbre and loudness domains, may suggest that

TEPs may only exist amongst time judgement tasks. However this implication should

not be considered definite. It has still been demonstrated that temporal expectancy does

have some effect on loudness discrimination tasks (Geiser et al., 2012; Lawrance et al.,

2014) and several other pitch tasks (Bausenhart et al. 2007; Ellis & Jones, 2010; Jones

et al., 2006), none of which were standard-comparison tasks. Putting this all together,

the present findings may extend findings by Bauer et al. (2015), and to a certain degree

those by Prince et al. (2009), by providing evidence for the limited capability of a

standard-comparison task in particular to demonstrate TEPs in accuracy scores. To

effectively convey evidence for DAT in non-temporal domains, TEPs may need to be

demonstrated through means other than a standard-comparison task.

No theoretically significant effects of musical experience were observed. In both

experiments musical experience was not correlated with greater performance. The

relatively smaller sample size in Experiment 2 is a limitation since the imbalanced sizes

based on musical experience led to no statistically significant effects for musical

experience at all. By contrast, Experiment 1 presented an interaction between

expectancy levels and musical experience. Despite this, differences in musical

experience still did not demonstrate a significant movement towards a TEP pattern, and

the experiment still faced imbalanced sample sizes. It may be that the measure used to

determine and define musical experience in this study was a limitation. Specifically,

what classified as musical experience was loosely defined as years spent playing an

instrument, and relied on a basic self-report measure (see Appendix A). To properly

analyse the factor of musical experience, it may have been more appropriate to use

Goldsmith’s Musical Sophistication Index as used by Bauer et al. (2015). Without a

proper definition of what counted as musical experience, as well as the uncertainty of

participants’ reports, interpretations of musical experience in this study may be invalid.

Overall however, the lack of musical experience differences in the current study fits

with Bauer et al. (2015) who found no performance differences across musicians and

non-musicians in their study either.

Although there were no apparent effects of timbre, there were effects of

loudness on performance. Experiment 2 demonstrated that on average, larger

differences in loudness between the standard and comparison tone led to increased

accuracy; a finding consistent with Geiser et al. (2012). Results also suggested that this

increased accuracy was regardless of whether the comparison was softer or louder,

evident in Figure 8 where the increase went in both directions of loudness. This implies

that, in tasks where the loudness of one stimulus is compared to another, performance

mainly follows the degree of loudness differences between the stimuli. There was also

enhanced performance when the comparison tone was louder, at least for when the

difference in loudness was large. Future research may test and extend these ideas with

more appropriately designed experiments, for example by investigating the lowest

detectable intensity level where this relationship exists.

There are several potential reasons for why results of the current study did not

demonstrate an effect of temporal expectancy. The simplest explanation was presented

by Bauer et al. (2015), who suggested that paying attention to the right time of the

comparison was not necessary to task performance. Having a non-temporally based task

means that judgements are not based on the time domain, and so attention for the most

part does not focus on time. In the present study participants were instructed to ignore

distractor tones, but DAT would dictate that attention would have been at least

implicitly subject to temporal effects the tones. However regardless of whether temporal

expectancy was implicitly induced, majority of attention would focus on what the

timbre or loudness of the standard and comparison tones were, subsequently leading to

weaker effects in manipulating the when of the comparison onset.

The studies by Jones et al. (2002) and Prince et al. (2009) both involved

standard-comparison tasks on a non-temporal domain of sound, and both appeared to

convey a TEP. One possible reason that the present study could not do the same was

due to musical experience of participants. In Jones et al. (2002), all participants had no

more than 6 years of musical experience. By contrast, Prince et al. (2009) only

examined “musicians” who had at least 8 years of musical training. Perhaps placing

strict constraints on musical experience in these studies allowed for more robust results.

In the present study, and in Bauer et al. (2015), many participants had mixed levels of

musical experience. Therefore differences in the way participants were measured and

split into groups for musical experience may be a factor that contributes to the lack of

temporal expectancy effects.

Participants in the present study were also diverse in age. McAuley et al. (2006)

demonstrated that abilities of entrainment towards events appear to change over

different periods of life and development. Since the present study grouped participants

as a repeated measures analysis, overall performance scores could be confounded by

differences across ages. However there was still a lack of quadratic patterns observed in

the individual performance of participants (Appendix C and F). Thus it is unlikely that

age differences greatly contributed to lack of TEPs in this study, given that TEPs were

not present to begin with. Nonetheless, taking the findings of McAuley et al. (2006) into

consideration, age diversity could be an important factor to consider when examining

temporal expectancy. Future research using larger and more age balanced samples than

the present study has to offer could examine this.

Another explanation for lack of temporal expectancy effects was the pitch

interference in Experiment 1, which is important to consider. Timbre is a complex

concept to grasp, especially when people have no musical training. In Experiment 1 the

pitch of the standard and comparison tones remained constant, however the distractor

tones varied in pitch. Previous studies suggest a complex interaction between pitch and

timbre, despite the two being independent properties, which often affect perception of

timbre and subsequent performance on tasks (Handel & Erickson, 2004; Krumhansl &

Iverson, 1992; Margulis & Levine, 2006; Mercer & McKeown, 2010). Furthermore

Prince et al. (2009), as an explanation to their results on reaction times and lack of

temporal expectancy effects in their more tonal sequences, argued that pitch is a much

more accessible perceptual dimension of sound, to a point where it is difficult to ignore

even when it is task irrelevant. Thus it may be that using randomised pitches of tones

within sequences drew more attention to pitch and left little attention for forming

temporal expectancies. Therefore the lack of a TEP in Experiment 1 of this study may

conform to the findings of previous studies in demonstrating a bias of attention towards

pitch, thus interfering with the perception of timbre and time domains.

The possibility of pitch interference has implications on the discriminability and

salience of other musical dimensions. There have been several cases where dimensions

of musical properties seem to combine and consequently influence results. In particular,

among the conflicts between pitch and timbre, there have been reports of volume

influencing time duration judgements (Rammsayer & Lina, 1991) and pitch

discriminations (Rinne et al., 2006). In fact, an early study by Melara & Marks (1990)

suggest that perception of loudness, pitch and timbre are all processed jointly. They

proposed a model dictating interconnectedness of these three dimensions, where

information being selected from one dimension is contextualised in part by the other

two, whether or not they may be relevant to a task. Given that the dimension of time is

theoretically perceived separately, the model therefore additionally offers an

explanation as to why TEPs are common only amongst time duration experiments

(Barnes & Jones, 2000; Large & Jones, 1999; McAuley & Jones, 2003). The

experiments in these studies were only concerned with manipulating time specifically

for the judgement of time. As a result there may have been fewer confounding effects of

any other dimensions involved, providing more robust patterns in temporal expectancy.

The Interconnectedness of these musical dimensions may further explain lack of results

in Experiment 1 of the present study, however it does not explain findings of

Experiment 2, which controlled for task irrelevant domains of sound.

The final explanation that can be offered for the findings of this study is the

process of reactive attending. This form of attending was also discussed by Barnes &

Jones (2000) and Jones et al. (2002). It describes stimulus attending as a brief, very

sudden change in attention from an expected point to an unexpected point along time.

Specifically, attention reflexively shifts to the temporal area just after the onset of a

sound. The alternate form of attention to this is anticipatory attending, which involves

more stable attentional shifts anticipating the onset of tones. When reactive attending is

combined with anticipatory attending, the latter forms the foundation of DAT. Some

studies however have demonstrated a stronger lean towards reactive attending than

anticipatory attending. In their pitch discrimination tasks, Barnes & Johnston (2010)

reported that attention was drawn more towards unexpected events than events that fell

at expected points in time, resulting in higher accuracy at the more unexpected points in

time. They manipulated the onset of target tones to occur earlier, later or on time based

on isochronous sequences. When the position of the target tone was known to

participants there was no difference across expectancy manipulations, much like the

findings of this study. However Barnes & Johnston also tested for when the target

position became unknown. One condition involved a cue preceding the target, which led

to increased performance for when the target onset was early or late. In another

condition when there was no cue, performance was only higher for when the target

onset was later than expected. Barnes & Johnston used reactive attending as an

explanation for these results. This study also seemed to display more of this form of

attention. Specifically, the lack of temporal expectancy effects suggests that the tone

before the comparison note acted as a cue to shift attention fully to the entire temporal

space that followed it, preparing the participant for any onset of the comparison tone at

more unexpected points of time. A hint of this appeared in the one of the loudness

conditions in Experiment 2, where higher accuracy was observed in the Very Early

condition as opposed to the On Time condition. Shifting attention to the temporal space

on the whole would also have eliminated the entrained temporal expectancy from the

rest of the sequence, therefore removing accuracy differences across expectancy levels

and favouring reactive attending over anticipatory attending.

The present study offers insight for the investigation of temporal expectancy in

non-temporal dimensions of musical perception. Evidently TEPs did not exist for timbre

or loudness in the standard-comparison tasks of this study. In terms of DAT, instead

there was a stronger lean toward reactive attending than anticipatory attending.

However, other studies have shown that effects of temporal expectancy still exist in

dimensions of pitch (Jones et al., 2002; Prince et al., 2009) and loudness (Geiser et al.,

2012). Given this, future research should still continue to examine temporal expectancy

in tasks involving non-temporal dimensions of sound. As a precaution, the

interrelatedness of auditory dimensions (Melara & Marks, 1990) should be explored and

taken into account before designing an adequate task that can distinguish the time

dimension from the non-temporal dimension being tested. This would involve removing

interference from other auditory dimensions by isolating them, which may be difficult

particularly for timbre tasks given the complexity of timbre. Because of this it is

encouraged that future research more thoroughly examines performance on loudness

tasks, especially given the effects of loudness observed in Experiment 2. Overall the

generalisability of DAT remains indefinite. This study, alongside the findings of Bauer

et al. (2015), narrows down how to solve this problem by demonstrating how

inconsistent the standard-comparison task is in providing evidence for dynamic

attending, suggesting that alternate tasks are required for a more valid investigation into

the theory.

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