Content uploaded by Eckart Altenmüller
All content in this area was uploaded by Eckart Altenmüller on Oct 25, 2019
Content may be subject to copyright.
Auditory and Visual Timing Performance 85
Music Perceptio n VOLU ME 30, ISSUE 1, PP. 85–96. ISSN 0730-7829, ELECTRONIC ISSN 1533-8312. © 2012 BY THE REGENTS OF THE UNIVERSITY OF CALIFORNIA ALL
RIGHTS RESERVED. PLEASE DIRECT ALL REQUESTS FOR PERMISSION TO PHOTOCOPY OR REPRODUCE ARTICLE CONTENT THROUGH THE UNIVERSITY OF CALIFORNIA PRESS’S
RIGHTS AND PERMISSIONS WEBSITE, HTTP://WWW.UCPRESSJOURNALS.COM/REPRINTINFO.ASP.DOI: 10.1525/MP.2012.30.1.85
THOMAS H. RAMMSAYER
University of Bern, Bern, Switzerland
FRANZISKA BUTTKUS, & ECKART ALTENMÜLLER
University of Music, Drama and Media, Hannover,
THE PRESENT STUDY WAS DESIGNED TO INVESTIGATE
differences in auditory and visual temporal information
processing between musicians and nonmusicians. For
this purpose, timing performance on a set of six different
psychophysical temporal tasks for both the auditory and
visual sensory modalities was compared in 40 formally
trained musicians and 40 controls without musical
experience. Across modalities, superior temporal acuity
for musicians compared to nonmusicians could be
shown for all temporal tasks except for temporal
generalization. When comparing the two sensory
modalities, temporal acuity was superior to auditory
stimuli as compared to visual stimuli, with the exception
of the temporal generalization task in the 1-s range. The
overall pattern of our findings is consistent with the
notion that musicians’ long-lasting intensive music
training, starting in childhood, improves general timing
ability irrespective of sensory modality.
Received July 5, 2011, accepted December 21, 2011.
Key words: interval timing, rhythm perception, audi-
tory, visual, sensory modality
T HE DEFINITION OF MUSICAL ABILITY HAS ALWAYS BEEN A
highly disputed issue (for a review see Sloboda,
2005). There is no clear agreement on a definition yet
(Bentley, 1966; Colwell, 1970; Lundin, 1967; Radocy &
Boyle, 1979; Shuter-Dyson, 1999), although good timing
ability is considered explicitly or inexplicitly as an important
attribute of musical ability. Musical aptitude tests often
include temporal discrimination or rhythm perception tasks,
and are based on various aspects of auditory performance
and tonal and rhythmic concepts (e.g., Drake, 1954;
Kw al w as s er , 1 9 53 ; S e as h or e , 1 9 19 ) . F o r example, the
Seashore Measures of Musical Talents (Seashore, Lewis,
& Saetveit, 1956) were intended to assess musical aptitude
by means of various tests referred to as Sense of Pitch,
Sense of Intensity, Timbre, Tonal Memory, Sense of
Time, and Sense of Rhythm. The construct validity of
such musical aptitude tests has been doubted by several
researchers (e.g., Anastasi, 1961; Henson & Wyke, 1982;
Sloboda, 2005; Winner & Martino, 1993). One major
objection refers to the fact that musical aptitude tests
often assess isolated sensorimotor abilities rather than
considering the complexity of musical abilities, such as
emotional appreciation, which is required in a real
musical context (Haroutounian, 2000; Henson & Wyke,
1982; Rainbow, 1965; Sloboda, 1985, 2005).
A positive functional relationship between musical
ability and performance on temporal information
processing is supported by various findings of superior
timing accuracy in musicians compared to nonmusicians.
Many of these findings were obtained with tasks based
on the reproduction or production of temporal patterns
(Aschersleben 2002; Drake, Penel, & Bigand, 2000; Repp,
2006). In these studies, temporal information processing
was investigated in combination with motor skills,
showing a good ecological validity for music performance
skills. It is possible, therefore, th at the superior
performance on timing and time perception tasks
observed with musicians largely depends on the strong
central nervous coupling of auditory and sensory-motor
In music performance, musicians frequently attain
extremely precise timing control. Wagner (1971) assessed
the rhythmic precision of playing a C-major scale in
professional pianists. At a required speed of about six
key-strokes per s, a standard deviation of 6 to 10 ms was
found when calculating the temporal deviations of 30
subsequent keystrokes. An even higher degree of
regularity of cyclic trill movements in a pianist was
reported by Moore (1992) using MIDI-technology.
Another study showed that cumulative practice time of
MUSICIANS DO BETTER THAN NONMUSICIANS IN BOTH
AUDITORY AND VISUAL TIMING TASKS
86 Thomas H. Rammsayer, Franziska Buttkus, & Eckart Altenmüller
professional percussionists was correlated with the
ability to produce regular sequences of synchrony
(Trappe, Katzenberger, & Altenmüller, 1998). There is
also a less pronounced negative asynchrony in profes-
sional musicians compared to nonmusicians when asked
to tap in beat with a metronome (Aschersleben, 2002).
Investigation of temporal information processing
should also include tasks of a purely perceptual nature.
There are only few studies based on perception tasks
without any motor component. Two studies by Jones
and Yee (1997) and Yee, Holleran, and Jones (1994)
found that musicians showed superior performance in
detecting small time changes embedded in regular
auditory sequences, although these results may apply
only to specific aspects of temporal judgments. In
another study, when musicians were asked to judge
whether the last of six regular sequential auditory or
tactile stimuli occurred 50 or 25 ms later or earlier,
musicians did not perform better than nonmusicians
(Lim, Bradshaw, Nicholls, & Altenmüller, 2003). A study
by Rammsayer and Altenmüller (2006) also investigated
temporal information processing of a purely perceptual
nature in musicians and nonmusicians. Seven different
auditory psychophysical timing tasks without a motor
component were included. Superior temporal acuity for
musicians compared to nonmusicians was shown for
auditory fusion, rhythm perception, and three temporal
discrimination tasks. The two groups did not differ,
however, in terms of their performance on two tasks of
temporal generalization. Unlike the other timing tasks,
temporal generalization involves a longer period of time
during which temporal information is stored in memory
before being processed (McCormack, Brown, Maylor,
Richardson, & Darby, 2002), Therefore, musicians’
superior performance appeared to be limited to aspects
of timing that do not require long-term storage of
Given mus ician s’ su perior aud itor y tem poral
accuracy compared to nonmusicians, the major goal of
the present study was to determine whether musical
experience also exerts a beneficial effect on temporal
information processing in the visual domain. The
nature of temporal information processing represents
a highly disputed issue concerning the underlying
mechanisms of timing. Most researchers assume a gen-
eral timing ability that is active in temporal information
processing independent of the actual modality of the
task and also independent of the task itself (cf.,
Grondin, 2003; Guttman, Gilroy, & Blake, 2005;
Merchant, Zarco, & Prado, 2008; Rammsayer & Ulrich,
2001). Nevertheless, several findings suggest a multi-
dimensional model of temporal information processing,
assuming several underlying mechanisms that may be
active depending on the sensory modality and specific
timing task used (cf., Block, 1990; Lapid, Ulrich, &
Rammsayer, 2009; Penney, 2003). In the present study,
therefore, also the dimensional structure of timing
performance of musicians and nonmusicians was
investigated with principal components analysis.
Our approach is based on a comparison of timing
performance between formally trained musicians and
participants without any specific musical experience,
who were matched with regard to age, gender, and level
of education. For this purpose, we employed a set of six
basic timing tasks for psychophysical assessment of
performance on different aspects of temporal informa-
tion processing. As indicators of individual temporal
acuity, performance measures on temporal fusion,
rhythm perception, and interval timing in the range of
seconds and milliseconds were obtained. In the following,
a brief description of the six timing tasks will be given.
Temporal fusion refers to the size of the temporal
interval between two sensory events that is required for
them to be perceived as two separate events rather than
fused as one event. Thus, temporal fusion thresholds
represent a psychophysical indicator of temporal
resolving power for central sensory information process-
ing (Robin & Royer, 1987; van Wassenhove, 2009).
A focus of research on rhythm perception is on
discrimination of serial temporal patterns (ten Hoopen
et al., 1995). Commonly, in a rhythm perception task, a
participant is presented with a simple pattern of brief
auditory or visual stimuli. The participant’s task is to
detect a deviation from regular, periodic interstimulus
For assessment of performance on interval timing, two
temporal discrimination and two temporal generaliza-
tion tasks were used. In a typical temporal discrimination
task, a participant is presented with two intervals and
his/her task is to decide which of the two intervals was
longer. While timing of brief intervals in the range of
milliseconds appears to be dependent on sensory
processes beyond cognitive control, temporal processing
of longer intervals is likely to be cognitively mediated
(Lewis & Miall, 2003; Michon, 1985; Rammsayer, 1999;
Rammsayer & Lima, 1991; Rammsayer & Ulrich, 2011).
This latter mode of processing implies a cognitive
representation of temporal information that draws on
central executive resources and is subject to a limited-
capacity attentional system (Rammsayer & Ulrich, 2011).
Based on th e s e conside r a t i o n s , t w o temp o r al
discrimination tasks were employed: one task with a
50-ms standard duration and one task with a 1,000-ms
Auditory and Visual Timing Performance 87
In addition to the temporal discrimination tasks, two
temporal-generalization tasks were used with standard
durations of 75 and 1,000 ms, respectively. Unlike
temporal discr imination, temporal generalization
requires a longer period of time during which temporal
information is stored in memory. This is because with
the latter task, participants are presented with a standard
duration during a preexposure phase and are required
to judge whether the durations presented during a
subsequent test phase were the same as the standard
duration that they have encountered earlier.
The short and long standard durations of the interval
timing tasks were selected because the hypothetical shift
from one timing mechanism to the other may be found
at an interval duration somewhere between 100 and
500 ms (Abel, 1972; Buonomano & Karmarkar, 2002;
Buonomano, Bramen, & Khodadadifar, 2009; Michon,
1985; Spencer, Karmarkar, & Ivry, 2009). Furthermore,
when participants are asked to compare time intervals,
many of them adopt a counting strategy. Since explicit
counting becomes a useful timing strategy for intervals
longer than approximately 1,200 ms (Grondin, Meilleur-
We ll s , & L a ch a nc e , 1 9 99 ; Gr o n di n , O u el l e t, & R o u ss e l,
2004), the “long” standard duration was chosen not to
exceed this critical value.
Two groups of participants, musicians and nonmusi-
cians, participated in the study. The musician group
included 20 male and 20 female musicians ranging in
age from 18 to 30 years (mean age ± standard deviation:
22.7 ± 2.6 years). All participants of the musician group
were graduate students at the University of Music,
Drama and Media, Hannover, Germany, enrolled in the
music master program with major “musical perfor-
mance.” All musicians had played their instruments for
16.5 ± 0.5 years on average. The nonmusician group
included 20 male and 20 female nonmusicians ranging
in age from 18 to 28 years (mean age: 22.8 ± 2.6 years).
All nonmusicians were graduate students at the
University of Göttingen and reported that they had
never been playing any musical instrument nor were
they especially interested in music. Thus, none of the
nonmusicians were occupied with music to a greater
extent than occasionally listening to music. The level of
education was matched between the two groups as both
musicians and nonmusicians possessed the German
Abitur, a high school degree required to enroll at
PSYCHOMETRIC ASSESSMENT OF GENERAL INTELLIGENCE
As a psychometric index of general intelligence, the
Zahlen-Verbindungs-Test (ZVT; Oswald & Roth, 1987)
was used. The ZVT measures general intelligence by the
assessment of information processing speed (Oswald &
Roth, 1987). The ZVT is a trail-making test in which
participants draw lines to connect, in order, circled
numbers or letters that are positioned more or less
randomly on a sheet of paper. Vernon (1993) introduced
additional versions of the ZVT varying in task complexity.
He found more complex versions to be more highly
correlated with conventional measures of general intel-
ligence. Therefore, we administered a ZVT version with
high task complexity (Version 4) selected from Vernon’s
(1993) survey. This version represents one of the most
valid ones as it shows a substantial correlation of r = .71
with full-scale IQ obtained by Jackson’s (1983)
Multidimensional Aptitude Battery. More recently,
Rammsayer (2005) reported correlation coefficients of
similar magnitude between ZVT Version 4 and a g factor
of psychometric intelligence extracted from 15 subtests
assessing different aspects of intelligence. With Version 4,
participants are required to connect alternate numbers
and letters backward (26-Z-25-Y-24-X… etc.). In the
present study, ZVT Version 4 was administered in a
speeded format that counted the number of items
completed within 45 s. Test-retest reliability reported by
Ve r n o n ( 1 9 9 3 ) f o r t e s t a d m i n i s t r a t i o n u n d e r t i m e l i m i t a -
tions was r = .80.
PSYCHOPHYSICAL TMING TASKS
Auditory and visual duration discrimination in the sub-
second range. The presentation of the intervals to be
judged and the recording of participants’ responses
were controlled by a computer. The standard and the
comparison stimuli were filled auditory or visual inter-
vals. Auditory stimuli were white-noise bursts from a
computer-controlled sound generator (Phylab Model
1), presented binaurally through headphones (Vivanco
SR85) at an average intensity of 63 dB(A) SPL. Visual
stimuli were generated by a red LED (diameter 0.48°,
viewing distance 60 cm, luminance 48 cd/m2) positioned
at eye level of the participant. The intensity of the LED
was clearly above threshold, but not dazzling.
The duration discrimination task consisted of the
presentation of one block of auditory and one block of
visual intervals. The order of blocks was counterbalanced
across participants. Each block consisted of 64 trials, and
each trial consisted of one standard interval and one
comparison interval. The duration of the comparison
interval varied according to an adaptive rule (Kaernbach,
88 Thomas H. Rammsayer, Franziska Buttkus, & Eckart Altenmüller
1991) to estimate x.25 and x.75 of the individual psycho-
metric function; that is, the two comparison intervals at
which the response “longer” was given with a probability
of .25 and .75, respectively.
For both the auditory and the visual task, the standard
inter val was 50 ms and initia l durations of the
comparison interval were 15 ms below and above the
standard interval for x.25 and x.75, respectively. To
estimate x.25, the duration of the comparison interval
was increased for Trials 1-6 by 3 ms if the participant had
judged the standard interval to be longer and decreased
by 9 ms after a “short” judgment. For Trials 7-32, the
duration of the comparison interval was increased by
2 ms and decreased by 6 ms, respectively. The opposite
step sizes were employed for x.75. In each experimental
block, one series of 32 trials converging to x.75 and one
series of 32 trials converging to x.25 were presented.
Within each series, the order of presentation for the
standard interval and the comparison interval was
randomized and balanced, with each interval being
presented first in 50% of the trials. Trials from both
series were randomly interleaved within a block.
Each participant was seated at a table with a keyboard
and a computer monitor. To initiate a trial, the participant
pressed the space bar; auditory presentation began 900 ms
later. The two intervals were presented with an interstim-
ulus interval of 900 ms. The participant’s task was to
decide which of the two intervals was longer and to
indicate his decision by pressing one of two designated
keys on a computer keyboard (two-alternative forced-
choice technique). One key was labeled “First interval
longer” and th e oth er was la bel ed “Second interval longer.”
The instructions to the participants emphasized accuracy;
there was no requirement to respond quickly. After each
response, visual feedback (“+”, i.e., correct; “−”, i.e., false)
was displayed on the computer screen. The next trial
started when the participant pressed the space bar again.
As a measure of performance, mean differences
between standard and comparison inter vals were
computed for the last 20 trials of each series. Thus,
estimates of the 25% and 75% difference thresholds in
relation to the 50 ms standard intervals were obtained
for the auditory and the visual task, respectively. In a
second step, half the interquartile range, (75%-threshold
value - 25%-threshold value)/2, representing the differ-
ence limen, DL (Luce & Galanter, 1963), was determined
for both duration discrimination tasks. With this
psychophysical measure, better performance on duration
discrimination is indicated by smaller values.
Auditory and visual duration discrimination in the
second range. Apparatus, stimuli, and the psychophysical
procedure were the same as in the previous task except
that the temporal intervals to be discriminated were
longer. For duration discrimination of intervals in the
second range, the standard interval was 1,000 ms and the
initial values of the comparison interval were 500 ms and
1,500 ms for x.25 and x.75, respectively. To estimate x.25,
the duration of the comparison interval was increased
by 100 ms if the participant had judged the standard
interval to be longer and decreased by 300 ms after a
“short” response. For Trials 7-32, the duration of the
comparison interval was increased by 25 ms and
decreased by 75 ms, respectively. Again, the opposite step
sizes were employed for x.75. As a psychophysical indica-
tor of performance on auditory and visual duration
discrimination, DLs were determined.
Auditory and visual temporal generalization in the
sub-second and second range. In addition to the duration
dis c r i m ination ta s k s , two a u d itory and visual
temporal-generalization tasks were used with base
durations of 75 and 1,000 ms, respectively. Participants
were presented with a standard duration during a pre-
exposure phase and were required to judge whether the
durations presented during the test phase were the
same as the standard duration that they had encoun-
Apparatus and stimuli were the same as in the previous
experimental tasks. For auditory and visual temporal
generalization of intervals in the sub-second range, the
non-standard stimulus durations were 42, 53, 64, 86, 97,
and 108 ms and the standard duration was 75 ms. For
auditory and visual temporal generalization of intervals
in the second range, the standard stimulus duration was
1,000 ms and the non-standard durations were 700, 800,
900, 1,100, 1,200, and 1,300 ms.
Performance on temporal generalization in the sub-
second and second range was assessed separately for
auditory and visual intervals. For both time ranges, order
of the auditory and visual temporal-generalization tasks
was randomized and balanced across participants. With
all generalization tasks, participants were required to
identify the standard stimulus among the six non-
standard stimuli. In the first part of the experiment,
participants were instructed to memorize the standard
stimulus duration. For this purpose, the standard inter-
val was presented five times accompanied by the display
“This is the standard duration.” Then participants were
asked to start the test. Each generalization task consisted
of eight blocks. Within each block, the standard duration
was presented twice, while each of the six non-standard
intervals was presented once. All duration stimuli were
presented in randomized order.
Auditory and Visual Timing Performance 89
On each test trial, one duration stimulus was presented.
Participants were instructed to decide whether or not the
presented stimulus was of the same duration as the
standard stimulus stored in memory. Immediately after
presentation of a stimulus, the display “Was this the
standard duration?” appeared on the screen, requesting
the participant to respond by pressing one of two
designated response keys. Each response was followed by
visual feedback. As a quantitative measure of perfor-
mance on temporal generalization an individual index of
response dispersion (cf., Wearden, Wearden, & Rabbitt,
1997) was determined. For this purpose, the proportion
of total “yes” responses to the standard duration and the
two non-standard durations immediately adjacent (e.g.,
900, 1,000, and 1,100 ms) was determined. This measure
would approach 1.0 if all “yes” responses were clustered
closely around the standard duration.
Auditory and visual rhythm perception. Apparatus and
stimuli were the same as in the previous experimental
tasks. For the auditory rhythm perception task, the stimuli
consisted of 3-ms clicks presented binaurally through
headphones, while, for the visual task, light flashes with a
duration of 3 ms were used. Participants were presented
with rhythmic patterns, each consisting of a sequence of
six clicks (auditory task) or six flashes (visual task) mark-
ing five beat-to-beat intervals. Four of these intervals were
of a constant duration of 150 ms, while one interval was
variable (150 ms + x). The magnitude of x changed from
trial to trial depending on the participant’s previous
response according to the weighted up-down procedure
(Kaernbach, 1991) that converged on a probability of hits
of .75. Correct responding resulted in a decrease of x and
incorrect responses made the task easier by increasing the
value of x. For each task, a total of 64 experimental trials
were grouped in two independent series of 32 trials each.
In Series 1, the third beat-to beat interval was the deviant
interval, while in Series 2 the fourth beat-to-beat interval
was the deviant interval. Trials from both series were
The participant’s task was to decide whether the presented
rhythmic pattern was perceived as “regular” (i.e., all beat-
to-beat intervals appeared to be of the same duration) or
“irregular” (i.e., one beat-to-beat interval was perceived as
deviant). Participants indicated their decision by pressing
one of two designated response keys. No f eedback was
given, as there were no perfectly isochronous (“regular”)
patterns presented. As a psychophysical indicator of perfor-
mance on auditory and visual rhythm perception, the 75%
threshold for detection of irregularity was determined.
Individual threshold estimates represented the mean
threshold value across Series 1 and 2.
Auditory and visual temporal fusion. Apparatus and
stimuli were the same as for the previous experimental
tasks. The stimuli consisted of 25-ms noise bursts and
25-ms light flashes for the auditory and visual fusion
tasks, respectively. Fusion threshold estimation consisted
of 12 trials, and each trial consisted of two noise bursts
(auditory fusion task) or two light flashes (visual fusion
task) separated by a variable ISI ranging from 1 to 40 ms.
After each trial, the participant’s task was to indicate by
pressing one of two designated response keys whether
he perceived the two successive noise bursts/flash lights
as one tone/light or two separate events. The ISI was
changed using an adaptive rule based on the Best PEST
procedure (Pentland, 1980) to estimate the 75% auditory
and visual fusion threshold.
The intelligence test and the experimental tasks were
administered in a testing session of approximately 90
min. All experiments were carried out in a sound-atten-
uated room with constant ambient light. The testing
session was initiated by the psychometric intelligence test
followed by the duration discrimination tasks in the sub-
second and second range, the temporal generalization
tasks in the sub-second and second range, the rhythm
perception tasks, and the two fusion tasks. For each type
of timing task, order of auditory and visual versions of
the task was counterbalanced across participants.
Experimental trials of all tasks were preceded by practice
trials to ensure that the participants understood the
instructions and to familiarize them with the stimuli.
Several studies suggest a positive association between
individual level of general intelligence and performance
on temporal information processing (e.g., Helmbold,
Troche, & Rammsayer, 2007; Rammsayer & Brandler,
2007). Therefore, in a first step, musicians’ and nonmu-
sicians’ ZVT scores as a psychometric indicator of
general intelligence were compared by means of a t-test.
There was no statistically significant difference in levels
of general intelligence between the musician and
nonmusician groups, t(78) = 0.41, p = .68; mean (±
SEM) ZVT scores were 19.78 ± 1.02 and 19.22 ± 0.95
for musicians and nonmusicians, respectively. Thus, it
appears highly unlikely that potential differences in
timing performance between both groups will be due
to differences in general intelligence.
In a second step, two-way analyses of variance were
performed on all psychophysical timing tasks with
90 Thomas H. Rammsayer, Franziska Buttkus, & Eckart Altenmüller
Group (two levels: musicians and nonmusicians) as an
independent factor and Sensory Modality (two levels:
auditory and visual) as a repeated measurement factor.
Simple main effect means (± SEM) of musicians and
nonmusicians for the auditory and visual versions of
each temporal task are given in Table 1.
Two-way analyses of variance revealed significantly
better timing performance for musicians compared to
nonmusicians for duration discrimination in the sub-
second, F(1, 78) = 6.37, p < .05, h2 = .08, and in the
second range, F(1, 78) = 17.51, p < .001, h2 = .18, rhythm
perception, F(1, 78) = 17.74, p < .001, h2 = .19, and
temporal fusion, F(1, 78) = 7.54, p < .01, h2 = .09. No
performance differences between both groups could be
observed for temporal generalization in the sub-second,
F(1, 78) = 0.91, ns, h2 = .01, and second range, F(1, 78) =
2.68, ns, h2 = .03.
A statistically significant main effect of Sensory
Modality yielded superior auditory compared to visual
timing acuity for duration discrimination in the sub-
second, F(1, 78) = 414.28, p < .001, h2 = .84, and in the
second range, F(1, 78) = 43.97, p < .001, h2 = .36,
temporal generalization in the sub-second, F(1, 78) =
10.44, p < .01, h2 = .12, rhythm perception, F(1, 78) =
9.58, p < .01, h2 = .19, and temporal fusion, F(1, 78) =
473.19, p < .001, h2 = .86. No modality-specific effect was
found for temporal generalization in the second range,
F(1, 78) = 0.62, ns, h2 = .01.
A statistically significant interaction between Group
and Sensory Modality could only be shown for duration
discrimination of intervals in the range of milliseconds,
F(1, 78) = 4.98, p < .05, h2 = .06. Post-hoc Scheffé tests
yielded reliably better temporal discrimination perfor-
mance for musicians than for nonmusicians with visual
intervals (p < .001) while no such difference was found
for auditorily presented intervals. In addition, for both
groups, timing performance was better with auditory
than with visual stimuli (p < .001 each). There were no
statistically significant interactions for all other temporal
tasks applied in the present study.
In order to elucidate the dimensional structure of
timing performance assessed in the present study, indi-
vidual performance scores on the six different types of
psychophysical timing tasks were subjected to a principal
component analysis. The index of response dispersion
obtained with the temporal generalization tasks was
positively related to performance, i.e., better performance
was indicated by higher values of response dispersion,
while the other psychophysical measures based on
threshold estimates were negatively associated with
temporal performance, i.e., better performance was
reflected by lower threshold values and lower DLs,
respectively. Therefore, to enhance clarity of data
presentation, the sign (+ or −) of the factor loadings
presented in Table 2 has been adjusted such that positive
values indicate a positive covariation of performance
and respective factor loading.
Principal component analysis resulted in three factors
with eigenvalues greater than unity; eigenvalues were
3.75, 1.40, and 1.28 for the first, second, and third
component, respectively. The additional scree test
(Cattell, 1966; Cattell & Vogelmann, 1977), however,
unambiguously supported a one-factor solution. This
first unrotated component accounted for 31.3% of total
timing variability (see Table 2).
All temporal tasks, except the two temporal generaliza-
tion tasks, consistently showed substantial factor loadings
ranging from .55 to .77 irrespective of sensory modality.
For the temporal generalization tasks, however, a rather
ambiguous pattern of factor loadings emerged. For
TABLE 1. Mean Performance Scores (± SEM) for Auditory and Visual Versions of Each Temporal Task for Musicians and Nonmusicians.
Temporal task Indicator of
Auditory Visual Auditory Visual
M SEM M SEM M SEM M SEM
DD1 DL [ms] 6.35 0.30 23.73 1.34 8.34 0.47 30.00 1.56
DD2 DL [ms] 107.79 10.01 150.21 8.45 140.51 9.30 211.97 12.01
TG1 Response dispersion .48 .02 .45 .02 .48 .02 .41 .02
TG2 Response dispersion .50 .02 .47 .02 .45 .02 .46 .02
RP 75% threshold [ms] 42.32 2.00 44.78 2.77 54.50 2.97 63.66 3.71
Fusion 75% threshold [ms] 5.90 0.47 26.08 0.98 9.68 1.59 29.08 0.99
Note: DD1: duration discrimination of intervals in the sub-second range, standard = 50 ms; DD2: duration discrimination of intervals in the second range, standard = 1,000
ms; TG1: temporal generalization, standard = 75 ms; TG2: temporal generalization, standard = 1,000 ms; RP: rhythm perception; Fusion: temporal fusion.
Auditory and Visual Timing Performance 91
temporal generalization in the sub-second range, factor
loadings were .20 and .41 for auditory and visual inter-
vals, respectively, while for temporal generalization in the
second range, factor loadings were .49 and .27 for
auditory and visual intervals, respectively. Since all timing
tasks other than auditory temporal generalization in the
sub-second range and visual temporal generalization in
the second range markedly contributed to this first
component, it appears reasonable to construe this factor
as task- and modality-independent general timing ability.
In a final step, resulting factor scores of musicians and
nonmusicians were compared by means of t tests. Mean
factor score was reliably higher in musicians than in
nonmusicians, t(78) = 5.24, p < .001, d = 1.19, indicating
that the musician group scores notably higher in general
timing ability than the nonmusician group.
The first goal of the present study was to replicate and
expand the finding that temporal information process-
ing in the auditory modality is more accurate in
musicians than in nonmusicians (Rammsayer &
Altenmüller, 2006). The second aim of the study was to
investigate whether the superior temporal information
processing of musicians compared to nonmusicians
also holds for the visual modality. For this purpose,
timing performance on a set of six different psycho-
physical temporal tasks for both the auditory and visual
sensory modality was compared in 40 formally trained
musicians and 40 controls without musical experience.
With respect to group differences, superior temporal
acuity for musicians compared to nonmusicians was
found for all temporal tasks except for the two temporal
generalization tasks. When comparing the two sensory
modalities, temporal acuity was superior with auditory
stimuli as compared to visual stimuli with the exception
of the temporal generalization task in the second range.
With this latter task, timing performance was almost the
same for both sensory modalities.
The musicians’ superior performance compared to the
nonmusicians confirms the results reported by
Rammsayer and Altenmüller (2006). The fact that
performance on various aspects of temporal perception
such as rhythm perception, temporal fusion, and
duration discrimination was consistently superior in the
musician group may lead to the assumption that one
general internal timing mechanism underlies all of these
different aspects of timing performance. To further
elucidate this assumption, a principal component
analysis was performed to identify the dimensional
structure of timing performance. All timing tasks, except
for auditory temporal generalization in the sub-second
range and visual temporal generalization in the second
range, exhibited substantial loadings on the first
unrotated principal component. This finding also
supports the notion of a task- and modality- independent
general internal timing mechanism.
The notion of a hypothetical general timing mechanism
that operates independent of sensory modality is in line
with both some experimental findings and a major theo-
retical account of temporal information processing.
Because perceptual timing tasks require processing of
events or changes in information over time, several
authors (e.g., Burle & Bonnet, 1997, 1999; Rammsayer &
Brandler, 2007; Surwillo, 1968) have put forward the idea
that a general internal timing mechanism in the brain is
responsible for various aspects of temporal information
processing such as rhythm perception or interval timing.
More specifically, performance on interval timing is often
explained by the general assumption of a hypothetical
internal clock based on neural counting (e.g., Creelman,
1962; Gibbon, 1977; Killeen & Weiss, 1987; Rammsayer &
Ulrich, 2001; Treisman, Faulkner, Naish, & Brogan, 1990).
The main features of such an internal clock mechanism
are a pacemaker and an accumulator. The pacemaker
emits pulses and the number of pulses relating to a phys-
ical time interval is recorded by the accumulator. Thus,
the number of pulses counted during a given time interval
is the internal temporal representation of the interval. The
higher the clock rate, the finer the temporal resolution of
the internal clock will be, which is equivalent to higher
TABL E 2. Results of the Principal Component Analysis: Factor Load-
ings, Eigenvalue, and Explained Variance.
Temporal task Factor 1
Explained variance [%] 31.29
Note: a: auditory; v: visual.
92 Thomas H. Rammsayer, Franziska Buttkus, & Eckart Altenmüller
temporal sensitivity, as indicated by better performance
on duration discrimination and rhythm perception
(Pashler, 2001; Rammsayer & Brandler, 2007).
Within the framework of a general, modality-indepen-
dent timing mechanism, better timing performance with
auditory than with visual stimuli can be envisioned as
an increase in neural firing rate in the case of auditory
temporal stimuli (cf. Grondin, 2001; Wearden, Edwards,
Fakhri, & Percival, 1998). This higher pacemaker rate
yields finer temporal resolution and, thus, better timing
accuracy for auditory compared to visual stimuli. In a
recent fMRI study, Shih, Kuo, Yeh, Tzen, and Hsieh (2009)
identified the supplementary motor area and the basal
ganglia as a common neural substrate involved in
temporal processing of both auditory and visual intervals
in the subsecond range. Both brain structures and their
precise interactions in the millisecond range have been
shown to be extremely sensitive to timing demands in
musicians and nonmusicians (cf., Chen, Penhune, &
Zatorre, 2008; Haslinger et al., 2005; Krause, Schnitzler,
& Pollok, 2010).
In a recent psychophysical study, Stauffer, Haldemann,
Troche, and Rammsayer (2011) confirmed higher
temporal sensitivity for rhythm perception and duration
discrimination in the range of milliseconds for the
auditory compared to the visual sensory modality.
Furthermore, their data also provide empirical evidence
for a hierarchical model with modality-specific visual
and auditory temporal processing at a first level and a
superordinate, modality-independent processing system
at a second level of the hierarchy.
In the present study, musicians also performed
superior to nonmusicians in the set of temporal process-
ing tasks presented in the visual sensory modality. If a
timing mechanism, such as the hypothetical internal
clock, underlies temporal information processing
independent of sensory modality, the influence of
extensive music training may indirectly shape also the
timing ability in the visual sensory modality.
An alternative possible explanation of the musicians’
superior performance in the visual sensory modality is
that the processing of visual temporal clues more directly
benefited from the years of music training. Musicians
rely very strongly on visual clues when playing with a
conductor in an orchestra as well as when playing in
small ensembles. Synchronizing to and anticipating the
movements of the conductor and of other members of
the ensemble are crucial to establish optimal timing
while making music together (cf., Pecenka & Keller 2011;
Repp, 2006). This latter explanation does not necessitate
a general, modality-independent timing mechanism but
would also be consistent with the notion of two distinct,
modality-specific timing mechanisms.
Supposedly converging evidence for this latter notion
can be derived from the significant interaction between
Group and Sensory Modality for the duration discrimi-
nation task in the sub-second range. Post-hoc tests
revealed a superior temporal discrimination performance
for musicians compared to nonmusicians for visually
presented but not for auditorily presented intervals. For
both groups, timing performance was better with
auditory than with visual stimuli. Another study
comparing timing performance in blind and sighted
participants also found performance differences in most
of the applied auditor y temporal tasks except for
discrimination of brief tones in the sub-second range
(Rammsayer, 1994). This lack of a performance differ-
ence has been attributed to the fact that temporal
discrimination of brief auditor y intervals can be
considered a process highly overlearned in everyday life.
The ability to discriminate the duration of sounds is very
critical for speech perception (Ackermann, Graber,
Hertrich, & Daum, 1999; Drullman, 1995; Liberman,
Delattre, Gerstman, & Cooper, 1956; Scott, 1982;
Shannon, Zeng, Kamath, Wygonski, & Ekelid, 1995;
Tallal et al., 1996). Therefore, temporal discrimination
of extremely brief tones in the range of milliseconds may
represent an overlearned perceptual function that does
not benefit from additional music training. Such an
explanation may account for the observed lack of a
performance difference between blind and sighted
individuals (Rammsayer, 1994), as well as between
musicians and nonmusicians in the present study.
Eventually, from a theoretical point of view, crossmodal
perceptual learning from the auditory to the visual
sensory modality might also account for musicians’
superior timing performance. It should be noted,
however, that in the present study, reliably superior
timing accuracy was revealed for musicians compared to
nonmusicians for visual, but not for auditory duration
discrimination in the range of milliseconds. Thus,
apparently no crossmodal transfer from the auditory to
the visual sensory modality occurred in nonmusicians.
This conclusion is consistent with the outcome of a study
employing a perceptual learning paradigm to examine
potential crossmodal transfer in a duration discrimina-
tion task. In this study, employing nine testing sessions
extending over two weeks. Lapid et al. (2009) showed
transfer within the auditory modality but failed to
confirm a transfer from the auditory to the visual
modality. One should keep in mind, though, that unlike
nonmusicians, all musicians tested in the present study
Auditory and Visual Timing Performance 93
had long lasting, intensive music training starting in
childhood. This extensive practice may represent a
crucial prerequisite for a crossmodal transfer from the
auditory to the visual domain to become effective. From
this perspective, Lapid et al.’s (2009) failure to show
crossmodal transfer in timing performance could be
attributed to insufficient practice rather than challenging
the idea of crossmodal perceptual learning in temporal
In the present study, musicians performed superior to
nonmusicians on all temporal tasks except for the
t emporal generalization task. This result replicates
former findings for the auditory domain (Rammsayer &
Altenmüller, 2006) and may be indicative of, at least
partly, qualitatively different timing mechanisms
involved in temporal generalization, on the one hand,
and duration discrimination, rhythm perception, and
temporal fusion, on the other. With the latter class of
timing tasks, temporal information has to be stored in
memory for a much shorter period of time than with the
temporal generalization paradigm. Based on this
consideration, our findings suggest that extensive music
training may exert a positive effect on timing perfor-
mance by reducing variability or noise associated with
the timing process itself. However, this advantage of
musicians compared to nonmusicians appears to wear
off with increasing memory retention time. Thus,
temporal judgments which cannot be derived directly
from perceptual processing seem to be less sensitive to
While it is plausible that the differences between
musicians and nonmusicians are the result of music
training, a cautionary note, however, is that innate
differences in timing ability between these groups cannot
be ruled out completely. Another possible moderating
variable that could account for the difference between
the two groups is that the musicians were more moti-
vated when performing the timing tasks (cf., McAuley,
Henry, & Tuft, 2011). Also, given that various cognitive
measures predict timing ability (e.g., Helmbold et al.,
20 0 7; Ramm saye r & Brandl e r, 2007 ; Tro che &
Rammsayer, 2009), an additional alternative explanation
could be that there is a selection bias in a sense that
individuals with higher cognitive ability go on to study
music. In the present study, however, all musicians and
nonmusicians were graduate students and the two
groups did not differ in mean ZVT score, a global
measure of general intelligence.
Taken together, the present study confirmed previous
findings of superior auditory timing performance in
musicians compared to nonmusicians. Furthermore, we
were able to expand these findings by providing first direct
experimental evidence that musicians’ superior temporal
information processing also holds for the visual modality.
The overall pattern of our results is consistent with the
notion that musicians’ long-lasting extensive music train-
ing, starting in childhood, can enhance general timing
ability irrespective of sensory modality.
We thank Kati Klopfleisch for her assistance in data
Correspondence concerning this article should be
addressed to Thomas Rammsayer, Department of
Psychology, University of Bern, Muesmattstrasse 45, CH-
3000 Bern 9, Switzerland. E-MAIL: thomas.rammsayer
ABEL, S. M. (1972). Discrimination of temporal gaps. Journal of
the Acoustical Society of America, 52, 519–524.
ACKERMANN, H., GRABER, S., HERTRICH, I., & DAUM, I. (1999).
Cerebellar contributions to the perception of temporal cues
within the speech and nonspeech domain. Brain and
Language, 67, 228–241.
ANASTASI, A. (1961). Psychological testing. New York: Macmillan.
ASCHERSLEBEN, G. (2002). Temporal control of movements in
sensorimotor synchronization. Brain and Cognition, 48, 66–79.
BENTLEY, A. (1966). Measures in musical ability. London, UK:
George G. Harrap.
BLOCK, R. A. (1990). Models of psychological time. In R. A.
Block (Ed.), Cognitive models of psychological time (pp. 1–35).
Hillsdale, NJ: Lawrence Erlbaum Associates.
BUONOMANO, D. V., BRAMEN, J., & KHODADADIFAR, M. ( 2009).
Influence of the interstimulus interval on temporal processing
and learning: Testing the state-dependent network model.
Philosophical Transactions of the Royal Society B, 364, 1865–1873.
BUONOMANO, D. V., & KARMARKAR, U. R. (2002). How do we tell
time? The Neuroscientist, 8, 42–51.
BURLE, B., & BONNET, M. (1997). Further argument for the
existence of a pacemaker in the human information
processing system. Acta Psychologica, 97, 129–143.
BURLE, B., & BONNET, M. (1999). What’s an internal clock for?
From temporal information processing to temporal
processing of information. Behavioural Processes, 45, 59–72.
CATTELL, R. B. (1966). The scree test for the number of factors.
Multivariate Behavioral Research, 1, 245–276.
94 Thomas H. Rammsayer, Franziska Buttkus, & Eckart Altenmüller
CATTELL, R. B., & VOGELMANN, S. (1977). A comprehensive trial
of the scree and KG criteria for determining the number of
factors. Multivariate Behavioral Research, 12, 289–325.
CHEN, J. L., PENHUNE, V. B., & ZATORRE, R. J. (2008). Moving on
time: Brain network for auditory-motor synchronization is
modulated by rhythm complexity and musical training.
Journal of Cognitive Neuroscience, 20, 226–239.
COLWELL, R. (1970). The evaluation of music teaching and
l earning. Englewood Cliffs, NJ: Prentice-Hall.
CREELMAN, C. D. (1962). Human discrimination of auditory
duration. Journal of the Acoustical Societ y of Amer ica, 34 , 582–5 93.
DRAKE, R. (1954). Drake Musical Aptitude Test. Chicago; IL:
Science Research Association.
DRAKE, C., PANEL, A., & BIGAND, E. (2000). Tapping in time with
mechanically and expressively performed music. Music
Perception, 18, 1–25.
DRULLMAN, R. (1995). Temporal envelope and fine structure
cues for speech intelligibility. Journal of the Acoustical Society
of America, 97, 585–592.
GIBBON, J. (1977). Scalar expectancy theory and Weber’s Law in
animal timing. Psychological Review, 84, 279–325.
GRONDIN, S. (2001). From physical time to the first and second
moments of psychological time. Psychological Bulle tin, 127, 22–44.
GRONDIN, S. (2003). Studying psychological time with Weber’s
Law. In R. Buccheri, M. Saniga, & W. M. Stuckey (Eds.), The
nature of time: Geometry, physics, and perception (pp. 33–41).
Dordrecht, The Netherlands: Kluwer.
GRONDIN, S., MEILLEUR-WELLS, G., & LACHANCE, R. (1999). When
to start explicit counting in time-intervals discrimination
task: A critical point in the timing process of humans. Journal
of Experimental Psychology: Human Perception and
Performance, 25, 993–1004.
GRONDIN, S., OUELLET, B., & ROUSSEL, M.-E. (2004). Benefits and
limits of explicit counting for discriminating temporal intervals.
Canadian Journal of Experimental Psychology, 58, 1– 12.
GUTTMAN, S. E., GILROY, L. A., & BLAKE, R. (2005). Hearing what
the eyes see: Auditory encoding of visual temporal sequences.
Psychological Science, 16, 228-235.
HAROUTOUNIAN, J. (2000). Perspectives of musical talent: A study
of identification criteria and procedures. High Ability Studies,
HASLINGER, B., ERHARD, P., ALTE N M Ü L LE R , E., SCHROEDER, U., BOECKER,
H., & CEBALLOS-BAUMA NN , A. O. (2005). Transmodal sensorimo-
tor networks during action observation in professional pianists.
Journal of Cogni tive Neurosc ience, 17, 282–293.
HELMBOLD, N., TROCHE, S., & RAMMSAYER, T. (2007). Processing
of temporal and non-temporal information as predictors of
psychometric intelligence: A structural-equation-modeling
approach. Journal of Personality, 75, 985–1006.
HENSON, R. A., & WYKE, M. A. (1982). The performance of
professional musicians on the Seashore Measures of Musical
Talent: An unexpected finding. Cortex, 18, 153–158.
JACKSON, D. N. (1983). Multidimensional Aptitude Battery. Port
Huron, MI: Research Psychologists Press.
JONES, M. R., & YEE, W. (1997). Sensitivity to time change: The
role of context and skill. Journal of Experimental Psychology:
Human Perception and Performance, 23, 693–709.
KAERNBACH, C. (1991). Simple adaptive testing with the
weighted up-down method. Perception and Psychophysics, 49,
KILLEEN, P. R., & WEISS, N. A. (1987). Optimal timing and the
Weber function. Psychological Review, 94, 455–468.
KRAUSE, V., SCHNITZLER, A., & POLLOK, B. (2010). Functional
network interactions during sensorimotor synchronization in
musicians and non-musicians. Neuroimage, 52, 245–51.
KWALWASSER, J. (1953). Kwalwasser Music Talent Test. New York:
Mills Music Co.
LAPID, E., ULRICH, R., & RAMMSAYER, T. (2009). Perceptual
learning in auditory temporal discrimination: No evidence
for a cross-modal transfer to the visual modality. Psychonomic
Bulletin and Review, 16, 382–389.
LEWIS, P. A., & MIALL, R. C. (2003). Distinct systems for automatic
and cognitively controlled time measurement: evidence from
neuroimaging. Current Opinion in Neurobiolog y, 13, 250–255.
LIBERMAN, A. M., DELATTRE, P. C., GERSTMAN, L. J., &
COOPER, F. S. (1956). Tempo of frequency change as a cue for
distinguishing classes of speech sounds. Journal of
Experimental Psychology, 52, 127–137.
LIM, V. K., BRADSHAW, J. L., NICHOLLS, M., & ALTENMÜLLER, E.
(2003). Perceptual differences in sequential stimuli across
patients with musicians and writer’s cramp. Movement
Disorders, 11, 1286–93.
LUCE, R. D., & GALANTER, E. (1963). Discrimination. In
R. D. Luce, R. R. Bush, & E. Galanter (Eds.), Handbook of
mathematical psychology (Vol. 1, pp. 191–243). New York:
LUNDIN, R. W. (1967). An objective psychology of music. New
MCAULEY, J. D., HENRY, M. J., & TUFT, S. (2011). Musician
advantages in music perception: An issue of motivation, not
just ability. Music Perception, 28, 505–518.
MCCORMACK, T., BROWN, G. D. A., MAYLOR, E. A., RICHARDSON,
L. B. N., & DARBY, R. J. (2002). Effects of aging on absolute
identification of duration. Psychology and Aging, 17, 363–378.
MERCHANT, H., ZARCO, W., & PRADO, L. (2008). Do we have a
common mechanism for measuring time in the hundreds of
milliseconds range? Evidence from multiple-interval timing
tasks. Journal of Neurophysiology, 99, 939-949.
MICHON, J. A. (1985). The compleat time experiencer. In
J. A. Michon & J. L. Jackson (Eds.), Time, mind, and behavior
(pp. 21–52). Berlin: Springer.
MOORE, G. P. (1992). Piano trills. Music Perception, 9, 351–360.
OSWALD, W. D., & ROTH, E. (1987). Der Zahlen-Verbindungs-Test
(ZVT). Göttingen: Hogrefe.
Auditory and Visual Timing Performance 95
PASHLER, H. (2001). Perception and production of brief durations:
Beat-based versus interval-based timing. Journal of Experimental
Psychology: Human Perception and Performance, 27, 485–493.
PECENKA, N., & KELLER, P. E. (2011). The role of temporal
prediction abilities in interpersonal sensorimotor
synchronization. Experimental Brain Research, 211, 505–515.
PENNEY, T. B. (2003). Modality differences in interval timing:
Attention, clock speed, and memory. In W. H. Meck (Ed.),
Functional and neural mechanisms of interval timing
(pp. 209–233). Boca Raton, FL: CRC Press.
PENTLAND, A. (1980). Maximum likelihood estimation: The best
PEST. Perception and Psychophysics, 28, 377–379.
RADOCY, R. E., & BOYLE, J. D. (1979). Psychological foundations of
musical behavior. Springfield, IL: Charles C. Thomas.
RAINBOW, E. L. (1965). A pilot study to investigate the constructs
of musical aptitude. Journal of Research in Music Education,
RAMMSAYER, T. H. (1994). Temporal information processing in
blind and sighted subjects. In L. M. Ward (Ed.), Fechner Day
94. Proceedings of the Tenth Annual Meeting of the
International Society for Psychophysics (pp. 179–184).
Vancouver, Canada: The International Society for
RAMMSAYER, T. H. (1999). Neuropharmacological evidence for
different timing mechanisms in humans. Quarterly Journal of
Experimental Psychology, Section B: Comparative and
Physiological Psychology, 52, 273–286.
RAMMSAYER, T. (2005, July). Mental ability and the concept of
“temporal g.” Paper presented at the 12th Biennial Meeting of
the International Society for the Study of Individual Differences,
RAMMSAYER, T., & ALTENMÜLLER, E. (2006). Temporal
information processing in musicians and nonmusicians.
Music Perception, 24, 37–47.
RAMMSAYER, T. H., & BRANDLER, S. (2007). Performance on
temporal information processing as an index of general
intelligence. Intelligence, 35, 123–139.
RAMMSAYER, T. H., & LIMA, S. D. (1991). Duration discrimination
of filled and empty auditory intervals: Cognitive and
perceptual factors. Perception and Psychophysics, 50, 565–574.
RAMMSAYER, T., & ULRICH, R. (2001). Counting models of temporal
discrimination. Psychonomic Bulletin and Review, 8, 2 70-277 .
RAMMSAYER, T. H., & ULRICH, R. (2011). Elaborative rehearsal of
nontemporal information interferes with temporal processing
of durations in the range of seconds but not milliseconds.
Acta Psychologica, 137, 127–133.
REPP, B. H. (2006). Musical synchronisation. In E. Altenmüller,
M. Wiesendanger, & J. Kesselring (Eds.), Music, motor c ontrol,
and the brain (pp. 55–76). Oxford, UK: Oxford University Press.
ROBIN, D. A., & ROYER, F. L. (1987). Auditory temporal processing:
Tw o- t o n e f lu t t e r f u s i o n a nd a m o d el o f t e m p or a l i n t eg r a t i o n .
Journal of the Acoustical Society of America, 82, 1207–1217.
SCOTT, D. R. (1982). Duration as a cue to the perception of a
phrase boundary. Journal of the Acoustical Society of America,
SEASHORE, C. E. (1919). Seashore measures of musical talent. New
York: Columbia Phonograph Co.
SEASHORE, C. E., LEWIS, D., & SAETVEIT, J. (1956). Seashore measures
of musical talents. New York: Psychological Corporation.
SHANNON, R. V., ZENG, F. G., KAMATH, V., WYGONSKI, J., &
EKELID, M. (1995). Speech recognition with primarily
temporal cues. Science, 270, 303–304.
SHIH, L. Y. L., KUO, W. J., YEH, T. C., TZEN, O. J. L., & HSIEH, J. C.
(2009). Common neural mechanisms for explicit timing in
the sub-second range. Neuroreport, 20, 897–901.
SHUTER-DYSON, R. (1999). Musical ability. In D. Deutsch (Ed.),
The psychology of music (pp. 627–651). San Diego, CA:
SLOBODA, J. A. (1985). The musical mind: The cognitive psychology
of music. Oxford, UK: Clarendon Press.
SLOBODA J. A. (2005). Exploring the musical mind: Cognition,
emotion, ability, function. Oxford, UK: Oxford University Press.
SPENCER, R. M. C., KARMARKAR, U., & IVRY, R. B. (2009).
Evaluating dedicated and intrinsic models of temporal
encoding by varying context. Philosophical Transactions of the
Royal Society B, 364, 1853–1863.
STAUFFER, C., HALDEMANN, J., TROCHE, S. J., & RAMMSAYER, T. H.
(2012). Auditory and visual temporal sensitivity: Evidence
for a hierarchical structure of modality-specific and
modality-independent levels of temporal information pro-
cessing. Psychological Research, 76, 20–31.
SURWILLO, W. W. (1968). Timing of behaviour in senescence
and the role of the central nervous system. In G. A. Talland
(Ed.), Human aging and behavior (pp. 1–35). New York:
TALLAL, P., MILLER, S. L., BEDI, G., BYMA, G., WANG, X.,
NAGARAJAN, S. S., ET AL. (1996). Language comprehension
in language-learning impaired children improved with
acoustically modified speech. Science, 271, 81–84.
TEN HOOPEN, G., HARTSUIKER, R., SASAKI, T., NAKAJIMA, Y.,
TANAKA, M., & TSUMURA, T. (1995). Auditory isochrony: Time
shrinking and temporal patterns. Perception, 24, 577–593.
TRAPPE, W., KAT ZEN B ER G ER, U., & ALTE N M Ü L LE R , E. (1998). 3-d
measurement of cyclic motion patterns in drummers with
different skill. In G. Wolfe & T. Brooks (Eds.), Proceedings of the
Fifth International Symposium on the 3-D Analysis of Human
Movement (pp. 97–99). Chicago, IL: North Western Press.
TREISMAN, M., FAULKNER, A., NAISH, P. L. N., & BROGAN, D.
(1990). The internal clock: Evidence for a temporal oscillator
underlying time perception with some estimates of ist
characteristic frequency. Perception, 19, 705–743.
TROCHE, S. J., & RAMMSAYER, T. H. (2009). The influence of
temporal resolution power and working memory capacity on
psychometric intelligence. Intelligence, 37, 479–486.
96 Thomas H. Rammsayer, Franziska Buttkus, & Eckart Altenmüller
VAN WASSENHOVE, V. (2009). Minding time in an amodal
representational space. Philosphical Transactions of the Royal
Society B, 364, 1815–1830.
VERNON, P. A. (1993). Der Zahlen-Verbindungs-Test and other
trail-making correlates of general intelligence. Personality and
Individual Differences, 14, 35–40.
WAGNER, C. (1971). The influence of the tempo of playing on
the rhythmic structure studied at pianist’s playing scale.
Medicine and Sport, 6, 129–132.
WEARDEN, J. H., EDWARDS, H., FAKHRI, M., & PERCIVAL, A. (1998).
Why “sounds are judged longer than lights”: Application of a
model of the internal clock in humans. Quarterly Journal of
Experimental Psychology, 51B, 97–120.
WEARDEN, J. H., WEARDEN, A. J., & RABBITT, P. M. A. (1997). Age
and IQ effects on stimulus and response timing. Journal of
Experimental Psychology: Human Perception and Performance,
WINNER, E., & MARTINO, G. (1993). Giftedness in the visual
arts and music. In K. A. Heller, F. J. Mönks, & A. H. Passow
(Eds.), International handbook of research and development
of giftedness and talent (pp. 253–281). Elmsford, NY:
YEE, W., HOLLERAN, S., & JONES, M. R. (1994). Sensitivity to
event timing in regular and irregular sequences:
Influences of musical skill. Perception and Psychophysics,