olympia karampela - diva portalumu.diva-portal.org/smash/get/diva2:1069970/fulltext01.pdf · order...
Post on 23-Aug-2019
213 Views
Preview:
TRANSCRIPT
Exploring models of time processing
Effects of training and modality, and the relationship with cognition in rhythmic motor
tasks
Olympia Karampela
Department of Psychology
Umeå 2017
Responsible publisher under swedish law: the Dean of the Social Sciences
This work is protected by the Swedish Copyright Legislation (Act 1960:729)
ISBN: 978-91-7601-668-8
Cover design by George Andreou
Elektronisk version tillgänglig på http://umu.diva-portal.org/
Tryck/Printed by: UmU Print Service, Umeå University
Umeå, Sweden 2017
i
Ithaca
When you start on your journey to Ithaca,
then pray that the road is long,
full of adventure, full of knowledge.
Do not fear the Lestrygonians
and the Cyclopes and the angry Poseidon.
You will never meet such as these on your path,
if your thoughts remain lofty, if a fine
emotion touches your body and your spirit.
You will never meet the Lestrygonians,
the Cyclopes and the fierce Poseidon,
if you do not carry them within your soul,
if your soul does not raise them up before you.
Then pray that the road is long.
That the summer mornings are many,
that you will enter ports seen for the first time
with such pleasure, with such joy!
Stop at Phoenician markets,
and purchase fine merchandise,
mother-of-pearl and corals, amber and ebony,
and pleasurable perfumes of all kinds,
buy as many pleasurable perfumes as you can;
visit hosts of Egyptian cities,
to learn and learn from those who have knowledge.
Always keep Ithaca fixed in your mind.
To arrive there is your ultimate goal.
But do not hurry the voyage at all.
It is better to let it last for long years;
and even to anchor at the isle when you are old,
rich with all that you have gained on the way,
not expecting that Ithaca will offer you riches.
Ithaca has given you the beautiful voyage.
Without her you would never have taken the road.
But she has nothing more to give you.
And if you find her poor, Ithaca has not defrauded you.
With the great wisdom you have gained, with so much experience,
you must surely have understood by then what Ithacas mean.
-K. P. Kavafis (C. P. Cavafy), translation by Rae Dalven
ii
iii
To my son and my husband
iv
v
TABLE OF CONTENTS
ABSTRACT vii
LIST OF PAPERS ix
SAMMANFATTNING PÅ SVENSKA x
ABBREVIATIONS xi
INTRODUCTION 1 Aims of the thesis 2 Time perception and the production of timed intervals 4 Dedicated models of timing 6 One clock? 9 Timing at different timescales 12 Task differences 14 The effect of feedback 15 The intrinsic models of timing 16 Cognitive capacities in timing performance 20 Impaired timing in clinical populations with an attention disorder 24 Attentional models of timing 25 Concluding remarks 28 The Main objectives of the Thesis 28
METHODS 29 Participants 29 Instruments-Procedure 31 Cognitive measurements (Study II) 32 Eye tracking (Study III) 33 Dependent measurements computation 33 Saccade onsets (Study III) 34 Statistical Analysis 34
Results 35 Study I 35 Study II 40 Study III 44
GENERAL DISCUSSION 47 Effects of training. Motor and cognitive gains 47 Modality differences – dedicated or intrinsic mechanisms? 51 Implications 54 Limitations 55
vi
Future 56
REFERENCES 58
ACKNOWLEDGEMENTS 76
vii
ABSTRACT
Timing can be defined as the ability to perceive temporal sequences and
regulate timed behaviors. As in other animals, our ability to make accurate
time estimations is crucial in order to accomplish several activities.
Organisms can process time over a wide range of durations ranging from
microseconds to days. In the middle of these extremes is the hundreds of
milliseconds to seconds range which is important for many everyday
behaviors, such as walking, speaking and dancing. Yet, how this is managed
remains poorly understood. Some central issues with regard time processing
in this particular time range are whether timing is governed by one, or by
several different mechanisms, possibly invoked by different effectors used to
perform the timing task, as well as, if cognitive capacities are also involved in
rhythmic motor timing.
This thesis includes three studies. Study I investigated the effects of short-
term practice on a motor timing task. Analyses of the timing variability
indicated that a substantial amount of learning occurred in the first hour of
practice and declined afterwards, exhibiting no trend for further decrease
across the remaining 60 or 210 minutes. This effect was similar across
effector, amount of feedback, and interval duration. Our results suggested
that training effects influenced mainly motor precision and raised the
question of whether motor timing training influenced also cognitive
capacities.
Study II investigated the relationship between motor timing and cognition.
Specifically, participants had to train a sensorimotor synchronization task
(SMS) over several days, and the question was whether this training would
improve cognitive performance. A near transfer effect was found between the
sensorimotor synchronization task and the sustained attention task,
indicating that sustained attention is involved in motor timing.
Study III compared the timing variability between the eyes and the hands, as
a function of four different intervals, in order to examine whether these
systems are temporally controlled by the same or different mechanism(s).
The results showed several positive correlations in variability, between the
eye and the finger movements, which, however, were significant only for the
longer intervals. In addition, they were differences in variability between the
eye and the hand, for the different interval durations.
In general, the pattern of results from these studies suggested that voluntary
motor timing is managed by overlapping distributed mechanisms and that
viii
these mechanisms are related to systems that manage cognitive processes,
such as attention. The results partially explain the well-known relationships
between cognitive ability and timing.
ix
LIST OF PAPERS
I. Madison, G., Karampela, O., Ullén, F., & Holm, L. (2013).
Effects of practice on variability in an isochronous serial
interval production task: Asymptotical levels of tapping
variability after training are similar to those of musicians.
Acta Psychologica, 1, 119-128.
II. Karampela, O., Madison, G., & Holm, L. (manuscript). Motor
timing training improves sustained attention performance.
III. Karampela, O., Holm, L., & Madison G. (2015). Shared timing
variability in eye and finger movements increases with interval
duration: Support for a distributed timing system below and
above 1 second. The Quarterly Journal of Experimental
Psychology, 68, 1965-1980.
Papers I and III have been reproduced with the permission of the
copyright holders.
x
SAMMANFATTNING PÅ SVENSKA
Timing innebär att uppfatta tidsmässiga förlopp och tidsmässigt reglera
beteende. Exakt timing är nödvändig för att koordinera vårt eget beteende
med andra personer och processer i vår miljö, inom ett spann, från
millisekunder till dagar eller år. För många vardagliga beteenden, som att
tala, promenera, dansa och köra bil är det kritiska spannet från några 10-tals
millisekunder till några sekunder. Hur detta går till är relativt outforskat.
Några centrala frågor är huruvida sådan timing styrs av en eller flera olika
mekanismer, om olika delar av kroppen regleras av samma eller olika
mekanismer, och i vilken mån kognitiva processer och förmågor är
involverade i timing av repetitiva motoriska förlopp.
Denna avhandling omfattar tre empiriska studier. Studie 1 undersökte
effekterna av träning på variabiliteten i timing av en motorisk rörelseuppgift.
Den största delen av minskningen i tidsvariabilitet inträffade under den
första timmen. Ytterligare träning i upp till 3.5 timmar verkade inte minska
tidsvariabiliteten. Detta gällde alla intervaller, från 500 ms till 1.6 s, oavsett
deltagarna använde ett finger eller en trumstock, och oavsett de fick taktil
eller auditiv återkoppling från sina egna responser. Resultaten tyder på att
träning främst påverkade motorisk precision, och väckte frågan om motorisk
timing påverkas av kognitiv förmåga.
Studie II undersökte sambandet mellan motorisk timing och kognition.
Deltagarna fick träna en sensomotorisk synkroniseringsuppgift (SMS) över
flera dagar, och frågan var om det skulle förbättra kognitiv prestation. Sådan
”near transfer” kunde ses från timing-träningen och prestationen på ett
uppmärksamhetstest, men inte på två spatiala intelligenstester.
Studie III jämförde timing-variabilitet mellan ögon och händer som en
funktion av fyra olika intervaller mellan 500 ms och 1.4 s, för att undersöka
om dessa system tidsmässigt styrs av samma eller olika mekanismer. Det
fanns positiva samband i variabilitet mellan ögon- och fingerrörelser, som
dock var signifikanta endast för längre intervall. Det fanns också stora
skillnader i relationen mellan intervall och variabilitet mellan ögon- och
fingerrörelser.
Sammantaget indikerar de tre studierna att motorisk timing hanteras av
delvis överlappande, distribuerade mekanismer och att dessa mekanismer är
relaterade till system som hanterar kognitiva processer som
uppmärksamhet. Resultaten förklarar delvis de välkända sambanden mellan
kognitiv förmåga och timing.
xi
ABBREVIATIONS
ADHD (Attentional Deficit Hyperactivity Disorder)
AGM (Attentional Gate Model)
ANOVA (Analysis of Variance)
CPT II (Continuous Performance Task II)
CTBG (Cortico-Thalamic-Basal Gnglia)
CV (Coefficient of Variation)
DAMP (Deficits in Attention, Motor control and Perception)
DNA (Deoxyribonucleic Acid)
f-MRI (functional Magnetic Resonance Imaging)
IOI (Inter-Onset-Interval)
IQ (Intelligence Quotient)
IRI (Inter-Response-Interval)
ISI (Inter-Stimulus-Interval)
M (Mean)
PET (Positron Emission Tomography)
PFC (Parietal Frontal Cortex)
RT (Reaction Time)
SBF (Striatal Beat Frequency)
SD (Standard Deviation)
SDN (State-Dependent Network model)
SET (Scalar Expectancy Theory)
xii
SMA (Supplementary motor area)
SMS (Sensorimotor Synchronization Task)
TBI (Traumatic Brain Injury)
5-HT (5-Hydroxy-Tryptamine or serotonin)
1
INTRODUCTION
Timing is fundamental for our existence. As in other animals, our ability to
regulate and estimate timed behaviors with high precision is crucial in order
to accomplish several activities. Organisms can process time over a wide
range of durations ranging from microsecond to days (Fig. 1). In this extreme
range of time processes, the hundreds of milliseconds to seconds range is
important for everyday behavior, including motor control and cognitive
operations, such as directing attention and language processing (Justus &
Ivry, 2001; Meck & Benson, 2002; Schirmer, 2004). For example, walking,
and dancing require precise temporal control of body movements.
Furthermore, to ensure accurate speech production and comprehension,
speakers must successfully time the production of individual sounds
precisely. Timing in the hundreds of milliseconds to seconds range is
essential for learning and for representing sequential relationships between
stimuli (Bangert, Reuter-Lorenz, & Seidler, 2011). Yet, there is no dedicated
sensory system, such as an organ or brain area for time, such as there is for
sight, hearing, and taste. There has been a surge in research dedicated to
timing over the past decades, but it has still not led to any consensus about
the nature of the mechanism(s) for timing in the order of the hundreds of
milliseconds and seconds.
Fig. 1. Scales of temporal processing. Adapted from Buonomano &
Karmarkar, (2002).
Two general approaches have been proposed to describe how the passage of
time is perceived in the hundreds of milliseconds to seconds range. The first
is the dedicated approach, where a specialized mechanism that represents
2
the temporal relationships between events is assumed (Droit-Volet, Meck, &
Penney, 2007; Woodrow, 1930). The second approach includes models that
assume intrinsic explanations, and describe time as a general and inherent
property of neural dynamics (Dragoi, Staddon, Palmer & Buhusi, 2003;
Karmarkar & Buonomano, 2007). Specifically, models in this framework
propose that the representation of duration could be an omnipresent
manifestation of intrinsic capacities of neural mechanisms, without any
apparent dedicated function to the treatment of time.
Aims of the thesis
The aims of this thesis were to narrow down these possibilities and provide
further understanding of the phenomena, so that these frameworks could be
evaluated. To these ends, three areas of specific interest have been identified.
They are the role of practice, the role of cognition, and the role of the
modality used to perform a motor timing task, in relation to timing
performance. This led to three more specific aims for the present thesis. The
first was to investigate the impact of practice on a motor timing task, in
order to explore the differential effects of training on two different
measurements of variability, namely the cognitive and the motor component
of timing variability. The second research aim concerned the role of
cognition in motor timing and specifically, the role of sustained attention
and fluid intelligence in motor timing performance, in order to explore
further the relationship found between timing variability and measurements
of sustained attention and fluid intelligence. The third research aim was to
study whether motor timing in the hundreds of milliseconds to seconds
range is managed by a dedicated single timing mechanism or by multiple,
effector-dependent timing mechanisms. Below, the rationale of choosing to
study these particular scientific questions will be described.
In study I we investigated the effect of practice on motor timing variability.
Relatively few studies within timing research have considered learning as an
important research question to assess. The study of learning and
generalization to other behaviors and modalities can provide important
insights into the nature of temporal processing in the hundreds of
milliseconds to seconds range. Specifically, we would expect that an
improvement in motor timing followed by training would generalize to
untrained conditions, and would display no differences with regard to the
interval, the effector used or the feedback provided if time is processed by a
dedicated mechanism. The rationale behind this is that the existence of a
dedicated timing mechanism implies that the “internal clock” should be
independent of modality, task, and context (Buonomano & Karmarkar 2002;
3
Zelaznik, Spencer, & Ivry, 2002). Previous studies have shown that timing
training generalized to different duration intervals (Karmarkar &
Buonomano, 2003), sensory modalities, as well as, from perceptual to
motor-timing tasks (Meegan, Aslin, & Jacobs, 2000). Here, we expand on
the previous research by using a motor timing task and by testing how these
training effects depend on a number of key variables; the duration of the
interval to be timed, the presence of auditory feedback, and the particular
way used to produce the intervals.
In study II we explored the transfer gains from a sensorimotor
synchronization task (SMS) to fluid intelligence and sustained attention
tasks, by comparing a treatment group with an untrained control group.
Despite the vast amount of studies that have shown a relationship between
timing and cognition, the central question about the role of cognitive
functions in timing processing remains largely unanswered. To draw
stronger inferences about this relationship, one possibility would be to study
transfer effects between timing learning and other cognitive functions. If
there is a causal link between motor timing and cognition, it should be
possible to train one and see effects also on the other.
In study III we investigated the effects of modality on performance on a
motor timing task. Specifically, we compared timed finger and eye
movements. Several correlations have been found between different
modalities used to perform a timing task. These correlations imply that
participants who perform well in one behavioral context will also perform
well in another, and they have been used in support of the dedicated timing
models. Consistent with this idea, a dedicated timing mechanism should be
independent of modality, task, and context (Buonomano & Karmarkar 2002;
Zelaznik, Spencer, & Ivry, 2002). So far, the correlations reported have been
between effectors displaying similar response requirements, such as the
hand and the foot. It is possible that similarity between tasks or effectors
involves recruitment of the same or similar timing mechanisms. Timing
behavior and correlations between less similar effector systems would
provide a stronger test and evidence of the dedicated mechanism hypothesis.
4
Time perception and the production of timed intervals
An important issue is whether the processing of time intervals is based on
the same timing process in perception (discrimination) and motor timing
(tapping) tasks (Keele, Pokorny, Corcos, & Ivry, 1985; Ivry & Hazeltine,
1995; Repp, 1998) (Fig. 2). Several researchers have posed the question of
whether tasks with different response requirements depend on the same
dedicated timing mechanism(s) (Ivry & Hazeltine, 1995; Keele et al.,
1985; Merchant, Zarco, & Prado, 2008). A general consensus among these
studies is that being able to translate durations into motor programs for
production (motor timing), is rather different than comparing two durations
represented in memory (perceptual timing). Therefore, cognitive capacities
might be differently involved in perceptual and motor timing.
Time perception refers to the subjective experience of time, for example
someone's own perception of a specific duration. Four main tasks are
commonly used to investigate the mechanisms involved in time perception.
These are (1) time discrimination, (2) time production, (3) verbal estimation,
and (4) time reproduction (Grondin, 2010). In time discrimination tasks,
subjects first compare interval durations and then indicate whether these
durations were different or not, or whether the second interval duration was
shorter or longer than the first. In the time production method, a participant
produces an interval that the experimenter has specified. Two finger “taps”
marking the beginning and end of the interval are usually involved in time
production. Another way is by pushing a button for a duration that the
participant decides was equivalent to the target interval. In the verbal
estimation method, the participant has to provide a verbal estimation of a
target interval in seconds or minutes. Finally, in the reproduction method
the participant has to reproduce the length of an interval that an
experimenter has presented, usually with a continuous auditory or visual
stimuli (Grondin, 2010).
Motor timing refers to the motoric implementation of a timed action. One of
the most common experimental paradigms when investigating motor timing
is the synchronization-continuation tapping. In this task, subjects have to
produce a continuous series of intervals with their taps. Specifically, subjects
listen to sounds which they synchronize their tap with and then continue
tapping at the same pace without listening to the external sounds (Repp,
2005). Two dependent variables are extracted in this procedure, the mean
(M) produced intervals, and variability expressed as the standard deviation
(SD) of the distribution of the intervals. From the SD it is possible to extract
the coefficient of variation (CV) which is the ratio of SD to M, as well as other
5
metrics (Madison, 2001). Based on assumptions of underlying functional
models of both synchronization and production, higher-order parameters of
the behavioral processes can also be estimated (e.g. Wing & Kristofferson,
1973; Semjen, Vorberg, & Schulze, 1998; Vorberg & Wing, 1996). For
example, Wing and Kristofferson (1973) proposed that variability during the
continuation phase arises from two sources: an internal clock, which triggers
the time for a movement, and a motor implementation system, that
translates this signal into a movement. The clock and the motor processes
are considered to be independent. However, this independence is violated if
tempo tends to drift, meaning that there is a slow change in the tempo.
Fig. 2. Motor timing and perceptual timing. Adapted from Breska and Ivry,
2016.
Following the introduction, dedicated models of timing as well as behavioral
and neuroimaging data that support their existence will be presented. The
Scalar Expectancy Theory (SET), which is the most influential model within
this category will be described in more detail. Other dedicated models, as
well as the limitations concerning these particular models, will also be
introduced. After that, the second approach which includes intrinsic models
that describe time as a general and inherent property of neural dynamics will
be presented and discussed, in comparison to the dedicated models. Finally,
the correlations found between timing variability and several cognitive traits,
6
such as working memory, attention and intelligence will be highlighted and
various cognitive models of timing that can accommodate and provide
explanations for these results will be presented. The role of sustained
attention in motor timing will be particularly addressed, given that most
timing models address the role of sustained attention in timing. A more
recent attempt to combine the existing traditional theories and models for
temporal processing in the hundreds of milliseconds to seconds range will
also be presented and discussed. Finally, a number of unresolved issues will
be discussed, based on their crucial role in increasing our understanding of
the nature of temporal processing in the hundreds of milliseconds to seconds
range.
Dedicated models of timing
The dedicated approach assumes dedicated neural mechanisms that are
specialized at representing the temporal relationships between events (Bueti,
2011; Ivry & Schlerf, 2008). This category includes models that are clock-
based, where a pacemaker-accumulator system is proposed. According to
these models, a pacemaker emits ‘ticks’ that are collected by an accumulator
which stores the number of ticks (Droit-Volet et al., 2007; Gibbon, Church &
Meck, 1984; Woodrow, 1930). The more ticks that are accumulated, the
greater the duration perceived. In other words, a 300 ms and 400 ms
auditory interval would involve the same pacemaker but the longer interval
would achieve a larger count in the accumulator (Spencer, Karmakar & Ivry,
2009). Creelman (1962) and Treisman (1963) formulated two of the earliest
models in this category. They both proposed a pacemaker-accumulator
system but disagreed on whether external variables might affect the
frequency with which the pacemaker emits pulses. Greelman assumed that
this frequency is fixed, whereas Treisman argued that various external
factors can affect the frequency of the pacemaker (Creelman, 1962; Treisman
1963).
The most influential model in the dedicated timer category is the Scalar
Expectancy Theory (SET) proposed by Gibbon et al. (1984) (Fig 3). This
model posits an internal clock, memory and decision processes. Specifically,
SET proposes a clock system that consists of a pacemaker that generates or
emits neural ticks which are sent to the accumulator. This accumulation of
ticks represents the elapsed time. The representation of the duration is then
transferred to working memory and then compared to a reference standard
in the long-term memory. Finally, in the decision stage, a comparison is
made between the contents of the long term and the working memory
(Gibbon, et al., 1984). The rate of the pacemaker could be influenced by the
7
stimulus intensity (Zelkind, 1973) and switch latencies (Wearden, Edwards,
Fakhri, & Percival, 1998). The relationship between timing variability and
the target duration should follow Weber’s law; that is the standard deviation
of the represented duration increases proportionally with increasing
duration. In other words, the coefficient of variation (CV), which is the
standard deviation divided by the mean target interval, should be constant
across durations (Gibbon, et al., 1984).
Fig 3. Outline of the scalar expectancy theory (SET). The pacemaker-
accumulator clock is shown to the left, the long term reference memory and
the short-term working memory in the middle, and the decision mechanism
to the right. Adapted from Gibbon et al. (1984).
Neural underpinnings of dedicated timing models
Dedicated models of timing are by their nature modular. It is therefore not
surprising that much research has been aimed at identifying specific brain
areas that constitute basic elements of a dedicated system. The basal ganglia
have been widely considered as a core element of the timing system (e.g.
Buhusi & Meck, 2009; Meck & Benson, 2002) This finding is based on
evidence which shows that dopamine manipulations known to affect the
basal ganglia also affect time performance (Rammsayer, 1993; Meck &
Benson 2002; Ivry & Spencer 2004), as well as, on evidence which has
8
shown that Parkinson patients, who suffer from problems in the basal
ganglia, exhibit deficits in timing in the hundreds of milliseconds to seconds
range (Artieda, Pastor, Lacruz, & Obeso, 1992; Harrington, Haaland &
Hermanowicz, 1998).
The cerebellum has also been also implicated in dedicated timing, by several
studies indicating that the cerebellum has a central role in tasks that require
precise timing (Ackermann, Graber, Hertrich & Daum, 1999; Ivry, Keele, &
Diener, 1988). An extensively used example here is the eyeblink conditioning
method, which involves the relative simple procedure of pairing an auditory
or visual stimulus, with an eyeblink-unconditioned stimulus (air puff). It is
well established that people with damage in the cerebellum exhibit poor
performance on this task. In general, it has been reported that an intact
cerebellum and interpositus nucleus are required for this form of classical
conditioning. Taken together, different research findings indicate that the
cerebellum has a computational role in different timing tasks (Ivry &
Hazeltine, 1995). In addition, prefrontal and parietal cortices seem to be
among the main cortical regions involved in timing and time perception
(Koch, Oliveri, Carlesimo, & Caltagirone 2002; Lewis & Miall, 2006) while
the basal ganglia has been proposed to be the main subcortical cerebral
structure involved in the processing of temporal information (Pouthas et al.,
2005; Jahanshahi, Jones, Dirnberger & Frith, 2006).
Other dedicated models do not suggest a specific area in the brain but
instead, they propose that the successful representation of time intervals is
the result of activity deriving from a distributed network of regions. For
example, the operation of some areas could be specific to timing, such as a
pacemaker function, whereas other areas might be related to more general
functions, such as working memory which stores temporal information.
Deficits in any part of this network would disrupt performance on timing
tasks (Ivry & Schlerf, 2008).
Another model of dedicated systems suggests that timing is managed by a
frontal-striatal circuitry (Meck & Benson, 2002; Matell & Meck, 2004;
Matell, Meck, & Nicolelis, 2003). In this model, the striatum plays a critical
computational role by receiving millions of impulses from the cortical cells.
These cortical cells, which have firing rates from 10 to 40 cycles per second,
begin firing simultaneously for a moment, creating a specific pattern of
neural activity. Then, the substantia nigra sends a message to the striatum
when the timekeeping activity must stop. Through this dopaminergic
activation, identification of a specific interval length is achieved
(Rammsayer, 2008).
9
The idea of a dedicated mechanism has been widely used and validated by
many behavioral studies, in both animals and humans. Below, several
present behavioral and neuroimaging results which support the existence
and the highly adaptive nature of the dedicated models in several different
settings will be described.
One clock?
Timing variability in the hundreds of milliseconds to seconds range has been
found to increase monotonically as a function of the interval to be timed, in
accordance with Weber’s law. This scalar property compliance is true for a
wide range of duration from 200 ms to 2 s (for a review see Grondin, 2001).
Weber’s law implies the constancy of the coefficient of variation, across
different intervals (Allan & Kristofferson, 1974; Gibbon, 1977; Killeen &
Weiss, 1987). Weber’s law is given as SD (T) = kT, where k is a constant
corresponding to the Weber fraction. Furthermore, the Weber fractions
display similar values in the range of hundreds of milliseconds in tasks with
different response requirements and different modalities (Merchant, Zarco &
Prado, 2008). These findings are suggestive of a common dedicated
mechanism.
The overall variability in a motor timing task can be dissociated into both
time-dependent and time-independent sources of variation (Wing &
Kristofferson 1973). Slope analysis is one of the methods used to dissociate
the total performance variability. Assuming a linear relationship between
timing variability (SD) and the mean of the inter- response intervals (IRIs),
the slope of the regression line represents the duration-dependent timer SD,
and the intercept represents the duration-independent component (e.g.,
motor). It has been demonstrated that the slopes of interval discrimination
and motor timing tasks are similar for a range of intervals ranging from 325
to 550 ms (Ivry & Hazeltine, 1995), supporting the view of one dedicated
clock in different timing contexts. In addition, it has been shown that
temporal variability is correlated between different tasks, such as time
perception and time production tasks (Keele et al. 1985). This implies that
individuals who perform well in time perception tasks also perform well in
other behavioral contexts, such as the time. The fact that individuals can be
equally good in many different behavioral contexts with regard to their
timing performance provides additional evidence for the dedicated
perspective of timing.
Support for the dedicated models of timing stems from neuroimaging studies
that propose that specific brain areas contribute to the successful processing
of time. The cerebellar timing hypothesis, for example, suggests that
10
cerebellum contributes in many different types of tasks that require precise
timing. Damage to the cerebellum can disrupt timing performance on a
number of tasks, such as duration discrimination, the organization of skilled
movements and in eyeblink conditioning. Ivry and colleagues (2002)
proposed that while the cerebellum contributes in many different skilled
tasks, its contribution might be more important when the tasks involve event
based timing (Breska & Ivry, 2016).
Finally, results from training studies have demonstrated that training in one
timing context can be generalized to other timing contexts. For example,
Meegan et al (2000), showed that training to discriminate time intervals
caused an improvement in temporal performance in a motor timing task,
favoring the dedicated mechanism hypothesis (Buonomano & Karmarkar,
2002).
The reasons why the concept of a dedicated mechanism is favored are its
conceptual simplicity and reasonable account of many empirical
observations. However, the concept of a dedicated, specialized mechanism is
not unanimously supported. The assumptions of a dedicated mechanism
have been violated several times in many different ways. Therefore, a better
and a more neurally plausible explanation for the processing of time in the
hundreds of milliseconds to seconds range than the centralized, supramodal
timer is called for. Below, various different contexts where these violations
occur and where other possibilities for processing time seem more
reasonable will be presented.
The existence of a dedicated mechanism implies that the timing behavior
should be independent of modality, task, and context (Buonomano &
Karmarkar, 2002; Zelaznik et al., 2002). Indeed, several correlations have
been found between different modalities, but violations have also been
reported. For example, it has been shown that performance in timing tasks is
more accurate for auditory rather than visual stimuli and for multiple
isochronous intervals rather than single intervals (Ivry & Hazeltine, 1995).
The question arising here is how several non temporal factors, such as the
nature of the tasks used and the different modalities, can affect timing
performance. Do all these external factors directly affect the dedicated clock
or should we start thinking about different clocks? (Grondin, 2001).
One clock or several clocks?
Transitions and non- linearities in timescales provide additional evidence
against the dedicated clock. Temporal variability does not always conform to
11
Weber’s law, and several breakpoints (violations of Weber’s law) have been
found at 1s (Allan & Gibbon 1991; Jingu, 1989, Madison 2001), and between
1 and 2 sec intervals (Getty, 1975; Matthews & Grondin, 2012; Madison
2006;) suggesting multiple clock timing mechanisms across those time
frames. This resonates with Michon’s (1985) view, who argued that there is a
distinction between automatic (< 500 ms) versus cognitive (> 500 ms)
temporal processes, as well as with Karmarkar and Buonomano (2007), who
suggested that this duration is the transition between a state-dependent
(< 500 ms) and a scalar timer (> 500 ms). Finally, several neuroimaging
experiments implicate a shift between motor and cognitive timing systems in
the region above 1 s (Lewis & Miall, 2003a; 2003b; 2006).
Additional evidence against the dedicated clock mechanism comes from
research findings showing differences in duration discrimination across
sensory modalities. For example, a well-established finding is that interval
discrimination is more accurate for auditory compared to visual stimuli
(Grondin & Rousseau, 1991; Jaskowski, Purszewicz, & Swidzinski, 1990).
There is also evidence that these differences can be specific to a single
sensory system. It has been found, for example, that saccadic eye movements
cause compression of the perceived duration of visual but not auditory
stimuli (Burr, Cicchini, Arrighi, & Morrone, 2011; Morrone, Ross, & Burr,
2005). The general notion of a single clock has also been challenged by the
finding that temporal perception in any given spatial region of the visual
field seems to be independent of perception in other regions of the visual
field (Burr, Tozzi, & Morrone, 2007). Moreover, significant differences in
timing accuracy have been reported when participants perform a timing task
with the hand and the feet, as well as with the dominant and the non-
dominant side (Kauranen & Vanharanta, 1996). Failure to observe a cross
modality transfer adds to the non-dedicated clock mechanism hypothesis.
Several studies failed to find an improvement in visual duration
discrimination after temporal training in the auditory mode (Grondin
Gamache, Tobin, Bisson, & Hawke 2008; Grondin, Bisson, Gagnon,
Gamache, & Matteau, 2009; Lapid, Ulrich & Rammsayer, 2009). These
unsuccessful attempts for improving visual duration discrimination with
audition have been interpreted as an indirect support against the idea of the
one dedicated clock and suggest that processing of timing events is modality-
specific.
Although one dedicated mechanism has been proposed also for the
perception and motor timing tasks, several research findings challenge that
notion, by pointing out differences in timing accuracy for motor timing and
discrimination timing tasks. Bangert, Reuter-Lorenz, & Seidler (2011)
compared timing variability between a motor timing and a discrimination
12
task and found that the coefficients of variation, (CV) values, and Weber
fractions were larger for discrimination than for motor timing tasks. Also,
tap latency increased with increasing duration for motor timing, but
remained constant for discrimination taks. Finally, no between-task
correlations for CV were found. All these results are suggestive of a non-
dedicated timing mechanism across the two tasks. Given the different nature
of the discrimination and reproduction tasks, it seems reasonable that the
patterns of performance variability are rather different between these tasks.
Specifically, in a motor timing task, individuals first encode an interval
duration and then via movement they express the target duration. In a
temporal discrimination task, individuals compare only two or more abstract
representations of durations in memory and then produce a non-timed
response to indicate whether or not they match. While it may not seem
surprising that tasks with different response requirements produce different
patterns of timing variability, that finding is in contradiction with the
dedicated models where correlations would have been expected between the
tasks, regardless the differences in sensorimotor processing and response
requirements (Bangert et al., 2011).
Interestingly, recent work has found between-task correlation accompanied
by task-specific differences in timing variability (Merchant, Zarco & Prado,
2008) for durations of 1 s and less (350 ms to 1000 ms). This latter finding is
again suggestive of a non-dedicated timing system. More evidence against
the dedicated models of interval timing comes from studies which have
shown differences in timing performance between different timescales
(Buhusi & Meck, 2005).
Timing at different timescales
Several differences found in the existing models of time processing have also
been attributed to differences between different timescales (Buhusi & Meck,
2005). Indeed, several differences in timing performance have been found
between different timescales. Overall, the research findings agree with the
notion that duration intervals in the hundreds of milliseconds to seconds
range are more dependent on sensory processes, whereas temporal intervals
above the seconds range are more related to cognitive capacities, and are
therefore processed differently from the shorter ones (Buhusi & Μeck, 2005;
Rammsayer, 1999). Studies manipulating cognitive load and attention provide further evidence
for the role of cognitive functions in timing performance above the 1 second
range (Brown, 1997; Fortin & Breton, 1995; Fortin & Rousseau, 1998;
Rammsayer, 1992; 1997; 1999; 2006). On the other hand, the involvement of
13
cognitive capacities is less clear for timing durations in the hundreds of
milliseconds to second range (Grondin & Rammsayer, 2003; Macar,
Grondin, & Casini 1994; Rammsayer & Lima, 1991, Rammsayer & Ulrich,
2005). While some studies point to common mechanism(s) across such tasks
for timing in the hundreds of milliseconds range (Keele et al., 1985; Ivry &
Hazeltine, 1995), other studies have pointed towards different mechanisms
for different timescales (e.g. Rammsayer, 1999).
In addition, evidence from pharmacological studies adds to the distinction
between the neural systems of the hundreds of milliseconds and seconds
range. It has been shown, for example, that benzodiazepines and
remoxipride impair the discrimination ability for intervals above the second
range, while the ability to discriminate intervals in the hundreds of
milliseconds to seconds range remained unaffected. In addition, it has been
shown that ethanol impaired performance in the hundreds of milliseconds to
second range but it dit not affect timing performance above the second range
(Rammsayer, 1997; 1999). According to these results, any pharmacological
treatment that affects cognitive capacities, such as working memory capacity,
interferes with temporal processing of intervals above the second range,
whereas does not affect the intervals below the second scale.
Several neuroimaging studies also provide evidence for the duration-specific
timing mechanisms. Functional magnetic resonance imaging (fMRI) and
positron emission tomography (PET) studies have revealed areas in the brain
that were selectively activated during the hundreds of milliseconds and the
seconds range. For instance, Lewis and Miall (2003a) reported specific brain
areas that were more strongly activated during a 0.6 s temporal
discrimination task, compared to a 3 s discrimination task. Rao et al (1997),
found that the selective brain areas were more activated when participants
were performing a motor timing task in the hundreds of milliseconds to
seconds range (300 and 600 ms) compared to when they performed a motor
timing task above the 1 sec range. Specifically, Rao et al (1997) showed that
time estimation for the hundreds of milliseconds to seconds range recruited
the primary sensorimotor and supplementary motor areas and the
cerebellum, whereas time estimation above the second range recruited the
posterior dorsolateral parietal frontal cortex (Lewis & Miall, 2003a; 2003b;
2006). Lewis and Miall (2006), showed that when the time interval was
increasing, there was more activation in the dorsolateral prefrontal cortex, a
brain region that is associated with executive functions and working
memory. Consistent with that neuroimaging finding, Brown (1997) reported
greater variability in timing performance when participants had to
simultaneously solve a difficult math problem when the target interval was at
5 seconds, whereas there was no effect at 2 seconds. These results indicate
14
that tasks demanding higher attentional and executive resources might
engage different mechanisms, support for which has been presented by
Miyake, Onishi & Poppel (2004).
Behavioral data might point to the same direction but results are rather
inconclusive. For example, Merchant, Zarco, and Prado (2008), used a wide
range of intervals from 350 to 1000 ms, and found a complex pattern of
correlations between tasks with different response requirements. These tasks
differed in sensorimotor processing (perception–production), in modality
(visual–auditory), and in the number of intervals produced (one–four). The
results appeared somehow mixed. There was a linear increase in timing
variability in accordance to Weber's law, but there was also a strong effect of
sensorimotor processing, the modality used, and the number of intervals, on
participants’ temporal performance. Some temporal tasks were correlated
with each other and others were not, suggesting the existence of a possible
context-specific timing mechanism based on the different requirements of
the tasks.
Task differences
One fundamental question with regard timing in the hundreds of
milliseconds to second range is whether the same mechanism(s) control
timing in different modalities such as the fingers, feet, and arms or whether
different mechanisms are engaged by different modalities. The most
common approach which has been used to answer this question is by testing
the correlations between diferent effectors. If there is some common timing
mechanism used by different effectors, then subjects who perform precisely
with one effector should also perform precise with another. Indeed, several
correlations have been found across motor timing tapping tasks, duration
judgement tasks as well as eye blink classical conditioning tasks. In addition,
significant correlations have been reported between temporal variability in
finger tapping and in tapping with the forearm and the foot (Keele &
Hawkins, 1982). However, the motor demands of these tasks are relatively
small. It seems reasonable that in such tasks, an explicit representation of
time would be important. These results are suggestive of one dedicated clock
involved in probably all the aforementioned tapping tasks. However,
different results have been reported for different kind of tasks that require
for example smooth movements and different movement trajectories such as
continuous drawing. For example, research findings have shown that
variability in a tapping task was not significantly correlated with variability
in a circle-drawing task (Zelaznik, Spencer, & Doffin, 2000; Zelaznik, et al.,
2002). An explanation for these results has been provided by pointing to two
classes of timing processes, the explicit and the implicit timing.
15
An explicit representation of the interval to be timed is used in explicit
timing tasks, such as the tapping tasks, whereas implicit timing is thought to
be an emergent property of the trajectory produced (Turvey, 1977). Explicit
timing is assumed to be a shared process across tasks and relies on a central
dedicated timer (Lorås, Sigmundsson, Talcott, Öhberg, & Stensdotter, 2012).
In contrast, intrinsic timing tasks such as circle drawing are considered to be
continuous and timing is assumed to be regulated on the basis of a different
process, such as the control of movement dynamics. The physical guidance
provided during different tasks can be one way to explain individual
differences in performance variability. For example, individual differences in
the accuracy of synchronizing repetitive discrete movements to an external
pace stimulus can be attributed to the relative efficiency of the matching
process, of participants’ owns beats from the contact to the drum pad. In
general, a different kind of sensory feedback has been considered to be an
important factor with regard performance in motor timing tasks.
The effect of feedback
Motor learning has been proposed to involve three different phases. The first
phase is assumed to be an attention-demanding phase, where learning
progresses really fast and the motor program to be learned is formed. In the
second phase, the motor presentations are further refined, and error
detection/correction mechanisms emerge and sensory afferences of the
ongoing movement are compared with the intended motor output. In a third
phase, movements have become highly automatic (Fitts & Posner, 1967;
Schmidt & Wrisber, 2008). The informational role of feedback (e.g., how the
participants use augmented feedback to correct errors, how error estimation
influences learning), as well as, the motivational aspects of feedback, have
been considered as an important factor for successful motor timing learning
(Schmidt & Wrisber, 2008; Wulf, 2010).
In the dedicated timing models, such as the Wing and Kristofferson’s
(1973) model, it is assumed that timing is performed in an open-loop
manner which assumes a negligible role of feedback and error correction
clearly present during synchronization (Studenka & Zelaznik, 2011).
However, several findings have shown that time keeping might not be
attributable to an open-loop, central clock-like timing process. For example,
there are some observations showing that sensory feedback can affect
accuracy as well as variability in timing tasks (Billon et al. 1996; Chen, Repp,
& Patel, 2002; Drewing, Hennings & Aschersleben, 2002; Kolers &
Brewster, 1985). These findings contradict the dedicated, open-loop
mechanisms described above, suggesting that a closed loop mechanism
16
(where each interval is made with reference to the previous one) seems more
plausible (Madison, & Delignières, 2009).
The intrinsic models of timing
Intrinsic models suggest that temporal information may be encoded in a
more flexible way, depending on the task demands (Buonomano &
Merzenich 1995; Buonomano 2000). In contrast to the dedicated models of
timing, no specialized mechanism is needed and time estimation relies on
local and general multimodal neural activity.
In a motor timing task, for example, as the one we have used in our studies,
the rhythmic responses produced by the individual are assumed to emerge
not from a dedicated clock, but from the continuous activation and
deactivation of the signals that control the consecutive actions. Since the
neural firing is distributed in the brain, it means that timing of an auditory
stimulus, for example, would be represented by changes in the neural
dynamics of the auditory cortex, whereas timing a visual stimulus would be
represented by changes in the neural dynamics in the visual cortex (Spencer
& Ivry, 2009). These models imply no explicit or linear measure of time, like
the ticks, as in the dedicated approach. Instead, a general-purpose network
of neurons can process time and time co-exists with the processing of other
sensory properties of that stimuli.
In some models of intrinsic timing, the representations of timing are
inherently interdependent. For example, one form of intrinsic timing
proposes that duration is encoded in the magnitude of neural activity. In
other words, the amount of energy spent over time (cognitive and emotional
effort) would be the basis for the subjective experience of time. As such,
there will be a bias to perceive a stimulus of a fixed duration as longer, if it is
brighter or louder (Eagleman, 2008; Pariyadath & Eagleman, 2007).
Other models suggest that timing of isochronous sounds is represented in
neuromagnetic beta oscillations (Fujioka, Trainor, Large & Ross, 2012). This
hypothesis proposes that beta power oscillations serve as a transporter for
temporal information during motor timing tasks. Higher neural oscillation
beta power is hypothesized to be associated with longer temporal durations,
whereas lower neural oscillation power is hypothesized to be associated with
shorter temporal durations. Through this change in dynamics of neural
oscillations, it becomes possible to match the temporal scales of perceptual
phenomena (Buzsáki & Draguhn, 2004). One mechanism which has been
proposed to underlie these changes in dynamics of neural oscillations is the
synaptic plasticity in cortico-striatum connections. That is an interesting
17
finding given that the striato-thalamo-cortical circuit has been suggested to
be involved in coincidence detection of oscillatory activities for temporal
coding (Matell & Meck, 2004).
An alternative form of intrinsic models suggests that the precise
representation of timing arises as a result of patterns of activity throughout
the brain (Buonomano & Merzenich 1995). A well known intrinsic timing
model of this type is the state-dependent network model (SDN), in which
time is implicitly represented in the synaptic properties or state of a neural
network (Buonomano & Mauk, 1994; Yamazaki & Tanaka, 2005; Karmarkar
& Buonomano, 2007). SDN models propose that any cortical network could
potentially process temporal information, as populations within each region
are capable of processing duration (Fig 4). In other words, time can be
inferred through changes in the pattern of activity over the network. In order
to better understand the functional properties of an SDN model, Karmakar &
Buonomano (2007) provided a simple example: imagine that we have two
auditory tones presented 100 ms apart from each other. When the first tone
arrives, it will initiate a series of synaptic processes resulting in the activation
of various neuron groups. The response to the second one will produce a
different pattern of neural activity, which changed from S0 to S100. The final
state of the network can be informative in that the previous tone was
presented 100 ms before (Karmakar & Buonomano, 2007).
18
Fig 4. Neural models for temporal representation. The figure was adapted
from Ivry and Schlerf (2008, p 2). (a) Cerebellum as a specialized neural
structure to represent temporal information. Some models postulate a
specialized role for the basal ganglia, supplementary motor area or right
prefrontal cortex. (b) A dedicated system could involve activity across a
distributed network of neural regions. (c) In a state-dependent network,
temporal patterns are represented as spatial patterns of activity across a
neural network.
It has been proposed that the intrinsic models of timing may be especially
suited for timing at the hundreds of milliseconds to seconds range scale,
while dedicated timing models may be more suitable for timing behaviors in
the above the seconds range (Ivry & Schlerf, 2008; Mauk & Buonomano,
2004). However, other researchers have raised several concerns with regard
to the feasibility of a state-dependent network over the hundred of
milliseconds timescale (Spencer, Karmarkar & Ivry, 2009). For example,
human studies testing the state-dependent network model of time
19
perception revealed that specific experimental manipulations influenced
only the perception of intervals lasting up to 300 ms, reducing the
applicability of this model to very short temporal intervals (Buonomano,
Bramen & Khodadadifar, 2009). This raises the question of how an intrinsic
model could account for transfer between different modalities. For example,
how could an intrinsic model of timing explain how training on an auditory
duration discrimination task would facilitate performance for judging the
duration of a visual stimulus?
An attempt to combine the dedicated and the intrinsic models is provided by
the Striatal Beat Frequency (SBF) model (Matell & Meck, 2004; Meck,
Penney, & Pouthas, 2008). The SBF model tries to exemplify the neural
mechanisms of timing by the coincidence detection of oscillatory processes
(Matell & Meck, 2004; Lustig, Matell, & Meck, 2005; Harrington
Zimbelman, Hinton, & Rao, 2010). The SBF model involves a set of cortical
neurons (timekeepers) that oscillate at regular, but distinct frequencies. This
vacillatory activity of brain cells in the upper cortex allows a pattern of
activation to occur at each point in time. A specific rhythm characterizes the
activity of each oscillator cell. These activations are assisted by striatal
integrators that combine their information with feedback and form the basis
of interval timing. The prefrontal cortex, striatum, and thalamus (Allman &
Meck, 2012; Buhusi & Meck, 2005; Hinton & Meck, 2004; Meck, 2006a;
2006b) constitutes the hypothesized frontal–striatal system corresponding
to the functional components of the SBF model (Meck, 1996; Meck &
Benson, 2002; Meck et al., 2008; Matell, Meck, & Nicolelis, 2003; Matell &
Meck, 2004; .
Taken together, both the dedicated and the intrinsic models of timing are
well supported but both have limitations that cannot be disregarded. The
main limitation of the dedicated models concerns their inability to explain
modality specific differences in time estimation. On the other hand, intrinsic
models have limited processing capacity which makes them inappropriate
for time estimation in more complex and real life tasks (Maniadakis &
Trahanias, 2014).
A hybrid model has been also proposed as an explanation of how humans
process timing. That model suggests that a core timing system, the cortico-
thalamic-basal ganglia (CTBG), interact with areas that are selectively
engaged based on the task requirements (Buhusi & Meck 2005; Merchant et
al., 2013). Other researchers do not refer to internal clock models or intrinsic
models but they offer a purely cognitive explanation for time perception and
motor timing (Block, 2003; Block, Hancock & Zakay, 2010).
20
In conclusion, the neural basis of time processing in the hundreds of
milliseconds to seconds range remains poorly understood, despite the vast
amount of research within the field. One reason for this could be that the
existing and highly influential timing models do not take into consideration
other factors that are independent of the internal mechanisms. These factors
can be strategies and previous experiences, as well as attention, memory,
and decision processes that have been found to be correlated with timing
performance. Exploration of these factors, and the precise way in which they
are involved in timing, seems to be required in order to advance the existing
timing models.
In general, many different areas of the frontoparietal cortex, such as the
insular cortex, the striatum and the cerebellum, have been implicated to be
involved in temporal processing, as has been shown in neuroimaging studies
(Lewis & Miall, 2003a; 2003b; Wiener, Turkeltaub, & Coslett, 2010).
Moreover, deficits in different brain areas can result in problems with
temporal processing. These findings indicate that in addition to core timing
mechanisms, the whole brain is needed for the accurate processing of time.
Specifically, distributed brain networks that are responsible for cognitive
control also seem to be essential for the successful processing of time. Next,
several findings that provide evidence for the relationship between timing
performance and cognitive capacities will be described.
Cognitive capacities in timing performance
Several studies have shown a relationship between timing variability and
cognitive traits (Deary et al. 2000; Rammsayer, 2011; Ullén et al. 2012;
2015). Growing evidence from dual-task studies suggests that executive
resources are recruited during timing tasks, above 1 second (Brown, 2006;
Ogden et al., 2011). Brown (1997), in a meta-analytic review article, showed
that several timing tasks exhibit an interference effect when non-temporal
tasks disrupted timing performance. Specifically, the time responses were
shorter, more variable, or more inaccurate when the individuals had to
perform a timing task simultaneously with a non-temporal task.
Furthermore, behavioral data have indicated a strong negative relationship
between reaction time (RT) and various elementary cognitive tasks, as well
as, psychometric intelligence (Jensen, 2006).
As it was described earlier in the introduction, the involvement of cognitive
capacities in timing has been shown to vary with the timed duration. For
example, Miyake, Onishi, & Poppel (2004) showed that participants’ ability
21
to synchronize to a metronome while they performed a memory task was
more impaired at longer intervals, specifically at intervals above two
seconds, compared to shorter intervals. A general finding is that in the range
from approximately 350 ms to 1 s, time perception and motor timing
variability has been found to be linear with time (Getty 1975; Zarco et al.,
2009), whereas above 1 second, there are several non-linearities in the
relationship between interval duration and performance (Madison, 2001).
Madison’s findings are in line with previous research findings (Karmarkar &
Buonomano, 2007; Lewis & Miall, 2006; Maes, Wanderley & Palmer, 2015;
Rammsayer, 1999; Rammsayer & Troche, 2014). In general, it has been
proposed that durations between 0.45 and 1.5 s are automatically processed,
whereas processing of intervals in the range from 1.8 to 3.6 s is affected by
attention and working memory processes (Miyake et al., 2004).
Other researchers, have also shown the role of attention and executive
control in motor timing tasks by using dual-task paradigms. The results from
a recent study suggested that attention and executive control was involved
when participants performed a rhythmic timing task in a slow tempo
compared to when the tempo was faster. The tempo of the sequences ranged
from 600 ms to 3000 ms between each beat. The distractor task was a novel
covert n-back task. A fast tempo was defined as an interstimulus interval
(ISI) shorter than 1500 ms and a slow tempo as an ISI longer than 1500 ms.
The largest increase in interference occurred in two different time ranges,
specifically, between an ISI of 897 and 1342 ms and between an ISI of 200
and 300 ms. This is consistent with models of time perception that assume
that different timing mechanisms are recruited at different time scales
(Bååth, Tjøstheim & Lingonblad, 2016).
Although the effect of cognitive control has been evident for the longer
durations, it remains unclear whether the same holds for voluntary timing in
the hundreds of milliseconds to seconds range. Some studies using dual
tasks paradigms provided support for cognitive control also in that range
(Maes, Wanderley & Palmer, 2015; McFarland & Ashton, 1978; Sergent,
Hellige & Cherry, 1993) In healthy individuals, disruptions of attention have
been found to affect perception of very brief durations in the hundreds of
milliseconds range, as well as estimation of longer durations, in the seconds
range (Casini & Macar, 1997; Grondin & Macar, 1992). Kee et al. (1986)
reported that solving anagrams and thinking out loud simultaneously led to
more timing variability compared to when subjects performed a single task.
Another study found that variability in finger tapping increased when
participants performed a recognition memory task simultaneously
(McFarland & Ashton, 1978). On the other hand, other researchers have
reported limited effects on timing variability as a result of dual task
22
interference on finger tapping (Michon, 1966; Nagasaki, 1990) in the
hundreds of milliseconds to second range. In addition, another study found
no reliable evidence for cognitive control on repetitive motor timing
variability in the range from 0.5 to 2.0 s. (Holm et al. 2013).
In general, it has been shown that the ability to accurately estimate and
produce the duration of a stimulus depends upon processes of sustained
attention and working memory capacity, and that timing and other cognitive
processes are known to share brain networks (Gómez, Molero, Atakan, &
Ortuño, 2014). Therefore, many researchers attribute the interference effect
in terms of a competition for attentional resources (e.g., Brown, 1985; Brown
& Bennette, 2002; Thomas & Weaver, 1975; Zakay, 1989) which leads to a
mutual deterioration of timing performance.
Furthermore, psychometric intelligence has been found to be correlated with
reaction time tasks (Rammsayer & Brandler 2002; 2007) as well as with
performance in time discrimination and production tasks (Deary, 2000;
Jensen, 2006, Madison et al., 2009; Ullén et al., 2008). In addition,
intelligence and time production performance seem to share a common
neural basis indicated by correlations found between self-paced tapping
performance and regional white matter volume in the prefrontal lobes (Ullén
et al., 2008). So far, the source of the associations between intelligence and
timing accuracy have been attributed to bottom-up mechanisms, meaning
that timing accuracy is under the influence of the same low-level processes
that also provide the foundation for the cognitive processes involved in
intelligence (Ullén et al., 2008; Ullén & Madison, 2009). Another source of
associations could be due to individual differences in cognitive capacities, for
example, sustained attention. Indeed, this explanation seems plausible given
that several research findings have shown that reaction time tasks (RT)
depend on attention, which in turn is correlated with intelligence (Schweizer,
Moosbrugger, & Goldhammer, 2005; Schweizer & Moosbrugge, 2004).
Below, several research findings that support the role of attention in timing
performance will be presented.
The role of attention in timing performance
Attention has been found to be highly related with our subjective and
objective experience of timing (Brown 1997; Zakay & Block 2004). For
example, time intervals are judged to be longer when we pay more attention
to time or when we are involved in pleasant activities, but time seems
prolonged during periods of boredom. Attention has also been defined as an
important concept in many models of time perception and time production,
23
including also the cognitive ones in which no reference is made to a
dedicated timing mechanism (Block, 2003; Block et al., 2010).
An extensive literature provides evidence for the hypothesis that attention
plays a crucial role in the estimation of time durations (James, 1950; Mattes
& Ulrich, 1998). Temporal productions have been found to be substantially
more variable under dual-task conditions compared with single-task
(timing-only) conditions. Other studies have also shown that temporal
productions were longer for dual task compared to single task conditions
(Brown, 1997; Miyake et al., 2004). Specifically, longer intervals
corresponded to an underestimation of time. According to an attentional
resource account, these effects occur because a concurrent distractor task
disrupts the accumulation of temporal cues.
Overall, a central idea in research on time performance is that when more
attention is directed towards a nontemporal task, attention towards the
timing task is reduced. It has been shown, for instance, that timing accuracy
was decreased when participants performed a timing task with the presence
of a concurrent task compared with a single task timing condition (Brown,
2006; 2008; Brown & Merchant, 2007). In the same vein, the results of
Chaston and Kingstone (2004) suggested that as attentional demands for a
search task increased, the estimated time duration spent searching
decreased. Lamotte, Izaute, and Droit-Volet (2012) suggested that the source
of underestimation of time durations was the higher attentional resources
demanded for the dual tasks. In line with this, Woehrle and Magliano (2012)
found differences in timing variability between participants with high
working-memory capacities and low working memory capacities.
Specifically, individuals with greater working memory capacity estimated the
length of the timing task as shorter than those with low working-memory
capacities. These differences could be due to those with higher working-
memory capacities directing more attention to the cognitive task, which
resulted in less accurate time estimations (Woehrle & Magliano, 2012).
Overall, the results of this study suggest that the amount of attention
directed toward a stimulus is reciprocally related to the accuracy of time
estimation.
Zélanti and Droit-Volet (2012) found that adults were more accurate in
perceiving time durations compared to children. The same researchers
reported that time perception was less accurate for visual than for auditory
stimuli for both adults and children, while the magnitude of this difference
was larger for the children. An explanation for these observations could be
that attention is involved in the accuracy of time processing. Children, for
whom attentional abilities are considered to be not fully developed,
24
performed poorly in the time perception of visual stimuli as a result of visual
signals requiring more attentional resources than the ones needed for the
processing of auditory signalas.
In line with this behavioral evidence, neuroimaging studies have shown that
attention can modulate time estimation accuracy and brain activity for time
estimation Increasing attention to time selectively increased activity in the
corticostriatal network, including pre–supplementary motor area and right
frontal areas, specifically when the task required a greater degree of
attentional resources, defining this as the core neuroanatomical substrates of
timing behavior (Coull, Vidal, Nazarian & Macar, 2004). Evidence for the
critical role of attention in time perception and motor timing comes from
various clinical populations where a deficit in time accuracy co exists with
distortions in attentional capacity. Below, some of the clinical cases will be
presented.
Impaired timing in clinical populations with an attention disorder
Evidence from clinical populations also supports the role of attention in time
perception and motor timing. It has been shown, for example, that autistic
children or children diagnosed with Attentional Deficit Hyperactivity
Disorder (ADHD) exhibit mixed problems in both motor control and
attentional capacity. These children face problems not only in the execution
and control of cognitive tasks, but also in motor coordination (Kaplan,
Wilson, Dewey & Crawford, 1998; Piek, Pitcher & Hay, 1999). Indeed, several
investigators have shown strong relationships between attention and aspects
of motor regulation, including inhibition, speed, and rhythmicity (Barkley &
Biederman, 1997; Denckla et al., 1985). Valera and colleagues (2010)
performed a neuroimaging study where they compared 20 unmedicated
adults with ADHD and 19 control subjects comparable on age, sex, and
estimated IQ, by using a finger-tapping paradigm. What they found was an
atypical pattern of neural activity in the cerebellum and basal ganglia areas
that have been considered important for motor timing. A common finding
was that individuals with ADHD were more inaccurate in temporal duration
judgments across the hundreds of milliseconds to seconds range, for both
visual and auditory stimuli. Specifically, ADHD individuals tended to
underestimate durations, and their judgements were more variable than
those of typically developing children (Allman & Meck, 2012).
Variable or inaccurate time judgments have also been reported in patients
with damage in the frontal lobes. These timing problems have been related
to the involvement of the frontal lobes in working memory and attention
25
processes. Nichelli, Always, & Grafman, (1996) found that patients with
frontal lesions, and specifically patients who suffered from traumatic brain
injury (TBI), judged short intervals as more similar to the long standard, at
two time ranges (100–900 ms and 8–32 s), a pattern related to problems
with attention, working memory, and processing speed mechanisms.
In another study, depressed patients had difficulties to estimate time
durations (Gualtieri, Johnson & Benedict, 2006). It is well known that
individuals suffering from depression face difficulties in terms of vigilance,
as well as sustained and selective attention, which affects the processing of
durations and especially the longer ones. Therefore, these deficits in the
processing of interval durations had been attributed to their limited
attentional resources or working memory capacity, which led to more
variability in the timing tasks (Gualtieri, Johnson & Benedict, 2006).
All these findings have challenged the traditional clock models by pointing
out their weakness in explaining the effect of cognitive functions in the
estimation of time. Thus, other researchers have proposed models for timing
which have scalar-timing properties but also consider the role of cognitive
factors, such as attention and memory.
Attentional models of timing
Attentional models for time perception and estimation propose that the
existence of non–temporal stimuli can affect attentional processes.
Specifically, they suggest that when more attention is given to non-temporal
information processing, less attentional resources are allocated to temporal
processing. In turn, less attention to timing tasks results in more timing
variability. Specifically, they propose that estimating time intervals that
involve more non-temporal information processing result in greater
problems estimating time, compared to estimating intervals when there is no
attention directed to a task.
The first attentional model of time proposed by Thomas and Weaver (1975)
was for very short (less than 100 ms) durations. It was later extended it to
longer durations by Zakay (1989). The first assumption of the model is that
organisms draw attentional resources from the same pool and distribute
them between all tasks. Consequently, when more attention is needed to
process a non- temporal task, fewer resources are available for the temporal
one, and temporal performance is impaired. Moreover, considering that
subjective duration depends on the number of accumulated pulses, as
proposed in the model of Gibbon et al. (1984), the second assumption is that
fewer pulses are accumulated and the duration is perceived as shorter when
26
the temporal processor cannot function optimally. This hypothesis explains
why durations seem shorter when more attentional resources are allocated to
non-temporal tasks. In this model, attention is supposed to influence the
gate opening, but other hypotheses have also been proposed. Casini and
Macar (1997) suggested that attentional disruption during a target interval
might have two different effects, either stopping pulse accumulation by
opening the switch or affecting the rate of the pacemaker.
A more advanced type of this kind of model is the Attentional Gate Model
(Zakay & Block, 1995). The gate is considered to be a cognitive mechanism
regulated by the distribution of attention to time. If more attention is
dispensed, the gate opens wider and more pulses can pass through and be
transferred to the accumulator.
Fig 4. The Attentional Gate Model (AGM). Adapted from Zakay & Block,
1995.
The attentional gate model by Zakay and Block (1995) used properties from
different models of time perception to explain the relationship between
attentional allocation and time estimation. This model combines most
internal clock theories by suggesting that the mind counts pulses, but also
adds that an "attentional gate" controls how many pulses can enter the
cognitive counter. The proposed model has been widely used in explaining
human timing behavior. Nevertheless, the model must be validated
27
empirically by testing both scalar properties of human-timing behavior and
the necessary properties of the attentional gate.
Lejeune (1998) introduced a similar model, also based on attention. In this
model a dynamic switch is controlled by the attentional resources allocated
for timing. The switch is opened and closed at a frequency dependent on the
amount of attentional resources allocated for timing. When there are more
attentional resources, the frequency and the number of pulses that can enter
and be accumulated in the cognitive counter increase (Zakay, 2000). The
attentional gate, as well as, the dynamic-switch described in the previous
models serve as mechanisms for the control of attentional resources between
timing- and non-temporal tasks. A common ground in both models is that
duration intervals will be judged as longer when non-temporal tasks are
simple and non-demanding compared to when non-temporal tasks are
complex and demanding. A difference between the two models is that in the
Attentional Gate Model, a gate determines the flow of pulses when attending
to time, while in Lejeune’s model the switch is associated with attending to a
duration-onset signal.
The attentional models of time perception are also in symphony with Mihaly
Csíkszentmihályi's theory of flow (1990). This theory considers what
happens when an individual is fully absorbed in the activity at hand. These
activities are usually internally motivated, meaning that the activity itself
rather than the result of it offers satisfaction (e.g. playing a sport or
painting). This intense concentration in activities results in a distortion of
time perception, leading to underestimating the passage of time (Nakamura
& Csikszentmihalyi, 2002). The flow theory suggests that the most important
factor that makes people underestimate time perception is the focused
attention toward an activity that also involves non-temporal information.
Specifically, they suggest that processing 0f non -temporal information can
take away attentional resources from estimating time accurately
(Csíkszentmihályi, 1990).
A similar procedure described in the models above seem to be also involved
in a motor timing task such as the one we have also employed in our studies.
Specifically, subjects must accumulate and store pulses while accumulating
new ones and then make the comparison of these pulses in memory (Bangert
et al., 2011). It seems reasonable that variability in duration reproductions
could be due to variable representations of the target duration in memory
and attention (Harrington & Haaland, & Hermanowicz, 1998). That could
account for the greater timing variability obtained by several clinical
populations with a clear deficit in memory or in attention, such as patients
28
with traumatic brain injury. A failure to fully attend to temporal information
will lead to inaccurate timing performance.
Concluding remarks
Despite the explosion of research on time perception and motor timing,
there are still critical questions that need to be resolved. Firstly, it seems
interesting how reasonable it is to believe that a timing mechanism applies to
a large interval durations when so many violations have been observed. Now,
that we also know that other external factors, such as previous experience,
the context, and the modality can affect timing performance, it seems more
important than ever to test the relative importance of these factors and how
they may vary with interval duration. Secondly, given the fact that several
studies have shown a relationship between timing accuracy and different
cognitive capacities it seems crucial to study the role of cognition in timing
performance.
The Main objectives of the Thesis
The overall aim of this thesis was to explore the nature of temporal
processing in motor timing in the hundreds of milliseconds to seconds range.
Is timing managed by one dedicated system or is it generated intrinsically
based at the different demands of the different timing tasks? This general
aim was broken down into three specific aims. The first aim was to study the
effects of short-term training in a motor timing task. The second aim was to
study the relationship between motor timing and cognition. Specifically, the
transfer gains and differences between a control and a treatment group, in a
sustained attention task, measurements of fluid intelligence and a motor
timing task were tested. The third aim was to study the effects of using
different means to express timed behavior. Specifically, variations across
distinctly different effectors such as the hand and ocular motor system were
tested.
The specific research questions were:
1. Can timing variability in a simple motor timing task be improved
with practice? How do these training effects depend on a number of
key variables, such as the duration of the interval to be timed, the
auditory feedback and the effector used to produce the intervals?
2. Does motor timing training improve performance in cognitive tasks,
such as sustained attention and measurements of fluid intelligence?
29
3. Is motor timing governed by one dedicated mechanism or by several
different mechanisms, possibly invoked by different effectors used to
perform the timing task?
METHODS
A schematic overview of the three different studies is presented in Table 1. In
all studies the synchronization-continuation timing task was employed. In
Study II, we also used a sensorimotor synchronization timing task, as well as
a sustained attention task and two measurements of fluid intelligence.
Common to all studies was that written informed consent was obtained in
accordance with the Declaration of Helsinki, and all studies were approved
by the Regional Ethics Committee at Umeå University. Participation was
voluntary.
Participants
All participants in three studies were students from Umeå University. They
were recruited via flyers at the Umeå University campus and through an
internet Facebook group. Exclusion criteria were report of neurological
disorders as well as being a professional musician. All participants gave their
informed consent to participate.
In Study I, in the first experiment, eight right-handed participants (5 men, 3
women) between 25 to 36 years participated. In the second experiment ten
right-handed participants between 22 and 41 years participated, none of
whom had participated in experiment 1. All participants were paid the
equivalent of 8 Euro per session. In study II, forty right-handed students (16
men, 24 women) between 18-35 years participated. In study III, sixty
participants (31 men, 29 women) between 20-35 years participated. Fifty-
nine participants were right-handed according to a self-report and fifty-nine
participants were right-eye dominant as determined by the Convergence
near-point test (Chen et al., 2000).
30
Table 1. A schematic overview of the three studies.
Study 1 Study 2 Study 3
Purpose To study the effects of training on timing variability in a synchronization-continuation tapping task. Of additional interest was if the training effects depended on the interval duration, the effector used to perform the task and the feedback.
To study transfer effects between a
motor timing task, a sustained
attention task, as well as,
measurements of fluid intelligence. To
this end, we compared timing
performance between a control group,
which received no training, and a
training group, which received motor
timing training
To test whether voluntary milliseconds
to seconds timing is managed by a
single or by multiple effector-dependent
timing mechanisms. To this end we
compared timing variability produced
by two different effectors, the eyes and
the fingers.
Participants Students from Umeå University.
N=(18)
Students from Umeå University.
N= (40)
Students from Umeå University
N=(60)
Tasks Synchronization-Continuation paradigm with 4 diferent responsemodes (Finger, Lightbeam, Drumstick, Cuff) with and without auditory feedback
Synchronization-Continuation
paradigm with a drumstick
Sensorimotor-Synchronization Task
with a drumstick
Corners’ Continuous Performance Task
II (CPT II)
Block Design
Figure Weights
Synchronization-Continuation paradigm
on a Synthesizer
Eye tracking
Analysis Repeated measurements of Anova Repeated measurements of Anova
Pearson’s correlation coifficients
Dependent sample t- test
Repeated measurements of Anova
Pearson’s Correlation coifficients
Slope Analysis
Exploratory Factor Analysis
31
Instruments-Procedure
Synchronization-Continuation paradigm (Study I, II, III)
In this task subjects had to synchronize their beats with external rhythmic
stimuli, and then continue producing those intervals without hearing the
external pacing signal. Specifically, each trial consisted of synchronization to
40 stimuli followed immediately by the production of 200 unsupported
intervals. The stimuli interval (ISI) in a trial were: 524, 733, 1,024, or 1,431
ms with two replications of each ISI. The tapping task lasted about 35
minutes.
An alesis DM5 sound module produced the stimulus sounds which were
presented through a pair of Peltor HTB7A headphones. The rhythmic stimuli
the participants synchronized with, consisted of a repeated sound sampled
from a pair of claves. Sounds were presented at 84 dBA.
Each participant was tested individually, sitting upright on a chair with the
feet on the floor. A computer was at the left side of the participant and in
front was the drum pad. The computer generated a warning signal if the
mean response inter-onset-interval (IOI) across the last 6 responses was
shorter than 66% of the stimulus IOI or if the stimulus IOI had elapsed since
the last response more than three times
In Study I, each participant made responses using four response modes
(Finger, Lightbeam, Drumstick and Cuff), both with and without auditory
feedback. In Study II, the responses were made with a drumstick. In Study
III, the Synchronization- Continutation paradigm was employed on a
synthesizer. The participants’ task was to synchronize their finger presses on
each of three keys on the keyboard, to the sounds presented from a
loudspeaker for 16 times. Then they continued making 39 key presses at the
same frequency and pattern as during the synchronization phase. Responses
were given by pressing keys on a synthesizer keyboard. In study III, an eye
tracking system was also employed. In those kind of tasks the accuracy
(mean distance of taps from the beat) and precision (variability of tap
position relative to the beat) of the motor movement can provide valid
information about participants’ timing performance. This type of task is
considered to be an automatic rather than a cognitive timing task according
to the classification criteria of automatic and cognitive temporal processing
provided by Lewis and Miall (2003a). One reason is that, in this type of type
of task there are no explicit instructions to involve working memory, as in
the time discrimination and time judgement tasks for example.
32
Sensorimotor synchronization task (Study II)
In the sensorimotor synchronization task, participants had to synchronize
drumstick beats to external rhythmic stimuli. Participants were seated
upright on a chair in front of a PC running the Windows 7 operating system
and an LCD screen with a resolution of 1920x1080 pixels. A drum pad was
positioned at the right of the participant, at a convenient position for beating
with the right hand. The metronome sounds were produced by an Arduino
Uno 1.6.3 microcomputer and consisted of a sine tone lasting 50 ms. The
inter-stimulus-interval (ISI) of the metronome was 1024 ms. The beats were
recorded and time stamped by the Arduino in real time and sent for further
processing to an in-house developed matlab code running in the Matlab
2014b environment. Participants received feedback simultaneously with
their beats via the matlab code on the PC. The basic data that can be
extracted from this task are the asynchronies which can be defined as the
difference between the time of a tap and the time of the time onset in the
external rhythm. The standard deviation of asynchronies (SDasy) is an index
of stability (Repp, 2013).
Cognitive measurements (Study II)
Block design
In the Block Design task, participants were shown a pattern and they had to
reproduce the presented pattern using red and white colored blocks. Scores
were calculated based on speed and accuracy, with a maximum score of 51
(WAIS- IV).
Figure Weights
In the Figure Weights, the participant viewed scales with missing weights
and they had to select the weights out of several options to balance the
scales. Scores were calculated based on the number of correct answers with a
maximum score of 27 (WAIS -IV).
Sustained Attention Task (CPT II)
Conners’ Continuous Performance Test II (CPT II) was used for the
measurement of sustained attention. The CPT II task was computerized on a
33
PC running the Windows 7 operating system. Participants had to press the
space bar or click the mouse button when any letter except the target letter
‘’X’’ was presented on a screen. The inter-stimulus-intervals (ISIs) between
letters were 1, 2, and 4 seconds in the same condition. There were 6 blocks,
with 60 trials each. There were 54 targets ‘nonX’ (90%) and 6 non-targets ‘X’
in one block (10%). Target order was randomly permuted in each block. The
CPT II took fourteen minutes to complete.
Eye tracking (Study III)
An Eyelink 1 eye tracker (SR research) recorded participants’ eye movements
at 250 Hz. The visual stimulus material was a mid grey image consisting of
three black dot forming an equilateral triangle with its base rotated
approximately 15 degrees clockwise.
Participants’ task was to synchronize their eye movements to sound pulses
presented from a loud speaker. Furthermore, they had to move their eyes in
a clockwise pattern between the dots at the same rate as the sound pulses.
The sound was switched off after 16 sound pulses, and the participants had
to continue making eye movements at the same frequency and pattern as
they did when they synchronized to the sounds. The trial was ended after 40
times. The same sound stimulus order and the same intervals 524 ms,
733ms, 1024 ms, 1431 ms were used in the key pressing and eye movement
conditions, which were alternated across participant (For a more detailed
description of the design see Article 2).
Dependent measurements computation
SD, CV, Local and Drift Variability (Study I, II, III).
Apart from the standard deviation (SD), and the coefficient of variation (CV)
two additional measurements were used in order to calculate the total
variability: Local, a measure of interval-to interval variability that was
calculated for each trial as follows:
22
1
2
1
2
2
N
xx
xLocal
N
ii
where xi is the duration of the temporal interval between response i and
response i + 1, x is the mean of all intervals of the trial and N is the number
of intervals in a trial. The expression inside the square root is a variance
34
measure based on lag 2 local differences between data points, which is
further divided by the mean duration of all intervals in the trial (to make
Local comparable to a coefficient of variation and inter-onset-intervals), and
was computed as the difference between total variability and Local:
22
1
2
1
2
22
N
xx
xDrift
N
ii
where variables are notated as for Local;
σ2 refers to the total variance in a trial.
Detectability (Study II)
As a measure of sustained attention Discriminability (d') was used, which
was calculated as the difference between the omissions (non X) and
commissions (X) distributions. d' = Z(hit rate) − Z(false alarm rate). A hit
corresponded to not responding in the presence of the X target and a false
alarm corresponds to responding in the absence of a target.
Saccade onsets (Study III)
For the eye task, saccade onsets were used to define the timing of the eye
movements. A saccade onset was defined as a change in position exceeding
15° across a four-sample window (16 ms), or the time at which the eye
velocity became greater than 35° s–1, or reached an acceleration greater than
9500° s–2. The nearest vector angle to the angle between the different points
in the triangle determined the target of each saccade. Since the triangle was
equidistant, the angle between each point of the triangle was 60°.
Statistical Analysis
Different statistical tools were used based on the specific research design in
the three studies. However, a common statistical method that was employed
in all threes studies was the repeated measurements of ANOVA. In study I,
in the first experiment, a 6 Practice × 4 Response Mode × 2 Feedback × 3
Inter-onset-interval (IOI) repeated measurements of ANOVA was employed
to investigate the effects of practice on timing performance. This analysis
resulted in a full factorial design with 144 conditions. Conditions were
blocked with respect to Response Mode and the order was rotated within
each session. The order of IOI and Feedback conditions were randomly
varied within each Response Mode block. In the second experiment of study
35
I, the number of intervals increased to 18. This resulted in an 18 IOI × 20
Practice design with 360 conditions repeated measurements of ANOVA.
In study II, a repeated measurements of ANOVA was employed to test the
effects of training in the control and in the treatment group, using training
group as a between factor and session order as a within factor. A dependent
sample t-test was also employed to access the performance difference in the
sensorimotor synchronization task between the first and last session.
Pearson’s product–moment correlation coefficients was computed to access
the rate of improvement between the motor timing task, the sustained
attention task as well as the tasks of fluid intelligence. In study III, a 2
Effector × 4 Interval duration repeated measurements (ANOVA) was
employed with CV as dependent variable to test if there were any mains
effect of interval duration, as well as, any interactions between the eye and
the finger. Pearson’s product–moment correlation coefficients were
computed to assess the relationship between the timing variability across the
two effectors, using the mean standard deviation across the two repetitions
for all eight trials. Furthermore, an exploratory factor analysis across the
eight conditions (correlations) was employed, in order to further indicate the
source of the variances that underlied the results reported.
A slope analysis was also conducted in study III, to partition the time
dependent and the time independent variability for each effector. For the
slope analysis, the two equations proposed by Ivry and Hazeltine (1995) were
used: σ2Total = kD+ c, where σ2 is overall variance, k is the proportionality
constant, D is the duration-dependent variability, and c is a constant
representing timing-independent variability. Furthermore, the squared
version of Equation 1 was also employed, producing the following equation:
σ2 Total = k2 D2 + c where k is the slope, which approximates the Weber
fraction, D2 is the mean subjective duration squared, and the intercept c is a
constant representing the time-independent component of variability. To
this end a linear regression was also performed on the timing variance and
the squared interval duration according to Equations 1 and 2 (Ivry &
Hazeltine, 1995).
Results
Study I
Madison, G., Karampela, O., Ullen, F., & Holm, L. (2013). Effects of
practice on variability in an isochronous serial interval production
task: Asymptotical levels of tapping variability after training are
similar to those of musicians. Acta Psychologica, 1, 119-128.
36
Aim
The overall aim of the study was to explore to what extent temporal accuracy
in a simple motor timing task can be improved with practice. Moreover, we
investigated how training effects depend on a number of key variables such
as the duration of the interval to be timed, the auditory feedback and the
effector used to produce the intervals.
Results
Experiment 1
Local Variability
Local variability decreased in the first 90 min of practice, across all response
modes and reached a stable level at about 4% thereafter. That was
demonstated by employing a 6 Practice × 2 fFedback × 4 Response mode × 3
IOI repeated measurements of ANOVA. There was a main effect of practice
(F5, 35 = 10.09, p < .00001), response mode, (F3, 21 = 23.89, p < 0.0001),
and feedback, (F1, 7 = 10.91, p = .010). There was a trend for a main effect of
IOI (F2, 14 = 3. 63, p=0.54) suggestive of higher rates of variability at the
longer durations. No significant interaction was found between practice and
response mode as well as between response mode and feedback F < 1,
Ηowever, there was a significant interaction between feedback and response
mode, (F7, 3 = 10.3, p = 0.14) indicating that variability in the Lightbeam
condition reduced more compared to the other response modes.
There was also a significant interaction between practice and IOI (F7,
10 = 2.39, p = .017) suggesting that longer IOIs benefited more from practice.
Furthermore, there was a significant interaction between IOI and response
mode, (F7, 6 = 3.24, p = .010), indicating that longer IOIs caused more
variability in the Lightbeam condition compared to the other response
modes. No more significant interactions were found.
Drift variability
Drift Variability decreased across the first 90 min of practice and reached a
stable level at about 0.5% for the Drumstick and Finger. There was a
significant main effect of practice (F3, 35 = 5.19, p = .001) response mode (F3,
21 = 28.16, p = 0.0001), as well as a main effect of feedback (F1,
7 = 51.03, p = .010). There was a trend for a main effect of IOI (F2,
7 = 3.23, p = .070) indicating higher variability at longer IOIs. Finally, a
37
significant interaction between feedback and response mode was found (F7,
3 = 3.81, p = .025).
Experiment 2
Local Variability
There was no effect of practice as it was demonstrated by a 20 Practice × 18
IOI repeated measures ANOVA. All main effects were for F < 1 and the
interaction effects was at F = 1.4.
Drift Variability
There was a trend towards a significant effect of practice, F19, 171 = 1.50, p =
.088, and a significant effect of IOI, F17, 153 = 18.97, p < .0001. The interaction
between practice and IOI was not significant, F = 1.
Given the results of Experiment 1, which showed that most of the learning
occurred over the first hour of practice, we considered it important to
analyze these data separately. Therefore, we analyzed data from the first two
sessions in a 4 Practice × 18 IOI repeated measures ANOVA.
Local and Drift variability across the first 60 min, in four 15 min
blocks
For Local variability, the main effect of practice was now statistically
significant, F3, 27 = 4.24, p < 0.014. There were no effects of IOI, nor any
interaction between practice and IOI, both F's < 1.
For Drift variability, there was no significant effect of Practice, F < 1, but a
significant effect of IOI, F17, 153 = 7.19, p < 0.0001. There was no significant
interaction, F = 1.
Combined results from both experiments
Here, we considered local and standard deviation, (SD) in order to compare
across different studies. Drift was excluded because the effect of training was
not consistent in Expt 1 and not significant in Expt 2 (Table 2).
Performance for the response mode Drumstick was really close in both
experiments indicating high reliability of the results. The smaller decrease in
variability exhibited in Expt 2 (19 vs. ~30%), was likely because the training
spread across a wide range of intervals from 500 to 1624 ms which was less
effective compared to the training on a few close IOIs. As one can notice in
38
Table 2, the decrease in variability was smaller for Local than for SD. That
might be attributable to the fact that SD is reflecting more sources of
variance (such as drift and other sources) therefore, has more room for
improvement. The initial SD, was larger for the Finger than for the
Drumstick, allowing more improvement overall (31.2%), while the
corresponding decrease in Local was limited by the finger production
method (13.4%) as compared to the Drumstick (27.9%). Only the two 500
and 574 ms were considered in Expt 2, to render the results comparable with
500, 536, and 574 ms used in Expt 1.
Table 2.
Summary of comparable practice effects across the two experiments.
Pre
training
(Local
ms)
Post-
training
(Local
ms)
Decrease
(Local %)
Pre-
training
(SD ms)
Post-
training
(SD ms)
Decrease
(SD %)
Expt 1
Finger 26.1 22.6 13.4 36.1 25.2 31.2
Expt 1
Drumstick 18.3 13.2 27.9 18.9 13.5 29.6
Expt 2
Drumstick
500 and
574 ms
IOI
18.0 15.4 14.5 19.5 15.8 19.0
Conclusions
In general, both Local and Drift variability decreased after a short amount of
training and regardless of the amount of feedback provided and the effector
used. Taken together, the response mode Drumstick came closest to
providing a measure of the individual’s lower limit of timing variability. In
addition, the finger and drumstick conditions yielded similar proportional
decrease in variability. Earlier studies on training effects on timing
performance are not so many, as it was mentioned in the introduction. These
studies covered a range of different timing tasks, but only a few have
considered synchronization-continuation tapping. Furthermore, most of
them have compared experts and novices such as musicians and non-
musicians (Billon & Semjen, 1995; Keele et al., 1985). To our knowledge,
39
only one synchronization-continuation tapping study compared the same
participants pre- and post training (Nagasaki, 1990). By employing a direct
comparison of both the absolute standard deviations and the differences
between pre- and post practice, we have gained some interesting results. The
first important result was that that the pre- and post practice differences
were all around a narrow range of about 30 percent, despite the pronounced
differences between the studies.
The substantial learning was found in the first hour of practice and exhibited
no trend for further decrease thereafter. This learning effect, which is likely
to occur within one single experimental session has many implications for
many timing studies that have not taken into account training. If not
carefully monitored, the effects of experimentally induced manipulations
might be confounded with learning.
Given the relationship between cognition and motor timing (e.g, Deary et
al. 2000) an interesting question with regard to the results of this study
would be to understand if training reflected improvements only in motor
precision or in cognitive performance also. Our results could not provide
direct evidence to this question, as we did not find any support for
differences between the shorter and the longer IOIs. Consistent with the
automatic and cognitive distinction of timing proposed by several
researchers (Lewis & Miall, 2003a; 2003b; Rammsayer, 2008), there were
also no differences in variability produced by the Local and the Drift
component.
In general, two approaches have been proposed with regard to the timing-
cognition relationship. The first is a top-down approach which proposes that
cognitive functions, such as memory or sustained attention, can affect timing
performance. The second approach is a bottom-up one, which suggests that
timing performance is independent of cognitive control. For drawing
stronger inferences about the relationship between timing and cognition, it
would be useful to study transfer effects between timing learning and other
cognitive functions. For instance, if training in a timing task also improves
verbal short term memory span, it would suggest that the timing training
effects reflects the improvement of intrinsic timing capacity and not just the
motor pattern involved in the timing task.
Here, it is important to mention that the differences found between different
response modes might be attributable to differences in motor control. For
example, the inertia found to a great extent in the forearm which is involved
in the drumstick movements may have helped to produce more stable
consecutive intervals. On the other hand, finger tapping, may be more
40
susceptible to small errors from muscle tremor. Other factors that have
might influenced timing performance and learning are genetic influences
(Madison et al., 2009b; Ullén et al., 2008), which might have a large effect
given the small sample of these studies.
Study II
Karampela, O., Madison, G., & Holm, L. (manuscript). Motor timing training
improves sustained attention performance.
Aim
The aim of this study was to test the transfer effects between a motor timing
task, a sustained attention task, and measurements of fluid intelligence.
Specifically, we tested if there were differences in transfer gains between a
training group, that received motor training, and a control group that
received any training.
Results
As you can see in Fig. 9, participants in the training group performed better
at the last training session compared to the first one. That was demonstrated
by employing a dependent samples t-test where a significant difference in
the scores for training session one (M = 45.05, SD = 4) and training session
five (M = 37.1, SD = 5), t (18) = 6.27, p < 0.05 was found.
Overall, the training and the control group performed rather equally in all
the cognitive tasks except for the sustained attention task, where the training
group improved more after the motor timing training (Fig 10). That was
employed by a repeated measurement of ANOVA, where a significant main
effect of session was found for the sustained attention task, F1, 38 = 24.910, p
< 0.05, ηp2 = .396, as well as a significant interaction was found between the
session and group, F1, 38 =5.865, p = .0.02, ηp2 = .134. No other main effects
and interactions were found.
With regard to the synchronization-continuation tapping task, there was no
main effect of group F1, 38 = 0.33 p = 0.56, neither a significant effect of
session, F1, 38 = 0.11 2.6 p = 0.56 for the SD measurement. Furthermore, the
results indicated a non-significant interaction between session and group, F1,
38 = 3.63, p = .0.06. With regard the Local measurement there was a
significant main effect of group, F1, 38 = 2.7, p = .010. However, there was
also a significant main effect of session, F1, 38 = 4.06, p = .0.05, as well as a
41
significant interaction between the group and the session, F1, 38 = 5.92, p =
.0.019.
Fig 9. Period synchronization variability as a function of training. Error
bars indicate the SEM.
42
Fig 10. Performance on Sustained Attention (CPT II), as a function of test session for control and training group. Error bars express the SEM.
Conclusions
The aim of this study was to test if cognitive capacities are involved in motor
timing, by assessing transfer effects from a sensorimotor timing task to a
sustained attention task, as well as, measures of fluid intelligence.
Furthermore, we were interested to test if training in the sensorimotor
timing task would enhance performance in a continuation tapping task. The
hypothesis, that the timing trained group would improve more in the
sustained attention task compared to the control group was confirmed.
Furthermore, we found no evidence for timing training improvements on
measures of other types of cognitive ability that we used in the study.
Specifically, the training and the control group improved equally in the Block
design and the Figure Weight task. In addition, we found an improvement in
43
the continuation tapping task after the sensorimotor synchronization
training.
Overall, the results of this study provide support that motor timing in the
milliseconds to seconds range involves cognitive capacities, and specifically
sustained attention. The close match in execution requirements between the
sensorimotor timing task and the sustained attention task could be one
reason for the transfer found between those two tasks. Specifically, the
sensorimotor timing task seems to be a task that involves regulation of
attention over time. The CPT II, also involves regulation of behavior over
time. In other words, it seems very likely that we might have trained
sustained attention and not motor timing ability per se. Therefore, we view
our results as near transfer rather than far transfer.
The present results also suggest that top-down control might be involved in
the relationship between intelligence and motor timing. Specifically, people
who perform better in intelligence tasks could also perform better in the
timing tasks because of better cognitive control capacity. For example, better
focus capacity and less lapses of attention could lead to enhanced
performance in timing tasks (Ullén et al., 2008). That is in line with several
studies which have shown an association between intelligence and sustained
attention (Schweizer, Moosbrugger, & Goldhammer, 2005).
With regard to the transfer effect from the sensorimotor timing task to the
continuation tapping task, one explanation could be that training in the
synchronization task reduced the motor implementation error expressed as
reduced timing variability, both in the sensorimotor synchronization and the
continuation tapping task. Another explanation could be that training in the
sensorimotor synchronization task increased predictive control. The ability
to coordinate with a motoric action an external predictable event, as happens
in the sensorimotor timing task, increased during the sensorimotor
synchronization, affecting at the same time the internal, tap to tap
variability, during the continuation task.
Taken together, after the relationship found between sustained attention and
the motor timing, it seems reasonable to expect an improvement in
sustained attention after intense training in a sensorimotor timing task, at
least, under the experimental conditions we used in our study. That would be
beneficial for several clinical populations, such as, people with ADHD and
people with autism who exhibit deficits both in timing and attentional
capacity.
.
44
Study III
Karampela, O., Holm, L., & Madison G. (2015). Shared timing variability in
eye and finger movements increases with interval duration: Support for a
distributed timing system below and above 1 second. The Quarterly Journal
of Experimental Psychology, 68, 1965-1980.
Aim
The aim of this study was to test differences in timing variability produced by
the eye and the finger so as to check whether motor timing in the hundreds
of milliseconds to seconds range is managed by a single or multiple timing
mechanisms. To test these possibilities, we compared the timing variability
between and within the eye and the finger as a function of the interval to be
produced.
Results
First we computed the accuracy of the sequence reproduction by calculating
the proportion of hits for the fingers and the eyes. Six participants were
excluded as they had really low response hit rates for the eye conditions.
The variability (SD) was increasing monotonically for the eyes and the
fingers for each IOI and the coefficients of variation was flat in accordance
with Weber’s law. There was no significant main effect of interval duration as
it was indicated by a 2 (effector) x 4 (interval duration) repeated measures
ANOVA with CV as dependent variable duration, F3, 159 = 1.48, p = .22. A
significant difference was found for the two effectors of the eye and the
finger, F1, 53 = .859, p < .001, as well as a significant interaction F (3,128)
=11.2, p<0.01, residing in differences between effectors across the interval
durations of 1024 and 1431 ms. Specifically, the CV for the fingers increased,
whereas the CV for the eyes decreased.
Pearson’s product–moment correlation coefficients were computed to test
the correlations between the two different effectors. Table 3 shows all eye
finger correlations for each IOI combination. All four between-effector
correlations were positive, but only those for 1024 and 1431 ms were
statistically significant with Bonferroni correction. Seven different-interval
comparisons were also significant. That was suggestive of a relatively strong
relationship between fingers and eyes when fingers produce the two longer
intervals (1024 and 1431 ms) and relatively low relationships for the lower
intervals. The Equations 1 and 2 (see Statistical Analysis) for the slope
45
analysis were submitted to a 2 (effector) × 4 (interval duration) repeated
measures ANOVA to test whether the slopes of eye and finger variability
differed. There were significant main effects for interval duration, effector
and their interaction for both equations, p<.001. The significant interaction
in both analyses resided in a steeper slope of eye than finger variability
across interval duration.
Table 2. Correlations between the two effectors and all IOIs.
Intervals 524 ms
hand
733 ms
hand
1024 ms
hand
1431 ms
hand
524 ms eye 0.09
0.18 0.46* 0.47*
733 ms eye 0.16
0.16 0.39* 0.34*
1024 ms eye 0.18
0.27 0.36* 0.35*
1431 ms eye 0.32* 0.30* 0.31* 0.32*
Conclusions
The overall aim of this study was to test whether temporal processing in the
hundreds of milliseconds to seconds range is governed by one or by multiple
timing mechanisms. The most important result found in this study were the
several positive correlations found between the two different effectors.
Specifically, individuals who varied more in the finger movements also
varied more in the eye movements and vice versa. That result was consistent
with the idea of a single timing mechanism where no differences between
different effectors should be expected. Several other findings were also
consistent with the notion of a single timing mechanism. For example,
temporal variability in both fingers and eyes followed Weber’s law and the
CV tended to increase for the fingers, consistent with previous studies (e.g.
Madison, 2001).
46
On the other hand, several results were inconsistent with a single timing
mechanism. For example, between effector correlations were substantially
different across IOIs. Furthermore, these correlations were not statistically
significant for intervals below one second after correcting for reliability
finally, the slopes derived from the slope analysis were significantly different
for the eyes and the fingers. An interesting finding was that CV values were
decreasing for the eyes with increasing IOIs. However, that cannot invalidate
the single clock hypothesis as these differences can be due to larger motor
noise dominating eye variability at shorter intervals and hiding possible
correlations between the two effectors at the shorter intervals.
Overall, the pattern of results from this study could not support either the
single clock or the multiple clock hypothesis. Instead, in symphony with
other studies these results proposed that voluntary motor timing is managed
by a partially overlapping distributed timing system. That system could be
responsible for temporal processing for both in the eyes and the fingers but
changing according to the task demands. (Merchant et al., 2008).
47
GENERAL DISCUSSION
The overall aim of this thesis was to explore and increase our knowledge of
the nature of timing processing in the hundreds of milliseconds to seconds
range. To these ends, three areas of specific interest were identified.
Specifically, the studies in this thesis examined whether motor timing
training can influence timing performance and whether motor timing
training can improve performance on a variety of cognitive tasks.
Furthermore, the effect of using different means to express timed behavior
on timing performance was assessed. The discussion will initially be
organized according to these three themes. The effects of training, as well as,
the transfer gains and differences between motor timing and cognitive tasks
will be presented and critically discussed. Furthermore, the findings with
regard modality differences in relationship to timing performance will be
presented and discussed, in light of the existing models for time processing.
Following the discussion of the findings related to these themes, the
limitations of the studies, as well as, implications of the results found will be
presented. Finally, several ideas for future studies will be presented.
Effects of training. Motor and cognitive gains
The results of Study I indicated that participants’ performance reached a
stable plateau after 90 minutes of practice regardless of the response mode
employed. In symphony with study I the results of study II showed a
substantial effect of training after 5 training sessions, also around 90
minutes. The importance of these results lies in that repetitive motor timing
can be improved and reaches a stable plateau after a few training sessions,
even with different intervals. That is an important finding as it is indicative
of motor timing being a skill, which can be learned and improved, and not a
predetermined ability that cannot be changed. These results are on the one
hand in line with previous research, which has shown that practice can
enhance timing performance (Nagarajan et al., 1998; Wright et al., 1997),
while on the other hand, they raise methodological concerns about other
studies that have not controlled for practice and response modes. For
example, different levels of practice among participants might have affected
the validity of the timing performance data in previous studies and thus have
led to incorrect conclusions. Interestingly enough, there is only one study
which has used naïve participants at a pre and post session (Nagasaki, 1990).
All earlier studies testing the training effects on timing performance have
compared experts and novices such as musicians and non-musicians (Billon,
48
& Semjen, 1995; Keele et al., 1985) and they have usually used the finger as
the response mode.
Furthermore, these results raise the interesting question of what property do
we train when we receive training in a motor timing task. Performance
improvements may reside in either improved timing ability or task
adaptation, such as adapting the sensorimotor model to the response
requirements imposed by the task. That is a really interesting question for
researchers dealing with the nature of the relation between timing and
cognition (Helmbold & Rammsayer, 2006; Helmbold, Troche, &
Rammsayer, 2007). The bottom-up account, which suggests that basic
neural properties influence the amount of temporal variability, which is
reflected both in timing tasks and cognitive performance, is strongly
supported so far (Madison et al. 2009; Ullén et al, 2008;2012).
Local variability has been considered an automatic process which is
inaccessible to conscious control. On the other hand, Drift has been
considered to be dependent on short-term memory of previously produced
intervals and it has been considered to be more susceptible to top–down
influences (Forsman et al., 2009; Madison & Delignières, 2009; Madison et
al., 2009b). We hypothesized that if training affected Local and Drift
variability differently, this would be suggestive of which functional parts of
the timing mechanisms are more adoptive to learning. For example, a
decrease in Local variability would suggest that training affected motor
control more (Flanagan, Vetter, Johansson, & Wolpert, 2002), whereas a
decrease in Drift would suggest that training would affect processes related
to short-term memory during several intervals.
Study I could not provide a direct answer to this question. Specifically,
despite our hypotheses that Drift and Local variability would be affected
differently from the training, we found that Drift did not decrease more than
Local, according to the idea that training would mainly affect cognitive
processes. In addition, we used a wide range of durations in order to explore
the differences of the training effect as a possible function of IOI. Lewis and
Miall’s (2003a) classification criteria for the distinction between the
automatic and the cognitive based timing are (1) different psychophysical
characteristics at different durations (several breakpoints above 1 sec), (2)
differential responses to pharmacological manipulations (durations above 1
sec seem to be affected), (3) differential impairment of performance by dual
task (durations above 1 sec are affected) and, (4) different patterns of brain
activation during the measurement of sub-second and supra- second
intervals (the sub-second timing activates more motor circuits whereas the
supra seconds system activates more prefrontal and parietal regions). Study
49
I found no differences in training effects between the longer and shorter
intervals, indicating no dominance of cognitive effects of training. That is in
line with Holm, Ullén and Madison (2013), who did not find an effect on
cognitve load; nor did they find an interaction between cognitive load and
sequence.
The results of study II, however, provided evidence for a direct involvement
of cognitive capacities, especially sustained attention, in repetitive motor
timing. Previous studies dealing with the timing-cognition relationship have
mostly used dual task paradigms where participants perform a timing task
simultaneously with a cognitive task. In study II, we extended previous
research by employing a transfer study. The transfer hypothesis was based
on the condition that executive functions are involved in timing performance
found in the previous dual task studies. Furthermore, transfer enabled us to
draw stronger inferences about which specific cognitive capacity is involved
in the relationship between timing and cognition. The results of study II
could be attributed to the close similarity in the executive demands of the
two tasks we used. Motor timing behavior is a regulated process that involves
attentional control over time. The CPT II, the sustained attention task that
we used, is also a regulation process of sustained behavior over time.
Therefore, it seems reasonable that the close nature of the two tasks led to
near transfer between motor timing and sustained attention. Therefore, it
seems possible that through the motor timing training we have trained the
regulation of motor and cognitive control and specifically attention. Overall,
study II provided support that repetitive motor timing in the hundreds of
milliseconds to seconds range employs regulation and is subject to
controlled cognitive processes and specifically sustained attention. That
agrees with research findings which suggest that measurements of sustained
attention, as well as, working memory capacity seem to control the
sensorimotor and the cognitive demands of a timing task (Block, Hancock, &
Zakay, 2010).
While the involvement of cognitive capacities in longer durations has been
reported by several studies, the involvement of cognitive capacities in shorter
durations remains rather unclear. The presence of cognitive control also in
the hundreds of milliseconds to seconds range seems to suggest that it is the
predictive control demands that determine the involvement of cognitive
control rather than time itself (Holm, et al., 2016). Task difficulty and the
time frame of a planned action can affect anticipatory timing. For example,
when the time frame increases predictive uncertainty also increases, and
therefore more cognitive control is needed. So, the recent view of automatic
versus cognitive timing for intervals below and above 1 sec could be replaced
with the view of at least some cognitive control being present throughout the
milliseconds to seconds timing, possibly increasing with increased durations.
50
Since we did not employ a wide range of durations below 500 ms in our
studies we cannot give much support towards that direction but this is
evident from other studies. Furthermore, that is in line with timing models
which acknowledge the role of attention in successful time processing (Zakay
& Block, 1995). In support of the involvement of cognitive control in motor
timing, research findings suggest a partial overlap of brain regions engaged
in time processing tasks, with regions engaged in tasks requiring increased
cognitive effort. Specifically, timing and other cognitive processes were
found to be interlinked when there was an increase in the level of difficulty
or increased effort (Alustiza et al., 2016). In general cognitive control seems
to be recruited in response to the execution demands of the task employed,
such as cognitive effort demanded by the task difficulty.
The present results shed light on the established relationship between timing
and intelligence. In general, intelligence has been positively correlated with
accuracy of performance in a wide range of timing tasks (Jensen, 2006). This
relationship holds true also for the synchronization-continuation task used
in various studies (Holm, Ullén, & Madison, 2011; 2013). Mainly, this
relation has been attributed to bottom-up mechanisms that are independent
of cognitive control (Holm et al., 2011; Madison et al, 2009; Ullén et al.,
2008). Variability in the isochronous tapping had been considered to be
inaccessible to top-down control and unaffected under dual task conditions
(Holm, et al., 2013). In addition, relations with intelligence are stronger for
Local than for Drift (Ullén et al. 2008; Madison et al., 2009). Consistent with
this, children diagnosed with Attention Deficit Hyperactivity Disorder
(ADHD), who exhibit deficits in executive functions, exhibit larger deviations
in Drift than in Local (Jucaitė et al. 2008). Finally, manipulation of
motivation states did not affect the magnitude of relations between motor
timing and intelligence (Ullén & Madison, 2009) indicating a lack of top-
down control.
However, a general conclusion from study II was that there is also a top-
down relationship between cognitive capacities and motor timing in the
hundreds of milliseconds to seconds range. The timing-intelligence
relationship could therefore be attributed to individual differences in top-
down control and specifically sustained attention, which influence
performance in both timing tasks and intelligence. For example, more
intelligent people could perform better in timed tasks due to better cognitive
control mechanisms which might lead to less lapses of attention (Madison et
al., 2009).
51
That accords with converging results from several studies which have found
that sustained attention and psychometric intelligence are related (Burns,
Nettelbeck, & McPherson, 2009; Ren et al., 2013; Schweizer & Moosbrugger,
2004). For example, several research findings have shown that reaction time
tasks (RT) depend on attention, which in turn is correlated to intelligence
(Schweizer & Moosbrugger, 2004; Schweizer et al. 2005). Further support
derives from the Worst Performance Rule, which states that the worst trials
in reaction timing tasks, which are the trials most likely to be affected by
attentional lapses, are more strongly associated with intelligence, provides
further support for the role of attention on the relationship between
intelligence and timing tasks (Coyle, 2003). In summary, both top-down and
bottom-up processes are likely to be involved in associations between
intelligence and performance in timing tasks.
The involvement of cognitive control in repetitive motor timing is also
indicated by the results of study III. Several positive correlations were found
between the effectors of the eye and the hand of the order of .5 for the two
longer IOIs. That is supported from the increase in correlations between the
two different effectors at the longer IOIs. The lack of significant correlations
in the shorter interval could be due to independent noise dominating at the
hundreds of milliseconds range and covering this effect. On the other hand,
cognitive control might take over in the longer intervals, which may be why
we found stronger correlations for them. These results support that there
might at least be some cognitive control, below one second, which varies
with the task characteristics (Holm, Ullén & Madison, 2013; Holm et al.,
2016).
The behavioral results described in this thesis challenge the traditional
models of processing time in the hundreds of milliseconds and seconds
range, by pointing out their inability to fully accommodate the results of our
studies as well as the results of several others. This pattern of results opens
up to investigation of long-standing issues in complex motor behavior, like
the interplay between different effectors (fingers or hands) as well as the
manipulation of different modalities such as vision and audition and
between different target durations. Moreover, a long-standing question is
whether time is intrinsically encoded in the activity of general purpose
networks of neurons or time is processed through a time-dedicated neural
circuit.
Modality differences – dedicated or intrinsic mechanisms?
A fundamental question regarding timing processing is whether it relies on a
single centralized mechanism or is distributed throughout different areas.
52
The results of study III showed several positive correlations between the
effectors of the eye and the hand, on the order of .5 for the two longer
intervals. In addition, the variability of the eye and the finger movements
increased with the interval duration. Under the assumption of a centralized
clock, no differences between different modalities should be expected.
Therefore, these results could be viewed as a support of the single timing
mechanism hypothesis. In the same vein, others have shown that temporal
training generalized across audition and touch, across different skin
positions (Nagarajan et al., 1998), as well as, across different frequencies of
the stimulus tone (Wright et al., 1997) in support of the centralized clock
hypothesis.
Furthemore, the lack of differences in variability between short and long
intervals (> ~ 1 s,) (Lewis & Miall, 2003a) indicated in study I, was in
accoradance with models of timing that assume one dedicated clock for all
time durations. However, several other results of study I and study III were
inconsistent with a single timing mechanism. For example, cross-effector
correlations were different across duration intervals. Moreover, the slopes of
variability differed markedly for the eyes and the fingers. In addition, in
study I, there were differences in the variability produced by the different
effectors, as well as, differences in the effect of feedback, with the effect of
feedback being more pronounced in the Lightbeam condition. These findings
were suggestive of multiple timing mechanisms operating and they were in
accordance with research findings that have failed to find any transfer
between different modalities. For example, no improvement in performance
found in visual processing after temporal auditory training (Grondin et al.,
2008; Grondin & Ulrich, 2011). The lack of transfer or any training or
association found in these studies, does not simply imply that visual
processing cannot be enhaced by auditory processing. One explanation
accounted for these results could be the moderate amount of training
provided. It has been shown that for interval duration discrimination
extensive training is needed in order to exert a strong effect on performance
(Kristofersson, 1980). Another explanation could be that different timing
mechanisms are involved in different sensory modalities in the
discrimination of brief durations (Rammsayer & Lima, 1991).
The results of study II suggested that cognitive control expressed via
sustained attention directly affected timing performance, as well as, that
training in sensorimotor synchronization produced improvements in a
continuation tapping task. These results are also consistent with the
executive control hypothesis (Krampe, Kliegl & Mayr, 2005), which assumes
that timing and sequencing in paced movement production reflect two
distinct processes: a low level and a high-level component. According to
53
these researchers, during the production of timed sequences, executive
functions program the low-level timing mechanism such updating,
maintenance, and adjustment of the specific action plan. In the same vein,
evidence from recent studies suggest that modulation of attention resources
predicts the speed and accuracy of visual and visuomotor processing
(Klimesch et al. 2008; Serences & Yantis 2006), indicating that the nervous
system’s ability to modulate its representation of time is highly influenced by
the attentional state of the observer. These results act against a dedicated
approach which is unaffected from the computational demands of the tasks.
Taken together, the results of the three studies cannot support unanimously
the dedicated or the distributed timing system. The mixed pattern of results
might be suggestive of an intermediate hypothesis, namely a partially
distributed timing system. These results suggest that timing mechanisms
interact with a distributed brain network which is engaged in all tasks
requiring timing processing, whereas this activation can be altered
depending on task demands. That would be in agreement with Merchant and
colleagues (2008) who also found a mixed pattern of results when analyzing
timing variability produced by four timing tasks that differed in many ways,
such as the duration interval and the the modality used. Timing variability
increased linearly as a function of the interval to be timed in all tasks, in
accordance to the scalar property, but a strong effect of the nontemporal
variables was also found (Merchant et al., 2008).
As proposed by Merchant et al (2013), temporal estimation may depend on
the interaction of multiple areas including regions that conform to the main
core timing network, including the cortico-basal ganglia-thalamus circuit
(CBSTc) and areas that are activated in a context-dependent fashion
(Merchant et al., 2008; 2013). The main core timing network consists of the
supplementary motor area (SMA) and the basal ganglia. The pattern of
results found in study III could also be explained on the properties of that
circuitry. The common system proposed in the CBSTc circuit could be
dedicated to temporal processing for both the eyes and the fingers (given the
similarities found), but other areas could also be activated according to task
incident (given the differences found). Furthermore, a system like that could
explain the results of study II where general, cognitive control mechanisms
may constitute a general purpose resource for controlling timed behavior
and be recruited in response to the computational demand of the task, such
as increased attention.
The Striatal Beat Frequency (SBF) model described in the introduction could
be complementary to the CTBG circuit. The SBF models propose an innate
interactive nature of timing networks. Specifically, SBF model assumes a
54
dedicated timing mechanism in the basal ganglia which monitors distributed
neural activity in the cortex. The mechanisms proposed by the SBF model as
described by Matell and Meck (2004) is constructed such that it is consistent
with brain regions which are thought to be involved in timing (e.g., frontal
cortex and striatum). Furthermore, its output is consistent with physiological
recordings and behavioral results from interval-timing experiments (Matell
et al. 2003). Critically, the SBF model reproduces the scalar property,
(Gibbon et al. 1984, 1997), which occurs because of variability in the firing
patterns of striatal neurons and because cortical activity is assumed to be
oscillatory.
Implications
In general, the results of the three studies support models that propose a
core timing mechanism that has access to multi modal information and is
engaged in the synchronization and the production of timed intervals.
Furthermore, our cross-modal and transfer studies propose that there are
other behavioral parameters that can influence this core timing mechanism
in a specific way, such as higher cognitive components like attention. In
other words, in addition to bottom-up, intrinsic circuitry mechanisms can
affect timing.
Apart from the theoretical contribution of the results of the studies included
in this thesis, they do have many practical implications for both healthy
people and clinical populations. First, they provided valuable information on
how much timing training would someone need to improve in motor timing
and which way would be the most effective. That could be beneficial for
people’s everyday activities such as sports performance, speech production
and a variety of motor activities. Furthermore, the effects of near transfer
found between timing and sustained attention can be beneficial for clinical
population exhibiting attention and motor control problems, such as ADHD
and autism. Future research can test if people with severe problems in
sustained attention capacity could benefit from a motor timing intervention
program that eventually can improve cognitive performance, at least under
the experimental conditions that we employed in our studies.
Given the direct relationship found between sustained attention and
repetitive motor timing, it seems reasonable to expect an improvement in a
number of capacities, including sustained attention and motor control, at
least under the experimental conditions we employed. For example, training
in a sensorimotor synchronization task could facilitate performance on tasks
that require sustained attention and timing accuracy. These results have
55
implications not only for clinical populations but also for people exhibiting
no cognitive or motor deficits. A systematic motor timing training might
improve motor timing and sustained attention in adults and children. The
facilitation of motor and cognitive properties can lead to increased
concentration when performing several tasks that may be important for
academic achievement. In addition, it is possible that other cognitive
capacities can be improved following motor timing training. Based on the
proposed interconnectivity between motor and cognitive functions, it seems
reasonable that designing rehabilitation strategies for people with motor
problems should include a focus also on the cognitive aspects of motor
control and behaviour. Based on the results of our studies it seems crucial
that other training studies with a larger sample of participants should be
performed. Furthermore, the inclusion of various cognitive tasks seem
crucial.
To my knowledge, there are no neurological disorders that are characterized
by pure timing deficits. It is thus difficult to judge whether the observed
timing processing deficits are due to deficits in attention and working
memory since all three processes are known to engage same brain areas,
specifically, the right prefrontal cortex (PFC). Further research is warranted,
since the results of our study cannot provide direct evidence.
Given the peculiar nature of the timesense it is not surprising that there are
so many controversies and so many different models proposed.
Furthermore, neglecting various cognitive processes, which might contribute
to the perception and production of time, can cause several problems and
inconsistencies in identifying the underlying neural mechanisms of timing
(Grondin, 2010).
Limitations
The studies included in the thesis have several limitations that would
motivate further work. A methodological limitation of study I was that a
within-participant design was used. That means that the type of practice was
not varied across groups, but was mixed across the different training
sessions. Thus, although this type of design could address the effects of
training, as well as the effect of a number of different variables, such as the
feedback and the interval used, it could not provide more information about
which specific condition has been more efficient. Another methodological
consideration was the number of the training sessions included, which did
not allow us to get a clear picture of whether the training effect decreased or
flattened out, after the few hours of practice.
56
In study II, in the sensorimotor synchronization training task, participants
received both visual and auditory feedback during the training sessions.
Therefore, it was not possible to distinguish which condition (visual
auditory, or a combination) had been more beneficial for motor timing
learning. In addition, it would have been beneficial, if we had studied, not
only the immediate effects of training, but also the effects of training over
time. That would allow us to draw stronger conclusions about the
sustainability of motor timing training over time. Furthermore, we used a
passive control group, which made it possible to rule out retest effects, but a
question arising here was whether an active control group would have
improved more from pretest to posttest on the transfer task compared to a
passive control group. Having included a control group, with at least a low
level of motor training, it would have made it possible to draw firm
conclusions. Moreover, another limitation of study II, was the limited
number of cognitive tasks included. It would be more informative, if we had
incorporated test batteries that would allow for direct calculation of the g
component, as well as of other cognitive factors. Finally, more cognitive,
motor, as well as, time perception and production tasks should have been
included, in order to allow stronger inferences about the relationship found
between motor timing and cognition.
Given that several different suggestions have been proposed for the
breakpoints above and below 1 second, a limitation of study III seems to be
the limited range of intervals used. The inclusion of a wider range of
intervals would have probably provided important information for the
nature of time processing in the different timescales. Specifically, the
inclusion of intervals below 500 ms, would have made it possible to identify
several sources of motor noise, which are more likely to be reflected in the
short interval durations. It seems that an avenue for advancing our
knowledge about timing mechanisms, would be by using different
modalities, while trying to eliminate independent noise.
Another limitation concerning all the studies was the study population. A
larger number of participants would have increased the statistical power in
the studies. Moreover, the study population was highly homogeneous. This
specific selection could have led to limited generalizability of the results to
the general population, while on the other hand it might have led reduced
within groups’ variance and noise.
Future
Given that there are many time-dependent capacities, it is really surprising
that cognitive research has largely neglected the flow of time. Recent
57
reasearch has luckily been directed towards the temporal aspects of
cognition, by incorporating a range of perceptual and motor skills into time
(Battelli et al., 2008; Taatgen et al., 2007). Overall, in order to expand the
theoretical understanding of timing processes, future research should
investigate in depth the interplay between cognitive control and timing in
many timing tasks. Considering the results of our studies, as well as previous
research, it seems crucial to study how cognitive control affects accuracy in
timing performance. In addition further research is required to more closely
map the relationship between motor timing and intelligence.
Finally, future research should focus on whether time is represented in a
context-dependent manner or in a more context-independent manner in a
dedicated region (or network of regions) that acts as an internal timer. The
new models point towards a joint operation of several mechanisms, with
perhaps a switch from one to the other as the duration to be timed increases
(Lewis and Miall, 2003b; Ivry and Schlerf, 2008). In general, it is important
for future research to study how different brain areas communicate, such as
specific timing circuits, with other brain networks (Cheng, Dyke, McConnell,
& Meck, 2011; Harrington, Castillo, Fong, & Reed, 2011), as well as changes
in the brain, in cognitive and timing processes that occur throughout
childhood and into old age.
58
REFERENCES
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.
Allan, L. G., & Gibbon, J. (1991). Human bisection at the geometric mean.
Learning and Motivation, 22, 39-58.
Allan, L. G., & Kristofersson, A. B. (1974). Psychophysical theories of duration
discrimination. Perception & Psychophysics, 16, 26-34.
Allman, M. J., & Meck W. H. (2012). Pathophysiological distortions in time
perception and timed performance. Brain, 135, 656–677.
Alústiza, I., Radula, J., Albajes-Eizagirre, A., Domíngue, M., Aubá, E., & Ortuño, F.
(2016). Meta-analysis of functional neuroimaging and cognitive control
studies in schizophrenia: Preliminary elucidation of a core dysfunctional
timing network. Frontiers in Psychoogy, 7, 192.
Artieda, J., Pastor, M. A., Lacruz, F., & Obeso, J. A. (1992). Temporal
discrimination is abnormal in Parkinson’s disease. Brain, 115, 199–210.
Bangert, A. S., Reuter-Lorenz, P. A., & Seidler, R. D. (2011). Dissecting the clock:
understanding the mechanisms of timing across tasks and temporal
intervals. Acta Psychologica, 136, 20–34.
Barkley, R. A., & Biederman, J. (1997). Towards a broader definition of the age of
onset criterion for attention deficit hyperactivity disorder. Journal of the
American Academy of Child and Adolescent Psychiatry, 36, 1204-1210.
Battelli, L., Walsh, V., Pascual-Leone A., & Cavanagh, P. (2008). The when
pathway explored by lesion studies. Current Opinions in Neurobiology, 18,
120–126.
Billon, M. & Semjen, A. (1995). The timing effects of accent production in
synchronization and continuation tasks performed by musicians and
nonmusicians. Psychological Research, 58, 206-217
Billon, M., Semjen, A., Cole, J., & Gautier, G. (1996). The role of sensory
information in the production of periodic finger tapping sequences.
Experimental Brain Research, 110, 117-130.
59
Block, R. A. (2003). Psychological timing without a timer: The roles of attention
and memory. In H. Helfrich (Ed.), Time and mind II (pp. 41- 60). Göttingen:
Hogrefe & Hube.
Block, R. A., Hancock, P. A., & Zakay, D. (2010). How cognitive load affects
duration judgments: a meta-analytic review. Acta Psychologica. 134, 330–
343.
Block, R. A., & Zakay, D. (2008). Timing and remembering the past, the present,
and the future. In S. Grondin (Ed.), Psychology of time (pp. 367–394).
Bingley, England: Emerald.
Breska, A., & Ivry, R. (2016). Taxonomies of timing: where does the cerebellum fit
in? Current Opinion in Behavioral Sciences, 282-288.
Brown, S. W. (2008). Time and attention: Review of the literature. In S. Grondin
(Ed.), Psychology of time (pp. 111-138). Bingley, U.K.: Emerald Group
Brown, S. W. (2006). Timing and executive function: bidirectional interference
between concurrent temporal production and randomization tasks. Memory
& Cognition, 34(7), 1464–1471.
Brown, S. W. (1997). Attentional resources in timing: interference effects in
concurrent temporal and nontemporal working memory tasks. Perception
and Psychophysics, 59 (7), 1118–1140.
Brown, S. W. (1985). Time perception and attention: the effects of prospective
versus retrospective paradigms and task demands on perceived
duration. Perception & Psychophysics, 38, 115–124.
Brown, S.W., & Benette, E. D (2002). The role of practice and automaticity in
temporal and non temporal dual –task performance. Psychological
Research, 66, 80-89.
Brown, S. W., & Merchant, S. M. (2007). Processing resources in timing and
sequencing tasks. Perception & Psychophysics, 69, 439-449.
Bueti, D. (2011). The sensory representation of time. Frontiers in Integrative.
Neuroscience, 5:34.
Buhusi, C. V, & Meck, W. H. (2009). Relative time sharing: new findings and an
extension of the resource allocation model of temporal processing.
60
Philosophical Transactions of the Royal Society B: Biological Sciences, 364
1875–85.
Buhusi, C. V., & Meck, W. H. (2005). What makes us tick? Functional and neural
mechanisms of interval timing. Nature Neuroscience, 6, 755–765.
Buonomano, D. V. (2000). Decoding temporal information: a model based on
short-term synaptic plasticity. Journal of Neuroscience, 20: 1129–114.
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: Biological Sciences, 364, 1865–1873.
Buonomano, D. V., & Karmarkar, U. R. (2002). How do we tell time?
Neuroscientist, 8, 42–51.
Buonomano, D. V., & Mauk, M. D. (1994). Temporal discrimination and the timing
of motor responses. Neural Computation, 6:38–55.
Buonomano, D. V., & Merzenich, M. M. (1995). Temporal information
transformed into a spatial code by a neural network with realistic properties.
Science 267, 1028-1030.
Burns, N. R., Nettelbeck, T., & McPherson, J. (2009). Attention and intelligence: A
factor analytic study. Journal of Individual Differences, 30(1), 44-57.
Burr, D. C., Cicchini, G M, Arrighi, R, & & Morrone, M. C. (2011). Spatiotopic
selectivity of adaptation-based compression of event duration”. Journal of
Vision, 11(2), 21, 1–9.
Burr, D. C., Tozzi, A., & Morrone, M. C. (2007). Neural mechanisms for timing are
spatially selective in real-world coordinates. Nature Neuroscience, 10, 423–
425.
Buzsáki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks.
Science, 304, 1926-1929.
Bååth, R., Tjostheim, T. A., & Lingonblad, M. (2016). The role of ececutive control
in rythmic timing att diferent tempi. Psychomonic Bulletin & Review, 1954-
1960.
61
Casini, L. & Macar, F. (1997). Effects of attention manipulation on judgments of
duration and on intensity in the visual modality. Memory & Cognition, 25,
812- 818.
Chaston, A., & Kingstone, A. (2004). Time estimation: The effect of cortically
mediated attention. Brain and Cognition, 55, (2), 286-289.
Chen, A. H., O’ Leary, D. J., & Howell, E. R. (2000). Near visual function in young
children. Part I: near point of convergence. Part II: amplitude of
accommodation. Part III: Near heterophoria.Ophthalmic and Psysiological
Optics, 20 (3), 185-198.
Chen, Y., Repp, B. H., & Patel, H. D. (2002). Spectral decomposition of variability
in synchronization and continuation tapping: comparisons between auditory
and visual pacing and feedback conditions. Human Movement Science, 21,
515-532.
Cheng, R. K., Dyke, A. G, McConnell, M. W., & Meck, W. H. (2011). Categorical
scaling of duration as a function of temporal context in aged rats. Brain
Research, 1381, 175–86.
Coull, J. T., Vidal, F., Nazarian, B., & Macar, F. (2004). Functional anatomy of the
attentional modulation of time estimation. Science, 303, 1506–1508.
Coyle, T. R. (2003). A review of the worst performance rule: Evidence, theory and
alternative hypotheses. Intelligence, 31, 567−587.
Creelman, C. D. (1962). Human discrimination of auditory duration. Journal of
the Acoustical Society of America, 34, 582–593.
Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience. New
York: Harper and Row.
Deary, I. J. (2000). Looking down on human intelligence: From psychometrics to
the brain. Oxford, UK: Oxford University Press.
Denckla, M. B., Rudel, R. G., Chapman, C., & Krieger, J. (1985). Motor proficiency
in dyslexic children with and without attentional disorders. Archives of
Neurology, 42, 228–231.
Dragoi, V., Staddon, J. E., Palmer R. G., & Buhusi, C. V. (2003). Interval timing as
an emergent learning property. Psychological Review, 110, 126–144.
62
Drewing, K., Hennings, M., & Aschersleben, G. (2002). The contribution of tactile
reaffernce to temporal regularity during bimanual finger tapping.
Psychological Research, 66, 60-70.
Droit-Volet, S., Meck, W. H., & Penney, T. B. (2007). Sensory modality and time
perception in children and adults. Behavioural Processes, 74, 244-250.
Eagleman, D. M. (2008). Human time perception and its illusions. Current
Opinion in Neurobiology, 18, 131–136,
Fitts, P. M, & Posner, M. I. Human Performance. Brooks/Cole Pub. Co; Belmont,
CA: 1967.
Flanagan, J. R., Vetter, P., Johansson, R. S., & Wolpert, D. M. (2002). Prediction
precedes motor control in motor learning. Current Biology, 13, 146-150.
Forsman, L., Madison, G., & Ullén, F. (2009). Neuroticism is correlated with drift
in serial interval production. Personality and Individual Differences, 47,
229-232.
Fortin, C., & Breton, R. (1995). Temporal interval production and processing in
working memory. Perception & Psychophysics, 57 (2), 203–215.
Fortin, C., & Rousseau, R. (1998). Interference from short-term memory
processing on encoding and reproducing brief durations. Psychological
Research, 61 (4), 269–276.
Fujioka, T., Trainor, L. J., Large, E. W., & Ross, B. (2012). Internalized timing of
isochronous sounds is represented in neuromagnetic beta oscillations. The
Journal of Neuroscience, 32 (5), 1791-1802.
Getty, D. (1975). Discrimination of short temporal intervals: A comparison of two
models. Perception & Psychophysics, 18, 1– 8.
Gibbon, J. (1977). Scalar expectancy theory and Weber’s Law in animal timing.
Psychological Review, 84, 279–325.
Gibbon, J., Church, R. M., & Meck, W. H. (1984). Scalar timing in memory. In J.
Gibbon & L. Allan (Eds.), Annals of the New York Academy of sciences (Vol.
423). Timing and time perception (pp. 52–77). New York: New York
Academy of Sciences.
63
Gibbon, J., Malapani, C., Dale, C. L., & Gallistel, C. (1997). Toward a neurobiology
of temporal cognition: advances and challenges. Current Opinion in
Neurobiology, 7, 170–184.
Gómez, J., Marín-Méndez, J., Molero, P., Atakan, Z., & Ortuño, F. (2014). Time
perception networks and cognition in schizophrenia: a review and a
proposal. Psychiatry Research, 220, 737–744.
Grondin, S. (2010). Timing and time perception: a review of recent behavioral and
neuroscience findings and theoretical directions. Attention. Perception &
Psychophysics, 72, 561–582.
Grondin, S. (2001). From physical time to the first and second moments of
psychological time. Psychological Bulletin, 127, 22–44.
Grondin, S., Bisson, N., Gagnon, C., Gamache, P. L., & Matteau, A. A. (2009).
Little to beexpected from auditory training for improving visual temporal
discrimination. NeuroQuantology, 7, 95–102.
Grondin, S., Gamache, P.L., Tobin, S., Bisson, N., & Hawke, L. (2008).
Categorization of brieftemporal intervals: An auditory processing context
impair visual performances. Acoustical Science & Technology, 29, 338–340.
Grondin, S., & Macar, F. (1992). Dividing attention between temporal and
nontemporal tasks: A performance operating characteristics – POC –
analysis. In F. Macar, V. Pouthas, & W. Friedman (Eds.), Time, action,
cognition: Towards bridging the gap (pp. 119-128). Dordrecht, The
Netherlands: Kluwer.
Grondin, S., & Rammsayer, T. H. (2003). Variable foreperiods and temporal
discrimination. The Quarterly Journal of Experimental Psychology, 56 (4),
731–765.
Grondin, S., & Rousseau, R. (1991). Judging the relative duration of multimodal
short empty time intervals. Perception Psychophysics, 49, 245–256.
Grondin, S., & Ulrich, R. (2011). Duration discrimination performance: No cross-
modal transfer from audition to vision even after massive perceptual
learning. Frontiers in Psychology, 5, 140.
64
Gualtieri, T. C., Johnson, L. G., & Benedict, K. B. (2006). Neurocognition in
depression: Patients on and off medication versus healthy comparison
subjects. Journal of Neuropsychiatry and Clinical. Neuroscience, 18, 217–
225.
Harrington, D. L., Castillo, G. N., Fong, C. H., & Reed, J. D. (2011). Neural
underpinnings of distortions in the experience of time across senses.
Frontiers in Integrative Neuroscience, 5, 32.
Harrington, D. L., Haaland, K. Y., & Hermanowicz, N. (1998). Temporal
processing in the basal ganglia. Neuropsychology, 12, 3–1210.
Harrington, D. L., Zimbelman, J. L, Hinton, S. C, & Rao, S. M. (2010). Neural
modulation of temporal encoding, maintenance, and decision processes.
Cereberal Cortex, 20, 1274–85.
Helmbold, N., & Rammsayer, T. H. (2006). Psychometric intelligence as measured
by speed and power tests. Journal of Individual Differences,, 20-37.
Helmbold, N., Troche, S., & Rammsayer, T. (2007). Processing of temporal and
nontemporal information as predictors of psychometric intelligence: A
structural-equation-modeling approach. Journal of Personality, 75 (5), 985-
1006.
Hinton, S. C, & Meck, W. H. (2004). Frontal-striatal circuitry activated by human
peak-interval timing in the supraseconds range. Cognitive. Brain Research,
21, 171–82.
Holm, L., Karampela, O. Ullén, F., & Madison, G. (2016). Executive control and
working memory are involved in sub-second repetitive motor timing.
Experimental Brain Research, 4839-4846.
Holm, L., Ullén, F., & Madison, G. (2013). Motor and executive control in
repetitive timing of brief intervals. Journal of Experimental Psychology:
Human Perception and Performance, 39, 365-380.
Holm, L., Ullén, F., & Madison, G. (2011). Intelligence and temporal accuracy of
behavior: unique and shared associations between intelligence, reaction time
and motor timing. Experimental Brain Research, 214, 175- 183.
Ivry, R. B., & Hazeltine, R. E. (1995). Perception and production of temporal
intervals across a range of duration: Evidence for a common timing
65
mechanism. Journal of Experimental Psychology: Human Perception and
Performance, 21, 3–18.
Ivry, R. B., Keele, S. W., & Diener, H. C. (1988). Dissociation of the lateral and
medial cerebellum in movement timing and movement execution.
Experimental Brain Research, 73, 167–180.
Ivry, R. B., Schlerf, J. E. (2008). Dedicated and intrinsic models of time
perception. Trends in Cognitive Science, 12 (7), 273–280.
Ivry, R. B., & Spencer, R. M. C. (2004). The neural representation of time. Current
Opinion in Neurobiology, 14, 225-232.
Ivry, R. B., Spencer, R. M. C., Zelaznik, H. N., & Diedrichsen, J. (2002). The
cerebellum and event timing. Annals of the New York Academy of Sciences,
978, 302–317.
Jahanshahi, M., Jones, C. R. G., Dirnberger, G., & Frith, C. D. (2006). The
substantia nigra pars compacta and temporal processing. Journal of
Neuroscience, 26, 12 266–12 273.
James, W. (1950). The principles of psychology. New York: Dover. (Original work
published 1890)
Jaskowski, P., Purszewicz, A., & Swidzinski, P. (1990). VEP latency and some
properties of simple motor reaction-time distribution. Psychological
Research, 52, 28–34.
Jensen, A. R. (2006). Clocking the mind: mental chronometry and individual
differences. Oxford, UK: Elsevier.
Jingu, H. (1989). Numerical processing of duration in temporal tracking behavior.
Japanese Psychological Research, 31, 169-178.
Jucaité, A., Dahlström, A., Farde, L., Forssberg, H., & Madion, G. (2008). Time
perception and production in children with ADHD correlates to the central
dopaminergic transmission (submitted for publication).
Justus, T. J. & Ivry, R., B. (2001). The cognitive neuropsychology of the
cerebellum. International Review of Psychiatry, 13, 276-282.
66
Karampela, O., Holm, L., & Madison G. (2015). Shared timing variability in eye
and finger movements increases with interval duration: Support for a
distributed timing system below and above 1 second. The Quarterly Journal
of Experimental Psychology, 68, 1965-1980.
Kaplan, B. J., Wilson, B. N., Dewey, D., & Crawford, S. G. (1998). DCD may not be
a discrete disorder. Human Movement Science, 17, 471-490.
Karmarkar, U. R., & Buonomano, D. V. (2007). Timing in the absence of clocks:
encoding time in neural network states. Neuron, 53, 427–438.
Karmarkar, U. R., & Buonomano, D. V. (2003). Temporal specificity of perceptual
learning in an auditory discrimination task. Learning & Memory, 10(2), 141-
7.
Kauranen, K., & Vanharanta, H. (1996). Influences of aging, gender, and
handedness on motor performance of upper and lower extremities.
Perceptual and Motor Skills, 82, 515-525.
Kee, D. W., Morris, K., Bathurst, K., & Hellige, J. (1986). Lateralized interference
in finger tapping: Comparisons of rate and variability measures under speed
and consistency tapping instructions. Brain and Cognition, 5, 268 –279.
Keele, S. W., & Hawking, H. L. (1982). Exploration of Individual differences
relavenat to high level skills. Journal of Motor Behavior, 3-23.
Keele, S. W., Pokorny, R. A., Corcos, D. M., & Ivry, R. B. (1985). Do perception and
motor production share common timing mechanisms: A correlational
analysis. Acta Psychologica, 60, 173-191.
Killeen, P. R., & Weiss, N. A. (1987). Optimal timing and the Weber function.
Psychological Review, 94, 455-468.
Klimesch, W., Freunberger, R, Sauseng, P, & Gruber, W. A. (2008). Short review of
slow phase synchronization and memory: evidence for control processes in
different memory systems? Brain Research, 1235, 31–44.
Koch, G., Oliveri, M., Carlesimo, G. A., & Caltagirone C. (2002). Selective deficit of
time perception in a patient with right prefrontal cortex lesion. Neurology,
59, 658–1659.
67
Kolers, P. A., & Brewster, J. M. (1985). Rhythms and responses. Journal of
Experimental Psychology. Human Perception Performance, 11,150–167.
Krampe, R. T., Kleigl, R., & Mayr, U. (2005). Timing, Sequencing, and Executive
Control in Repetitive Movement Production. Journal of Experimental
Psychology, 31, 379–397.
Kristofferson, A. B. (1980). A quantal step function in duration discrimination.
Perception & Psychophysics, 27, 300–306.
Lamotte, M., Izaute, M., & Droit-Volet, S. (2012). Awareness of time distortions
and its relation with time judgment: A metacognitive approach.
Consciousness and Cognition, 2, 835-842.
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 & Review 16, 382–389
Lejeune, H. (1998). Switching or gating? The attentional challenge in cognitive
models of psychological time. Behavioral. Processes, 44, 127–145.
Lewis, P. A., & Miall, R. C. (2006). Remembering the time: A continuous
clock. Trends in Cognitive Sciences, 10 (9), 401–406.
Lewis, P. A., & Miall, R. C. (2003a) Brain activation patterns during measurement
of sub-second and supra-second intervals. Neuropsychologia, 41(12):1583–
1592.
Lewis, P. A., & Miall, R. C. (2003b). Distinct systems for automatic and cognitively
controlled time measurement: Evidence from neuroimaging. Current
Opinion in Neurobiology, 13 (2), 250–255.
Lorås, H., Sigmundsson, H., Talcott, J. B., Öhberg, F., & Stensdotter, A. K. (2012),
Timing continuous or discontinuous movements across effectors specified by
different pacing modalities and intervals. Experimental Brain
Research, 220, 335–347.
Lustig, C., Matell, M. S., & Meck, W. H. (2005). Not “just” a coincidence: frontal-
striatal synchronization in working memory and interval
timing. Memory, 13, 441–448.
68
Macar, F., Grondin, S., & Casini, L., (1994). Controlled attention sharing
influences time estimation. Memory & Cognition, 22 (6), 673–686.
Madison, G. (2006). Duration-specificity in the long range correlation of human
serial interval production. Physica D, 216, 301−306.
Madison, G., (2001). Variability in isochronous tapping: Higher order
dependencies as a function of intertap interval. Journal of Experimental
Psychology: Human Perception and Performance, 27, 411– 421.
Madison, G., & Delignières, D. (2009a). Auditory feedback affects the long-range
correlation of isochronous serial interval production. Support for a closed-
loop or memory model of timing. Experimental Brain Research, 193, 519-
527.
Madison, G., Forsman, L., Blom, Ö., Karabanov, A., & Ullén, F. (2009).
Correlations between general intelligence and components of serial timing
variability. Intelligence, 37, 68- 75.
Madison, G., Karampela, O., Ullen, F., & Holm, L. (2013). Effects of practice
on variability in an isochronous serial interval production task:
Asymptotical levels of tapping variability training are similar to those
of musicians. Acta Psychologica, 1, 119-128.
Maes, P. J., Wanderley, M. M., & Palmer C. (2015). The role of working memory in
the temporal control of discrete and continuous movements. Experimental.
Brain Research, 233, 263–27.
Maniadakis, M., & Trahanias, P. E. (2014). Time models and cognitive processes: a
review, Frontiers in NeuroRobotics, Research Topic: Towards embodied
artificial cognition. doi: 10.3389/fnbot.2014.00007
Matell, M., & Meck, W. (2004). Cortico-striatal circuits and interval timing:
coincidence detection of oscillatory processes. Cognitive Brain Research, 21,
139-170.
Matell, M. S., Meck, W. H., & Nicolelis, M. A. L. (2003). Interval timing and the
encoding of signal duration by ensembles of cortical and striatal neurons.
Behavioral Neuroscience, 117, 760–773
Mattes, S., & Ulrich, R. (1998). Directed attention prolongs the perceived duration
of a brief stimulus. Perception & Psychophysics, 60, 1305-1317.
69
Matthews, W. J., & Grondin, S. (2012). On the replication of Kristofferson’s (1980)
quantal timing for duration discrimination: some learning but no quanta and
not much of a Weber constant. Attention, Perception and Psychophysics, 74,
1056-1072.
Mauk, M. D., & Buonomano, D. V. (2004). The neural basis of temporal
processing. Annual. Revisions in Neuroscience, 27, 307–340.
McFarland, K., & Ashton, R. (1978). The influence of concurrent task difficulty on
manual performance. Neuropsychologia, 16 (6), 735-741.
Meck, W. H. (2006a). Frontal cortex lesions eliminate the clock speed effect of
dopaminergic drugs on interval timing. Brain Research, 1108, 157–167.
Meck, W. H. (2006b). Neuroanatomical localization of an internal clock: a
functional link between mesolimbic, nigrostriatal, and mesocortical
dopaminergic systems. Brain Research, 1109, 93–107.
Meck, W. H. (1996). Neuropharmacology of timing and time perception. Cognitive
Brain Research, 3, 227–242.
Meck, W. H., & Benson, A. M. (2002). Dissecting the brains internal clock: How
frontal-striatal circuitry keeps time and shifts attention. Brain and
Cognition, 48, 195-211.
Meck, W. H., Penney, T. B, & Pouthas, V. (2008). Cortico-striatal representation of
time in animals and humans. Current Opinions in Neurobiology, 18, 145–
152.
Meegan, D. V., Aslin, R. N. & Jacobs, R. A. (2000). Motor timing learned without
motor training. Nature Neuroscience, 3, 860-862.
Merchant, H., Harrington, D. L., & Meck, W. H (2013). Neural basis of the
perception and estimation of time. Annual Revisions in Neuroscience, 36,
313–336.
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: Jackson JAMJL,
editor. Time, mind, and behavior.Berlin: Springer Verlag; pp. 20–54.
70
Michon, J. A. (1966). Tapping regularity as a measure of perceptual motor load.
Ergonomics, 9, 401-412.
Miyake, Y., Onishi, Y., & Poppel, E. (2004). Two types of anticipation in
synchronization tapping. Acta Neurobiologiae Experimentalis, 64 (3), 415–
426.
Morrone, C., Ross, J., & Burr, D. (2005). Saccade cause compression of time as
well as space. Nature Neuroscience, 8, 950-954.
Nagarajan, S., Blake, D. T., Wright, B. A., Byl, N., & Merzenich, M. M. (1998).
Practice-related improvements in somatosensory interval discrimination are
temporally specific but generalize across skin location, hemisphere, and
modality. The Journal of Neuroscience, 18, 1559-1570.
Nagasaki, H. (1990). Rhythm in periodic tapping is centrally produced. Perceptual
and Motor Skills, 71, 985-986.
Nakamura, J., & CSíkszentmihályi, M. (2002). The concept of flow. Handbook of
positive Psychology, 89-105.
Nichelli, P., Always, D., & Grafman, J. (1996). Perceptual timing in cerebellar
degeneration. Neuropsychologia, 34, 863–871.
Ogden, R. S., Salominaite, E., Jones, L. A, Fisk J. E & Montgomery, C. (2011). The
role of executive functions in human prospective interval timing. Acta
Psychologica, 137 (3), 352–358.
Pariyadath, V., & Eagleman, D. (2007). The effect of predictability on subjective
duration. PLoS ONE 2(11): e1264. doi:10.1371/ journal.pone.0001264
Piek, J. P., Pitcher, T. M., & Hay, D. A. (1999). Motor coordination and
kinaesthesis in boys with attention deficit hyperactivity disorder.
Developmental Medicine and Child Neurology, 41(3), 159-165.
Pouthas, V., George, N., Poline, J. B., Pfeuty, M., VandeMoorteele, P. F.,
Hugueville, L., Ferrandez, A. M., Lehericy, S., Lebihan, D., & Renault, B.
(2005). Neural network involved in time perception: an fMRI study
comparing long and short interval estimation. Human Brain Mapping, 25,
433-441.
71
Rammsayer, T. H. (2008). Neuropharmacological approaches to human timing. In
S. Grondin (Ed.), Psychology of time (pp. 295-320). Bingley, U.K.: Emerald
Group.
Rammsayer, T. H. (2006). Effects of pharmacologically induced changes in NMDA
receptor activity on human timing and sensorimotor performance. Brain
Research, 1073, 407–416.
Rammsayer, T. H. (1999). Neuropharmacological evidence for different timing
mechanisms in humans. Quarterly Journal of experimental Psychology,
52, 273–286.
Rammsayer, T. H. (1997). Are there dissociable roles of the mesostriatal and
mesolimbocortical dopamine systems on temporal information processing in
humans? Neuropsychobiology, 35 (1):36–45.
Rammsayer, T. H (1993). On dopaminergic modulation of temporal information
processing. Biological Psychology, 36, 209–222.
Rammsayer, T. H. (1992). Effects of benzodiazepine-induced sedation on temporal
processing. Human Psychopharmacology: Clinical and Experimental
Psychology, 7 (5):311–318.
Rammsayer, T. H, & Brandler, S. (2007) Performance on temporal information
processing as an index of general intelligence. Intelligence, 35,123-139.
Rammsayer, T. H, & Brandler, S. (2002). On the relationship between general
fluid intelligence and psychophysical indicators of temporal resolution in the
brain. Journal of Research in Personality, 36,507-530.
Rammsayer, T. H, Lima, S. D. (1991). Duration discrimination of filled and empty
auditory intervals: Cognitive and perceptual factors. Perception &
Psychophysics, 50 (6), 565–574.
Rammsayer, T. H., & Troche, S. J. (2014). In search of the internal structure of the
processes underlying interval timing in the sub-second and the second
range: A confirmatory factor analysis approach. Acta Psychologica. 147, 68–
74.
Rammsayer, T. H, & Ulrich, R. (2005). No evidence for qualitative differences in
the processing of short and long temporal intervals. Acta Psychologica, 120
(2), 141–171.
72
Rao, S. M., Harrington, D. L., Haaland, K. Y., Bobholz, J. A., Cox, R. W, & Binder,
J. R. (1997). Distributed neural systems underlying the timing of
movements. Journal of Neuroscience, 17, 5528–5535.
Ren, X., Schweizer, K., & Xu, F. (2013). The sources of the relationship between
sustained attention and reasoning. Intelligence, 41, 132-128.
Repp, B. H. (2005). Sensorimotr synchronization: A review of the tapping
literature. Psychonomic Bulletin & Review, 12, 969-992.
Repp, B. H. (1998). The detectability of local deviations from a typical expressive
timing pattern. Music perception, 14, 419-444.
Schirmer, A. (2004). Timing speech: A review of lesion and neuroimaging
findings. Cognitive Brain Research, 21, 269–278.
Schmidt, R. A., & Wrisberg, C. A. (2008). Motor learning and performance (4th
ed.). Champaign, IL: Human Kinetics Publishers. (Translations into
Traditional Chinese, Polish, Greek, Japanese, Persian, Turkish, and an
electronic version).
Schweizer, K., & Moosbrugger, H. (2004). Attention and working memory as
predictors of intelligence. Intelligence, 32, 329-347
Schweizer, K., Moosbrugger, H., & Goldhammer, F. (2005). The structure of the
relationship between attention and intelligence. Intelligence, 33, 589-611.
Semjen, A., Vorberg, D., & Schulze, H. H. (1998). Getting synchronized with the
metronome: Comparisons between phase and period
correction. Psychological Research, 61, 44–55.
Serences, J. T., & Yantis, S. (2006). Selective visual attention and perceptual
coherence. Trends in Cognitve Science, 10, 38–45.
Sergent, V., Hellige, J. B. & Cherry, B. (1993). Effects of responding hand and
concurrent verbal processing on time-keeping and motor-implementation
processes. Brain and Cognition, 23(2), 243–262.
Spencer, R. M, & Ivry, R. B. (2009) Sequence learning is preserved in individuals
with cerebellar degeneration when the movements are directly cued. Journal
of Cognitive neuroscience, 21 (7), 1302–1310.
73
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: Biological Sciences, 364 (1525), 1853–
1863.
Studenka, B. E., & Zelaznik, H. N. (2011). Circle drawing does not exhibit auditory-
motor synchronization. Journal of. Motor. Behavior, 43, 185–191.
Taatgen, N., Rijnvan, H., & Anderson, J. (2007). An integrated theory of
prospective time interval estimation: the role of cognition, attention and
learning. Psychological Review, 114, 577–598.
Thomas, E. A. C., & Weaver, W. B. (1975). Cognitive processing and time
perception. Perception & Psychophysics, 17, 363-367.
Treisman, M. (1963). Temporal discrimination and the indifference interval:
Implications for a model of the “internal clock”. Psychological Monographs,
77, 576, 1-31.
Turvey, M. T. (1977). Preliminaries to a theory of action with reference to vision. In
R. E. S. J. Bransford (Ed.), Perceiving, acting and knowing (pp. 211-265).
Hillsdale, NJ: Lawrence Erlbaum.
Ullén, F., Forsman, L., Blom, Ö, Karabanov, A., & Madison, G. (2008). Intelligence
and variability in a simple timing task share neural substrates in the
prefrontal white matter. The Journal of Neuroscience, 28, 4239- 4243.
Ullén, F., & Madison, G. (2009) There is a bottom-up relation between temporal
accuracy and intelligence - further arguments from studies of correlations
between tapping variability and intelligence during high and low motivation.
In: International Society for Intelligence Research, Madrid
Ullén, F., Söderlund, T., Kääriä, L., & Madison, G. (2012). Bottom-up mechanisms
are involved in the relation between accuracy in timing tasks and intelligence
- further evidence using manipulations of state motivation. Intelligence, 40,
100-106.
Valera, E. M., Spencer, R. M. C., Zeffiro, T. A, Makris, N., Spencer, T. J, & Faraone,
S. V. (2010). Neural substrates of impaired sensorimotor timing in adult
attention-deficit/hyperactivity disorder. Biological Psychiatry, 68, 359–
367.
74
Vorberg, D., & Wing, A. (1996). Modeling variability and dependence in timing. In:
Heuer H, Keele SW, editors. Handbook of perception and action. Vol. 2.
London: Academic Press, 181–262.
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.
Wiener, M., Turkeltaub, P., & Coslett, H. B. (2010). The image of time: a voxel-
wise meta-analysis. Neuroimage, 49(2), 1728-1740.
Wing, A. M., & Kristofferson, A. B. (1973). The timing of interresponse intervals.
Perception & Psychophysics, 13, 455– 460.
Woehrle, J. L., & Magliano, J. P. (2012). Time flies faster if a person has a high
working-memory capacity. Acta Psychologica, 139(2):314-319.
Woodrow, H. (1930). The reproduction of temporal intervals. Journal of
Experimental Psychology, 13, 473-499.
Wright, B. A., Buonomano, D. V., Mahncke, H. W., & Merzenich, M. M. (1997).
Learning and generalization of auditory temporal-interval discrimination in
humans. The Journal of Neuroscience, 17, 3956-3963.
Wulf, G. (2010). Social-comparative feedback affects motor learning. Psychology
Press, 4, 738-749.
Yamazaki, T., Tanaka, S. (2005). Neural modeling of an internal clock. Neural
Computation, 17, 1032–1058.
Zakay, D. (2000). Gating or switching? Gating is a better model of prospective
timing. Behavioural Processes, 50, 1–7.
Zakay, D. (1989). Subjective time and attentional resource allocation: an
integrated model of time estimation. In I. Levin & D. Zakay (Eds.), Time and
human cognition: A life-time perspective (pp. 365–397). Amsterdam: North-
Holland.
Zakay, R. A., & Block (2004). Prospective and retrospective duration judgments:
An executive-control perspective. Acta neurobiologiae experimentalis, 64,
319–328.
75
Zakay, D., & Block, R. A. (1995). An attentional gate moded of prospective time
estimation. In: M. Richelle (Ed.), Time and the Dynamic Control of
Behavior: IPA Symposium; Liége, November 7-8, 1994. France: Université
Liége.
Zarco, W., Merchant, H., Prado, L., & Mendez, J. C. (2009) Subsecond timing in
primates: comparison of interval production between human subjects and
rhesus monkeys. Journal of Neurophysiology, 102, 3191–3202.
Zelanti, P. & Droit-Volet, S. (2012). Auditory and visual differences in time
perception. An investigation from a developmental perspective with
neuropåsychological tests. Joural of Experimental Child Psychology, 112,
296-311.
Zelaznik, H. N., Spencer, R. M., & Doffin, J. (2000). Temporal precision in tapping
and circle drawing movements at preferred rates is not correlated: Further
evidence against timing as a general purpose ability. Journal of Motor
Behavior, 32, 193-199.
Zelaznik, H. N., Spencer, R. M., & Ivry, 2002. Dissociation of explicit and implicit
timing in repetitive tapping and drawing movements. Journal of Experiment
Psychology: Human perception & Performance, 28, 575-588.
Zelkind, I. (1973). Factors in time estimation and a case for the internal clock. The
Journal of General Psychology, 88, 295-301.
76
ACKNOWLEDGEMENTS
‘’The essence of all beautiful art, all great art is gratitude’’
Friedrich Nietzsche
After an intensive period of several months, today is finally the day! I am
really glad to write this note of thanks, the finishing touch on my thesis. This
thesis becomes a reality with the kind support of many individuals. I would
like to reflect on these people who have supported and helped me so much
throughout this period.
Firstly, I would like to express my gratitude to my principal supervisor, Guy
Madison, for giving me the chance to become a PhD student, as well as, for
all the constructive suggestions, availability and support throughout this
journey. I am also grateful to my co-supervisor, Linus Holm, for all the
guidance, support and helpful criticism through my PhD research studies. In
addition, I would like to thank my other co-supervisor, Fredrik Ullén, for the
really nice collaboration and all insightful suggestions in article writing.
Thank you all three for giving me the chance to share of your exceptional
scientific knowledge.
A big thank you goes to all the participants, for taking part in the studies.
Without you, there would be no thesis.
My gratitude goes to those who spent time reading this thesis at an earlier
stage. Anders Friberg, Anna-Maria Johansson, Erik Marsja, Johan Eriksson
and Maria Grazia Carelli, I am very grateful for the invaluable comments and
suggestions for improvement.
My appreciation also extends to all the colleagues at the Department of
Psychology at Umeå University. Particularly, I would like to thank all my
PhD colleagues for the encouragement, support, and all the fun times.
Elisabeth, once a roommate always a roommate! Your support has been
invaluable along the PhD journey. Thanks for being a good friend and a great
colleague. Thank you for helping through the hard times and then laughing
together when the hard times have passed. You are a unique person!
77
Johan, you have always trying to encourage me. I like your GO FOR IT
attitude. Thank you for listening, offering me advice, and for all the fun
times! You are a great person!
Kalyani, thank you for being part of this journey. All the help, the chats, and
the beautiful moments we shared, have been fundamental in supporting me
during these stressful moments. I really appreciate all you have done for me.
Thanks for your friendship!
Anna –Maria, I have to thank you again, as a friend this time. Your
genuine kindness is so exceptional!
Eva, our conversations on Lync should be saved for ever. Only by reading
those one could actually understand, how important social support is, during
the writing of the thesis. I do not forget our favorite word .Thank you and I
wish you the best luck!
Sincere thanks goes also to Maria Nordin, for all the support and the
valuable advice whenever needed.
I would like to send my sincere love, to the rest of my big Greek family and
friends. Thank you, one and all, for your love, support, and patience with me.
My godparents deserve a special note of thanks for providing me a blissful
and carefree childhood, as well as, for supporting me in acquiring the skills
and tools, in order to get into the higher education. I am also grateful to my
mother in law, for helping with all the housework and for taking care of
Alkinoos, while I was mentally and physically absent during the writing of
this thesis.
A very special thanks to you Giorgos, for designing the cover of the thesis.
How many talents can a person possibly have?
Aggele, thanks for all the help with the graphs and the funny moments in
your office! Please keep the Oly file.
I would also like to thank my parents for their endless support. You have
taught me about hard work and persistence. Bασίλη και Γεωργία
Καράμπελα, σας ευχαριστώ. Δεν υπάρχουν λόγια για να περιγράψω τις
θυσίες που κάνατε και τα όνειρα που αναγκάστηκατε να αφήσετε για να μου
δώσετε την ευκαιρία να πραγματοποιήσω τα δικά μου. Ελπίζω να είστε
περήφανοι για εμένα όπως είμαι εγώ για εσάς.
78
To my beloved brother, thank you for your love and support, as well as, for
the understanding you showed to me in every circumstance. You deserve the
best!
Alkinoe, my son, thank you for teaching me every day what life is all about
and for making all the troubles go away with just one smile. If you could see
yourself through my eyes, you would understand how special you are to me.
You have been a blessing from the start! Mommy is back now
Last but certainly not least, I must acknowledge with deep thanks my dearest
husband. Gianni, you have always been the driving force in our relationship.
You have given me confidence and your love has been motivated me in so
many different ways. I am sure that I could not face all the life’s challenges if
you have not been by my side. You are my personal compass, always pointing
the correct direction. Well, actually I don’t know if I can find any good words
that can express my gratitude towards you. My heart and soul want to say so
many things. Could those be possibly summed up in just two words?
Ευχαριστώ, Σ’αγαπώ.
Olympia Karampela,
Umeå, January, 2017.
top related