Embodiment and the origin of interval timing: kinematic and electromyographic data

Abstract

Recent evidence suggests that interval timing (the judgment of durations lasting from approximately 500 ms. to a few minutes) is closely coupled to the action control system. We used surface electromyography (EMG) and motion capture technology to explore the emergence of this coupling in 4-, 6-, and 8-month-olds. We engaged infants in an active and socially relevant arm-raising task with seven cycles and response period. In one condition, cycles were slow (every 4 s); in another, they were fast (every 2 s). In the slow condition, we found evidence of time-locked sub-threshold EMG activity even in the absence of any observed overt motor responses at all three ages. This study shows that EMGs can be a more sensitive measure of interval timing in early development than overt behavior.

Full text

1 3
Exp Brain Res (2017) 235:923–930
DOI 10.1007/s00221-016-4842-y
RESEARCH ARTICLE
Embodiment and the origin of interval timing: kinematic
and electromyographic data
Caspar Addyman1 · Sinead Rocha2 · Lilian Fautrelle3 · Robert M. French4 ·
Elizabeth Thomas5 · Denis Mareschal2
Received: 19 August 2016 / Accepted: 18 November 2016 / Published online: 9 December 2016
© The Author(s) 2016. This article is published with open access at Springerlink.com
Keywords Interval timing · Infants · Electromyography ·
Embodiment · Open Data · Open Materials
Introduction
Interval timing concerns our ability to judge the length
of events taking from a few seconds up to a few min-
utes (Buhusi and Meck 2005; Grondin 2008; Zakay and
Block 1997). It is different from precision timing (occur-
ring on a scale of less than 500 ms), which is involved
in online motor control, and from long-term time percep-
tion (occurring on a scale of hours) involved in culturally
specific notions of time. Interval timing or the “sense” of
time passing is essential in structuring our interactions
with the physical and social worlds. It is an ability that
we share with many other species, and which appears
to be present from early in human development. Indeed,
over the last 15 years, substantial evidence using heart-
rate measures (Colombo and Richman 2002), ERP meas-
ures (Brannon et al. 2004, 2008), looking time measures
(Brannon et al. 2007), and eye-tracking measures (Addy-
man et al. 2014) has established that infants as young as
4 months old are sensitive to the unexpected interrup-
tion of a temporally regular event on the scale of several
seconds.
But where does this ability come from? Our answer to
this question starts from the view that action structures
the sensory world. This is a classic developmental view
that resonates with both the work of Piaget (e.g., 1952,
1957) and Eleanor Gibson (1969), as well as in more con-
temporary embodied approaches to perceptual and cogni-
tive development (e.g., Thelen and Smith 1996; Goldfield
1995). Addyman et al. (2011; French et al. 2014) argue that
the ubiquitous repetitive action cycles observed in young
Abstract Recent evidence suggests that interval timing (the
judgment of durations lasting from approximately 500 ms.
to a few minutes) is closely coupled to the action control
system. We used surface electromyography (EMG) and
motion capture technology to explore the emergence of this
coupling in 4-, 6-, and 8-month-olds. We engaged infants in
an active and socially relevant arm-raising task with seven
cycles and response period. In one condition, cycles were
slow (every 4 s); in another, they were fast (every 2 s). In
the slow condition, we found evidence of time-locked sub-
threshold EMG activity even in the absence of any observed
overt motor responses at all three ages. This study shows
that EMGs can be a more sensitive measure of interval tim-
ing in early development than overt behavior.
* Caspar Addyman
c.addyman@gold.ac.uk
1 Department of Psychology, Goldsmiths, University
of London, New Cross, London SE14 6NW, UK
2 Centre for Brain and Cognitive Development, Department
of Psychological Sciences, Birkbeck University of London,
London WC1E 7HX, UK
3 Unité de Formation et de Recherche en Sciences et
Techniques des Activités Physiques et Sportives, Université
Paris Ouest, Nanterre La Défense, Nanterre, France
4 UMR 5022, Laboratoire d’Etude de l’Apprentissage et
du Développement, Centre National de la Recherche
Scientifique (CNRS), 21065 Dijon, France
5 Unité de Formation et de Recherche en Sciences et
Techniques des Activités Physiques et Sportives, Institut
National de la Santé et de la Recherche Médicale
(INSERM), U1093, Cognition, Action et Plasticité Sensori
Motrice, Université de Bourgogne, Campus Universitaire,
21078 Dijon, France
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

924 Exp Brain Res (2017) 235:923–930
1 3
infants (e.g., Thelen 1979, 1981) can help calibrate tempo-
ral durations on the scale of interval timing.
There is, in fact, substantial evidence from studies with
children with attention deficit and hyperactivity disorder
(ADHD) and with adults that motor actions and temporal
perception can mutually influence one another (see Kran-
jec and Chatterje 2010 for a full review). Firstly, conscious
actions can alter adults’ perception of time (Gavazzi et al.
2013). For example, Haggard et al. (2002) showed that
voluntary initiation of a button press shortened the time to
the perceived onset of a resulting stimulus. This effect was
reversed when movement was involuntarily induced with
transcranial magnetic stimulation. Conversely, temporal
awareness can affect the motor system. For example, Thom-
aschke and Dreisbach (2013) found that a predictable tem-
poral pattern could improve the accuracy of motor responses
but did not improve the accuracy of temporal judgments.
Finally, recent work (e.g., Fautrelle et al. 2015; Carlini and
French 2014) has revealed that, in adults, regular repeated
motor activity improves interval timing accuracy at novel
time scales. Participants’ motor responses to a regular target
were improved by motor training or imagined motor train-
ing with a (different) regular target but not by mere obser-
vation or practice with an irregular target. Taken together,
these studies suggest that, at least in adults, there is a close
coupling between repeated regular movements and subse-
quent temporal judgment accuracy. Finally, children with
ADHD show deficits in both interval timing and in motor
coordination tasks, perhaps because both time perception
and motor coordination depend on a right hemispheric
fronto–striato–cerebellar network (Smith et al. 2003).
Studying the emergence of this coupling in infants is dif-
ficult because of their limited motor control. Thus, in the
current article, we use surface electromyography (EMG)
as an implicit measure of temporal anticipation and motor
activity in young infants. Surface EMG recordings are non-
invasive measures that allow the study of muscular activ-
ity through the electrical signal it emits. From the point
of view of motor control, surface EMGs provide a win-
dow onto the neural commands used by the central nerv-
ous system to pilot the body. Importantly, EMG analyses
allow us to measure anticipatory muscle activity and antici-
patory postural adjustments (Bouisset and Zattara 1987;
Hadders-Algra 2005; Thelen and Smith 1998; Witherington
et al. 2002). These are essential for effective motor control
because of the electromechanical delay in sending a com-
mand and realizing that command in the physical body
(Schenau et al. 1995). In our case, EMG recordings and
analyses will enable us to identify muscle activations syn-
chronized with or anticipating rhythmic stimuli, whether
they have observable kinematic consequences or not.
We focus on 4- to 8-month-olds because this is an age
over which infants’ motor abilities change dramatically
(Piek 2006). This is especially true for target reaching,
object manipulations, and locomotion. It is, therefore, a
critical age range with respect to the embodied timing
hypothesis described above. We investigated interval-
timing abilities directly in the context of a physical and
socially interactive task. In our study, an experimenter
raised an infant’s arms seven times at fixed intervals, but
omitted to do so at the 8th interval, waiting to see whether
the infant would initiate a time-sensitive movement during
a pre-specified interval. This was repeated at two speeds,
first on a slow 4-s cycle for four blocks; then, after a short
break, on a fast 2-s cycle for four blocks. Responses were
recorded using bipolar EMG and an infrared motion cap-
ture system. We hypothesized that, even if overt time-
locked motor responses were not observed, evidence of
temporal anticipation would be detected in the EMG signal.
Method
Participants
Fifty-seven infants took part in the 4-s condition compris-
ing 20 four-month-olds (ten female; mean age = 129 days,
range = 111 days to 142 days), 20 six-month-olds (14
female; mean age = 189 days, range = 157 days to
205 days), and 17 eight-month-olds (ten female; mean
age = 243 days, range = 230 days to 267 days). Of these,
52 infants then took part in a faster 2-s condition including
16 four-month-olds (nine female; mean age = 128 days,
range = 111 days to 141 days), 20 six-month-olds (14
female; mean age = 189 days, range = 157 days to
205 days), and 17 eight-month-olds (ten female; mean
age = 243 days, range = 230 days to 267 days). Across
both conditions, a total of 12 infants were excluded: eight
infants because of fussiness or computer failure and four
due to insufficient EMG data.
Stimuli and procedure
Each condition (4- or 2-s intervals) consisted of four blocks
of seven learning trials followed by one test trial (Fig. 1).
During the learning trials, the experimenter, holding the
infant’s hands in a relaxed position on the infant’s lap,
gave the verbal cue “Ready?” (400 ms), paused (2600 ms
or 600 ms), and then said “Go!,” while raising the infant’s
arms to infant shoulder head height and lowering them
back to the relaxed lap position (1000 ms). To ensure accu-
rate timing, the experimenter had visual access to a monitor
(behind and out of sight of the infant) displaying a colored
ball and a numerical countdown of the trial duration. The
beginning of the countdown prompted the experimenter’s
verbal cue. At the end of the countdown, the ball moved
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

925Exp Brain Res (2017) 235:923–930
1 3
from the bottom of the screen to the top of the screen and
down again. The experimenter said “Go!” and lifted the
infant’s arms in time with the ball on the screen. During the
test trial, the experimenter used the verbal cue but remained
in the relaxed lap position with her hands open for twice the
length of a learning trial. In other words, she did not raise
the infant’s arms during the test trial, but left the infant free
to raise his/her arms.
Infants were seated either on a baby seat or on their car-
er’s lap opposite and within comfortable arm’s reach of the
experimenter. Surface EMG was used to record the elec-
trical activity of the infant and experimenter’s arm. Elec-
trodes were placed on the infant’s upper left arm (Bicep
brachii), chest (Pectoralis major), and left thigh (Rectus
femoris), as well as on the experimenter’s right anterior
deltoid (DE). Movements were simultaneously recorded
through motion capture technology. Two spherical reflec-
tive markers (10 mm diameter) were attached to the infant’s
sleeve over the left posterior forearm and connected as a
free joint to three triangulated markers on the experiment-
er’s right anterior forearm. Thus, although the experimenter
initiated a bilateral movement, only unilateral kinematic
data were collected. In each block, the experimenter raised
the infant’s hands seven times in a row (learning trials) on
a fast (4 s) or slow (2 s) arm-lift cycle. On the eighth trial
(test trial), the experimenter did nothing and waited for the
infant’s spontaneous response.
Apparatus
EMG data were collected using four bipolar pediatric sur-
face electrodes (3 M monitoring electrodes with micropore
tape and solid gel) and the Myon 320 wireless EMG
system, at a sampling rate of 1000 Hz. Motion capture data
were recorded using four infrared (IR) reference cameras
(Bonita 10), inputting to a PC (Dell Precision T1600), at a
sampling rate of 100 Hz. Synchronization of EMG and kin-
ematic data was achieved using Vicon Nexus software (Ver-
sion1.7.1). An in-house experimental control script written
using Matlab® R2009b generated the task timing signal for
the experimenter to follow. This was displayed on a 17-inch
screen out of view of the infant. This script also sent tim-
ing signals to the Vicon Nexus to mark the start of each
cycle and each movement. This was achieved by sending
transient on–off voltages directly to an unused EMG chan-
nel via an Arduino circuit driven by the MATLAB control
script. All control scripts are included with the open dataset
(Addyman et al. 2016b). Simultaneous video recording of
the testing session was conducted using a webcam (Log-
itech HD 1080p) connected to the control PC.
Data processing
Motion capture data were processed automatically by the
Vicon Nexus software. The three-dimensional path coordi-
nates of the infant arm markers were captured, reconstructed,
and labeled by the Vicon software, following which, smooth-
ing was performed using cross-validation splines (Woltring
1986). Trajectory data were filtered using a fourth-order
Butterworth filter with zero lag and cut-off frequencies of 6
and 300 Hz. Relevant information concerning the infant arm
movements was obtained from the marker z coordinates.
Raw EMG data were captured within the Vicon Nexus
system providing synchrony with the motion capture data.
However, all processing was performed in MATLAB using
scripts written by the first author. Various methods have been
Fig. 1 Schematic representa-
tion of hand-holding task.
Infants completed four blocks
of the slow version of the task
(4-s cycles) had a break and
then completed four blocks of
the fast version (2-s cycles). In
each block, an experimenter
cued then lifted infants arms
seven times in a row before
finally cueing the infant and
waiting for a response. The
response period was twice the
length of the learning cycle
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

926 Exp Brain Res (2017) 235:923–930
1 3
used to interpret infant EMG signals (e.g., Van der Fits et al.
1999; Spencer and Thelen 2000; Nishida et al. 2006), with
no established standard practice. We therefore used two inde-
pendent analyses, one based on low-pass filtering and the
other on a moving average, which are known to give similar
shape and amplitude profiles (Winter 1990; Konrad 2005).
First, EMG data from the infant bicep were visually
examined, and experimental blocks with noisy data were
excluded. The remaining data (93%) were rectified and
processed with a fourth-order Butterworth filter with zero
lag and cut-off frequencies of 20 and 300 Hz using the
MATLAB “filtfit” function. The data were then rectified
a second time. These filtered data were then used in both
analyses. For the continuous or analogue analysis, a low-
pass fourth-order Butterworth filter was applied with zero
lag and a 6 Hz cut-off. EMG amplitudes at selected points
were then compared with each other. For the second binary
method, we used a method identical to Van der Fits et al.
(1999): For each block of data, a simple RMS moving aver-
age was calculated with a 200-ms window, which was then
followed by calculating the first derivative of the smooth
EMG trajectory. A moving baseline RMS for the preceding
3.4 s was also calculated. Activity was then coded as ON or
OFF, where the moving average was, respectively, 1.4 times
above or below the moving baseline (this is identical to the
procedure reported in Van der Fits et al. 1999). From these
data, we can then calculate the frequency of ON–OFF states
or bursts in any given time window. All analysis scripts are
included with the open dataset (Addyman et al. 2016b).
Results
Our hypothesis was that infants will learn to anticipate the
hand rising during the first seven training episodes and so
will show time-locked movement and associated muscle
activity in the subsequent missing beat period. We were
therefore specifically interested in the infants’ data in the
missed-beat test periods (time period 8) and how this com-
pares to the movement and activity in the experimenter-led
training periods (time periods 1–7). Infants’ responses were
measured by looking at the movement along the z-dimen-
sion of the marker on the infant’s wrist and by the timing
of sustained bursts of bicep EMG activity during the test
period. We first grouped the infants by age and by condi-
tion, then collapsed the data across the four trial blocks for
each baby in each timing condition. Using the EMG tim-
ing signal from the MATLAB control script, we were able
to synchronize the starts of all the blocks so that the data
could be superimposed on each other. As is done with ERP
data, we aligned the EMG data in this way and averaged
across events to explore group performance. For the pur-
poses of illustration, the data recorded from 8-month-olds
are shown in Fig. 2. As shown by the movement deflections
in the z direction, the infants participated fully in the learn-
ing trials, letting the experimenter raise and lower their
hands at the appropriate times. However, there was no evi-
dence of overt (i.e., visible from the kinematics) response
in the critical test period at either cycle length.
Continuous EMG data analysis
Although the kinematic data suggest that infants were
not reacting in the test period (i.e., interval 8), previous
research has shown that infant responses can be sub-thresh-
old for actual movement when anticipating interaction with
a social partner (e.g., Reddy et al. 2013) or when mak-
ing postural adjustments in preparation for a reach action
(van der Fits et al. 1999; Witherington et al. 2002; Thelen
and Spencer 1998). The EMG recording from the infants’
left biceps allows us to examine this. The second row in
Fig. 2a, b shows the average low-pass filtered EMG signal
for the 8-month age group and interval length, averaged
across the four test trials for each baby. In the 4-s condi-
tion, each raising of the arms (time points 1–7) is accompa-
nied by an increase in EMG activation, while each lowering
is accompanied by a decrease in EMG activity. This is also
the case in the 8th (test) trial, even though no overt move-
ment was observed. In the 2-s condition, the same pattern
is found during the training phase, but not in the test trial.
Thus, it appears that infants in the slow 4-s condition
show a time-locked motor response, albeit one that is
sub-threshold and not accompanied by overt movement.
To test this hypothesis statistically, we averaged the acti-
vations over a 200-ms interval situated in the middle of
the down part of the cycle (trough value) and compared
this to the groupwise highest activation in the response
period (peak value) in the last learning trial (shaded
blue) or in the test trial (shaded red). These values are
displayed in top three rows in Table 1. A mixed analysis
of variance was carried out with Age (4, 6, or 8 months)
as a between-subject factor and Trial Type (learning vs
test) and Cycle Point (trough, peak) as a within subject
factors. This analysis revealed a main effect of Age, F(2,
54) = 3.69, p = .032, partial eta squared = .12, reflect-
ing a lower mean voltages in the oldest age group. There
were also main effects of Trial Type, F(1, 54) = 11.06,
p = .002, partial eta squared = .17 and of Cycle Point
F(1, 54) = 22.75, p < .001, partial eta squared = .30.
These main effects were modulated by a significant
Trial Type × Age interaction F(2, 54) = 3.21, p = .048,
partial eta squared = .11 and a marginally significant
Trial Type × Cycle Point interaction F(1, 54) = 3.95,
p < .052, partial eta squared = .068. Responses were
larger in the learning period as compared to test period,
and the response during test trials differed with age. This
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

927Exp Brain Res (2017) 235:923–930
1 3
interpretation was confirmed using post hoc t tests (see
Table 1, top 3 rows). No other interaction was significant.
An identical (3 × 2 × 2) ANOVA was performed on the
2-s data. At this time, there was no main effect of Age, F(2,
52) = 1.70, p = .192, but there were main effects of Trial
Type, F(1, 54) = 5.63, p = .021, partial eta squared = .10
and of Cycle Point F(1, 52) = 7.98, p = .007, partial eta
squared = .13. These main effects were modulated by
Fig. 2 Average displacement in the z direction and average low-pass
filtered EMG activity for 8-month-olds in the (a) 4-s, and (b) 2-s
conditions. In each plot, the dark line represents the mean, and the
shaded area is the 95% confidence interval. Double asterisk indicates
t test significant at p < .005, Single Asterisk significant p < .05
Table 1 Comparison of EMG activities in critical points in the experiment
The middle set of columns compares peak and troughs in final learning trial, while rightmost columns compare same points in test period
Columns show mean (±SD) activation, one-tailed repeated measures t tests, and p values. (* p < .05; ** p < .01; *** p < .001)
Task timing Age NFinal learning trials Test trial
Cycle 6 arms down Cycle 7 arms
up
t test pCycle 7 arms
down
Cycle 8 arms
up
t test p
SLOW 4 s 8 m 17 .045 ± .12 .095 ± .18 2.60 .006** .032 ± .05 .091 ± .25 1.81 .036*
6 m 20 .11 ± .17 .28 ± .41 4.56 .001*** .14 ± .25 .17 ± .30 .97 .17
4 m 20 .16 ± .28 .32 ± .40 2.97 .002** .10 ± .15 .17 ± .28 1.87 .032*
FAST 2 s 8 m 16 .11 ± .20 .18 ± .36 1.68 .049* .12 ± .22 .12 ± .27 .25 .40
6 m 20 .17 ± .28 .28 ± .39 2.92 .002** .16 ± .21 .16 +.25 .02 .49
4 m 16 .20 ± .32 .31 ± .38 2.46 .008** .24 ± .37 .24 ± .38 .1 .54
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

928 Exp Brain Res (2017) 235:923–930
1 3
a significant Trial Type × Cycle Point interaction F(1,
52) = 8.64, p = .005, partial eta squared = .14. No other
interactions were significant. In this condition, a consistent
pattern of response was seen across all ages with a clear
difference between peak and trough at the end of learning,
but no differences with age of the response during the test
trial. This interpretation was confirmed using post hoc t
tests (see Table 1, bottom 3 rows).
Binary EMG data analysis
We ran a similar analysis on the ON–OFF burst frequency
data (Table 2). These are binary data, so we therefore
adopted a nonparametric approach. First, we compared the
average burst frequencies at the same time points identified
in the first analysis using one-tailed Wilcoxon signed-rank
tests. This analysis revealed a highly significant trough-
to-peak difference for all age groups during the learning
cycles. During the test phase, this trough-to-peak difference
was significant for the 6- and 8-month-olds, and margin-
ally significant for the 4-month-olds, in the 4-s condition.
There were no significant differences at any age in the 2-s
condition.
Discussion
This study investigated 4-, 6- and 8-month-olds’ responses
to a regularly repeating socially driven motor interaction.
An experimenter raised the infant’s arms seven times in
a row at regular intervals. We then observed the infant’s
response in a ‘missed-beat’ interval. Infants were tested
on slow (4 s) and fast (2 s) cycles. We found no evidence
of behavioral (i.e., overt) anticipation at any age or time
scale, in that motion capture data showed the infants did
not raise their arms in the test response period. However,
low-pass filtered EMGs from infants’ left biceps revealed
a significant time-locked signal in the 4-s condition for
4- and 8-month-olds. A similar time-locked response was
observed in the burst frequencies of 6- and 8-month-olds,
in the 4-s condition. No EMG effects were found at any
age in the 2-s condition. Thus, across the two measures, we
found evidence of time-locked EMG activity in all three
age groups, with the most robust response (as evidenced
in both measures) present in the older 8-month-old group
only.
How do these findings relate to what is already known
about infant timing ability? Although there has been no
previous research on infant motor timing, our results are
consistent with more general surprise-based measures of
timing ability. These include evidence of physiological cor-
relates of time discrimination detected in heart-rate varia-
tion at 4-month-olds (Colombo and Richman 2002), visual
preference at 6-months (Brannon et al. 2007), and in event-
related potentials (ERPs) in 10-month-olds (Brannon et al.
2008). The current results are also consistent with our pre-
vious work using an event-based paradigm in the visual
domain (Addyman et al. 2014), which found evidence of
interval timing from 4 months of age with no developmen-
tal trend.
The results from the faster time cycle stand in contrast to
previous reports suggesting that infants as young as 4 months
of age are sensitive to interruptions of temporal regularity
(e.g., Addyman et al. 2014; Colombo and Richman 2002).
There are two reasons for why this discrepancy might have
appeared. First, the current task requires a volitional upper-
body response from the infant rather than just anticipatory
eye movements, reflexive pupil dilation, or heart-rate decel-
eration of previous studies. The physical response is more
complex and requiring coordination of numerous postural
muscles which develops slowly in the first 18 months (Van
Der Fits et al.1999). The second reason is that this task
involved a highly engaging—and consequently potentially
distracting—social interaction that may have captured the
infant’s attention and undermined their ability to demon-
strate fully their accurate time-keeping abilities.
Investigating interval timing in infants is very difficult.
Researchers must balance the need to expose infants to a
Table 2 Average number of ON bursts per infant per trial during middle of arms down and arms ups intervals during final learning trial and test
trial
Columns show mean number of bursts and p values from one-tailed Wilcoxon signed-rank tests (* p < .05; ** p < .01; *** p < .001)
Trial timing Age (m) NFinal learning trials Test trials
Cycle 6 arms down Cycle 7 arms up Sign test pCycle 7 arms down Cycle 8 arms up Sign test p
SLOW 4 s 8 17 .10 .34 .001*** .06 .15 .04*
6 20 .06 .36 .001*** .10 .21 .01**
4 20 .08 .30 .001** .08 .13 .06
FAST 2 s 8 16 .09 .22 .01* .12 .11 .64
6 20 .10 .22 .01* .10 .16 .12
4 16 .09 .26 .002** .14 .17 .38
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

929Exp Brain Res (2017) 235:923–930
1 3
stimulus that extends through time and which is repeated
sufficiently often for the infant to learn the regularity, while
simultaneously avoiding boredom and the loss of atten-
tion. The current research introduces a new paradigm to the
study of timing of infants under one year of age, and cru-
cially, demonstrates that even in the absence of an observed
overt response, EMGs can carry time-locked information
revealing the infant’s expectations about the onset of a
stimulus occurring on the interval time scale. Thus, EMGs
are a more sensitive measure of infant timing abilities than
are overt behavioral responses; EMG methods reveal pro-
ficiency in the physical domain that these latter methods
have not previously detected.
This is important because understanding the very early
development of interval timing places strong constraints
on the plausibility of timing models in older children and
adults (See Addyman et al. 2016a, for a recent review). Our
findings are consistent with the suggestion that embodied
experience is essential to acquisition of cognitive skills (e.g.,
Kermoian and Campos 1988). We provide evidence for an
emerging coupling between the motor system and interval
timing. In this sense, interval timing is indeed embodied (cf.
Kranjec and Chatterje 2010; French et al. 2014).
We believe that infant interval timing abilities are highly
embodied in a “Gibsonian” or ecological sense. Namely,
it is the infant’s physical interactions with the world that
provides much of the structure to help develop their inter-
nal representation of time. Eleanor and James Gibson pro-
posed that action and perception are tightly coupled to an
environment that provides physical affordances (Gibson
1982; Gibson 1979). Although the term “affordance” is
often used in relation to objects, the environment or eco-
logical setting provide affordances too. This is sometimes
also referred to as the “unity of perception” (Thelen 1995).
Thus, we suggest that object manipulation and locomotion
will both play an important role in the early development
of time perception. When infants successfully reach for and
manually explore objects in their peripersonal space, they
will be afforded a much richer temporal experience than
when they were just passive observers. Moving around
affords even larger temporal changes in the world (the
room looks very different from over here), and temporal
judgments become more important (how long will it take
me to get to that toy?).
Conclusion
The current study introduces a new event-based paradigm
for investigating infant time perception that allow for the
first time the investigation of motor components of interval
timing. Moreover, it demonstrates how EMGs can be more
sensitive measures of infant interval timing abilities than
overt observed behavior. Finally, these results are consistent
with an embodied developmental model of interval timing.
Acknowledgements This work was funded by Economic and Social
Research Council (UK) Grant RES 062-23-0819 and Agence National
de la Recherche grant 10-ORAR-006-03 as part of the ORA inter-
national collaboration initiative. DM is supported in part by a Royal
Society Wolfson research merit award.
Open Access This article is distributed under the terms of the Crea-
tive Commons Attribution 4.0 International License (http://crea-
tivecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
made.
References
Addyman C, French RM, Mareschal D, Thomas E (2011) Learning
to perceive time: a connectionist, memory-decay model of the
development of interval timing in infants. In: Carlson L, Höls-
cher C, Shipley T (eds) Proceedings of the 33rd annual confer-
ence of the cognitive science society. Cognitive Science Society,
Austin
Addyman C, Rocha S, Mareschal D (2014) Mapping the origins
of time: scalar errors in infant time estimation. Dev Psychol
50:2030–2035
Addyman C, French RM, Thomas E (2016a) Computational models
of interval timing. Curr Opin Behav Sci 8:140–146
Addyman C, Rocha S, Fautrelle L, French RM, Thomas E, Mare-
schal D (2016b) Embodiment and the origin of interval timing:
kinematic and Electromyographic data [dataset] doi:10.6084/
m9.figshare.3201289
Bouisset S, Zattara M (1987) Biomechanical study of the program-
ming of anticipatory postural adjustments associated with volun-
tary movement. J Biomech 20(8):735–742
Brannon EM, Roussel LW, Meck WH, Woldorff MG (2004) Timing
in the baby brain. Cogn Brain Res 21:227–233
Brannon EM, Suanda S, Libertus K (2007) Temporal discrimination
increases in precision over development and parallels the devel-
opment of numerosity discrimination. Dev Sci 10:770–777.
doi:10.1111/j.1467-7687.2007.00635.x
Brannon EM, Libertus ME, Meck WH, Woldorff MG (2008) Elec-
trophysiological measures of time processing in infant and adult
brains: Weber’s law holds. J Cogn Neurosci 20:193–203
Buhusi CV, Meck WH (2005) What makes us tick? functional and neu-
ral mechanisms of interval timing. Nat Rev Neurosci 6:755–765
Carlini A, French RM (2014) Visual tracking combined with hand-
tracking improves time perception of moving stimuli. Sci Rep 4,
Art. number: 5363. doi:10.1038/srep05363
Colombo J, Richamn WA (2002) Infant timekeeping: attention and
temporal estimation in 4-month-olds. Psychol Sci 13:475–479
Fautrelle L, Mareschal D, French R, Addyman C, Thomas E (2015)
Motor activity improves temporal expectancy. PLoS ONE
10(3):e0119187-14. doi:10.1371/journal.pone.0119187
French RM, Addyman C, Mareschal D, Thomas E (2014) GAMIT:
a fading-Gaussian activation model of interval-time—unifying
prospective and retrospective time estimation. Timing Time Per-
cept Rev 1:1–17
Gavazzi G, Bisio A, Pozzo T (2013) Time perception of visual motion
is tuned by the motor representation of human actions. Sci Rep,
3, Art. no. 1168. doi:10.1038/srep01168
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

930 Exp Brain Res (2017) 235:923–930
1 3
Gibson EJ (1969) Principals of perceptual learning and development.
Appleton-Century-Crofts, East Norwalk
Gibson JJ (1979) The ecological approach to visual perception.
Houghton Mifflin, Boston
Gibson EJ (1982) The concept of affordances in development: the
renascence of functionalism. In: Collins WA (ed) The concept
of development: Minnesota symposium on child psychology, vol
15. Erlbaum, Hillsdale, pp 55–81
Goldfield E (1995) Emergent forms: early development of human
action and perception. Oxford University Press, Oxford
Grondin S (2008) Psychology of time. Emerald Publishing, Bingley
Hadders-Algra M (2005) Development of postural control during the
first 18 months of life. Neural Plast 12:2–3
Haggard P, Clark S, Kalogeras J (2002) Voluntary action and con-
scious awareness. Nat Neurosci 5:382–385
Kermoian R, Campos JJ (1988) Locomotor experience: a facilitator of
spatial cognitive development. Child Dev 59(4):908–917
Konrad P (2005) The ABC of EMG. Noraxon, Scottsdale
Kranjec A, Chatterjee A (2010) Are temporal concepts embodied? a
challenge for cognitive neuroscience. Front Psychol 1:1–9
Nishida Y, Kawakami G, Mizoguchi H (2006) Everyday grasping
behavior measurement with wearable electromyography. In: 5th
IEEE conference on sensors, 2006. IEEE, pp 988–991
Piaget J (1952) The origins of intelligence in children. International
Universities Press, New York
Piaget J (1957) The child’s construction of reality. Routledge and
Kegan Paul, London
Piek JP (2006) Infant motor development. Human Kinetics, Leeds
Reddy V, Markova G, Wallot S (2013) Anticipatory adjustments to
being picked up in infancy. PLoS ONE 8(6):e65289
Schenau GJVI, Dorssers WM, Welter TG, Beelen A, de Groot G,
Jacobs R (1995) The control of monoarticular muscles in multi-
joint leg extensions in man. J Physiol 484:247–254
Smith A, Taylor E, Lidzba K, Rubia K (2003) A right hemispheric
fronto-cerebellar network for time discrimination of several hun-
dreds of milliseconds. Neuroimage 20:344–350
Spencer JP, Thelen E (2000) Spatially specific changes in infants’
muscle coactivity as they learn to reach. Infancy 1(3):275–302
Thelen E (1979) Rhythmical stereotypies in normal human infants.
Anim Behav 27:699–715
Thelen E (1981) Rhythmical behavior in infancy: an ethological per-
spective. Dev Psychol 17(3):237–258
Thelen E (1995) Motor Development. Am Psychol 50(2):79–95
Thelen E, Smith LB (1996) A dynamic systems approach to the devel-
opment of cognition and action. MIT Press, Cambridge
Thelen E, Spencer J (1998) Postural control during reaching in young
infants: a dynamic systems approach. Neurosci Biobehav Rev
22(4):507–514
Thomaschke R, Dreisbach G (2013) Temporal predictability facili-
tates action, not perception. Psychol Sci 24(7):1335–1340.
doi:10.1177/0956797612469411
Van der Fits I, Otten E, Klip A, Van Eykern L, Hadders-Algra M
(1999) The development of postural adjustments during reaching
in 6-to 18-month-old infants evidence for two transitions. Exp
Brain Res 126(4):517–528
Winter DA (1990) Biomechanics and motor control of human move-
ment. Wiley, New York
Witherington DC, von Hofsten C, Rosander K, Robinette A, Wolla-
cott MA, Bennet BI (2002) The development of anticipatory pos-
tural adjustments in infancy. Infancy 3:495–517
Woltring HJ (1986) A FORTRAN package for generalized, cross-
validatory spline smoothing and differentiation. Adv Eng Softw
(1978) 8(2):104–113
Zakay D, Block RA (1997) Temporal Cognition. Curr Dir Psychol Sci
6(1):12–16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com