Available via license: CC BY 4.0
Content may be subject to copyright.
ORIGINAL RESEARCH
published: 03 May 2018
doi: 10.3389/fpsyg.2018.00602
Frontiers in Psychology | www.frontiersin.org 1May 2018 | Volume 9 | Article 602
Edited by:
Karen Zentgraf,
Goethe University Frankfurt, Germany
Reviewed by:
André Klostermann,
Universität Bern, Switzerland
Herbert Heuer,
Leibniz Research Centre for Working
Environment and Human Factors (LG),
Germany
*Correspondence:
Claudia Voelcker-Rehage
claudia.voelcker-rehage@
hsw.tu-chemnitz.de
Specialty section:
This article was submitted to
Movement Science and Sport
Psychology,
a section of the journal
Frontiers in Psychology
Received: 03 February 2018
Accepted: 10 April 2018
Published: 03 May 2018
Citation:
Janouch C, Drescher U, Wechsler K,
Haeger M, Bock O and
Voelcker-Rehage C (2018)
Cognitive—Motor Interference in an
Ecologically Valid Street Crossing
Scenario. Front. Psychol. 9:602.
doi: 10.3389/fpsyg.2018.00602
Cognitive—Motor Interference in an
Ecologically Valid Street Crossing
Scenario
Christin Janouch 1, Uwe Drescher 2, Konstantin Wechsler 2, Mathias Haeger 2, Otmar Bock 2
and Claudia Voelcker-Rehage 1
*
1Faculty of Behavioral and Social Sciences, Institute of Human Movement Science and Health, Chemnitz University of
Technology, Chemnitz, Germany, 2Institute of Physiology and Anatomy, German Sport University Cologne, Cologne, Germany
Laboratory-based research revealed that gait involves higher cognitive processes,
leading to performance impairments when executed with a concurrent loading task.
Deficits are especially pronounced in older adults. Theoretical approaches like the
multiple resource model highlight the role of task similarity and associated attention
distribution problems. It has been shown that in cases where these distribution problems
are perceived relevant to participant’s risk of falls, older adults prioritize gait and posture
over the concurrent loading task. Here we investigate whether findings on task similarity
and task prioritization can be transferred to an ecologically valid scenario. Sixty-three
younger adults (20–30 years of age) and 61 older adults (65–75 years of age) participated
in a virtual street crossing simulation. The participants’ task was to identify suitable
gaps that would allow them to cross a simulated two way street safely. Therefore,
participants walked on a manual treadmill that transferred their forward motion to forward
displacements in a virtual city. The task was presented as a single task (crossing only)
and as a multitask. In the multitask condition participants were asked, among others,
to type in three digit numbers that were presented either visually or auditorily. We found
that for both age groups, street crossing as well as typing performance suffered under
multitasking conditions. Impairments were especially pronounced for older adults (e.g.,
longer crossing initiation phase, more missed opportunities). However, younger and older
adults did not differ in the speed and success rate of crossing. Further, deficits were
stronger in the visual compared to the auditory task modality for most parameters.
Our findings conform to earlier studies that found an age-related decline in multitasking
performance in less realistic scenarios. However, task similarity effects were inconsistent
and question the validity of the multiple resource model within ecologically valid scenarios.
Keywords: multitasking, dual-tasking, aging, walking, cognitive-motor interference, ecological validity, virtual
reality, street crossing
INTRODUCTION
Many daily activities require us to manage sensory-motor tasks while we simultaneously engage in
cognitive tasks. One prominent example is pedestrian mobility, such as walking down a sidewalk
while avoiding a collision with another pedestrian, walking while screening items in a shop window,
or crossing a non-signalized street while paying attention to relevant traffic information. These
Janouch et al. Cognitive—Motor Interference While Street Crossing
activities become even more complex with the advent of portable
technologies such as smartphones or music players. In June
2013 the Pedestrian Survey Infographic published, that from
over 1,000 American respondents three out of five (60%) 18–
65 year olds stated to use smartphones while crossing a street,
even though this was considered as dangerous by 70% of these
respondents (Liberty Mutual Insurance, 2013).
Standardized laboratory paradigms have provided evidence
that sensory-motor performance decreases under dual-task
conditions, and that this decrease is especially pronounced
in older adults (Kray and Lindenberger, 2000; Verhaeghen
et al., 2003). This has been often explained by sensory-motor
and cognitive declines within the aging process (Baltes and
Lindenberger, 1997; Li and Lindenberger, 2002). Performance
decrements in dual-task situations have even been observed
with tasks that are considered to be highly automated, like gait.
It has therefore been argued that even gait requires cognitive
control and higher-level resources (Hausdorff et al., 2005; Yogev-
Seligmann et al., 2008). Especially in older adults, gait seems
to place high attentional demands and requires more cognitive
resources (Lindenberger et al., 2000; Woollacott and Shumway-
Cook, 2002; Hausdorff et al., 2008) which leads to greater dual-
task decrements in this age group (Al-Yahya et al., 2011).
Performance decrements in dual-task situations have been
interpreted in light of several theoretical positions, such as
capacity models of attention (Kahneman, 1973) or multiple
resource models (Wickens, 2002). In both types of models, two
or more tasks compete for common resources, either within a
limited attentional resource pool (Kahneman, 1973) or within
multiple resource pools (Wickens, 2002). In the latter case,
pools are thought to be devoted to different stimulus modalities,
signal codes, processing stages, and response channels (Wickens
and McCarley, 2007). Both theoretical approaches share the
idea that performance deteriorates when the competing tasks
are so complex that their combined resource demand exceeds
the available resource capacity. The multiple-resource model
additionally posits that the tasks must be similar enough in order
to compete for the same resource. The determinants of dual-
task decrements therefore are task complexity and—in case of the
multiple-resource model—task similarity.
Several studies provided evidence for the role of task
similarities. They documented interference between tasks that
share sensory modalities, processing levels or information
channels (Allport et al., 1972; Isreal et al., 1980; Duncan
et al., 1997; Talsma et al., 2006). In a street crossing context,
such interference could emerge when two tasks require to
simultaneously process similar visual signals. This is the case,
e.g., when we look for a suitable gap in traffic and concurrently
read walking directions on a mobile phone. In contrast, looking
for gaps while listening to walking directions over headphones
should cause less interference.
So far, most available knowledge about dual-task performance
came from traditional laboratory-based research that offers a high
controllability and standardization, but lacks ecological validity.
Even if real walking is required, tasks are often executed within
a laboratory surrounding and most of the applied loading tasks
are rather abstract like verbal fluency or arithmetic subtraction
tasks. For example, participants were asked to memorize word
lists while walking (walk as accurately and quickly as possible
on two narrow tracks with different path complexity/avoid
obstacles) (Lindenberger et al., 2000; Li et al., 2001). The results
revealed diminished performance when the tasks were performed
concurrently. Age-related differences were more pronounced
in the memory task than in the walking task. This result was
discussed as older adults prioritizing walking over memorizing
to protect themselves from falls, a view known as “posture first
hypothesis” (Shumway-Cook and Woollacott, 2000; Schaefer and
Schumacher, 2011; cf. Li et al., 2012 for discussion of mixed
results).
Everyday life typically differs from traditional laboratory
paradigms in that behavior is uninstructed and volitional, with
varying and often unpredictable stimuli and with a wider range
of possible and purposeful responses. Little is known about the
transferability of laboratory outcomes to more realistic settings.
Available literature documents marked differences between
laboratory and realistic behavior with respect to gait (Bock and
Beurskens (2010), manual grasping (Bock and Züll, 2013) and
cognitive performance (Verhaeghen et al., 1993). In a systematic
review on dual-task training effects in older adults, Wollesen and
Voelcker-Rehage (2014) found heterogeneous results regarding
the transferability of training effects to everyday situations.
Consequently, several authors cautioned against generalizing
laboratory results to real life (Chaytor and Schmitter-Edgecombe,
2003; Li et al., 2005) and questioned the extent to which especially
age-related decays apply on everyday-like behavior (Verhaeghen
et al., 2012).
Given the above considerations, it seems desirable to expand
dual-task research by using more ecologically valid paradigms,
without giving up the advantages of a laboratory setting such
as controllability and standardization. A promising approach to
do so can be seen in virtual reality (VR) settings (Lopez Maite
et al., 2016) which can provide an everyday-like, controllable
and safe surrounding that can be adapted to the need of the
experimenter. Thus, a realistic walking task (e.g., walking down
or crossing a street) can be combined with a realistic loading
task (e.g., watching for vehicles or using a smartphone) while
ambient stimuli are controlled for and relevant measures are
extracted. Indeed, several VR pedestrian street crossing studies
have been conducted recently (Dommes et al., 2014; Schwebel
et al., 2014; Morrongiello and Corbett, 2015). However, most
of these studies did not address multitasking (Dommes et al.,
2014; Schwebel et al., 2014; Morrongiello and Corbett, 2015) and
those which did rather focused on children and young adults than
older adults (Chaddock et al., 2011, 2012; Byington and Schwebel,
2013; Gaspar et al., 2014; Tapiro et al., 2016) or used only a single
loading task throughout the whole experimental block or session
(Neider et al., 2011). For example, Neider et al. (2011) confirmed
that dual-task crossing performance deteriorates in old age, but
did not evaluate performance changes of the cognitive loading
task (cell phone conversation) to control for interaction effects or
possible prioritization strategies.
The present study aims to overcome the mentioned
limitations by combining a VR street crossing task with a realistic
loading task. We posit that the ecological validity of our approach
Frontiers in Psychology | www.frontiersin.org 2May 2018 | Volume 9 | Article 602
Janouch et al. Cognitive—Motor Interference While Street Crossing
exceeds that of earlier approaches. Specifically, loading tasks are
administered either through the visual or the auditory modality,
in order to scrutinize the validity of the multiple resource model
(Wickens, 2002) for ecologically valid settings. We hypothesized
that street crossing requires visual resources and therefore will
interfere with visually presented loading tasks more than with
auditorily presented loading tasks, particularly in older persons.
In accordance with the posture first hypothesis (Lindenberger
et al., 2000; Li et al., 2001; Schaefer and Schumacher, 2011),
we expected that age-related deficits will be less pronounced
for walking than for loading task performance, even in an
ecologically valid setting.
METHODS
Participants
The study was conducted within the DFG (German Research
Foundation) Priority Program SPP 1772 “Multitasking,” In total,
134 healthy men and women between 20 and 30 (n=69) and
65 and 75 (n=65) years of age who actively participated in
traffic as drivers as well as pedestrians were recruited. Younger
participants were recruited via mailing lists from the student pool
of the Chemnitz University of Technology (Germany) and the
German Sport University Cologne. Older adults were acquired
via local newspaper advertising and (only in Chemnitz) further
via the participant pool of the Cognition, Brain, and Movement
Lab of Chemnitz University of Technology. About half of the
young and old participants were recruited and tested in Chemnitz
and the other half in Cologne. Both locations used standardized
and indentical set ups, test designs and instructions as well as
identical hardware and software.
Interested persons were screened in an initial telephone
interview for the following exclusion criteria: (a) age range
violations, (b) former or current health impairments (heart
attacks, brain injuries, strokes; motor impairments that inhibit
the participant to continuously walk for 30 min, eye diseases or
current relevant injuries), (c) obesity (Body Mass Index, BMI >
30), and (d) driving irregularity (driving a car less than once a
week). Further exclusion tests were performed on participant’s
first laboratory test session (cf. below). No person had to be
excluded based on these tests. Before testing began, participants
obtained medical clearance from their local physician and signed
an informed consent statement to our study. This experiment
was part of a larger project in which the same participants
were additionally given a car-driving test (reported in another
contribution to this issue) and a cardiovascular fitness test. The
project was approved by the Ethics Committee of the German
Sport University, Cologne.
Six participants dropped out over the study time without
giving reasons, three had to be excluded because of simulator
sickness, and one participant left the study for personal reasons.
The remaining 124 participants were subdivided with respect to
age into 61 older adults (OA) with a mean age of 69.97 (SD =
2.96) years [females: n=22; BMI =25.09 (SD =2.44); MMSE
=29.15 (SD =0.85)] and 63 young adults (YA) with a mean age
of 23.17 (SD =2.83) years [females: n=40; BMI =22.04 (SD =
2.30); MMSE =29.67 (SD =0.62)]. OA received 15 eper session
as monetary compensation (60 ein total) and YA received course
credits. Further, all participants received an individual report of
their cardiovascular fitness test as compensation.
Laboratory Screening
Normal hearing was assessed by the Freiburg speech intelligibility
test (Freiburger Sprachverständlichkeitstest) with a set cutoff
word recognition rate of 50 %. Normal vision was assessed
by the Freiburg Visual Acuity Test (FrACT; version 3.9.0)
with a cutoff score of 20/60 since driving is presumed to be
safe above that score (Keeffe et al., 2002). Lack of visual-field
deficits was confirmed by the online version of the Damato
Multifixation Campimeter (Damato and Groenewald, 2003). All
participants who used visual and hearing aids in their daily
life did so in testing as well. Normal overall cognition was
assessed by the Mini-Mental State Examination (Folstein et al.,
1975) with a cutoff score of 27/30. Finally, the Edinburgh
Handedness Inventory (Oldfield, 1971) was used to determine
hand dominance. Five participants were left handed, one was
ambidextrous but preferred the right hand for typing, and all
others were right handed.
Apparatus and Setup
Hardware for the street crossing task consisted of a non-
motorized treadmill (DRAX, Speedfit 1000c, Vibrafit R
, Solms)
and three 46′′ TV flat screens that featured a 195 degree
horizontal field of view. Treadmill speed was registered opto-
electronically, and was synchronized with a first person
perspective view of a 3D world. Thus, as participants walked
at their own pace, sped up, and slowed down, their viewpoint
in the visual 3D world moved accordingly. To reduce physical
exertion, participants were asked not to run. For safety reasons,
each participant was equipped with a drop guard and asked to
keep the non-dominant hand on the treadmill’s handrail for the
entire test duration.
Headphones (Shark Zone H10 Gaming Headset, Sharkoon
Technologies GmbH, Linden, Germany) were used to deliver
auditory stimuli and a microphone to register verbal responses.
A keypad with 2 ×3 digits was attached within easy reach of the
participants’ dominant/preferred hand to register manual typing
responses.
Software consisted of a modified, commercially available
driving simulator (Carnetsoft R
, version 8.0 Groningen, NL) that
was adapted to the needs of a street crossing task: it displayed
the 3D model of a city street from a first person perspective (see
section Street Crossing Task).
Figure 1 illustrates the set up and displays the modeled city
street.
Street Crossing Task
The street crossing task was designed similarly to a study by
Neider et al. (2010), in which the participant’s task was to safely
cross a street presented in virtual reality. To do so, they had
to detect suitable gaps between the oncoming vehicles. In our
scenario, the street consisted of one three-meter wide lane in
each direction and was flanked by typical downtown buildings.
Vehicles traveled along both lanes at 50 km/h, which is the legal
Frontiers in Psychology | www.frontiersin.org 3May 2018 | Volume 9 | Article 602
Janouch et al. Cognitive—Motor Interference While Street Crossing
driving speed in German towns. At the onset of each trial,
participants walked 15 m through a virtual back alley to reach the
curb of the street, stopped, watched for a suitable intervehicle gap
and then crossed. Intervehicle gaps increased during each trial
according to the sequence 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6 s, and this
sequence was repeated if participants did not cross the street yet.
Cars on the far lane reached the crossing area one second later
than those on the near lane. Pilot work yielded that this traffic
pattern allows safe crossing even for older participants. Far lane
traffic was implemented to detect possible behavioral anomalies
during the crossing process (e.g., stopping in the middle of the
street to let pass the traffic on the far lane before continuing
crossing). However, as no such anomalies occurred and analyses
of near and far lane provided equally results, only near lane
analyses will be reported. A crossing trial was completed when
participants reached the opposite walkway, when they caused an
accident or when 80 s elapsed.
Loading Task
We used two realistic loading tasks that resembled rehearsal of
a shopping list (shopping task) and smartphone usage (typing
task). Each given loading task was presented repeatedly from
trial onset until trial end to ensure that the crossing task and the
loading task could not be dealt with sequentially. Loading tasks
were presented visually on some, and auditorily on other trials.
In the shopping task, grocery products were sequentially
presented either visually on billboards across the street or
auditorily through headphones. In the typing task, three-digit
numbers were sequentially presented either on the billboards
(for 4 s each) or through headphones, for about 1.7 s each.
Participants reacted by depressing, with their preferred hand,
the corresponding numbers on a keypad that was attached to
the treadmill handrail. Task type (shopping, typing) and task
modality (visually or auditorily) varied quasi-randomly between
trials, with the constraint that each type∗modality combination
was presented a total of ten times. To limit the complexity
of the present paper, we decided to focus our analyses on the
FIGURE 1 | Street crossing simulator set up.
typing task. However, it is important to note that this task was
not administered alone but rather intermixed with the shopping
task, to mimic the diversity of everyday multitasking. Possible
switching costs, resulting from such a loading task intermix will
be discussed in a car driving simulator study by Wechsler et al.
(under review).
The simulation offered three task conditions. In the control
condition “single-task crossing (STcross),” participants walked
on the treadmill and crossed the virtual street without loading
tasks. In the condition “single-task loading,” participants stood
still on the treadmill while the virtual reality display advanced
automatically and the loading tasks were displayed sequentially.
In this condition, each type∗modality combination [typing
auditory (STtype_aud), typing visual (STtype_vis), shopping
auditory (STshop_aud) and shopping visual (STshop_vis)]
was presented with a total of 10 trials. In the condition
“multitask,” participants walked on the treadmill and crossed
the virtual street while concurrently engaged in a loading task
(MTtype). Again, there where 10 trials of each type∗modality
combination [auditory typing task (MTtype_aud), visual typing
task (MTtype_vis), auditory shopping task (MTshop_aud), and
visual shopping task (MTshop_vis)].
Ten control trials of STCross were randomly intermixed with
ten trials each of MTtype_aud, MTtype_vis, MTshop_aud, and
MTshop_vis. The total of 50 trials was presented in blocks of
ten trials each that were characterized as active blocks in which
participants had to actually walk on the treadmill. These active
blocks alternated with passive blocks of ten single-task loading
trials each in which the participants stood still on the treadmill.
The latter blocks were formed by intermixing ten trial each
of STtype_aud, STtype_vis, STshop_aud, and STshop_vis. The
alternation of active and passive blocks was introduced to avoid
fatigue. All participants received the same sequence of trials,
which took about 40 min.
Procedure
All data were collected in four sessions of about 2 h each,
scheduled one to seven days apart. The first session included
TABLE 1 | Means (M) and Standard Deviations (SD) of crossing parameters
during single-task crossing (STCross), multitask typing visually (MTtype_vis) and
multitask typing auditorily (MTtype_aud).
STCross MTtype_vis MTtype_aud
M (SD) M (SD) M (SD)
YA OA YA OA YA OA
Stay Time 6.29
(0.97)
6.48
(0.99)
7.26
(4.19)
9.51
(5.99)
6.27
(0.91)
6.41
(1.01)
Back-alley
Speed (km/h)
3.94
(0.61)
3.75
(0.59)
3.80
(0.60)
3.51
(0.61)
3.72
(0.61)
3.37
(0.62)
Crossing
Speed (km/h)
6.37
(0.97)
6.70
(1.03)
6.26
(0.94)
6.14
(1.02)
6.30
(1.02)
6.33
(1.08)
Crossing
Failure (%)
10.63
(16.74)
9.51
(14.19)
16.35
(19.12)
21.31
(21.64)
11.43
(14.69)
15.08
(16.29)
Gap (#) 4.87
(1.32)
5.17
(1.30)
5.11
(1.24)
5.98
(1.53)
5.10
(1.33)
5.98
(1.39)
Frontiers in Psychology | www.frontiersin.org 4May 2018 | Volume 9 | Article 602
Janouch et al. Cognitive—Motor Interference While Street Crossing
a screening (see above) and a familiarization phase in which
participants walked on the treadmill. This phase ended when
participants and experimenter considered starting, walking and
stopping on the treadmill to be smooth and effortless, which
took about 10–15 min for YA, and 15–20 min for OA. Afterwards,
participants received one practice trial each for STtype_vis,
STtype_aud, and STcross. MTtype was not practiced.
Street crossing performance was registered in one of the
remaining three test sessions, depending on participant’s test
order randomization. It was assured that the street crossing
experiment was never scheduled on the same day as the
cardiovascular fitness test, to avoid fatigue.
Data Reduction
The following performance measures were calculated.
Back-alley Speed (km/h): Mean velocity of walking toward
the curb. Triggers were set at trial onset, and when participants
stopped at the curb.
Stay Time (s): Length of time that participants stood still at the
curb while watching traffic. Time triggers were set when treadmill
pace dropped to 0 m/s and when treadmill pace exceeded 0 m/s
thereafter.
Crossing Speed (km/h): Mean velocity of crossing the street.
Triggers were set when participants left the curb to cross
(treadmill pace >0 m/s) and when they reached the opposite
curb.
Crossing Failures: Percentage of unsuccessful trials, as a result
of timeouts (i.e., participant did not complete street crossing
within 80 s) or experienced a collision (i.e., participant was hit
by a car).
Gap Number: Serial order of the gap selected for crossing.
Typing Accuracy (%): Percentage of trials on which all three
digits were typed correctly.
Typing Reaction Time (ms): Interval from stimulus onset until
typing the first digit.
Multitasking Effects, MTE: Relative performance change under
multitask conditions, with negative values indicating poorer
performance (cf. Kelly et al., 2010; Plummer and Eskes, 2015).
For Back-alley Speed, Crossing Speed, and Typing Accuracy MTE
was calculated as
MTE =Multitask performance −Single task performance
Single task performacne x100%
(1)
while for Stay Time, Crossing Failure, Gap Number, and Typing
Reaction Time it was calculated as
MTE = − Multitask performance −Single task performance
Single task performancne x100%
(2)
Statistical Analyses
Outliers were eliminated by applying the ±3.29 SD criterion
(Tabachnick and Fidell, 2001), separately for each participant and
task. Data were then averaged across repetitions if at least seven
repetitions remained, which was the case for all 124 participants.
Each street crossing parameter was submitted to an analysis of
variance (ANOVA) with Age (OA, YA) as between-subject factor
and Condition (STcross, MTtype_aud, MTtype_vis) as within-
subject factor. Typing parameters were submitted to an ANOVA
with Age (OA, YA) as between-subject factor and Condition
(STtype, MTtype) and Task Modality (visual, auditory) as within-
subject factors.
Each MTE score was tested against zero with one-sample
t-tests in case of normal distributions, and Wilcoxon Signed
Rank tests otherwise. Further, MTE scores were submitted to
an ANOVA with Age (OA,YA) as between-subject factor and
Parameter Type (Stay Time, Walking Speed, Crossing Speed,
Failures, Gap, Typing Accuracy and Typing Reaction Time) as
well as Task Modality (visual, auditory) as within-subject factors.
Effect sizes are reported as partial eta squares. Homogeneity of
variances was determined by Mauchly-tests and, if the sphericity
assumption was violated, Greenhouse-Geisser adjustments were
applied. When omnibus ANOVA was significant, Bonferroni
post-hoc tests were conducted. All statistical analyses were
conducted with SPSS for Windows, version 25 (IBM Corp.,
Armonk, NY, USA).
RESULTS
Crossing Task
Table 1 and Figure 2 show descriptive data on each street
crossing parameter and Table 2 pertinent ANOVA results.
Back-alley Speed was significantly slower in OA than YA
and differed between conditions (Figure 2A). It was significantly
slower in the MTtype conditions compared to the STCross
condition but also differed significantly between both MTtype
TABLE 2 | ANOVA results for street crossing parameters.
df (Error) FSig η2
p
BACK-ALLEY SPEED
Condition 1.672 (203.929) 95.064 <0.001** 0.438
Age 1 (122) 7.038 0.009** 0.055
Condition ×Age 1.672 (203.929) 5.930 0.005** 0.046
STAY TIME
Condition 1.009 (123.112) 18.344 <0.001** 0.131
Age 1 (122) 6.937 0.010* 0.054
Condition ×Age 1.01 (123.112) 4.855 0.029* 0.038
CROSSING SPEED
Condition 2 (244) 40.003 <0.001** 0.247
Age 1 (122) 0.220 0.640 0.002
Condition ×Age 2 (244) 17.677 <0.001** 0.247
CROSSING FAILURE
Condition 1.895 (231.246) 18.423 <0.001** 0.131
Age 1 (122) 0.915 0.341 0.007
Condition ×Age 1.895 (231.246) 2.407 0.095 0.019
GAP
Condition 2 (244) 33.970 <0.001** 0.218
Age 1 (122) 9.228 0.003** 0.070
Condition ×Age 2 (244) 11.609 <0.001** 0.087
*p<0.05; **p<0.01.
Frontiers in Psychology | www.frontiersin.org 5May 2018 | Volume 9 | Article 602
Janouch et al. Cognitive—Motor Interference While Street Crossing
conditions (slowest in MTtype_aud followed by MTtype_vis
and fastest in STCross), especially in OA (significant age ×
condition interaction). Pairwise post-hoc comparisons revealed
that age differences only emerged in the MTtype conditions
(MTtype_vis: p=0.007; MTtype_aud: p=0.002), but not in
STCross (p =0.066).
Stay time was significantly longer in OA than YA and different
between conditions (Figure 2B). It was significant longer in
MTtype_vis compared to MTtype_aud and compared to STCross
(always, p<0.001), particularly so in older persons as shown
by a significant age by condition interaction. However, post-
hoc tests revealed significant age differences in MTtype_vis only
(p=0.016).
Crossing Speed did not differ as a function of age, but
again differed between conditions (Figure 2C). Also the age
by condition interaction was significant, indicating that only
for older adults Crossing Speed was significantly affected by
condition. OA were significantly slower in both MTtype
conditions compared to STcross, but this time with the slowest
Crossing Speed in MTtype_vis that was also significantly slower
compared to the MTtype_aud condition (p=0.001).
Crossing Failure revealed a significant condition effect only
(Figure 2D). Post-hoc comparisons revealed that this effect
was driven by MTtype_vis for which Crossing Failure was
significantly higher compared to STCross (p<0.001) as well as
to MTtype_aud (p<0.001).
FIGURE 2 | Condition differences between single-task crossing (STCross), multitask typing visually (MTtype_vis) and multitask typing auditorily (MTtype_aud),
grouped by age (M and SE) for (A) Back-alley Speed; (B) Stay Time; (C) Crossing Speed; (D) Gap Number; (E) Crossing Failures.
TABLE 3 | Means (M) and Standard Deviations (SD) of typing parameters for younger (YA) and older adults (OA).
STTyping_visual MT_visual STTyping_auditory MT_auditory
M (SD) M (SD) M (SD) M (SD)
YA OA YA OA YA OA YA OA
Accuracy in % 95.70 (1.17) 95.10 (1.36) 95.80 (1.13) 94.11 (3,66) 96.58 (3.51) 96.74 (7.80) 92.73 (5.05) 89.61 (8.31)
Reaction Time (s) 1.44 (0.19) 1.73 (0.18) 1.62 (0.24) 1.94 (0.22) 1.75 (0.25) 1.57 (0.21) 1.78 (0.23) 1.72 (0.21)
Frontiers in Psychology | www.frontiersin.org 6May 2018 | Volume 9 | Article 602
Janouch et al. Cognitive—Motor Interference While Street Crossing
FIGURE 3 | Condition differences between single-task typing visual (STtype_vis) and multitask typing visual (MTtype_vis) and between single task typing auditory
(STtype_aud) and multitask typing auditory (MTtype_aud) for (A) accuracy visual; (B) accuracy auditory; (C) reaction time visual; (D) reaction time auditory.
TABLE 4 | ANOVA results for typing parameters.
df F Sig η2
p
Accuracy
Age 1 (122) 8.894 0.004** 0.067
Condition 1 (122) 51.217 <0.001** 0.296
Condition ×Age 1 (122) 6.937 <0.001** 0.054
Task Modality 1 (122) 7.992 0.005** 0.061
Task Modality ×Age 1 (122) 0.143 0.706 0.001
Condition ×Task Modality 1 (122) 36.702 <0.001** 0.231
Condition ×Age ×Task Modality 1 (122) 1.728 0.191 0.014
REACTION TIME
Age 1 (122) 9.401 0.003** 0.072
Condition 1 (122) 112.852 <0.001** 0.481
Condition ×Age 1 (122) 6.912 0.010* 0.054
Task Modality 1 (122) 1.094 0.298 0.009
Task Modality ×Age 1 (122) 145.077 <0.001** 0.543
Condition ×Task Modality 1 (122) 31.572 <0.001** 0.206
Condition ×Age ×Task Modality 1 (122) 6.283 0.014* 0.049
*p<0.05; **p<0.01.
OA selected later Gaps than YA (significant effect of Age),
and gap selection was significant earlier within STcross compared
to both MTtype conditions (significant condition effect; both
p<0.001) (Figure 2E). As indicated by the age by condition
interaction, this condition effect was only driven by OA. Pairwise
post-hoc comparisons revealed significant age differences in
MTtype_vis (p=0.001) and MTtype_aud (p<0.001).
Typing Task
Table 3 and Figure 3 show descriptive data on all typing task
parameters and Table 4 pertinent ANOVA results.
Accuracy scores for typing were significantly lower in OA than
to YA, significantly lower in MTtype conditions than to STtype
conditions and significantly lower in the auditory than the visual
task modality (Figures 3A,B). The condition by age interaction
and corresponding post-hoc tests revealed, that age differences
only occurred in the MT conditions (p<0.001). Differences
between STtype and MTtype conditions occurred for the auditory
task modality (p<0.001), while task modality differences
occurred within both conditions (significant condition ×task
modality interaction).
Reaction Time was significantly longer for OA than YA (age
effect) and longer in MTtype conditions than STtype conditions
(condition effect) (Figures 3C,D). This was particularly true for
OA as shown by the age ×condition interaction. Pairwise
comparisons revealed, that age differences only occurred in the
MTtype conditions (p<0.001) but within both task modalities
(auditory: p=0.001; visual p<0.001; significant age ×task
modality interaction).
Condition differences were found within both task modalities
but task modality differences were only present in STtype
Frontiers in Psychology | www.frontiersin.org 7May 2018 | Volume 9 | Article 602
Janouch et al. Cognitive—Motor Interference While Street Crossing
condition (significant condition ×task modality interaction).
Pairwise comparisons for the condition ×modality ×age
interaction revealed that within the visual task modality, age
differences were present in the single task (p<0.001) as well
as the multitasks (always p<0.001). Within the auditory
task modality, age differences were only present in the STtype
condition (p<0.001).
Multitasking Effects
Table 5 shows MTE scores for crossing as well as typing
parameters, and their differences from zero. Within the visual
task modality, significant non-zero MTE scores emerged for both
age groups in all street crossing parameters and in Reaction Time.
Within the auditory task modality, significant non-zero MTE
scores in both age groups were only yielded for Walking Speed,
Gap, and Accuracy, while significant non-zero MTE for Crossing
Speed,Crossing Failure, and Reaction Time were only found in
OA. These data did not support a consistent relationship between
multitasking deficits and task modality.
Prioritization: Street Crossing-Related
MTE vs. Typing-Related MTE
The ANOVA results for MTE are depicted in Table 6, and the
pertinent post-hoc comparisons are summarized in Table 7. MTE
differed significantly between street crossing-related and typing-
related parameters. OA were more likely to produce significant
differences between street crossing- and typing-related MTE
compared to YA. Further, in the visual task modality significantly
higher MTE occurred more frequently for the street crossing-
related parameters than for the typing-related ones, especially in
OA. This was contrary to findings in the auditory task modality,
where significantly higher MTE were produced more frequently
in the typing task. However, the direction of those differences
was not consistent overall, for a given age group or for a given
modality.
These data argued against an overall or an age-dependent
prioritization of the street-crossing or the typing task. To
emphasize this lack of an overall prioritization strategy, we
plotted the Means of significant street crossing-related vs. typing-
related MTE differences, grouped by age (see Figure 4).
DISCUSSION
The aim of this study was to expand available dual-task
research by using an ecologically valid task, street crossing
in virtual reality, and by including realistic loading tasks and
stimulus modalities. The loading task was delivered via two
different task modalities (visual vs. auditory) to further provide a
theoretical contribution toward the multiple resource theory and
Mulittasking Effects were considered to identify possible general
prioritization strategies.
We expected to confirm that even in our ecologically valid
scenario the costs of multitasking increase in older age and
that this increase is more pronounced in the loading tasks as
compared to the street crossing task, in accordance with the
posture-first hypothesis (Lindenberger et al., 2000; Li et al., 2001;
Schäfer et al., 2006). Further we expected that this increase is
TABLE 5 | Means (M) and Standard Deviations (SD) of multitasking effects within
the visual task modality (MTE visual) and within the auditory task modality (MTE
auditory), and their difference from zero.
MTE visual MTE auditory
YA OA YA OA
M (SD) M (SD) M (SD) M (SD)
CROSSING PARAMETERS
Stay Time (s) −25.91
(68.91)**
−86.94
(191.59)**
−0.79
(6.15)
−1.01
(7.04)
Back-alley Speed
(km/h)
−3.56
(5.29)**
−6.28
(8.45)**
−5.69
(5.08)**
−10.05
(8.13)**
Crossing Speed
(km/h)
−1.61
(4.94)*
−8.10
(8.36)**
−0.95
(5.66)
−5.48
(6.57)**
Crossing Failure
(%)
−5.71
(16.24)**
−11.80
(19.79)**
−7.94
(14.29)
−5.57
(16.38)*
Gap (#) −6.93
(15.65)**
−17.59
(24.93)**
−5.12
(15.39)*
−19.16
(26.91)**
TYPING PARAMETERS
Accuracy 0.12
(1.79)
−1.03
(3.87)
−3.69
(5.28)**
−7.41
(6.36)**
Reaction time −13.18
(11.52)**
−12.74
(13.11)**
−2.60
(11.35)
−10.44
(12.19)**
*p<0.05; **p<0.01.
TABLE 6 | ANOVA results for multitasking effects.
df (Error) FSig η2
p
Age 1 (122) 9.566 0.002** 0.073
Parameter Type 6 (154.025) 19.637 <0.001** 0.139
Parameter Type ×Age 6 (154.025) 5.296 0.016* 0.042
Task Modality 1 (122) 24.151 <0.001** 0.165
Task Modality ×Age 1 (122) 0.068 0.068 0.027
Parameter Type ×Task
Modality
6 (132.331) 18.744 <0.001** 0.133
Parameter Type ×Age
×Task Modality
6 (132.331) 6.180 0.012* 0.048
*p<0.05; **p<0.01.
more pronounced in the visual than the auditory task modality,
in accordance with the multiple-resource model (Wickens, 2002).
In accordance with our first expectation, we found that
street crossing as well as typing performance suffered under
multitasking conditions, and that impairments were more
pronounced in older adults. When multitasking, older adults
slowed down more than young ones when approaching the
curb and when crossing the street, waited longer at the curb,
and therefore selected a later gap for crossing. This is in line
with previous street crossing studies which also found longer
approach durations (Banducci et al., 2016), longer preparation
durations (Neider et al., 2010, 2011; Chaddock et al., 2011, 2012;
Byington and Schwebel, 2013; Gaspar et al., 2014; Banducci et al.,
2016) and more missed crossing opportunities (Stavrinos et al.,
2011; Byington and Schwebel, 2013). Our findings are also in
line with traditional laboratory studies, which found stronger
Frontiers in Psychology | www.frontiersin.org 8May 2018 | Volume 9 | Article 602
Janouch et al. Cognitive—Motor Interference While Street Crossing
TABLE 7 | Post-hoc comparisons between street crossing-related (rows) and typing-related (columns) multitasking effects, separately for each age group (YA; OA) and
task modality.
MTE (%) Visual task modality Auditory task modality
YA OA YA OA YA OA YA OA
Reaction Time Reaction Time Accuracy Accuracy Reaction Time Reaction Time Accuracy Accuracy
Back-alley Speed <0.001** <0.001** 0.003** <0.001** <0.001** <0.001** <0.001** <0.001**
Stay Time 0.002** <0.001** 0.041*<0.001** <0.001**
Crossing Speed <0.001** <0.001** <0.001** 0.001** <0.001** <0.001**
Crossing Failure <0.001**
Gap <0.001** 0.002**
*p<0.05; **p<0.01. Bold, Street crossing MTE >Typing MTE; Italic, Typing MTE >Street crossing MTE.
effects of multitasking on gait speed for older than young persons
(Lindenberger et al., 2000; Hausdorff et al., 2008).
The age-related decrement of multitasking abilities manifested
not only in four of our five street-crossing parameters, but also
in both loading-task parameters. This conforms earlier findings
about differential effects of age on loading-task performance
(Lindenberger et al., 2000; Li et al., 2012), and extends them to
an ecologically valid scenario.
Surprisingly, we did not find age-related decrements in
Crossing Speed, even though a reduced walking speed in OA
compared to YA under dual-task conditions has been reported in
dual-task gait studies before (Lindenberger et al., 2000; Hausdorff
et al., 2008). In contrast, Neider et al. (2011) reported even
smaller crossing durations (i.e., faster crossing speeds) in OA
as compared to YA and interpreted this finding as a greater
perceived urge in OA to avoid (virtual) collisions. In our study,
both age groups crossed the street very quickly (around 6 km/h)
which means that especially OA accelerated their regular walking
speed (4.6–4.9 km/h; Samson et al., 2001) and invested more
motor (and cognitive) resources in order to complete the crossing
as fast as possible. Especially in a demanding multitasking
situation, this additional invest of resources might exceed the
limits of their processing capacity.
In accordance with our second expectation, multitasking had
more pronounced effects on the loading tasks than on the street-
crossing task, but this was the case for only about half of the
age ×modality ×parameter combinations (cf. Table 5). For
the other half, multitasking had more pronounced effects on
the street-crossing task. This heterogeneity persists even when
only the two crossing parameters with the closest link to posture
and gait are considered, namely, Back-alley Speed and Crossing
Speed. From this we conclude that the posture-first hypotheis
may not be applicable unconditionally (Li et al., 2012), and in
ecologically valid scenarios. These heterogeneous results could
either indicate implicit, individual prioritization strategies or a
limitation due to task difficulties. Thus, participants may either
have not perceived a risk to their health out of the virtual reality
which would limit the extent to what VR scenarios transmit a
real life impression or might have been limited by ceiling effects
within some tasks. Overall, it appears that realistic loading tasks
can be motivating enough to override older persons’ concerns
about postural stability. However, it has to be mentioned that
in our study, participants were allowed to keep one hand to the
treadmill’s handrail which might have influenced participant’s
perceived postural control. In this vein, Lövdén et al. (2005)
revealed that older adults’ navigation performance improves
when holding on to a handrail. Thus, future studies should
systematically investigate the influence of additionl support and
might also assess general as well as test set up-related anxiety
scores such as fear of falls.
Inconsistent with our third expectation, effects of multitasking
were not consistently more pronounced in the visual compared
to the auditory modality (cf. Tables 3,4). More pronounced
multitasking effects in the visual than auditory condition are
in accordance with an earlier virtual-driving study where visual
and auditory loading tasks were presented blockwise (Chaparro
et al., 2005). In our study, however, stronger effects of the visual
modality were observed for only a part of the age ×parameter
combinations. This was most striking for Stay Time, for which the
visual task modality (in YA as well as in OA) caused the highest
overall MTE of all parameters, implicating that within this phase
of the crossing process, vision might play an indispensable role.
However, for other combinations, both modalities yielded similar
effects or the auditory modality even yielded stronger effects e.g.,
for Back Alley Speed which was surprisingly more effected by
the auditory task modality. We therefore found no unequivocal
support for the multiple resource model in our ecologically
valid scenario. Possibly, our multitasking scenario was complex
enough to give participants a choice exactly what resources they
allocated to the task at hand. As a consequence, participants’
strategic choices could have upset any strict relationship between
task modality and multitasking effects.
The lack of a consistent relationship between task modality
and multitasking performance is particularly striking for our
loading task: multitasking effects on Typing Reaction Time were
more pronounced in the visual modality, while those on Typing
Accuracy were stronger in the auditory modality. We contribute
this particular dissimilarity to the fact that in the German
language, the ten’s and one’s of numbers are spoken in reversed
order (e.g., “two hundred and five-and-forty” instead of “two
hundred and forty-five”). If participants pressed the keys in the
same order in which digits were spoken, this would have reduced.
In conclusion, our findings confirm that age-related deficits
of multitasking exist even in ecologically valid scenarios, and
Frontiers in Psychology | www.frontiersin.org 9May 2018 | Volume 9 | Article 602
Janouch et al. Cognitive—Motor Interference While Street Crossing
document that those deficits can emerge in both concurrent
tasks. However, our findings provide no unequivocal support
for the posture-first hypothesis and for the multiple-resource
model. We attribute this lack of support to motivational and to
strategic factors, which are controlled for in traditional laboratory
paradigms but play a major role in realistic behavior.
AUTHOR CONTRIBUTIONS
CJ performed the acquisition of data and data analyses and wrote
the drafts of the manuscript. CV-R as a senior author as well
as OB contributed in terms of data selection, data analysis as
well as in editing the manuscript and providing additional ideas
at how to interpret our data. KW, UD and MH supported the
data collection as well as the data analysis and proof read the
manuscript.
ACKNOWLEDGMENTS
This research was supported by a grant within the Priority
Program, SPP 1772 from the German Research Foundation
(Deutsche Forschungsgemeinschaft, DFG), grants BO 649/22-1
and VO 1432/19-1 and by a grant of the European Social Fund
and the Free State of Saxony (CJ).
We wish to thank Wim Van Winsum and Thomas Kesnerus
for the modification of the simulator software and constant
support. We thank Rieke Schmale, Gisli Barthelmess, Sandra
Goertz, Verena Herzoff, Clara Rentz, Damaris Wallmeroth,
Jacqueline Kasemir, Lorenz Baumjohann, Martin Rebro.
Karim Aly, Stella Henn, Vincent Drießen, Isarn Babel, Maria
Andrea Neubert, Malte Liebl-Wachsmuth, and Karolina
Boxberger for their assistance in data collection and data
analyses.
REFERENCES
Allport, D. A., Antonis, B., and Reynolds, P. (1972). On the division of attention:
a disproof of the single channel hypothesis. Q. J. Exp. Psychol. 24, 225–235.
doi: 10.1080/00335557243000102
Al-Yahya, E., Dawes, H., Smith, L., Dennis, A., Howells, K., and Cockburn,
J. (2011). Cognitive motor interference while walking: a systematic
review and meta-analysis. Neurosci. Biobehav. Rev. 35, 715–728.
doi: 10.1016/j.neubiorev.2010.08.008
Baltes, P. B., and Lindenberger, U. (1997). Emergence of a powerful connection
between sensory and cognitive functions across the adult life span: a
new window to the study of cognitive aging? Psychol. Aging 12, 12–21.
doi: 10.1037/0882-7974.12.1.12
Banducci, S. E., Ward, N., Gaspar, J. G., Schab, K. R., Crowell, J. A.,
Kaczmarski, H., et al. (2016). The effects of cell phone and text message
conversations on simulated street crossing. Hum. Factors 58, 150–162.
doi: 10.1177/0018720815609501
Bock, O., and Beurskens, R. (2010). Changes of locomotion in old age depend on
task setting. Gait Posture 32, 645–649. doi: 10.1016/j.gaitpost.2010.09.009
Bock, O., and Züll, A. (2013). Characteristics of grasping movements in a
laboratory and in an everyday-like context. Hum. Mov. Sci. 32, 249–256.
doi: 10.1016/j.humov.2012.12.009
Byington, K. W., and Schwebel, D. C. (2013). Effects of mobile Internet use
on college student pedestrian injury risk. Accid. Anal. Prev. 51, 78–83.
doi: 10.1016/j.aap.2012.11.001
Chaddock, L., Neider, M. B., Lutz, A., Hillman, C. H., and Kramer, A. F. (2012).
Role of childhood aerobic fitness in successful street crossing. Med. Sci. Sports
Exerc. 44, 749–753. doi: 10.1249/MSS.0b013e31823a90cb
Chaddock, L., Neider, M. B., Voss, M. W., Gaspar, J. G., and Kramer, A. F. (2011).
Do athletes excel at everyday tasks? Med. Sci. Sports Exerc. 43, 1920–1926.
doi: 10.1249/MSS.0b013e318218ca74
Chaparro, A., Wood, J. M., and Carberry, T. (2005). Effects of age and auditory
and visual dual tasks on closed-road driving performance. Optom. Vis. Sci. 82,
747–754. doi: 10.1097/01.opx.0000174724.74957.45
Chaytor, N., and Schmitter-Edgecombe, M. (2003). The ecological validity of
neuropsychological tests: a review of the literature on everyday cognitive
skills. Neuropsychol. Rev. 13, 181–197. doi: 10.1023/B:NERV.0000009483.
91468.fb
Damato, B., and Groenewald, C. (2003). Multifixation campimetry on line: a
perimeter for the detection of visual field loss using the internet. Br. J.
Ophthalmol. 87, 1296–1298. doi: 10.1136/bjo.87.10.1296
Dommes, A., Cavallo, V., Dubuisson, J. B., Tournier, I., and Vienne, F. (2014).
Crossing a two-way street: comparison of young and old pedestrians. J. Saf.
Res. 50, 27–34. doi: 10.1016/j.jsr.2014.03.008
Duncan, J., Martens, S., and Ward, R. (1997). Restricted attentional capacity within
but not between sensory modalities. Nature 387, 808–810. doi: 10.1038/42947
Folstein, M. F., Folstein, S. E., and McHugh, P. R. (1975). “Mini-mental state”: a
practical method for grading the cognitive state of patients for the clinician.
J. Psychiatr. Res. 12, 189–198.
Gaspar, J. G., Neider, M. B., Crowell, J. A., Lutz, A., Kaczmarski, H., and Kramer,
A. F. (2014). Are gamers better crossers? An examination of action video game
experience and dual task effects in a simulated street crossing task. Hum. Factors
56, 443–452. doi: 10.1177/0018720813499930
Hausdorff, J. M., Schweiger, A., Herman, T., Yogev-Seligmann, G., and Giladi,
N. (2008). Dual task decrements in gait: contributing factors among
healthy older adults. J. Gerontol. A Biol. Sci. Med. Sci. 63, 1335–1343.
doi: 10.1093/gerona/63.12.1335
Hausdorff, J. M., Yogev, G., Springer, S., Simon, E. S., and Giladi, N. (2005).
Walking is more like catching than tapping: gait in the elderly as a
complex cognitive task. Exp. Brain Res. 164, 541–548. doi: 10.1007/s00221-005-
2280-3
Isreal, J. B., Chesney, G. L., Wickens, C. D., and Donchin, E. (1980).
P300 and tracking difficulty: evidence for multiple resources in dual-task
performance. Psychophysiology 17, 259–273. doi: 10.1111/j.1469-8986.1980.
tb00146.x
Kahneman, D. (1973). Attention and Effort, Vol. 1063. Prentice-Hall Englewood
Cliffs.
Keeffe, J. E., Jin, C., Weih, L., McCarty, C. A., and Taylor, H. (2002). Vision
impairment and older drivers: who’s driving? Br. J. Ophthalmol. 86, 1118–1121.
doi: 10.1136/bjo.86.10.1118
Kelly, V. E., Janke, A. A., and Shumway-Cook, A. (2010). Effects of
instructed focus and task difficulty on concurrent walking and cognitive
task performance in healthy young adults. Exp. Brain Res. 207, 65–73.
doi: 10.1007/s00221-010-2429-6
Kray, J., and Lindenberger, U. (2000). Adult age differences in task switching.
Psychol. Aging 15, 126–147. doi: 10.1037/0882-7974.15.1.126
Li, K. Z., Abbud, G. A., Fraser, S. A., and DeMont, R. G. (2012). Successful
adaptation of gait in healthy older adults during dual-task treadmill walking.
Aging Neuropsychol. Cogn. 19, 150–167. doi: 10.1080/13825585.2011.628375
Li, K. Z. H., Krampe, R. T., and Bondar, A. (2005). “An ecological approach to
studying aging and dual-task performance,” in Cognitive Limitations in Aging
and Psychopathology: Attention, Working Memory, and Executive Functions, eds
R. W. Engle, G. Sedek, U. von Hecker, and D. N. McIntosh (New York, NY:
Cambridge University Press), 190–218.
Li, K. Z., and Lindenberger, U. (2002). Relations between aging
sensory/sensorimotor and cognitive functions. Neurosci. Biobehav. Rev.
26, 777–783. doi: 10.1016/S0149-7634(02)00073-8
Li, K. Z., Lindenberger, U., Freund, A. M., and Baltes, P. B. (2001). Walking while
memorizing: age-related differences in compensatory behavior. Psychol. Sci. 12,
230–237. doi: 10.1111/1467-9280.00341
Liberty Mutual Insurance (2013). Liberty Mutual Insurance Pedestrian Safety
Survey Infographic.Available online at: http://www.libertymutualgroup.com/
Frontiers in Psychology | www.frontiersin.org 11 May 2018 | Volume 9 | Article 602
Janouch et al. Cognitive—Motor Interference While Street Crossing
omapps/ContentServer?fid=3237831502381&pagename=LMGroup%2FViews
%2FLMG&ft=8&cid=1240015377571 (Accessed November 10, 2017).
Lindenberger, U., Marsiske, M., and Baltes, P. B. (2000). Memorizing while
walking: increase in dual-task costs from young adulthood to old age. Psychol.
Aging 15, 417–436. doi: 10.1037/0882-7974.15.3.417
Lopez Maite, C., Gaetane, D., and Axel, C. (2016). Ecological assessment of divided
attention: what about the current tools and the relevancy of virtual reality. Rev.
Neurol. 172, 270–280. doi: 10.1016/j.neurol.2016.01.399
Lövdén, M., Schellenbach, M., Grossman-Hutter, B., Krüger, A., and Lindenberger,
U. (2005). Environmental topography and postural control demands shape
aging-associated decrements in spatial navigation performance. Psychol. Aging
20, 683–694. doi: 10.1037/0882-7974.20.4.683
Morrongiello, B. A., and Corbett, M. (2015). Using a virtual environment to
study child pedestrian behaviours: a comparison of parents’ expectations
and children’s street crossing behaviour. Inj. Prev. 21, 291–295.
doi: 10.1136/injuryprev-2014-041508
Neider, M. B., Gaspar, J. G., McCarley, J. S., Crowell, J. A., Kaczmarski, H.,
and Kramer, A. F. (2011). Walking and talking: dual-task effects on street
crossing behavior in older adults. Psychol. Aging 26, 260–268. doi: 10.1037/a00
21566
Neider, M. B., McCarley, J. S., Crowell, J. A., Kaczmarski, H., and Kramer, A. F.
(2010). Pedestrians, vehicles, and cell phones. Accid. Anal. Prev. 42, 589–594.
doi: 10.1016/j.aap.2009.10.004
Oldfield, R. C. (1971). The assessment and analysis of handedness: the
Edinburgh inventory. Neuropsychologia 9, 97–113. doi: 10.1016/0028-3932(71)
90067-4
Plummer, P., and Eskes, G. (2015). Measuring treatment effects on dual-task
performance: a framework for research and clinical practice. Front. Hum.
Neurosci. 9:225. doi: 10.3389/fnhum.2015.00225
Samson, M., Crowe, A., De Vreede, P., Dessens, J., Duursma, S., and Verhaar, H.
(2001). Differences in gait parameters at a preferred walking speed in healthy
subjects due to age, height and body weight. Aging Clin. Exp. Res. 13, 16–21.
doi: 10.1007/BF03351489
Schäfer, S., Huxhold, O., and Lindenberger, U. (2006). Healthy mind in healthy
body? A review of sensorimotor–cognitive interdependencies in old age. Eur.
Rev. Aging Phys. Act. 3, 45–54. doi: 10.1007/s11556-006-0007-5
Schaefer, S., and Schumacher, V. (2011). The interplay between cognitive and
motor functioning in healthy older adults: findings from dual-task studies
and suggestions for intervention. Gerontology 57, 239–246. doi: 10.1159/000
322197
Schwebel, D. C., Barton, B. K., Shen, J., Wells, H. L., Bogar, A., Heath, G.,
et al. (2014). Systematic review and meta-analysis of behavioral interventions
to improve child pedestrian safety. J. Pediatr. Psychol. 39, 826–845.
doi: 10.1093/jpepsy/jsu024
Shumway-Cook, A., and Woollacott, M. (2000). Attentional demands and postural
control: the effect of sensory context. J. Gerontol. Ser. A Biol. Med. Sci. 55,
M10–M16. doi: 10.1093/gerona/55.1.M10
Stavrinos, D., Byington, K. W., and Schwebel, D. C. (2011). Distracted walking:
cell phones increase injury risk for college pedestrians. J. Saf. Res. 42, 101–107.
doi: 10.1016/j.jsr.2011.01.004
Tabachnick, B. G., and Fidell, L. S. (2001). Using Multivariate Analysis. Boston,
MA: Allyn and Bacon.
Talsma, D., Doty, T. J., Strowd, R., and Woldorff, M. G. (2006). Attentional
capacity for processing concurrent stimuli is larger across sensory
modalities than within a modality. Psychophysiology 43, 541–549.
doi: 10.1111/j.1469-8986.2006.00452.x
Tapiro, H., Oron-Gilad, T., and Parmet, Y. (2016). Cell phone conversations and
child pedestrian’s crossing behavior; a simulator study. Safety Sci. 89, 36–44.
doi: 10.1016/j.ssci.2016.05.013
Verhaeghen, P., Marcoen, A., and Goossens, L. (1993). Facts and fiction about
memory aging: a quantitative integration of research findings. J. Gerontol. 48,
P157–P171. doi: 10.1093/geronj/48.4.P157
Verhaeghen, P., Martin, M., and Sedek, G. (2012). Reconnecting cognition in the
lab and cognition in real life: the role of compensatory social and motivational
factors in explaining how cognition ages in the wild. Neuropsychol. Dev. Cogn.
B Aging Neuropsychol. Cogn. 19, 1–12. doi: 10.1080/13825585.2011.645009
Verhaeghen, P., Steitz, D. W., Sliwinski, M. J., and Cerella, J. (2003). Aging
and dual-task performance: a meta-analysis. Psychol. Aging. 18, 443–460.
doi: 10.1037/0882-7974.18.3.443
Wickens, C. D. (2002). Multiple resources and performance prediction. Theor.
Issues Ergon. Sci. 3, 159–177. doi: 10.1080/14639220210123806
Wickens, C., and McCarley, J. (2007). Applied Attention Theory. Boca Raton, FL:
CRC Press.
Wollesen, B., and Voelcker-Rehage, C. (2014). Training effects on motor–cognitive
dual-task performance in older adults. Eur. Rev. Aging Phys. Act. 11, 5–24.
doi: 10.1007/s11556-013-0122-z
Woollacott, M., and Shumway-Cook, A. (2002). Attention and the control of
posture and gait: a review of an emerging area of research. Gait Posture 16,
1–14. doi: 10.1016/S0966-6362(01)00156-4
Yogev-Seligmann, G., Hausdorff, J. M., and Giladi, N. (2008). The role
of executive function and attention in gait. Mov. Disord. 23, 329–342.
doi: 10.1002/mds.21720
Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2018 Janouch, Drescher, Wechsler, Haeger, Bock and Voelcker-Rehage.
This is an open-access article distributed under the terms of the Creative Commons
Attribution License (CC BY). The use, distribution or reproduction in other forums
is permitted, provided the original author(s) and the copyright owner are credited
and that the original publication in this journal is cited, in accordance with accepted
academic practice. No use, distribution or reproduction is permitted which does not
comply with these terms.
Frontiers in Psychology | www.frontiersin.org 12 May 2018 | Volume 9 | Article 602
