www.nature.com/scientificreports

OPEN

Received: 17 May 2018
Accepted: 24 January 2019
Published: xx xx xxxx

Developing attentional control innaturalistic dynamic road crossing
situations
Victoria I. Nicholls 1, Geraldine Jean-Charles2, Junpeng Lao
Roberto Caldara2 & Sebastien Miellet 3

2, Peter de Lissa2,

In the last 20 years, there has been increasing interest in studying visual attentional processes undermore natural conditions. In the present study, we propose to determine the critical age at whichchildren show similar to adult performance and attentional control in a visually guided task; in anaturalistic dynamic and socially relevant context: road crossing. We monitored visual explorationand crossing decisions in adults and children aged between 5 and 15 while they watched road traffic
videos containing a range of traffic densities with or without pedestrians. 5–10 year old (y/o) children
showed less systematic gaze patterns. More specifically, adults and 11–15 y/o children look mainly at
the vehicles’ appearing point, which is an optimal location to sample diagnostic information for thetask. In contrast, 5–10 y/os look more at socially relevant stimuli and attend to moving vehicles further
down the trajectory when the traffic density is high. Critically, 5-10 y/o children also make an increased
number of crossing decisions compared to 11–15 y/os and adults. Our findings reveal a critical shift
around 10 y/o in attentional control and crossing decisions in a road crossing task.
Human visually guided behaviour relies on the selective uptake of information, due to sensory and cognitive
limitations1. In other words, human vision is a dynamic process, during which the observer actively samples theenvironment in order to gather diagnostic information for the task at hand. This is made possible by our attentional systems selecting information based on bottom-up stimulation2–6 and top-down influences7–11.
The focus on top-down processes has been of increasing interest in the last 20 years, as the study of visual processing has sought to involve more natural conditions and realistic stimuli. Many recent studies used photographs
and included eye movement recordings to look at the influence of context on visual attention11–16. For instance,these studies have shown influences of semantic17, episodic top-down processes18–20, as well as scene context onparafoveal processing of objects21,22. More recently, the role of the observer’s intention and understanding of thescene has been emphasized23,24. In this perspective, oculomotor planning is seen as making predictions about thelocations of diagnostic information for the task25.
These top-down processes are generally considered as being mainly under control of the frontal regions, which
are still maturing during childhood26–31. This protracted maturation of the frontal lobes has been associated witha lack of top-down attentional control32 and a deficit of top-down inhibition of reflexive, automatic saccades33,34.
This is consistent with research showing more express saccades, a slower pro- and anti-saccade reaction time anda higher error rate in the anti-saccade tasks for children compared to adults35–38. In terms of critical age, Leclercq
and Siéroff39 suggested that 6 and 8 (y/o) year old children fail to inhibit attentional capture by goal irrelevant
stimuli while 10 y/os and adults succeed. Klein,Fischer, Hartnegg, Heiss and Roth37; Klein and Foerster36 showedthat adults exhibited faster saccades and fewer prosaccades during the anti-saccade task than 10–11 y/os, who inturn had faster sacacdes and fewer prosaccades than 6 and 7 y/os. Additionally, Munoz and colleagues38 showedthat children between 5 and 8 y/o had slow saccadic reaction times (SRTs) and the most direction errors in theanti-saccade task, which is related to the protracted maturation of the frontal lobes33. These studies suggest thatchildren between 8 and 10 y/o have similar attentional control skills to adults. However, it is important to mention
that most of these studies used basic situations involving only flashing of very simple target stimuli. As explained
above, more naturalistic situations and stimuli involve top-down processes more strongly. Thus, it is possible that1

Bournemouth University, Faculty of Science & Technology, Bournemouth, BH12 1PH, United Kingdom. 2University
of Fribourg, Department of Psychology, iBM lab, Fribourg, 1700, Switzerland. 3University of Wollongong, School of
Psychology, ActiveVision lab, Wollongong, 2522, Australia. Correspondence and requests for materials should be
addressed to S.M. (email: smiellet@uow.edu.au)
Scientific Reports |(2019) 9:4176 | https://doi.org/10.1038/s41598-019-39737-7

1www.nature.com/scientificreports/

www.nature.com/scientificreports

the complexity and realism of the task influences the critical age at which children show stronger oculomotor
capture and decreased inhibition of responses to task irrelevant distractors compared to adults. Using static natural images, Açık and colleagues40 found that children under the age of 10 use oculomotor strategies particularly
influenced by bottom-up processes. These authors tested children from 7–9 y/o, adults from 19–27 y/o, and older
adults above 72 years of age. More recently, Kuhn and Teszka41 explored differences in attentional control between
adults and children within a more natural context. Their results suggest that children below the age of 10 are more
distracted than adults and this influences how they experience the world around them.
In the present study, we propose to determine the trajectory of the difference in attentional control, its effecton information sampling, and behaviour between children and adults. Moreover, the present study aims to determine whether there is a critical age at which children show similar attentional control skills and behavioural performance to adults, in a naturalistic, dynamic, and socially relevant task. To these aims, we created a road crossing
task which requires advanced attentional skills. Crucially, besides addressing an important theoretical question,
the present study aims to shed light on a critical practical issue associated with road safety. Road traffic accidents
killed 273,000 pedestrians worldwide in 2010 – 22% of all road traffic accidents that year42. Regarding childcasualties, 186,300 children died from road traffic related incidents across the world in 2012, and 38% of these
deaths were child pedestrians43. Recurrent observations seem to point towards specific perceptual, cognitive, andbehavioural aspects involved in children’s susceptibility to road traffic accidents. For instance, studies using various experimental techniques consistently showed that younger children take longer to enter a safe traffic gap thando older children (judgments on videos44, cycling in a virtual environment45, road crossing simulation46), which
overlaps with other developing skills such as perceptual and motor abilities47. Previous studies investigating theeffect of perceptual processes on road crossing performance reported that children aged under 8 y/o looked lessoften at traffic48,49, and when they did it was often in the opposite direction of oncoming traffic48. 8–10 y/os monitored traffic less when vehicles were further away than when they were closer50. These looking behaviours werecorrelated with children under 8–10 y/o making more unsafe crossing decisions.
However, these studies investigated perceptual processes using very general descriptions of the children looking towards or away from the traffic during simulated and real crosswalks49,51. Similarly, studies using virtual
reality (VR) reported head movements following the traffic50 or between computer screens, as well as duration
looking at a computer screen52. None of these studies, however, provide us with a fine-grained description of
how exactly children explore the visual field compared to adults, and how these explorations affect road crossing decisions. Tapiro and colleagues53 conducted a more fine-grained analysis of visual attention of adults andchildren in road crossing situations. Children looked preferentially at areas in front of them, while adults looked
preferentially at more distant locations. However, the analysis relies on areas of interest (AOIs) based on a-priori
segmentation of the stimulus space, preventing the data driven discovery of meaningful patterns (see54 for a
discussion on the limitations of AOIs). Critically, to the best of our knowledge, there is no study in the literaturethat includes distractors and their effects on visual exploration as a way to investigate attention switching andinhibitory control, and how these develop in children for real world scenarios.
The current study aimed to isolate the critical age at which road crossing decisions and oculomotor patterns ofchildren differ from those of adults. We wanted to characterise precisely children’s visual processing specificities
and explore the impact of distractors and task complexity. Based on the psychophysics and the road safety literatures our main hypothesis was that children under 10 y/o, for whom studies have shown a reduced inhibitory
control attributed to protracted maturation of the frontal lobes33 and fewer safe crossing decisions, would produce
an increased number of saccades towards task irrelevant stimuli which would, in turn, impact negatively on optimal information sampling for road crossing decisions. We therefore presented child participants aged 5 to 15 (andadult controls) with videos of naturalistic road crossing scenarios. Participants were then asked to decide when
to initiate a road crossing and to keep pressing the key as long as the crossing was possible. We included varying
levels of traffic density to investigate how this factor influences task difficulty and attention switching, and thus
crossing and eye movement behaviours. Additionally, we included pedestrians as they are known to be a potent
distractors for attentional capture, in order to test for inhibitory control.

Results

Eye Movement Results.

Global characteristics. General oculomotor characteristics were within a similar
range for each age group (see Table 1). Critically, all age groups showed an impact of pedestrians on their global
oculomotor characteristics, while only 5–10 y/os showed an impact of traffic density. There was an overall trend
for the number of fixations for 5–10 and 11–15 y/os to increase when pedestrians were present in the scene. Adults
and 11–15 y/os showed an overall trend to decrease their number of pursuits. All groups showed an overall trendof increasing trial time as fixation when pedestrians were present. Additionally, 11–15 y/os and adults showed an
overall trend of decreasing total trial time as pursuit when pedestrians were present. Finally, 5–10 y/os showed an
overall trend of decreasing the number of pursuits with lower traffic density. Supplementary Figures S2–S19 andsummary S20 provide a detailed description of subtle differences in the distributions at the decile level.
Gaze Similarity. In addition to looking at global eye movement characteristics, we investigated the variability
in gaze patterns across the trials and age groups through gaze similarity matrices (GSMs). GSMs are based on
pairwise correlations between the trials’ smoothed gaze maps. Thus, GSMs reveal the variability or consistency ingaze locations through the experiment (across trials). Figure 1d shows, for each of the 100 trials, the average correlation between its gaze map and the gaze maps generated by the other 99 trials. The trials are sorted according to
how consistent their gaze map is compared to all the other trials. The shaded areas represent bootstrap confidence
intervals across participants. Figure 1a–c suggests that 5–10 y/os have the least consistency in gaze behaviours
across trials, while adults are the most consistent. This is illustrated most clearly by Fig. 1d which shows that

Scientific Reports |(2019) 9:4176 | https://doi.org/10.1038/s41598-019-39737-72

www.nature.com/scientificreports

www.nature.com/scientificreports/
Global characteristics 5–10 yr old

11–15 yr old

AdultsFixations
Number of

19.15[18.95,19.36]

20.89[20.68,21.11]

21.92[21.58,22.26]

Trial time as(ms)3319.84[3261,3379]

3635.79[3578,3694]

3435.11[3332,3538]

Number of

8.79[8.65,8.93]

9.29[9.15,9.43]

10.38[10.11,10.64]

Trial time as(ms)4738.25[4667,4809]

5043.59[4977,5110]

5164.51[5048,5282]

Number of

41.61[40.40,42.80]

39.11[38.52,39.69]

45.02[43.65,46.40]

Trial time as(ms)906.26[888.3,924.4]

814.48[802.6,826.5]

850.119[829.3,870.9]

Smooth Pursuits

SaccadesTable 1. General oculomotor characteristics. The mean number of and proportion of trial time as each eye
movement type. Square brackets contain 95% confidence intervals.

Figure 1. Gaze similarity figures. Panels (a–c) are mean GSMs for each group. Panel (d) is the mean Fishertransformed correlation coefficient, with bootstrap confidence intervals for each trial, sorted by highest value.
Data in yellow are from adults, blue from the 11–15 y/o group, and green from the 5–10 y/o group.

5–10 y/os produce significantly less consistent gaze patterns across trials compared to 11–15 y/os, and adults, who
do not differ from each other.
Statistical Mapping. Statistical mapping using iMap455 allowed us to spatially isolate the effect of age on gaze pattern. Moreover, we explored how distractors and task difficulty specifically impact on gaze distribution across ages.
iMap analysis revealed that age group impacted on the favoured gaze location on the videos. The statistical
map for the main effect of age (Fig. 2a) shows significant differences at the beginning of the vehicle’s trajectory
and the sidewalks. This age effect can be characterised by representing the differential gaze distributions for eachage group. More precisely, older participants maintain their gaze within a smaller area (Fig. 2d–f) – adults gaze
mainly at the beginning of the vehicle’s trajectory while 11–15 and 5–10 y/os progressively show a wider gaze distribution covering the sidewalks and a larger proportion of the road (significant areas 2830, 3409 and 4183 pixels
Scientific Reports |(2019) 9:4176 | https://doi.org/10.1038/s41598-019-39737-73

www.nature.com/scientificreports/

www.nature.com/scientificreports

Figure 2. iMap4 analysis. (A) Statistical gaze maps with colour coding indicating the F-values. (B) Beta maps
for each age group. (C) Pairwise contrasts between each age group. Warm colours indicate areas where theolder age group looked more, compared to the younger age group. (D) Effect of pedestrian presence and traffic
density for 5–10 y/os.

for adults, 11–15 y/os and 5–10 y/os respectively). Figure 2g–i illustrates this by representing pairwise contrast
between all age groups, depicting statistical differences in gaze distributions across age groups.
The interaction between age group and pedestrian presence (Fig. 2b) reveals a significant area over the sidewalks. The interaction between age group and traffic density (Fig. 2c) shows a significant area on the part of theroad corresponding to approaching vehicles. These significant interactions were investigated further via simple
effects of pedestrian presence and traffic density for each age group (Fig. 2j,k). The effect of pedestrian presence
and traffic density were only significant for 5–10 y/os (Fig. 2j). When a pedestrian was present in the videos,
5–10 y/o children looked more at the sidewalks (Fig. 2k) which was not the case for 11–15 y/os and adults. Whenthe traffic was dense (more than 3 vehicles on screen) 5–10 y/o children looked further down the vehicle’s trajectory (compared to the maximum of their gaze distribution, at the appearing point). Such an effect was notobserved for 11–15 y/os and adults.

Road-crossing decisions.

A k-means analysis on the mean number of crossing decisions per participant
corroborated differences in performance for children below and above 11 y/o. Indeed, the k-means procedureisolated the following clusters: 5–10 y/os (mean = 8, SD = 1) and 11–15 y/os (mean = 13, SD = 1). The Yuen’s
test showed 5–10 y/os made significantly more button presses than 11–15 y/os (t = 10.70, df = 3414, p < 0.05,
d = 0.29, see Fig. 3a) and adults (t = 9.86, df = 2410, p < 0.05, d = 0.25). Contrastingly, 11–15 y/os do not differ
from adults in the number button presses (t = 1.27,df = 1755,p = 0.20,d = −0.043). The Yuen’s test showed 5–10 y/os pressed for longer than adults (t = 8.42, df = 1650.77, p < 0.05, d = 0.25; see Fig. 3b) and 11–15 y/os (t = 8.25,
df = 3361.33, p < 0.05, d = 0.23). 11–15 y/os did not press differently from adults (t = 0.876, df = 1440.98,
p = 0.38, d = 0.03).

Discussion

We recorded eye movements of adults and children while they watched videos of road traffic and were asked todecide when they believed they could cross the road. Eye movement data showed that 5–10 y/os exhibited a muchless systematic gaze scanpath than older children or adults. Indeed, older children and adults mainly looked at
the beginning of the vehicle’s trajectory. In contrast, younger children showed sparse gaze distributions, covering
Scientific Reports |(2019) 9:4176 | https://doi.org/10.1038/s41598-019-39737-7

4www.nature.com/scientificreports/

www.nature.com/scientificreports

Figure 3. The mean number of crossing decisions (a) and mean button press durations (b) per trial for
individual participants. Each figure is a scatter plot with coloured dots indicating the different groups
determined by k-means clustering. The yellow scatter points are the adult group data, blue represent 11–15 y/os,
and green are 5–10 y/os. The ellipses highlight the clusters identified by k means (button press number only).

sidewalks and the vehicle’s trajectory closer to them. All age groups showed disruptions in general oculomotor
characteristics depending on the presence of pedestrians in the scene. However, only younger children showed
direct gazing at the areas with pedestrian distractors. The traffic density had an effect on younger children’s general oculomotor characteristics, with more fixations in locations closer to the observer. The crossing decision
results are consistent with previous literature56,57, and confirm a critical age of 10, under which children mademore crossing decisions.
The higher number of road crossing decisions for 5–10 y/os compared to adults and older children were associated with gaze pattern biases. The young children’s gaze patterns were characterised by less consistency acrosstrials and more spread across the stimulus space. More specifically, younger children looked significantly more at
the sidewalk area than adults and older children when human beings were present in the scene. This suggests thathuman beings attract the overt attention of younger children but not of older children and adults. Interestingly,
human beings in the scene disrupted general oculomotor measures (more fixations, fewer pursuits, smaller proportion of trial as pursuit, and larger proportion of trial as fixation) for all age groups. Hence, it seems thatsocially relevant stimuli (including faces, body motion, etc.) capture the covert attention of all age groups but that
only younger children direct their gaze towards this type of stimuli, which are irrelevant to the crossing task. It
is possible that older children and adults are able to inhibit saccades towards irrelevant stimuli, while younger
children are lacking the inhibitory control to do so. This scenario is consistent with the findings of psychophysicaland neuroscientific studies that children are less able to inhibit automatic saccades, instead directing their overt
attention towards task irrelevant stimuli, which is linked in the literature to the ongoing maturation of executivefunctions due to a protracted maturation of frontal lobes33,34.
In all traffic density situations, adults and 11–15 y/os look at the top-left of the road in our paradigm – theappearing point of the cars. We propose that the reason this strategy is used is that this location represents an
Scientific Reports |(2019) 9:4176 | https://doi.org/10.1038/s41598-019-39737-7

5www.nature.com/scientificreports/

www.nature.com/scientificreports

ideal fixation position for assessing the vehicle’s speed and time to impact as early as possible. Moreover, this gazelocation allows the pedestrians to monitor for new vehicles entering the lane, thus to detect gaps, or end of gaps,
very early. As the vehicles approach closer to the pedestrians, they could easily be tracked using peripheral vision
as their retinal projection gets larger. Children aged between 5 and 10 also appear to be able to use this strategy,
as their gaze is also focused on the appearing point of the car. However, in high traffic density situations, children
look at the appearing point, as well as further down the road. We suggest that this is because they are following thecars down the road with their gaze, rather than just gazing at the appearing point. This may be due to an inabilityof 5–10 y/os to disengage their attention from task irrelevant stimuli, once their attention has been drawn by
them. This hypothesis is consistent with studies showing that individuals in general are drawn to stimuli before
disengaging to focus their attention on the target stimuli58,59. While pursuing the vehicles, the observers’ attention
is focused on the vehicle moving down the road and is not be able to attend to other vehicles entering the road.
This scenario is in line with studies showing that participants are not able to allocate much attention to objects inthe periphery while pursuing a target60,61. Without accurate information about vehicle position children would
not be able to make informed crossing decisions which could lead them to cross unsafely.
Overall, our results show systematic links between eye movement patterns and road crossing decisions acrossdevelopment. We propose that gaze locations have a direct impact on crossing decisions. Children orient theirovert attention towards human distractors more than 11–15 y/os and adults. This tendency would impair theirability to attend to the vehicles, thus making accurate judgements about a vehicle’s distance more difficult.
Our study provides new important insights in children’s deficits in attentional control in realistic situations,particularly their vulnerability as pedestrians. Our findings are consistent with recent studies that show a very
similar pattern of development. In real life situations, Connelly and colleagues56 demonstrated that children
below 11 years of age do not make safe decisions. Simpson, Johnston, and Richardson62 reach similar conclusions
using a VR head mounted display. Some recent studies used immersive VR environments allowing for realistic
pedestrian63 or cyclist actions64,65, and unveiled the developmental trajectory of the fine-tuning between perception, decision, and action.
This study isolated, for the range of situations tested, the critical age from which children’s attentional control
is at adult level in a road crossing task. Children below 11 years of age show differences in their visual explorations, characterised by a more spread gaze distribution, more overt attention to stimuli irrelevant to the task, andmore gazing following the vehicles closer to the participant. This specific oculomotor pattern was associated withriskier crossing decisions in shorter traffic gaps compared to older children and adults. Our findings suggest that
below 11 years of age, children do not employ attentional control to a level required for safe crossing decisions.
Thus, training and education programs might specifically target these vulnerable children and their caregivers. It
is also important to note that our task incorporated only one traffic direction. Thus, the critical age might occur
even later in more complex and taxing situations incorporating two traffic directions.
This work helps us to better understand general deficits in children’s attentional control in real world situations, and in particular their vulnerability as pedestrians. In future studies, these initial findings will be supplemented by ongoing research investigating questions such as the visual exploration in 3D, fine-grained analysesof time to impact and moment by moment crossing decisions, the mechanisms of attentional disengagement, theneural correlates of visuo-attentional processes for children as pedestrians, and large fields of view with two traffic
directions involving eye and head movement coordination.

Methods

All data is publicly available via the Open Science Framework through this link: https://doi.org/10.17605/OSF.
IO/B3YPC.

Participants. 67 participants were recruited: 57 aged between five and 15, and 10 adult controls aged between

20 and 40 (mean = 24, SD = 3). All children were recruited in schools in the Fribourg canton, Switzerland. Adults
were recruited from the University of Fribourg. All participants had normal or corrected to normal vision. The
study was approved by the Department of Psychology ethics committee at the University of Fribourg. Informed
consent was obtained from the schools, parents, children, and adult controls prior to taking part in the study. This
study was performed in accordance with all appropriate institutional and international guidelines and regulations, in line with the principles of the Declaration of Helsinki.

Apparatus. During the experiment participants’ eye movements were recorded at a sampling rate of 1000 Hz

with the SR-Research EyeLink 1000 (with a chin and forehead rest), which has an average gaze position error of
0.25°, a spatial resolution of 0.01°, and a linear output range over the range of the monitor used. Only the dominant eye was tracked. Stimuli were presented on an HP monitor with a screen resolution of 1920 by 1080 pixels,
a width of 521 mm and a height of 293 mm, a horizontal viewing angle of 46.9° and a vertical viewing angle of
27.4° at a distance of 600 mm. The experiment was coded in Matlab66 using Psychophysics (PTB-3) and EyeLink
Toolbox extensions67,68. Calibrations for eye fixations were conducted at the beginning of the experiment using anine-point fixation procedure as implemented in the EyeLink API (see EyeLink Manual) and using Matlab software. Calibrations were then validated with EyeLink software and repeated until the optimal calibration criterion
was reached.

Experimental Design.

At the beginning of the experiment participants were informed that they would bepresented with a series of videos of road crossing situations on screen and that they would have to indicate bypressing the spacebar on a keyboard when they could cross the road and hold the button pressed for as long as
they thought it was safe to cross. Participants were instructed to focus on approaching vehicles on the side of the

Scientific Reports |(2019) 9:4176 | https://doi.org/10.1038/s41598-019-39737-7

6

www.nature.com/scientificreports/

www.nature.com/scientificreports

Figure 4. Example video stimuli and illustration of eye parser algorithm (a) A screenshot taken from the avideo clip shown during a single trial of the experiment. The videos are filmed at an angle, so the participants
can see the approach of vehicles only from one side of the road. (b) Top left – velocity threshold to extract
saccades (bottom panel). Velocity of eye movement samples (top panel). Top centre – plotting X and Y
coordinates of eye movement samples across whole trial (top panel). Bottom left and right – extraction ofsegments of eye movement samples maintaining a velocity of 30 deg/s for at least 100 ms with a polynomial
fitted to the segments. Beside these are X and Y coordinates of the segments plotted on matching frames of theexperiment stimuli. Top right – completed labelling of eye movements as fixations (red lines), smooth pursuits(green lines), and saccades (blue lines) for a whole trial.

road closest to them (see Fig. 4a for a capture of the scene). Vehicles travelled at an average velocity of 50 km/h.
Each trial started with the presentation of a central fixation cross. Once the participants had fixated on the crossa blank screen was presented for 500 ms and then the video clip for the trial was presented (see Fig. 4a). Each trial
was followed by another blank screen for 500 ms and the next trial started with the central cross. 100 trials were
presented to the participants each with a different video clip, each lasting 10 seconds. All video clips were filmed
at a real road crossing in Fribourg with a variety of traffic densities, with or without pedestrians and cyclists (distractors). Number of presses for each trial were collected and analysed for the purpose of the present experiment.

Statistical Analyses.

All statistical analyses and figures were performed and created using Matlab 2016a66
and R69 with RStudio70.
The literature suggests that crossing decisions are different below and above 10 years old. We corroborated
this critical age, in a data-driven way to avoid confirmation bias, using a k-means clustering on the mean numberof crossing decisions per participant (Fig. 3a). We used the Matlab k-means function, based on the k-mean++algorithm, and ran 1000 iterations to verify that the centroids were grouping consistently. The k-means procedureisolated the following clusters: 5–10 y/os (mean = 8, SD = 1) and 11–15 y/os (mean = 13, SD = 1). The number ofand duration of button presses were analysed using a Yuen’s test with 20% trimmed means in R using the WRS2
package71. Eye movements were parsed into fixations, saccades, and smooth pursuits using a custom algorithm.
Saccades were extracted using the same parameters as the EyeLink software (a velocity threshold = 50 deg/s). If
the majority of samples in the trial were above this threshold then the trial was removed and if more than 50%of the trials were removed then the participant was excluded. In total, 31 trials were removed, and five participants were excluded for noisy recording. Potential smooth pursuit segments were first isolated as segments forwhich velocity was maintained below or equal to 30 deg/s for a minimum of 100 ms. From this initial extraction, smooth pursuit segments were identified using a dispersion threshold, based on the following algorithm.
A polynomial was fitted to the X and Y coordinates of the gaze samples in each smooth eye movement segment,
after having removed outliers using the Corr v2 toolbox72. The root-mean square error of the polynomial fit was
Scientific Reports |(2019) 9:4176 | https://doi.org/10.1038/s41598-019-39737-7

7www.nature.com/scientificreports

www.nature.com/scientificreports/then calculated and divided by the exponential of the arc length (calculated using the arclength toolbox73) of thepolynomial. A threshold was set at 1 × 10−9 and samples below that threshold were considered as smooth pursuit, while samples above were considered as part of a fixation. This algorithm is summarized in Fig. 4b and thefollowing equation:
PRMSE/exp(A)PRMSE is the root mean square error of the polynomial line, A is the arc length of the polynomial line. For each
video clip the presence of a human distractor was encoded in a dichotomous way (1 for one or more human distractors present in the trial, 0 for no human distractors in the trial). The number of vehicles on each trial (traffic
density) was determined using Matlab’s computer vision toolbox74. This toolbox uses a background subtraction
algorithm involving Gaussian mixture models to detect the foreground of each frame of the video. This is followed
by a blob analysis to detect and count moving objects – the vehicles in the trial videos (for an example see75).
Oculomotor characteristics were analysed using shift functions that were run in R using code from76. The oculomotor characteristics included fixation, pursuit and saccade durations, number, and proportion of trial time.
The shift functions were produced for the nine oculomotor characteristics according to age group, presence ofhuman distractors, and traffic density. High and low traffic density categories were produced using a kernel density plot of the number of cars on each trial (Fig. S1 in the Supplemental Material). The centre of the distributionof car traffic density was found to be three cars present in the trial. Gaze samples were further analysed using gazesimilarity matrices (GSMs). GSMs were computed by creating, for each participant and each trial, smoothed (1°of visual angle) Z-scored maps of the gaze positions as in55. The Fisher transformed correlations of the gaze map
on a single trial with the gaze maps for all other trials were calculated for each participant individually (Fig. 1a–c).
Finally, the mean similarity between the gaze map on a single trial and all the other maps were computed for eachparticipant, leading to 100 values per participant that were used to compute the age group with bootstrapped
confidence intervals (Fig. 1d).
Statistical maps were calculated with the iMap toolbox, version 455. iMap computes pixel-wise linear mixed
models (LMMs) across participants and trials on each z-score map. iMap uses a universal bootstrap clustering
test to resolve biases in parameter estimation and problems arising from multiple comparisons77,78. The LMM
included pedestrian presence, traffic density, and age group as fixed effects. The model also included random
intercepts for subject and video stimuli. Initially the model included random slopes of age group, pedestrianpresence, and traffic density for each random intercept; however, this initial model did not converge so all random
slopes were removed.

References

1. Ludwig, C. J., Davies, J. R. & Eckstein, M. P. Foveal analysis and peripheral selection during active visual sampling. Proceedings of the
National Academy of Sciences 111, 291–299 (2014).
2. Itti, L. & Koch, C. Computational modelling of visual attention. Nat. Rev. Neurosci. 2, 194 (2001).
3. Borji, A., Sihite, D. N. & Itti, L. What stands out in a scene? a study of human explicit saliency judgment. Vision Res. 91, 62–77(2013).
4. Bruce, N. & Tsotsos, J. Saliency based on information maximization. In Advances in neural information processing systems, 18,
155–162 (NIPS, 2006).
5. Bruce, N. D. & Tsotsos, J. K. Saliency, attention, and visual search: An information theoretic approach. Journal of Vision 9, 5–5(2009).
6. Marat, S. et al. Modelling spatio-temporal saliency to predict gaze direction for short videos. International Journal of Computer
Vision 82, 231 (2009).
7. Henderson, J. M., Weeks, P. A. Jr. & Hollingworth, A. The effects of semantic consistency on eye movements during complex scene
viewing. Journal of Experimental Psychology: Human Perception and Performance 25, 210–228 (1999).
8. Loftus, G. R. & Mackworth, N. H. Cognitive determinants of fixation location during picture viewing. Journal of Experimental
Psychology: Human Perception and Performance 4, 565–572 (1978).
9. Palmer, S. E. The effects of contextual scenes on the identification of objects. Memory & Cognition 3, 519–526 (1975).
10. Tatler, B. W., Hayhoe, M. M., Land, M. F. & Ballard, D. H. Eye guidance in natural vision: Reinterpreting salience. Journal of Vision
11, 5–5 (2011).
11. Wolfe, J. M., Võ, M. L.-H., Evans, K. K. & Greene, M. R. Visual search in scenes involves selective and nonselective pathways. Trends
in Cognitive Sciences 15, 77–84 (2011).
12. Eckstein, M. P., Drescher, B. A. & Shimozaki, S. S. Attentional cues in real scenes, saccadic targeting, and bayesian priors.
Psychological Science 17, 973–980 (2006).
13. Henderson, J. M., Chanceaux, M. & Smith, T. J. The influence of clutter on real-world scene search: Evidence from search efficiency
and eye movements. Journal of Vision 9, 32–32 (2009).
14. Henderson, J. M., Malcolm, G. L. & Schandl, C. Searching in the dark: Cognitive relevance drives attention in real-world scenes.
Psychonomic Bulletin & Review 16, 850–856 (2009).
15. Malcolm, G. L. & Henderson, J. M. Combining top-down processes to guide eye movements during real-world scene search. Journalof Vision 10, 4–4 (2010).
16. Võ, M. L.-H. & Henderson, J. M. The time course of initial scene processing for eye movement guidance in natural scene search.
Journal of Vision 10, 14–14 (2010).
17. Hwang, A. D., Wang, H.-C. & Pomplun, M. Semantic guidance of eye movements in real-world scenes. Vision Res. 51, 1192–1205(2011).
18. Castelhano, M. S. & Heaven, C. Scene context influences without scene gist: Eye movements guided by spatial associations in visual
search. Psychonomic Bulletin & Review 18, 890–896 (2011).
19. Castelhano, M. S. & Henderson, J. M. Initial scene representations facilitate eye movement guidance in visual search. Journal of
Experimental Psychology: Human Perception and Performance 33, 753–763 (2007).
20. Võ, M. L.-H. & Wolfe, J. M. When does repeated search in scenes involve memory? looking at versus looking for objects in scenes.
Journal of Experimental Psychology: Human Perception and Performance 38, 23–41 (2012).
21. Castelhano, M. S. & Pereira, E. J. The influence of scene context on parafoveal processing of objects. Quarterly Journal of
Experimental Psychology 71, 229–240 (2018).

Scientific Reports |(2019) 9:4176 | https://doi.org/10.1038/s41598-019-39737-7

8

www.nature.com/scientificreports/

www.nature.com/scientificreports

22. Pereira, E. J. & Castelhano, M. S. Peripheral guidance in scenes: The interaction of scene context and object content. Journal of
Experimental Psychology: Human Perception and Performance 40, 2056–2072 (2014).
23. Hayes, T. R. & Henderson, J. M. Scan patterns during real-world scene viewing predict individual differences in cognitive capacity.
Journal of Vision 17, 23–23 (2017).
24. Henderson, J. M. Gaze control as prediction. Trends in Cognitive Sciences 21, 15–23 (2017).
25. Friston, K., Adams, R., Perrinet, L. & Breakspear, M. Perceptions as hypotheses: saccades as experiments. Frontiers in Psychology 3,
151 (2012).
26. Booth, J. R. et al. Neural development of selective attention and response inhibition. NeuroImage 20, 737–751 (2003).
27. Bunge, S. A., Dudukovic, N. M., Thomason, M. E., Vaidya, C. J. & Gabrieli, J. D. Immature frontal lobe contributions to cognitive
control in children: evidence from fmri. Neuron 33, 301–311 (2002).
28. Durston, S. et al. A neural basis for the development of inhibitory control. Developmental Science 5, 9–16 (2002).
29. Kastner, S. & Ungerleider, L. G. Mechanisms of visual attention in the human cortex. Annual Review of Neuroscience 23, 315–341(2000).
30. Hwang, K., Velanova, K. & Luna, B. Strengthening of top-down frontal cognitive control networks underlying the development ofinhibitory control: a functional magnetic resonance imaging effective connectivity study. Journal of Neuroscience 30, 15535–15545(2010).
31. Konrad, K. et al. Development of attentional networks: an fmri study with children and adults. NeuroImage 28, 429–439 (2005).
32. Colombo, J. The development of visual attention in infancy. Annual Review of Psychology 52, 337–367 (2001).
33. Munoz, D. P. & Everling, S. Look away: the anti-saccade task and the voluntary control of eye movement. Nature Reviews.
Neuroscience 5, 218–228 (2004).
34. Paus, T. The development of sustained attention in children might be related to the maturation of frontal cortical functions. Acta
Neurobiologiae Experimentalis 49, 51–55 (1989).
35. Fukushima, J., Hatta, T. & Fukushima, K. Development of voluntary control of saccadic eye movements: I. age-related changes innormal children. Brain and Development 22, 173–180 (2000).
36. Klein, C. & Foerster, F. Development of prosaccade and antisaccade task performance in participants aged 6 to 26 years.
Psychophysiology 38, 179–189 (2001).
37. Klein, C., Fischer, B., Hartnegg, K., Heiss, W. & Roth, M. Optomotor and neuropsychological performance in old age. Experimental
Brain Research 135, 141–154 (2000).
38. Munoz, D. P., Broughton, J. R., Goldring, J. E. & Armstrong, I. T. Age-related performance of human subjects on saccadic eye
movement tasks. Experimental Brain Research 121, 391–400 (1998).
39. Leclercq, V. & Siéroff, E. Development of endogenous orienting of attention in school-age children. Child Neuropsychology 19,
400–419 (2013).
40. Açık, A., Sarwary, A., Schultze-Kraft, R., Onat, S. & König, P. Developmental changes in natural viewing behavior: bottom-up andtop-down differences between children, young adults and older adults. Frontiers in Psychology 1, 207 (2010).
41. Kuhn, G. & Teszka, R. Don’t get misdirected! differences in overt and covert attentional inhibition between children and adults. theQuarterly Journal of Experimental Psychology 71, 688–694 (2017).
42. World Health Organization. Pedestrian safety: a road safety manual for decision-makers and practitioners. (2013).
43. World Health Organization. Ten strategies for keeping children safe on the road (2015).
44. Pitcairn, T. & Edlmann, T. Individual differences in road crossing ability in young children and adults. British Journal of Psychology
91, 391–410 (2000).
45. Plumert, J. M., Kearney, J. K. & Cremer, J. F. Children’s perception of gap affordances: Bicycling across traffic-filled intersections in
an immersive virtual environment. Child Development 75, 1243–1253 (2004).
46. te Velde, A. F., van der Kamp, J., Barela, J. A. & Savelsbergh, G. J. Visual timing and adaptive behavior in a road-crossing simulation
study. Accid. Anal. & Prev. 37, 399–406 (2005).
47. Schwebel, D. C., Davis, A. L. & O’Neal, E. E. Child pedestrian injury: A review of behavioral risks and preventive strategies.
American Journal of Lifestyle Medicine 6, 292–302 (2012).
48. Zeedyk, M. S., Wallace, L. & Spry, L. Stop, look, listen, and think?: What young children really do when crossing the road. Accid.
Anal. & Prev. 34, 43–50 (2002).
49. Zeedyk, M. S. & Kelly, L. Behavioural observations of adult–child pairs at pedestrian crossings. Accid. Anal. & Prev. 35, 771–776(2003).
50. Morrongiello, B. A., Corbett, M., Milanovic, M. & Beer, J. Using a virtual environment to examine how children cross streets:
Advancing our understanding of how injury risk arises. Journal of Pediatric Psychology 41, 265–275 (2015).
51. Barton, B. K. & Schwebel, D. C. The roles of age, gender, inhibitory control, and parental supervision in children’s pedestrian safety.
Journal of Pediatric Psychology 32, 517–526 (2007).
52. Whitebread, D. & Neilson, K. The contribution of visual search strategies to the development of pedestrian skills by 4-11 year-old
children. British Journal of Educational Psychology 70, 539–557 (2000).
53. Tapiro, H., Meir, A., Parmet, Y. & Oron-Gilad, T. Visual search strategies of child-pedestrians in road crossing tasks. In de Waard, D.
et al. (eds) Proceedings of the Human Factors and Ergonomics Society (Perception, 2014).
54. Caldara, R. & Miellet, S. imap: a novel method for statistical fixation mapping of eye movement data. Behavior Research Methods 43,
864–878 (2011).
55. Lao, J., Miellet, S., Pernet, C. R., Sokhn, N. & Caldara, R. imap4: An open source toolbox for the statistical fixation mapping of eye
movement data with linear mixed modeling. Behavior Research Methods 49, 559–575 (2017).
56. Connelly, M. L., Conaglen, H. M., Parsonson, B. S. & Isler, R. B. Child pedestrians’ crossing gap thresholds. Accid. Anal. & Prev. 30,
443–453 (1998).
57. Lee, D. N., Young, D. S. & McLaughlin, C. M. A roadside simulation of road crossing for children. Ergonomics 27, 1271–1281 (1984).
58. Kim, M.-S. & Cave, K. R. Top-down and bottom-up attentional control: On the nature of interference from a salient distractor. Atten.
Percept. Psychophys. 61, 1009–1023 (1999).
59. Theeuwes, J., Atchley, P. & Kramer, A. F. On the time course of top-down and bottom-up control of visual attention. In Monsell, S. &Driver, J. (eds) Control of cognitive processes: Attention and performance XVIII, 105–124 (MIT Press, Cambridge,MA, 2000).
60. Hutton, S. & Tegally, D. The effects of dividing attention on smooth pursuit eye tracking. Experimental Brain Research 163, 306–313(2005).
61. Lovejoy, L. P., Fowler, G. A. & Krauzlis, R. J. Spatial allocation of attention during smooth pursuit eye movements. Vision Res. 49,
1275–1285 (2009).
62. Simpson, G., Johnston, L. & Richardson, M. An investigation of road crossing in a virtual environment. Accident Anal. & Prevention
35, 787–796 (2003).
63. O’Neal, E. E. et al. Changes in perception–action tuning over long time scales: How children and adults perceive and act on dynamic
affordances when crossing roads. Journal of Experimental Psychology: Human Perception and Performance 44, 18–26 (2017).
64. Grechkin, T. Y., Chihak, B. J., Cremer, J. F., Kearney, J. K. & Plumert, J. M. Perceiving and acting on complex affordances: How
children and adults bicycle across two lanes of opposing traffic. Journal of Experimental Psychology: Human Perception and
Performance 39, 23–36 (2013).

Scientific Reports |(2019) 9:4176 | https://doi.org/10.1038/s41598-019-39737-7

9www.nature.com/scientificreports/

www.nature.com/scientificreports

65. Chihak, B. J. et al. Synchronizing self and object movement: How child and adult cyclists intercept moving gaps in a virtualenvironment. Journal of Experimental Psychology: Human Perception and Performance 36, 1535–1552 (2010).
66. MATLAB. version 7.10.0 (R2016a) (The MathWorks Inc., Natick, Massachusetts, 2016).
67. Brainard, D. H. The psychophysics toolbox. Spatial vision 10, 433–436 (1997).
68. Cornelissen, F. W., Peters, E. M. & Palmer, J. The eyelink toolbox: eye tracking with matlab and the psychophysics toolbox. Behavior
Research Methods, Instruments, Computers 34, 613–617 (2002).
69. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria,
https://www.R-project.org (2016).
70. RStudio Team. RStudio: Integrated Development Environment for R. RStudio, Inc., Boston, MA, http://www.rstudio.com/ (2016).
71. Mair, P. & Wilcox, R. WRS2: Wilcox robust estimation and testing, http://CRAN.R-project.org/package=WRS2 0.10-0 (2018).
72. Pernet, C. R., Wilcox, R. R. & Rousselet, G. A. Robust correlation analyses: false positive and power validation using a new open
source matlab toolbox. Frontiers in Psychology 3, 606 (2013).
73. D’Errico, J. arclength (MATLAB Central File Exchange, https://uk.mathworks.com/matlabcentral/fileexchange/34871-arclength.1.0(2010)
74. MATLAB. MATLAB Computer Vision System Toolbox (The MathWorks Inc., Natick, Massachusetts, 2016a).
75. Kingdom, M. U. Detecting cars using gaussian mixture models- matlab and simulink example, https://uk.mathworks.com/help/
vision/examples/detecting-cars-using-gaussian-mixture-models.htmlresponsiveoffcanvas.1.0 (2017).
76. Rousselet, G. A., Pernet, C. R. & Wilcox, R. R. Beyond differences in means: robust graphical methods to compare two groups inneuroscience. European Journal of Neuroscience 46, 1738–1748 (2017).
77. Pernet, C. R., Chauveau, N., Gaspar, C. & Rousselet, G. A. Limo eeg: A toolbox for hierarchical linear modeling ofelectroencephalographic data. Intell. Neuroscience 2011, https://doi.org/10.1155/2011/831409 (2011).
78. Pernet, C. R., Latinus, M., Nichols, T. E. & Rousselet, G. A. Cluster-based computational methods for mass univariate analyses of
event-related brain potentials/fields: A simulation study. Journal of Neuroscience Methods 250, 85–93 (2015).

Acknowledgements

This study was supported by the Swiss National Science Foundation (100019_156541).

Author Contributions

S.M. and R.C. developed the study concept. S.M., R.C., G.J.-C. contributed to the study design. Testing and datacollection were performed by G.J.-C. V.I.N. and G.J.-C. performed the data analysis and interpretation under thesupervision of S.M., R.C., and assistance from JL on data analysis and P.dL. on interpretation. V.I.N. drafted themanuscript, and S.M., P.dL., and R.C. provided critical revisions. All authors approved the final version of themanuscript for submission.

Additional Information

Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-019-39737-7.
Competing Interests: The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps andinstitutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as 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. The images or other third party material in thisarticle are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to thematerial. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the
copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
© The Author(s) 2019

Scientific Reports |(2019) 9:4176 | https://doi.org/10.1038/s41598-019-39737-710