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Developing attentional control in naturalistic dynamic road crossing situations

Abstract

In the last 20 years, there has been increasing interest in studying visual attentional processes under more natural conditions. In the present study, we propose to determine the critical age at which children show similar to adult performance and attentional control in a visually guided task; in a naturalistic dynamic and socially relevant context: road crossing. We monitored visual exploration and 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 the task. 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.

Introduction

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 the environment 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 stimulation2,3,4,5,6 and top-down influences7,8,9,10,11.

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 attention11,12,13,14,15,16. For instance, these studies have shown influences of semantic17, episodic top-down processes18,19,20, as well as scene context on parafoveal processing of objects21,22. More recently, the role of the observer’s intention and understanding of the scene has been emphasized23,24. In this perspective, oculomotor planning is seen as making predictions about the locations of diagnostic information for the task25.

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 and children 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 literature that includes distractors and their effects on visual exploration as a way to investigate attention switching and inhibitory control, and how these develop in children for real world scenarios.

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 trend of 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 S2S19 and summary 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 in gaze 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 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 each age 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 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 the road 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. When the 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 not observed for 11–15 y/os and adults.

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 procedure isolated 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 to decide when they believed they could cross the road. Eye movement data showed that 5–10 y/os exhibited a much less 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 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 made more crossing decisions.

In all traffic density situations, adults and 11–15 y/os look at the top-left of the road in our paradigm – the appearing point of the cars. We propose that the reason this strategy is used is that this location represents an ideal fixation position for assessing the vehicle’s speed and time to impact as early as possible. Moreover, this gaze location 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 the cars down the road with their gaze, rather than just gazing at the appearing point. This may be due to an inability of 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 in the 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 across development. We propose that gaze locations have a direct impact on crossing decisions. Children orient their overt attention towards human distractors more than 11–15 y/os and adults. This tendency would impair their ability 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, and more gazing following the vehicles closer to the participant. This specific oculomotor pattern was associated with riskier 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 analyses of time to impact and moment by moment crossing decisions, the mechanisms of attentional disengagement, the neural 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 a nine-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 be presented with a series of videos of road crossing situations on screen and that they would have to indicate by pressing 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 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 cross a 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 number of 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 procedure isolated the following clusters: 5–10 y/os (mean = 8, SD = 1) and 11–15 y/os (mean = 13, SD = 1). The number of and 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 for which 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 then calculated and divided by the exponential of the arc length (calculated using the arclength toolbox73) of the polynomial. 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 the following equation:

$${P}_{RMSE}/\exp (A)$$

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 of human 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 distribution of car traffic density was found to be three cars present in the trial. Gaze samples were further analysed using gaze similarity 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 each participant, 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, pedestrian presence, 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. Journal of 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).

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 of inhibitory 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 in normal 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 and top-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. the Quarterly 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).

65. Chihak, B. J. et al. Synchronizing self and object movement: How child and adult cyclists intercept moving gaps in a virtual environment. 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 in neuroscience. 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 of electroencephalographic 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).

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Authors

Contributions

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

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Correspondence to Sebastien Miellet.

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Nicholls, V.I., Jean-Charles, G., Lao, J. et al. Developing attentional control in naturalistic dynamic road crossing situations. Sci Rep 9, 4176 (2019). https://doi.org/10.1038/s41598-019-39737-7

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• DOI: https://doi.org/10.1038/s41598-019-39737-7

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