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Navigating Virtual Collisions: Insights into Perception-Action Differences in Children and Young Adults Using a Head-On Avoidance Task

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Title: Navigating Virtual Collisions: Insights into Perception-Action Differences in Children and Young Adults Using a Head-On Avoidance Task
Language: English
Authors: Megan Hammill, Victoria Rapos, Michael Cinelli (ORCID 0000-0002-5802-2590)
Source: Journal of Motor Learning and Development. 2024 12(3):555-571.
Availability: Human Kinetics, Inc. 1607 North Market Street, Champaign, IL 61820. Tel: 800-474-4457; Fax: 217-351-1549; e-mail: info@hkusa.com; Web site: https://journals.humankinetics.com/view/journals/jmld/jmld-overview.xml
Peer Reviewed: Y
Page Count: 17
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Descriptors: Children, Young Adults, Motor Development, Decision Making, Task Analysis, Psychomotor Skills, Accidents, Accident Prevention, Perception, Human Body, Navigation, Reaction Time, Physical Activities, Accuracy
DOI: 10.1123/jmld.2024-0027
ISSN: 2325-3193
2325-3215
Abstract: Children tend to make more last-minute locomotor adjustments than adults when avoiding stationary obstacles. The purpose of this study was to compare avoidance behaviors of middle-aged children (10-12 years old) with young adults during a head-on collision course with an approaching virtual pedestrian. Participants were immersed in a virtual environment and completed a perceptual decision-making task in which they had to respond to the future direction of an approaching virtual pedestrian once they disappeared. Following the perceptual task, participants walked along an 8-m pathway toward a goal, while avoiding a collision with a virtual pedestrian who approached along the midline than veered toward the left, right, or continued walking straight. Results revealed that children were able to correctly predict the future directions of the virtual pedestrian similar to adults, albeit at a slower response time (0.55 s vs. 0.33 s). During the action task, children initiated a deviation in their travel path later (i.e., closer to the virtual pedestrian) compared to adults (1.65 s vs. 1.52 s). Children were also more variable in their onset of deviation and time-to-contact. Although children appear to have developed adult-like perceptual abilities and can avoid an approaching virtual pedestrian, children employ riskier avoidance strategies and are highly variable, suggesting middle-aged children are still fine-tuning their perception-action system.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1456430
Database: ERIC
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  Value: <anid>AN0181087342;[fqbl]01dec.24;2024Nov27.02:08;v2.2.500</anid> <title id="AN0181087342-1">Navigating Virtual Collisions: Insights Into Perception–Action Differences in Children and Young Adults Using a Head-On Avoidance Task </title> <p>Children tend to make more last-minute locomotor adjustments than adults when avoiding stationary obstacles. The purpose of this study was to compare avoidance behaviors of middle-aged children (10–12 years old) with young adults during a head-on collision course with an approaching virtual pedestrian. Participants were immersed in a virtual environment and completed a perceptual decision-making task in which they had to respond to the future direction of an approaching virtual pedestrian once they disappeared. Following the perceptual task, participants walked along an 8-m pathway toward a goal, while avoiding a collision with a virtual pedestrian who approached along the midline than veered toward the left, right, or continued walking straight. Results revealed that children were able to correctly predict the future directions of the virtual pedestrian similar to adults, albeit at a slower response time (0.55 s vs. 0.33 s). During the action task, children initiated a deviation in their travel path later (i.e., closer to the virtual pedestrian) compared to adults (1.65 s vs. 1.52 s). Children were also more variable in their onset of deviation and time-to-contact. Although children appear to have developed adult-like perceptual abilities and can avoid an approaching virtual pedestrian, children employ riskier avoidance strategies and are highly variable, suggesting middle-aged children are still fine-tuning their perception-action system.</p> <p>Keywords: motor development; decision making; perception–action integration; collision avoidance; time to contact</p> <p>When navigating through the world, humans regularly avoid collisions with environmental obstacles, such as objects or pedestrians. To successfully avoid a collision with other objects, individuals use sensory information, relying heavily on their visual system to guide their locomotion ([<reflink idref="bib31" id="ref1">31</reflink>]). The visual system provides information about the environment, self-motion, and the position of obstacles relative to themselves ([<reflink idref="bib18" id="ref2">18</reflink>]). The information provided by the visual system is used to perceive the motion of an approaching object and make (environment-based) appropriate locomotor adjustments to safely avoid a collision, a concept known as perception–action processing ([<reflink idref="bib7" id="ref3">7</reflink>]).</p> <p>Specific to head-on collision avoidance, determining when to initiate an avoidance requires individuals to use temporal information, known as time-to-contact (TTC). TTC is the amount of time remaining before an object will collide with a person if both continue moving at a constant speed and an avoidance is not initiated ([<reflink idref="bib11" id="ref4">11</reflink>]). The visual system can calculate TTC through the optical variable, tau (τ), which is specified as the inverse rate of dilation of the retinal image of an approaching obstacle ([<reflink idref="bib11" id="ref5">11</reflink>]). Individuals will execute an avoidance behavior once the size of the retinal image (i.e., the approaching object) reaches a certain optical expansion threshold on the retina ([<reflink idref="bib3" id="ref6">3</reflink>]). TTC may be significantly influenced by the object's properties or the individual being avoided. For instance, Cinelli and Patla ([<reflink idref="bib2" id="ref7">2</reflink>]) found adults do not maintain a constant TTC when initiating the avoidance an approaching object moving along a known trajectory. Instead, adults use visual information to regulate their avoidance behavior at a rate that is dependent on the approach speed of the object ([<reflink idref="bib2" id="ref8">2</reflink>]). However, when an approaching objects' travel path is uncertain, adults initiate an avoidance strategy at a constant TTC ([<reflink idref="bib19" id="ref9">19</reflink>]). Therefore, adults use TTC in uncertain scenarios as an unconscious quantitative metric to safely avoid a collision.</p> <p>Another important aspect of collision avoidance is the protective zone an individual maintains between themselves and nearby objects, a concept known as personal space ([<reflink idref="bib28" id="ref10">28</reflink>]; [<reflink idref="bib29" id="ref11">29</reflink>]). By maintaining personal space around oneself, individuals can perceive and react to unpredictable obstacles in their environment (i.e., a person changes direction). Specific to collision avoidance, adults maintain an elliptical-shaped protective zone that is approximately 2 m anteriorly and 0.5 m laterally when avoiding a stationary object ([<reflink idref="bib6" id="ref12">6</reflink>]). Similarly, young adults demonstrate an elliptical-shaped safety margin or clearance distance during a head-on collision avoidance task with an approaching pedestrian ([<reflink idref="bib19" id="ref13">19</reflink>]) and during a 90°-collision avoidance task with two walkers ([<reflink idref="bib16" id="ref14">16</reflink>], [<reflink idref="bib15" id="ref15">15</reflink>]).</p> <p>As the nervous system develops, the perception–action system undergoes a prolonged period of change, which to date has yet to be fully characterized ([<reflink idref="bib4" id="ref16">4</reflink>]; [<reflink idref="bib12" id="ref17">12</reflink>]; [<reflink idref="bib20" id="ref18">20</reflink>]; [<reflink idref="bib30" id="ref19">30</reflink>]). As a result, the age at which children develop mature avoidance strategies (i.e., collision avoidance) remains somewhat unknown. As mentioned, collision avoidance relies heavily on visual information and quick-processing adaptive behaviors to safely avoid objects and prevent injuries. Very few studies have examined perception–action processing related to collision avoidance in children. During obstacle avoidance, middle-aged children make last-minute locomotor adjustments, while adults make anticipatory adjustments well in advance of an obstacle ([<reflink idref="bib13" id="ref20">13</reflink>]; [<reflink idref="bib30" id="ref21">30</reflink>]). Plumert et al. ([<reflink idref="bib20" id="ref22">20</reflink>], [<reflink idref="bib21" id="ref23">21</reflink>]) found that children 5–12 years are still developing their perceptual system. Notably, 5-year-old children often select impassable gaps compared to 12-year-old children and young adults, suggesting younger children fail to account for one's own action capabilities, such as a slower walking speed due to body anthropometrics ([<reflink idref="bib4" id="ref24">4</reflink>]; [<reflink idref="bib12" id="ref25">12</reflink>]; [<reflink idref="bib20" id="ref26">20</reflink>]). This oversight in accounting for one's capabilities led to risker behaviors and near misses with an approaching virtual vehicle. Additionally, a 90°-collision avoidance task with two walkers resulted in children (8- to 12-years) maintaining a smaller clearance distance between one another at the time of crossing (TOC) compared with young adults ([<reflink idref="bib23" id="ref27">23</reflink>]). A smaller clearance distance further demonstrates middle-aged children's discrepancies with perception–action processing, resulting in a higher risk of collisions and injury. Although collision avoidance occurs daily, it is unknown whether middle-age children use similar optical variables (i.e., TTC) and avoidance strategies as adults during a head-on collision avoidance task.</p> <p>The purpose of the current study was to investigate perceptual and action capabilities of middle-aged children and adults during a head-on virtual collision avoidance task. As middle-aged children make more last-minute locomotor adjustments ([<reflink idref="bib30" id="ref28">30</reflink>]), it was hypothesized that children would have smaller TTC values (i.e., deviate closer to the approaching pedestrian) than adults. In addition, children (10- to 12-years old) were expected to be more variable in their avoidance behaviors, due to a developing perception–action system.</p> <hd id="AN0181087342-2">Methods</hd> <p></p> <hd id="AN0181087342-3">Participants</hd> <p>Sixteen middle-aged children ( <ephtml> <math overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mover accent="true"><mrow><mi>x</mi></mrow><mrow><mo stretchy="false">¯</mo></mrow></mover><mo>=</mo><mn>10.8</mn><mo>±</mo><mn>0.75</mn><mtext /><mtext /><mtext>years</mtext></mrow></math> </ephtml> ; eight males) and 16 young adults ( <ephtml> <math overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mover accent="true"><mrow><mi>x</mi></mrow><mrow><mo stretchy="false">¯</mo></mrow></mover><mo>=</mo><mn>22.94</mn><mo>±</mo><mn>2.08</mn><mtext /><mtext /><mtext>years</mtext></mrow></math> </ephtml> ; seven males) participated in the study (Table 1). Individuals were not included if any of the following criteria were present: (<reflink idref="bib1" id="ref29">1</reflink>) self-reported neurological disorders or deficits affecting postural control, (<reflink idref="bib2" id="ref30">2</reflink>) musculoskeletal injuries limiting an individual's walking ability, (<reflink idref="bib3" id="ref31">3</reflink>) self-reported visual impairments that could not be corrected, or (<reflink idref="bib4" id="ref32">4</reflink>) a concussion sustained in the previous 2 years. All participants and guardians of children provided informed consent and completed a Health History Questionnaire to obtain demographics such as age, sex, and height, as well as information on the health and physical activity level of each participant. In addition, informed assent was provided by children before the start of the experiment. All study procedures were approved by Wilfrid Laurier University's Research Ethics Board.</p> <p>Table 1 Participant Demographics Including Age, Height, Weight, Shoulder Width, and Approach Speed</p> <p> <ephtml> <table><colgroup span="1"><col align="left" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /></colgroup><thead><tr><th rowspan="1" colspan="1">Children</th><th rowspan="1" colspan="1">Sex</th><th rowspan="1" colspan="1">Age (years)</th><th rowspan="1" colspan="1">Height (cm)</th><th rowspan="1" colspan="1">Weight (kg)</th><th rowspan="1" colspan="1">Shoulder width (cm)</th><th rowspan="1" colspan="1">Approach speed (m/s)</th><th rowspan="1" colspan="1">Adults</th><th rowspan="1" colspan="1">Sex</th><th rowspan="1" colspan="1">Age (years)</th><th rowspan="1" colspan="1">Height (cm)</th><th rowspan="1" colspan="1">Weight (kg)</th><th rowspan="1" colspan="1">Shoulder width (cm)</th><th rowspan="1" colspan="1">Approach speed (m/s)</th></tr></thead><tbody><tr><td rowspan="1" colspan="1">1</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">12</td><td rowspan="1" colspan="1">147</td><td rowspan="1" colspan="1">43.54</td><td rowspan="1" colspan="1">36</td><td rowspan="1" colspan="1">1.26</td><td rowspan="1" colspan="1">1</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">22</td><td rowspan="1" colspan="1">185</td><td rowspan="1" colspan="1">81.65</td><td rowspan="1" colspan="1">43</td><td rowspan="1" colspan="1">1.29</td></tr><tr><td rowspan="1" colspan="1">2</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">142</td><td rowspan="1" colspan="1">40.80</td><td rowspan="1" colspan="1">33</td><td rowspan="1" colspan="1">1.24</td><td rowspan="1" colspan="1">2</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">21</td><td rowspan="1" colspan="1">173</td><td rowspan="1" colspan="1">74.39</td><td rowspan="1" colspan="1">43</td><td rowspan="1" colspan="1">1.38</td></tr><tr><td rowspan="1" colspan="1">3</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">140</td><td rowspan="1" colspan="1">39.00</td><td rowspan="1" colspan="1">32</td><td rowspan="1" colspan="1">1.41</td><td rowspan="1" colspan="1">3</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">26</td><td rowspan="1" colspan="1">177</td><td rowspan="1" colspan="1">76.20</td><td rowspan="1" colspan="1">42</td><td rowspan="1" colspan="1">1.30</td></tr><tr><td rowspan="1" colspan="1">4</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">152</td><td rowspan="1" colspan="1">39.00</td><td rowspan="1" colspan="1">35</td><td rowspan="1" colspan="1">1.20</td><td rowspan="1" colspan="1">4</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">25</td><td rowspan="1" colspan="1">185</td><td rowspan="1" colspan="1">87.00</td><td rowspan="1" colspan="1">45</td><td rowspan="1" colspan="1">1.28</td></tr><tr><td rowspan="1" colspan="1">5</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">152</td><td rowspan="1" colspan="1">47.60</td><td rowspan="1" colspan="1">33</td><td rowspan="1" colspan="1">1.26</td><td rowspan="1" colspan="1">5</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">20</td><td rowspan="1" colspan="1">170</td><td rowspan="1" colspan="1">68.04</td><td rowspan="1" colspan="1">43</td><td rowspan="1" colspan="1">1.29</td></tr><tr><td rowspan="1" colspan="1">6</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">150</td><td rowspan="1" colspan="1">40.80</td><td rowspan="1" colspan="1">31</td><td rowspan="1" colspan="1">1.27</td><td rowspan="1" colspan="1">6</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">26</td><td rowspan="1" colspan="1">175</td><td rowspan="1" colspan="1">54.40</td><td rowspan="1" colspan="1">41</td><td rowspan="1" colspan="1">1.27</td></tr><tr><td rowspan="1" colspan="1">7</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">152</td><td rowspan="1" colspan="1">36.30</td><td rowspan="1" colspan="1">33</td><td rowspan="1" colspan="1">1.25</td><td rowspan="1" colspan="1">7</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">23</td><td rowspan="1" colspan="1">181</td><td rowspan="1" colspan="1">79.37</td><td rowspan="1" colspan="1">44</td><td rowspan="1" colspan="1">1.29</td></tr><tr><td rowspan="1" colspan="1">8</td><td rowspan="1" colspan="1">M</td><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">145</td><td rowspan="1" colspan="1">34.00</td><td rowspan="1" colspan="1">34</td><td rowspan="1" colspan="1">1.45</td><td rowspan="1" colspan="1">8</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">25</td><td rowspan="1" colspan="1">160</td><td rowspan="1" colspan="1">53.50</td><td rowspan="1" colspan="1">38</td><td rowspan="1" colspan="1">1.35</td></tr><tr><td rowspan="1" colspan="1">9</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">9</td><td rowspan="1" colspan="1">135</td><td rowspan="1" colspan="1">28.58</td><td rowspan="1" colspan="1">31</td><td rowspan="1" colspan="1">1.27</td><td rowspan="1" colspan="1">9</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">24</td><td rowspan="1" colspan="1">163</td><td rowspan="1" colspan="1">54.43</td><td rowspan="1" colspan="1">39</td><td rowspan="1" colspan="1">1.38</td></tr><tr><td rowspan="1" colspan="1">10</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">150</td><td rowspan="1" colspan="1">43.10</td><td rowspan="1" colspan="1">33</td><td rowspan="1" colspan="1">1.27</td><td rowspan="1" colspan="1">10</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">24</td><td rowspan="1" colspan="1">153</td><td rowspan="1" colspan="1">83.00</td><td rowspan="1" colspan="1">43</td><td rowspan="1" colspan="1">1.27</td></tr><tr><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">10</td><td rowspan="1" colspan="1">152</td><td rowspan="1" colspan="1">39.00</td><td rowspan="1" colspan="1">31</td><td rowspan="1" colspan="1">1.28</td><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">22</td><td rowspan="1" colspan="1">178</td><td rowspan="1" colspan="1">83.01</td><td rowspan="1" colspan="1">40</td><td rowspan="1" colspan="1">1.42</td></tr><tr><td rowspan="1" colspan="1">12</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">137</td><td rowspan="1" colspan="1">36.29</td><td rowspan="1" colspan="1">34</td><td rowspan="1" colspan="1">1.34</td><td rowspan="1" colspan="1">12</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">22</td><td rowspan="1" colspan="1">167</td><td rowspan="1" colspan="1">59.87</td><td rowspan="1" colspan="1">39</td><td rowspan="1" colspan="1">1.38</td></tr><tr><td rowspan="1" colspan="1">13</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">146</td><td rowspan="1" colspan="1">37.19</td><td rowspan="1" colspan="1">35</td><td rowspan="1" colspan="1">1.46</td><td rowspan="1" colspan="1">13</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">20</td><td rowspan="1" colspan="1">167</td><td rowspan="1" colspan="1">75.74</td><td rowspan="1" colspan="1">42</td><td rowspan="1" colspan="1">1.21</td></tr><tr><td rowspan="1" colspan="1">14</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">10</td><td rowspan="1" colspan="1">142</td><td rowspan="1" colspan="1">34.02</td><td rowspan="1" colspan="1">33</td><td rowspan="1" colspan="1">1.20</td><td rowspan="1" colspan="1">14</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">25</td><td rowspan="1" colspan="1">171</td><td rowspan="1" colspan="1">104.3</td><td rowspan="1" colspan="1">45</td><td rowspan="1" colspan="1">1.28</td></tr><tr><td rowspan="1" colspan="1">15</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">10</td><td rowspan="1" colspan="1">138</td><td rowspan="1" colspan="1">32.21</td><td rowspan="1" colspan="1">32</td><td rowspan="1" colspan="1">1.23</td><td rowspan="1" colspan="1">15</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">21</td><td rowspan="1" colspan="1">162</td><td rowspan="1" colspan="1">79.38</td><td rowspan="1" colspan="1">42</td><td rowspan="1" colspan="1">1.36</td></tr><tr><td rowspan="1" colspan="1">16</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">12</td><td rowspan="1" colspan="1">158</td><td rowspan="1" colspan="1">36.29</td><td rowspan="1" colspan="1">36</td><td rowspan="1" colspan="1">1.28</td><td rowspan="1" colspan="1">16</td><td rowspan="1" colspan="1">F</td><td rowspan="1" colspan="1">21</td><td rowspan="1" colspan="1">156</td><td rowspan="1" colspan="1">52.62</td><td rowspan="1" colspan="1">38</td><td rowspan="1" colspan="1">1.33</td></tr><tr><td rowspan="1" colspan="1">Average</td><td rowspan="1" colspan="1">—</td><td rowspan="1" colspan="1">10.81</td><td rowspan="1" colspan="1">146.13</td><td rowspan="1" colspan="1">37.98</td><td rowspan="1" colspan="1">33.25</td><td rowspan="1" colspan="1">1.29</td><td rowspan="1" colspan="1">Average</td><td rowspan="1" colspan="1">—</td><td rowspan="1" colspan="1">22.94</td><td rowspan="1" colspan="1">170.19</td><td rowspan="1" colspan="1">72.93</td><td rowspan="1" colspan="1">41.69</td><td rowspan="1" colspan="1">1.32</td></tr><tr><td rowspan="1" colspan="1"><italic>SD</italic></td><td rowspan="1" colspan="1">—</td><td rowspan="1" colspan="1">0.75</td><td rowspan="1" colspan="1">6.61</td><td rowspan="1" colspan="1">4.70</td><td rowspan="1" colspan="1">1.65</td><td rowspan="1" colspan="1">0.08</td><td rowspan="1" colspan="1"><italic>SD</italic></td><td rowspan="1" colspan="1">—</td><td rowspan="1" colspan="1">2.08</td><td rowspan="1" colspan="1">9.78</td><td rowspan="1" colspan="1">14.69</td><td rowspan="1" colspan="1">2.30</td><td rowspan="1" colspan="1">0.06</td></tr></tbody></table> </ephtml> </p> <hd id="AN0181087342-4">Experimental Design</hd> <p>The experiment took place in a large rectangular room (14 m by 6 m), with an 8 m by 3-m pathway cleared along the midline of the room (Figure 1A). Participants were immersed in a virtual environment (Figure 1B) using the HTC Vive Pro 2 head-mounted display (HMD) and joysticks. The HMD was used to track each participant's position in space at a sampling frequency of 90 Hz. The virtual environment contained a ground plane along with a virtual adult male pedestrian in front of a white square box (on the ground) representing the goal for participants to walk toward (Figure 1B). Since the perceived sex and emotion are factors that can influence perception and human behaviors ([<reflink idref="bib26" id="ref33">26</reflink>]), all participants interacted with a nonreactive male virtual pedestrian to ensure consistency across participants.</p> <p>Graph: Figure 1 —(A) Illustration of the experimental space including an 8-m × 2-m pathway with a visible goal located on the opposite end of the path. Participants were instructed to walk along this pathway, toward the goal (X), while avoiding a collision with an approaching virtual pedestrian. (B) A snapshot of the virtual environment from the participant's point of view using the HTC Vive Pro 2 head-mounted display. Participants avoided the virtual adult male pedestrian while walking toward the goal (white square). (C) Young adult participant wearing the HTC Vive Pro2 head-mounted display unit and joysticks.</p> <hd id="AN0181087342-5">Experimental Protocol</hd> <p>Once outfitted with the HMD, participants completed three unobstructed baseline walking trials in the virtual environment. Baseline walking trials allowed participants to familiarize themselves walking with the HMD headset and within the virtual environment. Baseline walking trials were used to calculate each participant's average walking speed and to determine the relative speed of the virtual pedestrian. The virtual pedestrian approached the participant at one of three randomized speeds: (<reflink idref="bib1" id="ref34">1</reflink>) slow (0.8× the participant's walking speed), (<reflink idref="bib2" id="ref35">2</reflink>) matched to the participant's walking speed, or (<reflink idref="bib3" id="ref36">3</reflink>) fast (1.2× the participant's walking speed).</p> <p>Participants completed two blocks of trials: (<reflink idref="bib1" id="ref37">1</reflink>) a perceptual task and (<reflink idref="bib2" id="ref38">2</reflink>) a collision avoidance task.</p> <hd id="AN0181087342-6">Perceptual Task</hd> <p>Participants began with the perceptual task where they stood at the start position in the virtual environment and predicted the future travel path direction of the approaching virtual pedestrian. Each trial consisted of the virtual pedestrian walking along a straight path toward the observer for 4 s at one of three randomized speeds. The virtual pedestrian then initiated a change in travel path to the left or right, or continued walking straight relative to the participant's point of view, disappearing 100 ms following the direction change. Participants were instructed to use joysticks to indicate as quickly and accurately as possible, the direction they believed the pedestrian was heading. If participants failed to respond, the next trial was prompted after 2,000 ms and that trial was marked as a no response. Participants completed 36 randomized perceptual trials (i.e., 3 path directions × 3 approach speeds × 4 trials).</p> <hd id="AN0181087342-7">Avoidance Task</hd> <p>Following the perceptual task, participants completed the collision avoidance task where participants walked along an 8-m pathway toward a goal and were instructed to avoid colliding with an approaching virtual pedestrian (Figure 1). At the start of each trial, the virtual pedestrian stood on a white base on the ground which served as a visible goal 8 m from the participant's starting location. Participants were instructed to walk at their normal pace toward the goal without colliding with the virtual pedestrian. The participant and virtual pedestrian began moving toward each other simultaneously. One second after the start of each trial, the virtual pedestrian either initiated a change in travel path (i.e., toward the left or right of the participant) or continued walking along the midline of the pathway. In addition, the approach speed of the virtual pedestrian was randomized across trials to one of three relative speeds: (<reflink idref="bib1" id="ref39">1</reflink>) slow (0.8× the participant's average speed), (<reflink idref="bib2" id="ref40">2</reflink>) matched speed, or (<reflink idref="bib3" id="ref41">3</reflink>) fast (1.2× the participant's speed). Participants completed a total of 36 avoidance trials (i.e., three left trials, three right trials, and six straight trials × three approach speeds).</p> <hd id="AN0181087342-8">Data Analysis</hd> <p></p> <hd id="AN0181087342-9">Perceptual Task</hd> <p> <emph>Response Accuracy (%)</emph> was determined by whether participants correctly identified the travel path direction of the virtual pedestrian. Response accuracy was expressed as the percentage of correct responses out of the total number of trials.</p> <p> <emph>Response time (s)</emph> was the time required for participants to respond after the virtual pedestrian disappeared. Only trials where participants correctly identified the path direction of the virtual pedestrian were included in the analysis to represent participant's accurate response times.</p> <hd id="AN0181087342-10">Avoidance Task</hd> <p>The location of each participant's head in space (i.e., the center point of the participant and the virtual pedestrian) was recorded throughout the trial by the HTC Vive Pro 2 HMD at a sampling rate of 90 Hz. This position data allowed for the calculation of the following dependent measures:</p> <p> <emph>Onset of Deviation</emph> is the time (in seconds) from the start of the trial when the participant deviated from a straight trajectory and their medial-lateral (ML) position fell two <emph>SD</emph>s outside of their average ML approach position. The participant's average ML approach position was calculated during the first 1.5 s of each trial. This variable was used to calculate the rate of avoidance and the theoretical TTC.</p> <p> <emph>TTC</emph> is the time (in seconds) remaining before a theoretical collision would occur between the participant and the virtual pedestrian had they both continued to walk at their average approach speed, without initiating a change in travel path. The approach speed of each participant was calculated using an average of the instantaneous speeds during the approach phase until the onset of deviation. <ephtml> <math overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><mi>TTC</mi><mo /><mo stretchy="false">(</mo><mi mathvariant="normal">s</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mrow><mtext>Distance between participant and virtual pedestrian at deviation onset</mtext><mtext /><mtext /><mo stretchy="false">(</mo><mi mathvariant="normal">m</mi><mo stretchy="false">)</mo></mrow><mrow><mo stretchy="false">(</mo><mtext>Approach speed of participant</mtext><mo stretchy="false">)</mo><mo>+</mo><mo stretchy="false">(</mo><mi mathvariant="normal">A</mi><mtext>pproach speed of pedestrian</mtext><mo stretchy="false">)</mo><mtext /><mtext /><mo stretchy="false">(</mo><mi mathvariant="normal">m</mi><mo>/</mo><mi mathvariant="normal">s</mi><mo stretchy="false">)</mo></mrow></mfrac><mo>.</mo></math> </ephtml><emph>TOC, s</emph> is the point in time when the participant crossed the virtual pedestrian (i.e., when the difference between the participant's anterior-posterior (A-P) position and the virtual pedestrian's A-P position was zero. TOC was used to determine clearance distance.</p> <p> <emph>Clearance distance </emph>(in meters) is the absolute ML distance between the midpoints of the participant and virtual pedes1trian at the TOC.</p> <p> <emph>Rate of avoidance</emph> is the speed (in meters per second) at which participants move in their new locomotor trajectory following the onset of deviation. This value was calculated as the rate of change in displacement over time along the resultant vector (i.e., summation of the M-L and A-P directions) and was calculated as an average from the onset of deviation to the TOC.</p> <p>The dependent variable averages were determined and <emph>SD</emph>s were calculated to represent intertrial participant variability. Only straight walking trials were analyzed in the current study, as this condition required participants to produce a change in travel path to avoid a collision with the approaching virtual pedestrian.</p> <hd id="AN0181087342-11">Statistical Analysis</hd> <p></p> <hd id="AN0181087342-12">Participant Demographics</hd> <p>Height, shoulder width, and approach speed between children and young adults were determined using independent samples <emph>t</emph> tests.</p> <hd id="AN0181087342-13">Perceptual Task</hd> <p>To calculate <emph>response accuracy</emph>, a frequency count of the number of correct responses was converted to a proportion by dividing the number of correct responses by the total number of trials. This proportion (<emph>p</emph>) was converted to a parametric measure using the arcsine transformation with the equation: <ephtml> <math overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>Y</mi><mo>=</mo><mi>arcsine</mi><msqrt><mrow><mi>p</mi></mrow></msqrt></mrow></math> </ephtml> . The transformed response accuracy data (<emph>Y</emph>) was then used for the analysis to compare accuracy of responses between groups.</p> <p>Two independent samples <emph>t</emph> tests were conducted to determine whether there were differences in response accuracy and average response time between age groups (children and young adults). Data were assessed for normality and homogeneity of variances. Effect size was reported using Cohen's <emph>d</emph> value. Effect sizes were considered low (<emph>d</emph> = 0.2), medium (<emph>d</emph> = 0.5), and high (<emph>d</emph> ≥ 0.8).</p> <hd id="AN0181087342-14">Avoidance Task</hd> <p>For each dependent variable, data were assessed for normality, sphericity, and homogeneity of variances. The Greenhouse–Geisser correction factor was applied for any violations of sphericity. To determine whether the virtual pedestrian's approach speed and/or age group of participants influenced the outcome measures, a mixed repeated-measures analysis of variance was conducted for each of the variables discussed above with a within factor being speed of the virtual pedestrian (slow, matched, fast) and a between factor of age group (Child and Adult). In addition, a Tukey's honest significant difference post hoc analysis was completed to determine where significant differences existed as a result of the virtual pedestrian's approach speeds. Statistical significance was set to <emph>p</emph> <.05 and effect sizes were evaluated using Cohen's <emph>f</emph> (<emph>f</emph>). Effect sizes were considered low (<emph>f</emph> < 0.25), medium (0.25 ≥ <emph>f <</emph> 0.4), and high (<emph>f</emph> ≥ 0.4).</p> <hd id="AN0181087342-15">Results</hd> <p>Across all participants, no collision occurred with the virtual pedestrian. A total of 571 trials were analyzed as five trials were removed from the analysis due to connection issues where the HTC Vive Pro 2 HMD disconnected from the server midtrial.</p> <hd id="AN0181087342-16">Demographics</hd> <p>Children (146.13 ± 6.61 cm) were significantly shorter than adults (170.19 ± 9.78 cm), <emph>t</emph>(<reflink idref="bib30" id="ref42">30</reflink>) = 8.154, <emph>p</emph> <.001, <emph>d</emph> = 2.88. Children also had significantly smaller shoulder widths (33.25 ± 1.65 cm) than adults (41.69 ± 2.30 cm), <emph>t</emph>(<reflink idref="bib30" id="ref43">30</reflink>) = 11.911, <emph>p</emph> <.001, <emph>d</emph> = 4.21. There was no significant difference in the average walking speed of children (1.29 ± 0.08 m/s) and adults (1.32 ± 0.06 m/s), <emph>t</emph>(<reflink idref="bib30" id="ref44">30</reflink>) = 1.05, <emph>p</emph> =.302, <emph>d</emph> = 0.37 (Table 1).</p> <hd id="AN0181087342-17">Perceptual Task</hd> <p> <emph>Response accuracy</emph> was not significantly different between children and adults, <emph>t</emph>(<reflink idref="bib30" id="ref45">30</reflink>) = 0.385, <emph>p</emph> =.703, <emph>d</emph> =.136. Children were 68.1 ± 19.1% accurate and adults were 67.2 ± 22.1% accurate at responding to the perceptual task.</p> <p>There was a significant difference in <emph>response time</emph> between age groups such that children took longer to respond to the pedestrian direction (0.55 ± 0.21 s) compared with young adults, 0.33 ± 0.13 s; <emph>t</emph>(25.453) = 3.560, <emph>p</emph> =.001, <emph>d</emph> = 1.259 (Figure 2).</p> <p>Graph: Figure 2 —A box plot comparing response times between children and adults, displaying the distribution and variability between age groups. Children had significantly slower response times compared with adults (p =.001).</p> <hd id="AN0181087342-18">Avoidance Task</hd> <p>For <emph>onset of deviation,</emph> there was a main effect of age group, <emph>F</emph>(<reflink idref="bib1" id="ref46">1</reflink>, 30) = 9.974, <emph>p</emph> =.004, <emph>f</emph> = 0.33. On average, children initiated a deviation in travel path later (1.65 ± 0.17 s) than young adults (1.52 ± 0.10 s; Figure 3A). There was also a main effect of age group on the <emph>onset of deviation variability,</emph><emph>F</emph>(<reflink idref="bib1" id="ref47">1</reflink>, 30) = 11.537, <emph>p</emph> =.002, <emph>f</emph> = 0.39. Children were significantly more variable (0.22 s) in their onset of deviation than adults (0.13 s; Figure 3A). All interactions effects and effect of the virtual pedestrian's speed on <emph>onset of deviation</emph> average and variability were not significant (<emph>p</emph> >.05).</p> <p>Graph: Figure 3 —(A) A box plot illustrating the distribution of the onset of deviation time (in seconds) between children and adults, displaying the distribution and variability between groups. Children demonstrated a delayed onset of deviation and where highly variable compared with adults (p =.004). (B) A box plot displaying the distribution of TTC between children and adults. Average TTC was not different between groups (p >.05). TTC = time to contact. * p <.01.</p> <p>Average <emph>TTC</emph> did not differ between groups (children: 1.11 ± 0.31 s; adults: 1.13 ± 0.27 s; Figure 3B). Average <emph>TTC</emph> was affected by the speed of the virtual pedestrian, <emph>F</emph>(<reflink idref="bib2" id="ref48">2</reflink>, 60) = 280.559, <emph>p</emph> <.001, <emph>f</emph> = 9.3. Post hoc analysis identified a greater <emph>TTC</emph> when the virtual pedestrian approached at a slower speed (1.43 ± 0.19 s) compared with the matched speed (1.08 ± 0.16 s; <emph>p</emph> <.001) and a fast speed (0.85 ± 0.13 s; <emph>p</emph> <.001). Representative plots of the medial-lateral center of mass position of both a middle-aged child participant and a young adult as they avoided an approaching VP at the three different speeds is displayed in Figure 4. Furthermore, participants had a larger <emph>TTC</emph> when the virtual pedestrian approached at a matched speed compared with a fast speed (<emph>p</emph> <.001; Figure 5A). All interaction effects were not significant (<emph>p</emph> >.05).</p> <p>Graph: Figure 4 —Representative trials from a child participant (left column) and a YA participant (right column) for each of the VP approaching speed: (A) 0.8×, (B) 1.0×, and (C) 1.2×. The figure shows M/L COM positional data from the start of the trial to the point of deviation. The distance between the lines represents the A/P spatial margin at the time of deviation. The A/P spatial margin was affected by VP approach speed and not the age of the participant because children and the YA participants had similar approach speeds and similar TTC. A/P = anterior-posterior; M/L COM = medial-lateral center of mass; VP = virtual pedestrian; YA = young adult.</p> <p>Graph: Figure 5 —Box plots illustrating group distribution and variability across the virtual pedestrian's approach speeds (slow = 0.8× walking speed; matched = participant's average walking speed; fast = 1.2× walking speed) for TTC, clearance distance, and rate of avoidance. (A) TTC: Participants had a larger TTC when the virtual pedestrian approached at a slow speed compared with a matched speed and fast speed (p <.001). As well, participants had a greater TTC when the pedestrian approached at a matched speed compared with fast speed (p <.001). (B) Clearance distance (i.e., the distance between the participant and virtual pedestrian at time of crossing) did not differ between groups (p >.05). Participants maintained a larger clearance distance when the virtual pedestrian approached at a slow speed compared with a matched speed (p =.004) and a fast speed (p =.017). (C) Rate of avoidance (i.e., walking speed following a deviation in locomotor trajectory) did not differ between groups (p >.05). Participants avoided at a significantly faster speed when the virtual pedestrian approached at a fast speed compared with slow speed (p =.003). TTC = time to contact. * p <.01.</p> <p> <emph>TTC variability</emph> was affected by age group, <emph>F</emph>(<reflink idref="bib1" id="ref49">1</reflink>, 30) = 12.956, <emph>p</emph> =.001, <emph>f</emph> = 0.75, and the virtual pedestrian's speed, <emph>F</emph>(<reflink idref="bib2" id="ref50">2</reflink>, 60) = 4.974, <emph>p</emph> =.01, <emph>f</emph> = 0.17. Children had a higher variability in <emph>TTC</emph> (0.21 s) compared with adults (0.13 s) and both groups had a greater <emph>TTC variability</emph> when the virtual pedestrian approached at a slow speed (0.20 s) compared with a fast speed (0.14 s; <emph>p</emph> =.021). All interaction effects were not significant (<emph>p</emph> >.05).</p> <p>Children's clearance distance at TOC (0.76 ± 0.16 m) was not different from adult's clearance distance at TOC (0.72 ± 0.15 m; <emph>p</emph> >.05). <emph>Clearance distance</emph> was affected by the virtual pedestrian's approach speed, <emph>F</emph>(1.462, 43.873) = 6.066, <emph>p</emph> =.009, <emph>f</emph> = 0.20. Post hoc analysis identified a greater clearance distance when the virtual pedestrian approached at a slower speed (0.77 ± 0.15 m) compared with a matched speed (0.74 ± 0.15 m, <emph>p</emph> =.004) or a fast speed (0.72 ± 0.15 m; <emph>p</emph> =.017; Figure 5B). There were no interaction effects on clearance distance (<emph>p</emph> >.05). As well, clearance distance variability was not affected by age group, or the virtual pedestrian's speed (<emph>p</emph> >.05).</p> <p> <emph>Rate of avoidance</emph> was not different between groups (children: 1.11 ± 0.17 m/s); adults: 1.16 ± 0.14 m/s; <emph>p</emph> >.05). <emph>Rate of avoidance</emph> was affected by the virtual pedestrian's speed, <emph>F</emph>(<reflink idref="bib2" id="ref51">2</reflink>, 60) = 6.886, <emph>p</emph> =.002, <emph>f</emph> = 0.23. Post hoc analysis showed a faster rate of avoidance when the virtual pedestrian approached at a fast speed (1.16 ± 0.15 m/s) compared with a slow speed (1.11 ± 0.16 m/s; <emph>p</emph> =.003). However, there were no differences in the rate of avoidance between the slow and matched speeds (1.14 ± 0.15 m/s; <emph>p</emph> =.108) or the matched speed and fast speed (<emph>p</emph> =.390; Figure 5C). <emph>Rate of avoidance variability</emph> was not different between age groups or by the virtual pedestrian's approach speed (<emph>p</emph> >.05).</p> <hd id="AN0181087342-19">Discussion</hd> <p>The current study investigated perception and action capabilities of middle-aged children and adults during a head-on collision avoidance task. It was hypothesized that children would perform similar to adults on the perceptual decision-making task but would make more last-minute locomotor adjustments on the avoidance task compared with adults. As expected, 10- to 12-year-old children have adult-like perceptual skills and can successfully avoid an approaching virtual pedestrian. However, the avoidance strategies employed by children were highly variable, suggesting middle-aged children are still fine-tuning their perception–action system.</p> <hd id="AN0181087342-20">Perceptual Task</hd> <p>Using a decision-making task, the current study examined perceptual capabilities of 10- to 12-year old children. Similar to adults, children were able to correctly perceive and identify the future path directions of the approaching virtual pedestrian. The findings in the current study are similar to those from a previous study which demonstrated that by 10 years of age, children select the same size gaps in traffic as adults for safe road crossing ([<reflink idref="bib20" id="ref52">20</reflink>]). Despite similar accuracy scores, children in the current study required more time to respond to the perceptual task compared with adults (Figure 2). Ten to 12-year-old children may still be refining their perceptual–motor neural connections, specifically within the frontal lobe ([<reflink idref="bib25" id="ref53">25</reflink>]). A developing frontal lobe would result in children requiring more time to process visual information and respond to their environment. Alternatively, children's slower response times in this study may be due to challenges in using the joysticks to respond. The buttons on the joystick may have been difficult to press, especially for children with smaller hands compared with adults. Therefore, we speculate the slower response times in children, and overall lower accuracy scores for both groups are due to the joysticks rather than delayed processing speeds. Nonetheless, these findings confirm that by 10 years of age, children can perceive their environment, making adult-like decisions albeit at a slower response time.</p> <hd id="AN0181087342-21">Avoidance Task</hd> <p>During the avoidance task, middle-aged children use similar techniques as adults to avoid a collision, but are more exploratory with their actions. Children initiated a deviation in travel path closer to the virtual pedestrian compared with adults (Figure 3A), suggesting children may be riskier with their avoidance strategies. Similar to the perceptual task, risker avoidance strategies in 10- to 12-year-old children may be due to a developing dorsal stream which is responsible for perception–action coupling. If dorsal stream processing is not fully developed, 10- to 12-year-old children would require more time to process egocentric movements (i.e., movements of the approaching pedestrian relative to their location; [<reflink idref="bib8" id="ref54">8</reflink>]; [<reflink idref="bib14" id="ref55">14</reflink>]). As a result, a delayed onset of deviation to avoid an approaching person suggests middle-aged children may have difficulty with perception–action coupling ([<reflink idref="bib17" id="ref56">17</reflink>]; [<reflink idref="bib20" id="ref57">20</reflink>]; [<reflink idref="bib23" id="ref58">23</reflink>]).</p> <p>Further evidence that 10- to 12-year-old children have yet to develop adult-like dorsal stream processing is highlighted in the variability of their actions. Similar to previous adaptive walking tasks, middle-aged children were more variable in the control of their actions compared with adults ([<reflink idref="bib1" id="ref59">1</reflink>]; [<reflink idref="bib13" id="ref60">13</reflink>]; [<reflink idref="bib23" id="ref61">23</reflink>]; [<reflink idref="bib22" id="ref62">22</reflink>]). Specifically, children had highly variable behaviors when avoiding an approaching person as highlighted by variability in the onset of deviation and TTC compared with adults. Variability in behavior can be attributed to children exploring their limits of stability allowing them to fine-tune their developing perception–action abilities.</p> <p>Contrary to the hypothesis, TTC was not different between children and adults (Figure 3B). Children and adults did not appear to use optical expansion threshold to modulate the onset of their avoidance behaviors to the approach speed of the virtual pedestrian (i.e., TTC); instead walked to a consistent location in space before changing path direction (i.e., constant onset of deviation). The experimental design had the virtual pedestrian either initiate a change in travel path, or continue walking straight 1 s after the participant began to move. Since the average onset of deviation was approximately 1.6 s from the start of the trial, participants may have been using an internal clock and waited to determine the virtual pedestrian's travel path direction before initiating their avoidance. Such finding differs from previous head-on collision avoidance studies which found that young adults and athletes maintain consistent TTC to initiate an avoidance behavior with an approaching person ([<reflink idref="bib19" id="ref63">19</reflink>]). The discrepancy between the current study and Pfaff and Cinelli ([<reflink idref="bib19" id="ref64">19</reflink>]) is likely due to the virtual environment, specifically the virtual pedestrian being nonreactive (preprogrammed). As such, the findings in the current study are similar to Cinelli and Patla ([<reflink idref="bib2" id="ref65">2</reflink>]) who suggested that when the path of an approaching object is predetermined, individuals will not use a consistent optical expansion threshold (TTC) to initiate an avoidance. Therefore, future research using this head on-collision avoidance paradigm in real-world or constraining one's behaviors with nonresponsive objects is needed to determine whether middle-aged children use TTC similar to adults to avoid a collision.</p> <p>Furthermore, the current study reported no differences between clearance distance between the virtual pedestrian and children or adults. Previous collision avoidance literature reported children have a smaller clearance distance between themselves and others when on a 90°-collision course with another person ([<reflink idref="bib23" id="ref66">23</reflink>]). Observed differences in clearance distance is likely due to environmental differences. In the current study, children interacted with a head-on approaching pedestrian in virtual reality, whereas Rapos et al. ([<reflink idref="bib23" id="ref67">23</reflink>]) had participants interacting with other children and adults on a 90°-collision course. A head-on collision avoidance task in virtual reality removes egocentric information about oneself relative to their surroundings ([<reflink idref="bib9" id="ref68">9</reflink>]). Therefore, future research is needed to confirm whether the findings from the current study were due to the task-specific characteristics.</p> <p>The current study found TTC, clearance distance, and rate of avoidance were affected by the virtual pedestrian's approach speed (Figure 5). Regardless of group, participants modulated their avoidance behaviors based on the virtual pedestrian's approach speed. Specifically, when the virtual pedestrian approached at a slow speed (i.e., 0.8× walking speed), participants maintained a larger TTC and clearance distance compared with when the virtual pedestrian approached at faster speeds. One explanation for this finding could be related to the perception of affordances in a social context, where an individual must perceive their own action capabilities, as well as those of another person ([<reflink idref="bib5" id="ref69">5</reflink>]). In the current study, participants may have perceived the affordances of the virtual pedestrian approaching at a slow speed similar to that of an older adult. As such, both children and adults may have taken a more conservative approach (i.e., a larger clearance distance) when interacting with the virtual pedestrian. This finding aligns with previous research which suggests adults rely on information about the movement profile (i.e., speed) of an approaching individual rather than person-specific characteristics (i.e., height, age; [<reflink idref="bib5" id="ref70">5</reflink>]). For example, Rapos et al. ([<reflink idref="bib24" id="ref71">24</reflink>]) found adults employ a more conservative avoidance behavior when on a collision course with an older adult compared with another young adult. Therefore, the findings from the current study suggest middle-aged children modulate their behaviors based on movement profiles of who they are interacting with, similar to young adults.</p> <p>It is important to note, the current study had participants interacting with a virtual male pedestrian. A male was chosen as the virtual pedestrian as this virtual pedestrian had the highest contrast against the environment (i.e., the pedestrian was wearing all black clothing). Previous research has demonstrated sex differences on collision avoidance strategies (Pfaff & Cinelli, 2018a). Specifically, young adult research has demonstrated that males will leave a greater distance between themselves and a female during head on collision avoidance, compared with female–female interactions. Since head-on collision avoidance has yet to be explored in developmental research, it was important to first explore behaviors while keeping the task constraints the same. Furthermore, there is conflicting evidence on whether these sex differences are demonstrated in virtual reality ([<reflink idref="bib10" id="ref72">10</reflink>]; [<reflink idref="bib27" id="ref73">27</reflink>]). Therefore, the current study should be used as an initial exploration into collision avoidance studies and researchers should continue to explore the influence on social and personal factors (i.e., cultural background, sex, gender) on collision avoidance across the lifespan.</p> <hd id="AN0181087342-22">Conclusions</hd> <p>In conclusion, children (10–12 years old) continue to refine their perceptual–motor capabilities compared with young adults. Specifically, children can perceive their environment and make adult-like decisions albeit at a slower response time. Furthermore, during a head-on collision avoidance task, middle-aged children modulate their avoidance behaviors based on movement profiles of the approaching pedestrian, similar to young adults. However, children employ variable avoidance strategies, suggesting they are still fine-tuning their perception–action system. As such, the dorsal stream processing system of children is likely still undergoing development by 12 years of age, resulting in greater risk of injury in fast-paced, crowded environments.</p> <p>Cinelli (mcinelli@wlu.ca) is corresponding author, https://orcid.org/0000-0002-5802-2590</p> <hd id="AN0181087342-23">Acknowledgment</hd> <p>This work was supported by the Natural Sciences and Engineering Council of Canada under grant number 2019-05894 to Cinelli.</p> <ref id="AN0181087342-24"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref29" type="bt">1</bibl> <bibtext> Berard, J.R., & Vallis, L.A. (2006). Characteristics of single and double obstacle avoidance strategies: A comparison between adults and children. 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  Data: Navigating Virtual Collisions: Insights into Perception-Action Differences in Children and Young Adults Using a Head-On Avoidance Task
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  Data: <searchLink fieldCode="AR" term="%22Megan+Hammill%22">Megan Hammill</searchLink><br /><searchLink fieldCode="AR" term="%22Victoria+Rapos%22">Victoria Rapos</searchLink><br /><searchLink fieldCode="AR" term="%22Michael+Cinelli%22">Michael Cinelli</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5802-2590">0000-0002-5802-2590</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Motor+Learning+and+Development%22"><i>Journal of Motor Learning and Development</i></searchLink>. 2024 12(3):555-571.
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  Data: Human Kinetics, Inc. 1607 North Market Street, Champaign, IL 61820. Tel: 800-474-4457; Fax: 217-351-1549; e-mail: info@hkusa.com; Web site: https://journals.humankinetics.com/view/journals/jmld/jmld-overview.xml
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  Data: 17
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  Data: 2024
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Young+Adults%22">Young Adults</searchLink><br /><searchLink fieldCode="DE" term="%22Motor+Development%22">Motor Development</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+Making%22">Decision Making</searchLink><br /><searchLink fieldCode="DE" term="%22Task+Analysis%22">Task Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Psychomotor+Skills%22">Psychomotor Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Accidents%22">Accidents</searchLink><br /><searchLink fieldCode="DE" term="%22Accident+Prevention%22">Accident Prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Perception%22">Perception</searchLink><br /><searchLink fieldCode="DE" term="%22Human+Body%22">Human Body</searchLink><br /><searchLink fieldCode="DE" term="%22Navigation%22">Navigation</searchLink><br /><searchLink fieldCode="DE" term="%22Reaction+Time%22">Reaction Time</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+Activities%22">Physical Activities</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink>
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  Data: 10.1123/jmld.2024-0027
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  Data: 2325-3193<br />2325-3215
– Name: Abstract
  Label: Abstract
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  Data: Children tend to make more last-minute locomotor adjustments than adults when avoiding stationary obstacles. The purpose of this study was to compare avoidance behaviors of middle-aged children (10-12 years old) with young adults during a head-on collision course with an approaching virtual pedestrian. Participants were immersed in a virtual environment and completed a perceptual decision-making task in which they had to respond to the future direction of an approaching virtual pedestrian once they disappeared. Following the perceptual task, participants walked along an 8-m pathway toward a goal, while avoiding a collision with a virtual pedestrian who approached along the midline than veered toward the left, right, or continued walking straight. Results revealed that children were able to correctly predict the future directions of the virtual pedestrian similar to adults, albeit at a slower response time (0.55 s vs. 0.33 s). During the action task, children initiated a deviation in their travel path later (i.e., closer to the virtual pedestrian) compared to adults (1.65 s vs. 1.52 s). Children were also more variable in their onset of deviation and time-to-contact. Although children appear to have developed adult-like perceptual abilities and can avoid an approaching virtual pedestrian, children employ riskier avoidance strategies and are highly variable, suggesting middle-aged children are still fine-tuning their perception-action system.
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      – Text: English
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      Pagination:
        PageCount: 17
        StartPage: 555
    Subjects:
      – SubjectFull: Children
        Type: general
      – SubjectFull: Young Adults
        Type: general
      – SubjectFull: Motor Development
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      – SubjectFull: Perception
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      – SubjectFull: Reaction Time
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      – SubjectFull: Physical Activities
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      – SubjectFull: Accuracy
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      – TitleFull: Navigating Virtual Collisions: Insights into Perception-Action Differences in Children and Young Adults Using a Head-On Avoidance Task
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