The impact of human agents on spatial navigation and knowledge acquisition in a virtual environment

The impact of human agents on spatial navigation and knowledge acquisition in a virtual environment Tracy Sánchez Pacheco 1 *, Melissa Sarria Mosquera 1 , Kaya Gärtner 1 , Vincent Schmidt 1 , Debora Nolte 1 , Sabine U. König 1† , Gordon Pipa 1† and Peter König 1 , 2† 1 Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany, 2 Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany Concepts of spatial navigation rest on the idea of landmarks, which are immobile features or objects in the environment. However, behaviorally relevant objects or fellow humans are often mobile. This raises the question of how the presence of human agents influences spatial exploration and knowledge acquisition. Here, we investigate exploration and performance in subsequent spatial tasks within a virtual environment containing numerous human avatars. In the exploration phase, agents had a locally limited effect on navigation. They prompted participants to revisit locations with agents during their initial exploration without significantly altering overall exploration patterns or the extent of the area covered. However, agents and buildings competed for visual attention. When spatial recall was tested, pointing accuracy toward buildings improved when participants directed their attention to the buildings and nearby agents. In contrast, pointing accuracy for agents showed weaker performance and did not benefit from visual attention directed toward the adjacent building. Contextual agents and incongruent agent-environment pairings further enhanced pointing accuracy, revealing that violations of expectations by agents can significantly shape navigational knowledge acquisition. Overall, agents influenced spatial exploration by directing attention locally, with the interaction between agent salience and environmental features playing a key role in shaping navigational knowledge acquisition. KEYWORDS spatial navigation, human agents, virtual reality, exploration-exploitation, social facilitation 1 Introduction Spatial navigation is essential for goal-oriented movement and active environmental interaction (Epstein et al., 2017;Ito et al., 2015). In humans, regular engagement in spatial navigation, whether studied through navigation done in the context of professional activities (Griesbauer et al., 2022;Maguire et al., 2006;Woollett and Maguire, 2011), targeted training (Choi et al., 2012), or virtual environments (West et al., 2017), is related to the enhancement of cognitive functions, particularly memory and spatial awareness. This OPEN ACCESS EDITED BY Maria Pyasik, University of Udine, Italy REVIEWED BY Adriana Salatino, Royal Military Academy, Belgium Ruth Conroy Dalton, Lancaster University, United Kingdom *CORRESPONDENCE Tracy Sánchez Pacheco, [email protected] † These authors contributed equally and share senior authorship RECEIVED 16 September 2024 ACCEPTED 06 March 2025 PUBLISHED 02 April 2025 CITATION Sánchez Pacheco T, Sarria Mosquera M, Gärtner K, Schmidt V, Nolte D, König SU, Pipa G and König P (2025) The impact of human agents on spatial navigation and knowledge acquisition in a virtual environment. Front. Virtual Real. 6:1497237. doi: 10.3389/frvir.2025.1497237 COPYRIGHT © 2025 Sánchez Pacheco, Sarria Mosquera, Gärtner, Schmidt, Nolte, König, Pipa and König. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Frontiers in Virtual Reality frontiersin.org01 TYPE Original Research PUBLISHED 02 April 2025 DOI 10.3389/frvir.2025.1497237 intricate link between spatial navigation and cognitive processes highlights the crucial role of spatial navigation in developing and organizing spatial knowledge. The transformation of navigational experiences into spatial knowledge begins with recognizing key environmental elements, followed by their gradual integration into a cohesive reference system (Ekstrom and Isham, 2017). When individuals explore new surroundings freely, they can identify elements that aid their orientation and harness them as building blocks for knowledge construction, such as remembering shops when visiting a new town. This process, known as landmark identification, is essential for maintaining positional awareness and planning future pathways (Janzen et al., 2006). Spatial knowledge encompasses recognizing and recalling critical elements of the landscape, understanding their spatial relationships, and the routes connecting them. Despite extensive research on the role of static landmarks in spatial cognition (Malanchini et al., 2020), the dynamic human aspects influencing spatial exploration and knowledge acquisition have yet to be fully explored. Often, humans are viewed primarily as modifiers of navigational paths rather than as active contributors to spatial knowledge acquisition (Bicanski and Burgess, 2020;Ekstrom and Isham, 2017). This perspective overlooks the significant role other individuals play in real-world spatial cognition. Encounters with others can impact pedestrian dynamics (Dalton et al., 2019), influence visual exploration (Gert et al., 2020), provide vital information about the safety and usability of spaces (Bajorunaite et al., 2022), affect the recall of locations (Kuehn et al., 2018), and prompt a parallel social mapping of the environment (Schafer and Schiller, 2018). Thus, incorporating other humans into navigation research is essential for a deeper understanding of spatial cognition. However, studying the influence of fellow humans on concrete spatial knowledge faces the difficulties of maintaining a navigation scenario of real-world scale in controlled environments. When available, researchers often conduct retrospective studies using real-world data, such as mobile phone and GPS data, which can characterize human mobility patterns but lack the controlled variables necessary for isolating specific factors mediating the results (Pappalardo et al., 2015;Schläpfer et al., 2021). Moreover, these studies do not link exploration patterns to the future acquisition of spatial knowledge, hindering a comprehensive understanding of cognitive processes derived from navigation patterns. When moving to the other side of the spectrum, laboratory setups tend to be bound to small-scale environments and simplified tasks that lack the challenge of spatial scales of the real world (Wiener et al., 2020). Participants are asked to use humans as anchors inside a maze-like virtual reality (VR) or as landmarks in visual flow experiments (Kuehn et al., 2018). Dalton et al. (2019) point out that VR technologies in wayfinding research often exclude the presence of others, thereby underestimating their importance. They suggest that the mere presence of others (i.e., weak social cues), even without active interaction, can help infer the importance of a space within its environment, and actively call for more studies to be done in this realm of research. Hence, it is acknowledged that examining how the mere presence of others might impact navigation and knowledge acquisition when humans are not primary targets is essential but remains unstudied. With this work, we raise the question of whether spatially relevant information can be extracted from observing other humans in the space around us. It is important to note that humans do not inherently serve as fixed landmarks in natural navigation, primarily due to their mobility, preventing them from forming intrinsic associations with specific places. However, rather than considering other humans as reliable reference points, we explore the notion that their relevance within a given context can be harnessed as a source of information to enhance spatial knowledge. Essentially, it is not the presence of others per se that aids in spatial cognition but rather how they interact with the elements in their surroundings that could be relevant for spatial exploration and knowledge acquisition. This prompts us to investigate what minimal change in a human agent’s interaction with the environment can elicit participants’different behavioral responses. We seek to understand whether these human elements act as distractions, potentially diminishing the saliency of the surrounding stimuli, or if, conversely, they contribute to improved performance by anchoring individuals to a more vivid mental representation of specific locations. To address this complexity, we investigated how spatial knowledge acquisition developed in a controlled one-square-kilometer VR environment with human agents. The study incorporated human agents at two levels: one in which the agent interacted with the environment by holding an object relevant to the context, such as a toolbox in front of a hardware store (i.e., Contextual agent), and another in which the agent simply stood without interacting with any objects around it (i.e., Acontextual agent). These agents were placed in front of public buildings, such as stores, basketball courts, restaurants, or residential buildings. For our first group of participants, all contextual agents were placed in front of public buildings that matched their object interaction. For the second group, we disrupted this congruency to study the sole influence of the type of agent and their building on the context in which they are situated, under the hypothesis that having context-congruent agents will enhance the participant’s ability to recall. Specifically, we propose three hypotheses: 1) the Visit Hypothesis, which states that contextual agents will influence participants’exploration patterns by drawing them back to previously visited locations; 2) the Dwell Hypothesis, which posits that participants will allocate more visual attention to contextually congruent agents compared to acontextual ones; and 3) the Performance Hypothesis, which suggests that this increased engagement will lead to improved performance in spatial knowledge tasks, such as pointing accuracy. The aim of our study is to explore the role of these human agents and their congruency with context in spatial exploration and the development of spatial knowledge. 2 Results We examined the impact of human agents on spatial navigation and knowledge acquisition in a virtual city named Westbrook, consisting of 236 buildings. We identified 26 public buildings (e.g., shops, basketball court) and 26 residential buildings as taskrelevant, marking them with street art. Additionally, there were 180 buildings without graffiti and four large buildings on the city’s outskirts, which could serve as global landmarks given their dimensions. We designed two categories of human agents: contextual agents, who performed context-relevant actions (e.g., Frontiers in Virtual Reality frontiersin.org02 Sánchez Pacheco et al. 10.3389/frvir.2025.1497237 holding a toolbox in front of a hardware store), and acontextual agents, who held a resting position without interacting with objects. In the first experiment, contextual agents were placed in public areas, displaying actions congruent with the buildings, while acontextual agents were positioned in front of residential buildings without interaction. In the second experiment, both agent types were split evenly across public and residential areas, disrupting the congruent pairs. Participants in both experiments completed five 30-min exploration sessions, totaling 150 min. Additionally, to provide a baseline for comparison of the exploration strategies, we used the control group from Schmidtetal. (2023) who explored the same VR city (Westbrook) with the same session lengths and numbers but no agents present. We investigated the exploration phase by analyzing participants’navigational coverage of the city, their walking strategies, agent-induced bias in their exploration, and theirvisualbehaviorduringexploration. Finally, we tested their spatial knowledge acquisition in a separate session using VR pointing tasks. To establish comparability in spatial orientation abilities between the participants of the experiments (experiment 1, experiment 2, and control), participants completed the FRS (Fragebogen Räumlicher Strategien) questionnaire, and their scores were contrasted. There were no significant differences between the groups at baseline on any of the three subscales (global, survey, and cardinal), χ2(2,N67)≤1.68,p≥0.43. Therefore, the groups were comparable in their assessment of their use of spatial strategies before the start of the experiments. 2.1 Assessment of the exploration phase During the VR city experiment, we tracked participants’ exploration, including walking behavior, navigational coverage, decision-point strategies, and visual behavior. This FIGURE 1 Free exploration of Westbrook (a) Presents a heatmap that visualizes the spatial navigation data of all participants across the exploration phase. It compiles movement across all sessions, with participant locations discretized to the nearest centimeter and averaged on a second-by-second basis after removing the initial 2 s of each recording. We fitted a heatmap grid approximating a 1-cm resolution and created a color scale ranging from 0 to 10 visits, highlighting areas of varied exploration intensity (b) Shows Westbrook from above, overlaid with a graph structure we used to formalize navigational data as decision units (c) Quantifies exploration through a map coverage ratio, calculated by dividing the number of unique nodes visited by the total nodes in the city (d) Compiles these ratios on a cumulative basis, reflecting the participants’expanding discovery of the city over time. Frontiers in Virtual Reality frontiersin.org03 Sánchez Pacheco et al. 10.3389/frvir.2025.1497237 comprehensive analysis revealed their navigational strategies and engagement, highlighting the agents’impact on their exploration. 2.1.1 Navigational coverage of the city We quantified participants’walking behavior in the virtual city using a primal city graph (Neal, 2013) to analyze participants’free exploration patterns. In this graph, decision points (i.e., intersections of walkable paths) were represented as nodes and paths connecting them as edges. Participants’navigational coordinates (see Figure 1a) were assigned to the nearest graph element (see Figure 1b), defining their exploration as movements from one graph element to another. Out of 159 nodes, participants visited between 45 and 113 unique nodes during each 30-min session (M= 87.06, SD = 16.49). We calculated the coverage ratio by dividing the number of nodes visited at least once by the total number of nodes. To account for repeated measures, we implemented a linear mixed-effects model (LMM) that considered intrasubject variability and generated individual intercepts for each participant. This model predicted the individual session coverage ratio as a function of the session, the experimental group (control versus city with agents), and their interaction. Using the first session as a baseline, we observed significant cumulative increases in navigational coverage with each subsequent session. Starting from an individual coverage of roughly half the city (as shown by the mean of the blue curves in Figures 1c, d), the LMM analysis explained a substantial portion of the variance in navigational coverage, with marginal R2 m0.18 (variance explained by fixed effects) and conditional R2 c0.70 (variance explained by both fixed and random effects), emphasizing the role of session-based learning while accounting for individual differences. The coefficients indicate changes relative to session 1(η2 p.40), with positive values signifying an increase: βSession 2 0.03 (SE = 0.01, t2.48, p0.01), βSession 3 0.07 (SE = 0.01, t6.54, p<.001), βSession 4 0.09 (SE = 0.01, t8.53, p<.001), and βSession 5 0.12 (SE = 0.01, t11.49, p<.001). The effect of the experiment group (agents versus control) on the coverage ratio was not statistically significant (βExperiment −0.02, SE = 0.02, t−0.72, p0.48). Additionally, interactions between the session and experiment group were also not significant: βSession i:Experiment ≤−0.04, p≥.07. This demonstrated that as sessions advanced, participants covered more ground within the same timeframe, achieving this equally in both a city with agents and one devoid of them. In order to estimate if the participants were differentially accumulating unique decision points in the city as the sessions progressed, we calculated a cumulative coverage ratio, accumulating the number of uniquely visited nodes as the sessions advanced. A LMM with participants as random effects was used to predict the cumulative coverage ratio as a function of session, experiment group, and their interaction (R2 m0.74, R2 c0.91). This analysis showed a significant progression, with participants covering more of the city in each subsequent session (see Figure 1d). Starting from the same initial coverage ratio mean (M= 0.49), significant cumulative increments in navigational coverage were observed in each session (η2 p.91):βSession 2 0.19 (SE = 0.01, t24.71, p<.001), βSession 3  0.27 (SE = 0.01, t35.80, p<.001), βSession 4 0.31 (SE = 0.01, t40.75, p<.001), and βSession 5 0.33 (SE = 0.01, t43.48, p<.001). The effect of the experiment group on the coverage ratio was not statistically significant (βExperiment −0.02, p0.48). Additionally, interactions between the session and experiment group were also not significant: βSession i:Experiment ≤0.02, p≥.33. After the fifth session, the end of the exploration, the cumulative number of unique nodes visited (M= 0.83, SD = 0.04) indicated that most participants had seen the majority of the city. These findings suggest that the presence of an agent did not significantly influence the navigational coverage of the city. 2.1.2 Exploration strategies on decision points: Exploratory vs. conservative behavior To examine participants’walking strategies, we analyzed whether they tended to choose paths they had previously visited more frequently (conservative) or if they favored less-traveled routes (explorative). We defined discrete navigational decisions as movements from one node to another and quantified them using a strategy matrix (see Figures 2a,b). A linear mixed-effects model was fitted to the data to examine differences in decision numbers based on session, strategy (conservative vs explorative), and experiment (control vs city with agents), with random intercepts for participants to account for the nested data structure. The model’s fixed effects (R2 m0.49, R2 c0.83) indicated that the average number of decisions across all factors was β073.14 (SE = 4.02, t18.20, p<.001). The analysis showed significant increases in decisions as participants gained experience in Westbrook, with increases evident from the first session onward (η2 p.69):βSession 2  30.17 (SE = 2.49, t12.11, p<.001), βSession 3 51.23 (SE = 2.49, t20.57, p<.001), βSession 4 67.49 (SE = 2.49, t27.09, p<.001), βSession 5 80.32 (SE = 2.49, t32.24, p<.001). The difference in decisions between conservative and exploratory strategies was significant, β75.52 (SE = 3.52, t21.44, p<.001, η2 p.14). The interaction between session and strategy was significant (η2 p.46), showing that decision increases per session were lower for the exploratory strategy compared to the conservative one: βSession 2:Strategy1 −44.70 (SE = 4.98, t−8.97, p<.001), βSession 3:Strategy1 −67.66 (SE = 4.98, t−13.58, p<.001), βSession 4:Strategy1 −86.02 (SE = 4.98, t−17.26, p<.001), βSession 5:Strategy1 −102.37 (SE = 4.98, t−20.55, p<.001). No significant difference in decision numbers was observed between the two experiments (βExperiment −3.95, SE = 7.40, t−0.53, p0.84), indicating that the presence or absence of agents did not markedly influence decision-making strategies. Both behaviors increased as sessions progressed. Initially, participants prioritized exploratory behavior, but as they gained experience, they integrated conservative behavior, effectively combining both strategies (see Figure 2c), regardless of agent presence. This strategy adaptation, where exploratory decisions remained stable while conservative decisions increased, occurred without significant influence from agents, suggesting that the global exploration strategy is unaffected by human agents. 2.1.3 Agent-induced bias on walking strategies In order to test our Visit Hypothesis, we evaluated the impact of agents on participants’exploratory behavior; we analyzed decision points where participants could choose between a path with an agent and one without, assuming that agents might elicit visits. Data from these points were compared with a control group from Schmidt et al. (2023), who explored the same VR city (Westbrook) without agents Frontiers in Virtual Reality frontiersin.org04 Sánchez Pacheco et al. 10.3389/frvir.2025.1497237 (see Figure 3a). We used identical decision points in both scenarios. We applied a linear mixed-effects model to assess visit counts, accounting for session and experiment type (control vs city with agents) with random intercepts for participants (R2 m0.41, R2 c0.82). Results indicated a significant increase in visit counts across sessions (η2 p.71)compared to session 1. Specifically, visit counts in session 2 were, on average, 14.84 points higher (βSession 2 14.84, p<0.001), and this effect continued to grow in subsequent sessions, culminating in a 38.55 point increase by session 5(βSession 5 38.55, p<0.001). However, the overall difference between the experiment types was not significant (βExperiment −2.15, p0.50), suggesting that the presence of agents did not universally affect visit counts across all sessions. Notably, the interaction between session and experiment type was significant in session 5 (βSession 5:Experiment 5.47, p0.008, η2 p.01), showing a greater increase in visits in the city with agents group. To further explore the influence of agents, we calculated the likelihood of participants adopting exploratory versus conservative strategies. This was done by dividing the number of choices for each strategy cell (i.e., above and below the diagonal; see Figure 3b) by the total number of decisions made, as recorded in the mirrored cells of the strategy matrix. For instance, we summed the number of times a participant chose to move to a place they had visited only once over a place they had not visited (cell [1,0], conservative behavior) with the number of times they chose to go to a place they had never been over a place they had visited once (cell [0,1], explorative behavior), and divided each count by that total of decisions made in both cells (sum of the count in both [1,0] and [0,1] cells). This indicator showed a clear distinction between the behavior of participants in the city with agents and that of the control group. Within the first exploration session, participants in the city with agents had an average proportion of conservative behavior of M= 0.47 (SD = 0.16) compared to M= 0.20 (SD = 0.09) in the control data, while the explorative behavior was M= 0.53 (SD = 0.16) for the city with agents and M= 0.80 (SD = 0.09) for the control data. This indicates that agents prompt more local conservative behavior, reducing exploratory actions during participants’initial city exposure. These findings provide partial support for the Visit Hypothesis, suggesting that while agents influence local return patterns, their effect on overall visit counts is session-dependent rather than universal. 2.1.4 Assessment of visual behavior during exploration: Investigating dwell time on agents and buildings According to our Dwell Hypothesis, we expected participants to allocate more visual attention to contextually meaningful agents within the environment. To characterize what participants focused on in the city, we quantified their visual behavior by summing the cumulative time spent gazing at each object, termed dwell time. We hypothesized that participants would have higher dwell times for contextual agents and public buildings compared to acontextual agents and residential buildings, assuming contextual agents congruent with their surroundings would attract the most attention. The data revealed distinct patterns in the attention participants allocated to different types of objects in the city. Notably, general residential houses across the city were observed for a shorter duration compared to our experimental buildings, with FIGURE 2 Exploration strategy quantification (a) This panel illustrates the translation of continuous movement into quantifiable behavioral units. Movement coordinates, denoted by beige dots, are assigned to the closest node or edge centroid. Nodes, represented by blue circles, are positioned at decision points and are interconnected by black lines (edges). A conservative decision occurs when participants return to a frequently visited node, whereas an explorative decision is made when a less frequented node is chosen (b) This panel introduces a strategy matrix designed to analyze navigational choices. The matrix evaluates the path chosen by a participant (displayed on the y-axis) against all other possible paths at a decision point (x-axis). For instance, upon exiting node 157 (as seen on Panel A), the participant’s choice of node 156 (with two previous visits) is compared against nodes 155 and 158, which had 5 and 0 visits, respectively. The matrix is structured such that rows indicate visits to the selected node (in this case, 156) and columns to the unselected nodes (in this case, 155 and 158). Entries in the matrix at positions [2,0] and [2,5] increment by one, reflecting the selection of node 156 over alternatives. This matrix visually encodes decision-making patterns: counts above the diagonal suggest conservative decisions, and those below indicate exploratory actions (c) This panel synthesizes decision-making trends by displaying the average sum of decisions, categorized by strategy, by all participants across our two experiments and the control group in a single session. Frontiers in Virtual Reality frontiersin.org05 Sánchez Pacheco et al. 10.3389/frvir.2025.1497237 participants spending on average approximately 6.68 s (M= 6.68, SD = 6.69) gazing at these buildings. This is in contrast to the longer viewing times for our public task buildings (M= 11.82, SD = 8.48) and residential task buildings (M= 11.66, SD = 8.29), which attracted more sustained attention from participants. Furthermore, global landmarks within the city garnered significantly longer dwell times, with participants spending an average of 16.27 s (M= 16.27, SD = 10.69) focusing on these prominent features, nearly three times the average dwell time of general buildings. In terms of agents, contextual agents were observed for an average of 3.63 s (M= 3.63, SD = 3.74), while acontextual agents attracted slightly less attention, with an average dwell time of 2.66 s (M= 2.66, SD = 2.78). These findings indicate that our experimental manipulations effectively captured participants’gaze in the expected order for both buildings and agents. We further analyzed how each experimental factor influenced visual attention. On average, participants spent more time looking at contextual agents (M= 3.39, SD = 3.49) compared to acontextual agents (M= 2.73, SD = 2.91, see Figure 4a). Contextual agents also seemed to distract participants from focusing on the area behind them, as buildings with contextual agents had lower dwell times (M= 12.42, SD = 8.13, see Figure 4e) compared to those with acontextual agents (M= 13.28, SD = 9.27) see Figure 4b. These FIGURE 3 Agent-induced bias in exploration patterns (a) The figure compares decision-making in virtual environments with and without agent presence. It contrasts our experimental data against a control from Schmidt et al. (2021). Panel A shows the average visit count to agent locations in our experiment (black) and the visits to those same points in the control (grey) (b) The probability of participants opting for paths where agents are located, classifying decisions as conservative when going towards the agent was the more familiar path, and exploratory when choosing the agent path was the less frequented route. We only show the first two sessions as the difference disappears afterward. Frontiers in Virtual Reality frontiersin.org06 Sánchez Pacheco et al. 10.3389/frvir.2025.1497237 results underscore the adversarial relationship between agents and building attention, where increased focus on contextual agents corresponded with decreased attention to the buildings behind them. Regarding building type, dwell time on the agent was not significantly affected by the surrounding area, as agents in public areas (M= 3.07, SD = 3.07) and residential areas (M= 3.03, SD = 3.37, see Figure 4c) received similar attention. However, participants spent more time gazing at residential buildings (M= 13.39, SD = 9.01) compared to public buildings (M= 12.32, SD = 8.42, see Figure 4f). These results imply that incongruent agents divert focus from surroundings, as indicated by the anti-correlation in dwell times between agents and buildings. Examining congruency (see Figure 4d), participants looked at incongruent agents (M= 3.23, SD = 3.48, see Figure 4d) for longer periods compared to congruent agents (M= 2.60, SD = 2.44). This pattern was opposite for building gazing time, with participants spending more time looking at buildings and surroundings when the agent matched the context in which it was placed (M= 12.92, SD = 7.65) compared to when the agent did not match the surroundings (M= 12.83, SD = 9.15, see Figure 4g). These results imply that when faced with incongruent agents, participants redirect their focus away from the surroundings, as evidenced by the anti-correlation in dwell times between agents and buildings in the congruency factor. To account for the high inter-individual variability and the nested structure of the data, we employed a linear mixed-effects model to predict dwell time on agents, with subjects as a random effect. The fixed effects included the context (residential vs public), agent type level (acontextual vs contextual), the congruency of the agent with their surroundings (not congruent vs congruent), and the interaction between agent type and context. The results (R2 m0.03, R2 c0.36) indicated that participants gazed at agents for a significantly shorter period in residential contexts (βBuilding −0.41, SE 0.08, t−5.30, p<0.001, η2 p.0026). Additionally, participants spent more time gazing at contextual agents compared to acontextual agents (βAgent 1.32, SE 0.08, t16.94, p<0.001, η2 p.03). The congruency between the agent and its context also had a significant effect, with participants gazing at congruent agents for a shorter duration (βCongruency −0.58, SE 0.13, t−4.59, p<0.001, η2 p.0034). Moreover, the interaction between context and agent action level was significant (β−0.76, SE 0.16, t−4.88, p<0.001, η2 p.0022), suggesting that the longest dwell times were observed for contextual agents in residential settings. The findings reveal that residential contextual agents capture the most attention, especially when they clash with their surroundings. We fitted an analogous linear mixed-effects model for the dwell time on buildings (R2 m0.014, R2 c0.089). We found that FIGURE 4 Dwell times across sessions: This figure presents the dwell time, defined as the cumulative sum of fixations on specific objects during the exploration phase, measured in seconds. The bar graph shows the interplay of two factors, building and agent category (a) Illustrates the average dwell times for four distinct building and agent type combinations. Each bar represents a unique combination: public buildings with contextual agents, public buildings with acontextual agents, residential buildings with contextual agents, and residential buildings with acontextual agents. The height of each bar indicates the average dwell time, reflecting the relative visual attention each combination received. To the right, we examine one factor at a time for dwell time on the agent on the top (b–d), and on buildings on the bottom (e–g). Frontiers in Virtual Reality frontiersin.org07 Sánchez Pacheco et al. 10.3389/frvir.2025.1497237 participants gazed at buildings for a significantly shorter period in residential contexts (β−2.02, SE 0.25, t−8.19, p<0.001, η2 p.0067). Additionally, participants spent less time gazing at buildings with contextual agents compared to those with acontextual agents (β−1.44, SE 0.25, t−5.83, p<0.001, η2 p.0034). The congruency between the agent and the building also had a significant effect, with participants gazing at buildings with congruent agents for a longer duration (β3.07, SE 0.39, t7.77, p<0.001, η2 p.01). Moreover, the interaction between context and agent type was significant (β−1.61, SE 0.49, t−3.26, p0.001, η2 p.0011), indicating that the shortest dwell times were observed for buildings with contextual agents in residential settings. These findings support the Dwell Hypothesis, demonstrating that visual attention is modulated by both agent contextuality and environmental congruency. Specifically, contextual agents attracted more gaze time compared to acontextual agents, confirming that contextually meaningful elements elicit longer dwell times. However, the competitive relationship between agents and their surroundings suggests that when agents match their environment, attention shifts toward the broader spatial context rather than the agent itself. This highlights the dynamic interplay between agent presence and scene integration in guiding visual attention. 2.2 Testing for spatial knowledge acquisition We assessed participants’spatial knowledge acquisition using pointing tasks. In the Pointing to Buildings task, participants were given screenshots of target buildings, while in the Pointing to Agents task, they received screenshots of agents against a grey background. Participants were asked to point toward the target. Accuracy was measured by calculating the angular difference between their pointing direction and the direction to the center of the target building or agent. This angular error served as the performance indicator, with a perfect score yielding zero degrees of error and higher values indicating greater inaccuracy. 2.2.1 Pointing to buildings According to our Performance Hypothesis, participants would have more accurate pointing for public buildings, especially when targets had active contextually congruent agent-building pairs. We applied a linear mixed-effects model to predict pointing errors, incorporating fixed and random effects. The random effects accounted for the test location of the pointing task (28 distinct locations with repeated measures). The fixed effects included the type of building, type of agent, congruency pairs, and the interaction between agent and building (R2 m0.02, R2 c0.56). The analysis revealed that pointing accuracy was significantly better, with lower errors in public buildings (β−5.51, SE 1.69, t−3.28, p<.001, η2 p.0.0088). Contextual agents significantly improved pointing accuracy compared to acontextual agents (β−7.17, SE 1.72, t−4.17, p<.001, η2 p.0179). The congruency of agent actions also played a crucial role, with incongruent pairs (where agent actions did not match the context) leading to better performance than congruent pairs (β6.88, SE 1.97, t3.50, p<.001, η2 p.0122). Additionally, the interaction between agent and building type was non-significant, indicating that the main factor captured the relevant information regarding performance (β4.03, SE 2.45, t1.64, p.101). These results provide support for the Performance Hypothesis, suggesting that contextual agents aid spatial knowledge acquisition. After the linear mixed-effects analysis, we examined the estimated marginal means (EMM) to clarify how different factors influenced pointing accuracy. Public buildings (EMM 47.4, SE 2.81) resulted in significantly lower errors than residential buildings (EMM 50.9, SE 2.94). acontextual agents were associated with higher errors (EMM 51.7, SE 2.93) compared to contextual agents (EMM 46.6, SE 2.82). The interaction between agent and building type revealed that in residential contexts, acontextual agents resulted in the highest errors (EMM 54.5, SE 3.03), while contextual agents in residential contexts showed lower errors (EMM 46.8, SE 2.77). In public contexts, acontextual agents had higher errors (EMM 49.2, SE 2.75), while contextual agents in public buildings showed the lowest errors (EMM 45.8, SE 2.81). The congruency of agent actions also played a key role, with incongruent pairs performing better than congruent pairs. Specifically, incongruent pairs had lower errors (EMM 45.7, SE 2.81) compared to congruent pairs (EMM 52.6, SE 3.14). The findings emphasize that contextually incongruent agents improve pointing accuracy, reducing errors and equalizing performance across building types (see Figure 5a). 2.2.2 Pointing to agents According to our Performance Hypothesis, participants would demonstrate lower pointing errors for the locations of contextual agents, particularly those positioned in front of public buildings. To test this hypothesis, we employed a linear mixed-effects model similar to the one used for analyzing pointing-to-building performance. The model included crossed random effects for subjects and the starting locations of the pointing tasks, covering 28 distinct locations. The analysis revealed (R2 m0.01, R2 c0.49) a significant main effect for the building type (β4.41, SE 2.12, t2.08, p.038, η2.0022). However, the interaction between context and agent action was non-significant (β−0.21, SE 3.22, t−0.06, p.949). Consistent with the Performance Hypothesis, participants exhibited lower pointing errors for contextual agents (EMM 53.7, SE 3.06) compared to acontextual agents (EMM 57.2, SE 3.02), indicating that contextual agents were better remembered. However, contrary to expectations, pointing errors were greater for public buildings (EMM 56.6, SE 3.04) compared to residential buildings (EMM 54.3, SE 3.03). These findings suggest that while agent contextuality played a role in reducing pointing errors, the expected facilitative effect of public buildings did not emerge. Instead, participants demonstrated better recall of agents at residential locations, suggesting that memory encoding may have been influenced by other environmental or attentional factors beyond public-private distinctions see Figure 5b. 2.2.3 Accuracy differences between pointing to buildings and pointing to agents We expected participants to be less precise when pointing to agent stimuli compared to building stimuli. To investigate this, we first assessed whether participants exhibited significantly lower Frontiers in Virtual Reality frontiersin.org08 Sánchez Pacheco et al. 10.3389/frvir.2025.1497237 precision when pointing to agents. This was achieved by fitting a two-crossed random effects model (i.e., ID and pointing location) and predicting pointing error with the type of stimuli (agent vs building) as the sole predictor. The analysis revealed (R2 m0.008, R2 c0.51) that participants were indeed significantly less precise when pointing to agent stimuli (β−7.74, SE 0.98, p<.001, η2.0079) compared to their performance in the pointing-tobuilding task. The estimated marginal means showed that participants had higher pointing errors for agent stimuli (EMM 54.4, SE 2.55) compared to building stimuli (EMM 46.7, SE 2.53). This clear difference is illustrated in Figure 5c, where the average performance indicates that participants could recall the precise locations of buildings more accurately than human agents. The data reveal that while building locations were recalled more accurately, agents may have served as a salient feature, enhancing the overall spatial recall even if their precise positions were less accurately remembered. 2.2.4 Inclusion of gaze as a predictor of performance To assess whether the time spent looking at both agents and buildings would significantly predict participants’performance, we incorporated the dwell time in seconds each participant spent gazing at the agents and buildings as fixed effects in the pointing-tobuilding and pointing-to-agent tasks (see Figure 6). For the pointing-to-building task, the results showed (R2 m0.007, R2 c0.144) that only the dwell time on buildings significantly predicted performance (β−0.18, SE 0.05, t−3.62, p<.001, η2.0012), while the dwell time on agents did not have a significant effect (β−0.27, SE 0.15, t−1.79, p.073). To assess the robustness of our findings, we conducted a sensitivity analysis using bootstrap resampling (1,000 iterations). The results confirmed that all significant predictors in the model retained their effects, with bootstrap confidence intervals not crossing zero. Conversely, predictors that were non-significant in the original model had confidence intervals that included zero, indicating greater uncertainty in their effects. This alignment between the bootstrap and model-based results suggests that our findingsarestableandnotundulyinfluenced by sample variability as can be seen on Table 1.Thetypeofagent (contextual vs acontextual) remained a significant predictor, with contextual agents leading to better performance (β−6.79, SE 1.74, t−3.91, p<.001, η2.0016). The type of building also remained significant (β−5.71, SE 1.68, t−3.41, p<.001, η2.0010). Additionally, the congruency between the agents and the building showed a significant effect (R2 m0.007, R2 c0.144) revealed that neither the dwell time on agents (β−0.47, SE 0.24, t−1.72, p.086) nor the dwell time on buildings (β−0.002, SE 0.09, t−0.02, p.981) were significant predictors of performance. However, building type, with the opposite pattern as in pointing to buildings (i.e., residential locations being better remembered, β4.35, SE 1.53, t2.60, p.009, η2 p0.0012) with no other significant effects. Comparing the results from the two tasks, it becomes evident that there is an inverse relationship between the ability to locate agents and buildings, as both compete for attention. Contextual agents improved the recall of their locations, yet the presence of public buildings appeared to detract from the ability to remember the agent. Therefore, while agents may serve as useful proxies for FIGURE 5 Analysis of pointing accuracy across experimental conditions. This figure illustrates the absolute pointing angular error, a measure of spatial recall accuracy whose lower values indicate better performance, with data points representing the mean error rates and error bars indicating standard errors (a) pointing to buildings: This plot delineates participants’pointing accuracy to buildings paired with contextual and acontextual agents (b) Pointing to Agents: Here, participants’accuracy in pointing to agents is compared, with a focus on the agent’s activity level and the type of building (c) Aggregated Task Performance: This plot displays overall performance across both building and agent pointing tasks. Frontiers in Virtual Reality frontiersin.org09 Sánchez Pacheco et al. 10.3389/frvir.2025.1497237 the target. In experiment 1, the targets were solely buildings (i.e., pointing-to-building). In contrast, in experiment 2, we included atestthatdisplayedonlytheagentagainstagraybackground (i.e., pointing-to-agent). To account for the sequence effect, the task order was strategically randomized, with one-half of the participants starting with the pointing-to-agent task followed by the pointing-tobuilding task and the other completing the tasks in reverse order. Finally, all participants completed three self-assessment questionnaires that inquired about their perception of each agent, their social tendencies in real life, and the realism level of the VR. The questionnaire data has not been analyzed in this work. 4.5 Experimental setup The exploration and task assessment sessions were conducted with a desktop computer with an Intel (R) Xeon (R) W-2133 CPU, 16 GB RAM, and a Nvidia RTX 2080 Ti graphics card. The VR environment was rendered with a HTC Vive Pro Eye head-mounted display (HMD) setup with a refresh rate of 90 Hz and horizontal and vertical field of view of 106°and 110°, respectively. We used four SteamVR Base Stations 2.0, an HTC VIVE body tracker 2.0, and Valve Index controllers to monitor participants’positions within the environment. This combined setup achieved sub-millimeter precision in capturing the head, body, and eye positions, as well as rotation and orientation. 4.6 Spatial task Participants performed pointing tasks in VR from a first-person perspective. In the pointing-to-building task, they were teleported to 28 unique locations within the city, each serving as a distinct reference point. The sequence of these locations was randomized for each participant to prevent order effects. To further minimize systematic biases, participants’orientations after teleportation were also randomized, ensuring that they did not always begin facing the same direction. At each location, participants pointed repeatedly toward one of 56 potential targets, represented by static images of buildings, with agents positioned in front of them. These images were captured perpendicularly at a height of 1.80 m to ensure consistency in visual presentation. The trials began with a visual and auditory cue: a 25 ms green circular loading bar at the screen’s center accompanied by a beep. The target image appeared at the upper center of the screen, with a green dashed laser beam providing a visual guide for pointing. Participants indicated their direction by pressing a trigger button on their controllers. Each trial was timed for 30 s, automatically concluding if a direction was not indicated within this period. Performance was assessed by measuring the angular difference between the participant’s pointing direction and the precise center vector of the target location. In the initial experiment, participants completed 336 trials, pointing at 12 unique targets from each of the 28 reference locations. The reduction in the number of trials in the second experiment, where participants completed only 224 trials, pointing at eight distinct targets from each location, was necessitated by the additional pointing-to-agents task. In both experiments, the trials were balanced to ensure an even distribution of target types: 50% directed participants to public locations and 50% to residential areas. 4.7 Pointing to agents task This task was fundamentally equivalent to the pointing-tobuilding but specifically focused on agents as the target. The targets featured centered screenshots of individual agents set against a grey backdrop, captured perpendicularly at a height of 1.80 m. Participants undertook 224 trials and were instructed to point at eight unique agent targets from each of the 28 reference locations. The task mirrored the structure of the building task in terms of target placement, visual and auditory cues, and time constraints to maintain consistency in the testing conditions. Additionally, the sequence of these pointing locations was randomized for each participant, with the goal of avoiding order effects and preserving the integrity of the experimental data. 4.8 “Fragebogen Räumlicher Strategien” (FRS) questionnaire The “Fragebogen Räumlicher Strategien”[FRS; Münzer and Hölscher (2011)] questionnaire is a 7-point Likert scale that asks the participants to estimate their spatial orientation abilities in three areas of spatial knowledge in real-world scenarios. First, the global sub-scale consisted of 10 items (α0.89)inquiring about the subject’s ability to navigate routes from an egocentric perspective. The survey sub-scale incorporates seven items (α0.87)focused on the subject’sabilityfor mental mapping from an allocentric perspective. The cardinal sub-scale comprises two items (α0.80)that query the ability to point toward cardinal points. The FRS measures participants’likelihood to apply spatial strategies related to egocentric/global knowledge, survey knowledge, or cardinal directions, respectively. 4.9 Movement tracking in the city 4.9.1 Navigational tracking To analyze participant trajectories and their exploration decisions, we created a data-driven graph based on their actual trajectories while exploring the virtual city. Thus, this graph reflects only the paths and areas that participants walked through, constituted by the streets (edges) and crossings or decision points (nodes) that participants walked through. We first generated a heatmap of participants’movement to transition from raw sequential coordinates to spatial data. This heatmap accounted for the number of times a participant stood at a specific cell within a defined 4 m × 4 m grid on top of the city map. This heatmap was then turned into a binary image, where cells visited at least once were assigned a value of one and cells without visits a value of zero. The resulting image clearly outlined the walkable paths and connections within the city. We filled isolated holes within the streets to ensure the algorithm generating the graph did not create extraneous nodes in these areas. We then generated a skeleton from the binary image, reducing the city’s representation to a one-pixel Frontiers in Virtual Reality frontiersin.org16 Sánchez Pacheco et al. 10.3389/frvir.2025.1497237 width while preserving its topography and connectivity. This skeleton was the foundation for identifying the graph’s nodes and edges. Using the external Python library “sknw,”we generated a NetworkX graph from the city skeleton image. Nodes were numbered sequentially, and edges were named based on the nodes they connected (see Figure 2a). For example, edge [76,60] connected nodes 76 and 60. Some manual adjustments, such as adding an edge, were necessary to perfect the graph. Each pixel in the skeleton was recognized as belonging to a node or an edge. This information was later used to plot the graph and calculate distances to identify the graph elements visited by participants during their sessions. To analyze exploration strategies, we developed an algorithm that converts participants’coordinates data into a sequence of visited nodes and edges. This process involved mapping the coordinates to the skeleton heatmap’s 4 m × 4 m cells and determining the closest graph element using the Euclidean distance formula. The algorithm tracked transitions between edges and nodes, recording each visited element. This record included the time of entry, graph element type (node/edge), and number of visits to the graph with a, well as the available paths from the node of origin and their respective number of previous visits to the possible elements available from that position. We adjusted the nodes’radii to match the width of the corresponding streets. This adjustment ensured accurate detection of participant presence at nodes, preventing unnoticed transitions between edges. Overlapping node radii in areas with several short streets were resolved by assigning participant positions to the nearest node centroid. 4.9.2 Navigational pattern classification: Strategy matrix We condensed participants’decisions at nodes into a strategy matrix to analyze their exploration patterns. The strategy matrix was organized with the number of visits to the chosen node on the rows and the number of visits to the not-chosen nodes on the columns. Each decision was recorded in the row corresponding to the number of previous visits to the selected path. We added one to each column corresponding to the number of prior visits on the other available paths from that decision point that were not selected. For instance, if a participant was at a node that had three direct neighbors, visited zero, five, and two times, and chose to move in the direction of the node visited two times, we would add a count of one to the positions [0,2] and [5,2]. This represents that the participant moved to a node with two visits over the options that had been visited zero and five times (see Figure 2b). Decisions above the diagonal line in the strategy matrix were considered conservative, indicating that participants preferred nodes they had visited more frequently in the previous example, the one added at [0,2]. In contrast, decisions below the diagonal line were considered exploratory, as participants chose nodes with fewer visits compared to their neighboring nodes, in the example above the one added at [5,2]. Decisions exactly on the diagonal were neutral, as both the chosen and the adjacent nodes had the same number of previous visits. This method allowed us to quantify and compare participants’exploratory and conservative tendencies as they navigated the virtual city, providing insight into their spatial decision-making processes as they gained experience inside Westbrook. 4.9.3 Eye-tracking preprocessing and classification We applied a velocity-based algorithm that classified continuous eye movements into gazes and saccades and corrected the resulting gazes for the participants’movement in the 3D environment, as developed by (Nolte et al., 2024). In preparation for this algorithm, we preprocessed the data by excluding portions detected as invalid (e.g., blinks). In cases where more than one collider hit was detected within the same sample, we retained the closest hit to the participant, except for background colliders, such as leaves or fences, in which case we kept the second closest hit. As a last step, we dropped duplicated samples and applied a 5-point median filter to the gaze coordinates. The algorithm calculates the median velocity of eye movements within a time window (in our case, a 10-s window). Samples exceeding this velocity threshold are identified as saccades, while those falling below the threshold are classified as gazes. To ensure accuracy, we applied outlier detection based on median absolute deviation to correct for gaze events with anomalous durations (i.e., exceeding three median absolute deviations). Dwell time was defined as the cumulative time a subject spent gazing at a specific object within the city across all sessions. We computed the dwell time for each subject-object pair during their entire exploration period within the Westbrook environment. 4.10 Data analysis Given the hierarchical structure of our data, we employed Linear Mixed-Effects Models for our analysis. The modeling was done using R 4.3.2 with the lmer() function from the lme4 package. We used Restricted Maximum Likelihood for estimation and the nloptwrap optimizer (Bates et al., 2015). This approach allows us to handle the nested structure of our data, with random effects to account for within-subject variability. Fixed effects with two levels were effect-coded to ensure the betas reflect the differences between these levels, using the first level of each pair as the base for comparison. 4.10.1 Exploration phase analysis: Navigational coverage of the city To analyze participants’free exploration patterns, we quantified their walking behavior through the virtual city using a primal city graph Neal (2013). The coverage ratio, defined as the proportion of unique nodes visited during each session, was modeled using a linear mixed-effects approach. The model included fixed effects for the session, experiment, and interactions, with planned contrast testing for each session against the first. Random intercepts for participants were included to account for repeated measures within subjects. The model formula for the individual session coverage ratio was (see Equation 1): Individual Ratio ~ Session × Experiment +1|participant  (1) The same structure was used to test for the cumulative ratio of visited nodes, in which we kept track of how many unique decision points each participant had visited, accumulating them between sessions. The model formula for the cumulative coverage ratio was (see Equation 2): Cumulative Ratio ~ Session × Experiment +1|participant  (2) Frontiers in Virtual Reality frontiersin.org17 Sánchez Pacheco et al. 10.3389/frvir.2025.1497237 4.10.2 Walking strategies: Exploratory vs conservative navigational behavior To investigate the extent to which participants used conservative or exploratory walking strategies, we modeled the number of decisions at each node. A linear mixed-effects model (see Equation 3) was used with the session, strategy (conservative vs exploratory), and experiment as fixed effects and random intercepts for participants. Number of decisions ~ Session × Strategy +Experiment +1|participant  (3) 4.10.3 Visual behavior during exploration We quantified participants’visual behavior by summing the cumulative time spent gazing at objects (dwell time) during the entire exploration phase. Separate models predicted dwell time on agents (see Equation 4) and buildings (see Equation 5), with fixed effects for agent type (acontextual vs contextual), context effect (residential vs public), the congruency of the agent with their surroundings (not congruent vs congruent), and their interactions, including random effects for participants with the following formulas: Dwell Timeagent ~1+Building Category × Agent Category +1|participant  +1|pointing location  (4) Dwell Timebuilding ~1+Building × Agent Category +Congruence +1|participant  +1|pointing location  (5) 4.10.4 Pointing task: Pointing to buildings We assessed spatial knowledge using pointing tasks, calculating the angular error as the performance indicator. Two separate linear mixed-effects models (see Equations 6,7) predicted pointing error based on building type (residential vs public), agent type (acontextual vs contextual), the congruency of the agents with their surroundings, dwell time on agents, dwell time on buildings, and the interaction of agent and building type. Random effects for participants and pointing locations were included in each model: Pointing Errorbuilding ~1+Building Category × Agent Category +Congruency +Dwell Timebuilding +Dwell Timeagent +1|participant  +1|pointing location  (6) Pointing Erroragent ~1+Building Category × Agent Category +Dwell Timeagent +Dwell Timebuilding +1|participant  +1|pointing location  (7) 4.10.5 Comparing pointing accuracy between tasks To compare accuracy between pointing to buildings and pointing to agents, we used a model with the type of stimuli (agent vs building) as the fixed effect and participants and pointing locations as random effects (see Equation 8): Pointing Error ~ 1 +Test +1|participant  +1|pointing location  (8) Following model fitting, we performed likelihood ratio tests to compare each model against a null model containing only the intercept, evaluating the added predictive power of our factors. 4.10.6 Permission to reuse and copyright Figures, tables, and images will be published under a Creative Commons CC-BY licence and permission must be obtained for use of copyrighted material from other sources (including re-published/adapted/modified/partial figures and images from the internet). It is the responsibility of the authors to acquire the licenses, to follow any citation instructions requested by third-party rights holders, and cover any supplementary charges. Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Ethics statement The studies involving humans were approved by University of Osnabrück Ethics Committee. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Author contributions TS: Conceptualization, Formal Analysis, Investigation, Writing –original draft, Writing –review and editing. MS: Formal Analysis, Investigation, Resources, Writing –review and editing. KG: Formal Analysis, Investigation, Resources, Writing –review and editing. VS: Conceptualization, Resources, Writing –review and editing. DN: Formal Analysis, Investigation, Resources, Writing –review and editing. SK: Conceptualization, Supervision, Writing –original draft, Writing –review and editing. GP: Conceptualization, Funding acquisition, Supervision, Writing –review and editing. PK: Conceptualization Funding acquisition, Supervision, Writing –original draft, Writing –review and editing. Funding The author(s) declare that financial support was received for the research and/or publication of this article. The University of Osnabrück supported this work in cooperation with the Deutscher Akademischer Austauschdienst (DAAD), Grant No. 57440921 and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—GRK 2340. Frontiers in Virtual Reality frontiersin.org18 Sánchez Pacheco et al. 10.3389/frvir.2025.1497237 Acknowledgments The authors express their gratitude to everyone who contributed to this project. They would like to thank Nora Maleki and Linus Tiemann for their help in developing the VR city and the Pointing Tasks required for these experiments, and Philipp Spaniol for his 3dimensional art and implementation of differential loading of levels of details with the agents on this scene. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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