{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132792"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132792","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"The role of proximity in human-agent trust","abstract":"This dissertation examines how different forms of proximity—visual, cognitive, and spatial shape human trust and collaboration with embodied agents. As AI systems increasingly operate as social partners in virtual and mixed reality contexts, understanding the mechanisms that foster or erode trust becomes essential for designing effective and reliable human-agent relationships. Building upon theories of social cognition, embodiment, and proxemics, this research proposes that proximity—beyond physical distance—functions as a multidimensional construct that governs how humans perceive, interpret, and calibrate trust toward agents. Across three studies, this dissertation systematically investigates these dimensions. Study 1 explored visual proximity through self–avatar similarity, examining how users’ embodied representations influence perceived alignment and initial trust toward AI partners. Results showed that greater avatar similarity increased perceived identification, social presence, and baseline trust, demonstrating that visual embodiment shapes the psychological foundations of human–agent rapport. Study 2 examined cognitive proximity by manipulating agents’ communication framing and reasoning transparency. Findings indicated that when agents conveyed human-like reasoning styles and goal alignment, participants exhibited higher cognitive resonance, improved interpretability, and more stable trust trajectories. These results extend trust in automation models by highlighting that cognitive congruence rather than competence alone drives sustainable trust. Study 3 investigated spatial proximity in virtual navigation tasks, varying the agent’s distance (personal vs social zone) in a collaborative maze environment. Participants interacting with closer agents demonstrated stronger trust development, faster learning, and more fluid communication, while those with distant agents displayed improved trust calibration—showing reduced overcompliance and greater critical evaluation of AI guidance. Together, these findings reveal that proximity modulates both emotional engagement and analytical control in human-agent interaction. Integrating across studies, this dissertation demonstrates that proximity operates as a fundamental organizing principle in human-agent trust formation. Visual and cognitive proximity foster identification and understanding, while spatial proximity dynamically shapes the affective and behavioral calibration of trust. These multidimensional insights extend Hall’s proxemics theory to intelligent systems, showing that human-agent relationships are governed by social distance cues analogous to human–human interaction. Practically, the findings inform the design of embodied AI and virtual agents by emphasizing that optimal proximity—visual, cognitive, and spatial that supports balanced trust: strong enough to enable cooperation, yet calibrated enough to prevent overreliance.","abstract_html":"This dissertation examines how different forms of proximity—visual, cognitive, and spatial shape human trust and collaboration with embodied agents. As AI systems increasingly operate as social partners in virtual and mixed reality contexts, understanding the mechanisms that foster or erode trust becomes essential for designing effective and reliable human-agent relationships. Building upon theories of social cognition, embodiment, and proxemics, this research proposes that proximity—beyond physical distance—functions as a multidimensional construct that governs how humans perceive, interpret, and calibrate trust toward agents. Across three studies, this dissertation systematically investigates these dimensions. Study 1 explored visual proximity through self–avatar similarity, examining how users’ embodied representations influence perceived alignment and initial trust toward AI partners. Results showed that greater avatar similarity increased perceived identification, social presence, and baseline trust, demonstrating that visual embodiment shapes the psychological foundations of human–agent rapport. Study 2 examined cognitive proximity by manipulating agents’ communication framing and reasoning transparency. Findings indicated that when agents conveyed human-like reasoning styles and goal alignment, participants exhibited higher cognitive resonance, improved interpretability, and more stable trust trajectories. These results extend trust in automation models by highlighting that cognitive congruence rather than competence alone drives sustainable trust. Study 3 investigated spatial proximity in virtual navigation tasks, varying the agent’s distance (personal vs social zone) in a collaborative maze environment. Participants interacting with closer agents demonstrated stronger trust development, faster learning, and more fluid communication, while those with distant agents displayed improved trust calibration—showing reduced overcompliance and greater critical evaluation of AI guidance. Together, these findings reveal that proximity modulates both emotional engagement and analytical control in human-agent interaction. Integrating across studies, this dissertation demonstrates that proximity operates as a fundamental organizing principle in human-agent trust formation. Visual and cognitive proximity foster identification and understanding, while spatial proximity dynamically shapes the affective and behavioral calibration of trust. These multidimensional insights extend Hall’s proxemics theory to intelligent systems, showing that human-agent relationships are governed by social distance cues analogous to human–human interaction. Practically, the findings inform the design of embodied AI and virtual agents by emphasizing that optimal proximity—visual, cognitive, and spatial that supports balanced trust: strong enough to enable cooperation, yet calibrated enough to prevent overreliance.","abstract_has_math":false,"creators":["Tang, Liang"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Information Sciences","degree_department":null,"school":null,"contributors":["Bashir, Masooda","Bosch, Nigel","Ball, Christopher","Morrow , Daniel"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["Human Agent Interaction","Trust","Human AI Trust","HCI","Human centered design"],"languages":["en"],"rights":["Copyright 2025 Liang Tang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132792","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bashir, Masooda","Bosch, Nigel","Ball, Christopher","Morrow , Daniel"]},{"key":"dc:creator","label":"Author","values":["Tang, Liang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-12-03"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Information Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Human Agent Interaction","Trust","Human AI Trust","HCI","Human centered design"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Liang Tang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132792"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This dissertation examines how different forms of proximity—visual, cognitive, and spatial shape human trust and collaboration with embodied agents. As AI systems increasingly operate as social partners in virtual and mixed reality contexts, understanding the mechanisms that foster or erode trust becomes essential for designing effective and reliable human-agent relationships. Building upon theories of social cognition, embodiment, and proxemics, this research proposes that proximity—beyond physical distance—functions as a multidimensional construct that governs how humans perceive, interpret, and calibrate trust toward agents. Across three studies, this dissertation systematically investigates these dimensions. Study 1 explored visual proximity through self–avatar similarity, examining how users’ embodied representations influence perceived alignment and initial trust toward AI partners. Results showed that greater avatar similarity increased perceived identification, social presence, and baseline trust, demonstrating that visual embodiment shapes the psychological foundations of human–agent rapport. Study 2 examined cognitive proximity by manipulating agents’ communication framing and reasoning transparency. Findings indicated that when agents conveyed human-like reasoning styles and goal alignment, participants exhibited higher cognitive resonance, improved interpretability, and more stable trust trajectories. These results extend trust in automation models by highlighting that cognitive congruence rather than competence alone drives sustainable trust. Study 3 investigated spatial proximity in virtual navigation tasks, varying the agent’s distance (personal vs social zone) in a collaborative maze environment. Participants interacting with closer agents demonstrated stronger trust development, faster learning, and more fluid communication, while those with distant agents displayed improved trust calibration—showing reduced overcompliance and greater critical evaluation of AI guidance. Together, these findings reveal that proximity modulates both emotional engagement and analytical control in human-agent interaction. Integrating across studies, this dissertation demonstrates that proximity operates as a fundamental organizing principle in human-agent trust formation. Visual and cognitive proximity foster identification and understanding, while spatial proximity dynamically shapes the affective and behavioral calibration of trust. These multidimensional insights extend Hall’s proxemics theory to intelligent systems, showing that human-agent relationships are governed by social distance cues analogous to human–human interaction. Practically, the findings inform the design of embodied AI and virtual agents by emphasizing that optimal proximity—visual, cognitive, and spatial that supports balanced trust: strong enough to enable cooperation, yet calibrated enough to prevent overreliance.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-12-01","The student, Liang Tang, accepted the attached license on 2025-12-02 at 20:01.","The student, Liang Tang, submitted this Dissertation for approval on 2025-12-02 at 20:34.","This Dissertation was approved for publication on 2025-12-03 at 12:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #23030 on 2026-02-19 at 20:09:52"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["The role of proximity in human-agent trust"]}]}],"canonical_facts":{"dc:contributor":["Bashir, Masooda","Bosch, Nigel","Ball, Christopher","Morrow , Daniel"],"dc:creator":["Tang, Liang"],"dc:date":["2025-12","2025-12-03"],"dc:description":["This dissertation examines how different forms of proximity—visual, cognitive, and spatial shape human trust and collaboration with embodied agents. As AI systems increasingly operate as social partners in virtual and mixed reality contexts, understanding the mechanisms that foster or erode trust becomes essential for designing effective and reliable human-agent relationships. Building upon theories of social cognition, embodiment, and proxemics, this research proposes that proximity—beyond physical distance—functions as a multidimensional construct that governs how humans perceive, interpret, and calibrate trust toward agents. Across three studies, this dissertation systematically investigates these dimensions. Study 1 explored visual proximity through self–avatar similarity, examining how users’ embodied representations influence perceived alignment and initial trust toward AI partners. Results showed that greater avatar similarity increased perceived identification, social presence, and baseline trust, demonstrating that visual embodiment shapes the psychological foundations of human–agent rapport. Study 2 examined cognitive proximity by manipulating agents’ communication framing and reasoning transparency. Findings indicated that when agents conveyed human-like reasoning styles and goal alignment, participants exhibited higher cognitive resonance, improved interpretability, and more stable trust trajectories. These results extend trust in automation models by highlighting that cognitive congruence rather than competence alone drives sustainable trust. Study 3 investigated spatial proximity in virtual navigation tasks, varying the agent’s distance (personal vs social zone) in a collaborative maze environment. Participants interacting with closer agents demonstrated stronger trust development, faster learning, and more fluid communication, while those with distant agents displayed improved trust calibration—showing reduced overcompliance and greater critical evaluation of AI guidance. Together, these findings reveal that proximity modulates both emotional engagement and analytical control in human-agent interaction. Integrating across studies, this dissertation demonstrates that proximity operates as a fundamental organizing principle in human-agent trust formation. Visual and cognitive proximity foster identification and understanding, while spatial proximity dynamically shapes the affective and behavioral calibration of trust. These multidimensional insights extend Hall’s proxemics theory to intelligent systems, showing that human-agent relationships are governed by social distance cues analogous to human–human interaction. Practically, the findings inform the design of embodied AI and virtual agents by emphasizing that optimal proximity—visual, cognitive, and spatial that supports balanced trust: strong enough to enable cooperation, yet calibrated enough to prevent overreliance.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-12-01","The student, Liang Tang, accepted the attached license on 2025-12-02 at 20:01.","The student, Liang Tang, submitted this Dissertation for approval on 2025-12-02 at 20:34.","This Dissertation was approved for publication on 2025-12-03 at 12:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #23030 on 2026-02-19 at 20:09:52"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132792"],"dc:language":["en"],"dc:rights":["Copyright 2025 Liang Tang"],"dc:subject":["Human Agent Interaction","Trust","Human AI Trust","HCI","Human centered design"],"dc:title":["The role of proximity in human-agent trust"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Information Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}