Almost human: Anthropomorphism increases trust resilience in cognitive agents.
Open accessAbstract
We interact daily with computers that appear and behave like humans. Some researchers propose that people apply the same social norms to computers as they do to humans, suggesting that social psychological knowledge can be applied to our interactions with computers. In contrast, theories of human–automation interaction postulate that humans respond to machines in unique and specific ways. We believe that anthropomorphism—the degree to which an agent exhibits human characteristics—is the critical variable that may resolve this apparent contradiction across the formation, violation, and repair stages of trust. Three experiments were designed to examine these opposing viewpoints by varying the appearance and behavior of automated agents. Participants received advice that deteriorated gradually in reliability from a computer, avatar, or human agent. Our results showed (a) that anthropomorphic agents were associated with greater trust resilience , a higher resistance to breakdowns in trust; (b) that these effects were magnified by greater uncertainty; and c) that incorporating human-like trust repair behavior largely erased differences between the agents. Automation anthropomorphism is therefore a critical variable that should be carefully incorporated into any general theory of human–agent trust as well as novel automation design.
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Anthropomorphism is associated with greater trust resilience, as people are more resistant to updating their trust in anthropomorphic agents when seeing them err.
“Anthropomorphism is also associated with greater trust resilience, as people are more resistant to updating their trust in anthropomorphic agents when seeing them err14.”
From The influence of mental state attributions on trust in large language models (2025) · cites this paper as [14]
A third way to measure trust calibration was to measure over-, under-, and appropriate trust as behavioral deviation (reliance) from ideal behavior.
“A third way to measure the trust calibration was to measure over-, under-, and appropriate trust as the behavioral deviation (reliance) from the ideal behavior [27,50,55,104,110,132,137].”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [27]
de Visser et al. found more human-like systems were also perceived as more trustworthy, which the authors interpret as elicitation of unwarranted trust.
“This is in line with fndings by de Visser et al. [27], who found that more human-like systems were also perceived as more trustworthy -a fnding we interpret as the elicitation of unwarranted trust (see 4.4).”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [27]
Anthropomorphic features can increase trust and make it more resilient after errors.
“Anthropomorphic features can increase trust and make it more resilient after errors [55, 56]”
From Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration (2026) · cites this paper as [56]
Anthropomorphic design can make trust more resilient after errors-valuable when calibrated but risky when users overgeneralise from fluent expression to epistemic competence.
“Anthropomorphic design can make trust more resilient after errors [56]-valuable when calibrated but risky when users overgeneralise from fluent expression to epistemic competence.”
From Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration (2026) · cites this paper as [56]
Cites
Mixed-effects modeling with crossed random effects for subjects and items
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008) · Journal of Memory and Language · doi:10.1016/j.jml.2007.12.005 · Cited by 8,540 · see citing works
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605 · see citing works
Humans and automation: Use, misuse, disuse, abuse
Parasuraman, R., & Riley, V. (1997) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872097778543886 · Cited by 4,554 · see citing works
Self-citationAn Approximate Distribution of Estimates of Variance Components
Satterthwaite, F. E. (1946) · Biometrics Bulletin · doi:10.2307/3002019 · Cited by 4,373 · see citing works
Machines and Mindlessness: Social Responses to Computers
Nass, C., & Moon, Y. (2000) · Journal of Social Issues · doi:10.1111/0022-4537.00153 · Cited by 4,022 · cited 3× · see citing works
“In this view, human–computer interaction is a form of interpersonal communication: People are easily manipulated to act as if computers were human, even absent artificial intelligence or visual anthropomorphic features (Nass & Moon, 2000; Nass, Moon, Fogg, Reeves, & Dryer, 1995).”
BackgroundResultInfluentialAn Experimental Study of Apparent Behavior
Heider, F., & Simmel, M. (1944) · The American Journal of Psychology · doi:10.2307/1416950 · Cited by 3,340 · see citing works
Rempel, J. K., Holmes, J. G., & Zanna, M. P. (1985) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.49.1.95 · Cited by 3,146 · see citing works
Gray, H. M., Gray, K., & Wegner, D. M. (2007) · Science · doi:10.1126/science.1134475 · Cited by 2,342 · cited 10× · see citing works
“org/10.1518/ 001872006777724408 Manzey, D., Reichenbach, J., & Onnasch, L. (2012). Human performance consequences of automated decision aids: The impact of degree of automation and system experience. Journal of Cognitive Engineering and Decision Making, 6, 57–87. http://dx.doi.org/10.1177/1555343411433844 Mayer, R., D…”
BackgroundInfluentialA Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction
Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y. C., de Visser, E. J., & Parasuraman, R. (2011) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720811417254 · Cited by 2,051 · cited 1× · see citing works
“…& Carnevale, 2014; Fox et al., 2014), mind perception (Gray, Gray, & Wegner, 2007), intentionality (Wiese, Wykowska, Zwickel, & Müller, 2012) or physical presence (Brainbridge, Hart, Kim, & Scassellati, 2011; Hancock et al., 2011; van den Brule, Dotsch, Bijlstra, Wigboldus, & Haselager, 2014).”
Self-citationComplacency and Bias in Human Use of Automation: An Attentional Integration
Parasuraman, R., & Manzey, D. H. (2010) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720810376055 · Cited by 1,710 · cited 4× · see citing works
“Automation bias is a heuristic people use to reduce scrutiny and monitoring of an aid’s recommendations (Parasuraman & Manzey, 2010).”
BackgroundResultInfluentialSelf-citationThe role of trust in automation reliance
Dzindolet, M. T., Peterson, S. A., Pomranky, R. A., Pierce, L. G., & Beck, H. P. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00038-7 · Cited by 1,276 · see citing works
Bonaccio, S., & Dalal, R. S. (2006) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2006.07.001 · Cited by 1,011 · see citing works
MUIR, B. M., & MORAY, N. (1996) · Ergonomics · doi:10.1080/00140139608964474 · Cited by 993 · see citing works
MUIR, B. M. (1994) · Ergonomics · doi:10.1080/00140139408964957 · Cited by 873 · cited 1× · see citing works
“Although early research on trust in automation assumed that human trust was similar in character to human automation trust (Lee & Moray, 1992; Muir, 1987, 1994), this proposition has not been systematically and consistently tested by comparing humans and computers in the same study.”
BackgroundPerceptions of moral character modulate the neural systems of reward during the trust game
Delgado, M. R., Frank, R. H., & Phelps, E. A. (2005) · Nature Neuroscience · doi:10.1038/nn1575 · Cited by 729 · see citing works
Merritt, S. M., & Ilgen, D. R. (2008) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872008x288574 · Cited by 527 · cited 1× · see citing works
“Previous research has shown that using vignettes portraying an agent’s background effectively establishes dispositional trust (Boudreau, McCubbins, & Coulson, 2009; Delgado et al., 2005; Madhavan & Wiegmann, 2007a; Merritt & Ilgen, 2008).”
BackgroundNass, C., & Lee, K. M. (2001) · Journal of Experimental Psychology: Applied · doi:10.1037/1076-898x.7.3.171 · Cited by 522 · see citing works
Avatars Versus Agents: A Meta-Analysis Quantifying the Effect of Agency on Social Influence
Fox, J., Ahn, S. J. (., Janssen, J. H., Yeykelis, L., Segovia, K. Y., & Bailenson, J. N. (2015) · Human–Computer Interaction · doi:10.1080/07370024.2014.921494 · Cited by 320 · see citing works
Helldin, T., Falkman, G., Riveiro, M., & Davidsson, S. (2013) · Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications · doi:10.1145/2516540.2516554 · Cited by 286 · see citing works
Manzey, D., Reichenbach, J., & Onnasch, L. (2012) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343411433844 · Cited by 277 · see citing works
de Vries, P., Midden, C., & Bouwhuis, D. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00039-9 · Cited by 264 · see citing works
McGuirl, J. M., & Sarter, N. B. (2006) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872006779166334 · Cited by 212 · see citing works
de Visser, E., & Parasuraman, R. (2011) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343411410160 · Cited by 207 · see citing works
Self-citationPersuasiveness of expert systems
Dijkstra, J. J., Liebrand, W. B. G., & Timminga, E. (1998) · Behaviour & Information Technology · doi:10.1080/014492998119526 · Cited by 161 · see citing works
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“Anthropomorphism has been associated with greater trust resilience, which prevents loss of trust (de Visser et al., 2016).”
BackgroundExploring relationship development with social chatbots: A mixed-method study of replika
Pentina, I., Hancock, T., & Xie, T. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107600 · Cited by 522 · see citing works
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Konya-Baumbach, E., Biller, M., & von Janda, S. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107513 · Cited by 283 · see citing works
An Initial Model of Trust in Chatbots for Customer Service—Findings from a Questionnaire Study
Nordheim, C. B., Følstad, A., & Bjørkli, C. A. (2019) · Interacting with Computers · doi:10.1093/iwc/iwz022 · Cited by 233 · see citing works
Alabed, A., Javornik, A., & Gregory-Smith, D. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121786 · Cited by 233 · see citing works
The effect of social presence and chatbot errors on trust
Toader, D.-C., Boca, G., Toader, R., Măcelaru, M., Toader, C., Ighian, D., & Rădulescu, A. T. (2019) · Sustainability · doi:10.3390/su12010256 · Cited by 233 · see citing works
Texting with Humanlike Conversational Agents: Designing for Anthropomorphism
Seeger, A.-M., University of Mannheim, G., Pfeiffer, J., Justus Liebig University Giessen, G., Heinzl, A., & University of Mannheim, G. (2021) · Journal of the Association for Information Systems · doi:10.17705/1jais.00685 · Cited by 200 · see citing works
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Giroux, M., Kim, J., Lee, J. C., & Park, J. (2022) · Journal of Business Ethics · doi:10.1007/s10551-022-05056-7 · Cited by 194 · see citing works
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Kim, T. W., Jiang, L., Duhachek, A., Lee, H., & Garvey, A. (2022) · Journal of Service Research · doi:10.1177/10946705221120232 · Cited by 177 · see citing works
Wischnewski, M., Krämer, N., & Müller, E. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581197 · Cited by 146 · see citing works
Wang, Q., Saha, K., Gregori, E., Joyner, D., & Goel, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445645 · Cited by 120 · see citing works
Kim, T., & Song, H. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2021.101595 · Cited by 101 · see citing works
Esterwood, C., & Jr, L. P. R. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107658 · Cited by 98 · see citing works
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Haring, K. S., Watanabe, K., Velonaki, M., Tossell, C. C., & Finomore, V. (2018) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2018.2851569 · Cited by 78 · see citing works
Kulms, P., & Kopp, S. (2019) · Proceedings of Mensch und Computer 2019 · doi:10.1145/3340764.3340793 · Cited by 68 · see citing works
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Langer, M., Hunsicker, T., Feldkamp, T., König, C. J., & Grgić-Hlača, N. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517527 · Cited by 55 · see citing works
Daronnat, S., Azzopardi, L., Halvey, M., & Dubiel, M. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.642201 · Cited by 45 · see citing works
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Kopka, M., Schmieding, M. L., Rieger, T., Roesler, E., Balzer, F., & Feufel, M. A. (2022) · JMIR Human Factors · doi:10.2196/35219 · Cited by 37 · see citing works
The influence of mental state attributions on trust in large language models
Colombatto, C., Birch, J., & Fleming, S. M. (2025) · Communications Psychology · doi:10.1038/s44271-025-00262-1 · Cited by 34 · see citing works
“Anthro-pomorphism is also associated with greater trust resilience, as people are more resistant to updating their trust in anthropomorphic agents when seeing them err 14 .”
Kox, E. S., Siegling, L. B., & Kerstholt, J. H. (2022) · International Journal of Social Robotics · doi:10.1007/s12369-022-00871-4 · Cited by 22 · see citing works
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Williams, K. J., Yuh, M. S., & Jain, N. (2023) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3594715 · Cited by 18 · see citing works
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Ge, X., Li, D., Guan, D., Xu, S., Sun, Y., & Zhou, M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-23541-3_32 · Cited by 15 · see citing works
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Bridgwater, T., Giuliani, M., van Maris, A., Baker, G., Winfield, A., & Pipe, T. (2020) · Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/3319502.3374804 · Cited by 14 · see citing works
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Xu, Z., Wang, G., Zhai, S., & Liu, P. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2023.2176986 · Cited by 12 · see citing works
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“Anthropomorphic design can make trust more resilient after errors [56]—valuable when calibrated but risky when users overgeneralise from fluent expression to epistemic competence.”
- Authors
- de Visser, E. J., Monfort, S. S., McKendrick, R., Smith, M. A. B., McKnight, P. E., Krueger, F., & Parasuraman, R.
- Affiliations
- George Mason University (US)
- Venue
- Journal of Experimental Psychology: Applied
- Published
- 2016-09
- Volume
- 22
- Issue
- 3
- Pages
- 331-349
- ISSN
- 1076-898X (ISSN-L)
- 1939-2192
- OpenAlex
- W2499526086
- Cited by
- 614
- References
- 24
- Citations by year
- 2026: 83 · 2025: 107 · 2024: 89 · 2023: 72 · 2022: 88 · 2021: 69 · 2020: 45 · 2019: 39 · 2018: 12 · 2017: 10
- FWCI
- 31.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Experimental Psychology Applied (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Funders
- AFOSR (FA9550-10–1-0385)
- Center of Excellence in Neuroergonomics, Technology, and Cognition
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- anthropomorphism, trust repair, uncertainty, automation design, human-like agents
- MeSH terms
- Adolescent, Adult, Artificial Intelligence, Automation, Cognition, Computers, Female, Humans, Male, User-Computer Interface, Trust, Young Adult
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