Brief exposure increases mind perception to ChatGPT and is moderated by the individual propensity to anthropomorphize
Open accessAbstract
The rapid proliferation of advanced AI chatbots and large language models (LLMs), such as ChatGPT, have coincided with increased calls to use psychological tools to understand how people perceive interactions with these AI systems. Decades of research into how people perceive minds and anthropomorphize non-humans have led to the development of several prominent frameworks for investigating how people perceive minds in AI systems. Yet, due to the novelty of programs like ChatGPT, mind perception frameworks have not been applied to better understand how exposure to ChatGPT influences the perception of mind in AI. Furthermore, there is an absence of knowledge regarding how individual differences may moderate changes in mind perception as a function of exposure. Here, we reveal the results of a brief exposure manipulation to ChatGPT and its subsequent effect on changing mind perception ratings. We find that even brief exposure significantly increased people’s perceptions of agency and experience in ChatGPT. Moreover, individuals with a higher propensity to anthropomorphize were also more likely to show changes to experiential attributions to ChatGPT (i.e., its ability to feel). These findings suggest that as LLMs like ChatGPT grow in popularity, and people are exposed to them to a greater extent, the degree to which people attribute qualities of mind to AI systems will also increase. This study expands the field’s current understanding of how exposure to LLMs and individual differences may influence attribution of mind to AI.
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Exposure to as few as three answers from LLMs leads to an increase in attributions of mental capacities.
“exposure to as few as three answers from these systems leads to an increase in attributions of mental capacities7.”
From The influence of mental state attributions on trust in large language models (2025) · cites this paper as [7]
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On seeing human: A three-factor theory of anthropomorphism
Epley, N., Waytz, A., & Cacioppo, J. T. (2007) · Psychological Review · doi:10.1037/0033-295x.114.4.864 · Cited by 4,069 · see citing works
Allison, C., Auyeung, B., & Baron-Cohen, S. (2012) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2011.11.003 · Cited by 1,016 · see citing works
Causes and consequences of mind perception
Waytz, A., Gray, K., Epley, N., & Wegner, D. M. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.05.006 · Cited by 759 · see citing works
Nass, C., Steuer, J., & Tauber, E. R. (1994) · Conference companion on Human factors in computing systems - CHI '94 · doi:10.1145/259963.260288 · Cited by 579 · see citing works
Evaluating Large Language Models in Theory of Mind Tasks
Kosinski, M. (2023) · arXiv · doi:10.48550/arxiv.2302.02083 · Cited by 339 · see citing works
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People cannot distinguish GPT-4 from a human in a Turing test
Jones, C. R., Rathi, I., Taylor, S., & Bergen, B. K. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732108 · Cited by 76 · 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
Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation
Chien, J., & Danks, D. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3630106.3658946 · Cited by 22 · see citing works
- Authors
- Jacobs, O., Pazhoohi, F., & Kingstone, A.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Jacobs, Oliver
- Publisher
- Center for Open Science
- Published
- 2023-03-26
- OpenAlex
- W4360982940
- Cited by
- 16
- References
- 5
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 7
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- mind perception, anthropomorphism, ChatGPT, large language models, AI chatbots, short-term exposure, perceived agency, individual differences
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