Expert-Public Mental-Model Divergence in Risk-Benefit Judgments of Cultivated Meat Technologies
DOI:
https://doi.org/10.71465/jgs77Keywords:
Cultivated meat, Mental models, Risk perception, Public acceptanceAbstract
The rapid development of cultivated meat technologies presents a transformative opportunity to address the environmental, ethical, and public health challenges associated with conventional industrial animal agriculture. However, the successful commercialization and societal integration of these novel food technologies depend heavily on public acceptance, which often diverges significantly from expert assessments. This paper investigates the structural and conceptual disparities between expert and public mental models regarding the risk-benefit judgments of cultivated meat. Utilizing a comprehensive theoretical framework grounded in risk perception and cognitive psychology, this research systematically maps the divergent cognitive architectures that underpin how different cohorts evaluate cellular agriculture. Experts typically anchor their mental models in scientific paradigms, emphasizing bioprocess efficiency, toxicological safety, and quantifiable environmental life-cycle assessments. Conversely, public mental models are frequently dominated by heuristic evaluations, concerns regarding naturalness, neophobia, and skepticism toward corporate consolidation in the food system. By analyzing these divergent mental constructs, this paper elucidates the underlying mechanisms that drive public hesitation and potential rejection of cultivated meat. The findings underscore the limitations of the traditional information deficit model in science communication and highlight the necessity for dialogic engagement strategies that address foundational socio-cultural and moral concerns. Ultimately, bridging the expert-public cognitive divide is essential for the responsible development and equitable implementation of sustainable alternative protein technologies in the global food system.
References
Liu, J., Yang, X., Wang, C., & Tao, Y. (2018, September). A convolution neural network for dolphin species identification using echolocation clicks signal. In 2018 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) (pp. 1-4). IEEE.
Wu, Y., Jiang, J., & Rahbarimanesh, A. (2026). Simulation-Based Teaching in Project Management Education: A Systematic Literature Review and Bibliometric Analysis. Available at SSRN 6334578.
Zhang, X. (2026, June). Workforce Reskilling and Digital Transformation: Empirical Analysis of Human Capital Agility in Global E-Commerce Platforms. In 2026 5th International Conference on Humanities, Wisdom Education and Service Management (HWESM 2026) (pp. 692-703). Atlantis Press.
Liu, R., McKay, D., & Buchanan, G. (2023). Person‐Oriented Ontologies Analysis for Digital Humanities Collections from a Metadata Crosswalk Perspective. Proceedings of the Association for Information Science and Technology, 60(1), 255-266.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.
Ho, S. S., Ou, M., & Vijayan, A. V. (2023). Halal or not? Exploring Muslim perceptions of cultured meat in Singapore. Frontiers in Sustainable Food Systems, 7, 1127164.
Meng, X., Zhang, Y., & Lin, J. (2026). PEARL: A Task-Aware Framework for Evaluating Differentially Private Synthetic Educational Data. arXiv preprint arXiv:2609.10612.
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.
Zhou, Yuehong, et al. "Adaptive Behavior in Digital Nomads: An Interactive Spatial Structure Study Using Machine Learning."
Zhang, B., Wang, S., Jiang, Y., Sui, D., Tu, Z., & Chu, D. (2025, July). Ask and Retrieve Knowledge: Towards Proactive Asking with Imperfect Information in Medical Multi-turn Dialogues. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1055-1065).
Ou, M., & Ho, S. S. (2024). Does knowledge make a difference? Understanding how the lay public and experts assess the credibility of information on novel foods. Public Understanding of Science, 33(2), 241-259.
Mo, Z. (2025, May). Development of an Intelligent Retrieval and Recommendation System for Chinese Language Educational Resources Based on DNN Technology. In 2025 2nd International Conference on Intelligent Computing and Robotics (ICICR) (pp. 170-175). IEEE.
Yu, X., Wei, Y., Zhou, S., Yang, Z., Sun, L., Peng, H., ... & Yu, P. S. (2025, April). Towards effective, efficient and unsupervised social event detection in the hyperbolic space. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 12, pp. 13106-13114).
Yang, Z., & Tao, Z. CARE-Fin: A Causal-Aware and Robust Meta-Learning Framework for Safe and Reliable SME Financial Decision-Making. In Second Workshop on Safe AI.
Liu, X., Yu, Z., Yao, Y., Yang, K., Zeng, H., & Chen, C. P. (2026). A Comprehensive Survey of Cross-Modal Retrieval: Breaking Through Modal Barriers. ACM Computing Surveys.
Bian, X., Brown, A., & Marques, B. (2026). How children learn complex environmental concepts through digital storytelling: divergent pathways to knowledge and stewardship intention. Frontiers in Psychology, 17, 1893526.
Zhao, K., Li, G., Li, C., Jiang, T., Wang, W., & Yang, Z. (2026). Identification Markers for Salvia miltiorrhiza and Its Close Relatives Based on Cell Wall Component Characteristics. Engineered Science, 40, 2122.
Meng, X., & Lin, J. (2026). PRISM-Twin: Data-Minimizing and Interpretable Multimodal Campus IoT Student-State Modeling for Risk Support. IEEE Internet of Things Journal.
Bian, X., & Brown, A. (2023). Establishing the potential for the application of digital storytelling to support Chinese industrial heritage. Acta Horticulturae et Regiotecturae, 26(2), 147-156.
Rui, L., & Jingzhu, W. (2020). Research on public willingness to use the national public culture cloud APP. Information and documentation services, 41(4), 39-48.
Yan, T. (2019). Exploring the uncanny valley effect in VR/AR.
Bian, X., Brown, A., & Marques, B. (2026). Balancing Ecological Restoration and Industrial Landscape Heritage Values Through a Digital Narrative Approach: A Case Study of the Dagushan Iron Mine, China. Land, 15(1), 155.
Ho, S. S., Ou, M., & Ong, Z. T. (2023). Exploring the general public’s and experts’ risk and benefit perceptions of cultured meat in Singapore: A mental models approach. PLoS One, 18(11), e0295265.
Zhao, J., Zhang, C., Qin, M., & Yang, P. (2025). QuantFactor REINFORCE: mining steady formulaic alpha factors with variance-bounded REINFORCE. IEEE Transactions on Signal Processing, 73, 2448-2463.
Hu, J., Dowell, N., Brooks, C., & Yan, W. (2018, June). Temporal changes in affiliation and emotion in MOOC discussion forum discourse. In International conference on artificial intelligence in education (pp. 145-149). Cham: Springer International Publishing.
Bower, J. L., & Christensen, C. M. (1995). Disruptive technologies: Catching the wave. Harvard Business Review, 73(1), 43–53.
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