Wild animals suppress the spread of socially transmitted misinformation

Edited by Alan Hastings, University of California, Davis, CA; received September 8, 2022; accepted February 7, 2023 March 28, 2023 12...

Edited by Alan Hastings, University of California, Davis, CA; received September 8, 2022; accepted February 7, 2023

March 28, 2023

120 (14) e2215428120

Significance

Despite the benefits of learning about the world through social ties, social connections also provide a conduit for misinformation. Using underwater camera observatories to record the behavior of foraging coral reef fishes, we find that these animals produce and perceive visual motion cues produced by others, thereby forming dynamic information networks. These networks are surprisingly robust to false alarms that occur when one individual flees in the absence of a true shared threat. By reconstructing visual sensory inputs to each animal, we show that this robustness to misinformation about threats inherits from a specific property of their decision-making strategy: dynamic adjustments in sensitivity to socially acquired information. This property can be achieved through a simple and biologically widespread decision-making circuit.

Abstract

Understanding the mechanisms by which information and misinformation spread through groups of individual actors is essential to the prediction of phenomena ranging from coordinated group behaviors to misinformation epidemics. Transmission of information through groups depends on the rules that individuals use to transform the perceived actions of others into their own behaviors. Because it is often not possible to directly infer decision-making strategies in situ, most studies of behavioral spread assume that individuals make decisions by pooling or averaging the actions or behavioral states of neighbors. However, whether individuals may instead adopt more sophisticated strategies that exploit socially transmitted information, while remaining robust to misinformation, is unknown. Here, we study the relationship between individual decision-making and misinformation spread in groups of wild coral reef fish, where misinformation occurs in the form of false alarms that can spread contagiously through groups. Using automated visual field reconstruction of wild animals, we infer the precise sequences of socially transmitted visual stimuli perceived by individuals during decision-making. Our analysis reveals a feature of decision-making essential for controlling misinformation spread: dynamic adjustments in sensitivity to socially transmitted cues. This form of dynamic gain control can be achieved by a simple and biologically widespread decision-making circuit, and it renders individual behavior robust to natural fluctuations in misinformation exposure.

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Data, Materials, and Software Availability

Acknowledgments

We thank S. Hein, T. Gross, and S. Munch for comments and K. Fahimipour for illustrations. A.K.F. was supported by the Research Associateship Program from the National Research Council of the National Academies of Sciences, Engineering, and Mathematics. This work was supported by NSF grants IOS-1855956 and EF-2222478.

Author contributions

A.K.F. and A.M.H. designed research; A.K.F., M.A.G., and A.M.H. performed research; A.K.F., M.R.C., G.F.H., B.T.M., and A.M.H. contributed new reagents/analytic tools; A.K.F. and A.M.H. analyzed data; and A.K.F., M.A.G., M.R.C., B.T.M., and A.M.H. wrote the paper.

Competing interests

The authors declare no competing interest.

Supporting Information

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Information & Authors

Information

Published in

Proceedings of the National Academy of Sciences

Vol. 120 | No. 14
April 4, 2023

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Copyright

Data, Materials, and Software Availability

Submission history

Received: September 8, 2022

Accepted: February 7, 2023

Published online: March 28, 2023

Published in issue: April 4, 2023

Keywords

1. misinformation
2. decision-making
3. social networks
4. higher-order interactions
5. behavioral control

Acknowledgments

We thank S. Hein, T. Gross, and S. Munch for comments and K. Fahimipour for illustrations. A.K.F. was supported by the Research Associateship Program from the National Research Council of the National Academies of Sciences, Engineering, and Mathematics. This work was supported by NSF grants IOS-1855956 and EF-2222478.

Author Contributions

A.K.F. and A.M.H. designed research; A.K.F., M.A.G., and A.M.H. performed research; A.K.F., M.R.C., G.F.H., B.T.M., and A.M.H. contributed new reagents/analytic tools; A.K.F. and A.M.H. analyzed data; and A.K.F., M.A.G., M.R.C., B.T.M., and A.M.H. wrote the paper.

Competing Interests

The authors declare no competing interest.

Notes

This article is a PNAS Direct Submission.

Authors

Affiliations

Department of Biological Sciences, Florida Atlantic University, Boca Raton, FL 33431

Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, CA 95060

Department of Ecology & Evolutionary Biology, University of Colorado Boulder, Boulder, CO 80309

Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, CA 95060

OpenSpace Inc., San Francisco, CA 94108

Benjamin T. Martin

Institute for Biodiversity & Ecosystem Dynamics, University of Amsterdam, 1090 GE Amsterdam, The Netherlands

Department of Computational Biology, Cornell University, Ithaca, NY 14850

Notes

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