Dr Keith Ransom

Grant-Funded Research Fellow

School of Computer Science and Information Technology

College of Engineering and Information Technology

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.


I'm a Post Doctoral Researcher at the University of Adelaide in the School of Psychology where I collaborate with Dr. Rachel Stephens and supervise student research projects. I'm also a Postdoctoral Research Fellow at the University of Melbourne School of Psychological Sciences and member of the Computational Cognitive Science Lab led by Andrew Perfors and Charles Kemp. My current research projects focus on two related fronts:social meta-inference: this work seeks to extend theoretical models of human inference to models of communication and influence among individuals and groups, and to models of social consensus. This allows us to understand and predict patterns of bi-directional influence: namely, how people’s perception of the public information environment affects what they learn from information shared with them, and how patterns of inference and information transmission affect the public information environment. explainable reasoning: Increasingly, public information sharing is heavily mediated through technology, including online social networks architectures and AI. Thus there is a growing need to understand how such mediation impacts human reasoning at scale: for example, how can people correctly calibrate their trust not only in each other, but in the technology, and in the evidence extant in the information environment. This research seeks to develop applied tools that monitor and promote calibrated trust and vigilance towards shared information, using easily comprehensible, yet high fidelity representations and explanations of data and reasoning at scale.More broadly, my research looks at the challenges of everyday reasoning: how we learn about the world, and generalise from our experiences of it. I'm interested in how we infer and represent the structures and processes that lie beneath the data we do have and how we leverage these representations to reason beyond that data. I use a combination of computational modelling and simulation techniques as well as behavioural experiments to get at these issues in a variety of contexts and at multiple levels (individual, social group, and societal).

Date Position Institution name
2019 - ongoing Postdoctoral Research Fellow University of Melbourne

Year Citation
2025 Alister, M., Ransom, K., Connor Desai, S., Soh, E. V., Hayes, B., & Perfors, A. (2025). How Convincing Is a Crowd? Quantifying the Persuasiveness of a Consensus for Different Individuals and Types of Claims.. Psychological science, 36(7), 483-498.
DOI Scopus1 WoS1
2025 Fay, N., Ransom, K. J., Walker, B., Howe, P. D. L., Perfors, A., & Kashima, Y. (2025). Truth over falsehood: Experimental evidence on what persuades and spreads. Journal of Personality and Social Psychology, 1-17.
DOI
2024 Howe, P. D. L., Perfors, A., Ransom, K. J., Walker, B., Fay, N., Kashima, Y., . . . Dong, S. (2024). Self-certification: A novel method for increasing sharing discernment on social media. PLoS One, 19(6), e0303025-1-e0303025-17.
DOI
2024 Han, S. J., Ransom, K. J., Perfors, A., & Kemp, C. (2024). Inductive reasoning in humans and large language models. Cognitive Systems Research, 83, 101155-1-101155-28.
DOI Scopus44 WoS26
2024 Hayes, B. K., Pham, J., Lee, J., Perfors, A., Ransom, K., & Desai, S. C. (2024). Changing your mind about the data: Updating sampling assumptions in inductive inference. Cognition, 245, 105717-1-105717-15.
DOI Scopus2 WoS2 Europe PMC1
2023 Howe, P., Perfors, A., Ransom, K., Walker, B., Fay, N., Kashima, Y., & Saletta, M. (2023). Self-Censorship Appears to be an Effective Way of Reducing the Spread of Misinformation on Social Media.
DOI
2023 Alister, M., Ransom, K., & Perfors, A. (2023). Inferring the truth from deception: What can people learn from helpful and unhelpful information providers?.
DOI
2023 Ransom, K., Perfors, A., Hayes, B., & Connor Desai, S. (2023). What do our sampling assumptions affect: how we encode data or how we reason from it?. Journal of Experimental Psychology: Learning, Memory, and Cognition, 49(9), 1419-1438.
DOI Scopus3 WoS3 Europe PMC1
2022 Alister, M., Perfors, A., & Ransom, K. (2022). Source independence affects argument persuasiveness when the relevance is clear. PsyArXiv Preprints, 2767-2773.
DOI Scopus4
2021 Ransom, K., Perfors, A., Hayes, B., & Connor Desai, S. (2021). What do our sampling assumptions affect: how we encode data or how we reason from it?.
DOI
2020 Semmler, C. A., Kaesler, M., Dunn, J., & Ransom, K. (2020). Do sequential lineups impair underlying discriminability?. Cognitive Research: Principles and Implications, 5(1), 35-1-35-21.
DOI Scopus18 WoS16 Europe PMC7
2020 Kaesler, M. P., Dunn, J., Ransom, K., & Semmler, C. (2020). Do Sequential Lineups Impair Discriminability?.
DOI
2019 Ransom, K., Voorspoels, W., Navarro, D., & Perfors, A. (2019). Where the truth lies: how sampling implications drive deception without lying.
DOI
2019 Hayes, B., Navarro, D., Stephens, R. G., Ransom, K., & Dilevski, N. (2019). The diversity effect in inductive reasoning depends on sampling assumptions.
DOI
2019 Hendrickson, A. T., Perfors, A., Navarro, D., & Ransom, K. (2019). Sample size, number of categories and sampling assumptions: Exploring some differences between categorization and generalization.
DOI
2019 Voorspoels, W., Navarro, D., Perfors, A., Ransom, K., & Storms, G. (2019). How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning.
DOI
2019 Ransom, K., Perfors, A., & Navarro, D. (2019). Leaping to conclusions: Why premise relevance affects argument strength.
DOI
2019 Hendrickson, A. T., Perfors, A., Navarro, D. J., & Ransom, K. (2019). Sample size, number of categories and sampling assumptions: exploring some differences between categorization and generalization. Cognitive Psychology, 111, 80-102.
DOI Scopus17 WoS13 Europe PMC6
2019 Hayes, B. K., Navarro, D. J., Stephens, R. G., Ransom, K., & Dilevski, N. (2019). The diversity effect in inductive reasoning depends on sampling assumptions. Psychonomic Bulletin and Review, 26(3), 1043-1050.
DOI Scopus25 WoS18 Europe PMC7
2016 Ransom, K., Perfors, A., & Navarro, D. (2016). Leaping to Conclusions: Why Premise Relevance Affects Argument Strength. Cognitive Science, 40(7), 1775-1796.
DOI Scopus18 WoS12 Europe PMC5
2015 Voorspoels, W., Navarro, D., Perfors, A., Ransom, K., & Storms, G. (2015). How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning. Cognitive Psychology, 81, 1-25.
DOI Scopus27 WoS20 Europe PMC10

Year Citation
2022 Hayes, B., Connor Desai, S., Ransom, K., & Kemp, C. (2022). The dog that didn’t bark: Bayesian approaches to reasoning from censored data.. In K. Fiedler, P. Juslin, & J. Denrell (Eds.), Sampling in Judgment and Decision Making.. Cambridge University Press..
2022 Hayes, B., Connor Desai, S., Ransom, K., & Kemp, C. (2022). The dog that didn’t bark: Bayesian approaches to reasoning from censored data.. In K. Fiedler, P. Juslin, & J. Denrell (Eds.), Sampling in Judgment and Decision Making.. Cambridge University Press..

Year Citation
2025 Hoang, G. B., Ransom, K. J., Stephens, R., Semmler, C., Fay, N., & Mitchell, L. (2025). A Hybrid Theory and Data-driven Approach to Persuasion Detection with Large Language Models. In Workshop Proceedings of the 19th International AAAI Conference on Web and Social Media (ICWSM 2025) (pp. 1-12). Copenhagen, Denmark: Association for the Advancement of Artificial Intelligence.
DOI
2023 Vithanage, S., Ransom, K., Mendoza, A., & Karunasekera, S. (2023). Towards Better Truth Discernment on Social Media. In Australasian Conference on Information Systems, ACIS 2023.
2023 Simmonds, B., Stephens, R., Searston, R., Asad, N., & Ransom, K. (2023). The Influence of Cues to Consensus Quantity and Quality on Belief in Health Claims. In Proceedings of the Annual Meeting of the Cognitive Science Society (COGSCI 2023) Vol. 45 (pp. 828-834). Sydney, Australia: Cognitive Science Society : UC Merced.
2023 Vithanage, S., Ransom, K., Mendoza, A., & Karunasekera, S. (2023). Towards Better Truth Discernment on Social Media. In International Conference on Information Systems, ICIS 2023: "Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies".
2022 Han, S. J., Ransom, K. J., Perfors, A., & Kemp, C. (2022). Human-like property induction is a challenge for large language models. In Proceedings of the 44th Annual Conference of the Cognitive Science Society (CogSci 2022) (pp. 2782-2788). Toronto, Canada and Virtual Online: Cognitive Science Society, University of California.
DOI Scopus10
2022 Han, S. J., Ransom, K. J., Perfors, A., & Kemp, C. (2022). Human-like property induction is a challenge for large language models. In Proceedings of the 44th Annual Conference of the Cognitive Science Society (CogSci 2022) (pp. 2782-2788). Toronto, Canada and Virtual Online: Cognitive Science Society, University of California.
DOI Scopus10
2021 Ransom, K. J., Perfors, A., & Stephens, R. (2021). Social meta-inference and the evidentiary value of consensus. In Proceedings of the 43rd Annual Meeting of the Cognitive Science Society Comparative Cognition Animal Minds Cogsci 2021 (pp. 833-839).
DOI Scopus7
2021 Howe, P., Perfors, A., & Ransom, K. (2021). What interventions can decrease or increase belief polarisation in a population of rational agents?. In Proceedings of the Annual Meeting of the Cognitive Science Society Vol. 43 (pp. 1733-1739). Vienna, Austria: Cognitive Science Society.
DOI
2021 Ransom, K., Perfors, A., & Stephens, R. (2021). Social meta-inference and the evidentiary value of consensus. In Proceedings of the Annual Meeting of the Cognitive Science Society Vol. 43 (pp. 833-839). Berkeley, CA, United States: eScholarship, University of California.
2019 Ransom, K. J., & Perfors, A. (2019). Exploring the role that encoding and retrieval play in sampling effects. In A. Goel, C. Seifert, & C. Freksa (Eds.), Proceedings of the the 41st Annual Conference of the Cognitive Science Society (pp. 946-952). online: Cognitive Science Society.
DOI
2018 Ransom, K. J., Hendrickson, A. T., Perfors, A., & Navarro, D. J. (2018). Representational and sampling assumptions drive individual differences in single category generalisation. In The Proceedings of the 40th Annual Meeting of the Cognitive Science Society Vol. 1 (pp. 930-935). online: COGSCI.
Scopus7
2017 Ransom, K., Voorspoels, W., Perfors, A., & Navarro, D. (2017). A cognitive analysis of deception without lying. In Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition (pp. 992-997). London, UK: Cognitive Science Society.
Scopus10
2014 Perfors, A., Ransom, K., & Navarro, D. J. (2014). People ignore token frequency when deciding how widely to generalize. In Program of the 36th Annual Meeting of the Cognitive Science Society (pp. 2759-2764). Quebec City, Canada: Cognitive Science Society.
Scopus8
1995 Ransom, K. J., & Marlin, C. D. (1995). Supporting software reuse within an integrated software development environment. In ACM SIGSOFT Symposium on Software Reusability Ssr (pp. 233-237).
Scopus1
1995 Ransom, K. J., & Marlin, C. D. (1995). Modelling systems that integrate programming language and environment mechanisms. In Proceedings 1995 Asia Pacific Software Engineering Conference APSEC 1995 (pp. 274-281). IEEE Comput. Soc. Press.
DOI Scopus1

Year Citation
2019 Kaesler, M. P., Dunn, J. C., Ransom, K., & Semmler, C. (2019). Discriminability and Suspect Position in the Sequential Lineup. Poster session presented at the meeting of Society for Applied Research in Memory and Cognition XIII. Cape Cod, Massachusetts.

Year Citation
2025 Alister, M., Ransom, K., & Perfors, A. (2025). When a helpful bias is unhelpful: Limitations in reasoning about random and deliberately misleading evidence.
DOI
2025 Alister, M., Ransom, K., & Perfors, A. (2025). When a helpful bias is unhelpful: Limitations in reasoning about random and deliberately misleading evidence.
DOI
2025 Alister, M., Ransom, K., & Perfors, A. (2025). The impact of engagement and partisan influence campaigns in an isolated social media environment.
DOI
2024 Fay, N., Ransom, K., Walker, B., Howe, P., Perfors, A., & Kashima, Y. (2024). Truth Wins: True Information is More Persuasive and Shareable than Falsehoods.
DOI
2023 Howe, P., Perfors, A., Ransom, K., Walker, B., Fay, N., Kashima, Y., . . . Dong, S. (2023). Self-Certification: A novel method for increasing sharing discernment on social media.
DOI
2023 Hayes, B., Pham, J., Lee, J., Perfors, A., Ransom, K., & Connor Desai, S. (2023). Changing your mind about the data: Updating sampling assumptions in inductive inference.
DOI

Keith Ransom, Rachel Stephens, Carolyn Semmler & Lewis Mitchell.

In association with: Andrew Perfors & Christopher Leckie.

Digi+ FAME program (Information Capability mission), University of Adelaide.  $99,050, 18 months.

Project: MAGPIE: Monitoring And Guarding the Public Information Environment.

Rachel Stephens, Keith Ransom, Rachel Searston, Zygmunt Szpak, Fernando Marmolejo-Ramos, Dragana Pittas.

Collaborative Research Fund, Defence Innovation Partnership.  $150,000, 12 months.

Project: Advancing SOCRETIS (“SOCial REasoning Tool & Interactive System”): An AI-enabled collaborative reasoning aid for the information environment.

Zygmunt Szpak, Wojciech Chojnacki, Rachel Stephens, Keith Ransom & Rachel Searston.

AI for Decision Making Program Round 2, Department of Defence and the Office of National Intelligence, delivered in partnership with the Defence Innovation Partnership in South Australia.  $100,000, 6 months.

Project: A tool for human-in-the-loop contextual anomaly detection

Rachel Stephens & Keith Ransom.

AI for Decision Making Program, Department of Defence and the Office of National Intelligence, delivered in partnership with the Defence Innovation Partnership in South Australia.  $20,000, 3 months.

Project tile: Testing human responses to AI fact-checking and uncertainty. Project ID: 167650398

Date Role Research Topic Program Degree Type Student Load Student Name
2025 Co-Supervisor Quantifying Online Persuadability: Analysis, Metrics Development, and Application in Digital Discourse. Doctor of Philosophy Doctorate Full Time Mr Gia Bao Hoang
2025 Co-Supervisor Mixture-of-Experts for Multimodal Synthetic Media Detection Doctor of Philosophy Doctorate Part Time Mr Alessandro Cardoso Laudares Pereira
2025 Co-Supervisor Mixture-of-Experts for Multimodal Synthetic Media Detection Doctor of Philosophy Doctorate Part Time Mr Alessandro Cardoso Laudares Pereira
2025 Co-Supervisor Quantifying Online Persuadability: Analysis, Metrics Development, and Application in Digital Discourse. Doctor of Philosophy Doctorate Full Time Mr Gia Bao Hoang
2024 Co-Supervisor Reasoning from consensus: exploring perceptions of the value of consensus quality information in online reasoning. Doctor of Philosophy Doctorate Full Time Mr Joseph Higginson
2024 Co-Supervisor Enhancing Resilience to Misinformation through Tailored Interventions Doctor of Philosophy Doctorate Part Time Mr Steven Edward Davis
2024 Co-Supervisor Enhancing Resilience to Misinformation through Tailored Interventions Doctor of Philosophy Doctorate Part Time Mr Steven Edward Davis
2024 Co-Supervisor Reasoning from consensus: exploring perceptions of the value of consensus quality information in online reasoning. Doctor of Philosophy Doctorate Full Time Mr Joseph Higginson
2023 Co-Supervisor An exploration of the influence of cues to consensus quality on online reasoning and behaviour. Doctor of Philosophy Doctorate Full Time Mr Benjamin Paul Simmonds
2023 Co-Supervisor An exploration of the influence of cues to consensus quality on online reasoning and behaviour. Doctor of Philosophy Doctorate Full Time Mr Benjamin Paul Simmonds

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