Keith Ransom

Keith Ransom

School of Psychology

Faculty of Health and Medical Sciences


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 currently 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).

    Expand
  • Journals

    Year Citation
    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 Scopus1 WoS1
    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 Scopus7 WoS8 Europe PMC1
    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 Scopus6 WoS5
    2016 Ransom, K., Perfors, A., & Navarro, D. (2016). Leaping to Conclusions: Why Premise Relevance Affects Argument Strength. Cognitive Science, 40(7), 1775-1796.
    DOI Scopus8 WoS7 Europe PMC2
    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 Scopus15 WoS13 Europe PMC4
    Kaesler, M. P., Dunn, J. C., Ransom, K., & Semmler, C. (n.d.). Do Sequential Lineups Impair Discriminability?.
    DOI
    Ransom, K., Voorspoels, W., Navarro, D., & Perfors, A. (n.d.). Where the truth lies: how sampling implications drive deception without lying.
    DOI
    Hayes, B., Navarro, D., Stephens, R., Ransom, K., & Dilevski, N. (n.d.). The diversity effect in inductive reasoning depends on sampling assumptions.
    DOI
    Hendrickson, A. T., Perfors, A., Navarro, D., & Ransom, K. (n.d.). Sample size, number of categories and sampling assumptions: Exploring some differences between categorization and generalization.
    DOI
    Voorspoels, W., Navarro, D., Perfors, A., Ransom, K., & Storms, G. (n.d.). How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning.
    DOI
    Ransom, K., Perfors, A., & Navarro, D. (n.d.). Leaping to conclusions: Why premise relevance affects argument strength.
    DOI
    Ransom, K., Perfors, A., Hayes, B., & Connor Desai, S. (n.d.). What do our sampling assumptions affect: how we encode data or how we reason from it?.
    DOI
    Howe, P., Perfors, A., & Ransom, K. (n.d.). What interventions can decrease or increase belief polarisation in a population of rational agents?.
    DOI
  • Conference Papers

    Year Citation
    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., Hendrickson, A., Perfors, A., & Navarro, D. (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. online: COGSCI.
    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.
    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.
    1995 Ransom, K., & Marlin, C. (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
    Ransom, K., Perfors, A., & Stephens, R. G. (n.d.). Social meta-inference and the evidentiary value of consensus. Center for Open Science.
    DOI
  • Conference Items

    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.

Connect With Me
External Profiles

Other Links