Published in In process, 2025
We suggest a possible preference for producing explanations that allow the explainee to benefit from inferential work performed by the explainer.
Recommended citation: Stephanie Droop, Tadeg Quillien, Neil R. Bramley. (2025). "Unifying inference and selection in singular causal explanation." Pending publication .
Published in In process, 2025
We investigate whether online discourse contains a prevalence of descriptions of atypical situations or typical situations.
Recommended citation: Anonymous for review
Published in CHI Conference on Human Factors in Computing Systems, 2025
Insights from Cognitive Science for Explainable AI.
Recommended citation: Balint Gyevnar, Stephanie Droop, Tadeg Quillien, Shay B. Cohen, Neil R. Bramley, Christopher G. Lucas, Stefano V. Albrecht. (2025). People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI. In CHI Conference on Human Factors in Computing Systems (CHI ’25), April 26-May 1, 2025, Yokohama, Japan. ACM, New York, NY, USA, 18 pages. https:doi.org/10.1145/3706598.3713509
Published in Arxiv (for now), 2024
We found people systematically overestimate the use of personal questions in a Turing test. However, people are sensitive to brusque, cynical tone, slang and perceived idiosyncrasy as cues to natural human output.
Recommended citation: Stephanie Droop, Cansu Oranc, Neil R. Bramley, Azzurra Ruggeri. (2024). "Inverting the Turing test to track changing intuitions about artificial minds." Arxiv, pending publication .
Published in Proceedings of the 45th Annual Meeting of the Cognitive Science Society, 2023
We explore the hypothesis that decisions about who to learn from involve inferences about other agents’ reward functions.
Recommended citation: Max Taylor-Davies, Stephanie Droop & Christopher G. Lucas. (2023). "Selective imitation on the basis of reward function similarity." Proceedings of the 45th Annual Meeting of the Cognitive Science Society.
Published in ICML (International Conference on Machine Learning) Workshop on Counterfactuals in Minds and Machines, Honolulu 2023, 2023
We extend a counterfactual account of explanation to capture how people generate free explanations for someone’s behaviour across a set of scenarios.
Recommended citation: Stephanie Droop & Neil R. Bramley. (2023). "Extending counterfactual reasoning models to capture unconstrained social explanations." Proceedings of ICML (International Conference on Machine Learning) Workshop on Counterfactuals in Minds and Machines, Honolulu 2023.
Published in Proceedings of the 44th Annual Meeting of the Cognitive Science Society, 2022
We explore whether people can recognise the epistemic goal or intention of active learners interacting with simulated physical objects.
Recommended citation: Stephanie Droop & Neil R. Bramley. (2022). "Inferring epistemic intention in simulated physical microworlds." Proceedings of the 44th Annual Meeting of the Cognitive Science Society.