Bramley, Neil

Horne, Zachary

Gong, Tianwei

2023-11-10T14:08:07Z

2023-11-10T14:08:07Z

2023-11-24

Causes require time to propagate their effects. We can see stars at night because of the light they emitted hundreds of years ago. We can smell the fragrant aroma of baking bread because heat gradually changed the structure of the food, emitting particles that traveled on the breeze. In this thesis, I investigate how people use temporal information to make causal inferences. I propose a rational framework for causal induction based on continuous-time evidence, examine human performance in passive and active continuous-time causal learning tasks, and develop bounded rational accounts that can offer explanations for human causal judgments and intervention strategies. Chapters 2 and 3 review previous theoretical frameworks on causal induction, and empirical work on the role of time in causal induction, respectively. Chapter 4 develops a rational framework for processing temporal evidence. It provides an explanation for how delays shape human causal induction and accounts for human causal judgments across seven different temporal causal learning tasks. Chapter 5 and Chapter 6 test how people passively or actively learn causal structures based on events unfolding in real time. I found people are capable temporal causal learners who successfully identify structures that involve generative and preventative relationships, as well as acyclic and cyclic connections. Nevertheless, the computational demands of normative learning could easily exceed human capacity. People’s causal judgments align better with an algorithm that approximates the normative solution via a simulation and local summary statistics scheme, suggesting the reliance on structurally local computation and temporally local evidence. People’s intervention decisions align better with a resource-rational model that emphasizes a balance between expected information and expected inferential complexity when choosing interventions. Chapter 7 shows that when given a limited period of observation, people not only focus on existing data, but also consider future possibilities, relying on extrapolated data to make inferences. This demonstrates the unique “continuing” feature of time, and how generalization plays a role in the utilization of temporal information. Chapter 8 synthesizes the findings of this thesis and proposes future research directions of causal learning in temporal contexts.

https://hdl.handle.net/1842/41141

http://dx.doi.org/10.7488/era/3877

en

The University of Edinburgh

Gong, T., & Bramley, N. R. (2023). Continuous time causal structure induction with prevention and generation. Cognition, 240, 105530. (Materials, data, and analysis code available at https://osf.io/q8n72/)

Gong, T., & Bramley, N. R. (2020). What you didn’t see: Prevention and generation in continuous time causal induction. In S. Denison, M. Mack, Y. Xu, & B. Armstrong (Eds.), Proceedings of the 42th annual conference of the cognitive science society (pp. 2908–2914)

Gong, T., & Bramley, N. R. (2021). Learning preventative and generative causal structures from point events in continuous time.Proceedings of the Causal Inference & Machine Learning workshop at 35th Neural Information Processing Systems conference.

Gong, T., Gerstenberg, T., Mayrhofer, R., & Bramley, N. R. (2023). Active causal structure learning in continuous time. Cognitive Psychology, 140 (4), 101542. (Materials, data, and analysis code available at https://github.com/tianweigong/time_and_intervention)

Gong, T., & Bramley, N. R. (2023). Evidence from the future. PsyArXiv. Accepted at Journal of Experimental Psychology: General. (Materials, data, and analysis code available at https://osf.io/h2y3g/)

Gong, T., & Bramley, N. R. (2022). Intuitions and perceptual constraints on causal learning from dynamics. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th annual conference of the cognitive science society (pp. 1455–1461)

time information

causal relationships

human cognition

human-like algorithms

philosophy of science

continuous-time causal learning tasks

causal induction

processing temporal evidence

Causal induction in time

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy