Bonan ZHAO / 赵博囡

I am a final year PhD student at Bramley Lab and Lucas Lab at the University of Edinburgh, part of the joint Computational Cognitive Science Laboratory.

I work on computational models of how people generalize causal relations, advised by Neil Bramley and Chris Lucas. I am grateful for support from the CHASS/PPLS Postgraduate Research Award. Previously I worked in data science at Screen 6 (Amsterdam startup). I received my MSc in Logic at the ILLC from University of Amsterdam, and B.A. in philosophy from Tsinghua University in Beijing, China.

Feel free to contact me at b.zhao@ed.ac.uk.

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Research

I'm interested in people's causal representations and how they are actually used, i.e. generalized in novel situations. I combine interactive online experiments, symbolic generative grammars, and Bayesian (approximate) inference in my work.

* denotes equal contribution. Representative papers are highlighted.

Powering up causal generalization: A model of human conceptual bootstrapping with adaptor grammars
Bonan Zhao, Neil R. Bramley, Christopher G. Lucas
CogSci, 2022
OSF project / registrations / code / PDF

We find causal generalizations benefit from facilitory curriculums, neatly captured by an adaptor grammar model's native chunking mechanism.

Dissecting causal asymmetries in inductive generalization
Zeyu Xia*, Bonan Zhao*, Tadeg Quillien, Christopher G. Lucas
CogSci, 2022 (Oral)
OSF project / code / PDF

To induce causal asymmetries in object-based causal generalization tasks, the agent object needs to possess three interactions cues altogether: movement, stability, and visual-nominal indicator.

Categorizing perceived causal events
Nicolas Marchant, Bonan Zhao, Neil R. Bramley, Diego Morales, Sergio E. Chaigneau
CogSci, 2022
PDF

Perceptual causal stimuli work differently from well-established verbal stimuli in causal categorization tasks.

How do people generalize causal relations over objects? A non-parametric Bayesian account
Bonan Zhao, Christopher G. Lucas, Neil R. Bramley
Computational Brain & Behavior 5, 22-44 (2022)
DOI / code / preprint / PDF / BibTex

We account for generalization-order effects in one-shot causal generalization and causal asymmetries in few-shot tasks with a computational framework and its process variant.

Building object-based causal programs for human-like generalization
Bonan Zhao, Christopher G. Lucas, Neil R. Bramley
NeurIPS Causal Inference & Machine Learning Workshop, 2021
Code / arXiv / poster / PDF

We integrate a symbolic causal function generator with a Dirichlet Process to model causal generalizations that well-match people's.

Symbolic and sub-symbolic systems in people and machines
Simon Valentin, Bonan Zhao, Chentian Jiang, Neil R. Bramley, Christopher G. Lucas
CogSci Workshop, 2021
Website

We invite a wide range of speakers to debate and explore hybrid architectures linking symbolic systems with neural approaches.

Order effects in one-shot causal generalization
Bonan Zhao, Neil R. Bramley
CogSci, 2020
Code / poster / PDF

We find that people's causal generalization predictions are affected by the presentation order of generalization tasks.

Predicting cognitive difficulty of the deductive Mastermind game with dynamic epistemic logic models
Bonan Zhao*, Iris van de Pol*, Maartje Raijmakers, Jakub Szymanik
CogSci, 2018
PDF

We solve the deductive Mastermind game using DEL and find that it can explain children's performance in this game.

Logic of closeness revision: Challenging relations in social networks
Anthia Solaki, Zoi Terzopoulou, Bonan Zhao
28th European Summer School in Logic, Language and Information, 2016
(Springer Best Paper Award)
PDF

We propose a sound and complete logic where stubborn agents revise their friendship network according to their own opinions, rather than the other way around.


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