Bonan ZHAO / 赵博囡

I am a postdoctoral researcher working with Tom Griffiths and Natalia Velez at Princeton University. I am broadly interested in the cognitive mechanisms that drive conceptual discoveries, and in particular how individual cognitive constraints shape innovations and diverse understandings.

I completed my PhD at the University of Edinburgh advised by Neil Bramley and Chris Lucas. Previously I worked in data science at an adtech start-up. 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

CV  /  Google Scholar  /  Twitter

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* denotes equal contribution. Representative papers are highlighted.

A rational model of innovation by recombination
Bonan Zhao, Natalia Velez, Tom Griffiths
Preprint, 2024
PDF / preprint / pre-registration / code

We formalize a crafting game as a Markov decision process (MDP) and find that people can make near-optimal decisions in various game environments.

Using compositionality to learn many categories from few examples
Ilia Sucholutsky, Bonan Zhao, Tom Griffiths
Preprint, 2024
PDF / preprint / pre-registration

We show that people can learn 22 categories from just 4 examples, by leveraging soft compositional labels.

A model of conceptual bootstrapping in human cognition
Bonan Zhao, Neil R. Bramley, Christopher G. Lucas
Nature Human Behaviour, 2024 (Cover)
PDF / pre-registrations / data / code / cover

We present a model of conceptual bootstrapping - learning complex concepts by recursively combining simpler concepts. Our model predicts systematically different learned concepts when the same evidence is processed in different orders.

Local search and the evolution of world models
Neil R. Bramley, Bonan Zhao, Tadeg Quillien, Christopher G. Lucas
Topics in Cognitive Science, 2023
PDF / preprint / thread

We propose that stochastic program induction algorithms (MCMC & adaptor grammars) help explain how cognition innovates, via incremental selection among random local mutations and recombinations of (parts of) a cognizer's current world model.

A rational model of spatial neglect
Tianwei Gong, Bonan Zhao, Robert D. McIntosh, Christopher G. Lucas
CogSci, 2023; CCN, 2023 (Oral, top 4.5%)
PDF (CogSci) / PDF (CCN) / code

We propose a Bayesian computational model for the line bisection task, modeling neglect as rational inference in the face of uncertain information.

Powering up causal generalization: A model of human conceptual bootstrapping with adaptor grammars
Bonan Zhao, Neil R. Bramley, Christopher G. Lucas
CCN, 2023; CogSci, 2022
PDF (CCN) / PDF (CogSci)

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)
PDF / data / code

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

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)
PDF / preprint / code

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
PDF / poster

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
PDF / program

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
PDF / poster

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

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)

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.

A starter kit for program induction
Bonan Zhao (work in progress)
Google docs

Resources for those interested in program induction.

A computational framework of human causal generalization
Bonan Zhao
PhD thesis, 2023

A computational modeling framework that combines program induction (about the hidden causal laws) with non-parametric category inference (about their domains of influence) to account for unique patterns in human causal generalization.

Analyzing the Logic of Sun Tzu in “The Art of War”, Using Mind Maps
Peter van Emde Boas, Ghica van Emde Boas, Kaibo Xie, Bonan Zhao
Springer, 2022
Book / Project

We examined Sun Tzu's 2500-year-old Art of War from the perspectives of logic, mathematics, and computer science, using contemporary mind mapping methods.

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