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 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 bnz@princeton.edu.

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* denotes equal contribution. Representative papers are highlighted.

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

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

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.

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

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.

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

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

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

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

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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