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Building Object-based Causal Programs for Human-like Generalization

2021-11-20 15:21:17
Bonan Zhao, Christopher G. Lucas, Neil R. Bramley

Abstract

We present a novel task that measures how people generalize objects' causal powers based on observing a single (Experiment 1) or a few (Experiment 2) causal interactions between object pairs. We propose a computational modeling framework that can synthesize human-like generalization patterns in our task setting, and sheds light on how people may navigate the compositional space of possible causal functions and categories efficiently. Our modeling framework combines a causal function generator that makes use of agent and recipient objects' features and relations, and a Bayesian non-parametric inference process to govern the degree of similarity-based generalization. Our model has a natural "resource-rational" variant that outperforms a naive Bayesian account in describing participants, in particular reproducing a generalization-order effect and causal asymmetry observed in our behavioral experiments. We argue that this modeling framework provides a computationally plausible mechanism for real world causal generalization.

Abstract (translated)

URL

https://arxiv.org/abs/2111.12560

PDF

https://arxiv.org/pdf/2111.12560.pdf


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