Building Object-based Causal Programs for Human-like Generalization

TL;DR

This paper introduces a new task to measure human-like generalization of causal powers of objects, and proposes a computational model that captures how humans efficiently generalize causal functions based on object features and relations.

Contribution

It presents a novel task for assessing causal generalization and a resource-rational Bayesian model that replicates human patterns and explains causal asymmetries.

Findings

The model outperforms naive Bayesian approaches in behavioral fit.

It reproduces the order effect in generalization.

It captures causal asymmetry observed in experiments.

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.