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Found 5 papers, 1 papers with code
Designing Optimal Behavioral Experiments Using Machine Learning
As compared to experimental designs commonly used in the literature, we show that our optimal designs more efficiently determine which of a set of models best account for individual human behavior, and more efficiently characterize behavior given a preferred model.
Building Object-based Causal Programs for Human-like Generalization
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.
Bayesian Optimal Experimental Design for Simulator Models of Cognition
Bayesian optimal experimental design (BOED) is a methodology to identify experiments that are expected to yield informative data.
Formalizing Neurath's Ship: Approximate Algorithms for Online Causal Learning
Higher-level cognition depends on the ability to learn models of the world.
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