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Poster B42 in Poster Session B - Thursday, August 8, 2024, 1:30 – 3:30 pm, Johnson Ice Rink
An algorithmic account for how humans efficiently learn, transfer, and compose hierarchically structured decision policies
Jing-Jing Li1, Anne G. E. Collins1 (); 1UC Berkeley
Learning structures that effectively abstract decision policies is key to the flexibility of human intelligence. Previous work has shown that humans use hierarchically structured policies to efficiently navigate complex and dynamic environments. However, the computational processes that support the learning and construction of such policies remain insufficiently understood. To address this question, we tested 1,052 human participants on a decision-making task where they could learn, transfer, and recompose multiple sets of hierarchical policies. We propose a novel algorithmic account for the learning processes underlying observed human behavior. We show that humans use meta-learning and Bayesian inference to expand compressed policies into hierarchical representations over learning. Furthermore, our modeling suggests that these hierarchical policies are structured in a temporally backward-looking or retrospective fashion.
Keywords: computational cognitive modeling abstraction meta-learning decision-making