TY - JOUR
T1 - Exploring the hierarchical structure of human plans via program generation
AU - Correa, Carlos G.
AU - Sanborn, Sophia
AU - Ho, Mark K.
AU - Callaway, Frederick
AU - Daw, Nathaniel D.
AU - Griffiths, Thomas L.
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2025/2
Y1 - 2025/2
N2 - Human behavior is often assumed to be hierarchically structured, made up of abstract actions that can be decomposed into concrete actions. However, behavior is typically measured as a sequence of actions, which makes it difficult to infer its hierarchical structure. In this paper, we explore how people form hierarchically structured plans, using an experimental paradigm with observable hierarchical representations: participants create programs that produce sequences of actions in a language with explicit hierarchical structure. This task lets us test two well-established principles of human behavior: utility maximization (i.e. using fewer actions) and minimum description length (MDL; i.e. having a shorter program). We find that humans are sensitive to both metrics, but that both accounts fail to predict a qualitative feature of human-created programs, namely that people prefer programs with reuse over and above the predictions of MDL. We formalize this preference for reuse by extending the MDL account into a generative model over programs, modeling hierarchy choice as the induction of a grammar over actions. Our account can explain the preference for reuse and provides better predictions of human behavior, going beyond simple accounts of compressibility to highlight a principle that guides hierarchical planning.
AB - Human behavior is often assumed to be hierarchically structured, made up of abstract actions that can be decomposed into concrete actions. However, behavior is typically measured as a sequence of actions, which makes it difficult to infer its hierarchical structure. In this paper, we explore how people form hierarchically structured plans, using an experimental paradigm with observable hierarchical representations: participants create programs that produce sequences of actions in a language with explicit hierarchical structure. This task lets us test two well-established principles of human behavior: utility maximization (i.e. using fewer actions) and minimum description length (MDL; i.e. having a shorter program). We find that humans are sensitive to both metrics, but that both accounts fail to predict a qualitative feature of human-created programs, namely that people prefer programs with reuse over and above the predictions of MDL. We formalize this preference for reuse by extending the MDL account into a generative model over programs, modeling hierarchy choice as the induction of a grammar over actions. Our account can explain the preference for reuse and provides better predictions of human behavior, going beyond simple accounts of compressibility to highlight a principle that guides hierarchical planning.
KW - Chunking
KW - Hierarchical reinforcement learning
KW - Planning
KW - Program induction
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U2 - 10.1016/j.cognition.2024.105990
DO - 10.1016/j.cognition.2024.105990
M3 - Article
C2 - 39616822
AN - SCOPUS:85210535056
SN - 0010-0277
VL - 255
JO - Cognition
JF - Cognition
M1 - 105990
ER -