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Ten-month-old infants infer the value of goals from the costs of actions
Author(s) -
Shari Liu,
Tomer Ullman,
Joshua B. Tenenbaum,
Elizabeth S. Spelke
Publication year - 2017
Publication title -
science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 12.556
H-Index - 1186
eISSN - 1095-9203
pISSN - 0036-8075
DOI - 10.1126/science.aag2132
Subject(s) - action (physics) , ranking (information retrieval) , value (mathematics) , computer science , psychology , cognitive psychology , artificial intelligence , machine learning , physics , quantum mechanics
Infants understand that people pursue goals, but how do they learn which goals people prefer? We tested whether infants solve this problem by inverting a mental model of action planning, trading off the costs of acting against the rewards actions bring. After seeing an agent attain two goals equally often at varying costs, infants expected the agent to prefer the goal it attained through costlier actions. These expectations held across three experiments that conveyed cost through different physical path features (height, width, and incline angle), suggesting that an abstract variable-such as "force," "work," or "effort"-supported infants' inferences. We modeled infants' expectations as Bayesian inferences over utility-theoretic calculations, providing a bridge to recent quantitative accounts of action understanding in older children and adults.

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