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Sensitivity to the Sampling Process Emerges From the Principle of Efficiency
Author(s) -
JaraEttinger Julian,
Sun Felix,
Schulz Laura,
Tenenbaum Joshua B.
Publication year - 2018
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12596
Subject(s) - preference , computer science , sensitivity (control systems) , space (punctuation) , agency (philosophy) , process (computing) , dual (grammatical number) , artificial intelligence , sampling (signal processing) , spatial analysis , statistical model , statistical inference , machine learning , cognitive psychology , econometrics , psychology , mathematics , statistics , epistemology , art , philosophy , literature , filter (signal processing) , electronic engineering , engineering , computer vision , operating system
Abstract Humans can seamlessly infer other people's preferences, based on what they do. Broadly, two types of accounts have been proposed to explain different aspects of this ability. The first account focuses on spatial information: Agents' efficient navigation in space reveals what they like. The second account focuses on statistical information: Uncommon choices reveal stronger preferences. Together, these two lines of research suggest that we have two distinct capacities for inferring preferences. Here we propose that this is not the case, and that spatial‐based and statistical‐based preference inferences can be explained by the assumption that agents are efficient alone. We show that people's sensitivity to spatial and statistical information when they infer preferences is best predicted by a computational model of the principle of efficiency, and that this model outperforms dual‐system models, even when the latter are fit to participant judgments. Our results suggest that, as adults, a unified understanding of agency under the principle of efficiency underlies our ability to infer preferences.