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Spatial estimation: a non‐Bayesian alternative
Author(s) -
Barth Hilary,
Lesser Ellen,
Taggart Jessica,
Slusser Emily
Publication year - 2015
Publication title -
developmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.801
H-Index - 127
eISSN - 1467-7687
pISSN - 1363-755X
DOI - 10.1111/desc.12264
Subject(s) - bayesian probability , estimation , psychology , bayesian inference , bayesian statistics , task (project management) , bayes estimator , cognitive psychology , artificial intelligence , statistics , computer science , mathematics , management , economics
A large collection of estimation phenomena (e.g. biases arising when adults or children estimate remembered locations of objects in bounded spaces; Huttenlocher, Newcombe & Sandberg, 1994) are commonly explained in terms of complex Bayesian models. We provide evidence that some of these phenomena may be modeled instead by a simpler non‐Bayesian alternative. Undergraduates and 9‐ to 10‐year‐olds completed a speeded linear position estimation task. Bias in both groups’ estimates could be explained in terms of a simple psychophysical model of proportion estimation. Moreover, some individual data were not compatible with the requirements of the more complex Bayesian model.