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Media type:
E-Article
Title:
PROBabilities from EXemplars (PROBEX): a “lazy” algorithm for probabilistic inference from generic knowledge
Contributor:
Juslin, Peter;
Persson, Magnus
imprint:
Wiley, 2002
Published in:Cognitive Science
Language:
English
DOI:
10.1207/s15516709cog2605_2
ISSN:
0364-0213;
1551-6709
Origination:
Footnote:
Description:
<jats:title>Abstract</jats:title><jats:p>PROBEX (PROBabilities from EXemplars), a model of probabilistic inference and probability judgment based on generic knowledge is presented. Its properties are that: (a) it provides an exemplar model satisfying bounded rationality; (b) it is a “lazy” algorithm that presumes no pre‐computed abstractions; (c) it implements a hybrid‐representation, <jats:italic>similarity‐graded probability</jats:italic>. We investigate the <jats:italic>ecological rationality</jats:italic> of PROBEX and find that it compares favorably with Take‐The‐Best and multiple regression (Gigerenzer, Todd, & the ABC Research Group, 1999). PROBEX is fitted to the point estimates, decisions, and probability assessments by human participants. The best fit is obtained for a version that weights frequency heavily and retrieves only two exemplars. It is proposed that PROBEX implements speed and frugality in a psychologically plausible way.</jats:p>