Literature DB >> 28601709

From information processing to decisions: Formalizing and comparing psychologically plausible choice models.

Daniel W Heck1, Benjamin E Hilbig2, Morten Moshagen3.   

Abstract

Decision strategies explain how people integrate multiple sources of information to make probabilistic inferences. In the past decade, increasingly sophisticated methods have been developed to determine which strategy explains decision behavior best. We extend these efforts to test psychologically more plausible models (i.e., strategies), including a new, probabilistic version of the take-the-best (TTB) heuristic that implements a rank order of error probabilities based on sequential processing. Within a coherent statistical framework, deterministic and probabilistic versions of TTB and other strategies can directly be compared using model selection by minimum description length or the Bayes factor. In an experiment with inferences from given information, only three of 104 participants were best described by the psychologically plausible, probabilistic version of TTB. Similar as in previous studies, most participants were classified as users of weighted-additive, a strategy that integrates all available information and approximates rational decisions.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Bayes factor; Judgment and decision making; Minimum description length; Model selection; Take-the-best

Mesh:

Year:  2017        PMID: 28601709     DOI: 10.1016/j.cogpsych.2017.05.003

Source DB:  PubMed          Journal:  Cogn Psychol        ISSN: 0010-0285            Impact factor:   3.468


  1 in total

1.  Machine learning strategy identification: A paradigm to uncover decision strategies with high fidelity.

Authors:  Jun Fang; Lael Schooler; Luan Shenghua
Journal:  Behav Res Methods       Date:  2022-04-04
  1 in total

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