Literature DB >> 21038979

Preference reversal in multiattribute choice.

Konstantinos Tsetsos1, Marius Usher, Nick Chater.   

Abstract

A central puzzle for theories of choice is that people's preferences between options can be reversed by the presence of decoy options (that are not chosen) or by the presence of other irrelevant options added to the choice set. Three types of reversal effect reported in the decision-making literature, the attraction, compromise, and similarity effects, have been explained by a number of theoretical proposals. Yet a major theoretical challenge is capturing all 3 effects simultaneously. We review the range of mechanisms that have been proposed to account for decoy effects and analyze in detail 2 computational models, decision field theory (Roe, Busemeyer, & Townsend, 2001) and leaky competing accumulators (Usher & McClelland, 2004), that aim to combine several such mechanisms into an integrated account. By simulating the models, we examine differences in the ways the decoy effects are predicted. We argue that the LCA framework, which follows on Tversky's relational evaluation with loss aversion (Tversky & Kahneman, 1991), provides a more robust account, suggesting that common mechanisms are involved in both high-level decision making and perceptual choice, for which LCA was originally developed.

Entities:  

Mesh:

Year:  2010        PMID: 21038979     DOI: 10.1037/a0020580

Source DB:  PubMed          Journal:  Psychol Rev        ISSN: 0033-295X            Impact factor:   8.934


  29 in total

1.  Multialternative context effects obtained using an inference task.

Authors:  Jennifer S Trueblood
Journal:  Psychon Bull Rev       Date:  2012-10

2.  Response-time data provide critical constraints on dynamic models of multi-alternative, multi-attribute choice.

Authors:  Nathan J Evans; William R Holmes; Jennifer S Trueblood
Journal:  Psychon Bull Rev       Date:  2019-06

3.  A Bayesian model of context-sensitive value attribution.

Authors:  Francesco Rigoli; Karl J Friston; Cristina Martinelli; Mirjana Selaković; Sukhwinder S Shergill; Raymond J Dolan
Journal:  Elife       Date:  2016-06-22       Impact factor: 8.140

4.  Continuous track paths reveal additive evidence integration in multistep decision making.

Authors:  Cristian Buc Calderon; Myrtille Dewulf; Wim Gevers; Tom Verguts
Journal:  Proc Natl Acad Sci U S A       Date:  2017-09-18       Impact factor: 11.205

5.  Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates.

Authors:  Alec Solway; Matthew M Botvinick
Journal:  Psychol Rev       Date:  2012-01       Impact factor: 8.934

Review 6.  Advances in modeling learning and decision-making in neuroscience.

Authors:  Anne G E Collins; Amitai Shenhav
Journal:  Neuropsychopharmacology       Date:  2021-08-27       Impact factor: 7.853

Review 7.  Do humans make good decisions?

Authors:  Christopher Summerfield; Konstantinos Tsetsos
Journal:  Trends Cogn Sci       Date:  2014-12-06       Impact factor: 20.229

Review 8.  Diffusion Decision Model: Current Issues and History.

Authors:  Roger Ratcliff; Philip L Smith; Scott D Brown; Gail McKoon
Journal:  Trends Cogn Sci       Date:  2016-03-05       Impact factor: 20.229

9.  Natural selection can favour 'irrational' behaviour.

Authors:  J M McNamara; P C Trimmer; A I Houston
Journal:  Biol Lett       Date:  2014-01-15       Impact factor: 3.703

10.  Building a bridge into the future: dynamic connectionist modeling as an integrative tool for research on intertemporal choice.

Authors:  Stefan Scherbaum; Maja Dshemuchadse; Thomas Goschke
Journal:  Front Psychol       Date:  2012-11-20
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.