Reducing variance or helping the poorest? A mouse tracking approach to investigate cognitive bases of inequality aversion in resource allocation.
egalitarian concern
inequality aversion
maximin concern
mouse tracking
resource allocation
time-series analysis
Journal
Royal Society open science
ISSN: 2054-5703
Titre abrégé: R Soc Open Sci
Pays: England
ID NLM: 101647528
Informations de publication
Date de publication:
17 Mar 2021
17 Mar 2021
Historique:
entrez:
7
5
2021
pubmed:
8
5
2021
medline:
8
5
2021
Statut:
epublish
Résumé
Humans dislike unequal allocations. Although often conflated, such 'inequality-averse' preferences are separable into two elements: egalitarian concern about the variance and maximin concern about the poorest (maximizing the minimum). Recent research has shown that the maximin concern operates more robustly in allocation decisions than the egalitarian concern. However, the real-time cognitive dynamics of allocation decisions are still unknown. Here, we examined participants' choice behaviour with high temporal resolution using a mouse-tracking technique. Participants made a series of allocation choices for others between two options: a 'non-Utilitarian option' with both smaller variance and higher minimum pay-off (but a smaller total) compared with the other 'Utilitarian option'. Choice data confirmed that participants had strong inequality-averse preferences, and when choosing non-utilitarian allocations, participants' mouse movements prior to choices were more strongly determined by the minimum elements of the non-Utilitarian options than the variance elements. Furthermore, a time-series analysis revealed that this dominance emerged at a very early stage of decision making (around 500 ms after the stimulus onset), suggesting that the maximin concern operated as a strong cognitive anchor almost instantaneously. Our results provide the first temporally fine-scale evidence that people weigh the maximin concern over the egalitarian concern in distributive judgements.
Identifiants
pubmed: 33959311
doi: 10.1098/rsos.201159
pii: rsos201159
pmc: PMC8074914
doi:
Banques de données
figshare
['10.6084/m9.figshare.c.5330181']
Types de publication
Journal Article
Langues
eng
Pagination
201159Informations de copyright
© 2021 The Authors.
Références
Behav Res Methods. 2019 Oct;51(5):1987-1997
pubmed: 31197629
Psychol Sci. 2008 Jan;19(1):22-4
pubmed: 18181787
Nature. 2007 Apr 12;446(7137):794-6
pubmed: 17429399
Perspect Psychol Sci. 2011 Jan;6(1):9-12
pubmed: 26162108
Nature. 2015 Dec 10;528(7581):258-61
pubmed: 26580018
Neuroimage. 2020 Nov 15;222:117254
pubmed: 32800992
Nature. 2008 Aug 28;454(7208):1079-83
pubmed: 18756249
Neuron. 2013 Sep 4;79(5):836-48
pubmed: 24012000
Eur Econ Rev. 2015 May;76:85-103
pubmed: 26089571
Proc Natl Acad Sci U S A. 2016 Oct 18;113(42):11817-11822
pubmed: 27688764
Trends Cogn Sci. 2013 Jul;17(7):328-36
pubmed: 23790322
Science. 2008 May 23;320(5879):1092-5
pubmed: 18467558
Trends Cogn Sci. 2007 Feb;11(2):49-57
pubmed: 17188554
Proc Natl Acad Sci U S A. 2010 Apr 13;107(15):6753-8
pubmed: 20351278
Psychol Sci. 2015 Feb;26(2):122-34
pubmed: 25515527
R Soc Open Sci. 2017 Feb 15;4(2):160605
pubmed: 28386421
Sci Rep. 2018 Aug 27;8(1):12857
pubmed: 30150657
Trends Cogn Sci. 2018 Jun;22(6):531-543
pubmed: 29731415
Proc Natl Acad Sci U S A. 2005 Jul 19;102(29):10393-8
pubmed: 15985550
Psychol Methods. 2009 Sep;14(3):202-24
pubmed: 19719358
Behav Res Methods. 2019 Jun;51(3):1085-1101
pubmed: 30756261
Trends Cogn Sci. 2019 Dec;23(12):1058-1070
pubmed: 31679752