Wisdom of crowds benefits perceptual decision making across difficulty levels

Abstract Decades of research on collective decision making has claimed that aggregated judgment of multiple individuals is more accurate than expert individual judgement. A longstanding problem in this regard has been to determine how decisions of individuals can be combined to form intelligent grou...

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Autores principales:	Tiasha Saha Roy, Satyaki Mazumder, Koel Das
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/12e132b141e04d839dabb9df78bcc6b6
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spelling	oai:doaj.org-article:12e132b141e04d839dabb9df78bcc6b62021-12-02T14:12:46ZWisdom of crowds benefits perceptual decision making across difficulty levels10.1038/s41598-020-80500-02045-2322https://doaj.org/article/12e132b141e04d839dabb9df78bcc6b62021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80500-0https://doaj.org/toc/2045-2322Abstract Decades of research on collective decision making has claimed that aggregated judgment of multiple individuals is more accurate than expert individual judgement. A longstanding problem in this regard has been to determine how decisions of individuals can be combined to form intelligent group decisions. Our study consisted of a random target detection task in natural scenes, where human subjects (18 subjects, 7 female) detected the presence or absence of a random target as indicated by the cue word displayed prior to stimulus display. Concurrently the neural activities (EEG signals) were recorded. A separate behavioural experiment was performed by different subjects (20 subjects, 11 female) on the same set of images to categorize the tasks according to their difficulty levels. We demonstrate that the weighted average of individual decision confidence/neural decision variables produces significantly better performance than the frequently used majority pooling algorithm. Further, the classification error rates from individual judgement were found to increase with increasing task difficulty. This error could be significantly reduced upon combining the individual decisions using group aggregation rules. Using statistical tests, we show that combining all available participants is unnecessary to achieve minimum classification error rate. We also try to explore if group aggregation benefits depend on the correlation between the individual judgements of the group and our results seem to suggest that reduced inter-subject correlation can improve collective decision making for a fixed difficulty level.Tiasha Saha RoySatyaki MazumderKoel DasNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tiasha Saha Roy Satyaki Mazumder Koel Das Wisdom of crowds benefits perceptual decision making across difficulty levels
description	Abstract Decades of research on collective decision making has claimed that aggregated judgment of multiple individuals is more accurate than expert individual judgement. A longstanding problem in this regard has been to determine how decisions of individuals can be combined to form intelligent group decisions. Our study consisted of a random target detection task in natural scenes, where human subjects (18 subjects, 7 female) detected the presence or absence of a random target as indicated by the cue word displayed prior to stimulus display. Concurrently the neural activities (EEG signals) were recorded. A separate behavioural experiment was performed by different subjects (20 subjects, 11 female) on the same set of images to categorize the tasks according to their difficulty levels. We demonstrate that the weighted average of individual decision confidence/neural decision variables produces significantly better performance than the frequently used majority pooling algorithm. Further, the classification error rates from individual judgement were found to increase with increasing task difficulty. This error could be significantly reduced upon combining the individual decisions using group aggregation rules. Using statistical tests, we show that combining all available participants is unnecessary to achieve minimum classification error rate. We also try to explore if group aggregation benefits depend on the correlation between the individual judgements of the group and our results seem to suggest that reduced inter-subject correlation can improve collective decision making for a fixed difficulty level.
format	article
author	Tiasha Saha Roy Satyaki Mazumder Koel Das
author_facet	Tiasha Saha Roy Satyaki Mazumder Koel Das
author_sort	Tiasha Saha Roy
title	Wisdom of crowds benefits perceptual decision making across difficulty levels
title_short	Wisdom of crowds benefits perceptual decision making across difficulty levels
title_full	Wisdom of crowds benefits perceptual decision making across difficulty levels
title_fullStr	Wisdom of crowds benefits perceptual decision making across difficulty levels
title_full_unstemmed	Wisdom of crowds benefits perceptual decision making across difficulty levels
title_sort	wisdom of crowds benefits perceptual decision making across difficulty levels
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/12e132b141e04d839dabb9df78bcc6b6
work_keys_str_mv	AT tiashasaharoy wisdomofcrowdsbenefitsperceptualdecisionmakingacrossdifficultylevels AT satyakimazumder wisdomofcrowdsbenefitsperceptualdecisionmakingacrossdifficultylevels AT koeldas wisdomofcrowdsbenefitsperceptualdecisionmakingacrossdifficultylevels
_version_	1718391768892833792

Wisdom of crowds benefits perceptual decision making across difficulty levels

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