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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-020-80500-0 |
| _version_ | 1818431522836840448 |
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| author | Tiasha Saha Roy Satyaki Mazumder Koel Das |
| author_facet | Tiasha Saha Roy Satyaki Mazumder Koel Das |
| author_sort | Tiasha Saha Roy |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T15:50:39Z |
| format | Article |
| id | doaj.art-12e132b141e04d839dabb9df78bcc6b6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T15:50:39Z |
| publishDate | 2021-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-12e132b141e04d839dabb9df78bcc6b62022-12-21T22:55:22ZengNature PortfolioScientific Reports2045-23222021-01-0111111310.1038/s41598-020-80500-0Wisdom of crowds benefits perceptual decision making across difficulty levelsTiasha Saha Roy0Satyaki Mazumder1Koel Das2Department of Mathematics and Statistics, Indian Institute of Science Education and Research KolkataDepartment of Mathematics and Statistics, Indian Institute of Science Education and Research KolkataDepartment of Mathematics and Statistics, Indian Institute of Science Education and Research KolkataAbstract 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.https://doi.org/10.1038/s41598-020-80500-0 |
| spellingShingle | Tiasha Saha Roy Satyaki Mazumder Koel Das Wisdom of crowds benefits perceptual decision making across difficulty levels Scientific Reports |
| title | 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_short | Wisdom of crowds benefits perceptual decision making across difficulty levels |
| title_sort | wisdom of crowds benefits perceptual decision making across difficulty levels |
| url | https://doi.org/10.1038/s41598-020-80500-0 |
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