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Madirolas G, Zaghi-Lara R, Gomez-Marin A, Pérez-Escudero A. The motor Wisdom of the Crowd. J R Soc Interface 2022; 19:20220480. [PMID: 36195116 PMCID: PMC9532022 DOI: 10.1098/rsif.2022.0480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Wisdom of the Crowd is the aggregation of many individual estimates to obtain a better collective one. Because of its enormous social potential, this effect has been thoroughly investigated, but predominantly on tasks that involve rational thinking (such as estimating a number). Here we tested this effect in the context of drawing geometrical shapes, which still enacts cognitive processes but mainly involves visuomotor control. We asked more than 700 school students to trace five patterns shown on a touchscreen and then aggregated their individual trajectories to improve the match with the original pattern. Our results show the characteristics of the strongest examples of Wisdom of the Crowd. First, the aggregate trajectory can be up to 5 times more accurate than the individual ones. Second, this great improvement requires aggregating trajectories from different individuals (rather than trials from the same individual). Third, the aggregate trajectory outperforms more than 99% of individual trajectories. Fourth, while older individuals outperform younger ones, a crowd of young individuals outperforms the average older one. These results demonstrate for the first time Wisdom of the Crowd in the realm of motor control, opening the door to further studies of human and also animal behavioural trajectories and their mechanistic underpinnings.
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Affiliation(s)
- Gabriel Madirolas
- Research Centre on Animal Cognition (CRCA), Centre for Integrative Biology (CBI), Toulouse University, CNRS, UPS, 31062 Toulouse, France
| | - Regina Zaghi-Lara
- Behavior of Organisms Laboratory, Instituto de Neurociencias de Alicante (CSIC-UMH), Alicante, Spain
| | - Alex Gomez-Marin
- Behavior of Organisms Laboratory, Instituto de Neurociencias de Alicante (CSIC-UMH), Alicante, Spain,The Pari Center, via Tozzi 7, 58045 Pari (GR), Italy
| | - Alfonso Pérez-Escudero
- Research Centre on Animal Cognition (CRCA), Centre for Integrative Biology (CBI), Toulouse University, CNRS, UPS, 31062 Toulouse, France
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Jayles B, Sire C, Kurvers RHJM. Crowd control: Reducing individual estimation bias by sharing biased social information. PLoS Comput Biol 2021; 17:e1009590. [PMID: 34843458 PMCID: PMC8659305 DOI: 10.1371/journal.pcbi.1009590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 12/09/2021] [Accepted: 10/25/2021] [Indexed: 01/29/2023] Open
Abstract
Cognitive biases are widespread in humans and animals alike, and can sometimes be reinforced by social interactions. One prime bias in judgment and decision-making is the human tendency to underestimate large quantities. Previous research on social influence in estimation tasks has generally focused on the impact of single estimates on individual and collective accuracy, showing that randomly sharing estimates does not reduce the underestimation bias. Here, we test a method of social information sharing that exploits the known relationship between the true value and the level of underestimation, and study if it can counteract the underestimation bias. We performed estimation experiments in which participants had to estimate a series of quantities twice, before and after receiving estimates from one or several group members. Our purpose was threefold: to study (i) whether restructuring the sharing of social information can reduce the underestimation bias, (ii) how the number of estimates received affects the sensitivity to social influence and estimation accuracy, and (iii) the mechanisms underlying the integration of multiple estimates. Our restructuring of social interactions successfully countered the underestimation bias. Moreover, we find that sharing more than one estimate also reduces the underestimation bias. Underlying our results are a human tendency to herd, to trust larger estimates than one's own more than smaller estimates, and to follow disparate social information less. Using a computational modeling approach, we demonstrate that these effects are indeed key to explain the experimental results. Overall, our results show that existing knowledge on biases can be used to dampen their negative effects and boost judgment accuracy, paving the way for combating other cognitive biases threatening collective systems.
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Affiliation(s)
- Bertrand Jayles
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
- Institute of Catastrophe Risk Management, Nanyang Technological University, Singapore, Republic of Singapore
| | - Clément Sire
- Laboratoire de Physique Théorique, Centre National de la Recherche Scientifique (CNRS), Université de Toulouse – Paul Sabatier (UPS), Toulouse, France
| | - Ralf H. J. M. Kurvers
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
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How the wisdom of crowds, and of the crowd within, are affected by expertise. COGNITIVE RESEARCH-PRINCIPLES AND IMPLICATIONS 2021; 6:5. [PMID: 33544255 PMCID: PMC7865038 DOI: 10.1186/s41235-021-00273-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 01/12/2021] [Indexed: 11/30/2022]
Abstract
We investigated the effect of expertise on the wisdom of crowds. Participants completed 60 trials of a numerical estimation task, during which they saw 50–100 asterisks and were asked to estimate how many stars they had just seen. Experiment 1 established that both inner- and outer-crowd wisdom extended to our novel task: Single responses alone were less accurate than responses aggregated across a single participant (showing inner-crowd wisdom) and responses aggregated across different participants were even more accurate (showing outer-crowd wisdom). In Experiment 2, prior to beginning the critical trials, participants did 12 practice trials with feedback, which greatly increased their accuracy. There was a benefit of outer-crowd wisdom relative to a single estimate. There was no inner-crowd wisdom effect, however; with high accuracy came highly restricted variance, and aggregating insufficiently varying responses is not beneficial. Our data suggest that experts give almost the same answer every time they are asked and so they should consult the outer crowd rather than solicit multiple estimates from themselves.
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Jayles B, Escobedo R, Cezera S, Blanchet A, Kameda T, Sire C, Theraulaz G. The impact of incorrect social information on collective wisdom in human groups. J R Soc Interface 2020; 17:20200496. [PMID: 32900307 DOI: 10.1098/rsif.2020.0496] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
A major problem resulting from the massive use of social media is the potential spread of incorrect information. Yet, very few studies have investigated the impact of incorrect information on individual and collective decisions. We performed experiments in which participants had to estimate a series of quantities, before and after receiving social information. Unbeknownst to them, we controlled the degree of inaccuracy of the social information through 'virtual influencers', who provided some incorrect information. We find that a large proportion of individuals only partially follow the social information, thus resisting incorrect information. Moreover, incorrect information can help improve group performance more than correct information, when going against a human underestimation bias. We then design a computational model whose predictions are in good agreement with the empirical data, and sheds light on the mechanisms underlying our results. Besides these main findings, we demonstrate that the dispersion of estimates varies a lot between quantities, and must thus be considered when normalizing and aggregating estimates of quantities that are very different in nature. Overall, our results suggest that incorrect information does not necessarily impair the collective wisdom of groups, and can even be used to dampen the negative effects of known cognitive biases.
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Affiliation(s)
- Bertrand Jayles
- Laboratoire de Physique Théorique, Centre National de la Recherche Scientifique (CNRS), Université de Toulouse - Paul Sabatier (UPS), Toulouse, France.,Centre de Recherches sur la Cognition Animal-Centre de Biologie Intégrative, Centre National de la Recherche Scientifique (CNRS), Université de Toulouse - Paul Sabatier (UPS), Toulouse, France.,Center for Adaptive Rationality, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Ramón Escobedo
- Centre de Recherches sur la Cognition Animal-Centre de Biologie Intégrative, Centre National de la Recherche Scientifique (CNRS), Université de Toulouse - Paul Sabatier (UPS), Toulouse, France
| | - Stéphane Cezera
- Toulouse School of Economics, INRA, Université de Toulouse (Capitole), 31000 Toulouse, France
| | - Adrien Blanchet
- Toulouse School of Economics, INRA, Université de Toulouse (Capitole), 31000 Toulouse, France.,Institute for Advanced Study in Toulouse, 31015 Toulouse, France
| | - Tatsuya Kameda
- Department of Social Psychology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Clément Sire
- Laboratoire de Physique Théorique, Centre National de la Recherche Scientifique (CNRS), Université de Toulouse - Paul Sabatier (UPS), Toulouse, France
| | - Guy Theraulaz
- Centre de Recherches sur la Cognition Animal-Centre de Biologie Intégrative, Centre National de la Recherche Scientifique (CNRS), Université de Toulouse - Paul Sabatier (UPS), Toulouse, France.,Institute for Advanced Study in Toulouse, 31015 Toulouse, France
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