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Balsdon T, Mamassian P, Wyart V. Separable neural signatures of confidence during perceptual decisions. eLife 2021; 10:e68491. [PMID: 34488942 PMCID: PMC8423440 DOI: 10.7554/elife.68491] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 08/03/2021] [Indexed: 11/26/2022] Open
Abstract
Perceptual confidence is an evaluation of the validity of perceptual decisions. While there is behavioural evidence that confidence evaluation differs from perceptual decision-making, disentangling these two processes remains a challenge at the neural level. Here, we examined the electrical brain activity of human participants in a protracted perceptual decision-making task where observers tend to commit to perceptual decisions early whilst continuing to monitor sensory evidence for evaluating confidence. Premature decision commitments were revealed by patterns of spectral power overlying motor cortex, followed by an attenuation of the neural representation of perceptual decision evidence. A distinct neural representation was associated with the computation of confidence, with sources localised in the superior parietal and orbitofrontal cortices. In agreement with a dissociation between perception and confidence, these neural resources were recruited even after observers committed to their perceptual decisions, and thus delineate an integral neural circuit for evaluating perceptual decision confidence.
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Affiliation(s)
- Tarryn Balsdon
- Laboratoire des Systèmes Perceptifs (CNRS UMR 8248), DEC, ENS, PSL UniversityParisFrance
- Laboratoire de Neurosciences Cognitives et Computationnelles (Inserm U960), DEC, ENS, PSL UniversityParisFrance
| | - Pascal Mamassian
- Laboratoire des Systèmes Perceptifs (CNRS UMR 8248), DEC, ENS, PSL UniversityParisFrance
| | - Valentin Wyart
- Laboratoire de Neurosciences Cognitives et Computationnelles (Inserm U960), DEC, ENS, PSL UniversityParisFrance
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2
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Affiliation(s)
- Ali Abolhassani
- Department of Mathematics, Azarbaijan Shahid Madani University, Tabriz, Iran
| | - Marcos O. Prates
- Department of Statistics, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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Yuan M, Boston-Fisher N, Luo Y, Verma A, Buckeridge DL. A systematic review of aberration detection algorithms used in public health surveillance. J Biomed Inform 2019; 94:103181. [PMID: 31014979 DOI: 10.1016/j.jbi.2019.103181] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 04/16/2019] [Accepted: 04/17/2019] [Indexed: 12/21/2022]
Abstract
The algorithms used for detecting anomalies have evolved substantially over the last decade to take advantage of advances in informatics and to accommodate changes in surveillance data. We identified 145 studies since 2007 that evaluated statistical methods used to detect aberrations in public health surveillance data. For each study, we classified the analytic methods and reviewed the evaluation metrics. We also summarized the practical usage of the detection algorithms in public health surveillance systems worldwide. Traditional methods (e.g., control charts, linear regressions) were the focus of most evaluation studies and continue to be used commonly in practice. There was, however, an increase in the number of studies using forecasting methods and studies applying machine learning methods, hidden Markov models, and Bayesian framework to multivariate datasets. Evaluation studies demonstrated improved accuracy with more sophisticated methods, but these methods do not appear to be used widely in public health practice.
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Affiliation(s)
- Mengru Yuan
- Clinical and Health Informatics Research Group, McGill University, 1140 Pine Avenue West, Montreal, QC H3A 1A3, Canada
| | - Nikita Boston-Fisher
- Clinical and Health Informatics Research Group, McGill University, 1140 Pine Avenue West, Montreal, QC H3A 1A3, Canada
| | - Yu Luo
- Clinical and Health Informatics Research Group, McGill University, 1140 Pine Avenue West, Montreal, QC H3A 1A3, Canada
| | - Aman Verma
- Clinical and Health Informatics Research Group, McGill University, 1140 Pine Avenue West, Montreal, QC H3A 1A3, Canada
| | - David L Buckeridge
- Clinical and Health Informatics Research Group, McGill University, 1140 Pine Avenue West, Montreal, QC H3A 1A3, Canada.
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Zhao X, Zhou XH, Feng Z, Guo P, He H, Zhang T, Duan L, Li X. A scan statistic for binary outcome based on hypergeometric probability model, with an application to detecting spatial clusters of Japanese encephalitis. PLoS One 2013; 8:e65419. [PMID: 23785424 PMCID: PMC3681795 DOI: 10.1371/journal.pone.0065419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2013] [Accepted: 04/24/2013] [Indexed: 11/29/2022] Open
Abstract
As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff's methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff's statistics for clusters of high population density or large size; otherwise Kulldorff's statistics are superior.
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Affiliation(s)
- Xing Zhao
- Department of Biostatistics, West China School of Public Health, Sichuan University, Chengdu, Sichuan, China
- Department of Biostatistics, School of Public Health and Community Medicine, University of Washington, Seattle, Washington, United States of America
| | - Xiao-Hua Zhou
- Department of Biostatistics, School of Public Health and Community Medicine, University of Washington, Seattle, Washington, United States of America
| | - Zijian Feng
- Office for Disease Control and Emergency Response, Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Pengfei Guo
- Department of Biostatistics, West China School of Public Health, Sichuan University, Chengdu, Sichuan, China
| | - Hongyan He
- Department of Biostatistics, West China School of Public Health, Sichuan University, Chengdu, Sichuan, China
| | - Tao Zhang
- Department of Biostatistics, West China School of Public Health, Sichuan University, Chengdu, Sichuan, China
| | - Lei Duan
- School of Computer Science, Sichuan University, Chengdu, Sichuan, China
- State Key Laboratory of Software Engineering, Wuhan University, Wuhan, Hubei, China
| | - Xiaosong Li
- Department of Biostatistics, West China School of Public Health, Sichuan University, Chengdu, Sichuan, China
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Neill DB, McFowland E, Zheng H. Fast subset scan for multivariate event detection. Stat Med 2012; 32:2185-208. [DOI: 10.1002/sim.5675] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2011] [Accepted: 10/19/2012] [Indexed: 11/10/2022]
Affiliation(s)
- Daniel B. Neill
- Event and Pattern Detection Laboratory, H.J. Heinz III College; Carnegie Mellon University; 5000 Forbes Avenue Pittsburgh PA 15213 U.S.A
| | - Edward McFowland
- Event and Pattern Detection Laboratory, H.J. Heinz III College; Carnegie Mellon University; 5000 Forbes Avenue Pittsburgh PA 15213 U.S.A
| | - Huanian Zheng
- Department of E-commercial Product Marketing; Baidu Inc.; Beijing 100085 China
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