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Wheatley T, Thornton MA, Stolk A, Chang LJ. The Emerging Science of Interacting Minds. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2024; 19:355-373. [PMID: 38096443 PMCID: PMC10932833 DOI: 10.1177/17456916231200177] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
For over a century, psychology has focused on uncovering mental processes of a single individual. However, humans rarely navigate the world in isolation. The most important determinants of successful development, mental health, and our individual traits and preferences arise from interacting with other individuals. Social interaction underpins who we are, how we think, and how we behave. Here we discuss the key methodological challenges that have limited progress in establishing a robust science of how minds interact and the new tools that are beginning to overcome these challenges. A deep understanding of the human mind requires studying the context within which it originates and exists: social interaction.
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Affiliation(s)
- Thalia Wheatley
- Consortium for Interacting Minds, Psychological and Brain Sciences, Dartmouth, Hanover, NH USA
- Santa Fe Institute
| | - Mark A. Thornton
- Consortium for Interacting Minds, Psychological and Brain Sciences, Dartmouth, Hanover, NH USA
| | - Arjen Stolk
- Consortium for Interacting Minds, Psychological and Brain Sciences, Dartmouth, Hanover, NH USA
| | - Luke J. Chang
- Consortium for Interacting Minds, Psychological and Brain Sciences, Dartmouth, Hanover, NH USA
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Tang Q, Liu S, Tao C, Wang J, Zhao H, Wang G, Zhao X, Ren Q, Zhang L, Su B, Xu J, An H. A new method for vascular age estimation based on relative risk difference in vascular aging. Comput Biol Med 2024; 171:108155. [PMID: 38430740 DOI: 10.1016/j.compbiomed.2024.108155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/26/2024] [Accepted: 02/12/2024] [Indexed: 03/05/2024]
Abstract
OBJECTIVE The current models of estimating vascular age (VA) primarily rely on the regression label expressed with chronological age (CA), which does not account individual differences in vascular aging (IDVA) that are difficult to describe by CA. This may lead to inaccuracies in assessing the risk of cardiovascular disease based on VA. To address this limitation, this work aims to develop a new method for estimating VA by considering IDVA. This method will provide a more accurate assessment of cardiovascular disease risk. METHODS Relative risk difference in vascular aging (RRDVA) is proposed to replace IDVA, which is represented as the numerical difference between individual predicted age (PA) and the corresponding mean PA of healthy population. RRDVA and CA are regard as the influence factors to acquire VA. In order to acquire PA of all samples, this work takes CA as the dependent variable, and mines the two most representative indicators from arteriosclerosis data as the independent variables, to establish a regression model for obtaining PA. RESULTS The proposed VA based on RRDVA is significantly correlated with 27 indirect indicators for vascular aging evaluation. Moreover, VA is better than CA by comparing the correlation coefficients between VA, CA and 27 indirect indicators, and RRDVA greater than zero presents a higher risk of disease. CONCLUSION The proposed VA overcomes the limitation of CA in characterizing IDVA, which may help young groups with high disease risk to promote healthy behaviors.
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Affiliation(s)
- Qingfeng Tang
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China; Anhui Engineering Research Center of Intelligent Perception and Elderly Care, Chuzhou University, Chuzhou 239000, China.
| | - Shiping Liu
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China.
| | - Chao Tao
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China.
| | - Jue Wang
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
| | - Huanhuan Zhao
- Anhui Engineering Research Center of Intelligent Perception and Elderly Care, Chuzhou University, Chuzhou 239000, China; School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China.
| | - Guangjun Wang
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China.
| | - Xu Zhao
- Health Management & Physical Examination Center, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China.
| | - Qun Ren
- Health Management & Physical Examination Center, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China.
| | - Liangliang Zhang
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China.
| | - Benyue Su
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China; School of Mathematics and Computer Science, Tongling University, Tongling 244061, China.
| | - Jiatuo Xu
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
| | - Hui An
- Health Management & Physical Examination Center, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China.
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Liu N, Yuan Z, Chen Y, Liu C, Wang L. Learning implicit sentiments in Alzheimer's disease recognition with contextual attention features. Front Aging Neurosci 2023; 15:1122799. [PMID: 37266402 PMCID: PMC10231228 DOI: 10.3389/fnagi.2023.1122799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 04/05/2023] [Indexed: 06/03/2023] Open
Abstract
Background Alzheimer's disease (AD) is difficult to diagnose on the basis of language because of the implicit emotion of transcripts, which is defined as a supervised fuzzy implicit emotion classification at the document level. Recent neural network-based approaches have not paid attention to the implicit sentiments entailed in AD transcripts. Method A two-level attention mechanism is proposed to detect deep semantic information toward words and sentences, which enables it to attend to more words and fewer sentences differentially when constructing document representation. Specifically, a document vector was built by progressively aggregating important words into sentence vectors and important sentences into document vectors. Results Experimental results showed that our method achieved the best accuracy of 91.6% on annotated public Pitt corpora, which validates its effectiveness in learning implicit sentiment representation for our model. Conclusion The proposed model can qualitatively select informative words and sentences using attention layers, and this method also provides good inspiration for AD diagnosis based on implicit sentiment transcripts.
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Affiliation(s)
- Ning Liu
- School of Science/School of Big Data Science, Zhejiang University of Science and Technology, Hangzhou, China
| | - Zhenming Yuan
- School of Information Science and Technology, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Yan Chen
- International Unresponsive Wakefulness Syndrome and Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China
| | - Chuan Liu
- School of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou, Fujian, China
| | - Lingxing Wang
- Department of Neurology, Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
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