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Tao X, Gao D, Zhang W, Liu T, Du B, Zhang S, Qin Y. A multimodal physiological dataset for driving behaviour analysis. Sci Data 2024; 11:378. [PMID: 38609440 PMCID: PMC11014944 DOI: 10.1038/s41597-024-03222-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 04/03/2024] [Indexed: 04/14/2024] Open
Abstract
Physiological signal monitoring and driver behavior analysis have gained increasing attention in both fundamental research and applied research. This study involved the analysis of driving behavior using multimodal physiological data collected from 35 participants. The data included 59-channel EEG, single-channel ECG, 4-channel EMG, single-channel GSR, and eye movement data obtained via a six-degree-of-freedom driving simulator. We categorized driving behavior into five groups: smooth driving, acceleration, deceleration, lane changing, and turning. Through extensive experiments, we confirmed that both physiological and vehicle data met the requirements. Subsequently, we developed classification models, including linear discriminant analysis (LDA), MMPNet, and EEGNet, to demonstrate the correlation between physiological data and driving behaviors. Notably, we propose a multimodal physiological dataset for analyzing driving behavior(MPDB). The MPDB dataset's scale, accuracy, and multimodality provide unprecedented opportunities for researchers in the autonomous driving field and beyond. With this dataset, we will contribute to the field of traffic psychology and behavior.
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Affiliation(s)
- Xiaoming Tao
- Tsinghua University, Department of Electronic Engineering, Beijing, 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), 100084, Beijing, China
| | - Dingcheng Gao
- Tsinghua University, Department of Electronic Engineering, Beijing, 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), 100084, Beijing, China
| | - Wenqi Zhang
- Tsinghua University, Department of Electronic Engineering, Beijing, 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), 100084, Beijing, China
| | - Tianqi Liu
- Tsinghua University, Department of Electronic Engineering, Beijing, 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), 100084, Beijing, China
| | - Bing Du
- University of Science and Technology Beijing, School of Computer and Communication Engineering, Beijing, 100083, China
| | - Shanghang Zhang
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
| | - Yanjun Qin
- Tsinghua University, Department of Electronic Engineering, Beijing, 100084, China.
- Beijing National Research Center for Information Science and Technology (BNRist), 100084, Beijing, China.
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Sato K, Fukuhara K, Higuchi T. Age-Related Changes in the Utilization of Visual Information for Collision Prediction: A Study Using an Affordance-Based Model. Exp Aging Res 2023:1-17. [PMID: 37942547 DOI: 10.1080/0361073x.2023.2278985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 10/28/2023] [Indexed: 11/10/2023]
Abstract
The ability to predict collisions with moving objects deteriorates with aging. We followed the affordance-based model to identify optical variables that older adults had difficulty using for collision prediction. We reproduced a modified version of the interception task used in Steinmetz (Steinmetz, Layton, Powell, & Fajen, 2020, "Affordance-based versus current - future accounts of choosing whether to pursue or abandon the chase of a moving target," Journal of Vision, 20(3), 8) in a virtual reality (VR) environment and newly introduced perturbation for each of three optical variables (vertical and horizontal expansions of a moving object and the bearing angle produced between participants and a moving object). We expected that perturbation would negatively affect the performance only for those who rely on the optical variable to perform the interception task effectively. We tested 18 older and 15 younger adults and showed that older participants were not negatively affected by the perturbation for the vertical and horizontal expansion of a moving object, while they showed decreased performance when the perturbation was introduced with a bearing angle. These findings suggest that predicting collisions with moving objects deteriorates with aging because the perception of object expansion is impaired with aging.
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Affiliation(s)
- Kazuyuki Sato
- Department of Health Promotion Science, Tokyo Metropolitan University, Hachioji, Tokyo, Japan
| | - Kazunobu Fukuhara
- Department of Health Promotion Science, Tokyo Metropolitan University, Hachioji, Tokyo, Japan
| | - Takahiro Higuchi
- Department of Health Promotion Science, Tokyo Metropolitan University, Hachioji, Tokyo, Japan
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Fahimipour AK, Gil MA, Celis MR, Hein GF, Martin BT, Hein AM. Wild animals suppress the spread of socially transmitted misinformation. Proc Natl Acad Sci U S A 2023; 120:e2215428120. [PMID: 36976767 PMCID: PMC10083541 DOI: 10.1073/pnas.2215428120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/07/2023] [Indexed: 03/29/2023] Open
Abstract
Understanding the mechanisms by which information and misinformation spread through groups of individual actors is essential to the prediction of phenomena ranging from coordinated group behaviors to misinformation epidemics. Transmission of information through groups depends on the rules that individuals use to transform the perceived actions of others into their own behaviors. Because it is often not possible to directly infer decision-making strategies in situ, most studies of behavioral spread assume that individuals make decisions by pooling or averaging the actions or behavioral states of neighbors. However, whether individuals may instead adopt more sophisticated strategies that exploit socially transmitted information, while remaining robust to misinformation, is unknown. Here, we study the relationship between individual decision-making and misinformation spread in groups of wild coral reef fish, where misinformation occurs in the form of false alarms that can spread contagiously through groups. Using automated visual field reconstruction of wild animals, we infer the precise sequences of socially transmitted visual stimuli perceived by individuals during decision-making. Our analysis reveals a feature of decision-making essential for controlling misinformation spread: dynamic adjustments in sensitivity to socially transmitted cues. This form of dynamic gain control can be achieved by a simple and biologically widespread decision-making circuit, and it renders individual behavior robust to natural fluctuations in misinformation exposure.
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Affiliation(s)
- Ashkaan K. Fahimipour
- Department of Biological Sciences, Florida Atlantic University, Boca Raton, FL33431
- Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, CA95060
| | - Michael A. Gil
- Department of Ecology & Evolutionary Biology, University of Colorado Boulder, Boulder, CO80309
| | - Maria Rosa Celis
- Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, CA95060
| | | | - Benjamin T. Martin
- Institute for Biodiversity & Ecosystem Dynamics, University of Amsterdam, 1090GE Amsterdam, The Netherlands
| | - Andrew M. Hein
- Department of Computational Biology, Cornell University, Ithaca, NY14850
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Wang L, Chen S, Xiao W. Effect of real-world fear on risky decision-making in medical school-based students: A quasi-experimental study. Front Behav Neurosci 2023; 17:1030098. [PMID: 36935894 PMCID: PMC10017853 DOI: 10.3389/fnbeh.2023.1030098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 02/02/2023] [Indexed: 03/06/2023] Open
Abstract
Objective: To explore the effect of real-world fear on risky decision-making under certainty and uncertainty. Methods: This quasi-experimental study enrolled non-psychology undergraduate volunteers aged between 17 and 20 years old from the Preventive Medical Institute medical school in Xi'an. Participants were randomly divided into two groups, and each group received a two-stage crossover design intervention (of a calm and fearful situation) and completed the tasks of risky decision-making under uncertainty (the balloon analog risk task: BART) and certainty (the Cambridge gambling task: CGT), respectively. The primary outcomes included the behavioral impulsivity measured by the BART value, and the speed of decision-making, the quality of decisions, the adventure index, behavioral impulsivity, and risk adjustment measured by CGT. The secondary outcome was the concentration of cortisol in the saliva. Results: A total of 60 questionnaires and data were obtained from 60 participants (28 males and 32 females, aged 19.55 ± 0.75). Compared with the calm situation, participants were more likely to have a lower BART value (p = 0.013), slower speed of decision-making (p < 0.05), and higher adventure index (p = 0.018) in the fearful situation. The quality of decisions (p = 0.189), behavioral impulsivity index (p = 0.182), and risk adjustment (p = 0.063) between subjects in the fearful and calm situations were comparable. Furthermore, the mean value of the adventure index of CGT in male subjects was significantly higher than that in female subjects (p < 0.05), and the cortisol concentration in saliva during the fearful situation was significantly higher compared to the calm situation (p < 0.05). Conclusion: Fear might reduce behavioral impulsivity under uncertainty, and increase the adventure index under certainty in risky decision-making. Risky behavior might be influenced by gender: under certainty in risky decision-making, men were more adventurous. Additionally, fear increased the secretion of cortisol in saliva.
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Affiliation(s)
- Lei Wang
- Department of Medical Psychology, Strategic Support Force Medical Center, Beijing, China
| | - Sheng Chen
- Department of Medical Psychology, Strategic Support Force Medical Center, Beijing, China
| | - Wei Xiao
- Department of Military Medical Psychology, Air Force Medical University, Xian, China
- *Correspondence: Wei Xiao
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Lokesh R, Sullivan S, Calalo JA, Roth A, Swanik B, Carter MJ, Cashaback JGA. Humans utilize sensory evidence of others' intended action to make online decisions. Sci Rep 2022; 12:8806. [PMID: 35614073 PMCID: PMC9132989 DOI: 10.1038/s41598-022-12662-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/06/2022] [Indexed: 11/09/2022] Open
Abstract
We often acquire sensory information from another person's actions to make decisions on how to move, such as when walking through a crowded hallway. Past interactive decision-making research has focused on cognitive tasks that did not allow for sensory information exchange between humans prior to a decision. Here, we test the idea that humans accumulate sensory evidence of another person's intended action to decide their own movement. In a competitive sensorimotor task, we show that humans exploit time to accumulate sensory evidence of another's intended action and utilize this information to decide how to move. We captured this continuous interactive decision-making behaviour with a drift-diffusion model. Surprisingly, aligned with a 'paralysis-by-analysis' phenomenon, we found that humans often waited too long to accumulate sensory evidence and failed to make a decision. Understanding how humans engage in interactive and online decision-making has broad implications that spans sociology, athletics, interactive technology, and economics.
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Affiliation(s)
- Rakshith Lokesh
- Department of Biomedical Engineering, University of Delaware, Newark, DE, USA
| | - Seth Sullivan
- Department of Biomedical Engineering, University of Delaware, Newark, DE, USA
| | - Jan A Calalo
- Department of Mechanical Engineering, University of Delaware, Newark, DE, USA
| | - Adam Roth
- Department of Mechanical Engineering, University of Delaware, Newark, DE, USA
| | - Brenden Swanik
- Department of Biomedical Engineering, University of Delaware, Newark, DE, USA
| | - Michael J Carter
- Department of Kinesiology, McMaster University, Hamilton, ON, Canada.
| | - Joshua G A Cashaback
- Department of Biomedical Engineering, University of Delaware, Newark, DE, USA.
- Department of Mechanical Engineering, University of Delaware, Newark, DE, USA.
- Biomechanics and Movements Science Program, University of Delaware, Newark, DE, USA.
- Interdisciplinary Neuroscience Graduate Program, University of Delaware, Newark, DE, USA.
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