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      A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

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          Abstract

          As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.

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          Most cited references231

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          Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning.

          Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology's near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior.
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            Publication recommendations for electrodermal measurements.

            This committee was appointed by the SPR Board to provide recommendations for publishing data on electrodermal activity (EDA). They are intended to be a stand-alone source for newcomers and experienced users. A short outline of principles for electrodermal measurement is given, and recommendations from an earlier report (Fowles et al., ) are incorporated. Three fundamental techniques of EDA recording are described: (1) endosomatic recording without the application of an external current, (2) exosomatic recording with direct current (the most widely applied methodology), and (3) exosomatic recording with alternating current-to date infrequently used but a promising future methodology. In addition to EDA recording in laboratories, ambulatory recording has become an emerging technique. Specific problems that come with this recording of EDA in the field are discussed, as are those emerging from recording EDA within a magnetic field (e.g., fMRI). Recommendations for the details that should be mentioned in publications of EDA methods and results are provided. Copyright © 2012 Society for Psychophysiological Research.
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              Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness.

              This paper reviews published papers related to neurophysiological measurements (electroencephalography: EEG, electrooculography EOG; heart rate: HR) in pilots/drivers during their driving tasks. The aim is to summarise the main neurophysiological findings related to the measurements of pilot/driver's brain activity during drive performance and how particular aspects of this brain activity could be connected with the important concepts of "mental workload", "mental fatigue" or "situational awareness". Review of the literature suggests that exists a coherent sequence of changes for EEG, EOG and HR variables during the transition from normal drive, high mental workload and eventually mental fatigue and drowsiness. In particular, increased EEG power in theta band and a decrease in alpha band occurred in high mental workload. Successively, increased EEG power in theta as well as delta and alpha bands characterise the transition between mental workload and mental fatigue. Drowsiness is also characterised by increased blink rate and decreased HR values. The detection of such mental states is actually performed "offline" with accuracy around 90% but not online. A discussion on the possible future applications of findings provided by these neurophysiological measurements in order to improve the safety of the vehicles will be also presented. Copyright © 2012 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                19 March 2019
                2019
                : 13
                : 57
                Affiliations
                [1] 1Department of Educational Psychology, University of Utah , Salt Lake City, UT, United States
                [2] 2Department of Psychology, University of Utah , Salt Lake City, UT, United States
                Author notes

                Edited by: Bruce Mehler, Massachusetts Institute of Technology, United States

                Reviewed by: Joost De Winter, Delft University of Technology, Netherlands; Mickael Causse, National Higher School of Aeronautics and Space, France; Dick De Waard, University of Groningen, Netherlands; Edmund Wascher, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Germany

                *Correspondence: Monika Lohani monika.lohani@ 123456utah.edu
                Article
                10.3389/fnhum.2019.00057
                6434408
                30941023
                189568d1-1af7-4cdd-a7b9-149fa3140a4a
                Copyright © 2019 Lohani, Payne and Strayer.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 May 2018
                : 01 February 2019
                Page count
                Figures: 0, Tables: 2, Equations: 0, References: 265, Pages: 27, Words: 26072
                Funding
                Funded by: AAA Foundation for Traffic Safety 10.13039/100003550
                Categories
                Neuroscience
                Review

                Neurosciences
                psychophysiology,cognition,driving,traffic safety,real-world
                Neurosciences
                psychophysiology, cognition, driving, traffic safety, real-world

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