Literature DB >> 8652747

Psychophysiology and adaptive automation.

E A Byrne1, R Parasuraman.   

Abstract

Adaptive automation is an approach to automation design where tasks are dynamically allocated between the human operator and computer systems. Psychophysiology has two complementary roles in research on adaptive automation: first, to provide information about the effects of different forms of automation thus promoting the development of effective adaptive logic; and second, psychophysiology may yield information about the operator that can be integrated with performance measurement and operator modelling to aid in the regulation of automation. This review discusses the basic tenets of adaptive automation and the role of psychophysiological measures in the study of adaptive automation. Empirical results from studies of flight simulation are presented. Psychophysiological measures may prove especially useful in the prevention of performance deterioration in underload conditions that may accompany automation. Individual differences and the potential for learned responses require research to understand their influence on adaptive algorithms. Adaptive automation represents a unique domain for the application of psychophysiology in the work environment.

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Mesh:

Year:  1996        PMID: 8652747     DOI: 10.1016/0301-0511(95)05161-9

Source DB:  PubMed          Journal:  Biol Psychol        ISSN: 0301-0511            Impact factor:   3.251


  17 in total

1.  Study on Factors That Influence Human Errors: Focused on Cabin Crew.

Authors:  Jiyoung Kim; Myoungjin Yu; Sunghyup Sean Hyun
Journal:  Int J Environ Res Public Health       Date:  2022-05-07       Impact factor: 4.614

Review 2.  Neuroergonomics: a review of applications to physical and cognitive work.

Authors:  Ranjana K Mehta; Raja Parasuraman
Journal:  Front Hum Neurosci       Date:  2013-12-23       Impact factor: 3.169

3.  Wearable functional near infrared spectroscopy (fNIRS) and transcranial direct current stimulation (tDCS): expanding vistas for neurocognitive augmentation.

Authors:  Ryan McKendrick; Raja Parasuraman; Hasan Ayaz
Journal:  Front Syst Neurosci       Date:  2015-03-09

4.  Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight.

Authors:  Daniel E Callan; Gautier Durantin; Cengiz Terzibas
Journal:  Front Syst Neurosci       Date:  2015-02-17

5.  Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment.

Authors:  Pietro Aricò; Gianluca Borghini; Gianluca Di Flumeri; Alfredo Colosimo; Stefano Bonelli; Alessia Golfetti; Simone Pozzi; Jean-Paul Imbert; Géraud Granger; Raïlane Benhacene; Fabio Babiloni
Journal:  Front Hum Neurosci       Date:  2016-10-26       Impact factor: 3.169

6.  Mental workload and neural efficiency quantified in the prefrontal cortex using fNIRS.

Authors:  Mickaël Causse; Zarrin Chua; Vsevolod Peysakhovich; Natalia Del Campo; Nadine Matton
Journal:  Sci Rep       Date:  2017-07-12       Impact factor: 4.379

7.  Assessing the Driver's Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study.

Authors:  Anirudh Unni; Klas Ihme; Meike Jipp; Jochem W Rieger
Journal:  Front Hum Neurosci       Date:  2017-04-05       Impact factor: 3.169

8.  Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load.

Authors:  Jianhua Zhang; Zhong Yin; Rubin Wang
Journal:  Front Neurosci       Date:  2017-03-17       Impact factor: 4.677

9.  The Brain Is Faster than the Hand in Split-Second Intentions to Respond to an Impending Hazard: A Simulation of Neuroadaptive Automation to Speed Recovery to Perturbation in Flight Attitude.

Authors:  Daniel E Callan; Cengiz Terzibas; Daniel B Cassel; Masa-Aki Sato; Raja Parasuraman
Journal:  Front Hum Neurosci       Date:  2016-04-27       Impact factor: 3.169

Review 10.  Trusted Autonomy and Cognitive Cyber Symbiosis: Open Challenges.

Authors:  Hussein A Abbass; Eleni Petraki; Kathryn Merrick; John Harvey; Michael Barlow
Journal:  Cognit Comput       Date:  2015-12-23       Impact factor: 5.418

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