Literature DB >> 12502155

Malleable attentional resources theory: a new explanation for the effects of mental underload on performance.

Mark S Young1, Neville A Stanton.   

Abstract

This paper proposes a new theory to account for the effects of underload on performance. Malleable attentional resources theory posits that attentional capacity can change size in response to changes in task demands. As such, the performance decrements associated with mental underload can be explained by a lack of appropriate attentional resources. These proposals were explored in a driving simulator experiment. Vehicle automation was manipulated at 4 levels, and mental workload was assessed with a secondary task. Eye movements were also recorded to determine whether attentional capacity varied with mental workload. The results showed a clear decrease in mental workload associated with some levels of automation. Most striking, though, were the results derived from the eye movement recordings, which demonstrated that attentional capacity varies directly with level of mental workload. These data fully supported the predictions of the new theory. Malleable attentional resources theory suggests that future vehicle designers should employ their technology in driver support systems rather than in automation to replace the driver. The implications of this theory are discussed with regard to capacity models of attention as well as to the design of future vehicle systems.

Mesh:

Year:  2002        PMID: 12502155     DOI: 10.1518/0018720024497709

Source DB:  PubMed          Journal:  Hum Factors        ISSN: 0018-7208            Impact factor:   2.888


  16 in total

1.  Dynamics of Driver Distraction: The process of engaging and disengaging.

Authors:  John D Lee
Journal:  Ann Adv Automot Med       Date:  2014

2.  Neural signatures of vigilance decrements predict behavioural errors before they occur.

Authors:  Alexandra Woolgar; Anina N Rich; Hamid Karimi-Rouzbahani
Journal:  Elife       Date:  2021-04-08       Impact factor: 8.140

3.  Driving into the sunset: supporting cognitive functioning in older drivers.

Authors:  Mark S Young; David Bunce
Journal:  J Aging Res       Date:  2011-05-25

4.  On the relation between economic bubbles and effort gaps between sellers and buyers: An experimental study.

Authors:  Eldad Yechiam; Amitay Kauffmann; Nathaniel J S Ashby; Gal Zahavi
Journal:  PLoS One       Date:  2017-12-11       Impact factor: 3.240

5.  A Machine Learning Approach to the Detection of Pilot's Reaction to Unexpected Events Based on EEG Signals.

Authors:  Bartosz Binias; Dariusz Myszor; Krzysztof A Cyran
Journal:  Comput Intell Neurosci       Date:  2018-04-10

6.  Dealing With Unexpected Events on the Flight Deck: A Conceptual Model of Startle and Surprise.

Authors:  Annemarie Landman; Eric L Groen; M M René van Paassen; Adelbert W Bronkhorst; Max Mulder
Journal:  Hum Factors       Date:  2017-08-04       Impact factor: 2.888

7.  The Relationship Between Drivers' Cognitive Fatigue and Speed Variability During Monotonous Daytime Driving.

Authors:  Jinfei Ma; Jiaqi Gu; Huibin Jia; Zhuye Yao; Ruosong Chang
Journal:  Front Psychol       Date:  2018-04-04

Review 8.  A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance.

Authors:  Frédéric Dehais; Alex Lafont; Raphaëlle Roy; Stephen Fairclough
Journal:  Front Neurosci       Date:  2020-04-07       Impact factor: 4.677

9.  Measuring Mental Workload with EEG+fNIRS.

Authors:  Haleh Aghajani; Marc Garbey; Ahmet Omurtag
Journal:  Front Hum Neurosci       Date:  2017-07-14       Impact factor: 3.169

10.  Reduced Attention Allocation during Short Periods of Partially Automated Driving: An Event-Related Potentials Study.

Authors:  Ignacio Solís-Marcos; Alejandro Galvao-Carmona; Katja Kircher
Journal:  Front Hum Neurosci       Date:  2017-11-06       Impact factor: 3.169

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