Literature DB >> 8635447

An experimental evaluation of instantaneous self-assessment as a measure of workload.

A J Tattersall1, P S Foord.   

Abstract

Instantaneous self-assessment (ISA) is a technique that has been developed as a measure of workload to provide immediate subjective ratings of work demands during the performance of primary work tasks such as air traffic control. This paper reports a study that compared the results of ISA with those gathered from other established workload evaluation techniques; subjective ratings collected at the end of the task, mean heart rate and heart rate variability, and error in the primary task of tracking. ISA ratings were found to be correlated significantly with the post-task ratings of workload, heart rate variability, and task performance. Generally each of the techniques was sensitive to variations in task difficulty. However, performance on the primary tracking task was found to be poorer during periods when ISA responses were required, regardless of whether they were spoken or manual responses. This finding suggests that the usefulness of the technique is limited in comparison to less intrusive measures of workload.

Mesh:

Year:  1996        PMID: 8635447     DOI: 10.1080/00140139608964495

Source DB:  PubMed          Journal:  Ergonomics        ISSN: 0014-0139            Impact factor:   2.778


  5 in total

Review 1.  Human Mental Workload: A Survey and a Novel Inclusive Definition.

Authors:  Luca Longo; Christoper D Wickens; Gabriella Hancock; Peter A Hancock
Journal:  Front Psychol       Date:  2022-06-02

2.  A Systematic Review of Physiological Measures of Mental Workload.

Authors:  Da Tao; Haibo Tan; Hailiang Wang; Xu Zhang; Xingda Qu; Tingru Zhang
Journal:  Int J Environ Res Public Health       Date:  2019-07-30       Impact factor: 3.390

3.  Investigating mental workload-induced changes in cortical oxygenation and frontal theta activity during simulated flights.

Authors:  Anneke Hamann; Nils Carstengerdes
Journal:  Sci Rep       Date:  2022-04-19       Impact factor: 4.996

4.  Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers.

Authors:  Patricia Becerra-Sánchez; Angelica Reyes-Munoz; Antonio Guerrero-Ibañez
Journal:  Sensors (Basel)       Date:  2020-10-17       Impact factor: 3.576

5.  Supervised Classification of Operator Functional State Based on Physiological Data: Application to Drones Swarm Piloting.

Authors:  Alexandre Kostenko; Philippe Rauffet; Gilles Coppin
Journal:  Front Psychol       Date:  2022-01-06
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.