University of Oulu

E. Ferreira et al., "Assessing real-time cognitive load based on psycho-physiological measures for younger and older adults," 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), Orlando, FL, 2014, pp. 39-48. doi: 10.1109/CCMB.2014.7020692

Assessing real-time cognitive load based on psycho-physiological measures for younger and older adults

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Author: Ferreira, Eija1; Ferreira, Denzil1; Kim, SeungJun2;
Organizations: 1Department of Computer Science and Engineering, University of Oulu, Finland
2Human-Computer Interaction Institute, Carnegie Mellon University, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe201710098938
Language: English
Published: IEEE, 2014
Publish Date: 2017-10-09
Description:

Abstract

We are increasingly in situations of divided attention, subject to interruptions, and having to deal with an abundance of information. Our cognitive load changes in these situations of divided attention, task interruption or multitasking; this is particularly true for older adults. To help mediate our finite attention resources in performing cognitive tasks, we have to be able to measure the real-time changes in the cognitive load of individuals. This paper investigates how to assess real-time cognitive load based on psycho-physiological measurements. We use two different cognitive tasks that test perceptual speed and visio-spatial cognitive processing capabilities, and build accurate models that differentiate an individual’s cognitive load (low and high) for both young and older adults. Our models perform well in assessing load every second with two different time windows: 10 seconds and 60 seconds, although less accurately for older participants. Our results show that it is possible to build a realtime assessment method for cognitive load. Based on these results, we discuss how to integrate such models into deployable systems that mediate attention effectively.

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ISBN: 978-1-4799-4549-8
Pages: 39 - 48
DOI: 10.1109/CCMB.2014.7020692
OADOI: https://oadoi.org/10.1109/CCMB.2014.7020692
Host publication: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) Proceedings
Conference: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
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