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

Ferreira, Eija; Ferreira, Denzil; Kim, SeungJun; Siirtola, Pekka; Röning, Juha; Forlizzi, Jodi F.; Dey, Anind K. (2014-12-09)
 
Avaa tiedosto
Lataukset: 

URL:
https://doi.org/10.1109/CCMB.2014.7020692

Ferreira, Eija
Ferreira, Denzil
Kim, SeungJun
Siirtola, Pekka
Röning, Juha
Forlizzi, Jodi F.
Dey, Anind K.
Institute of Electrical and Electronics Engineers
09.12.2014

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

https://rightsstatements.org/vocab/InC/1.0/
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1109/CCMB.2014.7020692
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe201710098938
Tiivistelmä

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.

Kokoelmat