Analysing Behavioural and Eye-Tracking Data to Investigate Working Memory Load and Visuospatial Demands During Driving

Kelapanda, Nilma P. (2021) Analysing Behavioural and Eye-Tracking Data to Investigate Working Memory Load and Visuospatial Demands During Driving. Bachelor's Thesis, Artificial Intelligence.

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Abstract

Driving is a complex task, deeming research into adaptive automation, in which control of the car is split between the driver and a built-in system that takes over when necessary, a valuable study. For such a system, reliable measures of cognitive load, defined in this study as working memory load (WML) and visuospatial demands (VD), need to be determined in order to notify the system when the driver may need assistance. This study focused on analysing behavioural and eye-tracking data of participants in a simulated highway in order to assess cognitive load and effects on working memory (WM) performance and driving performance. The four research questions explored were the following; Does cognitive load have an influence on working memory performance? ; Does cognitive load have an influence on driving performance?; Can pupillometry predict cognitive load while driving? ; Does cognitive load have an influence on the frequency of speedometer checking? WML was manipulated by a speed regulation version of the n-back task. VD was manipulated through a construction site with narrower lanes. Results indicated a significant decrease in WM performance and frequency of speedometer checking as WML increased. Alongside this, a significant decrease in driving performance and increase in driving difficulty as VD increased was observed. Finally, it was found that pupil size was a predictor for WML.

Item Type:	Thesis (Bachelor's Thesis)
Supervisor name:	Borst, J.P.
Degree programme:	Artificial Intelligence
Thesis type:	Bachelor's Thesis
Language:	English
Date Deposited:	14 Jul 2021 08:21
Last Modified:	14 Jul 2021 08:21
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/25224

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