the influence of task characteristics on multiple objective and subjective cognitive load measures...

The Influence of Task Characteristics on Multiple Objective and Subjective Cognitive Load Measures

Mahdi MirhoseiniPierre-Majorique LégerSylvain Sénécal

Gmunden Retreat on NeuroISGmunden, AustriaJun 6-8, 2016

How can we use neurophysiological measures to uncover more aspects of cognitive load construct?

Copyright Mahdi Mirhoseini3

Workload and its consequenceWorking memory is the set of mental resources that people use to encode, activate, store, and manipulate information while they perform cognitive tasks (Baddeley 2003)

Workload reflects the interaction of mental demands imposed on operators by task (Cain 2007)

Workload has consequences: frustration, negative affects, mental fatigue, and user satisfaction (Mizuno et al., 2011, Gwizdka, 2010)


Measuring workloadSubjective workload measures prevent us from understanding the multiple variations of workload during a task and are subject to a retrospective bias

Measuring the mental workload construct has been a challenge to researchers to researchers in different fields

Researchers have compared subjective and objective mental workload and suggested that objective measures can provide a more comprehensive and richer understanding of the workload construct

However, thus far, research has only used one of many possible measures of objective workload when comparing it to subjective workload


Workload : Different perspective There are three types of cognitive load measures: subjective, performance, and physiological

Xie and Salvendy (2000) defined four types of workload:

Peak loadAverage loadAccumulated load, andOverall load


Hypotheses

H1: Task difficulty is positively associated with all workload types (Overall, Average,

Accumulated, Peak).

H2: Task uncertainty is positively associated with all workload types

(Overall, Average, Accumulated, Peak).


MethodologyA 2 (low or high task difficulty) X 2 (low or high task uncertainty) within-subject experiment was designed

Factors Low Uncertainty High Uncertainty

Simple5 non-perishable product

The same quantity as suggested in the recipe (4 people)

5 perishable product

The same quantity as suggested in the recipe (4 people)

Complex5 non-perishable product

Adjust quantities for 20 people

5 perishable product

Adjust quantities for 20 people


MethodologyN=10 (50% were male)

Tasks were randomly ordered

Timeline:


MethodologyMeasurement

Overall Load Cameron (2007)

Instantaneous load: A linear EEG algorithm, which includes calculating the ((delta+theta)/alpha) power ratio over a moving 2 second window and compare it with the average of previous 20 seconds Coyne et al., (2009)Accumulated load: The area under the instantaneous workload curve

Peak load: The number of times that the amplitude of instantaneous load exceeded 2.5 standard deviations of the instantaneous load


Results

Thus, H1 is supported.

H1 Regression results

Overall b= 0.88, p<0.01Average b=0.02, p<0.05

Accumulated b=44.45, p<0.01Number of Peaks b=4.87, p<0.05


Results

Thus, H2 is partially supported.

H2 Regression results

Overall b=0.51, p=0.18Average b=0.01, p=0.15

Accumulated b=35.24, p<0.05Number of Peaks b=3.12, p=0.15


DiscussionOur results suggest that all the extracted features of instantaneous load and the subjective measure of workload (i.e., Overall load) are sensitive to task difficulty; however only Accumulated load was able to capture the mental workload induced by task uncertainty

Posthoc Analysis:Four regression models with the same independent variables (Task difficulty and uncertainty) but with four different measures of workload as dependent variable (Overall, Accumulated, Average, and number of Peaks).


DiscussionResults

Dependent Variable

R2

Accumulated 21%Number of Peaks 16%

Overall 14%Average 5%

1) introducing a mental workload feature extraction method in order to benefit from the richness of EEG data

2) deriving three new metrics for measuring mental workload

Average load and overall load yield similar results while Accumulated load is a stronger indicator of the total workload experienced by users.Number of Peaks is also an appropriate metric to assess users’ mental

Summary Contributions

Copyright Nom de l’étudiant13


Next Steps1- Using Accumulated load as criteria for design:Testing multi search feature on an existing online shopping website

2- Testing primary versus recency effect

3- EFRP:Manipulating workload factors on a shoppingUsing users’ eye fixation as an event

the influence of task characteristics on multiple objective and subjective cognitive load measures...

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