THE IMPORTANCE OF HUMAN MENTAL WORKLOAD IN WEB

DESIGN

Luca Longo, Fabio Rusconi, Lucia Noce and Stephen Barrett

Department of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland

Keywords:

Human Mental Workload, Interaction Design, Web Design, Usability, Human Factors.

Abstract:

The focus of this study is the introduction of the construct of Human Mental Workload (HMW) in Web

design, aimed at supporting current interaction design practices. An experiment has been conducted using

the original Wikipedia and Google web-interfaces, and using two slightly different versions. Three subjective

psychological mental workload assessment techniques (NASA-TLX, Workload Proﬁle and SWAT) with a

well-established assessments usability tool (SUS) have been adopted. T-tests have been performed to study the

statistical signiﬁcance of the original and modiﬁed web-pages, in terms of workload required by typical tasks

and perceived usability. Preliminary results show that, in one ideal case, increments of usability correspond to

decrements of generated workload, conﬁrming the negative impact of the structural changes on the interface.

In another case, changes are signiﬁcant in terms of usability but not in terms of generated workloads, thus

raising research questions and underlying the importance of Human Mental Workload in Interaction Design.

1 INTRODUCTION

Human Mental Workload (HMW) is a multidimen-

sional complex construct mainly applied in Cognitive

Science and sporadically used in Human Computer

Interaction. Although a well-established deﬁnition is

absent in the literature, the goal of measuring mental

workload is to quantify the mental cost of perform-

ing tasks and having estimates of system and oper-

ator performance. Intuitively, mental workload is the

amount of mental work necessary for a person to com-

plete a task over a period of time. To be more pre-

cise, the construct emerges from the interaction be-

tween the requirements of a given task, the circum-

stances under which it is performed, the context and

the skills, behaviours, emotional state and perceptions

of the operator (Kantowitz, 1988). In the ﬁeld of In-

teraction Design, the application of the construct of

Human Mental Workload may have important practi-

cal implications in the interface design process and in

the evaluation of system usability. For instance, as-

sessments of the mental workload of users on tasks

performed on a web-site may be useful for evaluating

both the behavior of end-users upon it and the usabil-

ity of the web-site itself.

This research investigates the application of the

construct of Human Mental Workload in the ﬁeld

of Web Design. In detail, the main objective is to

compare different HMW assessments techniques over

tasks performed on selected web-sites, along with the

study of their correlation with a well-known subjec-

tive assessments tool of interface usability. The issues

investigated are:

• analysis of the distribution of mental workload

levels over selected web-tasks, produced by three

subjective assessment procedures, along with the

study of their inter-correlations;

• examination of the correlation of the outputs pro-

duced by the three HMW procedures with the one

by a well-established usability assessment tech-

nique (SUS - Subjective Usability Scale)

• impact analysis of HMW in Web Design.

The reminder of this paper is organized as follows.

We review related works in the ﬁeld of Human Mental

Workload and we describe the main subjective assess-

ment tools used in the research. We analyse applica-

tions of HMW in Human-Computer Interaction. We

then introduce the experiments conducted in line with

the research questions showing the outcomes follow-

ing a critical discussion. We conclude highlighting

the reasons and advantages that HMW can potentially

bring in the ﬁeld of Interaction and Web Design.

403

Longo L., Rusconi F., Noce L. and Barrett S..

THE IMPORTANCE OF HUMAN MENTAL WORKLOAD IN WEB DESIGN.

DOI: 10.5220/0003960204030409

In Proceedings of the 8th International Conference on Web Information Systems and Technologies (WEBIST-2012), pages 403-409

ISBN: 978-989-8565-08-2

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

2 RELATED WORK

Human Mental Workload (HMW) is a multifaceted

complex construct mainly applied in psychology and

other cognitive sciences. A plethora of deﬁnitions ex-

ists in the literature (Hancock and Meshkati, 1988;

Wickens, 1987; Cain, 2007; Gopher and Donchin,

1986). Intuitively, mental workload or cognitive

workload is the amount of mental work necessary for

a person to complete a task over a given period of

time. Generally, it is not an inherent property, rather

it emerges from the interaction between the require-

ments of a task, the circumstances under which it is

performed, and the skills, behaviors and perceptions

of the operator. The operational and practical na-

ture of the construct of human mental workload, in

the last few decades, has been acquiring interest in

Neuro-science, Physiology and even Computer sci-

ence (Kramer and Sirevaag, 1987; Kantowitz, 1988;

Donnell and Eggemeier, 1998; Young and Stanton,

2001). There is a wide application ﬁeld (Donnell and

Eggemeier, 1998; Tracy and Albers, 2006; Xie and

Salvendy, 2000; Gwizdka, 2009a; Gwizdka, 2009b)

and this new research domain may have an important

impact in the future, above all in Human-Computer

Interaction. The concept has become increasingly

important since modern interactive systems and in-

terfaces may impose severe requirements on mental

workload or information-processing capabilities.

There exist three major types of mental work-

load measures: performance-based, subjective and

physiological. The rationale behind performance-

based measures is that performance on a selected sec-

ondary task will decrease as a function of the de-

mands of a selected primary task. Subjective mea-

sures include self-assessments using uni-dimensional

or multi-dimensional scales. The former consider a

measures of overall mental workload, the latter take

into consideration individual dimensions of mental

workload, therefore being more accurate in determin-

ing the source of any potential workload problem.

Physiological measures are based on the premise that

mental workload will generate changes in the body

such as pupil dilation, changes in skin conductance,

body pressure and heart rate. Although they are ac-

curate and can work on a continuous scale, the equip-

ment they require is generally impractical for experi-

ments as it requires trained staff.

In this paper we focus on subjective multi-

dimensional measures and we use three well-

established tools: The NASA Task Load Index

(NASA-TLX) (Kantowitz, 1988); The Simpliﬁed

Subjective Workload Assessment Technique (SWAT)

(Luximon and Goonetilleke, 2001); The Workload

Proﬁle (WP) (Tsang and Velazquez, 1996). In the

following paragraphs, we brieﬂy describe each tech-

nique, introducing the formal models in section 3.

NASA-TLX (Hart, 2006) uses six dimensions to

estimate mental workload: mental demand, physical

demand, temporal demand, performance, effort and

frustration. Each of these is in a scale from 0 to 100.

The ﬁnal mental workload index is a weighted av-

erage of the six areas that provide an overall score.

The weights are obtained via a paired comparisons

which requires the operator to choose which dimen-

sion, across all pairs with the six dimensions, is more

relevant to mental workload. The number of times a

dimension has been chosen by the operator represents

the weight of that dimension scale, for a given task

(Kantowitz, 1988).

SWAT is a subjective multi-dimensional rating

procedure that uses three areas to evaluate mental

workload: time load, mental effort load, psychologi-

cal stress load, each of them in a three-levels scale. In

this paper we have adopted a simpliﬁed version of the

SWAT model, the Continuous SWAT dimensions with

weight (Luximon and Goonetilleke, 2001) which uses

a paired comparisons among the three dimensions ex-

actly as in the NASA-TLX model. The ﬁnal mental

workload is the average of the weighted areas (Luxi-

mon and Goonetilleke, 2001).

WP is a subjective workload assessment tech-

nique, based on the Multiple Resource Theory (MRT)

of Wickens (Wickens, 1987). In this procedure eight

dimensions are considered: perceptual/central pro-

cessing, response selection and execution, spatial pro-

cessing, verbal processing, visual processing, audi-

tory processing, manual output and speech output.

The WP procedure asks the operators to provide the

proportion of attentional resources, in the range 0 to 1,

used after the execution of a task. The overall work-

load rating is computed summing each of the 8 scores.

The three subjective techniques have low imple-

mentation requirements along with low intrusiveness

and high subject acceptability. These peculiarities

have promoted new research in which the construct of

Human Mental Workload has been adopted for evalu-

ating alternative interfaces. Tracy and Albers adopted

three different techniques for measuring mental work-

load applied to web-site design: NASA-TLX, The

Sternberg Memory Test and a tapping test (Tracy and

Albers, 2006) (Albers, 2011). They proposes a tech-

nique to individuate sub-areas of a web-site, in which

end-users manifested higher mental workload during

interaction. In turn, this allowed designers to mod-

ify those critical regions for enhancing their interface.

Zhu and Hou (Zhu and Hou, 2009) noted how roles

can be useful in interface design and proposed a role-

WEBIST2012-8thInternationalConferenceonWebInformationSystemsandTechnologies

404

based method to measure the mental workload. This

can be applied in the ﬁeld of Human-Computer Inter-

action for dynamically adjusting workload levels of

humans to enhance their performance of interaction.

3 METHODOLOGY

To investigate the research issues, we have designed

four web-tasks as depicted in ﬁgure 1. Nineteen peo-

ple aged between 19 and 35 years with different cul-

tures, native language and ethnic background partic-

ipated in the experiment. Web-tasks were designed

on top of two major web-sites: wikipedia.com and

google.com.

(a) W1 (Original Wikipedia) (b) W2 (Mod. Wikipedia)

Figure 1: Web-interfaces used in experiment web-tasks.

Task W1 was designed to be performed on the

original interface of Wikipedia (screenshot 1 a) while

task W2 on a modiﬁed version (screenshot 1 b). Sim-

ilarly task G1 was designed to be executed on the

original google interface (screenshot 1 c) and task G2

on the modiﬁed version (screenshot 1 d). For the in-

terface of picture 1 b, the layout has been modiﬁed,

removing the left menu and the searching box from

the original wikipedia along with the various tabs at

the top of the page. For the interface of picture 1 d,

the structural changes involved the removal of the left

menu and the application of a different background.

Volunteers needed to naturally interact with the web-

browser and execute as best as they could the de-

signed tasks. After the completion of each task, they

were asked to ﬁll in 4 questionnaires (random order):

• NASA-TLX questionnaire, as shown in table 2,

along with the pair-wise comparisons among the

6 dimensions. The question related to the “physi-

cal demand” dimension (NT Q

) was omitted due

to the cognitive nature of tasks, thus its pair-wise

comparison was automatically assigned to the

other 5 dimensions (Answers scale: 0 [strongly

disagree] to 100 [strongly agree]);

• WP questionnaire, as in table 3 (Answers scale: 0

[strongly disagree] to 100 [strongly agree]);

• SWAT questionnaire, as described in table4.

Users were asked to choose, for each of the three

areas (time load, mental effort load, psychological

load) one of three possible levels;

• SUS questionnaire, as reported in (Brook, 2008)

and shown in table 5, for evaluating the usability

of the interface used in the performed task.

NASA-TLX. The NASA-TLX model is not based

on a simple average of the 6 questions of Table 2

(NTQs), rather on a weighted aggregation.

These questions are formally expressed as:

NT Q

: [0..100] ∈ ℵ, x

: {M|PD|T |E|F} x

: {P}

The weight of each of the 6 considered areas needs to

be computed. Users needed to decide, for each possi-

ble pair of the 6 areas (binomial coefﬁcient), ‘which of

the two contributed more to their workload during the

task’ (Example: Mental or Physical Demand? Phys-

ical Demand or Performance? and so forth). This

cross tabulation generates 15 preferences:





2!(6 −2)!

= 15

The weights are the number of preferences, for each

area, in the 15 answers set (number of times that each

factor was selected). They range from 0 (not relevant)

to 5 (more important than any other factor). Formally:

NTW

: [0..5] ∈ ℵ. The ﬁnal human mental workload score

(HMW) of users is computed by multiplying the score

of each NTQ question by its computed weight NTW

(Note the Performance value is brought back to the

original NASA-TLX scale):

NASA

HMW

: [0..100] ∈ ℜ

NASA

HMW

∑

NT Q

· NTW

∑

(100 − NTQ

) · NTW

WP - Workload Proﬁle. For each questions of the

Workload Proﬁle questionnaire (table 3) subjects pro-

vided a number between 0 and 100. A rating of 0 in-

dicates that the task placed no demand on the dimen-

sion being rated, while a rating of 1 means that the

task required full attention. Formally: W PQ

: [0..100] ∈ R

The ratings of each individual dimensions, that means

the answer for each of the questions in table 3, are

summed toward an overall mental workload rating.

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405

Table 1: Experiment Tasks on Google and Wikipedia.

Task # Description

W 1 Using www.wikipedia.com ﬁnd out how many people currently live in Sydney.

W 2 Only using http://en.wikipedia.org/wiki/Main Page ﬁnd out how many people currently live in Sydney.

G1 Using www.google.com ﬁnd how many years passed between the foundation of Apple Computer Inc. and the year of the 14th FIFA world cup.

G2 Using www.google.com ﬁnd out how many years passed between the foundation of the Microsoft Corp. and the year of the 23rd Olympic games.

Table 2: Nasa Task Load Index (NASA-TLX) questionnaire.

Label Question Area

NT Q

How much mental and perceptual activity was required (e.g. thinking, deciding, calculating, remembering, Mental

looking, searching, etc.)? Was the task easy or demanding, simple or complex, exacting or forgiving? Demand

NT Q

How much physical activity was required (e.g. pushing, pulling, turning, controlling, activating, etc.)? Physical

Was the task easy or demanding, slow or brisk, slack or strenuous, restful or laborious? Demand

NT Q

How much time pressure did you feel due to the rate or pace at which the tasks or task elements occurred? Temporal

Was the pace slow and leisurely or rapid and frantic? Demand

NT Q

How hard did you have to work (mentally and physically) to accomplish your level of performance? Effort

NT Q

How successful do you think you were in accomplishing the goals, of the task set by the experimenter (or yourself)?

Performance

How satisﬁed were you with your performance in accomplishing these goals?

NT Q

How insecure, discouraged, irritated, stressed and annoyed versus secure, gratiﬁed, content,

Frustration

relaxed and complacent did you feel during the task?

Table 3: Workload Proﬁle (WP) questionnaire.

Label Question Area

W PQ

How much attention was required for activities like remembering, problem-solving decision-making, Perceiving / Remembering /

perceiving (detecting, recognizing and identifying objects)? Solving / Deciding

W PQ

How much attention was required for selecting the proper response channel Selection /

(manual - keyboard/mouse, or speech - voice) and its execution? Execution of Response

W PQ

How much attention was required for spatial processing (spatially pay attention around you)? Task and Space

W PQ

How much attention was required for verbal material (eg. reading, processing linguistic material, Verbal

listening to verbal conversations)? Material

W PQ

How much attention was required for executing the task based on the information visually received (eyes)? Visual Resources

W PQ

How much attention was required for executing the task based on the information auditorily received (ears)? Auditory Resources

W PQ

How much attention was required for manually respond to the task (eg. keyboard/mouse usage)? Manual Response

W PQ

How much attention was required for producing the speech response Speech

(eg. engaging in a conversation, talk, answering questions)? Response

Table 4: Simpliﬁed Subjective Workload Assessment Technique (SWAT) questionnaire.

Label Possibilities Value Area

SWATQ

Often have spare time. Interruptions or overlap among activities occur infrequently or not at all. 1

Time LoadOccasionally have spare time. Interruptions or overlap among activities occur infrequently. 2

Almost never have spare time. Interruptions or overlap among activities are very frequent, or occur all the time. 3

SWATQ

Very little conscious mental effort or concentration required.

1 Mental

Activity is almost automatic, requiring little or no attention.

Moderate conscious mental effort or concentration required. Complexity of activity is moderately high

2 Effort Load

due to uncertainty, unpredictability, or unfamiliarity. Considerable attention required.

Extensive mental effort and concentration are necessary. Very complex activity required total attention 3

SWATQ

Little Confusion, risk, frustration, or anxiety exists and can be easily accommodated 1

Psychological

Moderate stress due to confusion, frustration, or anxiety noticeably adds to workload.

StressSigniﬁcant compensation is required to maintain adequate performance

High to very intense stress due to confusion, frustration, or anxiety.

LoadHigh extreme determination and self-control required

W P

HMW

: [0..8] ∈ R W P

HMW

∑

i=1

(W PQ

) ·

100

SWAT - Simpliﬁed Subjective Workload Assess-

ment Technique. For the SWAT questions (table

4) subjects provided a number between 1 and 3 for

selecting the appropriate option among 3 possibili-

ties. SWAT Q

: [1..3] ∈ N After they indicated a prefer-

ence, for each pair combination among the 3 dimen-

sions, (Time Load, Mental Effort and Psychological

stress). This cross tabulation generates 3 preferences,

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Table 5: System Usability Scale (SUS) questionnaire.

Label Question Label Question

SUSQ

I think that I would like to use this interface frequently SUSQ

I thought there was too much inconsistency in this interface

SUSQ

I found the interface unnecessarily complex SUSQ

I would imagine that most people would learn to use this interface quickly

SUSQ

I thought the interface was easy to use SUSQ

I found the interface very unmanageable (irritating or tiresome) to use

SUSQ

I think that I would need the support of a technical

person to be able to use this interface

SUSQ

I felt very conﬁdent using the interface

SUSQ

I found the various functions in this interface were

well integrated

SUSQ

I needed to learn a lot of things before I could get going with this interface

so each dimension has max 2 occurrences:





2!(3 − 2)!

= 3 SWATW

: [0..2] ∈ ℵ

The number of occurrences, for each dimension, are

used to weight the original scores, producing a ﬁnal

human mental workload assessment as in the follow:

SWAT

HMW

: [1..3] ∈ R SWAT

HMW



∑

i=1

(SWAT Q

·SWATW

)



SUS - System Usability Scale. The original an-

swers of the SUS questionnaires (Bangor et al., 2008)

use a Likert scale, bounded in the range 1 to 5. Volun-

teers were asked to answer the questions with a scale

ranged 0 to 100:

SU SQ

: [1..100] ∈ N Individual scores are not mean-

ingful on their own. For odd questions (SUS

with

i = {1|3|5|7|9}), the score contribution is the scale

position (SUSQ

) minus 1. For even questions (SUS

with i = {2|4|6|8|10}), the contribution is 5 minus the

scale position. Formally:

SU S

TOT

: [0..100] ∈ ℵ , i

= {1, 3, 5, 7, 9} i

= {2, 4, 6, 8, 10}

SU S

TOT



∑

(SU SQ

) +

∑

(100 − SUSQ

)



4 RESULTS AND DISCUSSION

Experimental results are shown in table 6 and in ﬁgure

2 while the correlations between the outcomes of each

computational model are presented in table 7.

Figure 2: Boxplot of data grouped by model.

The Paired T-Test procedure was used to compare

the mean difference between the results of the HMW-

based models (NASA-TLX, WP, SWAT) and the ones

of the usability model (SUS) on the tasks (W1 against

Table 6: Distributions of data grouped by model.

Task

NASA SWAT WP SUS

Avg Std Avg Std Avg Std Avg Std

W1 21.3 14.3 46.1 15.2 26.0 14.8 78.3 18.7

W2 52.4 13.4 74.3 13.2 38.7 15.0 34.3 22.5

G1 41.9 15.1 66.6 18.7 34.9 16.2 84.9 13.7

G2 43.0 14.7 66.5 19.4 33.7 15.7 45.2 19.3

Table 7: Correlations of models.

Task

NASA/ SUS/ WP/ SUS/ WP/ WP/

SWAT NASA NASA SWAT SWAT SUS

W1 0.75 -0.17 -0.10 -0.08 -0.07 -0.27

W2 0.52 -0.29 0.50 0.04 0.35 0.27

G1 0.82 -0.22 0.75 0.01 0.48 -0.14

G2 0.88 -0.03 0.80 -0.15 0.76 -0.12

W2, and G1 against G2). In particular, we tested the

null hypothesis H

with a conﬁdence interval CI =

95%. The results are listed in tables 8 and 9.

The experiments conducted are preliminary,

aimed at showing the potential of Human Mental

Workload in Interaction and Web Design. From ta-

ble 7 a clear correlation between the three HWM-

based tools used (NASA-TLX, WP, SWAT) against

the usability assessment technique (SUS) (Columns

3, 5, 7) does not appear. Further investigation needs

to be carried out to discover correlations (if any). On

the other hand, the three HMW-based models highly

correlate, underlying a common view about the gen-

erated workloads on tasks (Columns 2, 4, 6). This

suggests further experiments could consider only one

of them, reducing the volunteers’ answer-set. The

only exception is for task W1 for the correlations

WP/NASA and WP/SWAT: here the proportions of at-

tentional resources required by the task do not clearly

emerge. A possible explanation may rely in the un-

certainty faced by volunteers, in answering the WP

questions (table 3) for task W1. Tables 8 and 9 show

the paired T-test of the four models for the task con-

ducted on Wikipedia (W1 against W2) and on Google

(G1 against G2). The ﬁrst line (W1, W2) clearly

shows that the null hypothesis is rejected in apply-

ing the four models (NASA-TLX, WP, SWAT, SUS):

the two interfaces W1 and W2 have generated statis-

tically signiﬁcant workloads and usability scores. In

turn this fact might be interpreted, by a web-designer,

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407

Table 8: Paired T-test for NASA-TLX and WP.

Task

NASA WP

T P H

W1, W2 −5.98 < 0.001 Rej. −3.39 0.003 Rej.

G1, G2 −0.37 0.718 Acc. 0.81 0.428 Acc.

Table 9: Paired T-test for SWAT and SUS.

Task

SWAT SUS

T P H

W1, W2 −5.56 < 0.001 Rej. 6.59 < 0.001 Rej.

G1, G2 0.02 0.987 Acc. 7.57 0.000 Rej.

negatively: the structural changes developed in the in-

terface W2 negatively affect perceived usability and

have an higher impact on the required mental work-

load of end-users. The second line of both the tables

(G1, G2) shows that the null hypothesis is accepted

for the three HMW-based tools, underling no statis-

tical difference, in workload levels, between the two

interfaces. On the contrary, for the paired T-test of the

two SUS outcomes, the hypothesis is rejected: the us-

ability of the two interfaces (G1 and G2) are perceived

being statistically different. Considering the above in-

terpretations, some potential research question arise:

• Is SUS, (or other usability assessment tool), sufﬁ-

cient for designing usable interfaces?

• Can the application of Human Mental Workload

be a alternative or supporting procedure in the

ﬁeld of Interaction and Web Design?

5 CONCLUSIONS

The main aim of this contribution is to show the po-

tential of the construct of Human Mental Workload in

Interaction and Web Design. It has been shown how

assessments of workload can be achieved on web-

based tasks and how they can be applied for evalu-

ating the impact of structural changes on web-sites.

Three subjective psychological techniques for assess-

ing mental workload were described: NASA Task

Load Index, Workload Proﬁle and a simpliﬁed Sub-

jective Workload Assessment Technique. An exper-

iment on two popular web-sites’ interfaces was con-

ducted: Wikipedia and Google. Four similar informa-

tion search tasks were performed by 19 volunteers,

two on the original interfaces and two on modiﬁed

versions. Results show how, in the tasks performed on

Wikipedia, increments in required mental workload

correspond to decrements in usability perception and

vice-versa, underlying an inverted correlation. This

ideal case does not occur on the tasks performed on

the two different Google interfaces where, for a decre-

ment of perceived usability, the mental workload re-

mains stationary. This suggests that usability should

not be used in isolation for evaluating interactive in-

terfaces but instead an analysis of the behaviour of

end-users on typical web-based task, on a given inter-

face, should be accounted. Our proposal is for the ap-

plication of mental workload as a evaluation measure.

This preliminary evidence should be supported by fur-

ther investigations and applications. Future work will

be focused on further experiments on other web-sites

towards a general-applicable paradigm for aggregat-

ing workload and usability scores.

ACKNOWLEDGEMENTS

We are grateful to Noce L. and Rusconi F. who con-

tributed to the design and execution of experiments.

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