E R G O N O M I C S I N D E S I G N bull S P R I N G 2 0 0 78

Cop

yrig

ht 2

007

by H

uman

Fac

tors

and

Erg

onom

ics

Soci

ety

Inc

All

righ

ts r

eser

ved

FEATURE AT A GLANCE In this articlewe describe a new soft-ware tool that was developed for modeling human performanceand mental workload in single- and dual-task situations The toolfeatures an interactive interface and is based on psychological theoryUsing this new modeling tool in most cases users can model andpredict human performance and workload by clicking buttons toselect options without needing to learn a new programming lan-guage They can also visualize the information-processing state ofthe model during simulation and compare and evaluate the sim-ulated human performance and mental workload for differentuser interface designs based on the simulation results

KEYWORDS user interface designmodeling toolqueuing network

In order to enhance system effectiveness and reducecosts it is necessary to integrate human factors knowl-edge into the design of a product or system as early aspossible during the development or design process(Feyen Liu Chaffin Jimmerson amp Joseh 1999)

Product design typically includes an iterative process thatinvolves developing prototypes testing them improving themto enhance the productrsquos usability and retesting until anacceptable prototype has been created (Magrab 1997)

Traditionally testing has involved labor-intensive experi-mental methods to assess user performance and workloadHowever we have recently witnessed a growing interest inthe use of computational models to predict performanceoutcomes (Olsen amp Olsen 1990) These methods ndash includingpaper-and-pencil calculation computer simulation andmathematical modeling ndash enable researchers to estimatehuman performance and mental workload without spendinga significant amount of time designing experimental materialsrecruiting subjects and conducting experiments Moreoverthese models may be used to predict human performance andworkload in some extreme situations which may be too dan-gerous or too unusual to be suitable for conducting humanexperiments

In this article we describe the outcome of our attempt todevelop an easy-to-use interface for one modeling tool ndashQueuing Network-Model Human Processor (QN-MHP) ndashfollowing an analysis of the skills and expectations of humanfactorsergonomics (HFE) practitioners The modelrsquos validityfor predicting performance in various types of tasks has beendemonstrated previously in a series of systematic experiments

Development and Use of PerformanceModeling Tools in HFE

Since the 1980s a number of computational models havebeen developed for psychological modeling product designand system evaluation These tools include KLM (CardMoran amp Newell 1980) GOMS (John amp Kieras 1996)CAT-HCI (Williams Hultman amp Graesser 1998) ACT-R(Anderson amp Lebiere 1998) EPIC (Meyer amp Kieras 1997a1997b) and QN-MHP (Liu Feyen amp Tsimhoni 2006 Wu ampLiu 2004a 2004b 2004c 2006a 2006b 2006c)

In addition several modeling tools with applications forHFE have been developed including iGEN (Emmerson2000) Micro Saint (Laughery 1989 Schunk 2000) MIDAS(Gore amp Corker 2002) and CSEES (Bolton amp Bass 2005) Wehave summarized these tools in Table 1

Notwithstanding the effectiveness of these models andtechniques it seems that they are not widely used in the humanfactors and human-computer interaction (HCI) communities(John et al 2004) One of the possible reasons might be thatprofessionals in these fields lack the necessary knowledge andskills Further in the time-stressed environment of commer-cial product development they are unlikely to have the timeto learn them

Accordingly in order to develop a modeling tool suitablefor researchers in human factors and HCI we believe it is crit-ical to test our assumptions and learn more about the skillsknowledge and expectations of current practitioners Onlywhen we take such knowledge about the users into consider-ation can we design tools that will receive wider acceptanceand use

The HFE Practitioner as UserWe began by conducting a small-scale survey based on 28

job advertisements posted on the two major HFE and HCIWeb sites in April 2006 the Human Factors and ErgonomicsSociety Career Center and the Association for ComputingMachineryrsquos SIGCHI Job Postings

The design of an interactive interfacefor this tool is based on the needsskills and expectations of human factors practitioners

Usability Makeover of aCognitive Modeling ToolB Y C H A N G X U W U amp Y I L I L I U

S P R I N G 2 0 0 7 bull E R G O N O M I C S I N D E S I G N 9

Figure 1 displays the proportion of available jobs in thesefields The majority of jobs are held by usability engineers andhuman factors engineers (68) and 14 of the jobs are forgraphic designers In the ldquootherrdquo category (18) are seniormanagers Web product managers project directors customercare personnel and sales engineers In these job advertise-ments the requirements mentioned most frequently forusability engineers and human factors engineers includedexperience with

bull development of prototypes and interaction models usingscenarios and storyboards

bull usability evaluation and testingbull user interface (UI) mockups HTML JavaScript and Visual

Basicbull Photoshop Illustrator Excel or other prototyping tools

Based on the data in this survey the lack of widespread useof modeling tools may not be too surprising It seems that thejob qualifications of most HFE and HCI practitioners often donot match the skills necessary for using the current modelingtools HFE professionals are familiar with designing and eval-uating various prototypes and HCI professionals typicallyhave skills in programming simulation and mathematicalmodeling

An ideal modeling tool that would meet the needs of HFEand HCI practitioners should employ the tools and methodswith which they are already familiar rather than requireadditional programming and mathematical skills Moreoverthe modeling tool should be capable of modeling bothsingle- and dual-task situations and should cover both humanperformance and mental workload

A Queing Network Model of HumanPerformance and Workload

QN-MHP is a simulation model of the cognitive systembased on the queuing network theory of human performancewhich has been adapted for modeling human behavior invarious tasks A queuing network is a network that is composedof servers (representing processors in the cognitive system forinformation processing) routes (representing the connectionsbetween these servers) and entities (representing the infor-mation that travels through and is processed by the network)It allows two or more servers to act serially in parallel or inany network arrangement Queuing network theory quantifiesmathematical properties of queuing networks and by treatingresponse time and multitask performance as special cases ofsuch networks it has managed to integrate a large number ofmathematical models within psychology (Liu 1996 1997)

In our simulation model of the cognitive system based onqueuing network theory ndash QN-MHP ndash response time is mod-eled in terms of entitiesrsquo waiting time and serversrsquo processingtime Mental workload is modeled in terms of subnetworksrsquoutilization We have used QN-MHP to simulate humanbehavior in real time including transcription typing (Wu ampLiu 2004a 2004b) psychological refractory period (Wu ampLiu 2004c) visual search (Lim amp Liu 2004) visual-manualtracking (Wu amp Liu 2006a) mental workload and humanperformance in driving (Liu et al 2006 Wu amp Liu 2006b2006c)

TABLE 1 A SUMMARY OF EXISTING MODELING TOOLS

Modeling Tools

iGEN(Emmerson 2000)

Micro Saint(Laughery 1989Schunk 2000)

MIDAS(Gore amp Corker 2002)

CSEES(Bolton amp Bass 2005)

Focus of Usage

Analyzing operational effectiveness in differentdomains (eg a battlespace) and decision aidingin human-machine interfaces (eg an interfacefor aircraft pilots)

Modeling human operator performance and interaction under changing conditions (eg modeling crew size for a helicopter)

Modeling and predicting human error especiallyin the aviation domain

Facilitating learning how to model air trafficconflict judgment task decision making signaldetection and rule-based navigation of agents

Feature

A modeling tool based on cognitive task analysis

A modeling software package based on the task network modeling method to predicthuman performance

An agent architecture composed of physicalcomponent agents and human operator agents

An integrated toolset designed to facilitate curricula and education related to human judgment and decision-making performancemodeling and evaluation

Usability Engineer and Human Factors Engineer

18

Graphic Designer14

Other

68

Figure 1 Distribution of jobs in human factorsergonomics andhuman-computer interaction

E R G O N O M I C S I N D E S I G N bull S P R I N G 2 0 0 71 0

One of the most important advantages of QN-MHP inmodeling multiple tasks is that multitask performance emergesas an outcome of multiple tasks (represented as multiplestreams of entities) traversing a network such as trucks andcars traveling on the same highway Multitask performanceand task interference emerge when two or more streams of

entities traverse the network concurrently and compete forservice at the various servers Accordingly there is no need todevise complex task-specific procedures to either interleaveproduction rules into a serial program or for an executiveprocess to interactively control task processes required byother cognitive architectures (Liu et al 2006)

Figure 2 A flow of screen shots of the modeling toolrsquos interface to choose a single or dual task

Single

(a) (b)

(d)(f)

(c)

(e)

Dual

S P R I N G 2 0 0 7 bull E R G O N O M I C S I N D E S I G N 1 1

The new user interface of QN-MHP was developed withVBA a common user interface rapid design tool that makesthe interface easier to use and more compatible with othercommonly used software and development tools includingMicrosoft Office software and Web design tools (eg Front-Page Visual Basic Visual C++) With our modeling tool inmost cases users need only the two frequently used toolsnoted earlier a storyboard tool developed with Visual Basicin Excel and Excel worksheets

In the next section we describe a detailed example toillustrate how to use QN-MHP to predict human performanceand mental workload in a dual task

Modeling a Dual Driving TaskSuppose users of the modeling tool wish to model a dual

task in driving where the primary task is a steering task inwhich the simulated driver steers a car on a freeway and thesecondary task is operation of a Global Positioning System(GPS) device while driving After seeing the welcome screenQN-MHPrsquos user simply clicks a button to indicate whether thetarget task is a single or a dual task (see Figure 2b) For eachtask (a single task or each component task of a dual task) the

tool allows the user to select an existing task module from thesoftware task library (including steering visual-manual track-ing or transcription typing) If no module is available the usercan define a new task to be modeled (see Figures 2c and 2d)

In this example the user chooses the dual-task option(steering and a new task) Next heshe interacts with thescreen as shown in Figure 2f On the left side of the interfaceis the existing steering module which can be modified byclicking the ldquoModify Steering Modulerdquo button On the rightside of the interface is the interactive storyboard tool whichcan be used to define a new task

From the interface shown in Figure 3 users simply demon-strate a task to be modeled with the storyboard method theyalready know Task 1 is displayed on the left side of the pageand Task 2 on the right side Figure 3 shows four potentialdisplays from the GPS system that QN-MHPrsquos user may beinterested in evaluating These include the main menu theaddress menu the list of addresses and the driving directions(Figures 3andash3d) The user of the modeling tool will need todefine the actions of users of the GPS system in each of theseuser interfaces before proceeding

Figure 3 A flow of screen shots of the modeling toolrsquos interface to define the tasksrsquos storyboard (a) Full screen (bndashd) right side of screen only

(a)

(c) (right half of the screen only)(b) (right half of the screen only) (d) (right half of the screen only)

E R G O N O M I C S I N D E S I G N bull S P R I N G 2 0 0 71 2

Figure 4 shows this detailed definition process in one ofthe four user interfaces of the GPS system The example is ofa QN-MHP user defining the actions of a target GPS user asheshe manipulates the mockups The QN-MHP user performsthe following actions

1 selects one of the images from the first UI mockup bychoosing an option in the box labeled ldquoUser interfacechange in this steprdquo then

2 selects a series of ldquoactionsrdquo that the GPS user might takeby pulling down the arrow in the Actions list box Forexample in order to model the GPS userrsquos selection of abutton on the interface the following actions might beselected

Action 1 ldquolook atrdquo Action 2 ldquostore (the caption of the button) to STM

(short-term memory)rdquo Action 3 ldquoretrieve (the caption of the button) from

STMrdquo Action 4 ldquoretrieve (the caption of the target button)

from LTM (long-term memory)rdquo Action 5 ldquodecide (if match then start action 6

else start action 7)rdquo Action 6 ldquoreach to (by hand)rdquo Action 7 ldquoupdate STMrdquo

3 defines whether or not the current series of actions (calleda step) have to wait for the previous step to finish andfinally

4 presses the ldquoNext Steprdquo button to initiate another series ofactions on the same mockup or on an object that belongsto a different mockup

After defining the tasks and the UI mockups users canbegin the simulation by clicking ldquoRun Simulationrdquo ThePromodel simulation software will generate human perform-ance and mental workload data as shown in Figure 5

QN-MHPrsquos users can compare and revise the current UIprototypes based on the simulation results (see Table 2) Forexample in the simulated subnetwork utilization of Design 1there is a peak in the utilization of the cognitive subnetworkwhich is regarded as an index of mental workload (Wu amp Liu2006b 2006c) By running the simulation model and observ-ing the activity of entities in the model again (eg in Table 2green entities queue up in front of server F) it becomes appar-ent that the peak might be caused by too many options on oneof the UI mockups (see Figure 3c) This increases the process-ing time of the cognitive subnetwork in that step and suggeststhat a design with fewer options (Design 2) may be preferableto the first design Simulating performance with both designsconfirms that Design 2 is better than Design 1 in terms of bothsimulated human performance and mental workload (seeTable 2)

At QN-MHPrsquos current stage of development if users needto evaluate a new device (for example a trackball) that is notcovered by the current device modules in the tool (simulatedkeyboard steering wheel joystick microphone pedal etc)they may need to write simple programs using the Promodelsimulation software However we are adding device modulesand features on the VBA user interface so users will be able toconveniently define new devices We are also creating featuresthat will allow QN-MHPrsquos users to describe properties of tar-get users of UI mockups (eg age perceptual motor speedmental operation speed handedness) and the conditions ofthe target environment in which the UI mockups are used(eg lighting condition noise level vibration level) with min-imal programming

ConclusionIn this article we have described a potentially useful and

proactive ergonomics tool that might be used in the early stageof user interface design to predict human performance andmental workload The modeling tool not only allows practi-

Figure 4 A screen shot of the modeling toolrsquos interface in defining actions

S P R I N G 2 0 0 7 bull E R G O N O M I C S I N D E S I G N 1 3

tioners to model both performance and mental workload insingle and dual tasks but also helps them to compare differentdesigns of UI mockups and improve the prototypes based onthe simulation results

Our new user interface is designed so that QN-MHPrsquosusers do not need to learn new programming languages to

perform modeling tasks in most cases The modeling can alsobe used in conjunction with other commonly used software

We believe that computational models of human per-formance and mental workload that are both easy to use andcomprehensive would significantly enhance their acceptanceand use by human factors and HCI professionals This in turn

Figure 5 A screen shot of the simulation model during the simulation process (see a short movie clip at httpwwwacsubuffaloedu~changxu)

LegendEntities Primary task entities

Secondary task entities

Plot Perceptual subnetworkCognitive subnetworkMotive subnetwork

1Subnetworkrsquos utilization as an index of mental workload (See Wu amp Liu 2006b 2006c for a detailed connection between the subnetworkrsquosunitization with the six scales in NASA-TLX mental workload measurements)

LPDDB (lane position deviationdifference)

Average TaskCompletion Time(Tasks 1 and 2)

Activities of Entities duringthe Simulation Process

Subnetwork Utilization1

Des

ign

1D

esig

n 2

TABLE 2 COMPARISON OF THE SIMULATION RESULTS OF DESIGNS 1 AND 2

S P R I N G 2 0 0 7 bull E R G O N O M I C S I N D E S I G N 9

Figure 1 displays the proportion of available jobs in thesefields The majority of jobs are held by usability engineers andhuman factors engineers (68) and 14 of the jobs are forgraphic designers In the ldquootherrdquo category (18) are seniormanagers Web product managers project directors customercare personnel and sales engineers In these job advertise-ments the requirements mentioned most frequently forusability engineers and human factors engineers includedexperience with

bull development of prototypes and interaction models usingscenarios and storyboards

bull usability evaluation and testingbull user interface (UI) mockups HTML JavaScript and Visual

Basicbull Photoshop Illustrator Excel or other prototyping tools

Based on the data in this survey the lack of widespread useof modeling tools may not be too surprising It seems that thejob qualifications of most HFE and HCI practitioners often donot match the skills necessary for using the current modelingtools HFE professionals are familiar with designing and eval-uating various prototypes and HCI professionals typicallyhave skills in programming simulation and mathematicalmodeling

An ideal modeling tool that would meet the needs of HFEand HCI practitioners should employ the tools and methodswith which they are already familiar rather than requireadditional programming and mathematical skills Moreoverthe modeling tool should be capable of modeling bothsingle- and dual-task situations and should cover both humanperformance and mental workload

A Queing Network Model of HumanPerformance and Workload

QN-MHP is a simulation model of the cognitive systembased on the queuing network theory of human performancewhich has been adapted for modeling human behavior invarious tasks A queuing network is a network that is composedof servers (representing processors in the cognitive system forinformation processing) routes (representing the connectionsbetween these servers) and entities (representing the infor-mation that travels through and is processed by the network)It allows two or more servers to act serially in parallel or inany network arrangement Queuing network theory quantifiesmathematical properties of queuing networks and by treatingresponse time and multitask performance as special cases ofsuch networks it has managed to integrate a large number ofmathematical models within psychology (Liu 1996 1997)

In our simulation model of the cognitive system based onqueuing network theory ndash QN-MHP ndash response time is mod-eled in terms of entitiesrsquo waiting time and serversrsquo processingtime Mental workload is modeled in terms of subnetworksrsquoutilization We have used QN-MHP to simulate humanbehavior in real time including transcription typing (Wu ampLiu 2004a 2004b) psychological refractory period (Wu ampLiu 2004c) visual search (Lim amp Liu 2004) visual-manualtracking (Wu amp Liu 2006a) mental workload and humanperformance in driving (Liu et al 2006 Wu amp Liu 2006b2006c)

TABLE 1 A SUMMARY OF EXISTING MODELING TOOLS

Modeling Tools

iGEN(Emmerson 2000)

Micro Saint(Laughery 1989Schunk 2000)

MIDAS(Gore amp Corker 2002)

CSEES(Bolton amp Bass 2005)

Focus of Usage

Analyzing operational effectiveness in differentdomains (eg a battlespace) and decision aidingin human-machine interfaces (eg an interfacefor aircraft pilots)

Modeling human operator performance and interaction under changing conditions (eg modeling crew size for a helicopter)

Modeling and predicting human error especiallyin the aviation domain

Facilitating learning how to model air trafficconflict judgment task decision making signaldetection and rule-based navigation of agents

Feature

A modeling tool based on cognitive task analysis

A modeling software package based on the task network modeling method to predicthuman performance

An agent architecture composed of physicalcomponent agents and human operator agents

An integrated toolset designed to facilitate curricula and education related to human judgment and decision-making performancemodeling and evaluation

Usability Engineer and Human Factors Engineer

18

Graphic Designer14

Other

68

Figure 1 Distribution of jobs in human factorsergonomics andhuman-computer interaction

E R G O N O M I C S I N D E S I G N bull S P R I N G 2 0 0 71 0

One of the most important advantages of QN-MHP inmodeling multiple tasks is that multitask performance emergesas an outcome of multiple tasks (represented as multiplestreams of entities) traversing a network such as trucks andcars traveling on the same highway Multitask performanceand task interference emerge when two or more streams of

entities traverse the network concurrently and compete forservice at the various servers Accordingly there is no need todevise complex task-specific procedures to either interleaveproduction rules into a serial program or for an executiveprocess to interactively control task processes required byother cognitive architectures (Liu et al 2006)

Figure 2 A flow of screen shots of the modeling toolrsquos interface to choose a single or dual task

Single

(a) (b)

(d)(f)

(c)

(e)

Dual

S P R I N G 2 0 0 7 bull E R G O N O M I C S I N D E S I G N 1 1

The new user interface of QN-MHP was developed withVBA a common user interface rapid design tool that makesthe interface easier to use and more compatible with othercommonly used software and development tools includingMicrosoft Office software and Web design tools (eg Front-Page Visual Basic Visual C++) With our modeling tool inmost cases users need only the two frequently used toolsnoted earlier a storyboard tool developed with Visual Basicin Excel and Excel worksheets

In the next section we describe a detailed example toillustrate how to use QN-MHP to predict human performanceand mental workload in a dual task

Modeling a Dual Driving TaskSuppose users of the modeling tool wish to model a dual

task in driving where the primary task is a steering task inwhich the simulated driver steers a car on a freeway and thesecondary task is operation of a Global Positioning System(GPS) device while driving After seeing the welcome screenQN-MHPrsquos user simply clicks a button to indicate whether thetarget task is a single or a dual task (see Figure 2b) For eachtask (a single task or each component task of a dual task) the

tool allows the user to select an existing task module from thesoftware task library (including steering visual-manual track-ing or transcription typing) If no module is available the usercan define a new task to be modeled (see Figures 2c and 2d)

In this example the user chooses the dual-task option(steering and a new task) Next heshe interacts with thescreen as shown in Figure 2f On the left side of the interfaceis the existing steering module which can be modified byclicking the ldquoModify Steering Modulerdquo button On the rightside of the interface is the interactive storyboard tool whichcan be used to define a new task

From the interface shown in Figure 3 users simply demon-strate a task to be modeled with the storyboard method theyalready know Task 1 is displayed on the left side of the pageand Task 2 on the right side Figure 3 shows four potentialdisplays from the GPS system that QN-MHPrsquos user may beinterested in evaluating These include the main menu theaddress menu the list of addresses and the driving directions(Figures 3andash3d) The user of the modeling tool will need todefine the actions of users of the GPS system in each of theseuser interfaces before proceeding

Figure 3 A flow of screen shots of the modeling toolrsquos interface to define the tasksrsquos storyboard (a) Full screen (bndashd) right side of screen only

(a)

(c) (right half of the screen only)(b) (right half of the screen only) (d) (right half of the screen only)

E R G O N O M I C S I N D E S I G N bull S P R I N G 2 0 0 71 2

Figure 4 shows this detailed definition process in one ofthe four user interfaces of the GPS system The example is ofa QN-MHP user defining the actions of a target GPS user asheshe manipulates the mockups The QN-MHP user performsthe following actions

1 selects one of the images from the first UI mockup bychoosing an option in the box labeled ldquoUser interfacechange in this steprdquo then

2 selects a series of ldquoactionsrdquo that the GPS user might takeby pulling down the arrow in the Actions list box Forexample in order to model the GPS userrsquos selection of abutton on the interface the following actions might beselected

Action 1 ldquolook atrdquo Action 2 ldquostore (the caption of the button) to STM

(short-term memory)rdquo Action 3 ldquoretrieve (the caption of the button) from

STMrdquo Action 4 ldquoretrieve (the caption of the target button)

from LTM (long-term memory)rdquo Action 5 ldquodecide (if match then start action 6

else start action 7)rdquo Action 6 ldquoreach to (by hand)rdquo Action 7 ldquoupdate STMrdquo

3 defines whether or not the current series of actions (calleda step) have to wait for the previous step to finish andfinally

4 presses the ldquoNext Steprdquo button to initiate another series ofactions on the same mockup or on an object that belongsto a different mockup

After defining the tasks and the UI mockups users canbegin the simulation by clicking ldquoRun Simulationrdquo ThePromodel simulation software will generate human perform-ance and mental workload data as shown in Figure 5

QN-MHPrsquos users can compare and revise the current UIprototypes based on the simulation results (see Table 2) Forexample in the simulated subnetwork utilization of Design 1there is a peak in the utilization of the cognitive subnetworkwhich is regarded as an index of mental workload (Wu amp Liu2006b 2006c) By running the simulation model and observ-ing the activity of entities in the model again (eg in Table 2green entities queue up in front of server F) it becomes appar-ent that the peak might be caused by too many options on oneof the UI mockups (see Figure 3c) This increases the process-ing time of the cognitive subnetwork in that step and suggeststhat a design with fewer options (Design 2) may be preferableto the first design Simulating performance with both designsconfirms that Design 2 is better than Design 1 in terms of bothsimulated human performance and mental workload (seeTable 2)

At QN-MHPrsquos current stage of development if users needto evaluate a new device (for example a trackball) that is notcovered by the current device modules in the tool (simulatedkeyboard steering wheel joystick microphone pedal etc)they may need to write simple programs using the Promodelsimulation software However we are adding device modulesand features on the VBA user interface so users will be able toconveniently define new devices We are also creating featuresthat will allow QN-MHPrsquos users to describe properties of tar-get users of UI mockups (eg age perceptual motor speedmental operation speed handedness) and the conditions ofthe target environment in which the UI mockups are used(eg lighting condition noise level vibration level) with min-imal programming

ConclusionIn this article we have described a potentially useful and

proactive ergonomics tool that might be used in the early stageof user interface design to predict human performance andmental workload The modeling tool not only allows practi-

Figure 4 A screen shot of the modeling toolrsquos interface in defining actions

S P R I N G 2 0 0 7 bull E R G O N O M I C S I N D E S I G N 1 3

tioners to model both performance and mental workload insingle and dual tasks but also helps them to compare differentdesigns of UI mockups and improve the prototypes based onthe simulation results

Our new user interface is designed so that QN-MHPrsquosusers do not need to learn new programming languages to

perform modeling tasks in most cases The modeling can alsobe used in conjunction with other commonly used software

We believe that computational models of human per-formance and mental workload that are both easy to use andcomprehensive would significantly enhance their acceptanceand use by human factors and HCI professionals This in turn

Figure 5 A screen shot of the simulation model during the simulation process (see a short movie clip at httpwwwacsubuffaloedu~changxu)

LegendEntities Primary task entities

Secondary task entities

Plot Perceptual subnetworkCognitive subnetworkMotive subnetwork

1Subnetworkrsquos utilization as an index of mental workload (See Wu amp Liu 2006b 2006c for a detailed connection between the subnetworkrsquosunitization with the six scales in NASA-TLX mental workload measurements)

LPDDB (lane position deviationdifference)

Average TaskCompletion Time(Tasks 1 and 2)

Activities of Entities duringthe Simulation Process

Subnetwork Utilization1

Des

ign

1D

esig

n 2

TABLE 2 COMPARISON OF THE SIMULATION RESULTS OF DESIGNS 1 AND 2

E R G O N O M I C S I N D E S I G N bull S P R I N G 2 0 0 71 0

One of the most important advantages of QN-MHP inmodeling multiple tasks is that multitask performance emergesas an outcome of multiple tasks (represented as multiplestreams of entities) traversing a network such as trucks andcars traveling on the same highway Multitask performanceand task interference emerge when two or more streams of

entities traverse the network concurrently and compete forservice at the various servers Accordingly there is no need todevise complex task-specific procedures to either interleaveproduction rules into a serial program or for an executiveprocess to interactively control task processes required byother cognitive architectures (Liu et al 2006)

Figure 2 A flow of screen shots of the modeling toolrsquos interface to choose a single or dual task

Single

(a) (b)

(d)(f)

(c)

(e)

Dual

S P R I N G 2 0 0 7 bull E R G O N O M I C S I N D E S I G N 1 1

The new user interface of QN-MHP was developed withVBA a common user interface rapid design tool that makesthe interface easier to use and more compatible with othercommonly used software and development tools includingMicrosoft Office software and Web design tools (eg Front-Page Visual Basic Visual C++) With our modeling tool inmost cases users need only the two frequently used toolsnoted earlier a storyboard tool developed with Visual Basicin Excel and Excel worksheets

In the next section we describe a detailed example toillustrate how to use QN-MHP to predict human performanceand mental workload in a dual task

Modeling a Dual Driving TaskSuppose users of the modeling tool wish to model a dual

task in driving where the primary task is a steering task inwhich the simulated driver steers a car on a freeway and thesecondary task is operation of a Global Positioning System(GPS) device while driving After seeing the welcome screenQN-MHPrsquos user simply clicks a button to indicate whether thetarget task is a single or a dual task (see Figure 2b) For eachtask (a single task or each component task of a dual task) the

tool allows the user to select an existing task module from thesoftware task library (including steering visual-manual track-ing or transcription typing) If no module is available the usercan define a new task to be modeled (see Figures 2c and 2d)

In this example the user chooses the dual-task option(steering and a new task) Next heshe interacts with thescreen as shown in Figure 2f On the left side of the interfaceis the existing steering module which can be modified byclicking the ldquoModify Steering Modulerdquo button On the rightside of the interface is the interactive storyboard tool whichcan be used to define a new task

From the interface shown in Figure 3 users simply demon-strate a task to be modeled with the storyboard method theyalready know Task 1 is displayed on the left side of the pageand Task 2 on the right side Figure 3 shows four potentialdisplays from the GPS system that QN-MHPrsquos user may beinterested in evaluating These include the main menu theaddress menu the list of addresses and the driving directions(Figures 3andash3d) The user of the modeling tool will need todefine the actions of users of the GPS system in each of theseuser interfaces before proceeding

Figure 3 A flow of screen shots of the modeling toolrsquos interface to define the tasksrsquos storyboard (a) Full screen (bndashd) right side of screen only

(a)

(c) (right half of the screen only)(b) (right half of the screen only) (d) (right half of the screen only)

E R G O N O M I C S I N D E S I G N bull S P R I N G 2 0 0 71 2

Figure 4 shows this detailed definition process in one ofthe four user interfaces of the GPS system The example is ofa QN-MHP user defining the actions of a target GPS user asheshe manipulates the mockups The QN-MHP user performsthe following actions

1 selects one of the images from the first UI mockup bychoosing an option in the box labeled ldquoUser interfacechange in this steprdquo then

2 selects a series of ldquoactionsrdquo that the GPS user might takeby pulling down the arrow in the Actions list box Forexample in order to model the GPS userrsquos selection of abutton on the interface the following actions might beselected

Action 1 ldquolook atrdquo Action 2 ldquostore (the caption of the button) to STM

(short-term memory)rdquo Action 3 ldquoretrieve (the caption of the button) from

STMrdquo Action 4 ldquoretrieve (the caption of the target button)

from LTM (long-term memory)rdquo Action 5 ldquodecide (if match then start action 6

else start action 7)rdquo Action 6 ldquoreach to (by hand)rdquo Action 7 ldquoupdate STMrdquo

3 defines whether or not the current series of actions (calleda step) have to wait for the previous step to finish andfinally

4 presses the ldquoNext Steprdquo button to initiate another series ofactions on the same mockup or on an object that belongsto a different mockup

After defining the tasks and the UI mockups users canbegin the simulation by clicking ldquoRun Simulationrdquo ThePromodel simulation software will generate human perform-ance and mental workload data as shown in Figure 5

QN-MHPrsquos users can compare and revise the current UIprototypes based on the simulation results (see Table 2) Forexample in the simulated subnetwork utilization of Design 1there is a peak in the utilization of the cognitive subnetworkwhich is regarded as an index of mental workload (Wu amp Liu2006b 2006c) By running the simulation model and observ-ing the activity of entities in the model again (eg in Table 2green entities queue up in front of server F) it becomes appar-ent that the peak might be caused by too many options on oneof the UI mockups (see Figure 3c) This increases the process-ing time of the cognitive subnetwork in that step and suggeststhat a design with fewer options (Design 2) may be preferableto the first design Simulating performance with both designsconfirms that Design 2 is better than Design 1 in terms of bothsimulated human performance and mental workload (seeTable 2)

At QN-MHPrsquos current stage of development if users needto evaluate a new device (for example a trackball) that is notcovered by the current device modules in the tool (simulatedkeyboard steering wheel joystick microphone pedal etc)they may need to write simple programs using the Promodelsimulation software However we are adding device modulesand features on the VBA user interface so users will be able toconveniently define new devices We are also creating featuresthat will allow QN-MHPrsquos users to describe properties of tar-get users of UI mockups (eg age perceptual motor speedmental operation speed handedness) and the conditions ofthe target environment in which the UI mockups are used(eg lighting condition noise level vibration level) with min-imal programming

ConclusionIn this article we have described a potentially useful and

proactive ergonomics tool that might be used in the early stageof user interface design to predict human performance andmental workload The modeling tool not only allows practi-

Figure 4 A screen shot of the modeling toolrsquos interface in defining actions

S P R I N G 2 0 0 7 bull E R G O N O M I C S I N D E S I G N 1 3

tioners to model both performance and mental workload insingle and dual tasks but also helps them to compare differentdesigns of UI mockups and improve the prototypes based onthe simulation results

Our new user interface is designed so that QN-MHPrsquosusers do not need to learn new programming languages to

perform modeling tasks in most cases The modeling can alsobe used in conjunction with other commonly used software

We believe that computational models of human per-formance and mental workload that are both easy to use andcomprehensive would significantly enhance their acceptanceand use by human factors and HCI professionals This in turn

Figure 5 A screen shot of the simulation model during the simulation process (see a short movie clip at httpwwwacsubuffaloedu~changxu)

LegendEntities Primary task entities

Secondary task entities

Plot Perceptual subnetworkCognitive subnetworkMotive subnetwork

1Subnetworkrsquos utilization as an index of mental workload (See Wu amp Liu 2006b 2006c for a detailed connection between the subnetworkrsquosunitization with the six scales in NASA-TLX mental workload measurements)

LPDDB (lane position deviationdifference)

Average TaskCompletion Time(Tasks 1 and 2)

Activities of Entities duringthe Simulation Process

Subnetwork Utilization1

Des

ign

1D

esig

n 2

TABLE 2 COMPARISON OF THE SIMULATION RESULTS OF DESIGNS 1 AND 2

S P R I N G 2 0 0 7 bull E R G O N O M I C S I N D E S I G N 1 1

The new user interface of QN-MHP was developed withVBA a common user interface rapid design tool that makesthe interface easier to use and more compatible with othercommonly used software and development tools includingMicrosoft Office software and Web design tools (eg Front-Page Visual Basic Visual C++) With our modeling tool inmost cases users need only the two frequently used toolsnoted earlier a storyboard tool developed with Visual Basicin Excel and Excel worksheets

In the next section we describe a detailed example toillustrate how to use QN-MHP to predict human performanceand mental workload in a dual task

Modeling a Dual Driving TaskSuppose users of the modeling tool wish to model a dual

task in driving where the primary task is a steering task inwhich the simulated driver steers a car on a freeway and thesecondary task is operation of a Global Positioning System(GPS) device while driving After seeing the welcome screenQN-MHPrsquos user simply clicks a button to indicate whether thetarget task is a single or a dual task (see Figure 2b) For eachtask (a single task or each component task of a dual task) the

tool allows the user to select an existing task module from thesoftware task library (including steering visual-manual track-ing or transcription typing) If no module is available the usercan define a new task to be modeled (see Figures 2c and 2d)

In this example the user chooses the dual-task option(steering and a new task) Next heshe interacts with thescreen as shown in Figure 2f On the left side of the interfaceis the existing steering module which can be modified byclicking the ldquoModify Steering Modulerdquo button On the rightside of the interface is the interactive storyboard tool whichcan be used to define a new task

From the interface shown in Figure 3 users simply demon-strate a task to be modeled with the storyboard method theyalready know Task 1 is displayed on the left side of the pageand Task 2 on the right side Figure 3 shows four potentialdisplays from the GPS system that QN-MHPrsquos user may beinterested in evaluating These include the main menu theaddress menu the list of addresses and the driving directions(Figures 3andash3d) The user of the modeling tool will need todefine the actions of users of the GPS system in each of theseuser interfaces before proceeding

Figure 3 A flow of screen shots of the modeling toolrsquos interface to define the tasksrsquos storyboard (a) Full screen (bndashd) right side of screen only

(a)

(c) (right half of the screen only)(b) (right half of the screen only) (d) (right half of the screen only)

E R G O N O M I C S I N D E S I G N bull S P R I N G 2 0 0 71 2

Figure 4 shows this detailed definition process in one ofthe four user interfaces of the GPS system The example is ofa QN-MHP user defining the actions of a target GPS user asheshe manipulates the mockups The QN-MHP user performsthe following actions

1 selects one of the images from the first UI mockup bychoosing an option in the box labeled ldquoUser interfacechange in this steprdquo then

2 selects a series of ldquoactionsrdquo that the GPS user might takeby pulling down the arrow in the Actions list box Forexample in order to model the GPS userrsquos selection of abutton on the interface the following actions might beselected

Action 1 ldquolook atrdquo Action 2 ldquostore (the caption of the button) to STM

(short-term memory)rdquo Action 3 ldquoretrieve (the caption of the button) from

STMrdquo Action 4 ldquoretrieve (the caption of the target button)

from LTM (long-term memory)rdquo Action 5 ldquodecide (if match then start action 6

else start action 7)rdquo Action 6 ldquoreach to (by hand)rdquo Action 7 ldquoupdate STMrdquo

3 defines whether or not the current series of actions (calleda step) have to wait for the previous step to finish andfinally

4 presses the ldquoNext Steprdquo button to initiate another series ofactions on the same mockup or on an object that belongsto a different mockup

After defining the tasks and the UI mockups users canbegin the simulation by clicking ldquoRun Simulationrdquo ThePromodel simulation software will generate human perform-ance and mental workload data as shown in Figure 5

QN-MHPrsquos users can compare and revise the current UIprototypes based on the simulation results (see Table 2) Forexample in the simulated subnetwork utilization of Design 1there is a peak in the utilization of the cognitive subnetworkwhich is regarded as an index of mental workload (Wu amp Liu2006b 2006c) By running the simulation model and observ-ing the activity of entities in the model again (eg in Table 2green entities queue up in front of server F) it becomes appar-ent that the peak might be caused by too many options on oneof the UI mockups (see Figure 3c) This increases the process-ing time of the cognitive subnetwork in that step and suggeststhat a design with fewer options (Design 2) may be preferableto the first design Simulating performance with both designsconfirms that Design 2 is better than Design 1 in terms of bothsimulated human performance and mental workload (seeTable 2)

At QN-MHPrsquos current stage of development if users needto evaluate a new device (for example a trackball) that is notcovered by the current device modules in the tool (simulatedkeyboard steering wheel joystick microphone pedal etc)they may need to write simple programs using the Promodelsimulation software However we are adding device modulesand features on the VBA user interface so users will be able toconveniently define new devices We are also creating featuresthat will allow QN-MHPrsquos users to describe properties of tar-get users of UI mockups (eg age perceptual motor speedmental operation speed handedness) and the conditions ofthe target environment in which the UI mockups are used(eg lighting condition noise level vibration level) with min-imal programming

ConclusionIn this article we have described a potentially useful and

proactive ergonomics tool that might be used in the early stageof user interface design to predict human performance andmental workload The modeling tool not only allows practi-

Figure 4 A screen shot of the modeling toolrsquos interface in defining actions

S P R I N G 2 0 0 7 bull E R G O N O M I C S I N D E S I G N 1 3

tioners to model both performance and mental workload insingle and dual tasks but also helps them to compare differentdesigns of UI mockups and improve the prototypes based onthe simulation results

Our new user interface is designed so that QN-MHPrsquosusers do not need to learn new programming languages to

perform modeling tasks in most cases The modeling can alsobe used in conjunction with other commonly used software

We believe that computational models of human per-formance and mental workload that are both easy to use andcomprehensive would significantly enhance their acceptanceand use by human factors and HCI professionals This in turn

Figure 5 A screen shot of the simulation model during the simulation process (see a short movie clip at httpwwwacsubuffaloedu~changxu)

LegendEntities Primary task entities

Secondary task entities

Plot Perceptual subnetworkCognitive subnetworkMotive subnetwork

1Subnetworkrsquos utilization as an index of mental workload (See Wu amp Liu 2006b 2006c for a detailed connection between the subnetworkrsquosunitization with the six scales in NASA-TLX mental workload measurements)

LPDDB (lane position deviationdifference)

Average TaskCompletion Time(Tasks 1 and 2)

Activities of Entities duringthe Simulation Process

Subnetwork Utilization1

Des

ign

1D

esig

n 2

TABLE 2 COMPARISON OF THE SIMULATION RESULTS OF DESIGNS 1 AND 2

E R G O N O M I C S I N D E S I G N bull S P R I N G 2 0 0 71 2

Figure 4 shows this detailed definition process in one ofthe four user interfaces of the GPS system The example is ofa QN-MHP user defining the actions of a target GPS user asheshe manipulates the mockups The QN-MHP user performsthe following actions

1 selects one of the images from the first UI mockup bychoosing an option in the box labeled ldquoUser interfacechange in this steprdquo then

2 selects a series of ldquoactionsrdquo that the GPS user might takeby pulling down the arrow in the Actions list box Forexample in order to model the GPS userrsquos selection of abutton on the interface the following actions might beselected

Action 1 ldquolook atrdquo Action 2 ldquostore (the caption of the button) to STM

(short-term memory)rdquo Action 3 ldquoretrieve (the caption of the button) from

STMrdquo Action 4 ldquoretrieve (the caption of the target button)

from LTM (long-term memory)rdquo Action 5 ldquodecide (if match then start action 6

else start action 7)rdquo Action 6 ldquoreach to (by hand)rdquo Action 7 ldquoupdate STMrdquo

3 defines whether or not the current series of actions (calleda step) have to wait for the previous step to finish andfinally

4 presses the ldquoNext Steprdquo button to initiate another series ofactions on the same mockup or on an object that belongsto a different mockup

After defining the tasks and the UI mockups users canbegin the simulation by clicking ldquoRun Simulationrdquo ThePromodel simulation software will generate human perform-ance and mental workload data as shown in Figure 5

QN-MHPrsquos users can compare and revise the current UIprototypes based on the simulation results (see Table 2) Forexample in the simulated subnetwork utilization of Design 1there is a peak in the utilization of the cognitive subnetworkwhich is regarded as an index of mental workload (Wu amp Liu2006b 2006c) By running the simulation model and observ-ing the activity of entities in the model again (eg in Table 2green entities queue up in front of server F) it becomes appar-ent that the peak might be caused by too many options on oneof the UI mockups (see Figure 3c) This increases the process-ing time of the cognitive subnetwork in that step and suggeststhat a design with fewer options (Design 2) may be preferableto the first design Simulating performance with both designsconfirms that Design 2 is better than Design 1 in terms of bothsimulated human performance and mental workload (seeTable 2)

At QN-MHPrsquos current stage of development if users needto evaluate a new device (for example a trackball) that is notcovered by the current device modules in the tool (simulatedkeyboard steering wheel joystick microphone pedal etc)they may need to write simple programs using the Promodelsimulation software However we are adding device modulesand features on the VBA user interface so users will be able toconveniently define new devices We are also creating featuresthat will allow QN-MHPrsquos users to describe properties of tar-get users of UI mockups (eg age perceptual motor speedmental operation speed handedness) and the conditions ofthe target environment in which the UI mockups are used(eg lighting condition noise level vibration level) with min-imal programming

ConclusionIn this article we have described a potentially useful and

proactive ergonomics tool that might be used in the early stageof user interface design to predict human performance andmental workload The modeling tool not only allows practi-

Figure 4 A screen shot of the modeling toolrsquos interface in defining actions

S P R I N G 2 0 0 7 bull E R G O N O M I C S I N D E S I G N 1 3

tioners to model both performance and mental workload insingle and dual tasks but also helps them to compare differentdesigns of UI mockups and improve the prototypes based onthe simulation results

Our new user interface is designed so that QN-MHPrsquosusers do not need to learn new programming languages to

perform modeling tasks in most cases The modeling can alsobe used in conjunction with other commonly used software

We believe that computational models of human per-formance and mental workload that are both easy to use andcomprehensive would significantly enhance their acceptanceand use by human factors and HCI professionals This in turn

Figure 5 A screen shot of the simulation model during the simulation process (see a short movie clip at httpwwwacsubuffaloedu~changxu)

LegendEntities Primary task entities

Secondary task entities

Plot Perceptual subnetworkCognitive subnetworkMotive subnetwork

1Subnetworkrsquos utilization as an index of mental workload (See Wu amp Liu 2006b 2006c for a detailed connection between the subnetworkrsquosunitization with the six scales in NASA-TLX mental workload measurements)

LPDDB (lane position deviationdifference)

Average TaskCompletion Time(Tasks 1 and 2)

Activities of Entities duringthe Simulation Process

Subnetwork Utilization1

Des

ign

1D

esig

n 2

TABLE 2 COMPARISON OF THE SIMULATION RESULTS OF DESIGNS 1 AND 2

S P R I N G 2 0 0 7 bull E R G O N O M I C S I N D E S I G N 1 3

tioners to model both performance and mental workload insingle and dual tasks but also helps them to compare differentdesigns of UI mockups and improve the prototypes based onthe simulation results

Our new user interface is designed so that QN-MHPrsquosusers do not need to learn new programming languages to

perform modeling tasks in most cases The modeling can alsobe used in conjunction with other commonly used software

We believe that computational models of human per-formance and mental workload that are both easy to use andcomprehensive would significantly enhance their acceptanceand use by human factors and HCI professionals This in turn

Figure 5 A screen shot of the simulation model during the simulation process (see a short movie clip at httpwwwacsubuffaloedu~changxu)

LegendEntities Primary task entities

Secondary task entities

Plot Perceptual subnetworkCognitive subnetworkMotive subnetwork

1Subnetworkrsquos utilization as an index of mental workload (See Wu amp Liu 2006b 2006c for a detailed connection between the subnetworkrsquosunitization with the six scales in NASA-TLX mental workload measurements)

LPDDB (lane position deviationdifference)

Average TaskCompletion Time(Tasks 1 and 2)

Activities of Entities duringthe Simulation Process

Subnetwork Utilization1

Des

ign

1D

esig

n 2

TABLE 2 COMPARISON OF THE SIMULATION RESULTS OF DESIGNS 1 AND 2

E R G O N O M I C S I N D E S I G N bull S P R I N G 2 0 0 71 4

should lead to improvements in the process of proactiveergonomic design and analysis

ReferencesAnderson J R amp Lebiere C (1998) The atomic components of thought

Mahwah NJ ErlbaumBolton M L amp Bass E J (2005) Cognitive systems engineering educational

software (CSEES) Educational software addressing quantitative models ofperformance IEEE International Conference on Systems Man andCybernetics Waikoloa HI

Card S K Moran T P amp Newell A (1980) The keystroke-level model foruser performance time with interactive systems Communications of theACM 23(7) 396ndash410

Emmerson P (2000) iGENtrade (software review) Ergonomics in Design 8(3)29ndash31

Feyen R Liu Y Chaffin D Jimmerson G amp Joseph B (1999) New soft-ware tools improve workplace design Ergonomics in Design 7(2) 24ndash30

Gore B F amp Corker K M (2002) Increasing aviation safety using humanperformance modeling tools An air man-machine integration designand analysis system application Military Government and AerospaceSimulation 34(3) 183ndash188

John B E amp Kieras D E (1996) The GOMS family of user interfaceanalysis techniques Comparison and contrast ACM Transactions onComputer-Human Interaction 3(4) 320ndash351

John B E Prevas K Salvucci D D amp Koedinger K (2004) Predictivehuman performance modeling made easy Paper presented at the CHI 2004Conference Vienna Austria

Laughery K (1989) Micro SAINT A tool for modeling human performancein systems In G R McMillan D Beevis amp E Salas (Eds) Applications ofhuman performance models to system design (pp 219ndash230) New YorkPlenum

Lim J amp Liu Y (2004) A queueing network model for eye movement Paperpresented at the International Conference on Cognitive Modeling Pitts-burg PA

Liu Y (1996) Queuing network modeling of elementary mental processesPsychological Review 103(1) 116ndash136

Liu Y (1997) Queuing network modeling of human performance of con-current spatial and verbal tasks IEEE Transactions on Systems Man andCybernetics Part A ndash Systems and Humans 27(2) 195ndash207

Liu Y Feyen R amp Tsimhoni O (2006) Queuing network-model humanprocessor (QN-MHP) A computational architecture for multitask per-formance ACM Transactions on Human Computer Interaction 13(1)37ndash70

Magrab E B (1997) Integrated product and process design and developmentBoca Raton FL CRC Press

Meyer D E amp Kieras D E (1997a) A computational theory of executivecognitive processes and multiple-task performance ndash Basic mechanismsPsychological Review 104(1) 3ndash65

Meyer D E amp Kieras D E (1997b) A computational theory of executivecognitive processes and multiple-task performance ndash Accounts of psy-chological refractory-period phenomena Psychological Review 104(4)749ndash791

Olsen J R amp Olsen G M (1990) The growth of cognitive modeling inhuman-computer interaction since GOMS Human-Computer Interaction5(2amp3) 221ndash265

Schunk D (2000) Modeling with the Micro Saint Simulation Package Paperpresented at the 2000 Winter Simulation Conference Orlando FL

Williams K E Hultman E amp Graesser A C (1998) CAT A tool for elicitingknowledge on how to perform procedures Behavior Research MethodsInstruments amp Computers 30(4) 565ndash575

Wu C amp Liu Y (2004a) Modeling behavioral and brain imaging phenomenain transcription typing with queuing networks and reinforcementlearning algorithms Proceedings of the 6th International Conference onCognitive Modeling (ICCM-2004 pp 314ndash319) Mahwah NJ Erlbaum

Wu C amp Liu Y (2004b) Modeling human transcription typing withqueuing network-model human processor Proceedings of the HumanFactors and Ergonomics Society 48th Annual Meeting (pp 381ndash385) SantaMonica CA Human Factors and Ergonomics Society

Wu C amp Liu Y (2004c) Modeling psychological refractory period (PRP)and practice effect on PRP with queuing networks and reinforcementlearning algorithms Proceedings of the 6th International Conference onCognitive Modeling (ICCM-2004 pp 320ndash325) Mahwah NJ Erlbaum

Wu C amp Liu Y (2006a) Queuing network modeling of a real-time psycho-physiological index of mental workloadmdashP300 amplitude in event-relatedpotential (ERP) Proceedings of the Human Factors and Ergonomics Society50th Annual Meeting (pp 1117ndash1121) Santa Monica CA Human Fac-tors and Ergonomics Society

Wu C amp Liu Y (2006b) Queuing network modeling of age differences indriver mental workload and performance Proceedings of the Human Fac-tors and Ergonomics Society 50th Annual Meeting (pp 190ndash194) SantaMonica CA Human Factors and Ergonomics Society

Wu C amp Liu Y (2006c) Queuing network modeling of driver workload andperformance Proceedings of the Human Factors and Ergonomics Society50th Annual Meeting (pp 2368ndash2372) Santa Monica CA Human Factorsand Ergonomics Society

Changxu Wu is a doctoral candidate enrolled inthe Department of Industrial and OperationsEngineering at the University of Michigan Heearned a BS in psychology at the ZhejiangUniversity and an MS in industrial engineering

at the University of Michigan His research interests includecognitive modeling human-computer interaction and compu-tational modeling in neuroergonomics He may be reached atthe Department of Industrial and Operations EngineeringUniversity of Michigan 1205 Beal Ave Ann Arbor MI48109-2117 changxuwumichedu

Yili Liu is an Arthur F Thurnau professor andassociate professor of industrial and operationsengineering at the University of Michigan Hereceived his PhD from the University of Illinoisat Urbana-Champaign His teaching and research

areas are human factors computational cognitive modelingand engineering aesthetics

This article is based on work supported by the National ScienceFoundation under Grant No NSF 0308000 However any opin-ions findings and conclusions or recommendations expressedin this article are those of the authors and do not necessarilyreflect the views of the National Science Foundation