Army Research Laboratory

Impact of Cognitive Architectures on Human-Computer

Interaction

by Sidney C Smith

ARL-TR-7092 September 2014

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Army Research Laboratory

Aberdeen Proving Ground, MD 21005-5067

ARL-TR-7092 September 2014

Impact of Cognitive Architectures on Human-Computer

Interaction

Sidney C Smith

Computational and Informational Sciences Directorate, ARL

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September 2014 Final

Impact of Cognitive Architectures on Human-Computer Interaction

ARL-TR-7092

Approved for public release; distribution is unlimited.

January 2014–June 2014

Sidney C Smith

US Army Research Laboratory

ATTN: RDRL-CIN-S

Aberdeen Proving Ground, MD 21005-5069

Author email: <sidney.c.smith24.civ@mail.mil>

Researchers have been developing, using, and implementing cognitive architectures in an attempt to understand how humans

gather, process, and use information. Cognitive architectures have been used to advance the study and application of artificial

intelligence. They have also been used to predict human performance and, in so doing, evaluate user interfaces. In this report we

will review the influence of several cognitive architectures—specifically, asking what promises were made, what impacts were

realized, and what potential impact can we reasonably expect in the future.

human-computer interaction (HCI), cognitive architectures, ACT-R, CLARION, EPIC, Soar

18

Sidney C Smith

Unclassified Unclassified Unclassified 410-278-6235

UU

ii
Contents

1. Introduction 1

2. Cognitive Architectures Overview 2

2.1 The Atomic Components of Thought–Rational (ACT-R)................................ 2

2.2 CLARION.................................................................................. 3

2.3 EPIC ........................................................................................ 3

2.4 Soar ......................................................................................... 3

3. Early Promises 4

4. Applications of Cognitive Architectures 5

5. Conclusions and Future Work 6

6. References 9

Distribution List 12

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INTENTIONALLY LEFT BLANK.

iv
1. Introduction

Newell and Card observed a phenomenon that they related to Gresham’s law, which states, “Bad

money drives out good.”1 Sir Thomas Gresham noticed that the newer coins were considered

more valuable; therefore, they began to disappear from circulation because people would rather

pay with the older coins, which were considered less valuable. Mundell argues that the expression

is backwards and should be rendered, “Good money drives out bad.”2I suppose it depends upon

whether the buyers or the sellers have more control over the currency. Newell applies this same

logic to science and observes that “hard science drives out soft.”1 Using three

examples—Operations Research, Human Factors, and Programming Languages—Newell and

Card illustrate how the hard sciences of linear programming, engineering, and parsing have

relegated the soft sciences into the background. I have seen this in software engineering, where

the hard functional requirements push the soft nonfunctional requirements into the background.

Our terminology, functional versus nonfunctional, displays the bias. We all agree that usability,

maintainability, and security are important, but since we do not really know how to measure these

things, they take a back seat to things that we can measure.

Working as an information assurance professional for 10 years, I have found that the progression

of security is directly proportional to our ability to measure it. Newell and Card are convinced that

for psychology to remain relevant in human-computer interaction (HCI), it must harden. Their

vision is for psychology to provide engineering style theory that influences the design of

computer interfaces. They acknowledge that there are competing visions. One vision uses

psychology primarily as an evaluation tool. The other vision provides an explanation where the

work is done to prove that the theory is correct and not to provide useful tools to designers. While

acknowledging the value of these visions, Newell and Card clearly state, “They will never beat

Gresham’s Law.”1 Hardening a science is difficult, and Newell and Card acknowledge four major

obstacles to realizing their vision: psychology, as it applies to computer interfaces, is too low

level, the scope is too limited, it is too late, and it is too difficult to apply.1 The hardest science in

this space deals with cognitive architectures and makes use of Fitts’s Law,3 which provides

empirical data that may be used to provide numbers that designers may incorporate into their

designs. Card et al. used this kind of science when they built the Model Human Processor and the

Goals, Operations, Methods, and Selection (GOMS) model, which they used to investigate text

editing with a line editor on a teletype machine.4 One of the key issues is that the problems

designers are trying to solve are much bigger than the problems researchers are investigating.

1
Moreover, the findings in the research do not scale up to solving the problems of the designers.

Newell and Card demonstrate that the average life cycle of a user interface device is about 20

years. By 1985 when they had completed their work, teletype machines had been replaced by

glass terminals, and line editors had been replaced by full screen visual editors. By the time I

started my career in 1989, teletypes where a relic of the past, and the only ones I ever saw were

the consoles of Digital Equipment Corporation Virtual Address eXtension DEC VAX 11-785

super minicomputers that were themselves considered old at the time. Because of the rapid

changes in techonology, by the time the research is concluded and the findings have been

published, the results are difficult to apply to modern technologies.

In this report, I will examine the progress that has been made since 1985 to discover how

successfully the science has been hardened and gauge the realization of Newell and Card’s vision.

In Section 2, I will review some of the cognitive architectures being used in HCI. In Section 3, I

will outline some of the early promises made in this field. In Section 4, I will cover the

applications of cognitive architectures. In Section 5, I will conclude by comparing the promises of

Section 3 to the deliveries in Section 4.

2. Cognitive Architectures Overview

Cognitive architectures are used to express the psychological theories in a quantitative format to

drive the models necessary to allow application designers to design more usable systems and to

generate intelligent behavior.5 They are also focused on those aspects that are constant over time.

Langley et al. related this to a building, saying, “There is also a direct analogy with a building’s

architecture, which consists of permanent features like its foundation, roof, and rooms, rather than

its furniture and appliances, which one can move or replace.”5 The goals of quantitative models

and intelligent behavior are complementary, and each serves to mature or harden this science.

Langley et al. include brief summaries of 18 separate cognitive architectures, but focus on only

4.5In a similar manner we will focus on only a few architectures.

2.1 The Atomic Components of Thought–Rational (ACT-R)

In their book The Atomic Components of Thought, Anderson and Lebiere present their ACT-R

cognitive model, which they claim “consists of a theory of the nature of human knowledge, a

theory of how this knowledge is deployed, and a theory of how that knowledge is acquired.”6

ACT-R is composed of modules that communicate with a central production system through

2
buffers. The number of modules is not key to the theory, and more may be added as necessary.

Theories and techniques have been incorporated from other cognitive architectures like Executive

Process–Interactive Control (EPIC) cognitive architecture. The core is the buffering to the central

production system because the central production system may work with only the data that

currently reside in its buffers. Anderson et al. go to great lengths to show how their architecture

maps to the regions of the brain as seen in integrated brain imaging.7

2.2 CLARION

The CLARION cognitive architecture is composed of four subsystems: the

action-centered-centered subsystem (ACS), the non-action-centered subsystem (NACS), the

motivational subsystem (MS), and the meta-cognitive subsystem (MCS).8 Each of these

subsystems has both an implicit and explicit representational structure. The ACS uses neural

networks to compute the quality of each action. It then selects the action with the highest quality.

There is a Q-learning algorithm that improves this assessment over time. The NACS provides the

memory for the architecture. MS sets the goals and evaluates whether the goal has been achieved.

The MCS monitors the system to improve its cognitive performance.8

2.3 EPIC

David Kieras and David Meyer presented the EPIC cognitive architecture and used it to explore

human performance.9 The EPIC cognitive architecture builds upon the Model Human Processor4

and is composed of a collection of models of human performance, fashioned together by a

simplified theory, and tuned using performance information gathered from the literature. To

validate the model, they used EPIC to examine common problems in HCI (e.g., choosing an item

from a pull-down menu, typing spoken data, and processing multiple visual information sources).

For each of these problems the predictions from EPIC were compared to actual human

performance.9 What differentiates EPIC from ACT-R is that EPIC has, from the beginning, used

empirical data to provide constraints on the model, like how long it takes a human to move a

hand, or the smallest area upon which an eye may focus.

2.4 Soar

The Soar cognitive architecture uses problem spaces connected to a production system that uses

subgoaling via impasse detection, learning, and chunking to create a model of human thought.10

The cognitive architecture was built iteratively with new modules and functionality being added

to almost every new version. In 2004 Nuxoll and Laird added episodic memory to the Soar

architecture.11 In 2008 Laird presented extensions to Soar that included nonsymbolic

3
representation, new learning mechanisms, and long-term memories. These extensions enable

working memory activation, reinforced learning, emotion, semantic memory, episodic memory,

and visual imagery.12 In 2010 Rosenbloom created a variant of the Soar architecture based upon

graphical models.13

3. Early Promises

Over the years researchers investigating cognitive architectures have identified the potential in

these models to impact HCI. Newell and Card discussed sound theories coming out of cognitive

architecture that would be used by interface designers the same way that engineers use the

theories of physics to build bridges.1 They also envisioned a design tool that would have the

theories and the quantitative data embedded inside of it, providing this capability to the designer

while insulating the designer from it.1 Kieras and Meyer talked about using cognitive

architectures to predict human performance and to compare different user interface approaches.9

Langley et al. discussed using cognitive architectures to simulate humans for pedagogical or

entertainment purposes.5 Pirolli asserted that psychology ought to be able to answer questions

about how to design application programs.14 In their 1990 work, Olson and Olson identified five

roles for cognitive architectures:15

1. Initially constraining the design space, so that one does not build an interface, for

example, that requires more items to be kept in memory than will fit in working

memory (WM).

2. Answering specific design decisions, so that one can decide, for example, between

a dialogue that requires few keystrokes but difficult retrieval from memory or one that

involves more keystrokes but is easier to remember.

3. Estimating the total time for task performance with sufficient accuracy to make

decisions about how many people are needed to staff the performance of a repetitive

operational task on a computer.

4. Providing the base from which both to calculate training time and to guide training

documentation to help the user determine in which situations which method is most

efficient.

5. Knowing which stages of activity take the longest time or produce the most errors,

in directing research toward the aspects of human-computer interaction that will have

strong future performance implications.

4
With the exception of using cognitive architecture to simulate humans, I believe that their list

provides a useful summary of the early promises of cognitive architectures to the HCI community.

4. Applications of Cognitive Architectures

In the last 30 years since Newell and Card published their work on the prospects of this

technology, researchers have made significant progress. Space will not permit an exhaustive

summary; however, I have highlighted some of this work:

• John and Vera were able to use GOMS analysis and the Soar cognitive architecture to

predict the behavior of an expert using a highly interactive machine-based graphic task (i.e.,

a video game) with a 60% success rate.16

• Gray et al. used GOMS analysis in Project Ernestine to compare the time it took telephone

company operators to complete certain tasks.17 This study showed that the performance of a

new workstation was actually slower than the previous workstation. Their theoretical

findings were validated with empirical data.17

• Byrne reported the work of Nelson et al. in constructing the NASA Test Director (NTD)

simulator in NTD-Soar.18 This simulation was able to perform the 3,000 pages of tasks that

an NTD would need to perform before a launch. Although this simulation probably will not

be used to launch spacecraft, it could be used to test new interfaces to automate some of the

NTD’s work.18

• Chella et al. proposed a cognitive architectural approach to the solve problems with

artificial vision for an autonomous agent.19

• Huguenard et al. conducted a study of telephone menus and discovered that contrary to

prevailing wisdom, smaller menus do not reduce error rate. The cognitive architecture

provided a theoretical explanation of why this happened.20

• Sweller et al. described their use of cognitive architectures for designing curriculum.21

• Byrne used the ACT-R cognitive architecture to successfully predict human performance in

menu selection.22

5
• Cockburn et al. used the Hick-Hyman and Fitts’s law to compare 4 different menu

technologies: traditional, recency, frequency, and adaptive. They compared their results

with empirical data and found that they matched extremely well.23

• Hornof used the EPIC cognitive architecture to compare the performance of several

different visual layouts.24

• Salvucci successfully used the ACT-R cognitive model to explain and predict the effects of

distraction upon driving an automobile.25

• Magerko et al. used the Soar cognitive architecture to implement characters in a computer

game based upon Unreal Tournament.26 They observed that every computer game in

existence is proof that you do not need realistic artificial intelligence (AI) in the nonplayer

characters (NPCs) for the game to be enjoyable. They lamented that many of the most

popular games like Quake have very limited violent adversarial relations with the NPCs,

which are basically just “computerized punching bags.”26 Their goal is to create a

nonviolent, plot driven game that really needs AI characters. The basic framework for this

game is complete, and now they are enriching it toward their ultimate goal.26

• Tambe et al. are using cognitive architectures to simulate human pilots in a battlefield

simulation. In this work they were preparing for the Synthetic Theatre of War-1997 exercise

where between 10,000 and 50,000 automated agents would work with up to 1,000

humans.27 The results of this exercise are documented by Laird et al.28

5. Conclusions and Future Work

To assess whether cognitive architectures have been able to fulfill their potential, in the following

paragraphs, I will take each role as outlined by Olson and Olson, plus the promises of simulated

humans, and compare it against the accomplishments listed in Section 3:

• Role #1: “Initially constraining the design space, so that one does not build an interface, for

example, that requires more items to be kept in memory than will fit in working memory

(WM).”15 Miller’s rule of 7, plus or minus 2,29 has made its way into the mainstream of

interface design thinking. It is not clear to me that cognitive architectures played a

significant role in establishing this rule; however, this is clearly a case where psychology

has influenced interface design.

6
• Role #2: “Answering specific design decisions, so that one can decide, for example,

between a dialogue that requires few keystrokes but difficult retrieval from memory or one

that involves more keystrokes but is easier to remember.”15 Byrne’s work on menu

selection,18 Cockburn et al.’s work on menu technology,23 and Hornof’s work on visual

layout24 show that cognitive architectures are indeed being used to fill the role; however, it

seems that these tools still have not made their way into the hands of interface designers.

When Tullis et al. were considering navigation architectures for a redesign of the Fidelity

Regional Security & Operations Website, no consideration was given to the information

gained from cognitive architectures. They simply used intuition and imitation to create 6

navigation strategies and conducted a usability study to discover which worked the best.30

• Role #3: “Estimating the total time for task performance with sufficient accuracy to make

decisions about how many people are needed to staff the performance of a repetitive

operational task on a computer.”15 The work of Gary et al. on Project Ernestine

demonstrates the value of cognitive architectures for this purpose.

• Role #4: “Providing the base from which both to calculate training time and to guide

training documentation to help the user determine in which situations which method is most

efficient.”15 Sweller et al. demonstrate that cognitive architectures can and have been used

for this purpose.

• Role #5: “Knowing which stages of activity take the longest time or produce the most

errors, in directing research toward the aspects of human-computer interaction that will

have strong future performance implications.”15 Huguenard et al.’s work with phone menus

demonstrates how cognitive architectures may be used for this purpose.

• Role #6: Providing a simulated human in simulated environments for training and

entertainment. The work of Magerko et al. and Tambe et al. demonstrates the ability of

cognitive architectures to fulfill this role.

Although there has been work which demonstrates that cognitive architectures are capable of

fulfilling each of the roles promised by early researchers, we are still a long way from the vision

Newell and Card described where psychology would provide design principles that would be used

by interface designers. Interface designers are still more likely to use intuition and imitation to

complete their work rather than consult psychologist and cognitive models. This might be the

time for the technology to establish itself in Website design. World Wide Web applications are

mature enough for the research to be able to provide relevant information. Palmer’s study clearly

indicates that more usable Websites have the ability to generate more revenue.31 Increased

7
revenue may provide the incentive necessary to utilize this technology if it were packaged in a

way that was easy for Web designers to use. I can envision a virtual Web user similar to the virtual

NTD described earlier that, given a Website and goals, would be able to assess the usability of the

site and provide some kind of score and suggestions on improvements. Over the past few months,

I have personally used 10 different automated Web usability tools, but almost all of them report

on compliance to the legal requirements to support usability of disabled persons. As a later

refinement, research into the cultural aspects of Web design may be incorporated.32,33

8
6. References

1. Newell A, Card SK. The prospects for psychological science in human-computer interaction.

Hum.-Comput. Interact. 1985;1(3):209–242.

2. Mundell R. Uses and abuses of Gresham’s law in the history of money. Zagreb Journal of

Economics. 1998;2(2):3–38.

3. Soukoreff RW, MacKenzie IS. Towards a standard for pointing device evaluation,

perspectives on 27 years of Fitts’ law research in HCI. International Journal of

Human-Computer Studies. 2004;61(6):751–789.

4. Card SK, Moran TP, Newell A. The psychology of human-computer interaction. Boca Raton

(FL): CRC Press; 1986.

5. Langley P, Laird JE, Rogers S. Cognitive architectures: research issues and challenges.

Cognitive Systems Research. 2009;10(2):141–160.

6. Anderson JR, Lebiere C. The atomic components of thought. New York (NY): Psychology

Press; 1998.

7. Anderson JR, Bothell D, Byrne MD, Douglass S, Lebiere C, Qin Y. An integrated theory of

the mind. Psychological Review. 2004;111:1036–1060.

8. Sun R. The importance of cognitive architectures: an analysis based on CLARION. Journal

of Experimental & Theoretical Artificial Intelligence. 2007;19(2):159–193.

9. Kieras DE, Meyer DE. An overview of the epic architecture for cognition and performance

with application to human-computer interaction. Human-Computer Interaction.

1997;12(4):391–438.

10. Laird JE, Rosenbloom P. The evolution of the Soar cognitive architecture. In: Mind matters: a

tribute to Allen Newell; Steier D, Mitchell T, editors. New York (NY): Psychology Press;

2014; p. 1–50.

11. Nuxoll A, Laird JE. A cognitive model of episodic memory integrated with a general

cognitive architecture. In: Proceedings of the International Conference on Cognitive

Modelling, ICCM 2004; 2004 July 30–August 1; Pittsburgh, PA. p. 220–225.

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12. Laird JE. Extending the soar cognitive architecture. Frontiers in Artificial Intelligence and

Applications. 2008;171:224.

13. Rosenbloom PS. Rethinking cognitive architecture via graphical models. Cognitive Systems

Research. 2011;12(2):198–209.

14. Pirolli P. Cognitive engineering models and cognitive architectures in human-computer

interaction. In: Handbook of applied cognition; Durso FT, Nickerson RS, Damis S,

Lewandoskey S, Perfect TJ, editors. West Sussex, England: John Wiley & Sons; 1999. p.

441–477.

15. Olson JR, Olson GM. The growth of cognitive modeling in human-computer interaction

since GOMS. Hum.-Comput. Interact. 1990;5(2):221–265.

16. John BE, Vera AH. A GOMS analysis of a graphic machine-paced, highly interactive task.

In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 1992 ;

Monterey, CA. CHI ’92, New York, NY, USA: ACM; 1992. p. 251–258.

17. Gray WD, John BE, Atwood ME. Project Ernestine: validating a GOMS analysis for

predicting and explaining real-world task performance. Human-Computer Interaction.

1993;8(3):237–309.

18. Byrne MD. Cognitive architecture. In: The human-computer interaction handbook:

fundamentals, evolving technologies and emerging applications; Jacko JA, Sears A, editors.

Hillsdale (NJ): Lawrence Erlbaum Associates Inc.; 2003; p. 97–117.

19. Chella A, Frixione M, Gaglio S. A cognitive architecture for artificial vision. Artificial

Intelligence. 1997;89(1):73–111.

20. Huguenard BR, Lerch FJ, Junker BW, Patz RJ, Kass RE. Working-memory failure in

phone-based interaction. ACM Transactions on Computer-Human Interaction (TOCHI).

1997;4(2):67–102.

21. Sweller J, Van Merrienboer JJ, Paas FG. Cognitive architecture and instructional design.

Educational Psychology Review. 1998;10(3):251–296.

22. Byrne MD. ACT-R/PM and menu selection: applying a cognitive architecture to HCI.

International Journal of Human-Computer Studies. 2001;55(1):41–84.

23. Cockburn A, Gutwin C, Greenberg S. A predictive model of menu performance. In:

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 2007. San

Jose, CA. p. 627–636.

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24. Hornof AJ. Cognitive strategies for the visual search of hierarchical computer displays.

Human-Computer Interaction. 2004;19(3):183–223.

25. Salvucci DD. Modeling driver behavior in a cognitive architecture. Human Factors: The

Journal of the Human Factors and Ergonomics Society. 2006;48(2):362–380.

26. Magerko B, Laird J, Assanie M, Kerfoot A, Stokes D. AI characters and directors for

interactive computer games. In: Proceedings of the Sixteenth Innovative Applications of

Artificial Intelligence Conference; 2004 July 27–29; San Jose, CA. Menlo Park (CA): AAAI

Press; 2004. p. 877–883.

27. Tambe M, Johnson WL, Jones RM, Koss F, Laird JE, Rosenbloom PS,

Schwamb K. Intelligent agents for interactive simulation environments. AI magazine.

1995;16(1):15.

28. Laird JE, Coulter KJ, Jones RM, Kenny PG, Koss F, Nielsen PE. Integrating intelligent

computer generated forces in distributed simulations: Tacair-soar in STOW-97. Paper

presented at: Simulation Interoperability Workshop. 1998 March 9-13; Orlando, FL.

29. Miller GA. The magical number seven, plus or minus two: some limits on our capacity for

processing information. Psychological Review. 1956;63(2):81.

30. Tullis TS, Connor E, LeDoux L, Chadwick-Dias A, True M, Catani M, Investments F. A

study of website navigation methods. Paper presented at: Usability Professionals Association

Conference. 2005 June 27–July 1; Montreal, Quebec.

31. Palmer JW. Web site usability, design, and performance metrics. Information Systems

Research. 2002;13(2):151–167.

32. Luna D, Peracchio LA, de Juan MD. Cross-cultural and cognitive aspects of web site

navigation. Journal of the Academy of Marketing Science. 2002;30(4):397–410.

33. Kralisch A, Eisend M, Berendt B. Impact of culture on website navigation behaviour. In:

Proceedings of the 11th International Conference on Human-Computer Interation; 2005 ; Las

Vegas, NV. Lawrence Erlbaum Associates, Inc.; 2005.

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