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Renk, Jeffrey M.; And OthersVisual Information Strategies in Mental ModelDevelopment.9313p.; In: Visual Literacy in the Digital Age:Selected.Readings from the Annual Conference of theInternational Visual Literacy Association (25th,Rochester, New York, October 13-17, 1993); see IR 055055.

Information Analyses (070) -- Speeches/ConferencePapers (150)

EDRS PRICE MF01/PC01DESCRIPTORS Cognitive

Software;Learning;Literacy;

IDENTIFIERS Examples;

ABSTRACT

Plus Postage.Processes; Computer Simulation; Computer*Information Transfer; Models; NonverbalSystems Approach; *Visual Learning; *VisualVisual Stimuli*Mental Models

This paper examines how visual information strategiesmay be used to facilitate the development of mental models. Topicscovered include: definition of mental models; mental models andvisual information; mental modeling concepts; power of modeling,including examples related to physical science, mathematics, writing,and depth of processing; information transformation; and a discussionof Visionnaire, a systems modeling software program. (Contains 16references.) (JLB)

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Reproductions supplied by EDRS are the best that can be madefrom the original document.

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Visual Information Strategies inMental Model Development

by Jeffrey M. RenkRobert C. BranchEcheol Change

Syracuse UniversitySchool of Education

Huntington HallSyracuse, NY 13244-2100

"PERMISSION TO REPRODUCE THISMATERIAL HAS SEEN GRANTED SY

Or Alice Walker

2

BEST COPY AVAILABLETO THE EDUCATIONAL RESOURCESINFORMATION CENTER (ERICI

Visual Information Strategies inMental Model Development

Jeffrey M. RenkRobert C. Branch

Echeol Chang

We are not born tabula rasa. It is aperception that an individual's cognitivestructure *s a result of the modelingmethodologies acquired throughexperience. Mentally constructinf modelsto serve as representations of objects andevents is fundamental to cognitivefunctioning. Donald (1991) suggests thatmodeling advances cognitive functions tohigher levels in an individual's search forthe power to resolve ambiguities and tosupport a certain level of culture. Thus, anindividual's inherent drive to mentallyengineer increasingly powerful models ofthe world, built upon Information, is theengine powering cognitive development.

It is an individual's natureto mentally model objects

and events.

Mental Models

A mental model is a domain-specificreprezentation of an object or event used toexplain or predict. The American HeritageDictionary (1979) defines mental as:"Done or performed by the mind; existingin the mind." Further, the AmericanHeritage Dictionary (1992) defines amodel as: "A schematic description of asystem, theory, or phenomenon thataccounts for its known or inferred

properties and may be used for futurestudy of its characteristics." Thus, amental model is creatul and exists in themind as a description of a system, theory,or phenomenon.

Scientifically, the term mental modelseems to elude such a concise dermition.Definitions range from Minsky's (1985,1986) suggestion that a mental model isanything that helps an individual answerquestions about something, to Norman's(1983) dermition of "an often technicallyinaccurate naturally evolving repre-sentation of a target system that is. .incomplete. . .unstable. . .lacks firmboundaries. . .unscientific. . .and areparsimonious."

Synthesizing available literature, men-tal model is defmed as:

personal mental representations ofobjects or events;domain-specific; andused by individuals to explain orpredict.

These three characteristics are the keysto crafting a visual environment withsoftware tools to assist in mental modeldevelopment. A mental model is arepresentation, thus it has structure. Amental model is domain-specific,therefore, a domain may be identified andmodeled. A mental model is used toexplain or predict, thus, causal relation-

ships are externally modeled then extractedfrom the model by the viewer to serve thisfunction. Employing visual information tomodel a system's structure and inherentcausal relationships are thus crucial inassisting the viewer with mental modeldevelopment.

Mental models are domain-specific representations used

to explain or predict

Mental Models and VisualInformation

Visual information may be presentedvia visual media to influence thedevelopment of a viewer's mental model.Visual information consists of the pictures,graphic symbols, or other imagery used invisual communication. Visual communi-cation is the use of visual information toconvey meaning. Mental models are builtfrom personal interpretation ofinformation. Information is stored inmental structures called knowledge basesand is acquired through the senses with thevisual system providing one of the densestpaths of information. Saunders' (1994)suggestion thst visuals appeal to theintellect and communicate, as well asBallstaedt, Mandl, and Molitor's (1989)suggestion that visual media may be usedto facilitate the construction of a mentalmodel, leads one to the conclusion thatvisual information has the potential toimpact mental model construction viacommunication of a system's structure andcausal relationships.

Visual InformationFacilitates Mental Model

Develo ment

Mental Modeling Concepts

Visual information presented to assistin the development of mental models mustprovide a visual model that is analogous toan actual system and allows the inferenceof causal relationships. Seel andStrittmatter (1989) suggest that theminimum precondition for mental modelconstruction is analogous structuresbetween the original system and themodel. The degree of correspondencebetween the model and original is furtherdetermined by the degree of similaritybetween the attributes and inferred causalrelationships existing between theattributes for each. Seel and Suittmatterseem to further postulate:

Mental models do not developautomatically. They develop throughconscious information processing.

Mental models are built from personalinterpretations of existing knowledgebases that repreient a specific domainof the external world. Mental modelsthus result from an individual'sinteraction with personal knowledgebases.

Since mental models are developedfrom knowledge bases, images onlyignite the development of mentalmodels and assist in making themodels concrete.

Illustration serves two functions:mapping and heuristic. The mappingfunction is the delivery of facts by themedia. The heuristic function initiatesthinking.

Analogy-making, important in Seeland Strittmatter's (1989) concept of mentalmodel development, was suggested byMitchell (1993) as the mechanism fordistilling the essence of a situation andtransportation to a different situation andadapted via conceptual slippages.Conceptual slippage occurs when aconcept embedded in an existing mentalmodel is put under pressure from

'such as degree of matchdon at hand and theder investigation) andhis slippage allows a

to embed itself in theag the model, thus*nation to be properlyidea of using visuals torill= is noteworthy,

in gnat depth.:lion of distilling thedtuation is thought-

from this suggestion,11 and ponraying the

tem is important inlopment of mentalinformation should

cture and allow therelationships, as well

of the system.

Modeling

facilitate mental inves-tem or relationships

Consider differentas a model of a train,

0, or a model of themodels facilitate

because they can beabout, taken apart, andodels such as those ofons or mathematicalmental investigationpulation of varutbles,interactive, dynamic

initiate mental inves-Mental models, though

y picked up, turnedand so on. Mentalintellectual skill of

an example. Paivio

(1986) suggens that the visual componentor system of information storage is initiallyused in problem-solving to rotate andexamine the problem space. The verbalsystem is then incorporavd into theexamination to probe and analyze theproblem space further. Mental modelsfacilitate mental investigation because theymay be manipulated during exploration.Usage of mental models in this capacity ispresent in the areas of physical sciences,mathematics, and writing. Mental !modelsalso facilitate the depth of informationprocessing, therefore, resulting in greaterunderstanding, retention, and uansferenceto novel situations.

EhnicaLicicom

In the field of physical science,Einstein achieved his greatest insights viaengagement with mentally visualizedsystems of light waves and idealizedphysical bodies (Shepard, 1978). JamesClark Maxwell arrived at Maxwell'sequations, not by logical reasoning, but bya series of increasingly abstracthydrodynamic and mechanical models ofthe medium (Shepard, 1978).

Mental model development specific tomathematics may benefit from engagementvia visual information with mathematicaltheories, principles, and concepts.Piaget's theory of cordlive developmentsuggests that cognition progresses fromconcrete representation to abstract

rezelentation as one ages. Perl (1993)that tools called Inanipulatives" are

used to assist this progression.Manipulatives are concrete, physicalobjects that can be manipulated andvisually inspected. In mathematics,manipdatives ate used to model abstractconcepts. Experimentation with mani-pulative: assists the movement fromconcrete to abstract representations; fromfragmented facts and concepts to mentalmodels.

Perl (1993) provides the example ofthe use of brightly colored geometric

shapes called attribute blocks asmanipulatives. The attribute blocks comein four shapes, each in three colors, and oftwo different sizes. School children builda "train" from the blocks, with adjacentblocks varying by only one characteristicat a time. The attribute blocks providehighly visual, concrete stimuli to fostermental model development of abstracttheories.

Writing

Carley and Palmquist (1992) suggestrepresenting mental models as visual mapsextracted from written and spoken prose.Underlying concepts and structures maythen be analyzed. An implication is thatsuch visualization could assist in thedevelopment of mental models necessaryfor one to write. The maps can beexplored and manipulated, serving tocreate and refine mental models of prosestructure and rules.

Dcpth.afitaccuing

Mental models are multi-dimensionaland possess informational depth. Visualthree-dimensional modeling provides avisual environment that is information-richand is multi-dimensional. Visual three-dimensional modeling, whether of objectsor events, is built on data. Data forspecific modeling applications may berequired from a vast array of disciplines.Therefore, data collection requires activethinking regarding what data is needed,from where, and in what form. Such anenvironment provides for the socialsharing of cognition (each disciplineparticipanes mental model is affected bythe other different discipline participants).Further, in the area of graphing, Schwartz(cited in Kinnaman, 1993) believes thatone must be able to enter a graph and seethe corresponding changes in symbolicform of the function itself. Visualinformation portrayed in this mannerwould allow the viewer to enter thesystem itself and navigate via their mentalmodel that is continually being updatedthrough feedback frlm hypothesis testing.

4

Mental models are powerful tools in anarray of disciplines. The models may bebuilt on visual information and serve tofacilitate mental investigation.

Mental models can be tools,built on information, thatfacilitate mental inquiry.

Information Transformation

Information, the building block ofmental models, is accelerating inproduction. Time, as a personal com-modity with which to attend and processthis accelerating information base, isdiminishing. Mental model strength isdependent on updated, comprehensiveinformation that may be perused andperceived timely. Thus, the nature ofinformation must change from primarilytext-bascd to imagery-based.

The natAwe of information is changing.White (cited in Fredette, 1994) suggests"the change in information is a shift fromprint to imagery as the medium forinformation delivery, transformation, andexchange." White further assert thatimagery is our major information carrier.

This trend is exemplified in today'sinstructional media such as multimedir, ofa world community requiringcommunications to be interpretable acrosscultures; and in mathematical conceptsbeing envisioned *tonally. Alas, thebold escape from Edward Tulle's (1990)"flatland" created by textual information isunderway. Imagery, or visualizedinformation, is becomthg the new natureof information. Increasing computationalpower may be harnessed to capture anddeliver visual information to assist in thedevelopment of mental models.

Imagery is becomingthe new natureof information.

Visionnairem A ToolVisionnairem is a systems modeling

software program developed by AT&TBell Laboratories initially to assist inclarifying large, complex telecommu-nications switching systems. The Instruc-tional Design, Development andEvaluation (IDD&E) department within theSchool of Education at SyracuseUniversity is engaged in a collaborativeeffort with Bell Laboratories to investigatethe apiplications of Visionnairem as aninstructional systems and mental modelingtool.

Visionnairen runs on a Sun"computer under OpenWindowsnl.Visionnaireng uses a Video StudioMetaphor to support rapid prototyping andimprove ease of use by non-programmers.The Video Studio Metaphor is based onthe analogy of video store operations andfilm production. The vaphical userinterface is that of a Vat as shown inFigure 1. The application developmentinterface incorporates visual programmingtechniques and revolves around theanalogy of film production. The developerdescnbes and records the "scripts" thatspecify the relationship and subsequentaction between two or more objects called"actors". Scripts may execute sequentiallyor in parallel and at various levels ofsystem information detail. A collection ofscripts and actors comprise I scenario andare stored as a tape, similar to a VCR tape.for later use.

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The core of Visionnaireng is aknowledge - based simulation engine.Scenarios are represented through object-oriented programming techniques thatconstruct rules of the 112...THEN format.Thus, scenarios are dynamic with pre-conditions and post-conditions of scriptsconsidered and altered during execution,thus changing the state of the actorsinvolved.

Visionnairelu has been used withswitching systems engineers as a vehiclefor interactive, dynamic presentations ofswitching system specifications at designreviews. Visionnairem, with its animatedgraphics, visual programming, and script-based scenarios has potential use inmodeling abstract complex systems. It isthis potential use that was investigated inthe collaboration between IDD&E andAT&T Bell Laboratories.

Visionnaire (m) isused to visually model

dynamic systems.

The Canvas Under Vision-nairelu

Building upon the aforementionedconcepts, Visionnairem was used tocreate an animated visual environment on aSure" computer that would ignite thedevelopment of a mental model of theInstructional Systems Design (ISD)process. This was accomplished byrepresenting the facts and structure of theprocess and the relationships that drive theprocess that constitute the essence of ISD.The animated visual environment serves toinitiate thinking by displaying changingrelationships amongst objects that changedthe objects themselves. Simultaneouslywith Seel and Strittmatter's (1989) theoryof mental model development. factsassociated with the concepts ofdimensionality, shape perception, andimage perception were utild.

Dimenakmalhat

For years artists have used basic truthsconcerning depth perception to give theirartwork a diree-dimensional orientation.Kelsey (1993) reports that size,perspective, and shadowing provide onewith clues as to the relationsMp betweenobjects. We perceive laiger objects ascloser than smaller ones, distant objects ashigher on the horizon than closer ones,an(' objects that appear shadowed by otherobjects as further away. An additionalclue is provided throu4h color. Boldercolored objects am perceived as closer thenlighter colored objects.

Sharlageotion

Marr (cited in Hendee, 1993), apsychologist who studied perception andits relation to cognition, suuested a three-stage process by which the perception ofobject shape occurs. Fust, there is the*primal sketch* of an object's features andintensity variations. Second, there is theidentification of more subtle characteristicssuch as depth referenced to a coordinateframe centered in the viewer. Third, thereis mental engineering on the part of theviewer of a three-dimensional model of theobject This process yields a mental modelof the object suspended in cognitive"space*.

BillIQULUILYSaittatm

Kelsey (1993) relates the twocompeting models for how an image isperceived: the bottom-up model and thetop-down model. The bottom-up modelsuggests that an image is perceived bysequentially building up individual featuresinto a recognizable whole. The top-downmodel sugguts that an immediate *whole"impression. or *gestalt*, is formed withthe individual features filled in at laterstages. Evidence supports both models.Because specific nerves only fire whenpresated with specific features, and thatfeature detectors exist in the visual system,support the bottom-up model. The top-down model is supported by our ability toquickly differentiate objects, such as faces

we recognize versus those we don't in acrowd.

Weidenmann's (1989) work providedan additional consideration in the design ofthe Visionnakem visual environmentWeidenmann (1989) suggests that aviewer may falsely perceive visualinformation as non-informative initiallyand stop processin; information. Thecieator of visual enviionments must makea conscious effort to protect againstsuperficial attention on the part of theviewer. That is, a creator's desire is tohave the viewer engage the visualenvironment rather then having the viewercomplete an initial scan of theenvironment, consider the environmentnon-informative, and mentally disengage.The visual environment is arranged so thefirst scan by the viewer creates anexpectation within the viewer that the

A

Key:A. Situational AssessmentB - Instructional AnalysisC - Objectives

environment is informative. Thus, infor-mation-proceuing of the visual informa-tion is not interrupted, or worse,termbiated.

Visionnairen,-- The Engineand Palette

The visual information environmentpainted by Visionnahetw incorporates Seeland Strittmatter's (1989) theory of mentalmodel construction, and the factsassociated with dimensionality, shapeperception, image perception, and super-ficial attention. Visionnairelw paints thevisual environment pictured in Figure 2.

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Figure 2

9

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D - kstructional StrategiesE - Media SelectionF. Pilot

- Summative Evaluation Plan

Initial viewer engagement with thisenvironment is at an animated level.Upon viewer selection of "play*, the ISDprocess is animated, with interactionoccurring amongst the objects (A, B, C,D, E, F, G). The interactions change thecolor and size of the objects signifying theinfluence that actions have on the differentstages (represented by the objec:s) of theISD process. Thus the essence of the ISDprocess is demonstrated. The ISD processis systematic and systemic. That is, theISD process is composed of interrelatedparts that interact to achieve a goal. Yeta change in any one part changes at

least one other part of the system. Thisinitial engagement is designed to guardagainst superficial attention on the part ofthe viewer. Additionally, this approachcomplies with the top-down model ofimage perception. The ISD process isinitially presented as a pstalt, withspecific features filled in gazduallythroughout the cognitive engagement.

Utilizing Seel and Strittmatter's (1989)theory of mental model construction, thestructure of this visual environment closelyparallels the original ISD schematicrepresented in Figure 3.

BratSpring 1994Syracuse UnivenityInstructional Design, Development and Evaluation

Figure 3Fundamental Components of the Instructional Design Process

COPY AVAILABLE

The structures are nearly identical thusproviding a general analogy to be drawnbetween the original and the model.Completion of the analogy requires thecausal relationships between objects to bedemonstrated. This is accomplishedthrough animated interactions amongstobjects. Textual messages move from oneobject to the next. Upon receipt of amessage, the object evaluates the message,and formulates an inquiry back to thesending object if the message is evaluatedas incomplete. Upon receipt of a returnedmessageohe sending object elaborates onthe message and re-transmits the messageto the recipient Upon re-transmittal, thesending object disappears from the visualdisplay. This signifies the true-to-life factthat time is a constraint on the overallprocess. There is not unlimited time toclarify and refme questions raised throughevaluation at various stages of the ISDprocess. At this point, the recipient re-evaluates the message, performs theactivity directed by the message, and!generates a new message to the next objectm the system. This activity parallels thebi-directional relationships that existbetween various components of the actualISD process itself. Dynamic activity ofthis nature is demonstrated in Figure 4.

Figure 4

In summary thus far, the visualenvironment portrays the structure,composed of objects representing the "at-

tributes", "facts" or "components" of theISD process, and displays the causalrelationships amongst the objects. Onelast component required portrayal toaccurately depict the essenc4 of the ISDprocess. This component is the sense ofmovement as a function of time. Thiscomponent is captured by the back-vounds of the various objects as shown inFigure 5.

Figure 5

Applying the concept that objects withdarker, bolder colors are perceived closerthen lighter colored ob,jects, black ischosen as the background for object A andB. Objects A and B represent thecomponents of the general "mput" phaseof the ISD process. These componentscomprise events that occur early in theISD. Extending this scheme, thecomponents representing the "process"(formative evaluation) phase of the ISDprocess are highlighted apinst a graybackground as depicted in Figure 6.Typically, these components occurmidway through ISD.

Figure 6

Completing this scheme, thecomponents that represent the "output"phase of ISD are highlighted against a

white background as demonstrated inFigure 7.

Figure 7

A final consideration concerns theobjects themselves. The perception ofdepth and movement in time as the viewerprogresses through the ISD process isaugmented by a gradual decrease in objectsize and color intensity. Object A is thelargest object; object 0 the smallest.Object A is a bold color. Object G iswhite. Objects B through F decreasegradually in color intensity.

Conclusion

Visual information strategies may beused to facilitate the development of mentalmodels. To be successful, visualinformation strategies must allow portrayalof the structure of the system te bemodeled, allow the inference of causalrelationships, and depict the essence of themodeled system. Software tools exist toassist in the generation and presentation ofvisual information in the execution ofthose strategies.

Mental models, and the world theyrepresent, are dynamic entities thatinterrelate in nonlinear, seemintiy chaoticways. Advanced strategies must bedeveloped in the design and use of visualinformation to capture and portray theessence of dynamic entities. Thus, furtherinvestigation is needed to explore theInteraction of visual information strategiesand the mental modeling of dynamicsystems that operate in non-pre-deterministic, non-linear sequences.

References

Ballstaedt, S.P., Mandl, H., & Molitor,S. (1989). Problems in knowledgeacquisition from text and pictures. In J.R.Levin & H. Mandl (Eds.), Knowledgeacquisition from text and pictures (pp. 3-29). Amsterdam, The Netherlands:Elsevier Science Publishers.

Ce.,y, K., & Palmquist. M. (1992).Extracting, represenung, and analyzingmental models. Social Forces, 70 , 601-636.

Donald, M. (1991). Origins of themodem mind: Three stages in theevolution of culture and cognition.Cambridge, MA: Harvard UniversityPress.

Fredette, B.W. (1994). Use of visuals inschools. In F.M. Dwyer & D.M. Moore(Eds.), Visual literacy: A spectrum ofvisual learning (pp. 235-256).Englewood Cliffs, NJ: EducationalTechnology Publications.

Hendee, W. R. (1993). Cognitiveinterpretation of visual signals. In W.R.Hendee & P.N.T. Wells (Eds.), Theperception of Waal biformadon (pp. 134-159). New York: Spnnger-Verlag.

Kelsey, C.A. (1993). Detection of visualinformation. In W.R. Hendee & P.N.T.Wells (Eds.), The perception of visualinformation (pp. 30-51). New York:Springer-Verlag.

Kinnaman, D.E. (1993). The next decade:What the future holds. In T.R. Cannings& L. Finkel (Eds.), The technology ageclassroom (pp. 604 - 611). Wilsonville,OR: Franklin, Beadle & Associates.

Minsky, M. (1985, 1986). The society ofmind. New York: Simon & Schuster.

Mitchell, M. (1993). Analogy-making asEerception. Cambridge, MA: The MITPress.

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Norman, D. (1983). Some observationson mental models. In D. Gentner & A.L.Stevens (Eds.), Mental models (pp. 7-14). Hillsdale, NJ: Lawrence ErlbaumAssociates.

Paivio, A. (1986). Mental representations:A dual coding approach. New York:Oxford University Press.

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Saunders, A.C. (1994). Graphics andhow they communicate. In F.M. Dwyer& D.M. Moore (Eds.), Visual literacy: Aspectrum of visual learning (pp. 183-192). Englewood Cliffs, NJ: EducationalTechnology Publications.

Seel, N.M., & Strittmatter. (1989).Presentation of information by media andits effect on mental models. In J.R. Levin& H. Mandl (Eds.), Knowledgeacquisition from text and pictures (pp.37-37). Amsterdam, The Netherlands:Elsevier Science Publishers.

Shepard, R.N. (1978). Externalization ofmental images and the act of creation. InW.E. Coffman & B.S. Randhawa (Eds.),Visual learning, thinking, andcommunication (pp. 133-189). NewYork: Academic Press.

Tufte, E. R. (1990). Envisioninginformation. Cheshire, CT: GraphicsPress.

Weidenmann, B. (1989). When goodpictures fail: An information-processingapproach to the effect of illustrations. InJ.R. Levin & H. Mandl (Eds.),Knowledge acquisition from text andpictures (pp. 137-170). Amsterdam, TheNetherlands: Elsevier Science Publishers.

Important Note

Visionnairen" is a trademark of AT&T.

Sun' and OpenWindowsm are trade-marks of Sun Microsystems, Inc.