2016-05-27 Venia Legendi (CEITER): Paul Seitlinger

The role of primary and secondary memory in organism-environment dynamics

... computa(onalmodelingofaccruingdataincollabora(ve

learningscenarios

paulseitlinger27.05.2016,tallinn

1

Outlineofmyresearch

•  Knowledgebuildinginopen/self-directedlearningseDngs

•  Challengesfromapsychologicalperspec(ve

–  Organism-Environmentdynamics

•  Interplayofprimarymemory(scopeandcontrolofaIen(on)andsecondary(long-term)memoryduringreflec(on–  Secondarymemory(SM):Integra(ngepisodicmemory(evolvingincollabora(velearning

seDng)intoseman(c(pre-exis(ng)memory[1]

–  Primarymemory(PM):controlleduseofcontextualcues(environmental,internal)tosearchsecondarymemory(interpreta(on,reflec(on)[1]

–  Socio-cogni(veprocesses/co-crea(onofenvironments

•  Howstabiliza(on(paIernedprac(ces,grounding)evolvesandaffectsPM-SMinterplay

[1]Usworth,N.&Engle,R.(2007).Thenatureofindividualdifferencesinworkingmemorycapacity:Ac(vemaintenanceinprimarymemoryandcontrolledsearchoffromsecondarymemory.PsychologicalReview,114,104-132.

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Outlineofmyresearch

•  Observingsocio-cogni(velearninginWebenvironments–  Collabora(velearningso`ware(school,university)–  Socialinforma(onsystems(bookmarking,tagging,informa(onsearch)

•  Makinguseofaccruingdatasetstovalidatemodelsofsocio-cogni(velearning

•  3studiesbytheexampleofsocialtagging–  Study1:Fieldstudy(universitycourse)abouteffectsofseman(c

stabiliza(ononindividuallearning[2]–  Studies2and3:Measurementandcomputa(onalmodelingtoshedlight

onvariablesgivingrisetostabiliza(on[3,4]

[2]Ley,T.&Seitlinger,P.(2015).Dynamicsofhumancategoriza(oninacollabora(vetaggingsystem:howsocialprocessesofseman(cstabiliza(onshapeindividualssensemaking.ComputersinHumanBehavior,51,140-151.

[3]Seitlinger,P.,Ley,T.&Albert,D.(2015).Verba(mandseman(cimita(oninindexingresourcesontheWeb:afuzzy-traceaccountofsocialtagging.AppliedCogni?vePsychology,29,32-48.

[4]Seitlinger,P.&Ley,T.(2016).Reconceptualizingimita(oninsocialtagging:areflec(vesearchmodelofhumanwebinterac(on.InP.Parigi&S.Staab(Eds.),Proceedingsofthe8thInterna?onalACMconferenceonWebScienceConference(inpress).NewYork:ACMpress. 3

•  Fromapost-phenomenologicalperspec(ve(e.g.,[5]):Tagsashermeneu(cmeansofcollabora(velearning

•  Hypothe(calstabiliza(oncyclei)Providecontextualcuesthattriggersearchofsecondarymemory(seman(cpriming;e.g.,[6])

ii)Subsequentresourcereflec(onsleadtosimilarinterpreta(ons/conceptualiza(ons(intersubjec(vity)

iii)Similartagchoices

Study1:Effectsofseman(cstabiliza(ononindividuallearning

àMutualreinforcementinchoosingsimilarwordsforsimilarconceptualiza(onsàSmallandconsistent(=stable)tagvocabularyandelaboratedknowledgeaboutunderlyingconcepts

[5]Verbeek,P.(2005).Whatthingsdo:philosophicalreflec?onsontechnology,agency,anddesign.UniversityPark,Pennsylvania:ThePennsylvaniaStateUniversityPress.[6]Fu,W.-T.,Kannampallil,T.,Kang,R.&He,J.(2010).Seman(cimita(oninsocialtagging.ACMTransac(onsonComputer-HumanInterac(ons,17,12:1-12:37.

Tag(contextual cue)

Search of memory(PM-SM interplay)

Semantic priming

Reflection(PM-SM interplay)

Intersubjectivity

ImitationSemantic

stabilization

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•  N=24studentsofauniversitycourseoncogni(vemodelsinTEL–  Socialbookmarkingsystem(SOBOLEO)tocollectandtagWebresources–  Trainingphasetobecomefamiliarwithpurposeoftagging

•  Manipula(ngstabiliza(on(λ)oftagvocabulary(highvs.lowstabiliza(on)–  Lowλ(n=12):‘Old’andinterferingtagsoftrainingphaseremaininthesystem–  Highλ(n=12):Environmentalswitch

•  Elici(ngindividuallearning:PerforminganaIributelis(ngtask–  Lis(ngaIributestogeneral,medium,andspecifictags(levelofspecificity)

•  Basic-levelshiN(e.g.[7])•  Hypothesis:Individualsofthehighλgroupgainmoreknowledgeaboutmedium

andspecifictagsthanindividualsofthelowλgroup.

[7]Close,J.&Pothos,E.(2012).“Objectcategoriza(on:Reversalsandexplana(onsofthebasic-leveladvantage”(Rogers&PaIerson,2007):Asimplicityaccount.QuarterlyJournalofExperimentalPsychology,65,1615-1632.

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0 50 100 150

010

3050

70

Consecutive tag assignments

Num

ber u

niqu

e ta

gs λ highλ low

N=H*(1–e-λt)λhigh=.009λlow=.006

Stabiliza(onongrouplevel:Higherstabiliza(oninhighthaninlowλgroup

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34

5Specificity

Num

ber l

iste

d at

tribu

tes

General Medium Specific

λ highλ low

Individuallearning:Moreknowledgeaboutmediumandspecifictags(basic-levelshi`)inhighthanlowλgroup

[2]Ley,T.&Seitlinger,P.(2015).Dynamicsofhumancategoriza(oninacollabora(vetaggingsystem:howsocialprocessesofseman(cstabiliza(onshapeindividualssensemaking.ComputersinHumanBehavior,51,140-151.

F2,21=5.06,p<.05

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•  Stabiliza(onduringcollabora(onsupportslearning

–  Tag-based(seman(c)priminginves(gatedby[6]

–  Goalsofstudies2and3:Revealinginterplayofremainingvariables•  Study2:Measuringi)contribu(onsofPM-SMinterplayandii)impactofintersubjec(vityonimita(on

•  Study3:Computa(onalmodelofmechanismsunderlyingthesevariables

[6]Fu,W.-T.,Kannampallil,T.,Kang,R.&He,J.(2010).Seman(cimita(oninsocialtagging.ACMTransac?onsonComputer-HumanInterac?ons,17,12:1-12:37.

Tag(contextual cue)


Semantic priming


Intersubjectivity

ImitationSemantic

stabilization

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Study2:Measuringtheimpactofintersubjec(vityonimita(on

•  Web-basedexperiment•  48studentsconduc(ngan

informa(onsearch•  Incidentallearning:Browsing

pictures(takenbyfamousphotographers;e.g.,HenriCar(er-Bresson)interpretedandannotatedbytagclouds

•  Taggingphase:Re-exposedtopicturestoreflectonitandderiveowninterpreta(onsandtagassignments

•  Frequencydistribu(onsfortheactofimita(ng(I)vs.notimita(ng(N)previouslyseentags•  Analysisintermsoftheore(calconstructs(e.g.,PM,SM,intersubjec(vity)through

Mul(nomialProcessingTree(MPT)derivedfromFuzzy-TraceTheory(e.g.,[8])

[8]Brainerd,C.&Reyna,V.(2010).Recollec(veandnon-recollec(verecall.JournalofMemoryandLanguage,63,425-445. 8


Automatic unloadingfrom PM

Reflective searchof memory

PM-SM interplay

Similar reflection(Intersubjectivity)

1-S

Same tag choice

1-C1

1-C2

Imitation, VI

Web resource

No Imitation, N

Imitation, ID

1-D S

C1

C2 Imitation, I

No Imitation, N

Same tag choice

Differentreflection


•  Performingmaximumlikelihoodes(ma(ontotestmodelfitandquan(fycontribu(onofcogni(veprocesses

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Automatic unloading from PM

Reflective Search of memory

PM-SM interplay

Similar reflection(Intersubjectivity)

1-S=0.81

Same tag choice

1-C1=0.37

1-C2=0.97

Imitation, I

Web resource

No Imitation, N

Imitation, ID=0.15

1-D=0.85 S=0.19

C1=0.63

Imitation, I

No Imitation, N

Same tag choice

C2=0.03Differentreflection

Modelfit,G2(4)=0.78,n.s.

P(I)=0.10

P(I)=0.02

ProbabilityP(I)=0.15


•  PM-SMinterplaycrucialtomodelstudents’interpreta(onsandannota(ons•  Intersubjec(vity(stateofreflec(veagreement)asadrivingforcebehindimita(on

andthus,stabiliza(on


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Study3:ApplyingCMRtomodelstudents’reflec(onsonWebresourcesasaPM-SMinterplay

•  MechanismsbehindPM-SMinterplaytomodelreflec(onsandstatesofreflec(veagreement(intersubjec(vity)–  Drawingoncontemporarytheoryofhumanmemory–  ContextMaintenanceandRetrieval(CMR)model[9]

•  SM:Integra(onofepisodicandseman(cknowledge•  PM:Controllinginternalcontextstate(aIen(on/’spotlight’)tosearchSM

[9]Polyn,S.,Norman,K.&Kahana,M.(2009).Acontextmaintenanceandretrievalmodeloforganiza(onalprocessesinfreerecall.PsychologicalReview,116,129-156.

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ContextMaintenanceandRetrievalModel(CMR[9])

PM-SMinterplaywhenreflec(ngonenvironmentalobjects

Article aboutlearning and

memory

“Brain” “Synapse”“Kandel”Item layer F

Context layer C

Context evolution (internal spotlight)Episodic learning (integration of item-context associations into MFC and MCF

MFC MFC MFC

MCF MCF MCF

Streamofthoughtstriggeredbyenvironmentalitem

*PM:Turningenvironmentalcuesintocontext**UsingcontexttosearchSM

[9]Polyn,S.,Norman,K.&Kahana,M.(2009).Acontextmaintenanceandretrievalmodeloforganiza(onalprocessesinfreerecall.PsychologicalReview,116,129-156.

*

**

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•  CMR:AvalidmodelofPM-SMdynamics–  Testedbyaseriesoflaboratoryexperimentsonepisodiclearning(e.g.,[9,10])

•  RQs:DoesPM-SMdynamicsformalizedbyCMRallowformodeling–  peoples’reflec(onsonWebresources?–  theeffectofintersubjec(vityonseman(cstabiliza(on?

•  RQsinves(gatedinalarge-scalesocialtaggingsystem(Delicious)–  Dataset[11]:1,685tagsfor49,691Bookmarksof2,003Wikipediaar(cles

from1,968Users•  Tes(ngaCMR-specifichypothesisaboutstabiliza(on(consensualtaguse)•  Simula(ngempiricalpaIernsbymeansofaCMR-basedmul(-agentsimula(on(MAS)

[9]Polyn,S.,Norman,K.&Kahana,M.(2009).Acontextmaintenanceandretrievalmodeloforganiza(onalprocessesinfreerecall.PsychologicalReview,116,129-156.[10]Healey,M.&Kahana,M.(2016).Afourcomponentmodelofage-relatedmemorychange.PsychologicalReview,123,23-69.[11]Zubiaga,A.(2009).Enhancingnaviga(ononwikipediawithsocialtags.InWikimania2009.WikimediaFounda(on,2009.

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Hypothesis:Decreasingintersubjec(vityduringreflec(ons

F

C

F

C

F

C

F

C

Evolving spotlight

tag1 tag2 tag3 tag4

Tag assignment TAS

•  TASasamanifesta(onofresourcereflec(on(Study2)

•  Eachsearchitera(onyieldsasingletag(posi(ont)withinTAS

•  Dri`ingspotlighthypothesis•  Thelongerwereflect,themoreindividualis(cthespotlight(internalcontext

state)shouldbe•  Intersubjec(vityshoulddecreasealongconsecu(vesearchitera(onst

(TASposi(ons)àLessimita(onandthus,seman(cstabiliza(on(consensualtaguse) atlaterTASposi(ons

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0.4

0.5

0.6

0.7

0.8

0.9

1.0

Prob

abilit

y ne

w ta

gConsecutive TAS

1 2 3 4 5 6 7 8 9 10

WitheachnewTAS,theprobabilityofanewtagdeclines~Stabiliza(on

Web resource

TAS1 = {Kandel, brain, synapse, learning}

TAS2

TAS3

TAS10…

Micro dynamics

Macrodynamics

TAS…Tagassignment

Indica(onofintersubjec(vity,implicitagreementonconceptualizinganobject(e.g.,[12])

Criterion:Seman(cstabiliza(oninasocialtaggingsystem

[12]S.Sen,S.,Lam,S.,Rashid,A.,Cosley,D.,Frankowski,D.,Osterhouse,J.,Harper,F.&Riedl,J.(2006).Tagging,communi(es,vocabulary,evolu(on.InProc.20thanniversaryconferenceonComputerSupportedCoopera(veWork(pp.181-190).ACMpress.15


•  DriNingspotlighthypothesis–  Stabiliza?onmorestronglypronouncedatearlythanlaterTASposi?onst

0.4

0.6

0.8

1.0

Prob

abilit

y ne

w ta

g p n

ew(r,t)

Consecutive TAS r1 2 3 4 5 6 7 8 9 10

1st position2nd position3rd position4th position

ExpectedpaIernofstabiliza(on

Predic(on:Stabiliza(on(slope)decreasesasTASposi(onincreases


…

…

MFC

MCF

1) Category combination of present Wikipedia article fi

3) Context evolution ci = ci-1 + β*cIN

2) Context retrievalcIN = MFCfi

4) Activation of item layerfIN = MCFci

SemanticPre-exist.

EpisodicEvolving

(1− !)!!"#!" + !!!"#!!


•  MAS–  EachagentbehavesaccordingtoCMRmodel

1)  Trainingphasebasedonarealuserhistory(sequenceofbookmarkedar(cles)Developingindividualstreamofconsciousness(episodiclearningandspotlightevolu(on)

2)  Taggingphase:Allagentsassign4tagstoeachof10furtherar(cles

Semantic utility based on reflection

u(w) = p(w|fIN)

u’(w) = u(w)[1+s(w)]Φ

O E

Environmental saliencebased on previous TAS

s(w) = p(w|fi)

5)

•  Gene(calgorithmexploringparameterspace

•  500simula(onrunswithbest-fiDngparameterset



0.4

0.6

0.8

1.0

Prob

abilit

y ne

w ta

g p n

ew(r,t)

Consecutive TAS r

DataCMR

1 2 3 4 5 6 7 8 9 10

t=1

0.4

0.6

0.8

1.0

Prob

abilit

y ne

w ta

g p n

ew(r,t)

Consecutive TAS r

DataCMR

1 2 3 4 5 6 7 8 9 10

t=3

0.4

0.6

0.8

1.0

Prob

abilit

y ne

w ta

g p n

ew(r,t)

Consecutive TAS r

DataCMR

1 2 3 4 5 6 7 8 9 10

t=4

•  Modelfit:χ2(29)=13.74,χ2crit=42.56–  CMR-basedmodelingofreflec(ngonresources

explainspaIernsqualita(velyandquan(ta(vely

•  Dri`ingspotlighthypothesisHDS

–  Slopeλofpnew(r,t)alongconsecu(verdecreaseswithincreasingt

Data CMR

pnew λ pnew λ

t=1 .580 .093 .584 .089

t=2 .639 .077 .633 .078

t=3 .669 .069 .665 .069

t=4 .708 .060 .700 .064

0.4

0.6

0.8

1.0

Prob

abilit

y ne

w ta

g p n

ew(r,t)

Consecutive TAS r

DataCMR

1 2 3 4 5 6 7 8 9 10

t=2


Tag(contextual cue)


Semantic priming


Intersubjectivity

ImitationSemantic

stabilization

Conclusion

•  Avalidmodelofpeoples’reflec(onsonWebresources–  PM-SMinterplay(spotlight-drivensearchofmemory)

•  Precisepredic(onsandmodelingofstabiliza(on–  Byimplemen(ngresultofstudy2:Imita(onasanepiphenomenonof

intersubjec(vity(stateofreflec(veagreement)

•  Studies1-3asatriangula(onof•  Fieldexperiment:Iden(fyingmutual

influencesbetweenobservablevariablesongroupandindividual

•  Mul(nomialmodelingofWeb-basedexperiments:Quan(fyingcontribu(onsoflatentvariablestoobservablebehavior

•  Mul(-AgentSimula(on:Tes(ngassump(onsondynamicsbetweenmul(plelatentandobservablevariables

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Methodologicalimplica(ons

•  Collabora(velearningscenariowellcapturedbynonlinearorganism-environmentdynamics–  Nosimplecause-effectrela(onships[13]–  Non-linearprocessesandmutualinfluencesbetweenvariables

•  Methodologicalapproach–  Goingbeyondcorrela(onalanalysis–  Computa(onalmodeling

•  Model-basedsimula(ons/predic(onsofsystemdevelopment•  Model-basedrepresenta(onandcomputa(on/analysisofcontextualinforma(onaboutastudent(temporal,seman(c,social)

[13]Larsen-Freeman,D.Cameron,L.(2008).Researchmethodologyonlanguagedevelopmentfromacomplexsystemsperspec(ve.TheModernLanguageJournal,08,200-213.

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Goingbeyondcorrela(onalanalysis

•  Advantageofcomputa(onalmodeling–  Closetophenomenatobeobserved

•  Distribu(onofinforma(onthroughnon-linearanditera(veprocesses–  Intertwiningtheoryandsta(s(cs

•  Parametersdirectlyrepresen(ngtheore(calconstructs–  Independenceofdomainanddata

•  Fundamentalmechanismsof–  learning(Hebbianlearningofseman(candepisodicassocia(ons)–  execu(vefunc(ons(scopeandcontrolofaIen(on~Spotlightandspotlight-

drivensearch)•  shouldaccountfordifferentbehavioraldata

–  Self-directednaviga(on(forma(onofinforma(ongoals,meta-cogni(veprocesses/control)»  spotlight->informa(ongoal»  PM-SMinterplaytoaccountformeta-cogni(veprocesses

–  Crea(vegroupcogni(on(trade-offbetweenstabiliza(onanddivergentthinking)»  InterplayofaIen(oncontrolandscopeofaIen(onwhenre-combining

pre-exis(ngassocia(ons

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Contribu(onstoresearchinfrastructure

•  Summerschoolontheory-drivenanalysesofhuman-webinterac(ons

–  Prof.Wai-TatFu(PartnerintwocurrentlyrunningFWFprojects)

•  DepartmentofComputerScience,UniversityofIllinoisatUrbana-Champaign

–  Topic1:Computa(onalmodelingofuserbehaviorincrea(veandself-directedlearningenvironments

–  Topic2:Designofcrea(velys(mula(ngrecommenda(onmechanisms:„Escapingtheechochamber“

•  Summerschoolonweb-basedexperimentson„accesstoknowledge“

–  Prof.HarryBahrick(PartnerinacurrentEUprojectproposal)

•  DepartmentofPsychology,OhioWesleyanUniversity

–  Topic:ApplyingMPTstoanalyzelearninginWeb-basedexperiments

•  Availabilityvs.Accessibilityofknowledge

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Contribu(onstoresearchinfrastructure

•  CoursesonR:Aflexibleenvironmentforlearninganaly(cs

–  toperformconven(onalinferen(alsta(s(cs(e.g.,ANOVA,SME,factoranalysis,etc.)

–  toperformtheory-drivenanalysesoflogfiles

–  implemen(ngandrunningsimula(ons

•  CoursesonJAVAScriptforpsychologistsandeduca(onalscien(sts

–  makinguseofexis(nglibrariestoturnresearchdesignsintoWeb-basedexperiments

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Education

2016-05-27 Venia Legendi (CEITER): Paul Seitlinger