Mental Models and Their Dynamics, Adaptation, and Control: A Self-Modeling Network Modeling Approach

611 Pages·2022·26.522 MB·English

by  Jan Treur, Laila Van Ments

Studies in Systems, Decision and Control 394
Jan Treur
Laila Van Ments   Editors
Mental Models
and Their
Dynamics,
Adaptation, and
Control
A Self-Modeling Network Modeling
Approach
Studies in Systems, Decision and Control
Volume 394
SeriesEditor
JanuszKacprzyk,SystemsResearchInstitute,PolishAcademyofSciences,
Warsaw,Poland
The series “Studies in Systems, Decision and Control” (SSDC) covers both new
developments and advances, as well as the state of the art, in the various areas of
broadly perceived systems, decision making and control–quickly, up to date and
with a high quality. The intent is to cover the theory, applications, and perspec-
tives on the state of the art and future developments relevant to systems, decision
making, control, complex processes and related areas, as embedded in the fields
of engineering, computer science, physics, economics, social and life sciences, as
wellastheparadigmsandmethodologiesbehindthem.Theseriescontainsmono-
graphs, textbooks, lecture notes and edited volumes in systems, decision making
and control spanning the areas of Cyber-Physical Systems, Autonomous Systems,
SensorNetworks,ControlSystems,EnergySystems,AutomotiveSystems,Biolog-
ical Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems,
Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems,
Robotics,SocialSystems,EconomicSystemsandother.Ofparticularvaluetoboth
thecontributorsandthereadershiparetheshortpublicationtimeframeandtheworld-
widedistributionandexposurewhichenablebothawideandrapiddisseminationof
researchoutput.
IndexedbySCOPUS,DBLP,WTIFrankfurteG,zbMATH,SCImago.
AllbookspublishedintheseriesaresubmittedforconsiderationinWebofScience.
Moreinformationaboutthisseriesathttps://link.springer.com/bookseries/13304
·
Jan Treur Laila Van Ments
Editors
Mental Models and Their
Dynamics, Adaptation,
and Control
A Self-Modeling Network Modeling
Approach
Editors
JanTreur LailaVanMents
SocialAIgroup AutoLeadStar,Jerusalem,Israel
DepartmentofComputerScience
VrijeUniversiteitAmsterdam,Amsterdam
Noord-Holland,TheNetherlands
ISSN2198-4182 ISSN2198-4190 (electronic)
StudiesinSystems,DecisionandControl
ISBN978-3-030-85820-9 ISBN978-3-030-85821-6 (eBook)
https://doi.org/10.1007/978-3-030-85821-6
©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature
SwitzerlandAG2022
Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether
thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse
ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and
transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar
ordissimilarmethodologynowknownorhereafterdeveloped.
Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication
doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant
protectivelawsandregulationsandthereforefreeforgeneraluse.
Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbook
arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor
theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany
errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional
claimsinpublishedmapsandinstitutionalaffiliations.
ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG
Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland
Preface
Formalisinghumanbehaviourandmentalprocessesinaformalisedformatthatcan
beusedbycomputationaldevicesisoneoftheholygrailsinthefieldsofartificial
intelligenceandcognitivescience.Thehumanbrainissodynamicandcomplexthatit
ishardtocomprehendthatacomputercanmimichowitworks,anideathathasbeen
thebasisformanyfantasies,booksandmovies.Thisbookismeantasacontribution
tothejourneytothisholygrailandtomakingthegapbetweenhumanbehaviourand
computationaldevicesabitsmaller,withafocusinparticularonmentalprocesses
involvingmentalmodels.
Our interests in mental models and their internal simulation and learning were
inspiredbyanumberofsources,includingliteratureinareassuchas
(cid:129)
Theoriesandpracticesinlearningpsychology,educationalscienceanddidactics
andinparticularthelearningandadaptationofmentalmodelsaddressedinthis
area
(cid:129)
Theconceptofas-ifbodyloopforinternalmentalsimulationofbodystatesfor
emotions
(cid:129)
Theconceptofsimulatedperception–actionchainsforimaginationandconscious
thought
(cid:129)
Theuseoftheoryofmindbasedoninternalmentalsimulation.
These areas that all concern crucial elements of human mental processes and
behaviourprovethatitisachallengetodesignformalisedandcomputationalmodels
forsuchphenomenainvolvingmentalmodelsaccordingtosomegeneralarchitecture.
Mentalmodelsareoftenconsideredasconsistingof(networksof)relationsbetween
nodes. Such an architecture needs different types of representation for the states
involved as nodes in a mental model (internal simulation can take place by their
activation)butalsofortherelationsbetweenthesestates(thattriggertheactivationsof
thestates).Whenmentalmodelsareusedforinternalsimulation,theirstateschange
overtimebasedontheserelations.Moreover,theserelationsadaptwhenthemental
modelislearnt,forgottenorrevisedovertime.Thissuggeststhattoaddressmental
processes involving mental models, dynamic representations are required both for
thestatesandfortherelations.Moreover,ontopofthatsomeformofmetacognition
(metacognitivecontrol)isneededtocontroltheseinternalsimulationandadaptation
v
vi Preface
processes. Together, these are non-trivial and challenging requirements to achieve
anappropriatetypeofformalisedcomputationalarchitecture.
In addition to the above-mentioned areas in the literature, our shared interest
in mental models was influenced by a number of case studies addressing specific
phenomena that involve mental models, for example, the influence of cognitive
metaphors on behaviour and the influence of mental God models on behaviour.
Moreover,theideathatapost-traumaticstressdisorderisbasedonalearntbutunde-
sired mental model gave another inspiration. It is in these application cases that
somefirstideasweredevelopedonhowcomputationalmodelscanbedesignedfor
mentalprocessesinvolvingmentalmodels.Asafirststep,usinganetwork-oriented
modellingapproach,examplenetworkmodelsweredevelopedforthesethreecases,
where:
(cid:129)
A mental model was modelled by a subnetwork of an overall mental network
model;thissubnetworkrepresentsthenodesandrelationsofthementalmodel.
(cid:129)
The use of a mental model for internal simulation as part of the overall mental
processwasmodelledaspropagationofactivationwithinthissubnetworkrepre-
sentingthementalmodel;outcomesofsuchformsofinternalsimulationthencan
beusedasabasistodecideaboutactionsorbehaviour.
(cid:129)
Learning or adaptation of the mental model was modelled and implemented
separatelyfromthenetworkmodel,tomakethenetworkanadaptivenetwork.
(cid:129)
Nometacognitivecontroloftheseprocesseswasexplicitlymodelled.
Asasecondstep,itwasfoundouthowadaptivenetworkscanalsobemodelledin
anetwork-orientedmannerbyusingso-calledself-modellingnetworks(alsocalled
reified networks). Self-modelling networks include self-models with nodes repre-
senting part of the network’s own structure, for example, nodes representing the
weights of some of its connections. Using this notion, to introduce new adapta-
tionprinciples,nonewimplementationshavetobemade,asthosenewadaptation
principlescanbeaddressedatthemodellinglevelinstead.Byusingaself-modelling
network,foramentalmodelwithitsnodesandrelationsmodelledasa(base)subnet-
work,therelationsofthementalmodelcanexplicitlyberepresented(andadapted)
byself-modelnodes.Thisindeedprovidesthedifferentrepresentationsneededfor
mentalprocessesbasedonamentalmodel.Then,inadedicatedsoftwareenviron-
ment,oneandthesamecomputationalself-modellingnetworkenginecanbeused
in a generic manner to process both the internal simulation of a mental model as
propagation of activation within the subnetwork for mental model and the mental
modeladaptationaspropagationofactivationwithintheself-modelofthesubnet-
workforthementalmodel.Moreover,astheconstructionofself-modellingnetworks
can easily be iterated to obtain multiple orders of self-models, it was found that
also metacognitive control can easily be modelled in this way by a second-order
self-modelintheself-modelingnetwork.
Inrecentyears,thisapproachhassuccessfullybeenappliedtomodeladiversityof
specificmentalprocessesbasedonmentalmodels,asillustratedinmanychaptersin
thisvolume.Inparticular,inPartsII(forindividualcases)andIII(forsocialcases),
each including six chapters. These cover the three examples mentioned above in
Preface vii
Chap.5(PTSDasundesiredmentalmodel),Chap.10(cognitivemetaphorsasmental
models)andChap.11(mentalGodmodels).OtherchaptersinPartIIaddressmental
modelsforlearninghowacarworksandhowtodriveit(Chap.3),usingmetacog-
nitivecontroltohandlemultiplementalmodels(Chap.4),learningbycounterfac-
tualthinking(Chap.6),complexandadaptiveanalysisandsupporttasks(Chap.7)
and self-referencing, self-awareness and self-interpretation within psychotherapy
sessions (Chap. 8). Other chapters in Part III address mental models for learner-
controlledlearning(Chap.9),attachmenttheory(Chap.12),controlledbondingby
homophily(Chap.13)andsharedmentalmodelsinhospitalteams(Chap.14).Note
that there is some overlap between the chapters concerning the general modelling
approachused,inordertoallowforreadingthechaptersseparately.
In addition, two chapters on philosophical perspectives on mental models are
includedinPartIV.ThefirstofthemisChap.15,whichproposescontext-dependent
realisationknownfromphilosophyofmindasaconceptualsolutionfortheissuethat
different mental models may have different types of neural correlates. The second
chapter from a philosophical perspective is Chap. 16, where it is shown how the
approach to dynamics and adaptivity used here for mental models can be used to
assign emerging informational content to mental models in a way that fits well in
thegeneralapproachestodynamicsandadaptivitybasedontemporalfactorisation
(Treur, 2007), relational specification of mental content (Kim, 1996), and criterial
causation(Tse,2013).
InPartV,somemoregeneralbackgroundisincludedonthemodellingapproach
based on self-modelling networks used here: on the supporting software environ-
ment(Chap.17),analysisofself-modellingnetworkbehaviourintermsofnetwork
structure(Chap.18),validationofnetworkmodelsbyparametertuning(Chap.19)
and mathematical analysis of the wide applicability of self-modelling networks to
modelanyadaptivedynamicalsystem(Chap.20).
InPartVIinChap.21,anevaluativeanalysisispresentedofwhattheformatof
aself-modellingnetworkcanoffertothemodellingofmentalprocessesinvolving
mentalmodels,coveringwhatalreadyhasbeenachievedandgeneratingoptionsthat
still are open for further exploration. As an example, while the current book was
alreadyinpress,foroneofthoseoptionsleftopeninthisbookithasbeenfoundout
byGülayCanbalog˘luandJanTreurthattheapproachintroducedhereisveryuseful
toobtaininasystematicmannercomputationalmodelsformultilevelorganisational
learning,anareawhichlackedsuchcomputationalmodelsuntilnow.Anextbookby
GülayandJan(togetherwithAnnaWiewiorafromtheareaofManagementScience)
specificallyaddressingthisareabasedonthemodelingapproachintroducedinthe
currentbookwillcomeoutlaterthisyearoratthebeginningofnextyear.
Finally, we would like to thank Raj Bhalwankar, Gerrit Glas, Peter Roelofsma,
Audrey Hermans, Selma Muhammad, and Jan Klein for their contribution to the
developmentofthisinterestingresearchareaandasauthorsofoneormorechapters
viii Preface
tothisbookinparticular,andSefvanMentsforhishelpinthefinalproofreadingof
thebook.
Amsterdam,TheNetherlands JanTreur
Jerusalem,Israel LailaVanMents
November,2021
Contents
PartI Introduction
1 Dynamics, Adaptation and Control for Mental Models:
ACognitiveArchitecture ...................................... 3
LailavanMentsandJanTreur
2 BringingNetworkstotheNextLevel:Self-modelingNetworks
forAdaptivityandControlofMentalModels .................... 27
JanTreur
PartII Self-ModellingNetworkModelsforMentalModelsin
IndividualProcesses
3 On Becoming a Good Driver: Modeling the Learning
ofaMentalModel ............................................. 59
RajBhalwankarandJanTreur
4 Controlling Your Mental Models: Using Metacognition
toControlUseandAdaptationforMultipleMentalModels ....... 81
JanTreur
5 DisturbedbyFlashbacks:AControlledAdaptiveNetwork
ModelAddressingMentalModelsforFlashbacksfromPTSD ..... 99
LailavanMentsandJanTreur
6 ‘WhatifIWouldHaveDoneOtherwise…’:AControlled
AdaptiveNetworkModelforMentalModelsinCounterfactual
Thinking ..................................................... 117
RajBhalwankarandJanTreur
7 Do You Get Me: Controlled Adaptive Mental Models
forAnalysisandSupportProcesses ............................. 139
JanTreur
ix