psychophysiological modeling current state and future ... · 1 psychophysiological modeling -...
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Psychophysiologicalmodeling-Currentstateandfuturedirections
DominikR.Bach1.2,3,GiuseppeCastegnetti1,2,ChristophW.Korn1,2,4,SamuelGerster1,2,Filip
Melinscak1,2,TobiasMoser1,2
1ClinicalPsychiatryResearch,DepartmentofPsychiatry,Psychotherapy,andPsychosomatics,PsychiatricHospital,UniversityofZurich,Zurich,Switzerland
2NeuroscienceCenterZurich,UniversityofZurich,Zurich,Switzerland3WellcomeTrustCentreforNeuroimagingandMaxPlanck/UCLCentrefor
ComputationalPsychiatryandAgeingResearch,UniversityCollegeLondon,UnitedKingdom
4InstituteforSystemsNeuroscience,UniversityMedicalCenterHamburg-Eppendorf,Hamburg,Germany
Correspondence
DominikR.Bach,DepartmentofPsychiatry,Psychotherapy,andPsychosomatics,PsychiatricHospital,UniversityofZurich,Lenggstrasse31,CH-8032Zurich;
Switzerland.Email:[email protected]
Abstract
Psychologistsoftenuseperipheralphysiologicalmeasurestoinferapsychologicalvariable.Itisdesirabletomakethisinverseinferenceinthemostpreciseway,ideally
standardizedacrossresearchlaboratories.Inrecentyears,psychophysiological
modelinghasemergedasamethodthatrestsonstatisticaltechniquestoinvert
mathematicallyformulatedforwardmodels(psychophysiologicalmodels,PsPMs).
ThesePsPMsarebasedonpsychophysiologicalknowledgeandoptimizedwithrespect
totheprecisionoftheinference.Buildingonestablishedexperimentalmanipulations,
knowntocreatedifferentvaluesofapsychologicalvariable,theycanbebenchmarkedin
termsoftheirsensitivity(e.g.,effectsize)torecoverthesevalueswehavetermedthis
predictivevalidity.Inthisreview,weintroducetheproblemofinverseinferenceand
psychophysiologicalmodellingasasolution.Wepresentbackgroundandapplicationfor
allperipheralmeasuresforwhichPsPMshavebeendeveloped:skinconductance,heart
period,respiratorymeasures,pupilsize,andstartleeyeblink.ManyofthesePsPMsare
taskinvariant,implementedinopen-sourcesoftware,andcanbeusedofftheshelffora
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widerangeofexperiments.Psychophysiologicalmodelingthusappearsasapotentially
powerfulmethodtoinferpsychologicalvariables.
Keywords
analysis/statisticalmethods,autonomicnervoussystem,computationalmodeling,
electrodermalactivity,heartrate,pupillometry
1Introduction
Peripheralphysiologicalmeasurementsareoftenusedtoinferpsychologicalvariables.
Forexample,totestapsychologicalinterventionthatreducesfearmemory,aresearcher
maybeinterestedinquantifyingthestrengthoffearmemoryfromskinconductance
responses(SCR).Thispsychologicalperspectiveistheoppositeofabasic
psychophysiologicalperspective,inwhichresearchersaimtodescribehowpsychologicalvariablesimpactontheperipheralmeasure.Toenableinferenceona
psychologicalvariable,thepsychophysiologicalmappingfromthisvariabletothe
measuredsignal(the"forwardmodel"instatisticalterminology)needstobeknown
withsomecertainty,anditneedstobeexploitedinthebestpossibleway(themodel
needstobe"inverted"),toarriveatthemostpreciseestimateofthepsychological
variable.Thisisthepsychophysiologicalinverseproblem(Figure1a).
Psychophysiologicalmodelingisastatisticalframeworktosolvethisproblemina
principledmanner(Bach&Friston,2013).Itcanprovideexperiment-invariant,off-the-
shelfapplicationsthatimproveoncurrentmethodsforinverseinferenceandthereby
suggestmeaningfulmethodologicalstandardstoenhancereproducibility.
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Indeed,psychophysiologicalmodelingapproacheshavebeenappliedtoanalyzeawide
varietyofexperiments:toinferattentionalvariablesfrompupilresponses(e.g.,deGee
etal.,2017;deGee,Knapen,&Donner,2014),toinferfearlearningfromSCR(e.g.,Bach,
Weiskopf,&Dolan,2011;Bulganin,Bach,&Wittmann,2014;Tzovara,Korn,&Bach,
2018)andstartleeyeblink(Bach,Tzovara,&Vunder,2017),andtoquantifyautonomic
arousalduringperception(e.g.,Bach,Seifritz,&Dolan,2015;Hayesetal.,2013;Koban,
Kusko,&Wager,2018;Koban&Wager,2016;Sulzeretal.,2013),decision-making(e.g.,
Alvarez,etal.,2015;Bach,2015a;deBerkeretal.,2016;Nicolle,Fleming,Bach,Driver,&
Dolan,2011;Talmi,Dayan,Kiebel,Frith,&Dolan,2009),andrest(Fanetal.,2012).
Thisreviewisstructuredinthefollowingway.First,wediscusshowtocompare
methodsforinverseinferenceonpsychologicalvariablesandintroducetheconceptof
predictivevalidity.Wethenpresentpsychophysiologicalmodelingasanovelapproach,
includingaspecificimplementationcreatedbytheauthorstogetherwithrelated
methods.Inthemajorpartofthereview,wegiveatutorial-styleoverviewofthevarious
forwardmodelsandinversionmethodsdevelopedoverthepastdecade,fordifferent
physiologicalmeasures.Thefieldismovingrapidly.Whileninemethodologicalarticles
Figure1.A:Thepsychophysiologicalinverseproblem.Top:psychophysiologicalperspective(forwardinference,e.g.,DoesaversivememoryinfluenceSCR?).Bottom:psychologicalperspective(inverseinference,e.g.,Doesmyprocedureestablishaversivememory,asindexedbySCR?)B:Benchmarkinganinverseinferencemethodbyassessingpredictivevalidity:Whatisthesensitivityforinferringtheeffectofaknownexperimentalmanipulation.
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onthetopicwerepublishedbetween1993-2013,11suchpaperscameoutinthe5
yearssincethelastreviewonthetopic(Bach&Friston,2013).Thislastreview
containedahistoricalperspectiveonthedevelopmentofmodelsforSCRinthe1990s
and2000s(Alexanderetal.,2005;Bach,Flandin,Friston,&Dolan,2009;Barry,
Feldmann,Gordon,Cocker,&Rennie,1993;Limetal.,1997)andontheemergent
critiqueofoperationalism(Green,1992);here,weapproachtheproblemina
systematic,nonhistoricalmanner.
2Predictivevalidity
Allanalysismethodsforpsychophysiologicalsignalsarebasedonsomeknowledge
abouttheforwardmappingfrompsychologytophysiology.Aplethoraof
psychophysiologicalliteraturehasaddressedsuchforwardmappings.However,even
withaperfectforwardmodeltherearedifferentwaysofmakinginverseinference.For
example,onecandefinedifferentpossibletimewindowstodetectanSCRpeakafteran
experimentalevent.Extendingthepeakwindowmayincreasethesensitivityofthe
methodtodetectatrueevent-relatedresponse,butdecreaseitsspecificitybecause
experiment-unspecificpeaksmaybemistakenforevent-relatedones.Crucially,the
optimalbalanceisdifficulttointuitasis,forexample,evidentfromthecoexistenceof
differentpeakdetectionwindowsinanalysisrecommendations(Boucseinetal.,2012),
andsometimesevenwithinthesamelaboratories.Hence,itwouldbedesirableto
quantitativelyevaluateaninverseinferencemethod.
Toassessthequalityofinverseinference,onewouldideallycomparetheinferredwith
theactualvalueofthepsychologicalvariable(i.e.,with"groundtruth").Ofcourse,
groundtruthisneverknownforpsychologicalvariables.1Tosolvethisconundrum,we
havepragmaticallyproposedtouseanexperimentalmanipulationthatcanbeassumed
toinfluencethepsychologicalvariableinacertainwayandisknowntoimpactonthe
peripheralmeasure.Onecanthenevaluatemethodsbytheirsensitivitytodetectthe
impactofthisexperimentalmanipulation(Figure1b).Wehaveintroducedtheterm
predictivevalidityforthismeasure(Bach,Daunizeau,Friston,&Dolan,2010),sinceit
1Thisistrueformanyareasofscienceandtechnology.
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evaluateshowwellthepsychologicalvariablecanbepredicted.2Predictivevalidity
analysishasoftenbeenperformedonacategoricalexperimentalmanipulation(e.g.,
anticipatingthreatorsafetyinfearconditioning).Althoughitmaythusappearonthe
surfacethatpredictivevalidityboilsdowntoclassificationperformance,oneusually
aspirestoinferpsychologicalvariablesonacontinuousscale,sothatthemethod
extendstosituationsinwhichthepsychologicalvariablevariesparametricallyacross
morethantwolevels.Crucially,sincemostpsychophysiologicalmeasuresarerelatively
unspecific,validationexperimentsrequirethattheexperimentalconditionsdifferon
onlyonedimension,thepsychologicalvariableofinterest.Thisistrueforanyinverse
inferencemethod.Onceamethodwithhighpredictivevalidityisidentified,onecan
applythismethodtoother(methodologicallysimilar)experimentstoinferthesame
psychologicalvariable.
Thus,inavalidationexperiment,agoodinferencemethodprovidesanestimatorofthe
(known)psychologicalvariablethathassmallervariancethananyothermethod.Fora
categoricalvalidationexperimentwithtwolevels,thissimplymeans-becausethescale
ofthepsychologicalvariableisarbitrary-thatthestandardizeddifferenceinthe
estimatedpsychologicalvariablebetweenthesetwolevelsshouldbelarge.Thiscanbe
evaluatedbyregardingtheeffectsize,orteststatistic,ortheresidualsumsofsquaresin
apredictivemodel,orthemodelevidenceofthatpredictivemodel.Allofthese
quantitiesaremonotonicallyrelated.Usingmodelevidenceadditionallyallowsusto
makestatementswhethertwomethodsaredecisivelydifferent(Bach&Friston,2013;
seeAppendixEquation1).Atthesametime,thedifferenceintheestimated
psychologicalvariablebetweentworandompartitionsofthesameexperimental
conditionshouldbezeroonaverage.
Predictivevaliditycanbeharnessedtovalidateanyinverseinferencemethod,includingoperationalanalysis.Forthepsychophysiologicalmodelingapproach,someadditional
considerationsarewarranted.Here,psychologicalvariablesareestimatedbyoptimizing
thegoodness-of-fitoftheforwardmodel.Yet,forcomparisonofdifferentmethods,the
goodness-of-fitoftheforwardmodelisnotasuitablecriterion.Thegoaloftheforward
2Becausethepsychologicalvariableisknownapriori,onecouldalsocallit“retrodictivevalidity”.
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modelistopredictthesignal,andthegoalofinferenceistofindthemostprecise
estimateofthepsychologicalvariable.Thesetwogoalscanalignwith,beorthogonalto,
orevenopposeeachother.Intuitively,onecouldassumethat,iftheforwardmodelis
knownwithcertaintyandformulatedinmathematicalterms,oneshouldeasilybeable
toinvertthemapping.However,thereareseveralstatisticalreasonswhythisintuition
isincorrectinthegeneralcase(althoughitmaybecorrectunderspecific
circumstances).First,theforwardmodelmayuseparametersthatoneisnotinterested
ininferring.Forexample,thebestknownforwardmodelforSCRassumesthatthe
strengthoramplitudeofpsychologicalinputintothesystemisdifferentoneachtrial
(Gerster,Namer,Elam,&Bach,2017).However,manyresearchersarenotinterestedin
psychologicalvariablesonatrial-by-trialbasisbutonlyintheaveragepsychological
variablewithinanexperimentalcondition.Thestandardgenerallinearmodel(GLM)
inversionapproachforSCR(Bachetal.2009;Bach,Friston,&Dolan,2013)willnormallyyieldthesameconditionwiseestimates,regardlessofwhetherestimationwas
doneonatrial-by-trialbasisfollowedbyaveraging,oronacondition-by-conditionbasis.
Inthiscase,thesimplermodelyieldsthesameinferenceonthepsychologicalstate,
althoughempiricallyitcannotpredictSCRdatasowell,becauseitwouldassumethe
same(average)SCRamplitudeforeachtrial(Bachetal.,2013).Hence,precisionofthe
forwardmodelandoftheinferenceareunrelated.Anexamplewheretheyareopposed
isgivenbyindividualresponsefunctionsforSCR.Allevidencesuggeststhatthemapping
fromsudomotornerveactivitytoskinconductancedependsonsubject-specific
anatomicalproperties,andisvariablebetweenpersons(Bach,Flandin,Friston,&Dolan,
2010;Gersteretal.,,2017).Hence,aforwardmodeltakingthisheterogeneityinto
accountwillhaveabettergoodness-of-fitthanamodelassumingacanonicalresponse
functionacrosssubjects,aswehavealsoshownempirically(Bachetal.,,2013).Atthe
sametime,itcanbedifficulttoestimatetheshapeofanindividual'strueresponse
functionfromalimitednumberoftrialswithshortintertrialintervals,andensuing"overfitting"canmakeinferenceonthepsychologicalvariableworse,reducing
predictivevalidity(Bach,Friston,&Dolan,2013).
Tosummarize,predictivevalidityallowsastatementonthequalityofinverseinference,
regardlessofthemethodunderstudy.Itcanbeusedtobenchmarkpsychophysiological
models,operationalmethods,orevenmachine-learningmethodsthattrytofind
statisticalregularitieswithoutanyknowledgeofthepsychologicalorbiophysical
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relationships(Grecoetal.,2017;Greco,Lanata,Valenza,DiFrancesco,&Scilingo,2016;
Greco,Valenza,&Scilingo,2016).Asastatisticalframework,ithasapotentialto
improveinverseinference,tostandardizemethodsacrosslaboratories(byselectingthe
bestone),andtoprovideanobjectivemeansforqualitycontrolwithinandbetween
laboratories.Wewillreturntotheselatterpointsinthediscussion.
3PsychophysiologicalModeling
Thegoalofinverseinferenceistofindthebestpossibleestimatorofapsychologicalvariable,fromameasureddatatimeseries.Thisincludesso-calledoperationalmethods,
which"operationalize"(i.e.,equateanoisyversionof)thepsychologicalvariablewitha
singlephysiologicaldatafeature,forexample,apeak-to-troughmeasure.Because
operationalmethodsuseoneoraverysmallnumberofdatafeatures,ratherthanthe
entiretimeseries,theymaysufferfrominformationloss.Psychophysiologicalmodeling
isawayofusinganentiredatatimeseriesforinference(Figure2).Inanutshell,a
psychophysiologicalmodel(PsPM)isaformal,quantitativemodelthatmapsa
psychologicalvariableontoanobserveddatatimeseries.PsPMsarespecifiedin
mathematicalform.TheearliestPsPMsweredevelopedforSCRandexplicitlyconstitutedasequenceoftwomodels(Figure3a):aneuralmodelthatspecifiesthe
mappingofthepsychologicalvariableontosudomotornerveactivity(SNA),anda
peripheral(effectororgan)modelthatspecifieshowSNAmapsontomeasuredSCR
(Alexanderetal.,2005;Bachetal.,2009;Limetal.,1997).ForSCR,thissplitisuseful
Figure2.Operationalanalysis(top)assumesthatselecteddatafeaturesare"equivalent"toapsychologicalvariable,wheretheselectionofdatafeaturesisoftenbasedoninformalmodels.Psychophysiologicalmodelling(bottom)estimatesthemostlikelypsychologicalvariable,giventheentiredatatimeseriesandastandard(experiment-invariant)responsemodel.
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becausetheperipheralmodelcanbeevaluatedonitsownbyintraneuralstimulation
andrecordingsfromwellaccessibleperipheralnerves(Gersteretal.,2017).Forsome
othermeasures,theperipheralmodelcanbeapproximatedbyspecificstimuli,for
example,onecanuseluminancechangestoelucidatepupilmechanics.However,for
mostPsPMsthathavebeencreatedtodate,neuralandperipheralprocessesaremore
difficulttoseparateexperimentallyastheefferentnervesarelessaccessible(atleastin
humans),andincurrentmodelstheyareeithercollapsed,orthedistinctionisonlyused
formathematicalconvenience.
3.1Hybridapproaches
TheempiricaldistinctionbetweenneuralandperipheralmodelsforSCRhasearlyon
motivatedahybridapproach(Alexanderetal.,2005),engendered,forexample,inthe
Figure3.A:Basicformalismofmostpsychophysiologicalmodels.B:Relatedhybridapproaches(e.g.Ledalab,cvxEDA)useastandardresponsemodeltoinfera(noisy)timeseriesofneuralinputs,andselectdatafeaturesofthattimeseriesas"equivalent"tothepsychologicalvariable.C:Lineartimeinvariant(LTI)systemslieatthecoreofallexistingpsychophysiologicalmodellingandhybridapproaches.Aneuralinputisconvolvedwithacanonical(experiment-invariant)responsefunction,toyieldapredictionforthemeasuredsignal.
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softwaresLedalab(Benedek&Kaernbach,2010a,2010b)orcvxEDA(Greco,Valenza,
Lanata,Scilingo,&Citi,2015).Inthisapproach,adeterministicperipheralmodelis
invertedtocomputeanoisySNAtimeseriesfromthetimeseriesofmeasuredSCRdata.
Tomakeinferenceonpsychologicalvariables,somedatafeatures(peak-to-trough
measures)oftheSNAtimeseriesareextracted,inlinewithmoretraditionaloperational
analysis(Figure3b).Thissecondmappingisheuristicallymotivated,notquantitatively
specifiedorevaluated.TwodifferentstudygroupshavecomparedLedalabwithpeak-
to-troughmethodsontheonehandandafullPsPM-basedapproachontheother,in
paradigmswithrelativelyshortintertrialintervals.Onaverage,thehybridLedalab
approachwasfoundtoyieldsimilarpredictivevalidityasdirectlyusingpeak-to-trough
measuresoftheSCRdata,anditspredictivevaliditywassurpassedbyinversionoffull
PsPMs(Bach,2014;Green,Kragel,Fecteau,&LaBar,2014).Systematicevaluationof
cvxEDAhasnotyetbeenconducted.Notably,theseapproachesdifferfromthePsPMapproachalsointheirperipheralforwardmodelsandstatisticalmethodsformodel
inversion.Theycouldpossiblybeextendedtodirectlyestimatepsychologicalvariables,
butretainingtheirspecificforwardmodelsandinversionmethods.
3.2Lineartimeinvariantsystems
AllPsPMsandhybridmodelsthathavebeenproposeduptotodaycontainattheirheart
alineartimeinvariant(LTI)system(Figure3c).ALTIsystemisasystemtheoutputof
whichdoesnotexplicitlydependontime(timeinvariance),andtheoutputtothesumof
twoinputsisjustthesumoftheoutputsoftheindividualinputs(linearity).Thefirst
principleimpliesthatdifferentresponseshapesareexplainedbydifferentinputs.
Accordingtothelinearityprinciple,themagnitudeofaresponsedoesnotdependonthe
baseline.LTIsystemsareunambiguouslydescribedbythemathematicaloperationof
convolution(overlapintegral)ofaninputtimeserieswitharesponsefunction(RF),
whichcorrespondsinsignalprocessingtermstoalinearfilter(seeAppendixEquation2).LTIsystemsconstituteamathematicalsimplificationofrealbiophysicalsystems,
whichcontainmanyparametersthatcannotbeusefullyconstrainedfrommeasured
data.WewillreportforeachmeasurehowcanitbedescribedbyaLTIsystem.
3.3Modelinversion
ManyPsPMsassumethatexperimentaleventsrapidlyengageapsychologicalprocess,
whichthenfeedsintothephysiologicalsystemwithconstantlatencyandshape.Under
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theseassumptions,theamplitudeofthepsychologicalinputcanbeestimatedina
generallinear(convolution)model(Bachetal.,2009),similartostandardapproaches
foranalysisoffMRIs(Friston,Jezzard,&Turner,1994).Inanutshell,theRFisconvolved
withatimeseriesofimpulses(deltafunctions)centeredonexperimentaleventsfor
eachcondition,andtheensuingtimeseriesformcolumnsinthedesignmatrixofa
multipleregressionmodel.Theestimatedcoefficientofanindividualeventcolumn
constitutestheamplitudeestimateforthatcondition(seeAppendixEquation3-5).
Looselyspeaking,theRFisregressedontotheobservedresponse,andtheregression
coefficientistheestimateoftheinputamplitude.Iflatencyand/orshapeofthe
psychologicalinputcannotbeassumedtobeconstant,thentheyneedtobeestimatedas
well,andtheinversionmodelbecomesnonlinear,forexample,inSCRmodelsforfear
conditioning(Bach,Daunizeauetal.,2010)orstartleeyeblinkresponse(SEBR)models
(Khemka,Tzovara,Gerster,Quednow,&Bach,2017).
4Psychophysiologicalmodelsfordifferentmeasurements
Table1givesanoverviewofthedifferentpsychophysiologicalmodelsproposeduntil
today.
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Table1:Psychophysiologicalmodelsdevelopeduntiltoday.PSR:pupilsizeresponses.SCR:skinconductanceresponses.HPR:Heartperiodresponses.RPR:respirationperiodresponses. 1RAR:respirationamplituderesponses.RFRR:respiratoryflowrateresponses.SEBR:startleeyeblinkresponses. 2Measure
ment
Psychological
variable
Neuralmodel Peripheralmodel Model
inversion
Software
implementation
Publishedin
SCR Genericphasicarousal Instantaneousimpulse LTIsystemwithparameters
fromempiricaldata
GLM PsPM Bach,2014;Bach,Flandin,Friston,&
Dolan,2009;Bach,Flandin,Friston,&
Dolan,2010;Bach,Friston,&Dolan,2013
Genericphasicarousal
(validatedforfear
conditioning)
ConstrainedGaussian
impulse
LTIsystemwithparameters
fromempiricaldata
Variational
Bayes
PsPM Bach,Daunizeau,Friston,&Dolan,2010;
Staib,Castegnetti,&Bach,2015
Generictonicarousal
(validatedforanxiety
andcognitiveload)
Gaussianimpulses
withconstantshape
andunconstrained
onset
LTIsystemwithparameters
fromempiricaldata
Variational
Bayes
PsPM Bach,Daunizeau,Kuelzow,Friston,&
Dolan,2011;Bach,Friston,&Dolan,2010
Generic Notspecified LTIsystemwithparameters
fromtheoreticalconsiderations
Deterministic
inversefilter
Ledalab Benedek&Kaernbach,2010a;Benedek&
Kaernbach,2010b
Generic Discreteimpulseswith
unconstrainedonset
LTIsystemwithparameters
fromtheoreticalconsiderations
Convex
optimisation
cvxEDA Greco,Valenza,Lanata,Scilingo,&Citi,
2015
PSR Luminanceadaptation Instantaneousimpulse Combinationof2LTIswith
parametersfromempiricaldata
GLM PsPM Korn&Bach,2016
Attention Instantaneousimpulse LTIwithparametersfrom
empiricaldata
OLSestimation
infrequency
domain
Pupil Hoeks&Levelt,1993
Fearconditioning Instantaneousimpulse LTIwithparametersfrom
empiricaldata
GLM PsPM Korn,Staib,Tzovara,Castegnetti,&Bach,
2017
HPR Notyetspecified Instantaneousimpulse LTIwithparametersfrom
empiricaldata
GLM PsPM Paulus,Castegnetti,&Bach,2016
Fearconditioning Instantaneousimpulse LTIwithparametersfrom GLM PsPM Castegnetti,etal.,2016
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empiricaldata
RPR Notyetspecified Instantaneousimpulse LTIwithparametersfrom
empiricaldata
GLM PsPM Bach,Gerster,Tzovara,&Castegnetti,2016
RFRR Notyetspecified Instantaneousimpulse LTIwithparametersfrom
empiricaldata
GLM PsPM Bach,Gerster,Tzovara,&Castegnetti,2016
RAR Notyetspecified Instantaneousimpulse LTIwithparametersfrom
empiricaldata
GLM PsPM Bach,Gerster,Tzovara,&Castegnetti,2016
Fearconditioning Instantaneousimpulse LTIwithparametersfrom
empiricaldata
GLM PsPM Castegnetti,Tzovara,Staib,Gerster,&
Bach,2017
SEBR Genericstartlereflex Instantaneousimpulse
withvariablelatency
LTIwithparametersfrom
empiricaldata
Template
matching/GLM
PsPM Khemka,Tzovara,Gerster,Quednow,&
Bach,2017
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4.1Skinconductance 3
4.1.1Forwardmodel 4
Skinconductanceisoftenusedtoinferphasicortonicsympatheticarousalgeneratedby 5
awiderangeofpsychologicalstimuliandtasks(Boucsein,2012).Openingofsweat 6
glands,elicitedviathesympatheticnervoussystemwithnegligibleparasympathetic 7
transmission,causesphasicincreasesofskinconductancethataretermedSCR(see 8
Boucsein,2012,forthephysiologyofSCR).SlowCfiberscarryingimpulsestothesweat 9
glandsaretermedsudomotor(SN),andtheiractivitycanbemeasuredbyintraneural 10
recordings.Fromtheirendterminal,theneurotransmitteracetylcholinediffuses 11
throughtheskintoreachsweatglands,aprocessonthetimescaleofuptoasecond.In 12
thehistoryofpsychophysiologicalmodeling,itwasrecognizedearlyonthat 13
nonoverlappingSCRcanbewelldescribedbyasimpleresponsefunction,and 14
overlappingSCRcanbeseenasbeinggeneratedbyaLTIsystem(Alexanderetal.,2005; 15
Bachetal,2009).AllpublishedSCRmodelshavethereforeassumedthatthemapping 16
fromSNAtoSCR(butnotnecessarilyfrompsychologicalvariabletoSNA)iswell 17
describedbyaLTIsystem. 18
19
Twotypesofresponsefunctionshavebeenproposed:onebasedonabiophysicalmodel 20
ofthesweatgland,withparameterssetfromtheoreticalconsiderations(Alexanderet 21
al.,2005;Benedek&Kaernbach,2010a,2010b;Grecoetal.,2015),andapurely 22
phenomenologicalfunctionwithparametersfittedtoadatabaseof1,278SCRsfrom64 23
individualsinsixdifferentexperimentalconditions(Bach,Daunizeauetal.,2010;Bach 24
etal.,2009;Bach,Flandinetal.,,2010).Whiletheformerapproachappearstheoretically 25
morerigorous,manyofthebiophysicalparameterswerenotknownfromphysiological 26
researchandhadtobeguessed.Theensuingforwardmodelhasnotbeensystematically 27
evaluated,anditisunclearhowwellthisresponsefunctionfitsactualSCR.Incontrast, 28
thelattermodelisdefinedbyitsfittoempiricaldata.Thisphenomenologicalresponse 29
functionismathematicallydescribedbyaGaussian-smoothedexponential(Bach, 30
Flandinetal.,2010)orathird-order(linear,constant-coefficient)ordinarydifferential 31
equation;seeAppendix-Equation6,7). 32
33
TherearegoodempiricalargumentstomotivatetheuseofLTIsystemstomodelthe 34
SNA/SCRrelationship,providedthatSCRdataarehigh-passfiltered.Threekindsoftests 35
havebeenexploitedtoevaluatetheforwardmodel.Indirecttestsmadetheauxiliary 36
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assumptionthatSNburstsfollowexternalstimulationwithconstantshapeandlatency 37
(thisassumptionisnotpartoftheLTIsystem).Thesetestsshowed,thatforshortevents 38
(<1sduration)thatareseparatedbyatleast30s,morethan60%ofthevariancein 39
(high-passfiltered)SCRcanbeexplainedunderaLTImodel(Bach,Flandinetal., 40
2010),supportingtheplausibilityofthetimeinvarianceapproximation.Intheabsence 41
ofstimulation,baselinevarianceexceededtheresidualvarianceunderstimulation, 42
implyingthattheresidualvarianceisduetonoiseratherthanLTIviolations(Bach, 43
Flandinetal.,2010).SCRtopairsofstimuliseparatedbydifferentintervals(2-9s)do 44
notdependontheinterval,inlinewiththelinearityprinciple(Bach,Flandinetal., 45
2010).Amoredirecttestofthetimeinvarianceprincipleisfurnishedbyintraneural 46
recordings,whichshowthat60%-75%ofSCRvarianceisexplainedbyaLTImodelthat 47
takesSNactivityasinput,althoughthisisstillsufferingfrominterferingnon-SN(e.g., 48
vasomotornerve)activity(Gersteretal.,2017).Athirdapproachreliesonintraneural 49
stimulationwhileblockinginterferingnervetrafficbyregionalanesthesia.Here,SNis 50
stimulatedatdifferentrepetitionfrequencies,thussimultaneouslyaddressingthe 51
linearityandtimeinvarianceprinciple.Inthiscase,93%-99%ofSCRvarianceis 52
explainedunderaLTImodelwhenstimulationfrequencyisbelow0.6Hz.Abovethis 53
stimulationfrequency,theLTImodelcannotbeusefullyappliedduetostrong 54
nonlinearities(Gersteretal.,2017);however,thislimitationshouldbelargelyirrelevant 55
formostpsychologicalexperimentswithslowerstimulationrates.Tosummarize,it 56
appearsthatundersuitableconditions,aLTImodelisnotjustanapproximationbut 57
ratheranaccuratedescriptionofbiophysicalrealityintheSNA/SCRsystem.Notably, 58
tonicskinconductancecomponents,whicharefilteredoutinallmodelingapproaches, 59
donotappeartobemodeledbyafiniteLTIsystem. 60
61
TheLTImodeldoesnotmaketheassumptionthatSCRshapeisconstantbetween 62
individuals.Onthecontrary,allevidencesuggeststhatthisisnotthecase.Nevertheless, 63
inferencebasedonacanonicalresponsefunctionalreadyprovidesbetterinferencethan 64
operationalmethods.Wehaveshownthatthisinferencecanbefurtherimprovedby 65
allowingsomevariabilitybetweenindividuals,butonlyifthepossibleindividual 66
responsefunctionsareverystronglyconstrainedtoavoidoverfitting(Bachetal.,2013; 67
Staib,Castegnetti,&Bach,2015). 68
69
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ForthemappingfrompsychologicalvariabletoSNA,threedifferentforwardmodels 70
havebeendevelopedandevaluated.First,shortstimulielicitrapidSNAwithconstant 71
Q6latency(Bachetal.,2009).Itappearsthatthemappingfrompsychologicalvariable 72
toSCRislargelyinvarianttothetypeofexperimentstimulus(aversivewhitenoise 73
bursts,aversiveelectricstimulation,aversivepictures,auditoryoddballs,andavisual 74
detectiontask;Bach,Flandinetal.,,2010),whichmotivatestheuseofthismodelfor 75
phasicarousalindependentoftheelicitingstimulusorexperiment.Forlongerstimulior 76
anticipationofstimuli,amodelwithvariableSNAlatencyandshapecanbeused,and 77
thesetwoparametersareestimatedfromtheexperimentaldata(Bach,Daunizeauetal., 78
2010).Thismodelwasmotivatedspecificallytoanalyzefearconditionexperimentsin 79
whichparticipantsareexposedtoconditionedstimuli(CS),oneofwhich(CS+)predicts 80
anaversiveevent(US).Typically,thereisatimedelaybetweenCS+andUS,andso 81
participantswillanticipatetheUSduringCS+and(toadiminishingextent)duringCS- 82
presentationandexpressSCRatsome(unknownandpossiblyvariable)timepoint 83
duringthisinterval.Notably(anddifferentfrommodelsdiscussedlater),responsesto 84
CS+andCS-arethoughttobegovernedbythesamepsychologicalprocess,buttobe 85
quantitativelydissimilar.Hence,inthisapplicationofthemodel,aconditioned 86
sudomotornerveresponsetoeachCSisestimated,andthedifferenceintheiramplitude 87
betweenCS+andCS-constitutestheinferenceonfearmemory.Finally,toaccountfor 88
spontaneousSCRfluctuations,amodelisproposedinwhichconstant-shapeSNbursts 89
occurwithvariableonsetandamplitude,whichareestimatedfromthedata(Bach, 90
Daunizeauetal.,,2011). 91
92
4.1.2Inferenceonpsychologicalvariables 93
Theconstant-latencyforwardmodelisinvertedinaGLMapproach(Bachetal.,,2009) 94
andhasbeenevaluatedonindependentdatasets(Bach,2014;Bachetal.,2013)in 95
comparisontodifferentpeak-scoringmeasures(Boucseinetal.,2012)andtomeasures 96
fromthehybridLedalabapproach(Benedek&Kaernbach,2010a,,2010b).Predictive 97
validitywasassessedforthecomparisonofnegativearousingversusneutralpicture 98
presentation,positivearousingversusneutralpictures,picturepresentationversusno 99
stimulus,andfearfulversusangryfacepresentation.Predictivevaliditywasdecisively 100
higherfortheGLM-basedapproachonthemajorityofcomparisons,anditwasnever 101
decisivelysurpassedbyanothermethodintheremainingcomparisons(Bach,2014).In 102
anevaluationstudyfromanindependentlaboratory,aGLMapproachhadhigher 103
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predictivevaliditythanpeak-scoringorLedalabfordistinguishingCS+andCS-in 104
aversivelearning(Greenetal.,,2014).Fordistinguishingfivedifferentphasesofafear 105
generalizationexperiment,peakscoringappearedtohavehigherpredictivevalidity 106
(Greenetal.,2014).However,notethatthepredictivevalidityevaluationassumesthat 107
distinguishabledifferentpsychologicalstatesarecreatedbytheexperiment,whichis 108
lesswellestablishedforthelattermanipulation.Thisstudydidnotallowforassessing 109
whetheranymethodwasdecisivelybetterorworsethananother.3 110
111
Theflexible-latencyforwardmodelisinvertedinavariationalBayesapproach(Bach, 112
Daunizeauetal.,2010)andwasevaluatedonindependentfearconditioningdatasets 113
(Staibetal,2015)incomparisontodifferentpeak-scoringmeasures(Boucseinetal., 114
2012)andtomeasuresfromthehybridLedalabapproach(Benedek&Kaernbach, 115
2010a,2010b).PredictivevaliditywasassessedforthecomparisonofCS+versusCS-, 116
andwasfoundtobedecisivelyhigherforthemodel-basedapproachthanforpeak- 117
scoringorLedalabmeasures(Staibetal.,2015). 118
119
Finally,theflexible-onsetmodelisinvertedwiththesamevariationalBayesapproach 120
(Bach,Daunizeauetal.,2011)andcanbeusedtoinfertonicarousal,whichisoften 121
operationalizedasthenumberofspontaneousskinconductancefluctuations(Boucsein 122
etal.,2012).Thismethodwasevaluatedwithrespecttodistinguishingpublicspeaking 123
anxietyfromrestornonpublicspeakinganxiety,andmentalloadfromrest.Predictive 124
validityofthemodel-basedapproachandofanautomatedpeak-countmeasurewas 125
comparable(Bach&Staib,2015).Furthermore,undertheperipheralLTImodel,the 126
areaunderthecurveofatimeseriesequalsthenumberofspontaneousfluctuations 127
timestheiramplitude.Whilethisrelationhasbeenempiricallyvalidated,thesamestudy 128
alsoshowedthatthenumberofspontaneousfluctuationsaloneallowsbetterinference 129
ontonicarousalthantheareaunderthecurve(Bach,Friston,&Dolan,2010). 130
131
4.1.3Futuredirections 132
3Bayesfactorsarereportedinthisstudyaswell,buttheyarenotcomparablebetweenthemodelsevaluated.Comparisonofmodelevidenceisonlypossibleifthedependentvariableinthemodelisthesame(Burnham&Anderson,2004)butGreenetal.(2014)useestimatesofpsychologicalstateasdependentvariable,whichareobviouslydifferentbetweenmethods.
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17
Theconstant-latencyGLMapproachappearsfairlymature.Ithasbeenoptimizedwith 133
respecttomodelcomplexityanddatapreprocessing(Bachetal.,,2013)andevaluated 134
bytwodifferentlaboratories(Bach,2014;Greenetal.,2014).Currentresearchfocuses 135
onincrementalimprovements,suchasdatapreprocessingandmodelingof 136
nonlinearitiesunderspecificconditionssuchasrapideventsuccession,orwhenthe 137
sweatductsqualitativelychangetheirresponsebehavior(Tronstad,Kalvoy,Grimnes,& 138
Martinsen,2013). 139
140
Theflexible-latencymodelisalsoinamaturestageandhasbeenoptimized(Staibetal., 141
2015),butaformalevaluationbydifferentresearchlaboratoriesislacking.Intermsof 142
theforwardmodel,aquestionremainsastounderwhatconditionsadditional 143
complexityaffordedbyestimatingresponselatencyimprovesinferenceonthe 144
psychologicalvariable(i.e.,Whatisthelevelofvariabilityinresponselatencythat 145
shouldmotivatepreferringthismethodtotheconstant-latencyGLMapproach?).This 146
questionisawaitingempiricalinvestigation.Aweaknessoftheflexible-latencymodelis 147
itscomplexity,suchthatitisimpossibletoestimateallparametersatthesametimeon 148
standardPCsandclustercores.Parametersarethereforeestimatedinatrial-by-trial 149
fashionsuchthatestimationerrorsthatoccurinonetrialwillpropagateintothenext 150
one.Whilesometechnicaltricksreduceadetrimentalimpactofthissequential 151
estimation,itmaybepossibletoimproveonparameterestimationwithaglobal 152
optimizationmethodthatevaluatestheentireparameterspaceatthesametime. 153
154
Finally,theflexible-onsetmodelistheleastmatureandhasbeenevaluatedononlya 155
smallnumberofquestionsanddatasets;thereisroomforoptimizationofthismethod. 156
157
BeyondSCR,othermeasuresalsoallowinferenceonSNAand,thus,onthesame 158
psychologicalvariables.Itremainstobedeterminedwhethermeasuressuchasskin 159
potentialandskinsusceptance(Tronstadetal.,2013)orthememristorpropertiesofthe 160
skin(Pabst,Tronstad,&Martinsen,2017)yieldadditionalinformationorcanhelp 161
reducenoiseintheinverseinference. 162
163
4.2Pupilsize 164
4.2.1Forwardmodel 165
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Awiderangeofpsychologicalvariablesimpactsonpupilsize(e.g.,deGeeetal.,2014; 166
Joshi,Li,Kalwani,&Gold,2016).Pupilsizeiscontrolledbytwoantagonistmuscles:the 167
radialM.dilatatorpupillae,whichreceivessympatheticinnervationviapreganglionic 168
neuronsfromthespinalcordandpostganglionicneuronsfromthesuperiorcervical 169
ganglion,andthecircularM.sphincterpupillae,whichreceiveparasympatheticinput 170
frompreganglionicneuronsin 171
theEdinger–Westphalnucleuswithinthemidbrainandpostganglionicneuronsinthe 172
ciliaryganglion(McDougal&Gamlin,2008).TheEdinger–Westphalnucleusreceives 173
bothluminance-mediatedinputsfromtheretinaandappearstorelayinputsthatarenot 174
relatedtoluminance(e.g.,fromthelocuscoeruleus;Joshietal.,2016;Liu,Rodenkirch, 175
Moskowitz,Schriver,&Wang,2017).Thisinnervationmotivatesusingluminance 176
changestoprobethebiophysicsofphasicpupilresponses.Thefactthatthepupilis 177
controlledseparatelybybothbranchesoftheautonomicnervoussystem,andby 178
antagonisticmuscleswithdifferentmechanicalproperties,alreadysuggeststhatpupil 179
responsesmaybestbemodeledbytwoparallelLTIsystems.Asaparsimonious 180
description,amodelisproposedthatdoesnotsplitthesystemintocontributionsofthe 181
twomusclesbutratherseparatesaslowerdilation/constrictionresponsefromafaster 182
componentthatonlyoccursforconstrictions(seeAppendixEquation9,10and 183
parametersforPSR_dilandPSR_con;Korn&Bach,2016).Interestingly,theformer 184
componentdirectly(exponentially)relatestoluminance,whilethecontributionofthe 185
lattercomponentappearsindependentfromtheamountofluminancechange(Korn& 186
Bach,2016).Becauseofthedifferenttimeconstantsofthetwosystems,themodel 187
makesaninterestingcounterintuitiveprediction:lightflashesshouldleadtopupil 188
constriction,butbriefdarknessperiodsshouldalsoleadtoconstriction,whenthefaster 189
responseinducedbythereturntolightprecedestheslowerresponseinducedbythe 190
darknessperiod.Indeed,thispredictionwasconfirmedbyexperimentalobservationin 191
ourownlaboratory(Korn&Bach,2016)andbyothers(Barbur,Harlow,&Sahraie, 192
1992).Thisforwardmodelexplainedaround60%ofsignalvariance,speakingtothe 193
validityofLTIapproximation(Korn&Bach,2016).Furthermore,itwasusedtoinferthe 194
neuralinputintothepupilsystemfordifferentpsychologicaltasks(visualdetection, 195
auditoryoddballdetection,listeningtoemotionalwords).Theinferredinputlatency 196
meaningfullyrelatedtoknownunderlyingpsychologicalprocesses(Korn&Bach,2016). 197
Thisalsosuggeststhatthefasttimecourseofpupilresponsesimpliesanecessityto 198
buildneuralforwardmodelsthataretosomeextentspecifictothepsychological 199
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processstudied-different,forexample,fromtheunspecificSCRmodels-becausethe 200
timeconstantsofthepsychologicalprocessesdiffer.Onesuchmodelwasdevelopedfor 201
fearconditioning(Korn,Staib,Tzovara,Castegnetti,&Bach,2017).Here,wefoundthat 202
CS-responsesaredifferentbetweenexperiments,dependingontheperceptualmodality 203
andphysicalpropertiesoftheCS.However,theaddedimpactoftheCS+wasrather 204
constantacrossexperimentsandcouldbemodeledwithaconstant-latencyCS-evoked 205
neuralinputthatpeaksbetweenCSandUS(Kornetal.,2017).Thisneuralinput 206
appearedtobetime-lockedtotheCS,fordifferentintervalsbetweenCSandUS. 207
DifferentfromSCRmodels,thismeansthatthereisnocommonCSresponsethatdiffers 208
onlyinamplitudebetweenconditions.Instead,themodelseekstoestimatetowhat 209
extentaspecificCS+componentisexpressedoneach(CS+andCS-)trial.SincethisCS+ 210
componentmaynotbeorthogonaltotheexperiment-specificCSresponse,thismeans 211
thatallestimates(CS+andCS-)areonlyinterpretableuptoaconstant:itispossibleto 212
interpretdifferencesbetweentrialsets,ortemporalchangesintheestimatedCS 213
response,butnotthemagnitudeofresponseestimatesforindividualtrialsor 214
conditions.Formathematicalconvenience,neuralandperipheralmodelarecollapsed 215
intooneresponsefunction,whichmakesGLMinversionpossible(Kornetal.,2017;see 216
AppendixEquation9andparametersforPSR_FC).Anotherpsychologicalmodelwith 217
similarstructurebutadifferentresponsefunctionwasproposedtocapturetheimpact 218
ofattentiononpupilsize(Hoeks&Levelt,1993). 219
220
4.2.2Inferenceonpsychologicalvariables 221
PupilPsPMshavebeenusedfortwopurposes.Thefirstisdirectinferenceona 222
psychologicalvariable.InferenceonCS+memoryinfearconditioningisimplementedin 223
aGLMinversionapproach,andyieldspredictivevaliditysuperiortopeakscoringor 224
area-under-the-curvemeasures(Kornetal.,2017).Giventheshortlatencyandsignal- 225
to-noiselevelssimilarorbetterthanSCR,inferencecanbeperformedonasingle-trial 226
level.Similarly,inferenceonattentionhasbeenusedinnumerousstudies(e.g.,deGeeet 227
al.,2014,2017;Knapenetal.,2016),althoughtoourknowledgethismethodhasnot 228
beensystematicallyinvestigatedandvalidatedbeyondtheinitialdevelopmentdataset 229
(Hoeks&Ellenbroek,1993;Hoeks&Levelt,1993).Anotherapplication,distinctfromall 230
othermodelspresentedinthisarticle,istoinferthetimecourseofapsychological 231
process.Thisispossiblebecauseluminance-relatedresponsesaremediatedbynear- 232
instantaneousneuralactivityandtherebyallowcharacterizingpupilbiomechanics. 233
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20
Comparingameasuredpupiltimecoursewiththetimecourseofaluminance-related 234
responsethereforeaffordsestimatingtheneuralinputintothepupillarysystem,and 235
thusmakesinferenceonthedynamicsoftheunderlyingpsychologicalprocess(Korn& 236
Bach,2016). 237
238
4.2.3Futuredirections. 239
Pupil-sizemodelingisarelativelynewapproachandrequiresfurtherstudy. 240
Importantly,anindependentvalidationofpsychologicalinferenceisyetlacking.PsPMs 241
existforluminance-conditioned,fear-conditioned,andattention-relatedresponses,and 242
appeartocruciallydependonthetimingofthepsychologicalprocessunderstudy. 243
Potentially,pupil-sizemodelingthusoffersamoreprecisewindowintothetemporal 244
dynamicsofcognitiveprocessesthanmanyotherpsychophysiologicalvariables.Finally, 245
alotofresearchiscurrentlybeingdoneonpupilsizeanditsrelationtocognitive 246
processes,inhumansandotherspecies(e.g.,Eldar,Cohen,&Niv,2013;Joshietal., 247
2016).Itislikelythatnewmodelsandmethodswillemanatefromthisbasicresearch. 248
249
4.3Heartperiod 250
4.3.1Forwardmodel 251
Heartrateorheartperiodareoftenusedtoinferemotionalarousal,forexample,while 252
watchingpicturesorduringfearconditioning(Berntson,Quigley,&Lozano,2007; 253
Bradley,Codispoti,Cuthbert,&Lang,2001)andcanbemeasuredwith 254
electrocardiogramorpulseoxymeters.Cardiacrhythmisgeneratedlocallyintheheart, 255
butmodulatedunderslowersympatheticandfasterparasympatheticinfluence 256
(Akselrodetal.,1981).Sympatheticstimulationfrequencyappearstolinearlyscalewith 257
heartperiodchanges,notheartrate(Berntson,Cacioppo,&Quigley,1995).Therefore 258
currentPsPMsforphasiccardiacresponsesmodelheartperiod,whichismappedonto 259
thefollowingRspikeandlinearlyinterpolated.Itappearsthatvariousshortstimuli 260
inducephasicheartperiodresponses(HPR),andsixresponsecomponentscouldbe 261
identifiedinasystematicinvestigationseeAppendixEquation8andparametersfor 262
HPR_E1-E6).However,neitherthisstudynorpreviousresearchbasedonoperational 263
methodsallowadefiniteconclusionastowhichpsychologicalvariablesinfluenceeach 264
ofthecomponentsandrelatedly,whetherthesecomponentsareindependently 265
controlled.Incontrast,awell-replicatedphenomenonisfear-conditionedbradycardia 266
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21
(Castegnettietal.,2016).Thisbradycardiaresponseappearstobeaddedtoastimulus- 267
specificHPRthatoccursforbothCS+andCS-infearconditioning,similartowhatis 268
observedforpupilsizeandwiththesamelimitationsforinterpretationofresponse 269
estimates.However,differentfrompupilresponses,itappearstobetime-lockedtothe 270
USwhentheCS-USintervalisvaried(Castegnetti,Tzovara,Staib,Gerster,&Bach,2017; 271
Castegnettietal.,2016).Furthermore,relatingthebradycardiaresponsetothefirst 272
responsecomponentelicitedbyshortstimulirevealedaputativeneuralinputthat 273
peaksattheUS(Castegnettietal.,2016).Pragmatically,thePsPMforfear-conditioned 274
HPRcollapsesaneuralandperipheralsystemintooneresponsefunction,allowingGLM 275
inversion(seeAppendixEquation9andparametersforHPR_FC). 276
277
4.3.2Inferenceonpsychologicalvariables 278
TherangeofpsychologicalvariablesthatcanbeinferredfromphasicHPRtoshort 279
stimuliappearsunclearatpresent.Fear-conditionedbradycardiaisanotable,well- 280
studiedexception.UsingtheGLMapproach,fearmemorycouldbeinferredwithhigher 281
predictivevaliditythanwithpeak-scoringmethods(Castegnettietal.,2016).Unlikefor 282
SCRandPSR,attemptstoperformsingle-trialanalysesinourownlaboratoryhavenot 283
succeeded,probablybecauseheart-periodtimeseriesaredominatedbyrespiratory 284
arrhythmia,andmanytrialsarerequiredtoreducetheimpactofthisnoisecomponent. 285
286
4.3.3Futuredirections 287
ElucidatingtheforwardmodelforHPRappearsanimportanttaskbothinthecontextof 288
PsPMsandoperationalapproaches.Thefidelityofinferenceonfearmemoryhasbeen 289
demonstratedinseveraldatasetsbutrequiresindependentconfirmationfromdifferent 290
laboratories.Castegnettietal.(2017)havesuggestedthatfear-conditionedbradycardia 291
couldpotentiallybe-atleastpartly-inducedbyincreasedthoraxpressureinducedvia 292
respirationamplituderesponses;thismaybeanotherinterestingavenueofresearch. 293
294
4.4Respirationmeasures 295
4.4.1Forwardmodel 296
Mostrespiratorypsychophysiologyresearchhasfocusedonhowpsychologicalstates 297
onatimescaleof10-20suptominutesinfluencerespirationparameters(Boiten, 298
Frijda,&Wientjes,1994;Grassmann,Vlemincx,vonLeupoldt,&VandenBergh,2015; 299
Ritzetal.,2010;Vlemincx,VanDiest,&VandenBergh,2015;Wuyts,Vlemincx,Bogaerts, 300
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VanDiest,&VandenBergh,2011),butrelativelylittleisknownaboutphasic 301
respiratoryresponses.PsPMshavebeendevelopedforrespiratoryperiod,respiratory 302
amplitude,andrespiratoryflowrateresponsestobriefexternalevents,allmeasured 303
withasimplesingle-chestbeltsystemasstandardlyemployedinfMRIlaboratories(see 304
AppendixEquation8andparametersforRPR,RAR_E,andRFRR).Externaleventscause 305
responsesinthesethreemeasuresthatarecapturedwithLTIsystems,butasyetitis 306
notclearwhichpsychologicalstatescouldbeinferredfromtheseresponses(Bach, 307
Gerster,Tzovara,&Castegnetti,2016).Incontrast,wehaveshowninseveral 308
experimentsthataCS+infearconditioningelicitsabiphasicrespiratoryamplitude 309
responsethatcanbemodeledinaLTIsystem,thusallowingGLMinversion(Castegnetti 310
etal.,2017)(seeAppendixEquation9andparametersforRAR_FC).Theapproachand 311
itsinterpretationaresimilartothatforpupilsizeandheartperiod. 312
313
4.4.2Inferenceonpsychologicalvariables 314
Itappearsthatfearmemorycanbeinferredfromrespirationamplituderesponses,and 315
predictivevalidityofthisinferenceishigherforaGLMinversionthanpeakscoring 316
(Castegnettietal.,2017);however,itislowerthanformanyotherpsychophysiological 317
measures.AdistinctadvantageoftherespiratoryPsPMcouldbethatitonlyrequires 318
single-chestbeltdata,whichisstandardlyavailableinmanyMRIscanners. 319
320
4.4.3Futuredirections 321
Moreresearchisrequiredtoelucidatetherangeofpsychologicalvariablesthatcanbe 322
inferredfromrespiratorymeasures.Modelingmoresophisticatedrespiratorymeasures 323
couldbeaninterestingpossibility. 324
325
4.5Startleeyeblinkelectromyogram 326
4.5.1Forwardmodel 327
Differentfromthepreviouslydiscussedmeasures,whichareunderthedirectinfluence 328
ofapsychologicalvariable,theimpactofpsychologicalvariablesonstartleeyeblinkis 329
onlymodulatoryandrequireselicitationofastartleresponsetorevealit.Thisstartle 330
eyeblinkresponse(SEBR)itselfhasratherstereotypicaldynamics,whileitsamplitudeis 331
modulatedbydifferentpsychologicalvariables(Yeomans,Li,Scott,&Frankland,2002). 332
Thismodulationhasbeensuggestedtobalancetheprotectiveutilityofthestartle 333
responsewithitsmetabolicandopportunitycost(Bach,2015b).Importantly,SEBR 334
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23
dissociatesCS+andCS-infearconditioning,aphenomenontermedfear-potentiated 335
startle(Brown,Kalish,&Faber,1951).APsPMwasdevelopedtomodeltheimmediate, 336
briefSEBRtostartleprobesintheabsenceofanypsychologicalmanipulation(Khemka 337
etal.,2017).Thismodelexplainedabout60%ofsignalvarianceunderLTIassumptions. 338
Forinference,theneuralinputwasallowedaflexiblelatency,tobettercaptureslight 339
latencyvariationbetweenindividualsandtrials(Khemkaetal.,2017).Ascommoninthe 340
literature,themodelassumesthattheshapeofSEBRisindependentofthepsychological 341
orcognitivestate,whichonlyimpactsonitsamplitude. 342
343
4.5.2Inferenceonpsychologicalvariables 344
ThisPsPMwasemployedtoinferfearmemory,bothduringacquisitionandmemory 345
recallunderextinction(Khemkaetal.,2017).Predictivevalidityoftheinferencewas 346
comparedtofourpeak-scoringmethodswithdifferentpreprocessingsteps.Foreachof 347
threeexperiments,adifferentpeak-scoringmethodperformedbest,butacrossall 348
experiments,thePsPMapproachyieldedhighestpredictivevalidity(Khemkaetal., 349
2017). 350
351
4.5.3Futuredirections 352
TheimpactofdifferentpreprocessingmethodsonSEBRanalysisappearsnotwell 353
understood.ThisleadstoaheterogeneouspicturewhencomparingPsPMwithdifferent 354
peak-scoringmethods,andshouldbeafocusoffutureresearch.Startle-independent 355
eyeblinksareatypicalsourceofnoiseinSEBRresearch,andmodelingtheseeyeblinks 356
couldbeanimportanttopicforfurtherinvestigation. 357
358
4.6Combiningpsychophysiologicalmodels 359
Inpsychophysiologicalresearch,differentmeasurementmethodsareoftenused 360
simultaneously(inthespiritofconvergentoperationalization),butnotcommonly 361
combinedforstatisticalinferenceonapsychologicalvariable.InaPsPMapproach,this 362
maybeapossibilityandcouldimproveinferenceundercircumstanceswhereseveral 363
measuresareindicativeofthesamepsychologicalvariable.Inordertoenablesuch 364
combination,itwouldbedesirabletoclarifyqualitativelythattwomeasuresare 365
impactedbythesamepsychologicalvariable,andtoinvestigatequantitativelythe 366
dimensionalstructureofthesemeasuresacrossdifferentindividuals. 367
368
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3695Discussion 370
Psychologicalinvestigationreliesonsolvingtheinverseproblem:makinginferenceon 371
essentiallyunobservablepsychologicalvariables.Psychophysiologybenefitsfrommany 372
decadesofresearchontheforwardmappingfrompsychologicalvariablesonto 373
physiologicalmeasures.Thishasallowedthebuildingofpreciseandexplicitforward 374
models,whichcanbespecifiedinmathematicalformandinvertedtoyieldinferenceon 375
thepsychologicalvariable:psychophysiologicalmodeling.Buildingonsimple 376
experimentalmanipulationsthatyieldaknownpsychologicalstate,methodscanbe 377
evaluatedintermsoftheirpredictivevalidity(i.e.,thefidelitywithwhichtheyrecover 378
theknownstate).Thisallowscomparisonofoperationalaswellasmodel-based 379
methodsandhasrevealedthatinmanycasesPsPMsallowmorepreciseinferencethan 380
traditionaloperationalmethods,forexamplepeakscoring. 381
382
Asalimitation,thisconclusionisbasedonalimitednumberofstudiesanddatasets, 383
mostofwhich-withoneexception(Greenetal.,2014)-comefromourlaboratoryand 384
arethusbasedonthesamerecordingequipmentandrathercomparableexperimental 385
procedures.ItispossiblethatthePsPMsandinversionmethodsdevelopedinthis 386
contextoverfittheseexperimentalcircumstancesanddonotgeneralizewelltodata 387
acquiredindifferentcontexts,forexample,usingdifferentexperimentaltimingsor 388
involvingothertypesofartifacts.Notably,thesamelimitationappliestothevarietyof 389
operationalmethodsthathaveoftenbeendevelopedinandforspecificlaboratories 390
suchthatamultitudeofoperationalanalysismethodscoexistsintheliterature.Wehave 391
providedexamplesforapplicationofthePsPMsindifferentlaboratories,whichprovides 392
circumstantialevidencethatoverfittingisnotamajorissuefortheapproachpresented 393
here.However,toentirelyruleoutsuchpossibility,itwouldbedesirabletocomparethe 394
PsPMswithdifferentoperationalanalysesmethodsinmanyexperimentalsituations. 395
396
Researchonpsychophysiologicalmodelingunderlinesthenecessityforprecise 397
specificationofforwardmodel,andthusforthedetailedandmeticulousworkofbasic 398
psychophysiology.TheapplicationofPsPMsremainsrestrictedtosituationsinwhich 399
thismappingiswellknown.Indeed,PsPMsexistonlyforasmallnumberof 400
experimentalscenarios.However,theseincludestandardexperimentssuchasfear 401
conditioningandpictureviewing,andmaywellcomprisealargeproportionofapplied 402
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25
psychophysiologyresearch.Forthese,PsPMsofferimprovedinferencecomparedto 403
currentlyusedmethods.Forexploratoryresearch,operationalmethodswiththeir 404
flexibilitymaybemoreappropriate.However,asanadvantageofmodel-basedmethods 405
theirprecisespecificationimplementationreducesresearcherdegreesoffreedom. 406
Analysisflexibilityisaproblemrecognizedacrosstheentirefieldofpsychology 407
(Simmons,Nelson,&Simonsohn,2011)andpossiblymoreprevalentwithflexible 408
operationalmethods.Whilestandardizationofmethodsisanongoingeffort(Boucsein 409
etal.,2012;Lonsdorfetal.,2017),PsPMoffersrationalcriteriabeyondcommunity 410
consensusforchoosingthestandards,namely,thequalityoftheinference.Notably,all 411
methodsdiscussedinthisreviewareavailableinopen-sourcetoolboxes,mostofthem 412
intheMatlab-basedPsPMtoolbox(pspm.sourceforge.net). 413
414
Asaframeworkforevaluatingmethods,wehaveproposedtobenchmarktheir 415
predictivevalidity,thatis,theirabilitytorecoverknownpsychologicalstates(Bach& 416
Friston,2013).Wenotethatthisframeworkhaspotentiallymanymoreapplications 417
thanevaluatingPsPMsorcomparingthemtooperationalmethods.Forexample,it 418
allowspoweranalyses.Ifthefidelityofamethodisknowninastandardexperiment,it 419
isoftenpossibletoderivebest-caseadditionalassumptionsthatdefineminimum 420
samplesizesrequiredtoachieveadesiredpowerlevel.Wehaveprovidedan 421
experimentalexampleforthisinthecontextofaninterventiontoreducesynaptic 422
plasticityduringfearconditioning,asmeasuredwithSEBR(Bachetal.,2017).Asan 423
importantinsight,poweranalysisbasedonpredictivevalidityresearchhasrevealed 424
thattherequiredsamplesizes-especiallywhenusingtraditionaloperationalmethods- 425
canbemuchhigherthanwhatisthestandardinthefield.Consequently,studiesnot 426
basingtheirsamplesizeonthisorotherformalanalysesmaybeunderpowered. 427
Anotherpotentialapplicationisqualitycontrol.Labscancomparetheirmeasurement 428
methodsbetweeneachother,assuremeasurementfidelityovertime,orbenchmark 429
researchtrainees,usingpredictivevalidityofstandardmeasures.Finally,apotentially 430
powerfuluseofthisframeworkistheoptimizationofexperimentaldesign.Iftheeffect 431
ofastandardexperimentonapsychologicalvariableisknownapriori,thenonecan 432
chooseanexperimentaldesignthatbestallowsdetectingthiseffect.Asanexample,this 433
approachcanhelptofindtheoptimalbalanceofretentiontrialstomeasurefear 434
memoryrecall,forwhichwehaveprovidedanempiricalexample(Khemkaetal.,2017). 435
436
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Tosummarize,thefieldofpsychophysiologicalmodelingismovingrapidlyandhasin 437
partsalreadymatured.Withthesedevelopments,wehopetohaveprovidedtask- 438
unspecifictoolsthatfreeresearchers'resourcesfromhavingtodevelopdataanalysis 439
proceduresforeverystudy,andinsteadfocusonthepsychologicalorcognitive 440
questionstheywanttoanswer. 441
442
Acknowledgments 443
Theauthorsthanktheircolleagueswhoovertheyearshelpedestablishingthesoftware 444
PsPMandthemethodsdescribedinthisreview:JeanDaunizeau,RaymondJ.Dolan, 445
GuillaumeFlandin,KarlJ.Friston,GabrielGräni,SaurabhKhemka,PhilippC.Paulus, 446
LinusRüttimann,MatthiasStaib,AthinaTzovara. 447
448
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644
645Appendix 646
647
Modelevidence 648
Toquantifymodelevidence,PsPMusesthefollowingidentitytocomputeAkaike 649
InformationCriterion(AIC): 650
wherenisthenumberofdatapointsinthepredictivemodel,Listhemaximumofthe 651
likelihoodfunction,andkthenumberofparametersinthepredictivemodel.kis 652
constantforallmethodsthatareevaluatedinamethodscomparison.Thepredictive 653
modelusestheaprioridefinedpsychologicalvariableasdependentvariable(this 654
ensuresitisthesameacrossallmethods),andthedesignmatrixcontainstheestimated 655
psychologicalvariableand(possiblysubject-specific)interceptterms. 656
657
LTIsystems 658
Lineartimeinvariantsystemsaredefinedbythefollowingconvolutionoperation: 659
whereu(t)istheinputintothesystemattimet,histheimpulseresponsefunction(RF), 660
andτisadummyvariableoverwhichintegrationisperformed.Notethatsinceweare 661
dealingwithacausalsystem(i.e,.intime),wehaveexplicitlysetthelowerlimitofthe 662
!"# = −2 ln ! + 2! = ! ln !""! + 2! (1)
! ! = !×ℎ = ! ! − ! ℎ ! d!!
! (2)
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31
integraltozero,suchthataninputthatoccursaftertcanhavenoimpactuponthe 663
outputattimet. 664
665
GLM 666
AGLMcanbewrittenas 667
whereYisthevectorofobservations,βisavectorofinputamplitudeparameters,andϵ 668
istheerror.Xisthedesignmatrixinwhicheachcolumnisobtainedbyconvolving 669
impulsefunctionsatknowntimepointswitheachcomponentoftheRF.Eachcolumn 670
takestheform: 671
where!!(!)istheneuralinputwithunitamplitudeforconditioni,andjistheindexof 672theRFcomponent.Finally,Xalsocontainsacolumnfortheintercept.Themaximum- 673
likelihoodamplitudeestimates!canbecomputedusingtheMoore-Penrose 674pseudoinverse!!,forexampleimplementedintheMatlabfunctionpinv.m: 675
676
Skinconductanceforwardmodel 677
(a)PhenomenologicalRFdescribedasaGaussian-smoothedexponential: 678
withestimatedparameters:!! = 3.0745 !forpeaklatency;! = 0.7013fordefinitionof 679therisetime;!! = 0.3176 and!! = 0.0708todefinethetwodecaycomponents. 680(b)PhenomenologicalRFdescribedasthird-order(linear,constant-coefficient)ordinary 681
differentialequation: 682
with!! = 1.342052,!! = 1.411425,!! = 0.122505,!! = 1.533879 683 684
! = !" + ! (3)
! = !!(!)×ℎ!(!) (4)
! = !!! (5)
ℎ ! ∝ ! ! − !!
!!! ! + !! ! !",
! ≥ 0,
! ! = 12!" !! !!!!
!!!! ,
!! ! = !!!!
(6)
! = !!! + !!! + !!! − ! ! − !! = 0 (7)
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32
ModelswithGaussianresponsefunctions 685
Gaussianfunction: 686
Parametersseetable(HPR_E1-6:constant-latencyheartperiodresponses;RPR: 687
respirationperiodresponses;RAR_E:constant-latencyrespirationamplituderesponses; 688
RFRR:respiratoryflowrateresponses). 689
Responsefunction(RF) ParametersofGaussianFunction
!(mean) !(standarddeviation)HPR_E1 1.0 1.9
HPR_E2 5.2 1.9
HPR_E3 7.2 1.5
HPR_E4 7.2 4.0
HPR_E5 12.6 2.0
HPR_E6 18.85 1.8
RPR 4.20 1.65
RAR_E 8.07 3.74
RFRR 6.00 3.23
690
Modelswithgammaresponsefunctions 691
Gammafunction: 692
Parametersseetable(HPR_RC:fear-conditionedheartperiodresponses;PSR_dil: 693
luminance-evokedpupildilation;PSR_con:luminance-evokedpupilconstriction;PSR_FC: 694
fear-conditionedpupilsizeresponses;RAR_FC:fear-conditionedrespirationamplitude 695
responses;SEBR:startleeyeblinkresponses).Notethattheamplitudeparameterisleftfree 696
forallmodelsotherthantheluminancemodelsinwhichithasaphysicalinterpretation. 697
698
Responsefunction(RF) ParametersofgammaFunction
!(shape) !(scale) !!(onset) !(amplitude)
! = 1! 2! !! !!!
!!!! (8)
! = ! − !!!!!!!
!!!!!
!!Γ ! (9)
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33
HPR_FC 48.5 0.182 -3.86 -
PSR_dil 2.40 2.40 0.2 0.77
PSR_con 3.24 0.18 0.2 0.43
PSR_FC 5.94 0.75 0.002 -
RAR_FC(early) 2.570×10! 3.124×10!! −8.024×10! -RAR_FC(late) 3.413 1.107 7.583 -
SEBR 3.5114 0.0108 -
699
Modelforsteady-statepupilsize 700
701where!isthezscoredsteady-statepupildiameterand!!is the respective illuminance 702level in (in [!"] ). The parameter values are: 703! = 49.79;! = −0.50 !!" ;! = −1.05. 704 705 706
! !! = ! + !!!!! (10)