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Characterizing the response to face pareidolia in human
category-selective visual cortex
Susan G Wardle*1, Kiley Seymour2 & Jessica Taubert3
1Department of Cognitive Science and ARC Centre of Excellence in Cognition and its Disorders, 16 University Avenue,
Macquarie University, New South Wales, 2109, Australia.
2School of Social Sciences and Psychology, Western Sydney University, Sydney, New South Wales, Australia.
3Section on Neurocircuitry, Laboratory of Brain and Cognition, The National Institute of Mental Health. 10 Center Drive,
Bethesda, Maryland, 20892, USA.
* Corresponding Author
Dr Susan Wardle
Department of Cognitive Science
16 University Avenue
Macquarie University
New South Wales, 2109, Australia.
Ph: +61 2 9850 4072
susan.wardle@mq.edu.au
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Abstract
Theneuralmechanismsunderlyingfaceandobjectrecognitionareunderstoodtooriginateinventral
occipital-temporalcortex.Akeyfeatureofthefunctionalarchitectureofthevisualventralpathwayis
itscategory-selectivity,yetitisunclearhowcategory-selectiveregionsprocessambiguousvisualinput
which violates category boundaries. One example is the spontaneous misperception of faces in
inanimateobjectssuchastheManintheMoon,inwhichanobjectbelongstomorethanonecategory
andfaceperceptionisdivorcedfromitsusualdiagnosticvisualfeatures.WeusedfMRItoinvestigate
therepresentationofillusoryfacesincategory-selectiveregions.Theperceptionofillusoryfaceswas
decodablefromactivationpatternsinthefusiformfacearea(FFA)andlateraloccipitalcomplex(LOC),
butnotfromothervisualareas.Further,activityinFFAwasstronglymodulatedbytheperceptionof
illusoryfaces,suchthatevenobjectswithvastlydifferentvisualfeatureswererepresentedsimilarlyif
allimagescontainedanillusoryface.TheresultsshowthattheFFAisbroadly-tunedforfacedetection,
not finely-tuned to the homogenous visual properties that typically distinguish faces from other
objects.Acompleteunderstandingofhigh-levelvisionwill requireexplanationof themechanisms
underlyingnaturalerrorsoffacedetection.
Keywords. face processing, fMRI, fusiform face area (FFA), lateral occipital complex (LOC), object
recognition,representationalsimilarityanalysis
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INTRODUCTION
Facepareidoliaistheperceptionofillusoryfacesininanimateobjects.Recently,facepareidoliahas
been demonstrated in non-human primates (Taubert et al. 2017), evidence that it arises from a
fundamentalaspectofafacedetectionsystemsharedacrossprimatespecies.Twokeyfeaturesof
face pareidolia are that it is typically a spontaneous andpersistent phenomenon,with the object
perceivedsimultaneouslyasbothanillusoryfaceandaninanimateobject,andsecondlythatthevisual
attributes perceived as illusory facial features are highly variable. Face pareidolia thus offers the
potential toexaminehowthebrainprocessesfaces inauniquesituationwherefaceperception is
decoupledfromthevisualpropertiesthattypicallydefinefacesasacategory.Inthehumanbrain,the
lateraloccipitalcomplex(LOC)(Malachetal.1995;KourtziandKanwisher2000)andfusiformface
area(FFA)(Kanwisheretal.1997)inoccipital-temporalcortexrespondpreferentiallytoeitherobjects
orfacesrespectively.Theinvolvementofthesecategory-selectiveregionsinthevisualventralstream
inprocessingfalsepositivefacesthathaveasimultaneousdualfaceandobjectidentityisunclear.
Several lines of evidence indicate that the response of the FFA is tightly-tuned to faces
(KanwisherandYovel2006).EarlyfMRIstudiesshowedthattheFFArespondspreferentiallytofaces
comparedtootherobjectcategories,evenifthefacesareschematicorimagined(Kanwisheretal.
1997;O'CravenandKanwisher2000;Tongetal.2000).Morerecently,face-relatedfeaturesincluding
identity and perceptual differences have been successfully decoded from fMRI BOLD activation
patterns in theFFAusingmultivariatepatternanalysis (Nestoretal.2011;GoesaertanddeBeeck
2013; Anzellotti et al. 2014; Axelrod and Yovel 2015; Zhang et al. 2016; Guntupalli et al. 2017).
ConvergingevidenceindicatesthattheresponseoftheFFAisrelatedtofaceperception.Activityin
the FFA correlateswith face detection and identification, but notwith the identification of other
complexobjects(e.g.cars)thatrequireasimilar levelofvisualexpertise(Grill-Spectoretal.2004).
Similarly,theFFAdisplaysagreaterresponsetouprightthaninvertedfaces,consistentwiththerobust
behavioral observation of superior face recognition for upright faces (Yovel and Kanwisher 2005).
Although the face-selectivity of the FFA is well-established, it is not yet known whether the
spontaneousperceptionofafaceinanexemplarfromanon-preferredcategory(e.g.inaninanimate
object)wouldmodulateactivityintheFFA.
Givenitsselectivityforshapeandobjectproperties(Malachetal.1995;KourtziandKanwisher
2000;2001),itisunclearwhetheractivityintheLOCwoulddifferentiatebetweeninanimateobjects
whichcontainillusoryfacescomparedtosimilarobjectswithoutanillusoryface.Althoughtheobject-
selectiveLOCisalsoresponsivetofacestosomedegree(YovelandKanwisher2004;2005),unlikethe
FFA,activity in LOCdoesnot correlatewithbehavioralmeasures suchas the face inversioneffect
(YovelandKanwisher2005)orthedegreeofperceivedface-likenessforsimplesilhouettes(Davidenko
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etal.2012).Thus,whilethemisperceptionofafaceinnoiseproducesanincreaseinthemagnitude
ofactivityintheFFA(Summerfieldetal.2006;Liuetal.2014)andissuggestiveofarolefortheFFA
inperceivingillusoryfaces,thepossibleroleofobject-selectivecortexinprocessingillusoryfacesis
moredifficulttopredict.
Toexaminetherepresentationofillusoryfacesinhumancategory-selectivecortex,weused
functionalmagneticresonance imaging(fMRI) torecordpatternsofblood-oxygen-level-dependent
(BOLD)activationinvisualbrainareaswhilesubjectsviewedpicturesofobjectseitherwithorwithout
illusory faces (Figure 2a). We collected 56 photographs of naturally occurring examples of face
pareidoliainobjectssuchasfood,accessories,andappliances.Foreachobjectwithanillusoryface,
wefoundasimilarimageofthesamecategoryofobjectbutwithoutanillusoryface.Importantly,this
yokedimagesetof56imageswasmatchedforobjectcontentandvisualfeaturestypicaloftheobject
category but did not contain any illusory faces. In total there were 8 unique image sets, each
containing14 images (Figure1),whichwerepresented in a standard fMRIblockeddesign (Figure
2b,c).Inordertocomprehensivelycharacterizetheresponseofvisualventralareastotheperception
of illusory faces in inanimate objects, we applied both multivariate pattern analysis and
representational similarity analysis techniques to the fMRI data. The aimswere firstly to discover
whichcategory-selectiveareasofvisualventralcortexaresensitivetotheperceptionofillusoryfaces,
andsecondlytodeterminetowhatextenttherepresentationintheseregions is influencedbythe
presenceofanillusoryface.
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Figure1.ExperimentalStimuli.All112experimentalstimulifromthe8imagesets.Fourimagesets
containimagesofillusoryfacesininanimateobjects,andfourimagesetscontainimagesofobjects
matchedforcontentand/orvisualfeatures,butwithoutanillusoryface.
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MATERIALS AND METHODS
Participants.Nineparticipants(8naivetoexperimentalaims(6female),1author)participatedin
returnforfinancialcompensationandprovidedinformedconsent.Oneadditionalparticipantwas
excludedfromanalysisduetoexcessivemovementintheMRIscanner(>20mm).Ethicalapproval
forallexperimentalprocedureswasobtainedfromtheMacquarieUniversityHumanResearchEthics
Committee(MedicalSciences).
MRIDataacquisition.MRIdatawereacquiredwitha3TSiemensVerioMRIscanneratMacquarie
Medical Imaging,MacquarieUniversityHospital.Ahigh-resolution (1x1x1mm)T1-weighted3D
whole-brain structural MRI scan was collected for each participant at the start of the session.
Functionalscanswereacquiredwitha2DT2*-weightedEPIacquisitionsequence:TR=2s,TE=32ms,
FA=80deg,voxelsize:2.5x2.5x2.5mm,inplanematrixsize:102x102.Apartialvolumecontaining
33sliceswascollectedorientedapproximatelyparalleltothebaseofthetemporallobeoranterior
commissure-posterior commissure (AC-PC) line for each participant, ensuring coverage of both
occipitalandtemporallobes.
Visualstimuliweredisplayedusingaprojectorforonesubject(1author)withresolution1280
x800andviewingdistance150cm.Experimentalstimuli(400x400pixels)subtended10.5x10.5°,
localizer stimuli (480 x 480 pixels) 12.6 x 12.6°. For the remaining 8 naive subjects, stimuli were
displayed using a flat-panel MRI-compatible 32" Cambridge Research Systems BOLDscreen with
resolution1920x1080andviewingdistance112cm.Experimentalstimulisubtended7.4x7.4°and
localizerstimuli8.8x8.8°.BehavioralresponseswerecollectedusingaLuminaMRI-compatiblebutton
box.
ExperimentalDesignandStimuli.ExperimentalcodewaswritteninMATLABwithfunctionsfromthe
Psychtoolbox(Brainard1997;Pelli1997;Kleineretal.2007),andrunonanAppleMacBookProwith
MacOSX.Thescanningsessionforeachsubjectconsistedofahigh-resolutionstructuralMRIscan,2
functionallocalizerrunstoidentifycategory-selectiveregionsFFA,LOC,andPPA,and8experimental
runs.Thefunctionallocalizerwasrunonceatthestartofthesessionfollowingthestructuralscanand
onceattheendofthesession.Theeightexperimentalrunsforeachparticipantwerecollectedin-
betweenthelocalizerruns.
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Figure2.fMRIMethods.(a)Examplestimuli:illusoryfaces(left)andmatchedobjects(right).(b)Trial
sequencewithineachimageblock.Whileinthescanner,participants(N=9)performeda1-backtask
tomaintainattention,pressingakeywheneveranimagewasshowntwiceinarow.(c)Sequenceof
blockdesign.Illusoryfaceblocksalternatedwithmatchedobjectblocks,separatedbyfixationperiods.
Fortheexperimentalruns,56colorimagesofnaturalexamplesofillusoryfacesininanimate
objectswerecollectedfromtheinternet.Foreachillusoryfaceimage,wesourcedaphotographofa
similarobjectwithoutanillusoryfaceandcroppedittomatchthefirstimageascloselyaspossible
(Figure2a). Imageswerecroppedtothesamesize,butnoothermanipulationwasmade.The112
photographsweregroupedinto8uniqueimagesetsof14imageseach,with4illusoryfacesetsand
4matchedobjectsets(Figure1).Stimuliwereshowncenteredonagrayscreenwithacentralblack
andwhite fixation bullseye (0.4° diameter for subject 1, 0.3° diameter for the all other subjects).
Duringfixationblocksthefixationbullseyeremainedonthegraybackground.Eachimageblockwas
16s in duration, followed by a 10s fixation period.Within each block, every imagewas shown in
random order for 800ms followed by a 200ms inter-stimulus-interval (Figure 2b). The order of
presentation of the images within each block was yoked between the face image sets and their
matchedobjectsets.Anewyokedrandomorderwasgeneratedeachtimeanimagesetblockwas
repeatedinthesession.Foreachblocktherewere16images:14uniqueimages,andanadditional2
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imageswhichwererepeatedforthe1-backtask.The1-backtaskwasyokedbetweenillusoryfaceand
matchedobjectblocks,suchthattherepeatedimagesonagivenblockpresentationwereidentical
between the matched image sets. Two 1-back trials occurred during each 16s block to maintain
attention,onceinthefirsthalfandonceinthesecondhalf.Participantspressedakeywhentheysaw
thesameimagetwiceinarow.Feedbackontaskperformancewasgivenattheendofeachrunafter
scanningwascomplete.Overall taskperformancewashighacross subjectsandexperimental runs
(Meanaccuracy=98.8%,SD=1.5%).
Theorderoftheexperimentalblocksalternatedbetweenillusoryobjectsandmatchedobjects
inanABAB/BABAdesign(Figure2c),withthestartingblocktypecounterbalancedacrossrunsforeach
participant.Theorderofblockpresentationwithineachrunwasrandomizedwithinthisdesignwith
theadditionalrestrictionsthat(1)amatchedimagesetneverdirectlyfolloweditsyokedfacesetand
viceversa,and(2)eachimagesetwascycledthroughonceintheruninarandomorderbeforebeing
repeated. Each of the 8 unique image sets was repeated twice per 7-minute experimental run.
Experimentalrunsbeganwitha4sfixationperiod.
Thefunctionallocalizerstimuliwerecolorpicturesoffaces,places,andobjects.54imagesfor
eachcategorywere selected fromTheCenter forVital Longevity FaceDatabase (MinearandPark
2004),theSUN397database(Xiaoetal.2010),andtheBOSSdatabase(Brodeuretal.2010;2014)
respectively. Scrambled objects for localizing object-selective region LOC (Kourtzi and Kanwisher
2000)werepre-generatedinMATLABbyrandomlyscramblingeachobjectimageinan8x8gridand
savingtheresultingimage.Foreachstimulusclass,therewere3uniqueblocksof18images.Every
timeablockwasrun,theimageswerepresentedinarandomorderandtworandomimageswere
repeatedtwiceforthe1-backtask.Participantsperformeda1-backtaskasfortheexperimentalruns,
pressingakeyeachtimeanimagewasrepeatedtwiceinarow(Meanaccuracy=96.8%,SD=3.8%).
Eachof the20 imageswithinablock (18unique+2 repeats)wasshownfor600ms followedbya
200msinter-stimulusinterval.The16sstimulusblocksalternatedwith10sfixationblocks.Eachofthe
4stimuluscategorieswasrepeatedthreetimesper5-minutelocalizerrun,onceperuniqueimageset.
The order of block presentation was in a pseudorandom order, with two different orders
counterbalancedacrossrunsforeachparticipant.Localizerrunsbeganwitha4sfixationperiodbefore
thefirststimulusblock.
fMRIdatapreprocessing.MinimalpreprocessingoftheMRIdatawasconductedusingSPM8.Foreach
observer,fMRIdataforallexperimentalandlocalizerrunswasmotioncorrectedandco-registeredto
their structural scan. No normalization or spatial smoothing was applied, and all analyses were
conductedinthenativebrainspaceofeachsubject.
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Region-of-interest(ROI)definition.CorticalreconstructionwasperformedusingFreesurfer5.3from
thestructuralscanforeachsubject.V1(range:886-1244voxels,M=1094)wasanatomicallydefined
ineachobserverbasedontheirindividualsurfacetopologyusingcorticalsurfacetemplatesapplied
totheirinflatedcorticalsurface(Bensonetal.2012).Thecategory-selectiveregions-of-interest(ROIs)
were functionally defined using standard procedures from the independent localizer runs
(Kriegeskorteetal.2009).FFA(range:29-78voxels,M=58)wasdefinedasthecontiguousclusterof
voxels inthefusiformgyrusproducedfromthecontrastbetweenfacesversusobjectsandscenes.
LOCscr(range:7-64voxels,M=39)wasdefinedasactivationonthelateraloccipitalsurfacefromthe
contrast between objects and scrambled objects. For comparison, LOCofs (range: 43-245 voxels,
M=113)wasdefinedasactivationonthelateraloccipitalsurfaceforthecontrastofobjectsversus
faces and places. Finally, PPA (range: 12-170 voxels,M=75) was defined as the peak cluster of
activationintheparahippocampalgyrusproducedbythecontrastbetweenscenesversusfacesand
objects(Kauffmannetal.2015).OverlappingvoxelswerepermittedbetweenLOCscrandLOCofs(range:
2-31voxels,M=12),butnotbetweentheotherregions.
Generallinearmodel.Toestimatethebetaweightsforeachexperimentalcondition,thefunctional
MRIdatafromtheexperimentalrunswasenteredintoaGLMinSPM8withaseparateregressorfor
eachofthe8stimulusblocks,producingoneparameterestimate(βweight)perconditionperrun(64
intotalperparticipant).Fixationwasnotexplicitlymodelledbutprovidedanimplicitbaseline.Percent
BOLDsignalchange(Figure3a)wascalculatedforeachparticipantandregion-of-interestacrossall
facevs.matchedobjectblocksusingSPM8andthemarsbar(Brettetal.2002)package.
Decodinganalysis.Decodinganalyseswereperformedseparatelyforeachparticipantintheirnative
brainspaceinMATLABusingfunctionsfromTheDecodingToolbox(Hebartetal.2014).Classification
usingalinearSVMwasconductedusingthebetaweightsforeachofthe8uniquestimulusblocks.For
leave-one-run-outclassification(Figure3b),theclassifierwastrainedontheactivationpatternsfor
each pair of the 8 conditions for 7 runs, and tested on the one run left out of the training set.
Classification accuracywas averaged across all permutations of leave-one-run-out cross-validation
folds(n=28).Forfacecross-decoding(Figure3c),theclassifierwastrainedonalldataforayoked
stimulusset (e.g.FacesSet1vs.MatchedObjectSet1)andtestedonalldata forasecondyoked
stimulusset(e.g.FacesSet2vs.MatchedObjectsSet2),andthenclassificationwasrepeatedwiththe
test and training sets swapped. This process was repeated for all possible yoked pairings, with
classificationaccuracyaveragedacrossallpermutations(n=6).Forobjectcross-decoding(Figure3d),
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two-way cross classificationwas conducted for each relevant pair as for face cross-decoding. For
example,theclassifierwastrainedontwodifferentimagesetseitherbothwithorwithoutfaces(e.g.
Faces Set 1 vs. Faces Set 2) and tested on their yokedmatched sets (e.g.MatchedObjects 1 vs.
MatchedObjects2),andthenviceversa.Classifieraccuracywasaveragedacrossallpermutationsof
possiblepairs(n=6).
Representationalsimilarityanalysis.Representationalsimilarityanalyses(Kriegeskorteetal.2008;
Kriegeskorte and Kievit 2013) was performed in MATLAB using functions from the toolbox for
representationalsimilarityanalysis(rsatoolbox)(Nilietal.2014).Categoricalmodelswereconstructed
basedonthestimulusdesign,todistinguishbetweenthetwopotentialoutcomesofinterest:whether
thesimilarityofbrainactivationpatternswasdeterminedprimarilybythepresenceofanillusoryface
orobjectcontent(Figure4c).Blueregionsindicatepredictedsimilarpatternsofactivationbetween
image set pairs, yellow regions indicate predicted dissimilar activation patterns, and grey regions
indicatemoderatedissimilarity.Theempiricaldissimilaritymatricesforeachbrainregion(Figure4a,b)
werecalculatedby1-correlationoftheBOLDactivationpatternsforeachimagesetpair.Dissimilarity
was scaled separately for each brain region to fall between 0 and 1 (min andmax dissimilarity).
CorrespondencebetweenthecategoricalRSAmodelsandthedissimilaritymatriciesforeachbrain
region(Figure4d)wereassessedbycomputingthecorrelation(Kendalltau-a)andapplyingcondition-
labelrandomizationwithFDRcorrectionappliedformultiplecomparisons(p<.05)(Nilietal.2014).
RESULTS
We focused our analysis on specific predefined regions-of-interest in ventral occipital-temporal
cortex.Ineachparticipant,welocalizedthreecategory-selectiveregions:thefusiformfacearea(FFA),
thelateraloccipitalcomplex(twoversions:LOCscrandLOCofs),andtheparahippocampalplacearea
(PPA),usingstandardfunctionallocalizers(Kanwisheretal.1997;EpsteinandKanwisher1998;Kourtzi
andKanwisher2000;Kravitzetal.2011;Parketal.2011;Kauffmannetal.2015).Asanadditional
comparisonregion,wedefinedV1anatomicallyineachsubject(Bensonetal.2012).
Univariateanalysis
Tocharacterizetheamountofactivationineachregion,wecalculatedBOLDpercentsignalchangefor
allillusoryfaceblocksversusallnon-faceblocks(Figure2a).Statisticalsignificancewasassessedfor
percentsignalchangeforillusoryfaceversusnon-faceblocksonthemeanactivationaveragedacross
participantsforeachregionofinterest(Figure2a)usingtwo-tailedpairedt-testswiththeBonferroni
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correctionappliedformultiplecomparisons(k=5)tomaintainα<0.05(α/k=.01).Illusoryfaceblocks
produced significantlyhigheractivation thannon-faceblocksasmeasuredbypercentBOLDsignal
changeforLOCscr(t(8)=4.25,p=.002814)andFFA(t(8)=8.15,p=.000038).Therewasnosignificant
differenceinactivationforillusoryfacesversusnon-facesforLOCofs(t(8)=2.89,p=.020278),PPA(t(8)=
-2.68,p=.027728),orV1(t(8)=-2.41,p=.042692).
Figure3.fMRIresultsaveragedacrosssubjects(N=9)foreachregion-of-interest.(a)BOLD%signal
change for illusory face blocks andmatched object blocks. Error bars are between-subjects SEM.
AsterisksdenoteasignificantdifferenceassessedwithBonferronipairedt-tests(*α<.05,**α<.01).
(b) Leave-one-run-out pairwise decoding of the 8 image sets. For panels b-d, asterisks denote a
significantdifferencefromchancedecodingperformance(50%)assessedwithBonferronione-sample
t-tests(*α<.05,**α<.01)anderrorbarsarebetween-subjectsSD.(c)Illusoryfacecross-decoding
results.Cross-classificationofillusoryfacesversusyokedobjectswithoutillusoryfacesacrossimage
sets(e.g.trainclassifieronFacesSet1vs.MatchedSet1;testclassifieronFacesSet2vs.MatchedSet
2).(d)Objectcross-decodingresults.Cross-classificationofobject identityacrossyokedimagesets
(e.g.trainclassifieronFacesSet1vs.FacesSet2;testclassifieronMatchedSet1vs.MatchedSet2).
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Multivariatepatternclassification
Leave-one-runoutclassification
Foraninitialsanitycheck,weperformedastandardleave-one-run-outclassificationanalysisonthe
patternsofBOLDactivationevokedby the8unique image sets.Usingpairwise classificationwith
cross-validation,alinearsupportvectormachinewastrainedonthedataforallrunsminusone,and
testedontheremainingset(Hebartetal.2014).Figure2bshowsthemeanclassificationaccuracyfor
each region-of-interest, averaged across subjects.Decoding accuracywas assessed statistically for
eachROIusingtwo-tailedone-samplet-testsonthemeanclassificationaccuraciesacrossparticipants
against chance decoding performance of 50%with the Bonferroni correction applied formultiple
comparisons(k=5)tomaintainα<0.05(α/k=.01).Decodingperformancewassignificantlyabove
chanceforallfiveROIs(Figure2b).V1:(t(8)=7.27,p=.000086),LOCscr(t(8)=4.35,p=.002444),LOCofs
(t(8)=7.40,p=.000076),PPA(t(8)=6.33,p=.000226),FFA(t(8)=11.16,p=.000004).Notethatasthe
classifieristrainedonexamplesofBOLDactivationpatternsevokedbyeachoftheuniquestimulus
blocksusingthisclassificationmethod,successfulclassificationcouldbebasedonsensitivitytolow-
levelvisualfeaturesratherthantheobjectorfacecontentoftheimagesets.Itislikelythatsuccessful
classification was based on different visual features in each ROI, however the consistently high
classifierperformancevalidatesthehighpowerofthefMRIdesignandverifiestherobustnessofthe
dataforthesubsequentanalysesofinterest.
Illusoryfacecross-classification
Weusedcross-decodingasthecriticaltestfordefiningwhethereachbrainregionwassensitivetothe
presenceorabsenceofillusoryfacesininanimateobjects(Figure2c).Alinearclassifierwastrained
onthepatternsofBOLDactivationevokedbyoneyokedsetofimages(e.g.FaceSet1vs.Matched
Set1),andtestedonthebrainactivationpatternsforanewyokedset(e.g.FaceSet2vs.MatchedSet
2). This analysis requires that the classifier be able to generalize across new images in order to
correctly classify the test set, excluding the possibility of illusory face decoding based upon brain
responsestothelow-levelvisualfeaturesofparticularimages.Cross-decodingaccuracywasassessed
usingtwo-tailedone-samplet-testswiththeBonferronicorrectionappliedformultiplecomparisons
(k=5)tomaintainα<0.05(α/k= .01).Cross-classificationof illusoryfaceswassignificantlyabove
chance in LOCscr (t(8)=4.97, p = .001096), LOCofs (t(8)= 7.21, p = .000092), and FFA (t(8)= 13.29, p =
.000001),butnotforV1(t(8)=3.10,p=.014737)orPPA(t(8)=2.44,p=.040749).
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Objectcross-classification
As our stimulus set included matched objects, we also tested whether object identity could be
decodedfromeachbrainregionacrossdifferentphotographs(Figure2d).Inthisanalysis,weignored
thepresence/absenceofanillusoryface,andinsteadtrainedtheclassifiertodiscriminatebetween
twodifferentobjectsetsthateitherbothdidordidnotcontainillusoryfaces(e.g.FaceSet1vs.Face
Set2).Theclassifierwasthentestedonthematchedsetscorrespondingtothetwotrainingsets(e.g.
MatchedSet1vs.MatchedSet2).Successfulclassificationrequiresextrapolationbasedonresponses
toeitherobject identityor sharedvisual featuresacrossdifferent images,but it isnotpossible to
distinguishbetweenthetwopossibilities.Objectcross-decodingwassignificantlyabovechancefor
LOCscr(t(8)=4.14,p=.003250),LOCofs(t(8)=5.63,p=.000491),andPPA(t(8)=4.37,p=.002379),butnot
forV1(t(8)=1.12,p=.296317)orFFA(t(8)=1.34,p=.216760).Thusbothfaceandobjectcross-decoding
weresuccessfulinLOC,butonlyfacesandnotobjectscouldbecross-decodedacrossimagesetsfrom
activityinFFA,andonlyobjectsbutnotfacesfromactivityinPPA.Neitherformofcross-decodingwas
successfulinV1,yettheidentityofthe8uniqueimagesetscouldbedecodedfromV1activityusing
leave-one-run-outclassification,consistentwithitsknownsensitivitytolow-levelvisualproperties.
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Figure4.Representationalsimilarityanalysis.(a)Dissimilaritymatrixshowingthenormalizedpairwise
dissimilarity(1-correlation)inBOLDactivationpatternsevokedbyeachofthe8uniqueimagesetsin
theFFA.Blueindicatesmoresimilaractivationpatterns,yellowmoredissimilar.Notethatthefour
uniquefaceblocksproducesimilarpatternsofactivity(upperleftquadrantisblue).(b)Dissimilarity
matrices for LOCscr, LOCofs,PPAandV1 (legendas forpanela). (c)Categoricalmodelsofexpected
patternsof similarity in theBOLDactivationpatternsevokedbyeach imageset if facesorobjects
dominatetheunderlyingrepresentation.(d)Correlation(Kendalltau-a)betweenthecategoricalRSA
models and the FFA RDM. Mean correlation across subjects (N=9) is shown, asterisks denote a
significantdifferencefromchancecalculatedusingcondition-labelrandomizationwithFDRcorrection
appliedformultiplecomparisons(p<.05).ErrorbarsareSEM.Thegraybandrepresentstheupper
andlowerboundariesofthenoiseceiling,indicatingthemaximumpossiblecorrelationpossiblefor
any model given the noise in the data (Nili et al. 2014). Note that no correlations between the
categoricalmodelsandtheotherregionsofinterest(LOCscr,LOCofs,PPA,V1)weresignificant.
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RepresentationalSimilarityAnalysis
To further characterize the response to illusory faces in visual ventral cortex, we applied
representationalsimilarityanalysis(KriegeskorteandKievit2013)toexaminesimilaritiesinactivation
patternsforthedifferentimagesets.Therepresentationaldissimilaritymatricesforeachregionof
interestquantifythedegreeofsimilarityintheBOLDactivationpatternsacrossvoxelsbetweeneach
pair of image sets (Figure 4a,b). Different models of the underlying representational space are
predictediffacesorobjectsarethedominantorganizingprincipleinaparticularbrainregion(Figure
4c). Notably, the dissimilarity matrix for the FFA (Figure 4a) reveals a strong dominance of the
presence of an illusory face on the brain activation patterns (upper left quadrant). Image sets
containingillusoryfacesproducemoresimilarresponsesinFFAthantheirmatchedimagesets,even
thoughthematchedimagesetssharemanymorevisualfeatureswiththefacesetsthanispresent
betweendifferentfacesets.Thisrelationshipiswell-describedbythefacemodel(Figure4c),which
significantlycorrelateswiththeFFAdissimilaritymatrix(Figure4d).
Beyond a similar representation of illusory faces irrespective of their visual features, the
responseoftheFFAisstronglymodulatedbythepresenceorabsenceofaface.Objectswhichdonot
haveanillusoryfaceelicitdissimilaractivationpatternstoeachotherinFFA(bottomrightquadrant
inFigure4a),aswouldbeexpectedfromdifferencesintheirobjectidentityand/orvisualfeatures.
Thisisinsharpcontrasttotheactivationpatternselicitedbytheiryokedobjectswithillusoryfaces,
whichelicithighlysimilaractivationpatternstoeachotherintheFFAdespitetheirsubstantialvisual
differences (compare upper left and bottom right quadrants in Figure 4a). In contrast, objects
containingillusoryfacesaremoderatelydissimilartothosewithoutillusoryfaces(toprightquadrant
inFigure 4a), despite their visual similarity resulting from the yoked stimulus design.Overall, the
results indicatethattheresponseofFFAisdominatedbythepresenceorabsenceofafacetothe
extent that itoverridesany influenceof thesimilarityofvisualpropertiesorobject identity in the
representation.
These relationships are well-captured by the face-nonface model (Figure 4c), which
significantlycorrelateswiththeFFAdissimilaritymatrix(Figure4d)andapproachesthetheoretical
limitofthemaximumpossiblecorrelationwiththefMRIdataasdefinedbythenoiseceiling(Nilietal.
2014).Notablytheobjectmodelfailstocapturetherepresentationalstructureofthevisualstimuliin
FFA,indicatedbyalackofasignificantcorrelationwiththeFFAdissimilaritymatrix(Figure4d).Insum,
theperceptionofanillusoryfacemediatesactivityinFFA,regardlessoftheparticularvisualproperties
that compose the facial features. Although the presence of an illusory face could be successfully
decodedfromactivationpatternsinLOCscrandLOCofs(Figure4b),noneofthedissimilaritymatrices
16
fortheotherregionsofinterest(Figure4b)significantlycorrelatedwiththecategoricalmodels(Figure
4c),suggestingthisorganizationoftherepresentationalspaceisuniquetotheFFA.
DISCUSSION
We investigated the representation of illusory faces in inanimate objects throughout category-
selectivehumanventralvisualcortex.Importantly,weusedayokedfMRIdesigntocomparepatterns
ofBOLDactivationevokedbyimagesofobjectscontainingillusoryfaceswiththatevokedbysimilar
matched objects without illusory faces. We selected only naturally-occurring examples of face
pareidoliainordertoinvestigatespontaneousfalsepositivesoffacedetectionwithahighdegreeof
varianceintheirspecificvisualproperties.Thecross-decodingresultsrevealedthatactivity inboth
face (FFA) andobject-selective (LOC) regionswasmodulatedby images that elicit facepareidolia,
regardlessof theparticularvisualelements thatcomposethe facial featuresand itsconfiguration.
GiventhatV1isparticularlysensitivetolow-levelvisualfeaturesandisimplicatedintheearlystages
ofretinotopicvisualprocessing,itisunsurprisingthattheclassifierfailedtodecodethepresenceof
an illusory face fromactivationpatterns in this region.Wealso foundnoevidence thatactivity in
scene-selectivePPAismediatedbytheperceptionofillusoryfaces.ThisisnotableasPPAisfrequently
functionally defined as voxelswhich exhibit a greater response to scenes than faces (Epstein and
Kanwisher 1998; Kravitz et al. 2011; Park et al. 2011; Kauffmann et al. 2015), thus a reduction in
activity for illusory facesmighthavebeenexpected.However, therewasalsonodifference in the
magnitudeofBOLDactivationinPPAforillusoryfacesversusmatchedobjects.
Inadditiontosuccessfulclassificationof illusory facesacross imagesets,activity inobject-
selectiveregionswasalsoinformativeaboutobjectcontent.SuccessfulobjectcrossdecodinginLOC
is consistent with previous results suggesting patterns of activation in these regions are strongly
associatedwithobjectidentity(MacEvoyandEpstein2009)andobjectshape/features(Kourtziand
Kanwisher2000).Notably,objectcrossdecodingwasalsosuccessful inPPA,butnot inFFAorV1.
Thereisconsiderableinfluenceoflow-levelvisualfeaturesontheresponseofscene-selectiveregions
(Groenetal.2017),thuscross-classificationofobjectsinPPAmaybebaseduponresponsesevoked
bysharedvisualfeaturesbetweendifferentpicturesofthesameobjectclass.
AlthoughbothFFAandLOCcontaineddecodableinformationabouttheperceptionofillusory
facesininanimateobjects,itislikelythatthestructureofthecorrespondingrepresentationsofthese
categoricallyambiguous stimuli is substantiallydifferentbetween these two regions.Beyond their
difference in preferred category (faces vs. objects) as measured by the magnitude of the BOLD
response (Kanwisher et al. 1997; Kourtzi and Kanwisher 2000), there are other known functional
differences.Previousstudieshaveobservedthatactivity inLOCdoesnotcorrelatewithbehavioral
17
measuresoffaceprocessing(YovelandKanwisher2005;Davidenkoetal.2012),whileactivityinFFA
does(Grill-Spectoretal.2004;YovelandKanwisher2005).Thisisconsistentwithourfindingthatthe
facemodelssignificantlycorrelatedwiththeFFAdissimilaritymatrix,andnotwiththat forobject-
selectiveLOC.Thissuggeststhatthepresenceofillusoryfacesisastrongerpredictoroftheunderlying
representationoftheseobjectsintheFFA,whiletherepresentationinLOCislikelytoinvolveother
factors.Further,theobjectcross-decodinganalysisrevealedthatactivityinLOCwasalsoinformative
aboutobjectidentity,whileobjectcontentcouldnotbedecodedfromtheFFA.Togethertheseresults
point to genuine functional differences in the representationof illusory faces in face- andobject-
selectivecorticalregions.
Thefunctionalarchitectureofhumanventraltemporalcortexisunderstoodtoinvolveboth
clustered category-selective regions such as the FFA, and more distributed patterns of category-
selectivityacrosslargerregionsofcortex(Grill-SpectorandWeiner2014).Animportantoutstanding
questionistounderstandthefunctionalsignificanceoftheinformationtheFFAcontainsaboutnon-
face objects (Haxby et al. 2001; O'Toole et al. 2005), which is a challenge to its face-specificity
(Kanwisher2017).ArelevantpointraisedbyourresultsisthattheresponseoftheFFAtoinanimate
objects is stronglymodulated by the spontaneous perception of an illusory face. Image setswith
diversevisualpropertiesandobjectidentitieselicitedsimilarrepresentationsiftheycontainedillusory
faces.Conversely,imagesetswiththesamedegreeofvarianceinvisualpropertiesandobjectidentity
elicitedhighlydissimilarrepresentationsifnoillusoryfaceswerepresent.Notablyevenyokedimage
setsmatchedforvisualfeaturesandobjectidentityproducedmarkedlydissimilarrepresentationsif
onlyonesetcontainedillusoryfaces,despitetheirconsiderablevisualandsemanticsimilarityonother
dimensions.Thissuggeststhatanynon-faceobjectinformationcontainedintheFFAissecondaryto
thedominanceoffaceinformationinitsrepresentationalorganization.
A significant current issue in studying the neural mechanisms of object recognition is in
disentangling the degree towhich category-selective responses in inferior temporal cortex reflect
categoryrepresentationsperse,versussensitivitytothelowlevelvisualfeatures(e.g.shape,color,
symmetry)thatco-varywithcategorymembership(CoxandSavoy2003;Baldassietal.2013;Riceet
al.2014;WardleandRitchie2014;Andrewsetal.2015;BracciandOpdeBeeck2016;Cogganetal.
2016;Kaiseretal.2016;Proklovaetal.2016;Braccietal.2017).Itisintuitivethatbydefinition,any
high-levelvisualrepresentationsmustberelatedtothevisualfeaturesthatdefineobjectcategories
to some degree. However, the impossibility of comprehensively accounting for all possible
combinations of visual features at different levels of description entails that it is empirically
challenging to provide evidence of category-representations that are not in principle reducible to
somesetofco-varyingvisualelements.Thenovelapproachwetakehereistouseastimulusclassin
18
whichfaceperceptionisdecoupledfromthetypicalvisualfeaturesthatdefinerealfaces.Secondly,
theuseofayokedexperimentaldesignmeansthatthesetsofillusoryfacessharemorevisualfeatures
withtheircorrespondingmatchedobjectsthanwitheachother,ensuringthatthevisualvarianceis
higherwithin-categorythanacross-categoryinourdesign.Thisisanextremeexampleofintroducing
highvarianceintothecategoryexemplars(BracciandOpdeBeeck2016;Braccietal.2017),asthe
visualfeaturesinourstimulussetareinsteaddiagnosticofnon-preferredobjectcategories(forthe
FFA).Afurtheradvantageofthisapproachisthatitdoesnotrequireartificiallycontrollingthevisual
contentoftheexperimentalstimuliandthereforereducingtheirecologicalvalidity(TalebiandBaker
2012).Instead,inourdesignweexploitthehighvariabilityinnaturallyoccurringfalsepositivesofface
detectionmadebytheprimatevisualsystem(Taubertetal.2017).
TogetherourresultsshowthattheFFAstronglyrespondstofalsepositivefacesininanimate
objects and is tolerant to a high degree of visual variance in what constitutes a "face". Previous
arguments for the face-selectivity of the FFA have been predominantly based on its preferred
responsetofaces,typicallyquantifiedbyahighermagnitudeoffMRIBOLDpercentsignalchangeto
faces compared to other categories (Kanwisher and Yovel 2006). In addition to human faces
(Kanwisheretal.1997),theFFAisknowntorespondtoanimalfaces(Tongetal.2000),schematic
faces(Tongetal.2000),andimaginedfaces(O'CravenandKanwisher2000).Morerecently,further
corroboratingevidencehasarisenfromsuccessfuldecodingofface-relatedpropertiesfromactivation
patternsintheFFAsuchasidentity(Nestoretal.2011;Zhangetal.2016),famousfaces(Axelrodand
Yovel2015),differencesinfacialfeaturesandtheirconfiguration(GoesaertanddeBeeck2013),and
tolerancetoviewpointrotation(Anzellottietal.2014).Importantly,ourresultsshowthattheface-
selective response in the FFA is not reducible to the specific low-level visual features that often
distinguish real faces from inanimateobjects. TheFFA isnot finely-tuned toahomogenous setof
visualfeatures.Instead,wefindthattheFFAisbroadly-tunedtothedetectionofaface,independently
oftheparticularvisualfeaturesthatformthisperception.
Object recognition and face processing are two of the most impressive computational
achievements of the human visual system, and the underlying neural mechanisms are still being
revealed.Todate thedevelopmentof the theoretical framework forunderstanding the functional
architecture of ventral temporal cortex and its role in high-level visual processing has focused on
exemplarswithdistinctcategorymembership(Kravitzetal.2013;Grill-SpectorandWeiner2014).Our
results with categorically ambiguous exemplars emphasize the complexity with which visual
informationismodulatedbyhigherlevelperceptionincategory-selectiveregionsoftheventralvisual
stream.Furtherexaminationofnatural'errors'orfalsepositivessuchasinthecaseoffacepareidolia
mayhelpconstrainmodelsofhigh-levelvisualprocessinginthefuture.
19
Acknowledgements
WethankJeffMcIntoshandthestaffofMacquarieMedicalImagingatMacquarieUniversityHospital
for assistance with the operation of the MRI scanner, and Robert Keys for assistance with data
collection.ThisworkwassupportedbyanAustralianNHMRCEarlyCareerFellowship(APP1072245)
andMacquarieUniversityResearchDevelopmentGrantawardedtoS.G.W.
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