Using neurocomputational modelling to investigate...

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Usingneurocomputationalmodellingtoinvestigatemechanisms

underlyingsocio-economicstatuseffectsoncognitiveandbrain

development

MichaelS.C.Thomas1,2

1DevelopmentalNeurocognitionLab,CentreforBrainandCognitive

Development,Birkbeck,UniversityofLondon,UK2UniversityofLondonCentreforEducationalNeuroscience

Toappearin:S.Lipina,M.SoledadSegretin,M.,&E.Pakulak(Eds.)(2019).

Proceedingsofthe12thMindBrainandEducationSchool,Erice,Sicily:

Neuroscienceofpoverty.CLACSO.

Runninghead:NeurocomputationalmodellingofSESeffectsondevelopment

Addressforcorrespondence:

Prof.MichaelS.C.ThomasDevelopmentalNeurocognitionLabCentreforBrainandCognitiveDevelopmentDepartmentofPsychologicalScience,BirkbeckCollege,MaletStreet,BloomsburyLondonWC1E7HX,UKEmail:m.thomas@bbk.ac.ukTel.:+44(0)2076316386Fax:+44(0)2076316312

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Abstract

Thischapterproposestheutilityofonetoolwithincognitiveneuroscience–neurocomputationalmodelling–forunderstandingthemechanismsthatunderlietheeffectsoflowsocio-economicstatus(SES)oncognitiveandbraindevelopment.ThelargeempiricalliteratureinthisfieldmainlycomprisescorrelationaldatalinkingmetricsofSEStocognitiveoutcomes,educationaloutcomes,andmeasuresofbrainfunctionandstructure.Mechanisticmodelsarerequiredtounifythesedataandprovideafoundationforeffectiveintervention.Amulti-levelartificialneuralnetworkmodelofcognitivedevelopmentisdescribedthatsimulateseffectsofSESintermsofdifferencesincognitivestimulation,againstabackgroundofgeneticvariationincognitiveabilityacrossapopulation.Fiveempiricaleffectsaresimultaneouslycapturedbythemodel:gapsinIQacrossSESthatwidenwithdevelopment(vonStumm&Plomin,2015),increasingrestrictiveeffectsoflowSESonchildrenwithearlyhighcognitiveability(Feinstein,2003),highheritabilityofcognitiveability(Plominetal.,2016),geneticeffectsonsocialmobility(Ayorechetal.,2017),andnon-lineareffectsofSESonmeasuresofbrainstructure,suchascorticalsurfacearea(Nobleetal.,2015).Implicationsandlimitationsofthemodelarediscussed.Computationalmodelsareavaluabletooltocomplementothercognitiveneurosciencemethodsforunderstandingcausalpathwaysofpovertyoncognitiveandbraindevelopment.Keywords:socio-economicstatus;computationalmodelling;artificialneuralnetworks;cognitivedevelopment;behaviouralgenetics;brainimaging

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Povertyisaboutpeople’slives.Inequality,oneofitsmajordrivers,isasocialissue.Cognitiveneuroscientistshavebecomeincreasinglyinterestedinhowbeingraisedinpovertyimpactschildren’sbrainandcognitivedevelopment.Buthowcanitbeusefultoreducepeopletoinstancesofindividualbrainfunction?Povertyistheresultofsocialstructuresandthereforeafocusonneurosciencewouldappeartobeadistraction(Farah,2017).

Thereareatleastthreereasonswhyacognitiveneuroscienceapproachmaybeuseful.First,asweshallsee,socioeconomicstatus(SES)–typicallymeasuredbyacombinationoffamilyincome,parentaloccupation,andparentaleducation–hasbeenfoundtocorrelatewithdifferencesinbrainstructure,brainfunction,cognitiveability,andeducationalachievement.However,manyfactorsco-occurwithlowSES(see,e.g.,Hackmanetal.,2015).Mothersmaybemorestressed,havepoorerdiets,andmoredrugexposurewhilepregnant;childrenmayberaisedinlessnurturing,morepolluted,andmoredangerousenvironments;theremaybelesssocialorneighbourhoodsupport,poorerschools,andlesssupportiveattitudestoeducation;childrenmayhavefewerresourcesandopportunitiesforcognitivestimulationandlearning.Thisarrayoffactorsmaynotallbeequallyresponsibleforproducinghealth,cognitive,andeducationaloutcomes.IfthebiologicalcausalpathwaysofSESeffectsareidentified,thiscanhelptotargetthemostefficientinterventionstoalleviatethedownstreameffectsofpoverty.Suchinterventionsoffershort-termmeasures,whilethelonger-termsocialgoalofreducingpovertycanbepursued.

Second,thereisastraightforwardsenseinwhichevidencethatpovertyaffectsthebraininmeasurablewaysisapowerfulmessagetopolicymakers.Abrainimageisworthathousandwords.Braindata,however,representadouble-edgedsword,becausepolicymakersmaybeliabletothinkthateffectsobservedonbrainstructureandfunctionarethenimmutable.Theyarenot,becauseweknowthatthebrainisplastic,andbehaviouralinterventionscanimproveoutcomes.Astudyofbrainmechanismsmustalso,therefore,emphasisethismessageandseektoidentifypathwaystoremediateobserveddeficits.

Third,workineducation,thesocialsciences,andthecognitivescienceshasgeneratedalargebodyofempiricaldataonoutcomesthatarecorrelatedwithSES.Butthesecorrelationaldataareopentomisunderstandingandmisinterpretationiftheunderlyingmechanismsarenotunderstood.Herearethreeexamplesofempiricaldataandthreerespectivepossibleinterpretations.

(1) Gapsinchildren’sIQs(cognitiveability)acrosslevelsofSESareevidentfrominfancyandthesegapswidenthroughchildhoodandadolescence(vonStumm&Plomin,2015).Someprocessmustbegettingworseacrosschildhoodtomakethegapswiden.

(2) Whenchildrenaresplitintobrighterandlessbrightgroupsaroundtwoyearsofageandthenfollowedup,overtimebrighterchildrenfrompoorerbackgroundsfallbackcomparedtotheirpeers,andbyage10,theyhavebeenovertakenbylessbrightclassmatesfromricherfamilies(Feinstein,2003).Withage,children’srankintheirclassisincreasinglyconstrainedbyenvironmentalfactorssuchasSES.Fromdatalikethese,policymakershaveconcludedthatearlypotentialislostthroughenvironmentalfactorssuchaspoorchildcare,poorearlyyearseducation,

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poorschoolingandlackofaccesstohealthservices(HMGovernment,2003).

(3) Onewaytomeasuresocialmobilityistoassesswhetherchildrenreacha

higherlevelofeducationalattainmentthantheirparents.Onthismeasure,however,atleasthalfthevariabilitycanbelinkedtogenes(Ayorechetal.,2017).Geneticswouldseemtoplacelimitsonhowmuchsocialmobilitycanbeinfluencedbyinterventions.Dogenesrestrictwhetherchildrencanescapepovertythrougheducation?

ThischapteroutlinesonemethodologywithincognitiveneurosciencetoinvestigatethemechanismsunderlyingSESeffectsonbrainandcognition:multi-levelneurocomputationalmodelsofcognitivedevelopment.Themodelpresentedherewasappliedtoeachoftheaboveempiricaleffects.Itgeneratedalternativeinterpretationsofeachsetofempiricaldata(Thomas,Forrester&Ronald,2013;Thomasetal.,2018;Thomas&Meaburn,2018).SESeffectsonbrainandcognitivedevelopmentWebeginwitha(very)briefoverviewoftheexistingempiricalliterature.WeknowthatdifferencesinSEShavemarkedeffectsoncognitivedevelopment(Farahetal.,2006).Theseeffectsarenotuniformacrossallareasofcognition,butareparticularlymarkedinthedevelopmentoflanguageandcognitivecontrol(oftenreferredtoas‘executivefunctions’).HackmanandFarah(2009)consideredthesedifferentialeffectsintermsofrelativelyindependent,anatomicallydefinedneurocognitivesystemsinthebrain.StrongesteffectsofSESwereobservedforthelanguagesystem(leftperisylvianregions)andtheexecutivesystem(prefrontalregions,decomposedintoworkingmemorysystem[lateralprefrontal],cognitivecontrol[anteriorcingulate]andrewardprocessing[ventromedialprefrontal]).SESexplained32%ofthevarianceinthelanguagecompositebehaviouralmeasure,6%incognitivecontrol,and6%inworkingmemory.

EffectsofSEShavebeenobservedonmeasuresofbrainstructureusingmagneticresonanceimaging.Forexample,Nobleetal.(2015)reportedeffectsoffamilyincomelevelsoncorticalsurfaceareainacross-sectionalsampleof1099childrenintheUSAaged3-20years.Therelationshipwasnon-linear,withthestrongesteffectsobservedinthelowestincomefamilies;differencesinincomeathigherlevelswereassociatedwithsmallerchangesincorticalsurfacearea.However,SESonlyexplainedafewpercentagepointsofthevariance;therewasagreatdealofvariationinbrainstructuremeasuresnotexplainedbySES.Notably,thestrongesteffectsofSESonbrainstructurewerefoundinregionssupportinglanguage,reading,executivefunctionsandspatialskills,consistentwithbehaviouralevidence.

SEShasalsobeenfoundtoimpactonneuraldevelopmentatmuchearlierages.Betancourtetal.(2016)examinedtherelationshipbetweenSESmeasures(income-to-needsratioandmaternaleducation)inasampleofAfrican-Americanfemaleinfantsaged5weeks.TheyobservedthatlowerSESwasassociatedwithsmallercorticalgreyanddeepgreymattervolumes,pointingtothebiologicalembeddingofadversityveryearlyindevelopment.

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Thelinkbetweenbrainstructureandfunctionisindirectandnotwellunderstood.Nevertheless,researchershaveobserveddifferencesinbrainfunctionassociatedwithSESbothwithfunctionalmagneticresonanceimaging(regionaloxygenatedbloodflowdifferences)andwithelectrophysiology(measurementofvoltagepotentialsonthescalpassociatedwithneuralactivity).Forexample,usingfunctionalmagneticresonanceimaging,Raizadaetal.(2008)foundthattheweakerlanguageskillsobservedin5-year-oldchildrenfromlowerSESbackgroundswereassociatedwithreducedhemisphericfunctionalspecialisationinleftinferiorfrontalgyrus.Specialisationtothelefthemisphereisamarkerofthefunctionalmaturationoflanguagesystems.Usingelectrophysiologywithasampleof3-8yearolds,Stevens,LauingerandNeville(2009)demonstratedreducedneuralsignaturesofselectiveattentioninchildrenfromlow-SESfamilies(indexedbymaternaleducation).Inanauditoryprocessingtaskwherethechildrenhadtoattendselectivelytooneoftwosimultaneouslypresentednarrativestories,theneuralprocessingdifferencesthatcharacterisedthelow-SESchildrenwererelatedspecificallytoareducedabilitytofilteroutirrelevantinformation.

Thesefewexamplesillustratethegeneralmethodsfromafastgrowingneuroscienceliterature(forwiderreviewsofstructuralandfunctionalbrainimagingandSESseeFarah,2017;Pavlakisetal.,2015).Importantly,cognitiveneuroscientistsdonotyetunderstandthecausalpathwaysofthesecognitiveandbraineffects,notleastbecausetheSESmeasurerepresentsadistalcauseanddoesnotisolatetheproximalcausesthatinfluencecognitiveandbraindevelopment.SomedifferencesassociatedwithlowSESmayrepresentdeficits(e.g.,poorerbraindevelopmentcausedprenatallybypoormaternalnutritionorpostnatallybychronicstress).Othersmayrepresentadaptations(e.g.,apparentpoorerselectiveattentionmayreflecthighervigilanceappropriatetoamoredangerousenvironment;apparentlyimpulsivitymayreflectmaximisingshort-termrewardsbecauselong-termrewardshaveprovedunreliable).

Hackman,FarahandMeaney(2010)classedpotentialcausalmechanismsintothreetypes,basedonresearchnaturalisticresearchwithhumansandexperimentalresearchwithanimalmodels:(1)thoseoperatingprenatallyonfoetaldevelopment,(2)thoseaffectingpostnatalparentalnurturing,and(3)thoseaffectingpostnatalcognitivestimulation.Explanatorymodelstendtodistinguishwhatislostfromlow-SESfamilies(resources,goodnutrition,learningopportunities)fromwhatisadded(stress,toxins,childhoodadversityexperiences)(Sheridan&McLaughlin,2016).Causalexplanationsarelikelytobecomplex:allthreeclassesoffactorscouldberesponsible;orcombinationscoulddifferperbrainsystem.Thecombinationoffactorsmaydependondetailsofthespecificpopulationandlocalfactors,intermsofabsolutelevelsofresources/poverty,wheretheeconomicandenvironmentalrestrictionslieinaparticularsociety,andtherelativelevelsofpoverty(inequality).

Againstthisbackgroundof(hopefully)remediableenvironmentaleffects,wealsoknowthatinWesternsocieties,afairproportionofchildren’svariabilityincognitiveandeducationaloutcomes,andindeedbrainstructure,canbepredictedbytheirgenotypes–thatis,abilitiesare‘heritable’(Plominetal.,2016).Thetermheritableisoftenmisunderstoodtorelatetonecessaryoutcomes(becausechildren’sgenesaren’tchangeable)butthisinterpretationisincorrect.Indifferentenvironments,geneticeffectsmaybeincreasedor

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decreased:observedgeneticeffectsarenotinevitableordeterministic.Theyshowwhatis,notwhatcanbe.Nevertheless,wecantakemeasuresofheritabilityascurrentsummarystatistics:giventhecurrentrangeoffamilyandeducationalenvironmentsthatchildrenareraisedin,andwhichshapetheworldtheycanexplore,heritabilityisastatisticthatcapturehowmuchvarianceiscurrentlybeingpredictedbygeneticsimilarity.

Therehasbeenaflurryofnewfindingswithrespecttolifeoutcomes,SESandbehaviouralgenetics.Forexample,researchershavereportedthateducationalachievementis‘highly’heritable,withasmuchas60%ofthevarianceinexaminationresultsin16yearoldsexplainedbygeneticsimilarity(Krapohletal.,2014).Thesegeneticeffectsappeargeneralacrosstopicsratherthanspecifictodifferentacademicsubjects(Rimfeldetal.,2015).DirectmeasuresofDNAvariationhavepointedtoregionsofthegenomeassociatedwithacademicachievement,albeitwithcoarseeducationalmeasuresastheoutcome(yearsofschoolingcompleted)andsmalleramountsofvarianceexplained(e.g.,11-13%variance;Leeetal.,2018).Notably,variationsinSEShavebeenreportedtopartlyalignwithgeneticvariation(e.g.,Trzaskowskietal.,2014).Moreover,socialmobility–whereanindividual’sSESdiffersfromthatoftheirparents,suchasineducationalattainment–hasitselfbeenreportedaspartlyheritable,withonestudyobservingthatjustunderhalfofthevarianceinsocialmobilitywaslinkedtogeneticvariation(Ayorechetal.,2017),andanotherreportingthatdirectmeasuresofDNAvariationcouldexplainaround3%ofthevarianceinupwardeducationalmobility(Belskyetal.,2018).

Evidenceoftheroleofgeneticvariationininfluencingcognitive,educationalandlifeoutcomes,andofthepossiblecorrelationsbetweenthegeneticvariationandSESgradients,drivesthedebatebetweensocialcausationandsocialselectionaccounts(Farah,2017).Underasocialcausationaccount,SESeffectsandtheirpersistenceacrossgenerationsaredrivenbytheenvironmentsinwhichchildrenareraised.Underasocialselectionaccount,SES-relateddifferencesinbrainandcognitionareundergeneticcontrol,withpopulationstratificationofgenotypesaccordingtoSES.

Ourconcernhereisnotthecompetingmeritsoftheseaccounts,butmerelythechallengeposedbyrespectivedataontherolesofenvironmentalfactorsandgeneticfactorsonbrainandcognitivedevelopment.Howcanthesebodiesofempiricaldatabereconciledintoacoherentcausalaccount?Giventhecomplexityandmulti-facetednatureofbothbraindevelopmentandcognitivedevelopment,howcanwebegintoformulateandtestcompetingexplanationsforthepathwaysbywhichSESeffectsoperate–andtheirimplicationsforintervention?Evenunderasocialcausationaccount,onemustaccepttheroleofgeneticvariationincontributingtodifferencesinoutcomes.Evenunderasocialselectionaccount,onemustacceptthatdifferencesinexperienceswillinfluencedevelopment.NeurocomputationalmodellingOnemethodusedincognitiveneurosciencetoformulateandtestcausalaccountsiscomputationalmodelling.Modelscanbeformulatedatdifferentlevelsofdescription:ofindividualneurons,ofcircuitsofneurons,orofwholebrainsystems.Ineachofthesecases,modelsseektocaptureempiricalevidence

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onpatternsofbrainactivationoranatomicalstructure.Modelscanalsobeformulatedatacognitivelevel:althoughcertainconstraintsmaybeincludedfromneuroscienceaboutthenatureofcomputation,thetargetisthentocaptureempiricaldataonhigh-levelbehaviour.Multi-levelmodelsincludeconstraintsfromseverallevelsofdescriptionandseektocapturedatabothatthelevelofbrainandbehaviour(Thomas,Forrester&Ronald,2016).Modelsmaybeconstructedtosimulatethecharacteristicsofthestaticpropertiesofasystematagivenpointintime,ortheymaybeconstructedtocapturedevelopmentalchange,wheretrajectoriesofbehaviouraresimulatedastheyalterovertime(Elmanetal.,1996;Mareschal&Thomas,2007).

Howmightweconstructamulti-levelcomputationalmodeltoexplainSESeffectsonbrainandcognitivedevelopment?Minimally,weneedtostipulateaneutrallyconstraineddevelopmentalmechanismwhichacquiresatargetbehaviourthroughinteractionwithastructuredlearningenvironment;weneedtostipulatehowgrowthofthatdevelopmentalmechanismandinteractionswiththestructuredlearningenvironmentmightalterasaconsequenceofvariationsinSES;andweneedtostipulateseparatelyhowgeneticvariationmightalterthepropertiesofthedevelopmentalmechanism,forexampleintermsofhowitgrows,operates,andrespondstostimulation.Thomas,ForresterandRonald(2013)beganthislineofresearchbyconstructinganartificialneuralnetworkmodeloftheeffectsofvariationinSESonlanguageacquisition,focusingonthespecificdomainofinflectionalmorphology(thatis,alteringthesoundsofwordstochangetheirmeaning,suchasinformingthepasttenseofaverb).Themodelwasabletosimulatehowchildren’slanguageskillsalteredacrosstheSESgradient,aswellasgeneratingtestablepredictionsaboutchildren’slanguageoutcomes(seealso,Thomas&Knowland,2014;Thomas,2018,forthemodel’sextensiontoconsideringdelayandgiftedness).Thomas,ForresterandRonald(2016)andThomas(2016)showedhowthesamemodel,treatedmoreabstractly,couldbeextendedintoamulti-levelformat,toincorporateageneticlevelofdescriptionandindicesofbrainstructureaswellasbehaviour.Inthefollowingsections,wedemonstratehowthemodelcanbeappliedtoconsideringSESeffectsonbrainandcognitivedevelopment(Thomasetal.,2018;Thomas&Meaburn,2018).ModelassumptionsandsimplificationsAschematicofthemodelisshowninFigure1.Inthemodel,cognitivedevelopmentoccursthroughtheinteractionofanexperience-dependentmechanismwithastructuredlearningenvironment.Themechanismisanartificialneuralnetwork,whichembodiescomputationalconstraintsfromneuralprocessing(Elmanetal.,1996).Theseconstraintsare,respectively,anetworkofsimplenon-linearintegrate-and-fireprocessingunits,distributedrepresentationsofknowledge,associativeerror-drivenlearningalteringnetworkconnectivitystrengthsandunitthresholds,andnetworkdevelopmentincludingphasesofgrowthandpruning.Thestructuredlearningenvironmentisdrawnfromthefieldoflanguagedevelopment.Thesingleprocessingstructureisassumedtoliewithinalargercognitivearchitecturebutisnotintendedinthismodeltocorrespondtoanyspecificbrainregion.

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Themechanismlearnsinput-outputmappingsthatdrivebehaviourrelevanttoitsdomain.Accuracyofinput-outputmappingsisusedasameasureofbehaviouralperformance.Structuralpropertiesoftheartificialneuralnetwork,includingthetotalnumberofconnectionsandthetotalstrengthofexcitatoryandinhibitoryconnections,areusedasanaloguesofbrainstructuremeasuressuchascorticalthickness,corticalsurfacearea,greymattervolume,andwhitemattervolume(Thomas,2016).

Individualdifferencesfactors,suchasSESandgeneticvariationarenotconsideredinisolationbutintermsofhowtheymodulatetheabovespecies-universalmechanismsthatunderpindevelopmentacrossallchildren.Inthissense,themodelconstruesindividualdifferencesasoperatingwithinadevelopmentalframework(Karmiloff-Smith,1998).VariousoptionsareavailabletoimplementtheeffectofSES:asamodulationofthelevelofstimulationavailableinthelearningenvironment(seeThomas,Forrester&Ronald,2013);asamodulationofthegrowthofthenetworkanditsprocessingproperties;orbothoftheseeffectsoperatinginacorrelatedfashion(seeThomasetal.,2018).Eachnetworkrepresentsasimulatedchildundergoingdevelopmentinafamilyenvironment.Eachfamilyisassignedavalue,between0and1,torepresentitsSES,whichisthenusedtomodulatethelearningenvironmentorthenetworkstructure.

Geneticvariationisassumedtooperatebyinfluencingtheneurocomputationalpropertiesoftheprocessingmechanism,intermsofitscapacity,plasticity,andnoisinessofprocessing(thesearebroadcharacterisationsoftheroleofalargersetofparameters,showinTable1).Sincebehaviouralgeneticresearchoncognitionhasindicatedthatcommongeneticvariationamountstolargenumbersofsmallgeneticeffectsonawiderangeofneuralproperties,geneticvariationisimplementedviaapolygeniccodingscheme:anartificialgenomecontainssetsofgeneswhicheachinfluencevariationonaneurocomputationalproperty(14properties,eachinfluencedby8-10genes);thecombinationofsmallvariationsacrossalargesetofpropertiesproducesnetworkswithanormaldistributionoflearningproperties(Thomas,Forrester&Ronald,2016,fordetails).Thecombinationofsimulatedchildrenwithdifferentlearningabilities,interactingwithenvironmentswithdifferentlevelsofstimulation,producesapopulationofchildrenwithdifferentdevelopmentaltrajectoriesinbothbehaviourandbrainstructure.Atanypointindevelopment,cross-sectionscanbetakenofbehaviourorstructureacrossthepopulation,andcorrelationsderivedtoSESorgeneticvariation.

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Figure1.StructureofneurocomputationalmodelsimulatingSESeffectsoncognitiveandbraindevelopment.Anexperience-dependentdevelopmentalmechanism(artificialneuralnetwork)interactswithastructuredlearningenvironmenttoacquireacognitivebehaviour.Themulti-levelmodelembodiesconstraintsatthelevelofgenes,brainstructure(connections,units),behaviour,andenvironment.Individualdifferencesfactors(SES,geneticvariation)areconsideredwithrespecttohowtheymodulatespeciesuniversalmechanismssupportingcognitivedevelopment.SimulationdesignAsinglenetworkwastrainedonitsfamily-specificsetofinput-outputmappings.Peritssourcecognitivedomain,inthiscasetheinputswerephonologicalrepresentationsofverbstemsandtheoutputswereinflectedformsofEnglishverbs.Lifespandevelopmentcorrespondedto1000exposures(or‘epochs’)ofthenetworktothetrainingset.Thetrainingsetcomprisedamaximumof500input-outputmappings.Thedevelopmentof1000individualchildrenwassimulated.Genomeswererandomlyinitialisedtoproducegeneticvariationinlearningabilityacrossthepopulation.Pairsof‘twin’networkswerecreatedwhicheithersharedthesamegenome(identical)orshared50%ofgenesonaverage(fraternal)andtwinpairsraisedinthesamefamily.Thisdesignenabledtheuseoftwincorrelationstocomputeheritabilitylevels.SESwasallowedtovarywidelyacrossfamiliestocapturethepotentialeffectsofpoverty.Inthesimulationsdescribedhere,SESwasimplementedasmodulationofthelevelofstimulationinthelearningenvironment,andwasallowedtovarybetween0and

StructuredlearningEnvironment

Experience-dependentdevelopmentalmechanismExperiencemodifiesprocessingStructurewhichmodifiesfunction

Behaviourdevelopsfrominteractionofmechanismwithenvironment

Geneticvariation

Socioeconomicstatus

(maybecorrelated)

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tiallyinfluence

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learningenviro

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Influencesgrowth,capacity,plasticity,maintenanceofmechanism

Individualdifferencesfactors Speciesuniversalfactors

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1.Afamilywithavalueof0.6wouldgenerateatrainingsetthatonlycontaineda(randomlysampled)subsetof60%ofthefulltrainingset(seeThomas,2016,forfurtherdetails,includingspecificationofneurocomputationalpropertiesandcalibrationoftheirrange;resultsarereportedfortheG-wideE-wideconditioninthatpaper).Simulation1:SESeffectsonIQchangeacrossdevelopmentThomasetal.(2018)firstconsidereddevelopmentaltrajectoriesofbehaviour.Thepopulationwassplitintothreegroups,thoseintheupperquartileofSES(trainingsetswith>75%ofavailableexperiences),thoseinthemiddletwoquartiles,andthoseinthelowestquartile(<25%ofavailableexperiences).Figure2(a)showsthelatentgrowthtrajectoriesofIQforchildrenfromlow,middle,andhighSESgroupsintheempiricaldataofvonStummandPlomin(2015),foraroundfifteenthousandUKchildrenfollowedfrominfancytoadolescence.Itshowsdivergingtrajectorieswithage.TheSESgapwidens.Figure2(b)showssimulateddataofIQscoresinthemodel,whereIQwascomputedaccordingtothepopulationdistributionateachmeasurementpoint[IQscore=((individualperformance–populationmean)/populationstandarddeviationX15)+100].Figure2(c)showsthedevelopmentaltrajectoriesofperformancewithoutthetransformationtoIQscores.Thesimulationisabletocatchthelowerinitiallevelsofperformanceattheyoungestage,aswellasthedivergenceofthetrajectoriesacrossdevelopmentaltime.

OnemightconcludefromtheempiricaldatathattheconditionsproducingSESdifferencesincognitivedevelopmentmustworsenovertimetoproducethedivergence.ThesimulationsreproducedthedivergingpatternwithaconsistentSESeffectovertime.Inthemodel,divergenceoccurredduetonon-lineartrajectoriesofdevelopment.IncreasinggapsbetweenSESgroupsdonot,then,necessarilyimplyworseningSEScausalfactors.

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Figure2.(a)EmpiricallongitudinaldatafromaUKsampleoftwins(N=14,853children)plottingIQchangeoverdevelopmentfrominfancytoadolescence,splitbysocioeconomicstatusandshownseparatelybygender(reproducedwithpermissionfromvonStumm&Plomin,2015).HighSES=>1standarddeviation(SD)aboveSESmean;low=<1SDbelowSESmean;middle=<1SDaboveSESmeanand>1SDbelowSESmean.(b)SimulationdataplottingIQchangeacrosschildren’sdevelopmentwhereSESiscapturedbydifferencesincognitivestimulation.HighSES=upperquartile,MiddleSES=middletwoquartiles,LowSES=lowerquartile.(c)Equivalentmeanperformanceontask(proportioncorrect)forsimulatedSESgroups.(a)(b) (c)Simulation2:SESanddevelopmentaleffectsonpopulationrankorderThomasandMeaburn(2018)usedthesamemodeltosimulatetheanalysisreportedbyFeinstein(2003).Theempiricaldatafromthe1970BirthCohortSurveyarere-plottedinFigure3.Around1,300UKchildrenwereclassifiedintohigh(upperquartile)andlow(lowerquartile)cognitiveabilityat22monthsand

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thenfollowedlongitudinallyto10yearsofage,withhighSES(top24%)andlowSES(bottom13%)subgroupstrackedseparately.Childrenaredepictedbythemeanpopulationrankorderoftheirgroup,where100ishighperformanceand1islowperformance.Somewherebetween5and10yearsofage,initiallyhigh-ability/low-SESchildrenfellbelowtherankoflow-ability/high-SESchildren.Followingpublicationofthesedata,thefindingswerecriticisedontwogrounds.First,thattheydonotrepresentarealeffectbutinsteadregressiontothemeanofinitiallyextremescoresthroughmeasurementerror(Jerrim&Vignoles,2013).Second,thatthemostemotivefinding,ofthecross-overofhigh-ability/low-SESandlow-ability/high-SESgroupsbetween5and10,washardtoreplicateanddependedoncut-offsusedtodefinegroups;forexample,crossing-overwasmorelikelyunderlessextremedefinitionsofhighandlowcognitiveability(Washbrook&Lee,2015;e.g.,Figure1).Figure3.Longitudinalempiricaldatafromthe1970BirthCohortSurveyfollowingthepopulationrankofchildrenoncognitiveabilitytasks,splitbyability(high,low)at22months,andfamilysocio-economicstatus(re-plottedfromFeinstein,2003).Y-axisshowsmeanpopulationrankofeachgroup,whereahigherrankmarksbetterperformanceonage-appropriatecognitivetests.

Figure4depictsthecomputationalsimulationofthesedata(Thomas&

Meaburn,2018).Earlyintraining(25epochsoutof1000epochs),simulatedchildrenweresplitintohighandlow‘ability’groupsbasedonbehaviour(accuracyofinput-outputmappings).Highabilitywasdefinedaspopulationrank>650(where1000isgood,1ispoor),lowabilityaspopulationrank<350.ThesegroupsweresubdividedbySES,asameansplit(simulatedSESvaried0to1;highSES>.5,lowSES<.5).Performanceofthegroupswasthenfollowedoverdevelopment.Figure4(a)depictsthemeanpopulationrankofeachgroup.AsintheFeinstein(2003)data,high-ability/high-SESandlow-ability/low-SESgroupsbroadlyheldtheirmeanrank.High-ability/low-SESshoweddecliningrankand

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LowSES,HighAbilityat22m(n=36)

LowSES,LowAbilityat22m(n=53)

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low-ability/high-SESshowascendingrank,suchthatthegroupsconverged.Notably,theydidnotcrossover.Figure4(b)showsthesamedatabutforperformance.Itisincludedtoemphasisethatweareobservingmodulationsindevelopmentaltrajectories,andthatchangesinrelativerankpositionsmayexaggeratesmalldifferencesinindividualswhoareneverthelessallshowingdevelopmentalimprovementswithage.

Cruciallyhere,therewasnonoiseinthemeasurementofperformanceinthegroups.Theconvergenceofthetrajectories,atleastinthesimulation,cannothaverisenfromregressiontothemeanfollowingmeasurementerror(Jerrim&Vignoles,2013).Itisarealreflectionoftheoperationofconstraintsondevelopment.Figure4(c)takesthesamepopulationofchildrenbutnowaltersthedefinitionofhighandlowabilitytobelessextreme(highability:populationrank>500;lowability:populationrank<500)andthedefinitionofSESmoreextreme(high:SES>.75;low:SES<.25).Nowthetrajectoriesofhigh-ability/low-SESandlow-ability/high-SESdidcrossover.Thesimulationscapturedtheempiricalobservationthatthecrossoverpatternissensitivetogroupdefinitions(Washbrook&Lee,2015).

OnesimpleinterpretationoftheFeinsteindataisthatchangesinchildren’spopulationrankperformanceincognitiveabilitytestsstemfromenvironmentalcauses.Forthesimulation,wehaveavailabletousthefullsetofparametersthatinfluenceseachsimulatedchild’sdevelopmentaltrajectory:boththestipulatedenvironmentaleffect,intermsofthelevelofcognitivestimulation,andthestipulatedgeneticindividualdifferences,intermsoftheneurocomputationalpatternsofeachartificialneuralnetwork.Wecanthenusetheseparametersinamultipleregressionanalysistoseewhichpredictedpopulationrankchangeacrossdevelopment.

Wasalltherankchangeduetotheenvironmentalmanipulation?Table1showstheresultsofthismultipleregression,withtheenvironmentalparametermarkedinbold,andtherespectiveinfluenceofeachneurocomputationalparameterbelow.First,itisworthnotingthatinthesimulation,sinceenvironmentaldifferencesactedthroughoutdevelopment,theyinfluencedmeasuresofabilityevenattheearlystageofdevelopment,hereexplaining22.7%ofthevarianceatthefirsttimepoint.Earlymeasurementdoesnotgiveanunbiasedmeasureof‘genetic’abilityfreefromSESinfluences.Second,asexpected,environmentaldifferencesdidaccountforasignificantamountofvarianceinchildren’schangeinrankacrossdevelopment,upto10%atthefinaltimepoint.Butnotably,anumberofneurocomputationalparametersalsocontributedtochangeinrank.Theseincludedparametersinfluencingthecapacityandplasticityofthemechanism,andconsequentlytheshapeofthedevelopmentaltrajectory.

Inotherwords,themodelhighlightsthatchildrendevelopatdifferentrates.Somechildrenarelatebloomers,othersslowlaterindevelopment.Thiswillcausechangesinpopulationrankorderthatarenotsolelyrelatedtovariationsinenvironmentalstimulation.Itisnotnecessary,therefore,toconcludefromtheFeinsteinplotthattheonlycauseofchangesinchildren’spopulationrankisduetoenvironmentalcausessuchasSES.Inturn,thisimpliesthatnotallthechangeinrankwouldberemovedbyreducingSESdisparities.

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Figure4.Simulationsoflongitudinalchangeinrankandchangeinperformanceacrossdevelopmentinthecomputermodel.Rank1000=best,rank1=worst.SESparametervariesbetween1(highest)and0(lowest).(a)Meanchangeinrankforhighandlowabilitygroupsdefinedattime1(epoch25),wherehighisrank>650andlowisrank<350,splitbySES,wherehigh>.5andlow<.5.(b)Equivalentperformanceontask(proportioncorrect).(c)Meanchangeinrankwherehighabilityistime1rank>500andlowabilityisrank<500,andwherehighSES>.75andlowSES<.25.(d)Equivalentperformanceontaskforthesegroupcriteria.

(a)

0100200300400500600700800900

1000

25 50 100 250 500 1000

Meanrnakinpop

ulation

Epochsoftraining(development)

HighSESHighAbility

LowSESHighAbility

HighSESLowAbility

LowSESLowAbility

00.10.20.30.40.50.60.70.80.91

25 50 100 250 500 1000

Meanpe

rforman

ceontask

Epochsoftraining(development)

HighSESHighAbility

LowSESHighAbility

HighSESLowAbility

LowSESLowAbility

01002003004005006007008009001000

25 50 100 250 500 1000

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ulation

Epochsoftraining(development)

HighSESHighAbility

LowSESHighAbility

HighSESLowAbility

LowSESLowAbility

00.10.20.30.40.50.60.70.80.91

25 50 100 250 500 1000

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rforman

ceontask

Epochsoftraining(development)

HighSESHighAbility

LowSESHighAbility

HighSESLowAbility

LowSESLowAbility

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Table1.Levelofenvironmentalstimulationandneurocomputationalparametersaspredictorsofdevelopmentalchangeinthemodel,measuredbyindividual’schangeinpopulationrankperformanceacrossdevelopment(scoresshowstandardisedbetacoefficientsfromalinearregressionmodel).Neurocomputationalparametersarelabelledaccordingtotheirapproximateprocessingrole.Bothenvironmentalstimulationandnetworkparametersexplainvarianceinrankchange(environmentismarkedbybold).Therightmostcolumnindicatespredictorsofwhetheranindividual’sperformance(rank)asanadultexceedstherankofthequalityoftheirenvironment,asanindicatorofsocialmobility.Time1=25epochsoftraining,Time2=50,Time3=100,Time4=250,Time5=500,Time6=1000. PredictorsofdevelopmentalchangeinPopulation

rankagainstTime1

Finalrank

vs.SESrank

Parameter Neuralnetwork

processingrole

Time2 Time3 Time4 Time5 Time6

Modelfit(R2) 0.181* 0.312* 0.368* 0.379* 0.384* 0.466*

SES Environment 0.158* 0.274* 0.332* 0.337* 0.333* -0.361*

Hidden Units Capacity -0.069+ -0.089* -0.079* -0.07* -0.053+ 0.356*

Architecture Capacity -0.185* -0.212* -0.171* -0.142* -0.129* 0.297*

Sparseness Capacity 0.028 0.037 0.036 0.032 0.036 0.016

Pruning Onset Capacity 0.044 0.074* 0.077* 0.074* 0.067* 0.061*

Pruning

probability Capacity 0.021 0.017 0.004 -0.002 -0.006 -0.007

Pruning

Threshold Capacity 0.033 0.013 0.006 0.023 0.025 -0.002

Learning

algorithm

Capacity /

plasticity -0.064+ -0.074* -0.107* -0.119* -0.138* 0.172*

Learning Rate Plasticity -0.148* -0.159* -0.177* -0.186* -0.199* -0.004

Momentum Plasticity -0.077* -0.091* -0.109* -0.108* -0.105* -0.089*

Weight variance Plasticity 0.006 0.004 0.033 0.043 0.052+ -0.1*

Unit activation

function

Plasticity /

signal -0.107* -0.147* -0.178* -0.184* -0.188* -0.053+

Noise Signal 0.019 0.036 0.069* 0.101* 0.116* -0.143*

Response

threshold Signal -0.223* -0.292* -0.304* -0.308* -0.309* 0.11*

Weight Decay Signal -0.004 -0.015 -0.011 -0.003 -0.003 -0.015

+ p < 0.05 * p < .01

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Simulation3:GeneticconstraintsonsocialmobilityThemodelconsideredSESeffectsagainstthebackgroundofgeneticallyinfluencedvariationsinlearningability.Thus,thesesimulationswereabletocapturethehighheritabilityofbehaviour.Forexample,heritabilityofbehaviourshowninFigure4(a)atthefinalmeasurementpointwas51%underanadditivemodel,computedfromthetwindesign.Thegeneticcomponentalsoallowsthesimulationtoaddressdataonsocialmobility.Inthemodel,socialmobilityisdefinedasadevelopmentaloutcomethatisgreaterorlesserthantheSESofthefamilyinwhichthechildisraised(Thomas&Meaburn,2018).Thiscanbemeasuredasthedifferenceinpopulationrankorderofafamily’sSEScomparedtothesimulatedchild’spopulationrankorderabilityattheendoftraining.Forexample,iftheSESrankwas500andtheabilityrankwas600,thiswouldqualifyasupwardssocialmobility;iftheSESrankwas500andthefinalabilityrankwas400,thiswouldqualifyasdownwardssocialmobility.Table1,rightmostcolumn,showstheresultsofamultiplelinearregressionpredictingtherankdisparitymeasureofsocialmobilityfromeachsimulatedchild’sparameters.Notably,SESitselfpredictedareliableamountofthedisparitymeasure.MuchofthisrelationshipwasdrivenbynetworksthatfellbelowexpectedlevelsinhighSESenvironments,lessbynetworksthatfinishedaboveexpectedlevelsinlowSESenvironments.Severaloftheneurocomputationalparametersrelatingtothenetwork’scapacitywerereliablepredictorsofthedisparitymeasure.Theseindexedwhetherthenetworkhadthecapacitytobesttakeadvantageoftheinformationthatwasavailableintheenvironment.

Totheextentthatthecapacityoflearningmechanismsisgeneticallyinfluenced,thissimulationthereforecapturedgeneticinfluencesonperformanceandonsocialmobility.ItisthesamesimulationthatcapturedempiricaldataonwideningIQgapsfromSESacrossdevelopment.ThesamesimulationthatcapturedtherestrictiveeffectsofSESonchildrendeemedhigh-abilityearlyindevelopment.Thesediversebehaviouraleffectswerecapturedinasinglemechanisticframework.Simulation4:SESeffectsonbrainstructureCanthemodelalsocapturedataonbrainstructure?Thelinksbetweenmodelandbraincanonlybeweak,becausethemodelhasaverylimiteddegreeofbiologicalrealism,necessitatedbytherequirementtomakecontactwithhigh-levelbehaviour.Moreover,thereisstillcontroversyhowthephysicalpropertiesthatstructuralbrainimagingmeasuresrelatetocognitivefunction.Despitethefactthatcognitiveabilityshowsbroadlyamonotonicallyincreasingfunctionwithage,someofthebrainstructuremeasuresreducefrommiddlechildhoodonwards(greymattervolume,corticalthickness),whileothersincrease(whitemattervolume,corticalsurfacearea);andtheunderlyingbiologicalmechanismsarestillamatterofdebate(Natuetal.,2018;Nobleetal.,2015).

Themodeldidnotsimulatethegrowthofeachnetwork,rathercapturingvariabilityintheoutcomeofthegrowthamongstitsparametersintermsofnetworkarchitecture(pathwayslinkinginputandoutput),numberofprocessingunits,anddensenessofconnectivity.Itdid,however,simulateareductioninconnectivityfrommid-childhoodonwards,intermsofapruningprocesswithvariablytimedonsetthatremovedunusedconnections(seeThomas,Knowland

17

&Karmiloff-Smith,2011).Fortheartificialneuralnetwork,twostructuralmeasuresofferedpossibleanaloguestobrainmeasures:thetotalstrengthofconnectionsinthenetworkandthetotalnumberofconnections.Duringtraining,thetotalstrengthincreasesasthoseusefulindrivingbehaviourarestrengthened,whilethenumberofconnectionsreducesasthosenotusefulfordrivingbehaviourareremoved.Thesetwonetworkmeasuresprovidepossibleanaloguestocorticalsurfacearea/whitematterdensityandcorticalthickness/greymatterdensity,respectively,byvirtueoftheirsimilardevelopmentaltrajectories.

Figure5takesamid-pointindevelopmentforthesimulatedpopulationconsideredintheprevioussections.Figure5(a)re-plotsdatafromasampleofover1000USchildrenaged3-20linkingcorticalsurfaceareatofamilyincome(Nobleetal.,2015).Asmallamountofvarianceisexplained,withanon-linearfunctionthatexhibitsstrongereffectsonbrainstructureatthelowestincomelevels.Figure5(b)plotstotalconnectionstrengthforthesimulatedpopulationagainstlevelofstimulation.Again,smallamountsofvarianceareexplained,andanon-linearfunctiongivesabestfit.Thus,thesamesimulatedpopulationthatcapturescross-sectionalempiricaldataonSESeffectsonbehaviourcanalsocapturecross-sectionalpatternsobservedinbrainstructuredata.

Themodelofferstwobenefitsatthislevel.First,itprovidesacandidatehypothesisaboutthefunctionalrelevanceofthebrainstructuremeasures–thattheyrepresentchangesofconnectivityarisingfromexperience-dependentdevelopmentalchange.Second,becausethefunctioningofanartificialneuralnetworkiswellunderstood–intermsofactivationsofnetworksofintegrate-and-fireneurons,andlearningalgorithmsthatupdateconnectivityandthresholds–itthendemonstrateshowindicesofnetworkstructureonlyserveasanindirectmeasureoffunction,andhowfunctionmodulatesstructureasaconsequenceof(variable)experience.

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Figure5.Empiricaldatare-plottedfromNobleetal.(2015)showingtherelationshipbetweenannualfamilyincome($)andcorticalsurfacearea(mm2)inasampleof1099USchildrenbetweentheageof3and20.(b)Computersimulationdatashowingtherelationshipbetweenlevelofcognitivestimulationintheenvironmentinwhichchildrenareraised,andthetotalmagnitudeofconnectionstrengthsineachartificialneuralnetwork,assessedatamid-pointindevelopment(500epochsoftraining).Bothplotsshowanon-linear(log)relationshipbetweentheenvironmentalmeasureandthestructuralmeasure,aswellasmuchunexplainedvariability(linearandnon-linearfitsareshown,alongwithrespectiveR2values).(a)(b)

y=5409.4x+6318.8R²=0.05492

y=1637.1ln(x)+10624R²=0.0593

0

5000

10000

15000

20000

25000

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Magnitude

ofcon

nections

Environment(SES)

y=0.0368x+167071R²=0.0263

y=3057.5ln(x)+136686R²=0.03077

100000

120000

140000

160000

180000

200000

220000

240000

0 50000 100000 150000 200000 250000 300000 350000

MRIcorticalsu

rfacearea

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DiscussionAmulti-levelneurocomputationalmodelwasabletocapturebothbehaviouraldataandbrainstructuredataontheeffectsofdifferencesinsocioeconomicstatusondevelopment.Itdidsowhilealsoincorporatingthecontributionofgeneticvariationtocognitivedevelopment,leadingtohighheritabilityofbehaviour;andbyassumingthatSESoperatesviadifferencesinlevelsofcognitivestimulation.Variationbetweenindividualswasconceivedasthemodulationoftrajectoriesofdevelopment,drivenbyspeciesuniversalmechanisms.

Inthesimulationdatapresented,SESwasimplementedasvariationsinthelevelofcognitivestimulation.However,amodellingframeworkprovidestheopportunitytoimplementandcomparealternativehypotheses,forexampleinhowwelltheycapturetheeffectsizeandshape(linear,log)ofSESeffectsonparticularmeasuresofbehaviourandbrainstructure.Thomasetal.(2018)comparedtwoalternativehypotheses:thatSESmayinsteadinfluencethegrowthofthenetworksthemselves(perthefindingsofBetancourtetal.,2016),andthereforeprocessingcapacity;orthatSESmayinfluencebothnetworkgrowthandcognitivestimulation,inacorrelatedmanner.Thecomputationalmodelthereforeprovidesafoundationtohypothesistestdifferentcausalaccountsofempiricaldata.

Thomasetal.(submitted)havearguedthatonceabasicdevelopmentalmodelofcognitivevariationexists,itprovidesthebasistoexploreinterventions,forexample,byalteringthequantityandqualityofcognitivestimulationthatindividualsexperience.Thenextstepforthemodel,then,istoexplorewhetherthegapsbetweenindividualsatdifferenceSESlevelscanbeclosedoreliminatedbyinterventionsthatequaliseenvironments,forinstancebysupplementingthestimulationreceivedbychildrenfromlow-SESfamilies.ThomasandMeaburn(2018)carriedoutthesesimulations,consideringtheextenttowhichopportunitiestoclosegapsdependedontheoriginofindividualdifferences(e.g.,howheritabletheywere)andwhetherinterventionsweremodulatedbychangesinplasticitywithage(Thomas&Johnson,2006).Thebroadpatternwasthatequalisedandenrichedenvironmentsimprovedpopulationmeansunderallconditions;whenheritabilitywashigher,improvementsweresmallerandgapsreducedless;butearlierinterventionsservedtoreducegapsmorethanlateinterventions.

Theresearchdescribedhereispresentedtoarguefortheutilityofneurocomputationalmodellingasoneresearchtooltofurthertheneuroscienceofpoverty.Oneshouldbecautious,however,toseesuchmodelsincontext.Modelsdonotdemonstratewhatisactuallythecase:theydemonstratethesufficiencyofparticularmechanisticaccountstoexplaintheobservedempiricaldata;andtherefore,indirectly,whatanygivenpatternofempiricaldatamustimplyaboutcausalmechanisms.Bydemonstratingthepossiblecausalexplanationsofdata,theydoatleastencouragetheavoidanceofmisinterpretationofthosedata.Forexample,thepatternofwideningIQgapsacrossSESgroupsacrossdevelopmentmightbeinterpretedtomeanthattheactionofSESdifferencesworsens;themodelshowedthepatternwouldemergeevenstaticcausalSESfactors.ThedeclineofpopulationrankforearlyhighabilitychildrenfromlowSESbackgroundscouldbeinterpretedtomeanthat

20

populationranksareentirelydependentonenvironmentalfactors;themodelshowedthattheempiricaldataareconsistentwithalimitedroleofenvironmentinchildren’srespectiveabilities.TheinfluentialroleofSESoncognitivedevelopmentandeducationalattainmentmightbetakenassupportingasocialcausationaccountofSESdifferences,andoftheprimaryroleofenvironmentinchildren’soutcome.ThemodeldisplayedrealisticSESeffectsbothonbehaviourandnetworkstructurewhilstdisplayinghighheritabilityofindividualdifferences,evenindeedtheheritabilityofdifferencesinsocialmobility.

Clearly,themodelpresentedhereishighlysimplified.Whileitsharedsomeprinciplesofneuralprocessing,itisnotamodelofbrainfunction.Itisessentiallyamachine-learningmechanismthatacquiresasmallsetofinput-outputmappings,representingatbestasinglecomponentofalargersystem.AmorerealisticmodelofSESeffectsondevelopmentwouldneedtodepictagoal-oriented,adaptive,autonomousagent,witharepertoireofbehavioursthatcanalteritssubjectiveenvironment;toincludeseparatecognitive,affectiveandreward-basedaspects;andprovideapathwayfornon-cognitivedimensions(diet,chronicstress,fitness)toalteritsprocessingproperties.Andclearly,thereisagreatdealmoretophenomenasuchassocialmobility(andthesocietalstructuresthatsupportorhinderit)thannotionsofcognitivestimulationandpropertiesofdevelopmentalmechanisms.

Nevertheless,thekeymotivationforconstructingamodelofthecurrentlevelofsimplicityistoemphasisetheimportanceofderivingcausal,mechanisticaccountstoexplainthelargebodyofcorrelationalevidencethathasaccumulatedonhowSESisassociatedwithdifferencesincognitive,educational,andlifeoutcomes.Computationalmodellingisbutoneamongstseveralneurosciencemethodsthatcanshedlightonmechanism,methodssuchasbrainimaging,anatomy,animalmodels,andgenetics.MechanisticinsightsultimatelyprovidethebasistoderivetargetedinterventionsthatcanamelioratetheconsequencesofdifferencesinSES,andespeciallypoverty(Thomas,2017).Thepotentialofmechanisticinsightstoinforminterventionisthemotivatingfactorbehindtheinvolvementofneuroscienceinasocialissuesuchaspoverty–evenifthewiderambitionistoaltersocietalstructuresthatcontributetopovertyinthefirstplace.

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AcknowledgementsThisworkwassupportedbyMRCgrantMR/R00322X/1andaWellcomeTrust/BirkbeckISSFCareerDevelopmentAwardheldattheUniversityofWesternOntario,Canada.

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