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Computationalmodellingofinterventionsfordevelopmental

disorders

MichaelS.C.Thomas1,AnnaFedor2,RachaelDavis1,JuanYang3,HalaAlireza1,

TonyCharman4,JackieMasterson5,&WendyBest6

1DevelopmentalNeurocognitionLab,Birkbeck,UniversityofLondon,

UK2MTA-ELTETheoreticalBiologyandEvolutionaryEcologyResearch

Group,Budapest,H-1117,Hungary3DepartmentofComputerScience,SichuanNormalUniversity

4InstituteofPsychiatry,Psychology&Neuroscience,King'sCollege

London5DepartmentofPsychologyandHumanDevelopment,UCLInstituteof

Education6DivisionofPsychology&LanguageSciences,UniversityCollege

London

Wordcount(maintext):21,521

Runninghead:Modellingmechanismsofintervention

Addressforcorrespondence:

Prof.MichaelS.C.ThomasDevelopmentalNeurocognitionLabCentreforBrainandCognitiveDevelopmentDepartmentofPsychologicalScience,BirkbeckCollege,MaletStreet,BloomsburyLondonWC1E7HX,UKEmail:m.thomas@bbk.ac.ukTel.:+44(0)2076316468Fax:+44(0)2076316312

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Abstract

Weevaluatethepotentialofconnectionistmodelsofdevelopmentaldisordersto

offerinsightsintotheefficacyofinterventions.Basedonarangeof

computationalsimulationresults,weassessfactorsthatinfluencethe

effectivenessofinterventionsforreading,language,andothercognitive

developmentaldisorders.Theanalysisprovidesalevelofmechanisticdetailthat

isgenerallylackinginbehaviouralapproachestointervention.Wereviewan

extendedprogrammeofmodellingworkinfoursections.Inthefirst,weconsider

long-termoutcomesandthepossibilityofcompensatedoutcomesandresolution

ofearlydelays.Inthesecondsection,weaddressmethodstoremediateatypical

developmentinasinglenetwork.Inthethirdsection,weaddressinterventions

toencouragecompensationviaalternativepathways.Inthefinalsection,we

considerthekeyissueofindividualdifferencesinresponsetointervention.

Togetherwithadvancesinunderstandingtheneuralbasisofdevelopmental

disordersandneuralresponsestotraining,formalcomputationalapproaches

canspurtheoreticalprogresstonarrowthegapbetweenthetheoryandpractice

ofintervention.(167words)

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Authornote

Amongthemodellingdatareportedhere,somefindingshavepreviouslybeen

publishedinBestetal.(2015,Figures6and7)andThomas&Knowland(2014),

inaconferenceproceedings(Alireza,Fedor&Thomas,2017),atechnicalreport

(Fedoretal.,2013),andanunpublishedPhDthesis(Davis,2017).Unpublished

dataarealsopresentedfromthreepublishedmodels,Thomas(2005),Thomas,

Knowland&Karmiloff-Smith(2011),andThomas(2016a).

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Keywords:Developmentaldisorders;intervention;computationalmodelling;

connectionism

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Inthisarticle,weconsidertheapplicationofconnectionistnetworksto

modellinginterventionstoremediatedevelopmentaldeficits,focusingon

disordersofspeech,language,communicationandliteracy.Recentconnectionist

modelshavemadeprogressinsimulatingpatternsofacquireddeficitsby

incorporatingneuroanatomicalconstraintsintotheirarchitectures.Forexample,

inUenoetal.’s(2011)modelofthereadingsystem,adualpathwaymodelof

readingwasconstrainedtoincludetheventralanddorsalanatomicalroutes

linkingprimaryauditorycortextomotorcortex,andwasabletosimulate

patternsofacquireddeficitsinwordrepetition,wordcomprehension,andword

naming.Chen,LambonRalphandRogers’(2017)modelofthesemanticsystem

employedaspokeandhubarchitecture,constrainedbytheheteromodal

integrativefunctionofanteriortemporallobe,thehublinkingrepresentationsof

conceptsindifferentsensoryandmotorsystems,andwasabletocapture

patternsofdeficitsinsemanticdementiaandvisualagnosiainpicturenaming

(seealsoHoffman,McClelland&LambonRalph,2018).Thesemodelssimulated

deficitsbyremovingconnectionsfromcertainregionsorpathwaysintheir

architecturesguidedbycognitiveneurosciencedata,andwerethenableto

simulatepatternsofrecoverybyrelearningintheimpairedmodel.Insomecases,

theeffectsofinterventionswereconsideredbyalteringpatternsofsubsequent

retraining(e.g.,Plaut,1996,1999).Modellingofdevelopmentaldisorders,

however,islessadvanced,todatemainlyfocusingonsinglenetworkmodelsof

individualabilities.Nevertheless,becausesuchmodelsfocusonmechanismsof

changeasacauseofdisorders,theyofferagoodfoundationtoconsider

interventions.

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Developmentaldisordersdifferfromacquireddisorders,inthatthecause

ofthedeficitisnotremovalofstructuressupportingestablishedfunctionality

butadevelopmentalprocessthatoccursunderatypicalconstraints.The

developmentalprocessischaracterisedbycomplexandinteractingcascades,by

effectsoftiming,andbyplasticitythataffordsopportunitiesforcompensation

(Karmiloff-Smith,1998;Thomas&Karmiloff-Smith,2002;Woollams,2014).

Over30years,arangeofdevelopmentalconnectionistmodelshasadvanced

explanationsforbehaviouraldeficitsindisorderssuchasdyslexia,

developmentallanguagedisorder,autism,andattentiondeficithyperactivity

disorder(seeThomas,Baughman,Karaminis&Addyman,2012;Thomas&

Mareschal,2007;Thomas&Karmiloff-Smith,2003a,forreviews).Having

establishedthisfoundation,someauthorsforesawaninfluentialrolefor

connectionismininterventionresearch.Daniloff(2002,p.viii)arguedthat

connectionism‘will…formthebackboneofmuchoflanguagetherapyinthe

nearfuture’,whilePoll(2011,p.583)arguedthat‘insightsfromconnectionist

researchontheacquisitionofearlymorphologyandsyntaxcanprovide

theoreticalguidanceforlanguageintervention’.Despitetheenthusiasm,this

potentialhasyettoberealised,withveryfewmodelsofdevelopmentaldeficits

beingextendedtoaddressbehaviouralinterventions(see,Bestetal.,2015;

Harm,McCandliss&Seidenberg,2003,forexceptions).

Oneshouldnotseethisasafailureofconnectionistapproachesperse.

Thegapbetweentheoriesofdeficitandtheoriesofinterventionisamore

generalphenomenon.Totakeoneexample,developmentaldisordersof

language,ithasbeenarguedthatdespiteextensivetheoriesaboutthecausesof

behaviouraldeficits,suchtheorieshaveplayedarelativelysmallroleinthe

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interventionpracticesofspeechandlanguagetherapists;andindeed,theoriesof

treatmenthaveoftendevelopedrelativelyindependentlyoftheoriesofdeficit

(Lawetal.,2008).Therearemultiplereasonsforthegap.Theseinclude(1)the

complexityoftheinterventionsituation,whichinvolvestreatmentofthewhole

childviaasocialinteractionwiththetherapist,andwherethetechniques

employedareoftendependentonthecharacteristicsoftheindividualchild,their

responsetointervention,andthetherapist’sexperienceandintuitions;(2)the

diversereal-worldconstraintsoninterventions,includingresourcesliketime

andcost;(3)theprimaryfocusofinterventiononbehaviouraloutcomes,which

donotinthemselvesnecessitateanunderstandingofcause;(4)frequentlackof

anevidence-basedconsensusonthemosteffectivetreatmentforagivendeficit;

(5)thefactthatchildrenoftendonothaveasingle‘deficit’eitherbehaviourally

orintermsofunderlyingmechanisms;and(6)evenwhenatheoryofdeficit

exists,thedifficultyofmovingstraightforwardlyfromthattheorytoaprediction

ofbesttreatment.AsByng(1994)argued,whiletheoriesofdeficitarea

necessaryprecursortodevelopinginterventions,‘simplyhavingadetailed

analysisofthedeficitdoesnotbyitselfsuggesttheformulationofspecific

therapeuticprocedurestoeffectchange’(p.270).Whatisneededisatheoryof

intervention.

Inwhatfollows,wereviewcontributionsfromexistingconnectionist

modelsandourownworktoassesswhetheranygeneralprinciplesof

interventioncanbeidentifiedfromthisapproach.Thefollowingbroadprinciples

willemerge:theexactnatureofthecomputationaldeficitmattersforthesuccess

ofintervention,asdoesitslocationinmorecomplexarchitectures;thetimingof

theinterventionmatters,anditscontentwithrespecttothetargetbehaviour;

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computationalmethodshavenotrevealedwaystotriggernewengagementof

compensatorymechanisms;asyetrelativelyunexploredaretheimplicationsof

dosage,duration,intensityandregimesofbehaviouralinterventions,andhowto

ensurebothgeneralisationbeyondtrainingitemsandpersistenceof

interventioneffects.Inthefollowingsections,wecharacterisethenatureofthe

interventionprocess,toestablishthechallengeofbuildingacomputational

modelofhowthisprocessmayactoncognitivemechanisms;wesummarisehow

developmentaldisordersarecapturedwithinconnectionistapproaches;andwe

outlinetwopreviousmodelsofinterventions,fordyslexiaandforword-finding

difficulties.

Theinterventionprocess:Theexampleofbehaviouralinterventionsfor

developmentaldisordersoflanguage

Interventionisabroadtermthatencompassesawiderangeofactivities.One

definition,inthecontextofimprovingthelanguageskillsofchildrenwithspeech,

language,andcommunicationneeds,describesaninterventionas‘anactionor

techniqueoractivityorprocedure(orindeedcombinationsofthese)that

reflectsasharedaimtobringaboutanimprovement,orpreventanegative

outcome…thiscanalsoincludethemodificationoffactorsthatarebarriersor

facilitatorstochangeandthemodificationofanenvironmenttofacilitate

communicationdevelopment’(Roulstone,Wren,Bakopoulou&Lindsay,2010,p.

327).Roulstoneetal.identifyseveraltermsthataresometimesused

interchangeably,includingtreatment,therapy,intervention,andremediation.

Oneprincipaldeterminingfactorinfluencingchoiceofintervention

methodisthechild’sage.Implicittechniquesareemployedwithyounger

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children,whileexplicittechniquesarefrequentlyemployedwitholderchildren

(Stokes,2014;Lawsetal.,2008).Foryoungerchildren(lessthan6years),the

mainaimisskillacquisition.Techniquesareinformalandnaturalistic,with

implicitgoalsandmethodsembeddedinchild-directedlearningcontexts.For

olderchildren(morethan6years),interventionalsotargetsmeta-cognitive

abilitiesandthedevelopmentofcompensatorystrategies.Thereisgreateruseof

formalmethods,employingexplicitgoalsandinstructioninatherapist-directed

learningcontext.Whilethereisageneralviewthattargetingcausalprocesses

earlyindisordereddevelopmentmaybemoreeffectivethanwaitinguntil

outcomesareestablished(Wass,2015),systematicevaluationsoftiming-of-

interventioneffectsarelesscommon.Importantdimensionsoftheintervention

methodincludetheprecisenatureoftheinterventionitself;whodeliversthe

respectivecomponentsofthetherapy(e.g.,aspeechandlanguagetherapist

[SLT],anSLTassistant,ateachingassistant,teacher,parent,oracomputer);if

thetherapyisdeliveredonetoone,orinagroup;andthedosageofthe

intervention,includingintensityandduration(Ebbels,2014).

Togiveaconcreteexampleofaninterventioninaspecificdomain,Seeff-

Gabriel,ChiatandPring(2012)evaluatedaninterventiontoimprove

performanceinproducingregularEnglishpasttensesfora5-year-oldchildwith

speechandlanguagedifficulties.Theinterventionwasdeliveredone-to-oneby

anSLT,withcarryoverfromthemotherandtheschool.Facilitationmethods

wereused,includingmodellingandelicitation,tohelpthechildproducethe

correctpasttenses,combinedwithvisualsymbolstoprovidemeta-linguistic

support.Theinterventiondosewas30minutesaweekfor10weekswiththe

SLTforatotalof5hours,plustheadditionalinputfromthemotherandschool.

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Thispatternisrepresentativeofasingleblockofintervention:inasurveyof

over500SLTsintheUK,Lindsayetal.(2010)reportedthemostcommon

frequencyofdeliveryofalanguagetherapywasonceaweekfor6weeksor

more,with42%askingteachersandparentstodelivertheinterventionmore

frequentlybetweenvisitstoincreasethedosage.Blocksmayberepeated.This

typicaldoseanddurationcanbecontrastedwiththemuchlargerdosages

sometimesusedwithotherdevelopmentaldisorders,forinstancetoaddressthe

widersocio-communicativedeficitsinautism.Inoneformoftheearlyintensive

behaviouralintervention(EIBI),interventionbeginsby2yearsofage,witha

rangeof20to40hoursperweekacrossonetofouryearsofthechild’slife,fora

rangeofinterventiondoseofbetween1000and8000hours(Eikeseth,2009;

Smith,2010).

Childrencanvarywidelyintheirresponsetointerventions.Apartfrom

theageofthechild,othercharacteristicsarerelevanttointerventionoutcome,

includingtheseverityofthedevelopmentaldeficitandthepresenceofotherco-

morbiddeficits(Ebbels,2014).Therelationshipbetweendosageandtheeffect

sizeofthebehaviouralimprovementproducedbytheinterventionalsovaries,

anddependsonthetargetability.Forexample,Lindsayetal.(2010)summarised

meta-analysisdatatoindicatethatforinterventionstargetingphonology,

intensiveinterventionsweremoreeffectivethanthoseoflongduration;for

thosetargetingsyntax,interventionsoflongdurationweremoreeffectivethan

shortintensiveones;forvocabulary,longdurationwasimportantbutnot

intensity–childrendidbetterwithshortburstsoveranextendedtime.Inawell

controlledstudyofagrammartreatmentfor5-year-oldswithDLD,Smith-Lock

etal.(2013)foundthatthesamedoseof8hourswasmoreeffectivedelivered

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weeklyover8consecutiveweeksthandailyover8consecutivedays.Differences

inoptimalregimespresumablydependonthefunctionalplasticityofthe

underlyingmechanisms,includingtimeforconsolidationandopportunitiesfor

practise.

Practicevariesastowhethertheprimaryaimofinterventionisto

remediatethedeficitortoencouragedevelopmentofpotentialcompensatory

strengths.Togiveanexample,word-findingdifficulties(WFD)representa

developmentalvocabularydeficitwherechildrenstruggletoproducewordsthat

theycanneverthelesscomprehend.WFDisviewedasaheterogeneousdisorder,

withpossiblecauseseitherinphonologicalaccessorimpoverishedsemantic

representations(Best,2005;Faust,Dimitrovsky&Davidi,1997).Inasurvey,

Best(2003)reportedthatSLTslistedphonologicalawarenessdifficultiesasco-

occurringwithWFD46%ofthetime,whilesemanticproblemsco-occurredonly

13%ofthetime.However,interventionapproachesthattargetedsemantics

wereusedmorefrequentlythanthosethattargetedphonology(79%ofthetime

comparedto54%).Inthiscase,therefore,SLTsoftensoughttobuttressareasof

strengthwithinthechildtoimproveword-retrievalskills.

Theorderoftargetingskillswithinadomainmayalsobeimportant.For

example,intheusage-basedapproachtoremediatingdevelopmentalproblems

insyntax,grammaticalstructuresaretargetedinthesameorderthatthey

developintypicallydevelopingchildren(e.g.,Riches,2013);andthatorderof

acquisitionreflectstheinteractionbetweenthechallengesoftheparticular

domainandtheconstraintsofdevelopmentalmechanisms.

Akeyquestioniswhichinterventionthetherapistshouldchoose.The

decisionisinfluencedbymultiplefactors.Akeyfactor,ofcourse,shouldbethe

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intervention’seffectiveness.However,Roulstoneetal.(2010)notedthat

evidenceforeffectivenessincorporateclinicalexperienceorlocalevaluations,in

additiontoresearchevidence.Roulstoneetal.identifiedseveralotherfactors

influencinginterventionchoice,includingreferencetounderlyingtheoretical

positions,andpragmaticreasonsrelatedtoefficiency,accessibility,popularity

andcost.Otherresearchershavetakenawiderperspectiveonthefactors

influencingthedesignandsuccessofinterventionsaimingtochangebehaviour.

Forexample,Michieandcolleagues(e.g.,Michie,vanStralen&West,2011)

constructedaframeworkthatincorporatesnotjusttheinternalcognitive

mechanismsabletodeliverbehaviouralchange(whichtheytermed‘capability’),

butalsomotivationandopportunitytochange.Theframeworkidentifies

environmentalinfluencesandstructures,suchasresourcesandpolicy,which

operateasconstraintsonorincentivesforsuccess.

Therearetwoimportantdimensionsintheevaluationofinterventions.

Thefirstistheextenttowhichtheinterventiongeneralisestootheritemsor

skillsbeyondthosetargetedintheinterventionitself.Thesecondisthe

persistenceofthebenefitsofinterventionaftertheinterventionhasceased.

UsingourexamplestudyofSeeff-Gabrieletal.(2012)thattargetedEnglishpast

tense,the5-hourinterventionwasfoundtogeneralisetountrainedregular

verbsbutnottootherirregularverbs,whileprogresswasmaintainedatfollow-

up8weekslater.Generally,achievinggeneralisationandpersistenceof

interventionshasprovedchallenging.Forexample,inherreviewof

interventionsforgrammardifficultiesinschool-agedchildren,Ebbels(2014)

concludedthatfollow-upgenerallyshowsthattheprogressproducedbythe

interventionismaintained,butdoesnotpromptaccelerationindevelopment

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aftertheinterventionhasceased.Thegainsareretainedbutnofurthergainsare

stimulated.Bailey,Duncan,OdgersandYu(2017)identifiedthediminishing

effectofaninterventionafteritscessation(so-called‘fade-out’)asa

characteristicofmanyinterventionstargetingcognitiveandsocioemotional

skillsandbehaviours.

Otherimportantfactorsinclude:(i)thechildpreferences(e.g.,achild’s

willingnesstoworkontargetAbutnotB);(ii)parentalinvolvement(whatare

appropriateactivitiesforhomepracticetomaximisedose);(iii)context(e.g.,

selectingvocabularyitemstomirrorthosecurrentlybeingtaughtintheschool

curriculum);and(iv)outcomeofintervention(suchthatthetherapistmay

modifytargets,methods,andfeedbackaccordingtotheresponseto

intervention).

Lastly,evenifaninterventionhasbeenshowntobeeffective,unlessits

key‘activeingredient’hasbeenunderstood,itisnotguaranteedthattheeffect

willbesimilarwhenappliedtonewchildren,whendeliveredbylessexpert

practitioners,orwhenadaptedtonewcontexts.Identificationoftheactive

ingredientinturnisfacilitatedbycomparisontoacontrolgroupwhose

treatmentdiffersonlyintheactiveingredient.Andthisinturnrequiresatheory

abouthowtheinterventionremediatesthedeficitorsupportsacompensatory

strategy.

Insummary,thisconcreteexampleofinterventionsfordevelopmental

disordersoflanguageillustratesthecomplexityoftheprocessandthemultiple

factorsinvolved.Interventionsinvolveactivitiestoimprovedevelopmental

outcomesinchildren;theiroutcomesarevariabledependingonthe

characteristicsofthechildandtherapist;boththedesignandthedosageofthe

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interventionareimportantforoutcome;andoutcomesneedtobeevaluated

againstkeycriteriaof(1)generalisationtootheritemsorskillsbeyondthose

targetedintheinterventionitself,and(2)maintenanceofgainsoncethe

interventionhasceased.

Connectionistmodelsofinterventions

Howdisordersaresimulated:monogenicversuspolygenicapproaches

Intheory,therecentneuroanatomicallyconstrainedconnectionistmodelsofthe

languagesystem(Chenetal.,2017;Uenoetal.,2011)lendthemselvesreadilyto

simulatingdevelopmentaldeficits,viainitialrestrictionstothepathwaysor

mechanismstakentounderlieagivenbehaviour.Forexample,Seidenberg

(2017)summarisedrecentcognitiveneurosciencehypothesesthat

developmentaldyslexiamaybetheresultoffourtypesofdeficit:anomaliesin

myelinisationaffectingthespeedandreliabilityofsignaltransmissionwithin

andbetweenreading/languageareasofthebrain;neuronalhyperexcitability

withinareas;anomaliesofneuralmigrationimpactingthefunctionalityofneural

networks;andincreasedvariability/noiseinneuralrepresentationsimpacting

thefunctionalconnectivitybetweenregionsofthereadingnetworkandthe

abilityofthesystemtobenefitfromlearningexperiences(seealsoHancock,

Pugh,&Hoeft,2017).Muchoftheexistingworkondevelopmentaldisorders,

however,hasfocusedonconnectionistmodelsofindividualmechanisms

acquiringsingletargetbehaviours.Inthiswork,adistinctioncanbedrawn

betweenmonogenicandpolygenicmodelsofdisorders.

Inasinglenetworkmodel,changesinbehaviouraretheresultof

experience-dependentalterationstothestructureofthenetwork,causedbyits

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interactionwithalearningenvironmentwithparticularinformationalcontent.

Artificialneuralnetworkshaveintrinsicconstraintsthataffectwhatinput-

outputmappingstheycanlearnandhowquickly.Theseconstraintsinclude

propertiessuchasthenumberofinternal(hidden)units,thepatternof

connectionsbetweenunits,therateatwhichconnectionstrengthschangein

responsetoexperience,andthewayexternalorenvironmentalinputsare

encodedforprocessing.Modelsofdevelopmentaldeficitsproposethatthese

constraintsareatypicalinsomechildren,deflectingdevelopmentaltrajectories

outsidethenormalrangeofvariation(Thomas&Karmiloff-Smith,2003a,b).For

instance,inanearlymodelofdevelopmentaldyslexia,thedeficitwassimulated

byattemptingtolearnthemappingsbetweenorthographyandphonologyina

modelwithtoofewhiddenunits(Seidenberg&McClelland,1989);inamodelof

autism,over-detailedcategoriesweresimulatedbyincreasingthenumberof

hiddenunitsinasemanticnetwork(Cohen,1994).

TheSeidenbergandMcClellandmodelofdyslexia(1989;seealsoHarm&

Seidenberg,1999;Plautetal.,1996)illustrateswhatmightbecalledthe

monogenicapproach.Connectionistmodelsusuallyhaveseveralfreeparameters,

suchasthenumberofinternalor‘hidden’units,thelearningrate,andthe

momentum.Valuesfortheseparametersaredeterminedsothatthemodel

capturesthetrajectoryoftypicaldevelopment.Inthedisorderedcase,justone

parameterissettoadifferentvalue.Thedisorder,then,hasasinglecause,against

abackgroundofverysmallorzerovariationinallothercomputational

parametersacrossindividuals(Thomas,2003a).

Morerecentmodelshaveadoptedapolygenicapproach(e.g.,Thomas,

Forrester,&Ronald,2015;Thomas&Knowland,2014;Thomas,2016a).

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Individualvariationinthedevelopmentofcognitiveabilitiesisviewedasarising

fromthecombinedinfluenceofsmallvariationsinmanyneurocomputational

parameters,includingthoseinvolvedintheconstruction,activationdynamics,

adaptation,andmaintenanceofnetworkarchitectures.Theapproachinvolves

simulatingdevelopmentinlargepopulationsofindividuals.Thecumulative

effectofmanysmallcontributionsproducesanormaldistributionofthe

developmentofbehaviourinthepopulation,againstwhicha‘normalrange’of

variationcanbedefined,andcasesofdevelopmentaldelayidentified(Thomas,

2016b).Disordersarethusviewedasthelowertailofacontinuousdistribution

ofdevelopmentalvariationinapopulation.

Themonogenicandpolygenicapproachesarenotmutuallyexclusive.For

example,Thomasandcolleaguesdemonstratedhowautismmightcombinetwo

groups,monogeniccaseswithageneticmutationcausingagiven

neurocomputationalparametertotakeupextremevalues,andpolygeniccases

withthesameparameterfallingintheuppernormalrangebuthavingitseffect

onbehaviouramplifiedbyacombinationofriskfactorsthatvaryacrossthe

wholepopulation(Thomas,Davisetal.,2015;Thomas,Knowland&Karmiloff-

Smith,2011;seeLeblondetal.,2019,forrecentgeneticresults).Furthermore,

interactionofamonogeniccauseandpopulation-widepolygenicindividual

differencescangiverisetoapparentsub-groupswithinthedevelopmental

disorderdespiteithavingasinglepathologicalcause:individualdifferencesthat

producesmalleffectsnormallycanbeexaggeratedbytheatypicalparameter,

causingdivergentmanifestationsofthedisorder(Thomas,Davisetal.,2015;

Thomas,2016b).

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Howinterventionsaresimulated

Whereadevelopmentaldeficitisidentifiedinachild,itispresumedthat

naturalisticexperience(ortypicaleducationalexperience)hasnotbeen

sufficienttoenabletheemergenceofage-appropriatebehaviours.Inasingle

networkmodel,twotypesofinterventionaresuggested:theadditionalofnew

informationtothestructuredlearningenvironment(insimulationterms,new/

replacementpatternsinthetrainingset);ormanipulationstothecomputational

propertiesofthesystem(equivalent,say,topharmacologicaltreatments,

transcranialmagneticstimulation,orneurofeedback).Insometypesofmodels,

changesincomputationalpropertiesmightsubsequentlyservetoalterthe

system’ssamplingofitslearningenvironment(suchasinreinforcementlearning

models;e.g.,Richardson&Thomas,2006).Inamodelthatsimulatesarangeof

behavioursinalargerarchitecture,suchasinafullreadingsystem,the

possibilityexistsnotonlyofinterveningtoremediateatypical

mechanisms/pathways,butalsotoexploitpathwayswithoutatypicalprocessing

constraints.Aswesawpreviously,actualinterventionsvaryastowhetherthey

targetremediationofdeficitorsupportofcompensatorystrengths,perhaps

dependingontheseverityofthedeficit(Woollams,2014).However,theexact

natureoftheinteractionbetweenprocessingcomponentsmaybeimportantin

understandingtheeffectsofeithertypeofintervention.

Howcouldoneselectfurthertrainingitems–aninterventionset–foran

atypicallydevelopingnetwork,whichwouldbemoresuccessfulindriving

developmentthannaturalexperience?Thestatisticallearningperspectiveof

whichconnectionismisaparthasgeneratedagrowingunderstandingof

environmentalfactorsthatproducestrongerorweakerlearningintypical

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development(Borovsky&Elman,2006;Gomez,2005;Onnisetal.,2005).This

includestheimportanceoffactorssuchasthefrequencyoftrainingitems,their

variability,andtheprovisionofnoveltyinfamiliarcontexts.Forexample,one

heuristicthatarisesfromthisapproachisthatinordertoimproveacquisitionof

compositionaldomains,whereconceptsaremadeupofdifferentcombinationsof

thesameprimitives,thesystemshouldbeexposedtothecomponentprimitives,

eitherinisolationorinmanydifferentcombinations(see,e.g.,Feyetal.,2003).

Thisalsoencouragessubsequentgeneralisationtonovelinstances.Potentially,

thesekindsoflessonscanprovideguidanceonhowtodesigninterventionsets

toachievethebestbehaviouraloutcomeforamodelwithatypicalcomputational

constraints.However,thiswouldbetoassumethatanunderstandingofthe

experiencesthatimproveorhinderlearningintypicallydevelopingsystemsis

informativeabouthowtoinfluencedevelopmentaloutcomesincognitive

systemswithatypicalconstraints(anassumptionthatdrives,forexample,the

usage-basedapproachforgrammardeficits;Richie,2013).Ifprinciplesoftypical

developmentareaguide,connectionistapproachestolanguageacquisition

highlightseveralfactors(Poll,2011):thatthestructureandquantityoftheinput

isimportantindrivingdevelopment;thatlanguagedevelopmentdoesnotoccur

throughpassiveexposurebutviaexperiencesrelatedtothechild’sown

expectations;andthatlanguagedevelopmentconcernslearningtherelationship

betweenlanguageformandlanguagemeaningsothatcontextualcueswhich

narrowthehypotheseswillaidlearning.However,itremainstobe

demonstratedinimplementedmodelsthatthefactorsproducingbest

developmentintypicalmodelsalsoholdforthosewithatypicalprocessing

properties.

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Cognitivecomputationalmodelspointtoanimportantdistinction

betweentwotypesofbehaviourinevaluatinginterventions.Thefirstis

performanceonthetrainingset,thatis,therangeofexperiencesthesystem

encountersinitsstructuredlearningenvironment.Thesecondisperformanceon

ageneralisationset,thatis,itemswhicharenoveltothesystembutwhichbear

similaritytothosewithwhichithasexperience.Thisechoestheconcernin

actualinterventionsonwhethertheinterventiongeneralisestootheritemsor

skillsbeyondthosetargetedintheinterventionitself.Computationalsystems

withaso-calledinductivebias(Mitchell,1997)cantakeadvantageoftheir

existingknowledgetoproduceresponsestonovelinputs.If–externally,as

modellers–westipulatethatthestructuredlearningenvironmentinfact

containssomeunderlyingregularityorfunction,wecanassessthe

generalisationperformanceofasystemdependingonwhetherithasextracted

thisunderlyingfunctionfromitstrainingexamples,andisthenabletoapplyit

appropriatelytonovelitems.Inmodels,thedistinctionbetweentrainingand

generalisationisimportantbecausedevelopmentaldeficitsmayoperate

differentiallyacrossperformanceonthetrainingsetandthegeneralisationset;

becauseactualinterventionsareoftenassessedspecificallyontheirabilityto

producegeneralisationbeyondthetreateditems;andbecauseinterventionscan

bechosenwhichdifferentiallytargettrainingsetorgeneralisationperformance.

Twopreviousmodelshavegivenseriousconsiderationtotheuseof

modelsofatypicaldevelopment(respectively,indyslexiaandinword-retrieval

difficulties)toevaluatingpotentialinterventions.Harm,McCandlissand

Seidenberg(2003)extendedthetrianglemodelofreading(Seidenberg&

McClelland,1989;Plautetal.,1996;Harm&Seidenberg,1999)toaddressan

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apparentparadoxthat,whileaphonologicaldeficitisoftenviewedasthe

primarycauseofdevelopmentaldyslexia,interventionsthattargetspoken

language(phonology)alonearerelativelyineffectiveatremediatingreading

deficitsonceachildhaslearnttoread.Instead,interventionstofacilitatereading

aloudneedtocombineworkonphonologyandondecoding,thatis,learningthe

mappingbetweenprintandsound(Bus&Ijzendoorn,1999).Harmetal.’s

(2003)modelofreadinginvolvedaphonologicalcomponent,whichfirstlearned

alexiconofEnglishwords.Anorthographiccomponentthenprovided

representationsofthewrittenformsofwords,whichhadtobeassociatedwith

theexistingphonologicalrepresentations.Dyslexicversionsofthemodelwere

producedbyapplyingatypicalconstraintstothephonologicalcomponent,which

impactedonitsinitialphaseofacquisition.Specifically,priortotraining,50%of

theconnectionweightsweresettoandheldatzero,andweightdecaywas

appliedtotheremainingweights,therebylimitingthemaximummagnitudethat

theycouldreachduringtraining.Beforereadingacquisitioncommenced,

phonologywasatypical.Theoutcomeofreadingacquisitionwasasystemwitha

particulardeficitinitsnonwordreading,thatis,itsgeneralisationofreadingto

novelforms.Suchadeficithasbeentermed‘phonological’developmental

dyslexia(Castles&Coltheart,1993).

Harmetal.(2003)thencomparedtwointerventions,eachappliedattwo

differentpointsintraining.Oneinterventionsimplyalleviatedthephonological

deficit–unfrozethe50%ofweightsandremovedweightdecay.Onecouldview

thisasaninterventionthatdirectlytargetedneurocomputationalproperties.The

secondinterventionaddednewitemstothetrainingset,tosimulateaparticular

behaviouralintervention(theWordBuildingIntervention;McCandlissetal.,

21

2003).Thistooktheformofextra‘lessons’onanorderedsequenceofwords

eachofwhichdifferedbychangingormovingonlyonegrapheme(e.g.,sat,sap,

tap);wherethemodelmadeanerror,extratrainingwasgivenontheindividual

componentgrapheme-phonememappingsofaword(for‘sat’,s=>/s/infirst

position,a=>/a/insecondposition,etc.).Bothinterventionsproducedbenefits

tononwordreading,albeitwithoutfullyremediatingthedeficittothelevels

observedinthetypicallydevelopingmodel.Thetimingofinterventionwasalso

important.Alleviatingthephonologicaldeficitaloneonlyshowedbenefitswhen

appliedearlyintraining,whilethesimulatedbehaviouralinterventionthat

targeteddecodingshowedbenefitsacrosstraining.Theexplanationforthisage-

relatedeffect,parallelingtheobservedempiricaldata,wasthatoncethenetwork

begantolearnmappingsbetweenorthographyandimpoverished

representationsofphonology,thesewerehardtoundoevenifphonologywas

remediatedlateron.Anapparentsensitiveperiodforremediationbytraining

phonologyalone,therefore,wasexplainedbyentrenchment:thedifficultyof

resettinginappropriatelyconfiguredconnectionweights(Thomas&Johnson,

2006).ViewingHarmetal.’s(2003)modelasrepresentingtwocomponentsin

thelargerreadingarchitecture(Uenoetal.,2011),thesetimingeffectsspeakto

theimportanceofunderstandingthedevelopmentalinteractionsbetween

multiplecomponentswiththearchitecture.

Inthismodel,then,theinitialdevelopmentaldeficitwasmainlyin

generalisationratherthanperformanceonthetrainingset.Thedeficitwas

remediatedbyshowingthenetworkthecomponentpartsofholistic

representations(inlinewiththeheuristicidentifiedinstatisticallearning

approaches)throughtheparticularsequenceofpresentationofitemsinthe

22

‘lesson’,andtheadditionofnewinformationtothetrainingsetintheformof

individualgrapheme-phonemecorrespondences.Lastly,therewasacontrast

betweenaninterventionthatdirectlytargetedcomputationalproperties,and

oneabehaviouralintervention,whichaddedsomethingnewtothetrainingset

and/orchangingthefrequencydistributionwithinthetrainingset.

ThesecondmodelbyBestetal.(2015)exploredinterventionsfor

childrenwithword-findingdifficulties(WFD).Namingwasimplementedasthe

activationofasemanticrepresentationoftheword’smeaningactivatingits

phonologicalform.Developmentaldeficitsinproductivevocabularymaybe

causedinatleasttwoways:byimpairmentsinthesemanticrepresentations

drivingnaming,orbyimpairmentsinaccessingphonologicaloutputforms.

Evidencesuggestsremediationofbothsemanticandphonologicalknowledge

canproducebenefitsforthesechildren(Bestetal.,2017).Theconnectionist

namingmodelhadtwocomponents:asemanticcomponentandaphonological

component,eachofwhichunderwentitsowndevelopmentalprocessto

establishitsinternalrepresentations;andtwopathwaystolearnthemappings

betweentheserepresentationsastheydeveloped,fromsemanticstophonology

tosimulatenaming,andfromphonologytosemanticstosimulate

comprehension.Constraintsappliedtoeitherofthesecomponents,ortothe

pathwaysbetweenthem,produceddevelopmentalnamingdeficits.Themodel

wasusedtopredicttheoutcomeofinterventionsontwoindividual6-year-old

childrendiagnosedwithWFD.Twoatypicalmodelswerecalibratedtoresemble

thedevelopmentalprofilesoftheindividualchildren,accordingtomeasuresof

thechildren’sphonologicalknowledge,semanticknowledge,naming,and

comprehensionabilities.Themodelmanipulationsinvolvedremoving

23

connections,reducingthenumberofhiddenunits,oralteringtheactivation

dynamicsofthesimpleprocessingunits,eitherinthecomponentsorthe

pathways,butalwayspriortotraining.

Theindividualmodelswerethengiveneithera‘semantic’ora

‘phonological’intervention.Thesemanticinterventioninvolvedadditional

trainingforthesemanticcomponenttoimproveitsinternalrepresentations,

whilethephonologicalinterventioninvolvedadditionaltrainingforthe

phonologicalcomponent.Theinterventionswereinterleavedwiththenormal

trainingregimeforvocabularydevelopment.Theresultwasapredictionfor

whichtypeofinterventionwouldworkbestforeachchild.Themodel

predictionswerethentestedinrealitybygivingeachchildbothasemanticanda

phonologicalinterventioninturn(1sessionof30minutesperweekfor6weeks,

foratotalof3hoursforeachinterventiontype,anda6-weekwash-outperiod

betweeninterventions).Itwasthendeterminedwhichimprovednamingskills

more.Foronechild,themodel’spredictionwascorrect(onlythephonological

interventionbenefitednamingperformance);fortheotherchilditwasnot(the

modelpredictedbothinterventionswouldwork;thechildonlybenefitedfrom

thesemanticintervention).

Inthismodel,abehaviouralinterventionwasagainsimulatedby

modifyingthetrainingset,herealteringtherelativeamountoftrainingon

differentcomponentsofthesystem,butwithouttheadditionofnewinformation.

Interventionsuccesswasmeasuredagainstperformanceonthetrainingset,

althoughtheinterventionoccurredonlyonasubsetofthefulltrainingset.The

modelfocusedondifferentialeffectsoftherapytypeanddidnotreportwhether

deficitswerefullyremediatedineithercase.

24

Table1summarisessomeofthekeyconceptsidentifiedinthe

introduction.

<InsertTable1here>

Outlineofmodelling

Wereviewanextendedprogrammeofmodellingwork(seeauthornote),infour

sections.Inthefirst,weconsiderlong-termoutcomes.Developmentaldisorders

arediagnosedinchildhood,whenachildisflaggedasnotmeetingage-

appropriateperformanceexpectations.Computationalmodelsallow

considerationofthelong-termoutcomes,ifthesesystemsarelefttodevelop

withoutinterventions.Weask(a)intheabsenceofintervention,what

compensatoryoutcomescanbereached?And(b)dosomeearlydelaysresolve,

andifsounderwhatconditions?Inthesecondsection,weaddressmethodsto

remediateatypicaldevelopmentinasinglenetwork.Weconsider(a)wherethe

disorderarisesthroughinsufficientearlystimulationofthetargetsystem;(b)

howtochoosebettertrainingitemstoachievelearninginasystemwithatypical

processingproperties;(c)howbetterperformancecanbeachievedfroman

atypicalnetworkbytargetingimprovementofitsinputandoutput

representations;and(d)howinterventionsmightinsteadalterthe

computationalpropertiesofthelearningsystem.Inthethirdsection,weaddress

interventionstoencouragecompensationviaalternativepathways.Inthefinal

section,weconsiderthekeyissueofindividualdifferencesinresponseto

intervention.

Computationalmodelling

25

Simulatingthelong-termoutcomeofatypicaldevelopmentwithout

intervention

Compensatedoutcomes

Animplementedmodelofadevelopmentaldeficitprovidesthefoundationto

investigatedifferentpossibleinterventionsappliedinchildhood.Butthe

modellercanalsorefrainfromintervening,andusethemodeltopredictthe

ultimatedevelopmentaloutcome.Forsomecomputationallimitations,sufficient

exposuretothetrainingseteventuallypermitsperformancetoreachthenormal

rangeonthisset.However,closeinspectionofthesenetworksindicatesthatthe

underlyingprocessingitselfhasnotnormalised.Thiscanbedemonstratedby

observingapersistingdeficitongeneralisation.Suchaneffectwasobservedina

connectionistmodelofEnglishpasttenseformationsimulatingchildrenwith

developmentallanguagedisorder(DLD).

ThemodelofThomas(2005)exploredthetheoreticalproposalofUllman

andPierpont(2005)thatchildrenwithDLDmighthaveaparticulardeficitin

morphosyntaxbecauseofamoregeneraldeficitintheirproceduralmemory

systems.Theso-called‘proceduraldeficithypothesis’addressedtheobservation

thatchildrenwithDLDoftenexhibitgreaterimpairmentingrammar

developmentthanvocabularydevelopment.Accordingtothehypothesis,the

disparitystemsfromadifferentialrelianceofthenormallanguagesystemon

twoseparate,moredomain-generalmemorysystems:grammardevelopmenton

theproceduralmemorysystem,whosecharacteristicsareslowacquisition,fast

automaticexecutionandsequenceprocessing;andvocabularydevelopmenton

thedeclarativememorysystem,whosecharacteristicsareparallelprocessing

andslowrecall.Notably,thehypothesisproposedacentralrolefor

26

compensationinexplainingobservedbehaviouralimpairmentsinDLD:the

profileoflanguageskillsisaconsequenceoftheproceduralsystem’ssub-

optimalattemptstoacquirethestructuralaspectsoflanguagecombinedwiththe

attemptsofthedeclarativememorysystemtocompensateforthisshortcoming

throughlexicalstrategies.

Thomas(2005)exploredthisideawithamodelofEnglishpasttense

acquisitioninwhichtheproductionofphonologicallyencodedpasttenseforms

attheoutputwasdrivenbyintegratinglexical-semanticandphonological

informationabouttheverbpresentedattheinput(Joanisse&Seidenberg,1999).

DLDwassimulatedasamonogenicdisorder,alteringtheactivationfunctionin

theinternalprocessingunitspriortotrainingtodecreasetheirdiscriminability,

inlinewithmorerecent‘neuralnoise’accountsofdevelopmentallanguage

deficits(Hancock,Pugh&Hoeft,2017).Unitdiscriminabilitywasreducedsuch

thatunitswerelessabletomakelargechangesintheiroutputforsmallchanges

intheirinput,implementedbyreducingthe‘temperature’parameterinthe

sigmoidactivationfunctionfrom1to0.25.Thisimpairedthenetwork’sabilityto

formsharpcategoricalboundariesinitsinternalrepresentations.Figure1

demonstratesthematchofmodeldatatoempiricaldatainapasttense

elicitationtaskforchildrenof10-11yearsofage,eitherwithorwithoutDLD.As

wellascapturingtheprofileofreducedaccuracy,themodelcapturedakey

‘compensatory’featureidentifiedbyUllmanandPierpontintheinflectionof

regularverbsinchildrenwithDLD:increasedfrequencyeffects(2005;seevan

derLely&Ullman,2001).UllmanandPierponttookthesefrequencyeffectstobe

akeyhallmarkoftheoperationofdeclarativememoryratherthanprocedural

memoryandreflectitsunusualinvolvementinmorphosyntaxinDLD.The

27

connectionistmodelalsocapturedcompensatoryhallmark.Inthemodel,itwas

instantiatedasagreaterroleforlexicalinformationindrivingpasttense

formation,ratherthanlearningthephonologicalregularitiesrelatingbaseand

inflectedverbformsthatcapturethepasttenseruleintheemergentistaccount

ofacquisition.Removinglexical-semanticinputintheDLDmodelimpaired

regularverbperformance,butdidnotinthetypicallydevelopingmodel.

Figure1nowshowswhathappenedwhentheatypicalmodelwasallowed

toruntoits‘adult’state.Performanceonthetrainingset,onbothregularand

irregularverbs,reachedceiling.Notably,however,therewasaresidualdeficiton

generalisation,theextensionoftheregularpasttenseruletonovelforms.The

model,withitsatypicalprocessingproperties,hadnotmanagedtoextractthe

generalfunctionwithinthetrainingset;butwithenoughexposuretothe

trainingset,hadeventuallymanagedtoproducenormal-lookingbehaviouron

thatset.Evenintheadultstate,theatypicalnetworkreliedmoreonlexical

informationatinputtodriveitsinflections.

Reducingthediscriminabilityofprocessingunitsparticularlyimpacted

ongeneralisationbecauseitaffectedtheformationofsharpcategoryboundaries.

Categoricalfunctioningallowsnovelformstobetreatedinthesamewayas

existingcategorymembers.Inunpublishedwork,thesimulationsreportedin

Thomas(2005)wererunwithothermonogeniccausesoftheinitialdeficit.For

twootherdeficits,processingnoiseandapurelylexicalstrategyforproducing

inflections,asimilarpatternwasobservedofresolvingdelayonthetrainingset

andaresidualgeneralisationdeficit;forrestrictednumbersofinternal

processingunits,therewasaresidualgeneralisationdeficitbutalsono

resolutionoftheearlydeficitonthetrainingset;foraveryslowlearningrate,

28

therewasnogeneralisationdeficitbutaresidualdeficitinirregularverb

performancewithinthetrainingset.Itisevident,then,thatthenatureand

possibilityoflong-termcompensatoryavenueswithinthissinglemechanism

modelweresensitivetothetypeofinitialprocessingdeficit.

Inonesense,onemightviewlong-termdeficitsinextractingregularities

intheproblemdomainasexamplesofawell-knowncharacteristicofsub-

optimalartificialneuralnetworks:over-fittingthetrainingdata.Wewishto

emphasiseanalternativeview,however:thatatypicalprocessingpropertiesmay

stillallowsomepartsoftheproblemdomaintobeacquiredwithenoughtraining.

Anotheraspectoflanguageandanothertypeofneuralnetworkarchitecture

illustratethispoint.ThomasandRedington(2004)usedasimplerecurrent

networktoinvestigatetheimpactofatypicalprocessingconstraintsonsyntax

processing.Givensufficienttraining,theyobservedthatsimplerecurrent

networkswithatypicalsequenceprocessingpropertiescouldeventuallyfind

compensatorysolutionsinclassifyingsyntacticconstructions,butonlyforthose

constructionsthatcouldbecomprehendedvialocallyavailablelexicalcues,not

thoserelyingsolelyonsequencinginformationfordecoding.

Insum,asystemthatexhibitsearlydelaysthroughatypicalprocessing

propertiesmaybe‘forced’throughmassiveexposuretoshownormal-looking

behaviouronthetrainingset–theitemsthatareintenselypractised.However,

thisdoesnotnormaliseprocessingproperties.Residualdeficitsmayremain,

suchasingeneralisationorinmoredemandingaspectsofthetask.Thispattern

ofeventualgoodaccuracyonpractiseditemsalongwithsubtleresidualdeficits

isobservedinsomedevelopmentaldisorders.Forexample,largedosagesof

readingexperiencecansometimesremediatereadingaccuracydeficitsin

29

dyslexia,butresidualdeficitscanbefoundinreadingspeedandinspelling,both

ofwhichsuggesttheinternalrepresentationshavenotbeennormalised(Hulme

&Snowling,2009).Thesedeficitsmayevenbesubtle:Leongetal.(2011)found

thathighlycompensatedadultswithdyslexia(undergraduatestudentsatthe

UniversityofCambridge)showedsignificantlylowersensitivitytosyllablestress

thanadultswithoutdyslexia.

<InsertFigure1abouthere>

Resolutionofearlydelays

Sometimes,forasubsetofchildren,earlyobserveddevelopmentaldeficitscan

resolveapparentlyoftheirownaccord.Theresolutionofdeficitshasbeen

reportedinseveraldevelopmentaldisorders,includinglanguage(e.g.,Dale,Price,

Bishop,&Plomin,2003),autism(e.g.,Charman,2014a;Feinetal.,2013),and

attentiondeficithyperactivitydisorder(ADHD;e.g.,Biedermanetal.,2010)and

hasgeneratedtheoreticaldebateineachcase.Whatdoesresolutionofdelay

implyaboutunderlyingcause?

ThomasandKnowland(2014)usedthesameconnectionistmodelofpast

tenseacquisitionasThomas(2005)toinvestigatewhyearly-identifieddelay

sometimesresolves.Theyarguedthatlimitationsintheplasticityof

developmentalmechanismscaninitiallyproducesimilarbehaviouralpatternsas

limitationsincomputationalcapacity.Systemswithlimitedplasticityrequire

moreexposuretolearningeventstoproduceanequivalentimprovementin

performance.Mechanismsexhibitingearlydelaysthroughlimitedplasticity

shouldthereforerespondtointerventionsthatsimplyenrichthelevelof

30

naturalisticexperience.Suchsystemsshouldremediatetothenormalrangejust

throughgreater‘practice’,withoutrequiringspeciallydesignedinterventions.

UnliketheThomas(2005)modelofpasttenseformation,Thomasand

Knowland(2014)tookapolygenicapproachtolanguagedelay.Variationinrates

ofdevelopmentwasmodelledinalargepopulationofsimulatedchildren

(N=1000).Variationwascausedbysimultaneoussmalldifferencesin14

computationalparameters,aswellasintherichnessofthelanguage

environmentinwhichthechildwasraised.Thecomputationalparameters

influencedpropertiesofthelearningmechanismsuchasnetworkconstruction

(e.g.,numberofinternalunits),networkactivation(e.g.,unitdiscriminability,

processingnoise),networkadaptation(e.g.,thelearningalgorithm,thelearning

rate),andnetworkmaintenance(e.g.,thelevelofpruningtoeliminateunused

connectivity,weightdecay).1Acrossthe14parameters,ThomasandKnowland

identifiedfourbroadtypesofprocessingrolethatparametersmightserve.These

roleswerecapacity,plasticity,signal,andregressiveevents.Parameters

contributingtocapacityinfluencethepotentialdimensionalityoflearned

representations,andincludethenumberofunitsandconnections;forplasticity,

1Thechoiceofparameterstovarywasbasedonpreviousconnectionistmodelsthathadusedindividualparametervariationstoexplainindividualdifferencesordisorders.Thesemodels

werepursuinghypothesesthat,forinstance,differencesincognitionmayarisefromneural

plasticityorfromtheactionsofcertainneurotransmitters.Variationsinarchitecturehavebeen

usedtoexplaindyslexia:Zorzi,HoughtonandButterworth(1998);inhiddenunitstoexplain

intelligence:Richardsonetal.(2006a,b)andautism:Cohen(1998);insparsenessofconnectivity

toexplainautism:McClelland(2000);inprocessingnoisetoexplainDevelopmentalLanguage

Disorder:JoanisseandSeidenberg(2003);inunitthresholdfunctiontoexplainschizophrenia:

Cohen&Servan-Schreiber(1992)andaging:LiandLindenberger(1999);inconnectionpruning

toexplainautism:Thomas,KnowlandandKarmiloff-Smith(2011);andinlearningratetoexplain

generalintelligence:Garlick(2002).Here,individualdifferenceswereproducedbysimultaneous

smallvariationsinallparameters.

31

contributingparametersmodulatethesizeoftheweightchangesproducedby

experience;forsignal,itisnoiseaddedtounitactivationsorthresholdsfor

drivingbehaviouralresponses;forregressiveevents,itisparametersinfluencing

maintenanceofconnectivity,suchaspruningandweightdecay.Some

parameterscontributemainlytoonerole,suchasnumberofprocessingunits

anddensenessofconnectivitycontributingtocapacity.Otherparameters

contributetomorethanonerole:thenatureofthelearningalgorithm

determinesbothwhatcanbelearnedandalsohowquickly;theunit

discriminabilityinfluencesthequalityofthesignalpropagatingthroughthe

networkbutalsomodulatestherateofconnectionchangesandtherefore

plasticity.Asystemwithlowcapacityhasareducedabilitytolearncomplex

information,onewithlowplasticityrequiresmoreexperiencetolearn,onewith

poorsignalstrugglestoacquireanaccuraterenditionofknowledge,whileone

withregressiveeventswillloseplasticityandpotentiallyknowledgeacross

development.

Ofthe1000networksinthesimulatedpopulation,287werediagnosed

withlanguagedelayatanearlypointindevelopment,basedonfalling1standard

deviationbelowthepopulationmean.Thesubsequentdevelopmental

trajectoriesofthesedelayednetworkswerefollowed,and169networkslater

resolvedbackintothenormalrange.Persistingdeficitswereobservedinthe

remaining118.Figure2showsthemeantrajectoriesofthetypicallydeveloping

anddelayedgroups.Theproportionsaresimilartothosereportedinthe

empiricalliterature,whereearlydiagnoseddelay(e.g.,aged3-4)resolvesin

morethanhalfofcases(e.g.,byage6)(Bishop,2005;Dale,Price,Bishop,&

Plomin,2003;seealsoUkoumunneetal.,2012,forresolutionatyoungerages).

32

<InsertFigure2abouthere>

Ifthenatureofinterventionshouldbedifferentiatedbywhetherdelay

resolvesorpersists,itisimportanttobeabletopredictoutcomesforchildren

withearly-diagnoseddelayassoonaspossible(Chiat&Roy,2008).However,

researchershavefoundthischallenging.Forexample,inalargeempiricalstudy,

Daleetal.(2003)exploredwhetheritwaspossibletopredictifchildrenwould

fallinthepersistingdelay(n=372)orresolvingdelay(n=250)grouponthe

basisoftheir‘time1’profilesat2yearsofage,comparedagainst‘time2’

outcomeat4years.Childrenwhosedelayswouldpersistscoredreliablylower

acrossanumberofparentalratingmeasures,includingvocabulary,grammar,

displacedreference(useoflanguagetorefertopastandfutureevents),and

nonverbalskills,aswellasscoringreliablylowermaternaleducationand

showingagreaterincidenceofearinfection.Nevertheless,theeffectsizeswere

small(.01–.06),andlogisticregressionanalysesfoundthatchildren’sprofilesat

age2offeredonlymodestclassificationofoutcomeatage4.Thestatistical

regressionmodelincludingvocabulary,displacedreference,andnonverbal

scoresattime1correctlypredictedonly45%ofcasesofpersistingdelay

(chance=50%),but81%ofcasesofresolvingdelay.Additionofgenderand

maternaleducationlevelbroughtupthepredictionofpersistingdelayto52%.

AsimilaranalysiswaspossibleintheThomasandKnowland’s(2014)

connectionistmodel.Here,time1behaviouralmeasureswerebroadlysimilar

acrosspersistingandresolvingdelaygroups.Thereweresubtledifferencesin

pasttenseaccuracy,withthepersistingdelaygroupperformingreliablyworse

33

onregularverbsandgeneralisationofthepasttenseruletonovelverbs(thatis,

inextractingtheunderlyingregularitiesofthedomain)comparedtothe

resolvinggroup.Butwhiletheseeffectswerehighlyreliable,aswiththe

empiricaldata,theywereofsmalleffectsize.Alogisticregressionmodel

enteringjusttime1behaviouralprofileswas80%accurateinpredicting

persistingdelaybutonly54%accuracyinpredictingresolvingdelay.Accuracy

wasnotincreasedbyaddingintherichnessofthelanguageenvironmentto

whicheachnetworkwasexposed.Inthemodel,variationsintherichnessofthe

trainingenvironmentimplementedonepathwaybywhichdifferencesin

maternaleducationhavebeenproposedtoinfluencelanguagedevelopment(see

Thomas,Forrester&Ronald,2013).AsperDaleetal.(2003),measuresofthe

environmentdidn’thelptopredictdevelopmentaloutcome.Inonesense,thisis

quitesurprising:inthemodel,experienceofthelanguageenvironmentwasthe

primarydriverofdevelopmentitself.Despitethiscentralrole,itwasaweak

predictorofindividualdifferences.

Computationalimplementationsprovidetheopportunitytoinvestigate

themechanisticreasonswhyamodelcapturesagivenbehaviouralprofile.Inthe

currentcase,wecanidentifywhichofthecomputationalparametersinfact

predictedwhetherdelaywouldresolveornot.Table2indicateswhich

parametershadpredictivepowerondevelopmentaloutcome.Limitsoncapacity

tendedtopredictpersistingdelay,whilelimitsinplasticitypredictedresolving

delay.Whenthefullsetofcomputationalparameterswasaddedintothelogistic

regression,acombinationoftime-1behaviourandinformationaboutprocessing

propertieswasabletopredictpersistingdelayat72%accuracyandresolving

delayat84%.(Inclinicalpractice,80%sensitivityandspecificityissometimes

34

viewedastherequirementofagoodscreeningtestfordevelopmental

disabilities;itislessthan100%sinceclinicalscienceisacceptedasoften

imprecise;Charmanetal.,2015,Glascoe,1999).Itisnotablethatinthemodel,

sensitivityandspecificitylevelsdidnotreach100%.Failuretopredictallthe

varianceinoutcomeinarelativelysimpleandwell-controlledmodelpointsto

thecomplexdynamicsinvolvedindevelopmentofnon-linearlearningsystems.2

Moreimportantly,themodelsuggestedthattopredictbehaviouraloutcomesin

casesofatypicality,measuresofbehaviourneedtobecomplementedwith

measuresofprocessing,asarguedbyFernaldandcolleagues(e.g.,Fernald&

Marchman,2012).

Predictionsderivedfromacomputationalmodelneedtobemappedto

cognitiveorbrainprocessesinthechild.Howdothepropertiesofthemodel

maptorealchildren?Practically,capacitycanbeoperationalisedasthequantity

ofinformationthatcanbeintegratedonline,suchasinaphonologicalawareness

task.Plasticity,bycontrast,canbeoperationalisedasperformanceonalearning

task,suchasinauditorystatisticallearning.Thecomputationallevelsuggests

thesepropertiesarelikelytoberelatedbutpotentiallydistinguishableby

focusingonchangeovertime,eitherinexperimentaltasksorinlongitudinal

trajectories.

Insum,resolutionofanearly-identifieddevelopmentaldeficitcanoccurif

theatypicalityinthesystemisalimitationinplasticityratherthancapacity.In

thiscase,naturalexperiencemaydrivetheresolution.Theimplicationisthat

interventionneedonlyincreasethedosageofnaturalisticexperience,for

2Predictivepowerislostduetotheinteractionsbetweenthecomputationalparametersinsuchmechanisms,wheremanyoftheeffectsarenon-linear(Thomas,Forrester&Ronald,2016).

35

examplebyencouragingmorefrequentlanguageinteractionsinthehome,

ratherthanemployaspeciallydesignedintervention.However,identifyingearly

onwhetheranemergingdelayisduetoaplasticityratherthanacapacity

limitationischallengingandrequiresattentiontoprocessingpropertiesrather

thanjustbehaviouralprofilesandenvironmentalmeasures.

<InsertTable2abouthere>

36

Simulatingmethodstoremediateatypicaldevelopmentinasinglenetwork

Fromacomputationalstandpoint,behaviouralinterventionsseekingto

amelioratedeficitscanbeconstruedaschangingtheexperiencesthesystemis

exposedto,forexamplethroughadiscreteblockofintervention.Thiscould

eitheramounttore-weightingofinformationavailableinpreviousexperience,to

blockedpracticeofcertainskills,toalterationsinsalienceorfeedback.Orit

couldbedifferentexperiencestothoseencounteredbefore.Thestartingpointis

theassumptionthatnaturalisticexperience(ortheusualrangeofeducational

experiences)hasnotbeensufficientforthesystemtoacquireage-appropriate

abilities;andthisisbecausethelearningmechanismhasatypicalprocessing

properties.Ifasystemhaslimitations,whyshouldaddingfurtherordifferent

experiencesimprovethesituation?Interventionmightcauseabeneficial

restructuringofrepresentations,anddosobyusingfeedbackorconcentrated

practicetoemphasisecertaindimensionsorassociationswithinthetaskdomain.

Ofcourse,thisispredicatedontheassumptionthatthemechanism,andindeed

thechildmorebroadly,hasindeedbeenexposedtotheappropriaterangeof

experiencespriortodiagnosisofthedisorder.Webeginbyconsideringthe

possibilitythatthisisnotthecase.

Disordersfrominsufficientearlystimulation

Althoughcliniciansusuallyattempttoruleoutenvironmentalcausesin

diagnosingdevelopmentaldisorders,languagedisordersareoftenobservedwith

increasedfrequencyinchildrenfromlowSESbackgrounds(AllParty

ParliamentaryGrouponSpeechandLanguageDifficulties,2013;Lockeetal.,

2002;Nelsonetal.,2011).OnefactorassociatedwithlowSESthatimpacts

37

languagedevelopmentistherichnessofthelanguageenvironmentinwhich

childrenareraised(Hart&Risely,1995).Anumberoflongitudinalstudieshave

shownthatdifferencesintherichnessoflinguisticinputresultinanincreasing

gapinchildren’slanguagedevelopment(Huttenlocheretal.,2010;Reillyetal.,

2010;Rowe,Raudenbush,&Goldin-Meadow,2012;Hoff,2013),whilebrain

imagingevidencehassuggestedthatyoungchildrenregularlyengagedin

conversationbyadultshavestrongerstructuralconnectivitybetweentwo

languageregions,Wernicke’sareaandBroca’sarea(Romeoetal.,2018).

Fromthepointofviewofasinglemechanismembeddedwithinawider

cognitivesystem,thedeficitininputneednotbeapropertyoftheexternal

environment,butcouldstemfromdeficitsinotherpartsofthesystem.For

instance,onetheoryofwhycomponentsofthesocialcognitivesystem(suchas

thoseunderlyingfacerecognition)donotdeveloptypicallyinautismisthatthe

infantasawholedoesnotpayattentiontotherelevantsocialcuesthatare

neverthelesspresentinhisorherenvironment(e.g.,Elsabbaghetal.,2011;

thoughseeElsabbagh&Johnson,2016).Thusafacerecognitionsystemmight

notdevelopappropriatelybecauseitisnotexposedtosufficientinformation

aboutfaces.

Behaviouralinterventionshouldthereforeinvolveenrichingthelearning

environmentfromtheperspectiveoftherelevantmechanism,toensure

sufficientinformationispresenttoacquirethetargetability.Inthedomainof

language,thereareinitiativestoencourageparentsfromlowerSESbackgrounds

totalkmoretotheirchildren(e.g.,Leffel&Suskind,2013;Suskind&Suskind,

2015);andwithinautism,interventionsarebeingdevelopedthatspecifically

38

traininfantsatfamilialriskofautismtopayattentiontosocialcues(Wass&

Porayska-Pomsta,2014).

Restorationofanenrichedinputshouldbringatypicallydeveloping

systemsbacktowardsthetypicalrangeofdevelopment.Thereisonecaveat

concerningtiming.Certaindomains,particularlythoseinvolvinglow-level

perceptualskills,mayexhibitsensitiveperiodsindevelopment,suchthatlater

acquisitiondoesnotreachthesameultimatelevelsofproficiency(Huttenlocher,

2001).Restorationofenrichedinputthatoccursaftertheplasticityofthesystem

hasbeguntoreducemaynotbeassuccessful;ineffect,theearlydisadvantage

willbeimprintedonthestructureofthesystem.Oneexampleofsuchanaccount

istheproposalthatDLDiscausedbyanearlyauditorydeficiteventhoughnotall

childrenwithDLDshowauditorydeficits.Theideaisthatanearlyauditory

deficitmayresolveinsomechildren,butduetosensitiveperiodsinthe

developmentofthelanguagesystem,thenow-enrichedauditoryinputcannot

bringthedevelopmentofthelanguagesystem(andspecifically,itsphonology)

backontothetypicaltrajectory(Bishop,1997).

Table3showsdatafromapolygenicmodelofindividualdifferences

(Thomas,2016a),againemployingtheexampledomainofEnglishpasttense.

Here,developmentissimulatedin1000children,withindividualdifferences

arisingfromtwosources:variationinmultiplecomputationalparametersand

variationintherichnessoftheinformationpresentinthelearningenvironment.

ThepopulationdepictedinTable3experiencedwidevariationintherichnessof

individuals’learningenvironments,whilethevariationincomputational

learningparameterswasmorerestricted,sothatenvironmentwasthemain

driverofindividualdifferences(seeThomas,2016a,forsimulationdetails;

39

GNEWpopulation).Variationintheenvironmentwasimplementedbyaone-

timefilteronthetrainingsetappliedtoeachfamily,analogoustotheeffectsof

SESonlanguageinput(Thomas,Forrester&Ronald,2013).Thetoplineofeach

sectioninTable3showshowthepopulationmeananddistributionof

performancechangesacrossdevelopment(inthiscase,alifespanof1000epochs

oftraining,where1epochwasasingleexposuretotheindividual’sfamily

trainingset).

Atepoch50,relativelyearlyindevelopment,everysimulatedchild’s

environmentwasfullyenrichedtoprovidethemaximumpossibletrainingset.

Table3showstheeffectonpopulationmeansandstandarddeviationsfollowing

theonsetofintervention.Regularverbsimmediatelyshowedanaccelerationin

responsetothiswhole-populationintervention,withvariationreducingandthe

lowestperformerseventuallyperformingabovethe50th-centileoftheoriginal

population.Irregularverbstookmoretimetoexhibittheacceleration,indeed

initiallyshowingadecline,buteventuallyexhibitedlargegains.Ingeneral,

acquisitionofirregularverbsintheseassociativemodelstendstobemore

sensitivetothecomputationalpropertiesofthenetwork.Forirregularverbs,

variationincomputationpropertiescontinuedtoproduceconsistentindividual

differencesinperformancedespitetheenrichedenvironment.Population

standarddeviationdidnotchangeinthedevelopmentalphasesfollowing

enrichment(Table3,middlesection,distributionsafter50epochs).Inother

words,thegapbetweensimulatedchildrendidnotclosefollowingenrichment.

Instead,thewholepopulationincreaseditsperformancelevel.Incontrast,gaps

didclosefortheeasierregularverbs,wherecomputationalpropertiesdidnot

constrainperformancesostrongly;poorerperformingchildrencaughtuponce

40

thehindranceofadisadvantagedenvironmentwaslifted.Inshort,theeffectsof

universalenrichmentonnarrowinggapsbetweenchildrendependedonthe

extenttowhichinternalcomputationalpropertiesconstraineddevelopment.

<InsertTable3abouthere>

Functionalplasticitycanreduceinassociativenetworkswith‘age’viaa

numberofmechanisms(Thomas&Johnson,2006).Inconnectionistmodels,age

maybeindexedbytheamountoftrainingthesystemhasexperiencedora

maturationalscheduleactingoncomputationalproperties.Amongthe

mechanismsthatcanreduceplasticityarethelossofresources,reductionsinthe

malleabilityofconnectionsinresponsetotrainingsignals,entrenchmentof

connectivity(thatis,wellestablishedconnectionstakelongertoreset),and

assimilation(wherebytopdownprocessesreducethedetectionofdifferencesin

analteredlearningenvironment,therebymitigatingtheresponsivenessofthe

systemtothenewconditions).

Thepopulationunderconsiderationhereexperiencedaged-related

reductionsinplasticitythroughpruningofconnectivity,whichreducedavailable

resources(orcapacity).Pruninghaditsonsetataround100epochs.Thebottom

sectionofTable3showstheeffectofpopulation-wideenrichmentonirregular

verbperformanceat250epochscomparedto,respectively,normal(untreated)

developmentandearlyintervention.Interventionhadreducedeffectiveness

whenitcommencedaftertheonsetofpruning.Forregularverbs,bytheendof

training,themeanimprovementinpopulationaccuracyfollowingearly

enrichmentwas22%,whilethatfollowinglaterenrichmentwas16%.For

41

irregularverbs,theimprovementfollowingearlyenrichmentwas31%andafter

laterenrichment13.5%(t-test,bothp<.001).Notably,thelateintervention

increasedthepopulationstandarddeviationforirregularverbs:intervention

increasedthegapsbetweenindividuals.

Ifearlyimpoverishedenvironmentscausedeficits,thesizeofthe

treatmenteffectavailablethroughenrichmentshouldbeinverselyproportional

tothequalityofthatearlyenvironment.Inotherwords,childrenwhoareheld

backmorebyanimpoverishedearlyenvironmentshouldhavegreaterscopefor

improvementfollowingenrichment.Inthesimulationofearlyenrichment,this

correlationwasobservedbothforregularandirregularverbs,withcorrelations

betweenenvironmentalqualityandtreatmenteffectof-.86and-.77,respectively

(Figure3a).

However,sensitiveperiodsindevelopmenteventuallytranslatethe

consequenceofbeingraisedinapoorenvironmentintoadeficitinthestructure

ofthenetwork,whichlaterenrichmentislessabletoundo.Inthisscenario,the

greatertheearlyimpoverishment,thegreatertheimpactonthedevelopmentof

processingstructures,andthepoorerthepredictedtreatmenteffect.Onemight

thusexpecttheinversecorrelationofearlyenvironmentalqualityandtreatment

effecttoweakenorevenreverse.Inlinewiththisexpectation,theequivalent

correlationsfollowinglateenrichmentwere-.76and-.25forregularand

irregularverbs,respectively(Figure3b).Thereductioninscopefortreatment

acrossdevelopmentfornetworksraisedinpoorerenvironmentswaslargerfor

irregularverbsthanregularverbs,sincetheyaremoresensitivetothe

processingcapacityofthenetwork(inafullyfactorialANCOVAoftreatment

42

effectswithfactorsofverbtypeandtiming,andenvironmentalqualityasthe

covariate,allmaineffectsandinteractionswerehighlysignificant).

Thepatternofmoresustainedearlydeprivationleadingtolesseasily

remediateddeficitscanbeseenindatafromarecentfollow-upstudyof

Romanianorphansexposedtosevereearlydeprivationbutthenadoptedinto

enrichedenvironments.Sonuga-Barkeetal.(2017)foundthat,whenfollowedup

intoyoungadulthood,Romanianadopteeswhoexperiencedlessthan6months

inaninstitutionhadsimilarlylowlevelsofsymptomsastypicallydeveloping

controls.Bycontrast,comparedtocontrols,Romanianadopteesexposedtomore

than6monthsinaninstitutionhadpersistentlyhigherratesofsymptomsof

autismspectrumdisorder,disinhibitedsocialengagement,andinattentionand

over-activitythroughtoyoungadulthood.

<InsertFigure3abouthere>

Thus,enrichmentinterventionstoalleviatedeficitscausedpurelybya

lackofappropriateexperienceneedtopayattentiontopossibletimingeffects

impactingplasticity.Ifplasticityreduces,enrichmentalonewillbeinsufficientas

anintervention.Howshouldinterventionsalterifplasticityhasreduced?The

bestbehaviouralinterventionmethodinthecaseoflateinterventionwilldepend

ontheparticularmechanismcausingtheplasticitylossforthedomainand

mechanisminquestion(see,e.g.,McClellandetal.,1999;Thomas&Johnson,

2006).Itmayinvolvemoreintensepractice,morefeedback,orperceptually

exaggeratedstimuli.Thekeymessage,however,isperhapsanobviousone.

Whereatheoreticalunderstandingofdevelopmentinthetargetdomainsuggests

43

reductionsinplasticitywithageinkeymechanisms,earlyinterventionsto

alleviateimpoverishedexperiencebecomemoreimportant.Ifenvironmental

factors(suchasSES)inverselypredictresponsetotreatmentinyoungerbutnot

olderchildren,thisisthehallmarkoftheoperationofsensitiveperiods.

Lastly,behaviouraldeficitsproducedbyimpoverishedlearning

environmentswillnotnecessarilyactindependentlyofdifferencesinintrinsic

learningproperties.Figure4showsthedifferencebetweenimpoverishedand

enrichedlearningenvironmentsforthesimulatedpopulation,stratifiedbytheir

unitdiscriminability.Theeffectoflearningenvironmentinteractedwiththis

internalcomputationalconstraint,suchthatthelessoptimalcomputational

constrainttendedtoexaggeratetheimpactoftheimpoverishedenvironment,

albeitthiswasamarginaleffectagainstthevariationofothercomputational

parametersinthepopulation(maineffectofenvironment:F(1,996)=89.61,

p<.001,ηp2=.083,maineffectoftemperature:F(1,996)=10.73,p=.001,ηp2=.011,

environmentxtemperature:F(1,996)=3.51,p=.061,ηp2=.004).Thisinteraction

occurredbecausebothinfluencesactonthestrengtheningofnetwork

connections,whichinturndrivesbehaviour.Anincreaseintheincidenceof

developmentaldisordersinlowSESfamiliesmay,therefore,representan

interactionbetweenriskfactors,ratherthanresultingfrompureenvironmental

effects.

<InsertFigure4abouthere>

Insum,interventionstoremediatedeficitsstemmingfrominsufficient

stimulationofadevelopingcognitivesystemmayeithertargettheexternal

44

environment,ortheinternalenvironmentofthesystembyseekingtoalterthose

aspectsoftheexternalenvironmenttowhichthechildattends.Enrichment

interventionswilleliminategapsbetweenchildrenunlessthetargetbehaviours

aresensitivetoother(independentlyoccurring)individualdifferencesin

computationalpropertiesoflearningmechanisms.Inthelattercase,enrichment

canimprovethewholepopulationlevelofperformancewithoutnarrowinggaps

betweenchildren.Lastly,environmentaleffectsmayinteractwithand

exacerbateunderlyingcomputationalriskfactors.

Choosingbettertrainingsetstosupportatypicalprocessingproperties

Inthefirstsection,weobservedhowaprocessingsystemwithatypical

computationalpropertiescouldeventuallyreachceilingperformanceonthe

trainingsetbutshowresidualdeficitsingeneralisation.Supporting

generalisationisanexamplewherespecificadditionalexperiencecanbeusedto

restructurerepresentations.

Fedoretal.(2013)exploredhowtheadditionofspeciallydesignedinput-

outputmappingscouldsupportgeneralisationinnetworkswithatypical

processingproperties.Theseauthorsalsoemployedafeedforwardconnectionist

modeldrawnfromthefieldoflanguagedevelopment,inthiscaseacquisitionof

theArabicplural(Forrester&Plunkett,1994).Theaimwastovisualisethe

formationandmediationofatypicalrepresentationsofcategories.Themodel

wastrainedtolearncategorisationsdefinedovera2-dimensionalinputspace

usinghigh-dimensionalinternalrepresentations.Fedoretal.considered

differentcategorisationproblems,ineachcaseonlygivingthenetworkalimited

45

sampleofthecategorisationproblem,andtestingitsabilitytoacquire

(generaliseto)thefullfunction.

Developmentaldisorderswerethensimulatedbyinitialchangesto

parameterssuchasthedensenessofconnectivity,numbersofinternal

processingunits,thelearningrate,theunitdiscriminability,andprocessing

noise.Next,casesofdevelopmentaldeficitswerere-runandinterventions

appliedearlyindevelopment.Interventionscomprisedadditionalinput-output

mappings(nomorethan10%ofthesizeofthetrainingset),whichoffered

differentinformationaboutthecategories.Forexample,interventionsmight

markoutprototypicalmembersofcategories,ordemarcatetheedgesof

categoryboundariesintheinputspace.Theresultsoftheseexploratory

simulationsindicatedthatthebestinterventionseithersampledthewhole

problemspaceorprovidedarepresentative‘slice’acrossallcategories.There

wasalsosomeevidencethatinterventionsweredifferentiallyeffective

dependingontheproblemdomain(mappingproblem)anddependingonthe

typeofdeficit.

Figure5illustratesoneexampleofatrainingproblemusedbyFedoretal.

(2013).Itshowsthearchitecture,thefullcategorisationproblem,thetrainingset

(whichrepresentsasubsetofthefullproblem),andthenanexample

interventionset.Here,thenetworkhadtolearnacategorythatspannedazone

aroundadiagonalofthetwo-dimensionalinputspace,withdifferentcategories

eitherside.Thetrainingsetonlyprovidedexamplesateitherendofthediagonal,

andthenetworkhadtolearntointerpolatethegeneralfunctionlinkingthetwo

ends.Figure6demonstratesanexampleofanetworklearningthisgeneral

functionsuccessfully.Althoughtheinternalrepresentationsofthenetworkhad

46

highdimensionality,theirstructurecouldbevisualisedbydeterminingthe

network’scategorisationofall10,000possiblelocationsintheinputspace.

Figure6showsthatinthetypicalcase,therewasquickformationofthediagonal

categorybutwithfuzzyboundaries,whichwerethenprogressivelysharpened

throughfurthertraining.Thefigurealsoshowstheformationofatypical

representationsinacaseofadevelopmentaldeficit,inthiscase,anetworkwith

only30%ofthenormallevelofconnectivity.Interpolationwasunsuccessful,and

eventualperformanceretainedaccuracyonlyintheregionofthetrainingset.

Finally,thefiguredemonstratestheconsequenceofaddinganeffective

intervention(asliceacrossallcategories)earlyintraining.Theseadditional

input-outputmappingsimprovedperformanceonthetrainingset,butcrucially

werealsoabletosupportacquisitionofthegeneralfunctiondespitetheatypical

processingproperties.Thisisanimportantdemonstrationthatatypical

processingpropertiesmayrequirethedesignofspecialinterventionsetsto

supportgeneralisation,evenincaseswherehighaccuracyonthetrainingsetcan

eventuallybereachedthroughextendedexposure.Alleviationofthedeficit

cannotbeachievedbymorenaturalisticexperience,butrequiresbespoke

additionaltrainingtorestructurerepresentationsbasedonatheoretical

understandingofthetargetdomain.

<InsertFigures5&6abouthere>

Simplifyingtheproblemtheatypicalsystemhastosolve

Whereacognitivemechanismisstrugglingtoacquireatargetability,a

behaviouralinterventionmightseektoreducethecomplexityoftheproblemthe

47

systemistryingtosolve.Itmightdosobyalteringtheinputandoutput

representations,orrestrictingtrainingtoasubsetofthetask.

Fromacomputationalperspective,ataskdomainisdefinedbythesetof

input-outputmappings.Thecomplexityoftheproblemisspecifiedbytheway

thedomainisencoded,withrespecttotheinputrepresentationsandtheoutput

representations,andthenumberofmappingstobelearnt.Wherealearning

mechanismhasinsufficientcomputationalresourcestosolvetheproblem,

developmentoccursmoreslowly,mayasymptoteatalowerlevel,show

acquisitionofsomepartsofthedomainbutnotothers,orshowgeneralisation

deficits.Wehavesofarconsideredbehaviouralinterventionasaddingsome

furtherinformationtothestructuredenvironmentoralteringitsfrequency

distribution.However,abehaviouralinterventioncouldservetoalterthenature

oftheinputoroutputrepresentations.Changingtherepresentationsmight

simplifytheproblemthatthelearningmechanismhastosolve,andbringit

withinwhatcanbeachievedwiththeexistingcomputationalconstraints.Thatis,

alesspowerfulmechanismmaybeabletolearnasimplerproblem.

Behaviouralinterventionsfordyslexiaandword-findingdifficultiesboth

appealtothisidea.Forreading,someinterventionstargetthestructureofthe

phonologicalrepresentations,theoutputofthedecodingsystem.ForWFD,

interventionsadditionallytargetimprovementsinsemanticrepresentations,the

driversofnaming.Computationalmodelsofinterventionhavealsoappealedto

thismethod.SeidenbergandMcClelland’soriginalconnectionistmodelof

reading(1989)waslaterdeemedtobeclosertotheperformanceofadyslexic,

becauseithadrepresentationsthatdidn’tshowsufficientsimilaritybetween

writtenlettersorbetweenspeechsoundstoallowthelearningmechanismto

48

generalisethereadingproblemtonovelwords.Thepresentationoftheproblem

domainmadeittoohardforthelearningmechanismtosolve.Alater

implementationutilisedmorecomponentialinputandoutputrepresentations

andwastakentobeabettermodeloftypicaldevelopment(Plautetal.,1996).

OneoftheinterventionsconsideredtoalleviatedyslexiaintheHarm,McCandliss

andSeidenberg(2003)modelwastoimprovetheoutputrepresentations

developedbythephonologicalcomponent.Bestetal.’s(2015)modelconsidered

interventionstoimprovenaming–capturedasthemappingbetweensemantic

andphonologicalrepresentations–bytreatmentsthatimprovedthe

representationsofsemanticsorphonologyinisolation,ratherthansimplymore

practiceinusingthecompromisedpathwaylinkingtheserepresentations.Lastly,

Harmetal.demonstratedthatimprovementsstemmingfromchangesininputor

outputrepresentationsmaybesubjecttotimingeffects;previouslearningmay

causeentrenchedconnectionsthatmeanthemechanismrespondslessreadily

whenrepresentationsarechangedlaterindevelopment.

TheBestetal.(2015)modelusedfairlyidealiseddepictionsofsemantics

andphonology.Figure7showsresultsfromamodelwithmorerealistic

representations(Alireza,Fedor&Thomas,2017).Usingthesamearchitectureas

theBestetal.model,thisimplementationemployedatrainingsetof400English

wordstakenfromtheMasterson,Stuart,DixonandLovejoy(2010)corpusof

wordsfoundinchildren’sbooks.Phonologywasencodedinaslot-basedscheme

usingarticulatoryfeatures,whilesemanticsusedafeature-basedschemeofover

1000featuresdrawnfromVinsonandVigliocco’s(2008)adultratingsofword

meanings.Figure7adepictsthetypicalmodelinitsdevelopmentofsemantic

knowledge,phonologicalknowledge,singlewordcomprehension,andsingle

49

wordnaming;andanatypicalnetwork,whichhadacomputationalrestrictionto

thenamingpathwaythatlinkedemergingsemanticandphonological

representations.Fortheatypicalnetwork,Figures7b-ddepicttheeffecton

namingofarelativelyshortinterventionearlyintraining(between100and200

epochs,inalifespanof1000epochs,depictedbytheshadedarea).Intervention

wastriggeredatapointwhenthetypicalmodelhadacquiredaproductive

vocabularysizeof67words,whiletheatypicalmodelshadavocabularysizeof

36words.Fivedifferentinterventionswerecontrasted,ofthreetypes:(1)

remediatingtheweakness–themodelwasprovidedwithadditionaltrainingon

thenamingpathway;(2)improvethestrength–themodelwasprovidedwith

additionaltrainingtoimprovethe(otherwisetypicallydeveloping)semantic

representations,thephonologicalrepresentations,orbothatonce;(3)both

types1and2werecombinedintoaninterventionthatsoughttosimultaneously

improvestrengthandremediateweakness.

<InsertFigure7abouthere>

Theinterventiontotargetthenamingweakness,extrapracticeforthe

semantics-to-phonologypathway,improvedperformanceinitially,butserved

onlytopropelthesystemfurtheralongitsatypicaltrajectory.Thefinallevelof

performancewasnohigher;eventually,theuntreatedconditioncaughtupwith

thetreatedcondition.Interventionstotargetstrengths,thesemanticand

phonologicalrepresentations,producedmoregradualimprovements(little

duringtheinterventionperioditself),butsubsequentimprovementswerelong-

termandraisedthefinallevelofperformance.Thisisbecauseextratrainingon

50

theinputandoutputrepresentationsfornamingservedtomakethemmore

distinguishable,andthereforemakethetaskoflearningthearbitrarymappings

betweenmeaningandsoundeasierfortherestrictedpathway.Thelargest

benefitoccurredwhenbothsemanticinputandphonologicaloutput

representationswereimproved(Fig.7c).Whentheinput/outputintervention

wascombinedwithextratrainingonthesemantics-to-phonologypathway,both

short-termandlong-termbenefitswereobserved(Fig.7d).

Alirezaetal.(2017)alsoconsideredtheeffectsoftiming,contrasting

interventionsat100,250,and750epochs.Inallmodels,unusednetwork

connectionswereprunedawaywithasmallprobabilityfrom100epochs

onwards,reducingtheplasticityofoldernetworks.Laterintraining,improving

strengthsbecamelesseffectiveandremediatingweaknessesbecamemore

effective.EchoingthefindingsofHarmetal.(2003),thebenefitofimproving

inputandoutputrepresentationswasmoremarkedearlyindevelopment,and

reducedoncepathwayshadcommittedtoutilisingthe(potentiallypoor)initial

representations.Atthatpoint,maximisingtheperformanceofthepathway

throughintensepracticebecamethebestrecourse.

Insum,behaviouralinterventionsthatimproveeithertheinputoroutput

representationsinvolvedinacquiringacognitivedomainmayimprovethe

ultimatelevelofperformancethatisattainablebythesystemwithatypical

computationalconstraints,butsuchimprovementsmaybesubjecttotiming

effects.Remediatingweaknessdidproduceimprovements,buttheseonly

propelledthesystemmorequicklyalongthesameatypicaltrajectory.Inthis

model,long-termbenefitsofanearlyinterventionarosefromimproving

51

strengths,notfromfocusingonweaknesses.However,theoppositewastrueofa

lateintervention.

Ifinputandoutputrepresentationscannotbealtered,howcanthe

problembesimplifiedtohelpanatypicalmechanism?Ifthemodelisunableto

learnthetrainingsettoagivenperformancelevelthroughlimitationsin

processingcapacity,addingfurtherinput-outputmappingstothetrainingsetis

unlikelytoenhanceaccuracyonthepatternsintheoriginaltrainingset.What

onemightcallnormalisationthroughbehaviouralinterventionistherefore

difficultifoneconceivesofdevelopmentaldeficitsasarisingfromlimitationsin

individualsystems.Wedefinenormalisationhereastheacquisitionofthe

abilitiesandknowledgethatanytypicallydevelopingsystemacquiresthrough

exposuretothenormaltrainingset.

However,onemighttaketheviewthat,foradequatefunctioningofachild

inhisorherday-to-dayenvironment,learningthefullrepertoireofbehaviours

inthetargetdomainisnotnecessary.Perhapsitissufficienttolearnjustsome

itemsinthetrainingset,themostfrequentlyrequired,themostprototypical?

Thismoremodestobjectivemightsuggestinterventionsthatfocusonlyona

subsetofthetrainingset.Forexample,inthepasttensedomain,onemightselect

themostfrequentlyusedverbs,betheyregularorirregular.Alternatively,one

mighttaketheviewthatwhattheatypicalsystemneedstolearnisnotthe

trainingsetperse(eventhoughthisiswhattypicalsystemsacquire),buta

generalfunctionimplicitintheitemsinthetrainingset.Acquisitionofthis

generalfunctioncanbeassessedbyperformanceongeneralisationsetsrather

thanthetrainingset.Theremaythenbeinput-outputmappingsthatcanbe

addedtothetrainingsetwhichcouldimprovethenetwork’sabilitytoextractthe

52

generalfunction,evenifperformanceontheoriginaltrainingsetdidnot

improve(orevenworsenedforthosepartsinconsistentwiththegeneral

function).Incontrasttonormalisation,wecouldtermthisapproach

compensation,sincetheaimistooptimiseasubsetofbehaviourspresentinthe

originaltrainingset.Inthepasttensedomain,suchanapproachmightseekto

improveacquisitionoftheregularpasttenserulebyshowingitsuseacrossa

varietyofverbforms.

Thedistinctionbetweenthesetwointerventionaims–improving

performanceonthefulltrainingsetversusonasub-setorafunctionimplicitin

thetrainingset–allowsustodrawaformaldistinctionbetweennormalisation

andcompensation,withrespecttooursingle-mechanismperspective.Itposes

thechallengeofhowonemightderiveinterventionsthatachievethesegoals.So

far,wehaveconceivedofabehaviouralinterventionastheadditionoftraining

patternstothenetwork’strainingsetforsomeduration.Whichadditional

patternswouldsupportnormalisation,underourdefinition?Whichadditional

patternswouldsupportcompensation?

YangandThomas(2015)exploredonemethodtoderiveintervention

setswithinamachine-learningframework.Themethodassumestheavailability

ofanartificialneuralnetworkthatisabletosuccessfullyacquirethetarget

domainthroughexposuretothetrainingset.Ageneticalgorithmtechniqueis

thenusedtoidentifywhichinputunitsweremostimportantforgeneratinggood

learningon,respectively,thetrainingsetorthegeneralisationset.Intervention

itemscanbeproducedwhichembodythefeaturesthatsupporteithertraining

setacquisitionorgeneralisation.Aninterventionsetthencomprisesaselection

oftheseitems,forexamplewhichspantheinternalrepresentationalspaceof

53

typicallydevelopingmodels.Theinternalrepresentationalspacecanbe

characterisedbyprincipalcomponentanalysesofhiddenunitactivations

producedbythetrainingset.Davis(2017)usedthismethodtoderive

interventionsetstoencourageeithernormalisationorcompensation,and

appliedthemtoamodelofautism.Interventionsetscontainedaround10%the

numberofpatternsasthetrainingset.Theresultsinthatcaseindicatedthat

compensationwasmoreeffectivethannormalisationfornetworkswith

compromisedconnectivity,sinceinartificialneuralnetworks,regularityisless

demandingonrepresentationalresources.

TheYangandThomasmethodforderivinginterventionsetsismodel

dependent.Itrequirestheavailabilityofafullyspecifiedtrainingset,and

commitmenttotherepresentationalformatinwhichtheproblemisspecified.

Moreover,compensationrequiresspecificationoftheimplicitfunctioninorder

toidentifythekeyinputdimensionsthatembodythefunction–inotherwords,a

theoryoftheinformationthatismostimportantinadomain.

Insum,behaviouralinterventionsmaybesuccessfulinmechanismswith

atypicalcomputationalconstraintsifthegoalofinterventionisrevisedfrom

normalisation(fullybehaviouralcompetency)toasubsetofskills,whichwe

termedcompensation.Machine-learningmethodssuggestpossiblewaysof

identifyingitemsthatwillsupportnormalisationandcompensation.

Alteringthecomputationalpropertiesofthesystem

Iftheatypicalcomputationalconstraintslimitingacquisitionofatargetcognitive

domaincannotberemediatedbyalteringorcomplementingtraining

experiences,interventionmayinsteadseektochangethecomputational

54

constraints.Notalltheoreticalapproachestodevelopmentviewthe

computationalpropertiesoflearningmechanismsinthecognitivesystemas

fixed.Ifcomputationalpropertiescanbeinfluencedbyexperience,thisopensup

thepossibilitythatbehaviouralinterventioncouldalleviatecomputational

limitationsandenablesuccessfulremediation.Initsdevelopment,thebrain

undergoesaphaseofelaborationofconnectivityfollowedbyregressiveevents

thatpruneawayconnectivity;inaddition,someexistingconnectivityis

enhancedbymyelination(Huttenlocher,2001).Itisasyetunclearwhatdirect

bearingsuchbrain-levelchangeshaveoncognitivedevelopment.Researchers

havesometimesincludedbothincreasesinconnectivityanddecreasesin

connectivityintheirdevelopmentalcognitivemodels.Forexample,

constructivistapproachesemploynetworksthatcanincreasethenumberof

processingunitsandconnectionsinanexperience-dependentmanner(see,e.g.,

Quartz&Sejnowski,1997;Mareschal&Shultz,1999;Westermann&Ruh,2012).

Othermodelshaveincludedpruningofconnectivity,wheretheconnections

removedarethosethathavenotbeenstrengthenedbyexperience(e.g.,Thomas,

2016a).Yetothermodelshaveincludedtheassumptionthatsome

computationalpropertiesalteraccordingtoamaturationalschedule.For

example,Munkata(1999)capturedage-relateddifferencesinaconnectionist

modeloftheinfantA-not-Btaskpartlythroughamaturationalincreaseinthe

system’sabilitytomaintainactiverepresentations,implementedbyagradual

increaseinthestrengthofrecurrentconnections.

Inprinciple,then,onecouldconceiveofabehaviouralintervention

modulatingamechanism’scomputationalpropertiesthroughalteringtheway

certainparameterschangeacrossdevelopment.Forexample,thismightequate

55

tostimulationcausinggreaterelaborationofconnectivityinthetarget

mechanism,orgreaterresistancetolossofconnectivityduringpruningof

connectivity.Toillustratehowthismightwork,consideramodelofautism

proposedbyThomas,KnowlandandKarmiloff-Smith(2011).Thisaccount

initiallyfocusedontheregressivesub-type.Itproposedthatautismiscausedby

anexaggerationofthenormalphaseofpruningofconnectivityoccurringfrom

infancyonwards;over-pruningoccursandparticularlyimpactslong-range

connectivity.Thomas,Davisetal.(2015)latershowedhowdifferencesinthe

timingofonsetofover-pruningcouldlinkearlyonset,lateonset,andregressive

sub-typesofautism(Landaetal.,2013).Davis(2017)thenconsideredwhether

thebehaviouraldeficitsshownbytheatypicalconnectionistmodelscouldbe

remediatedbyinterventionsofdifferenttypesandappliedatdifferenttimes.

Behaviouralimprovementswereonthewholerelativelysmall,andindividual

networksshowvariationintheirresponsetointervention.However,some

networksdidshowamarkedbehaviouralbenefitfromashort,discrete

interventionappliedearlyindevelopment.

Figure8showsthemeanperformanceofagroupofsuchnetworksthat

exhibitedastrongresponsetoearlyintervention.Networksweretrainedfor

1000epochs,withtheonsetofpruningbetween25and50epochs;atypical

networkswereexposedtoaninterventionatepoch30,lasting40epochs;the

interventionwasdesignedtoenhancegeneralisationbyincludingnovel

examplesofitemsfollowingtheimplicitrulepresentinthetrainingset,withthe

interventionsetapproximately10%thesizeofthetrainingset.Figure8(a)

showsthebehaviouraldeficitoftheimpairednetworks,comparedtoacontrol

conditionofthesamenetworkstrainedwithouttheatypicalsettingofthe

56

pruningparameter.Theshortinterventionshowedamarkedbenefitonaccuracy,

whichsustaineduntiltheendoftraining.Thesizeoftheinterventioneffectwas

highestinmid-training,anddidnotincreaseatthelatermeasurementpoint.

Figure8(b)showsthetotalnumberofconnectionsintheatypicalnetworksin

theuntreatedandtreatedconditions.Notably,duringtheintervention,

connectionlossacceleratedastheinternalrepresentationsunderwent

reorganisation.Thereafter,thetreatedconditionretainedagreaterproportionof

connections(t-test:250epochst(8)=3.91,p=.004,Cohen’sd=.43;1000epochs,

t(8)=3.85,p=.005,d=.37).Connectionnumberisassociatedwithimproved

computationalpower.3Thebehaviouralinterventionfortheseatypicalnetworks,

then,servedtoimprovetheircomputationalpropertiesduringsubsequent

developmentcomparedtotheuntreatedcondition.Here,thestimulationofthe

interventionproducedgreaterresistancetolossofconnectivity.

<InsertFigure8abouthere>

Underamaturationalview,computationalpropertiesmayalterwith

development,butthescheduleisnotinfluencedbybehaviouralinterventions,or

morebroadly,byexperience.(Undersuchanaccount,itisnotthatthe

experienceplaysnoroleindevelopment;itisjustthatexperienceisnotthe

limitingfactoronratesofgrowth).Insuchascenario,behaviouralinterventions

couldberenderedsuccessfulbywaitinguntilthecomputationalpropertieshave

3Inanequivalentpopulationof1000networkswithoutatypicalpruning,thenumberofconnectionsinanetworkcorrelated.134withbehaviouratepoch250and.163withbehaviour

atepoch1000(bothp<.001).

57

improved.MaturationalaccountshavebeenproposedindisorderssuchasDLD

(Bishop&McArthur,2004)andADHD(Battyetal.,2010;Shawetal.,2007).

Evidencefromneurosciencehasbeenusedtoarguethatinterventionsfor

anxietydisordersmaybemoreeffectiveafteradolescenceduetothe

developmentalstateoftheunderlyingmechanisms(Hartley&Casey,2013).

Withinthefieldofeducation,thebroadernotionof‘schoolreadiness’is

predicatedontheassumptionthatdevelopmentofskillssuchasexecutive

functionneedstohavereachedacertainlevelbeforetheclassroom-based

behaviouralmethodscanbeproperlyeffective(Noble,Tottenham&Casey,

2005).

Afurtheralternativewouldbetodirectlymanipulatethecomputational

propertiesoftheprocessingmechanism.Werefertotheseasbiological

interventions,sincetheyneednotinvolvebehaviouralmethodsdirectlyrelevant

tothetargetskill.Biologicalinterventionsmostobviouslywouldinclude

pharmacologicaltreatmentsthatalterthelevelsofneurotransmitters(e.g.,

dopamineforADHD,Volkowetal.,2002;serotoninforrepetitivebehavioursin

pervasivedevelopmentaldisorders,McDougle,Kresch&Posey,2000;oxytocin

inautism,Preckeletal.,2016).Morespeculatively,biologicalmethodsmight

targetneuralactivityviaelectricalmethods(e.g.,directcorticalstimulationfor

dyscalculia;Iuculano&CohenKadosh,2014)orbrainplasticityviadrug

treatments(e.g.,valproateacidforauditorylearning;Gervainetal.,2013).

Biologicalmethodsmightalsoemploybehaviouralpracticesthatdonotdirectly

targetcognitionbutinfluencebrainfunction,suchasexerciseanddiet(e.g.,for

treatingADHD:alterationsofdiet,Konikowska,Regulska-Ilow&Rózańska,

2012;useofexercise,Silvaetal.,2015).Ortheymightemploymethodsthat

58

indirectlytargetcognition,forexamplethroughtheeffectofsleeponmemory

consolidation,ormindfulnesstrainingonattention,oractionvideogameplaying

onvisualattention(e.g.,roleofsleepindevelopmentaldisabilities:Ashworth,

Hill,Karmiloff-Smith&Dimitriou,2017;Dodge&Wilson,2001;mindfulness

treatmentsforautism,dyslexia,ADHD:Sequeira&Ahmed,2012;Tarrasch,

Berman&Friedmann,2016;videogameplayingfordyslexia:Franceschinietal.,

2013).

Itshouldbepossibletoconstrueallsuchbiologicaleffectsintermsof

manipulationstoparameterswithincomputationalmodelsofdevelopment.For

example,impulsivityinADHDhasbeenmodelledintermsofacomputational

constraintonreward-basedorreinforcementlearning.WilliamsandDayan

(2004,2005;Richardson&Thomas,2006)usedoneformofreinforcement

learning,TemporalDifferencelearning,tosimulateadevelopmentalprofileof

impulsivityinADHD,basedonamodeloftheroleofdopamineinoperant

conditioning.Inthismodel,theagent(child)hadtolearntodelayanimmediate

actionthatgainedasmallrewardinfavourofalateractionthatgainedalarger

reward.WilliamsandDayansimulatedADHDbyalteringthe‘discountingrate’

parameter,whichdeterminedtherelativeweightingofimmediateversuslong-

termrewardsinguidingaction.Theatypicalsettingoftheparameter

correspondedtothelowerlevelsofdopaminefoundinthebrainsofchildren

withADHD.Asystemthatdiscountedlong-termrewardsdevelopedimpulsive

behaviouralpatterns,byallowingsmallimmediaterewardstoguideaction.

Althoughthismodelwasnotextendedtoconsiderintervention,thecommon

pharmacologicaltreatmentforADHD,methylphenidatehydrochloride,isa

stimulantthatoperatesbyincreasinglevelsofdopamineinchildren’sbrains

59

(Gottlieb,2001).Inthemodel,theeffectsofthebiologicalinterventioncouldbe

simulatedbyalteringthediscountingrateparameter,therebyremovingthe

atypicalconstraintonsubsequentdevelopmentofimpulsecontrolinreward-

basedactiondecision-making.

Harm,McCandlissandSeidenberg’s(2003)readingmodelineffect

includedabiologicalintervention.Inoneofitsconditions,aninitial

computationallimitationinthephonologicalcomponent(lowerconnectivityand

restrictionsonweightsize)wassimplyeliminatedbyanintervention.Lost

connectionswererestoredandweightswereallowedtotakeonlargersizes.Itis

worthnotingthatinthismodel,thisbiologicalinterventionwassubjecttotiming

effects.Laterinterventionswerelesseffectivebecausetheycouldnotreverse

entrenchedweightvaluesproducedbyearlierlearninginthenetwork

connectingorthographicinputstoatypicalphonologicaloutputs.Onthefaceofit,

biologicalinterventionsmightseemmorepowerful,buttheytoomaybesubject

tolimitations.

Interventionstoencouragecompensatoryresponsesthroughother

pathwaysandmechanisms

Wehavethusfarconstruedinterventionastargetingthemechanismexhibiting

thedevelopmentaldeficit.However,behaviouralinterventionsmightseek

insteadtoencouragetherecruitmentofothermechanismsorpathwaysableto

deliverorsupportthetargetbehaviour.Modelsofdeficitsfrequentlymake

referencetopathwaysoutsideofthesingleimplementedmechanismtoexplain

behaviouralpatterns.Forexample,inAbel,HuberandDell’s(2009)modelof

acquirednamingdeficits,theauthorsreferredtoarangeofadditionalstructures

60

notrealisedintheirimplementationaspossiblesourcesofnamingerrors.These

includedvisualinput,theconceptual-semanticsystem,aneditorcomponent,and

aphoneticcomponent.WhenPlaut(1996)’smodelofacquireddeepdyslexia

wasunabletoaccommodateacertainpatternofreadingerrorsduring

relearningafterdamage,Plautarguedthatthepatternoriginatedfromthe

operationofanunimplementedphonologicalroute.Intheirmodelof

developmentaldyslexia,Harm,McCandlissandSeidenberg(2003)arguedthat

interventionsactingonanunimplementedsemanticroutewouldimproveword

readingratherthanjustthenonwordreadingimprovementsshownbythe

implementedarchitecture.

Somedisordersmayevenoriginatefromatypicalorganisationof

pathways,ratherthanlimitationsinparticularmechanisms.Forexample,

Chang’s(2002)connectionistmodelofsentenceproductiondemonstratedhow

inappropriatesharingofinformationbetweenmechanisms(inthiscase,those

responsibleforprocessingsequencinginformationandmessageinformation)

causedamarkeddevelopmentalimpairmentingeneralisation(Dell&Chang,

2014).Themodellearnedtoproducesentencesinthetrainingset,butwaspoor

atgeneralisingwordstoappearinfunctionalrolesithadnotencountered.Ina

similarwaytoThomas’s(2005)modelofcompensatedmorphosyntaxinDLD,

thismodelhadacquiredanoverlylexicalisedapproachtoacquiringsyntax.More

generally,lackofseparationofinformationcaninsomecasesmakethe

computationaltaskmuchharderforasystemtosolve(seee.g.,Norris,1991;

Richardson&Thomas,2006).Disordersmayalsoarisewhenthebalance

betweendifferentinputsdrivingamechanismisdisrupted.Amblyopiaisawell

knownandmuchresearcheddisorderofvisionwheretheinputfromoneeyeis

61

weakerthantheother;oneeyecomestodominateprocessingatacorticallevel,

tothedisruptionofbinocularvision(Thompsonetal.,2015;seeCrewther&

Crewther,2015foraneurocomputationalaccount).

Evidencefromfunctionalbrainimagingofdevelopmentaldisordershas

encouragedtheviewthatinsomecasesofgooddevelopmentaloutcomes,

usuallyfollowingintensiveinterventions,compensatorymechanismshavebeen

engagedbeyondnormalcircuitry,therebyexploitingalternativepathways.For

example,argumentshavebeenmadeinthecaseofdyslexia(compensatory

activationinrightinferiorfrontalgyrus;Hoeftetal.,2011)andautism

(compensatoryactivationsinseveralleft-andright-lateralisedregionsidentified

inalanguagecomprehensiontask;Eigstietal.,2015).Researchershopethat

identificationofthesealternativebrainpathwayscanbetranslatedintonew

interventionsthatwillencourageadoptionofcompensatorystrategies.4

Similarclaimsforcompensatoryoutcomeshavebeenmadeon

behaviouralevidencealone.Forexample,DeHaan(2001)pointedoutthatin

childrenwithautism,despiteevidencethatindividualsprocessedfaces

atypically(suchastheunusualabsenceofcategoricalperceptionoffacial

expressions),someneverthelessperformedinthenormalrangeonexpression-

recognitiontasks.TheseindividualstendedtohavehigherIQs.DeHaanargued

thattheremustbe“adegreeofplasticityinthedevelopingsystemthatallowsfor

4Forexample:https://www.nih.gov/news-events/news-releases/brain-activity-pattern-signals-ability-compensate-dyslexia,retrieved17August2016:“Understandingthebrainactivity

associatedwithcompensationmayleadtowaystohelpindividualswiththiscapacitydrawupon

theirstrengths.Similarly,learningwhyotherindividualshavedifficultycompensatingmaylead

tonewtreatmentstohelpthemovercomereadingdisability”(AlanE.Guttmacher,M.D.,director

oftheNIH’sEuniceKennedyShriverNationalInstituteofChildHealthandHumanDevelopment,

commentingonHoeftetal.,2011)

62

developmentofalternativestrategies/mechanismsinfaceprocessing”(2001,p.

393).

Theproposalthatalternativecombinationsofmechanismscandeliver

similarbehaviours,whichunderpinshopesofcompensatoryoutcomes,requires

thatacertainkindofdevelopmentaltheorytobetrue–thatthereisasuiteof

cognitivemechanismswithdifferentialproperties,anddevelopmentpartly

involvesselectingacombinationthatwilldeliverbehaviouralmastery.Inthis

way,PriceandFriston(2002)havearguedfordegeneracyinthebrain’s

realisationofcognition.Thisisabiologicalconcept,wherebyelementsthatare

structurallydifferentcanperformthesamefunctionoryieldthesameoutput.

Forexample,objectscanberecognisedeitheronthebasisoftheirglobalshape

orbythepresenceofdistinguishingfeatures.Thedifferentcognitivefunctionsof

eitherglobalformorlocalfeatureprocessingcanthereforedeliverthesame

output:accurateobjectrecognition.Howwellaprocessingcomponentperforms

ataskthendependsonthefitofitsstructure(i.e.,itsneurocomputational

properties)totheintendedfunction;andhowmuchtrainingthecomponenthas

hadinperformingthetask.Evenwithinthenormalrange,individualsmayfollow

developmentaltrajectoriesthatharnessdifferentcombinationsofcomponents

toperformthesametask.Degeneracymaythereforeexplainbothindividual

variationinfunctionalbrainactivations,andvariationinimpairmentsfollowing

thesamelocalisedbraindamage(Price&Friston,2002).

However,relativelyfewcomputationalaccountshaveexplicitly

consideredhowdevelopmentcouldintegratemultiplemechanismstoperform

complextasks,letalonehowvariationinoutcomescouldarisebetween

individuals.Inthemixture-of-expertsapproach(Jacobs,1997,1999;Jacobsetal.,

63

1991),theinitialarchitectureiscomprisedofcomponentsthathavedifferent

computationalproperties.Aspecificmechanismgatesthecontributionofthese

componentstotheoutput.Whentheoverallarchitectureispresentedwithatask,

thegatingmechanismmediatesacompetitionbetweenthesetofcomponents,

allowingthemostsuccessfulcomponentforeachtrainingpatternbothtodrive

outputperformanceandtoupdateitsweightstobecomebetteratthatpattern.

Acrosstraining,certainmechanismscometospecialiseonsetsofpatterns,by

virtueofhavinganinitial(perhapssmall)advantageinprocessingthose

patterns.Whymightsuchaprocessofemergentspecialisationdifferbetween

individuals?Presumably,variationinoutcomescouldarisefromdifferencesin

thesetof‘experts’,differencesintheexperts’respectivecomputational

properties,theoperationofthegatingmechanism,andthecompositionofthe

trainingset(seeThomas&Richardson,2005).

Asyet,nocomputationalaccountshaveconsideredhowanintervention

mightaltertheorganisationofasetofmechanismstoimproveaccuracyona

givenbehaviour,forourpurposes,directinglearningtowardsmechanismswith

fewerrestrictionsontheirplasticity.Wedoknowthatinpractice,clinicianstend

toshiftfromimplicittoexplicitmethodswitholderchildren,inorderto

encouragecompensatorystrategies,suggestingthatmeta-cognitionmightbe

efficaciousintriggeringareorganisationofmechanisms.However,thereisa

missinglinkintheargument.Whilethereisevidenceofindividualvariabilityin

theuseofmechanisms,andevidenceofcompensatoryengagementofnew

mechanismsinsomedisorderswhereindividualsshowgoodoutcomes,thisdoes

notguaranteethatwecangenerateinterventionstoencouragetheuseof

alternativesetsofmechanisms.Thatis,evidenceofdifferentoutcomesacross

64

individualsisnotthesameasevidencethatalloutcomesareequallyaccessible

toasingleindividual.Oneviewisthatindividualvariabilityintheuseofdifferent

mechanismsforataskindexesthescopeforcompensatoryreorganisation(e.g.,

inthedomainofreading:Kherif,Josse,Seghier&Price,2009;Richardsonetal.,

2011;Seghieretal.,2008).Butevidencefromthefunctionalimagingof

compensatedbrainsminimallyrequirestranslationtothecognitivelevelto

understandwhatthecompensationsrepresent,beforeafacilitatoryintervention

canbedeveloped.

Howmightaninterventionpromptuseofcompensatorymechanisms?

Perhapsabehaviouralmethodcouldemphasisedifferenttask-relevant

information,ordifferentmodalities;orencouragedifferentialrelianceonmotor

versussensorydemandsofthetask;orengagementofdifferentrepresentational

formats,suchasgesturetosupportlanguage,orlanguagetosupportspatial

cognition.Perhapsatypicalover-connectivitycouldbediscouragedby

presentingmaterialsthatcarriedlessinformationandthereforeengagedfewer

mechanisms;disordersofdisruptedcompetitioncouldberemediatedby

blockingthestrongerpathwaytoallowtheweakertodevelop,asinthecaseof

patchingthestrongereyeinamblyopia.Thisremainstobeclarified.Thus,while

intrinsiccomputationallimitationsinatargetmechanismmightbeovercomeby

recruitingothermechanismsabletosupporttaskperformance,oralteringthe

competitionandcross-talkbetweenmechanisms,acomputationalanalysisof

thisstrategyisnotfaradvanced,noranunderstandingofhowtoencouragesuch

recruitmentviaaspecificbehaviouralintervention.

Individualdifferencesinresponsetointervention

65

Oneofthemostchallengingaspectsofinterventionisthevariationinchildren’s

responsetothesameintervention,andtheconsequentrequirementthat

interventionbetailoredtotheindividualchild.Howcanthetherapistdetermine

whichinterventionisthebesttopursueforagivenchild?

Monogenicmodelsofdisordersgivesomebasistoconsiderdifferential

responsestointervention.Forexample,intheirmodelofwordfinding

difficulties,Bestetal.(2015)wereabletousethreedifferentatypicalconstraints

(operatingonhiddenunits,connectivity,andunitactivationfunction)to

simulatethelanguageprofilesofindividualchildren.Figure9showsthe

responsetotwodifferentinterventions(semantictherapy,phonological

therapy)forthethreedifferent‘versions’ofeachchildwithWFD.Notably,the

differentcomputationaldeficitstoproducethesameatypicalbehaviouralprofile

wereassociatedwithdifferentresponsestointervention.AswithThomasand

Knowland’s(2014)modelthatsoughtmarkerstopredictresolutionor

persistenceofdelay,theimplicationhereisthatmeasuresofunderlying

processingarenecessarytocomplementbehaviouralprofiles.Indeed,usingthe

enrichmentintervention(seeFigure3),networkswhosedelaywouldresolveon

itsownwerefoundtorespondbettertointerventionthanthosewhosedelay

wouldpersist.Intheseassociativemodels,therefore,untreatedoutcomesare

linkedtoindividualdifferencesinresponsetointervention.5

5Theenrichmentintervention,describedinFigure3,wasappliedtotheThomas&Knowland(2014)model,andtrajectoriesofresponsetointerventionweretracedseparatelyforthose

whoseearlyidentifieddelay(ifuntreated)wouldresolveversusthosewhereitwouldpersist.

Themaximallyenrichedtrainingsetwasappliedtoallnetworksatepoch50.Forthefollowing

30epochs,resolversandpersistersimprovedbythesameamount.Thereafter,resolvers

(N=165)showedafasterrateofimprovementthanpersisters(N=64)(epochxgroupinteraction,

F(1,227)=5.06,p=.025,ηp2=.022),sothattherewasareliabledifferenceintreatmenteffect

66

<InsertFigure9abouthere>

Polygenicmodelsofdisordersofferamorereadyframeworktocapture

differentialresponse.Usingpopulation-levelmodels,atypicalcomputational

constraintscanbesimulatedagainstabackgroundofsmallpopulation-wide

variationsinmanycomputationalconstraints,suchasthoseinvolvedin

specifyingthenetworkarchitecture,processingdynamics,andplasticity,aswell

asdifferencesinenvironmentalstimulation.Onemightthinkofthisasthe

‘generalintelligence’ofanetwork.Figure10showsdistributionsoftreatment

effectsfromthesimulationsofDavis(2017)foramodelofregressiveautism.

Thedevelopmentaldeficitwascausedbyasingleatypicalparameteraffecting

connectionpruning,againstthebackgroundof,consideredseparatelyfor

trainingsetperformanceorgeneralisationperformance,andinresponseto

normalisationorcompensationinterventions.Thetreatmenteffectswere

generallysmall,oftheorderofafewpercentagepointsofaccuracyagainst

deficitsof20-40%;however,theyvariedwidelyacrossindividualnetworks,

includingcasesoflargegainsandlargelossesinresponsetointervention.Davis

(2017)wasthenabletoexploretheparametersetsofindividualnetworksto

predictthesizeofthetreatmenteffect,inordertoconstructamechanistic

accountoftheoriginofvariableresponsetointervention.

Table4showsasetofstandardisedcoefficientsfromlinearregressions

foreachinterventiontype,assessedontrainingsetandgeneralisation.The

betweenthegroupsby80epochspost-onsetofintervention(5.6%improvementinaccuracyfor

resolvers,3.4%improvementforpersisters,t(227)=2.56,p=.011).

67

shadedrowsrepresentparametersrelatedtothepathologicalprocess(over-

pruning),theresttogeneralintelligence.Severalpointsarenotable.First,the

maineffectsoftheseparametersexplainedtheminorityofthevariancein

responsetointervention.Whiletherewasastochasticelementtotheresponse,

replicationindicatedthatthetest-retestcorrelationwasaround0.5,indicating

thatafairproportionoftheresponsetointerventiondependedonthenetwork’s

developmentalconditions(itsparametersanditsenvironment).Mostlylikely

thosedevelopmentconditionsarosefromhigherorderinteractionsbetween

computationalparameters,enablingsomenetworkstogainfromintervention,

othersnottogain,andsometolose.Second,somepredictorsofindividual

responsedependedoninterventiontype(normalisationversuscompensation).

Third,predictorscouldbedifferentiallyimportantforinterventionresponseson

thetrainingsetversusgeneralisation,thatis,dependentonthetargetbehaviour.

Andlast,whilesomepredictorswereinvolvedinmodulatingtheimpactofthe

atypicalconnectivitypruningprocess,othersrepresentedparametersunrelated

tothepathology,consistentwiththeideathatgeneralindividualdifferences

factorsinfluencetheeffectivenessofbehaviouralintervention.

<InsertFigure10abouthere>

<InsertTable4abouthere>

Thenarrowfocusonindividualcognitivemechanismsfeelsparticularly

restrictinginthecontextofindividualdifferences,wheretheintervention

situationisinfluencedbymanyqualitiesofthewholechild,includingtheir

attentionskills,personality,motivation,andengagementwiththetherapistina

68

productivesocialinteraction(ordependingondeliverymode,withateaching

assistant,teacher,parent,groupofchildren,orcomputer).Fromthesingle-

mechanismperspective,wearerestrictedtoviewingtheseasfactorspotentially

influencingtheplasticityofthemechanism,theinformationexperiencedbythe

childinthetherapeuticsituation,andtheeffectivedosedeliveredbythe

intervention.Thechild’sattention/motivation/engagementinthetherapeutic

situationisanecessarypreconditionfortheinterventiontogainaccesstoand

alterthefunctioningofthetargetmechanism.Thisissomewhatunsatisfying,but

isanecessarysimplifyingstepintryingtobuildamechanisticaccountofthe

sourcesofindividualvariabilityinresponsetointervention.

69

Discussion

Wesetouttoinvestigatethepotentialofconnectionistmodellingtoincrease

understandingofthemechanismsunderlyinginterventionsindevelopmental

disorders.Wepresentedandanalysedarangeofmodelsandresults.Toevaluate

thepotential,letussetasceptical‘bar’thatneedstobecleared.Ontheonehand,

onecouldhavereservationsabouttheuseofcomputationalmodelstosimulate

developmentandindividualdifferencesinthatthemodelsaretoocomplex.

Connectionistmodelshavemanycomponentsandcomponentscanvaryalong

multipledimensions(e.g.,component:hiddenunits;dimensions:numberof

layers,unitsperlayer,patternofconnectivity,activationfunction).Components

andtheirdimensionsarenotindependent,andbehaviourresultsfromcomplex

interactionsamongthem(Thomas,Forrester&Ronald,2016).These

interactionscanbedifficulttoanalyse,makingithardtoderivedeeperprinciples

orgeneralisations.Perhapsthen,themodelsaretoocomplicatedtobeuseful;

andthechallengeofmappingfromthespecificpropertiesofthemodelto

propertiesofpeopletoogreat.Ontheotherhand,onecouldhavereservations

thatthecomputationalmodelsarenotcomplexenough.Wefocusedmostlyon

individualcognitivemechanismsorlimitednumbersofpathways.Theactual

cognitivesystemisfarmorecomplicated;wedidnotconsidersensori-motor

components,emotionalcomponents,socialcomponents,executivefunction

components,meta-cognition,andmotivation,letalonethedynamicsofthe

therapeuticsituationthatweoutlinedintheintroduction.Thecomputational

analysisdemonstratesthathigh-levelbehaviours,anddevelopmentaldeficitsin

thesebehaviours,aredeterminedbycomplex,non-obviousinteractionsamong

multiplefactors,someofwhichcan’tbedirectlymeasured.Moreover,the

70

modellingsuggestedthatsimilarlookingbehaviouraldeficitscanarisefrom

differentunderlyingcauses,whichinturnresponddifferentlytointervention.

Perhapsthesensibleconclusionwouldbethattointervene,ratherthan

investigatingunderlyingmechanisms,itwouldbebettertofocusonthe

behavioursinquestionandimprovethembywhatevermethodsseemeffective.

Dothefindingsclearthisbar?

Mainfindings

Acognitivemechanismexhibitingadevelopmentaldeficitinthebehaviourto

whichitcontributesdoessobecauseexposuretonaturalisticexperienceorto

typicaleducationalexperienceshasnotbeensufficienttoacquireage-

appropriateskills.Simplydrivingthismechanismharderwithmoreexperience

maynotremediatethedeficit,justservetopropelitfurtheralonganatypical

trajectory.Thisperhapsthischimeswiththegeneraldifficultyoftreating

developmentaldisorders,particularlythosewithpervasiveeffectssuchas

autism(Charman,2014b).Howcananinterventionsucceedwherenaturalistic

experiencehasnot?

Thesimulationswedescribedpursuedfourlinesofinvestigation.First,

weconsideredlong-termoutcomesintheabsenceofintervention,exploitingthe

opportunityofamodel,matchedtoanatypicalprofileearlyindevelopment,to

projectforwardtotheadultstate.Resultsindicatedthatprocessingmechanisms

couldreachcompensatedoutcomeswithexpertiseinskillslesssensitivetothe

atypicalprocessingconstraintsbutresidualdeficitsinotherareas.Resolutionin

earlydelaysoccurredwherethecauseoftheinitialdeficitwasalimitationin

plasticity,ratherthancapacity.Plasticitycouldbeoperationalisedintermsofa

71

childperformanceonlearningtasks,whilecapacitycouldbeoperationalisedas

thequantityofinformationthatthechildcanintegrateon-line.Resolutionmight

beacceleratedbyagreaterdosageofotherwisenaturalisticexperience(i.e.,

practice).However,earlybehaviouralprofileswerepoorpredictorsofthese

differentialoutcomes,andmeasuresofprocessingwereneededtoimprove

predictivepower(e.g.,Fernald&Marchman,2012).

Inthesecondlineofinvestigation,weconsideredmethodstoremediate

atypicaldevelopmentinasinglenetwork.Thesemodelsaddressed,respectively,

remediatingdisordersarisingfromalackofearlystimulation,choosingabetter

trainingsettosupportatypicalprocessingproperties,improvinginputand

outputrepresentations,andalteringthecomputationalpropertiesofthesystem.

Ifthedeficitinfactarisesthroughinsufficientstimulationofthetarget

mechanism,whetherexternallyinrichnessoftheenvironmenttowhichthechild

isexposedorinternallyintheinformationprovidedtothesinglemechanism(for

instance,byattentionalorientingsystems),thenthedeficitcanbetreatedby

alleviatingthisshortfall.Thismightamounttoenrichingtheenvironment(for

example,inthedomainoflanguage,withmorechild-directedspeech;e.g.,

Suskind&Suskind,2015);ortotrainingattentionalmechanisms(forexample,in

thecaseofyoungchildrenwithautism,trainingattentiontosocialcues,e.g.,

Powell,Wass,Erichsen&Leekam,2016;Wass&Porayska-Pomsta,2014).

Severalpossibilitiesaroseforaccommodatingtheatypicalprocessing

constraintsofthetargetmechanism:ofsupportinggeneralisationbyadditional

trainingonexperiencesthathighlightthestructureoftheproblemdomain;of

usinginterventiontoalterthequalityofthemechanism’sinputand/oroutput

representations,therebysimplifyingthecomputationalproblemthatthetarget

72

mechanismisrequiredtosolve;andoftrainingthetargetmechanismnotonthe

fullcognitivedomainbutasubsetoftheproblemadequateforeveryday

functioning.Thenthereweremethodsthatmightaltertheatypical

computationalconstraintsthemselves,perhapsinsystemswherestimulation

cancauseachangeincomputationalproperties;orthroughdelaying

interventioninsystemswherecomputationalpropertiesmature;orusing

biologicalinterventionstodirectlyaltercomputationalproperties(e.g.,through

pharmacologicaltreatments,orbehaviouraltechniquessuchaschangesindiet,

exercise,mindfulnesstraining,actionvideogameplaying,andsleepregimes).

Inthethirdline,weconsideredinterventionstoencouragecompensation

viaalternativepathwaysormechanismstoproducethesameorsimilar

behaviour.Here,computationalanalysisislessfaradvanced,mainlybecause

typicalmodelsofdevelopmenthavenotarticulatedhowacomplexsystemwitha

suiteofcognitivemechanismscanrecruitandintegratethemechanismsfor

behaviouralmastery.Itisthereforenotclearhowaninterventioncouldalterthe

organisationofmechanismstoimprovetaskperformance.Thefactthat

cliniciansshiftfromimplicittoexplicitmethodswitholderchildrentoencourage

compensatorystrategiessuggeststhatmeta-cognitionmightbeefficaciousin

triggeringareorganisationofmechanisms.Meta-cognitiveprocessesarerarely

implementedinmodels(thoughseeHoffman,McClelland&Lambon-Ralph,2018,

forarecentmodelofsemanticsthatincludesmechanismstocontrolretrieval).

Wetakemeta-cognitiontoactbyalteringinternalfeedbacktothetarget

mechanism,usingexecutivefunctionstoactivateorinhibitdifferentpathways

andmechanisms,oralteringattentiontodimensionsofthestimulusorrequired

73

response.Futuremodelsthatcapturesuchprocessesarerequiredforafirmer

foundationtoexploreinterventionsthatpromptreorganisation.

Inthefourthlineofmodelling,weconsideredindividualdifferencesin

responsetointervention.Morerecentpolygenicmodelsofdevelopmental

disorderswereusefulhere,sincetheysimulatedtheatypicalmechanismagainst

abackgroundoftypicalvariationinarangeofdevelopmentalfactors,orindeed

capturedthedevelopmentaldeficitaslyingonacontinuumofpopulation-wide

variation(Thomasetal.,2016).Amodelinvestigatingthecausesoflanguage

delay(Thomas&Knowland,2014)pointedtothelimitedpowerofearly

behaviouralmarkersinpredictingwhetherdelayswouldresolve,sinceearly

profilesarelargelyconditionedbythestructureofthetaskdomain.Themodel

suggestedthatpredictivepowercouldbeincreasedbymeasuresofunderlying

cognitiveprocesses(seeFernald&Marchman,2012).Notably,the

computationalpropertiesinthemodelthatledtoresolutionofearlydelayalso

increasedresponsivenesstointervention.Amodelinvestigatingindividual

differencesinresponsetointervention(Davis,2017)demonstratedthat

responsescouldbehighlyvariable,andthatbothdifferencesintheseverityof

atypicalcomputationalconstraintsandinotherpopulation-wideindividual

differencesfactorspredictedtheresponse.However,therewerestochastic

factors,andthepredictivefactorsthemselvesshowedstronginteractionssuch

thatmuchvarianceinoutcomeremainedunexplained,despitereplicable

individualdifferencesinresponsetointervention.Finally,alowerlevelof

stimulationfromtheenvironmentcouldalsoplayarole,exaggeratingtheeffect

ofatypicalcomputationalconstraints(Figure4),oritselfcausingdeficitsin

combinationwithmaturationalchangesinnetworkconnectivity(Figure3).

74

Overall,thisavenueofmodellingisimportanttosupportthesearchfor

stratificationbiomarkersinresearchondevelopmentaldisorders,workwhich

seekstoisolatemeasures(e.g.,age,gender,intellectualability,comorbidityof

deficits)thatpredictdevelopmentaloutcomesandresponsetointervention.

Computationalinsightsneedtobetranslatedtoactualinterventions.How

mightthefindingstranslateintoclinicaladvice?Generalisationmightbe

enhancedbyaninterventionthathighlightskeycues,orincompositional

domains,componentpartsofstimuli,whichwouldnormallybeextractedbya

typicallydevelopingsystembutneedtobeincludedintheexperienceofa

systemwithatypicalproperties.Ifabehaviourrequireslearningassociations

betweenrepresentationsindifferentdomains,improvingtheserepresentations

mayaidaninterventiontargetingtheassociationsthemselves.Ifthereisdomain

evidencesupportingmaturationinthetargetmechanism,waitingtoapplythe

interventionmayyieldbenefits,sincecomputationallimitationsmayreduce

withtime.Note,thisisatoddswiththegeneralrubricofinterveningearlierata

timeofpurportedlyhighlyplasticity,butitrequiresaspecificevidencebaseof

theimportanceofmaturationforagivenprocess(see,e.g.,Karmiloff-Smithetal.,

2014,fordiscussionoftheefficacyofCBTtotreatanxietydisordersatdifferent

ages,dependingonthematurationoffearextinctionmechanisms).Forolder

children,explicitinterventionsmayincreasetheopportunitytoengage

alternativemechanismstodrivetheimpairedbehaviour,totheextentthatmeta-

cognitionisefficaciousinenlistingthem.Ananalysisofthecognitivedomain

mayindicatesubsetsofbehaviourthatcouldprovideadaptivefunctioningin

everydaylife,andsoformacompensatoryintervention.Lastly,wherebehaviour

doesnotimprovethroughbehaviouralmeans,thenopportunitiescanbe

75

exploredforinterventionsthatalterthecomputationalpropertiesbybiological

orindirectbehaviouralmeans.

Generalprinciplesofintervention

Thereviewofcomputationalworkindicatesseveralimportantfactorsinthe

mechanismsunderlyinginterventioneffects.Firstly,thenatureofthe

computationaldeficitmatters.Similarbehaviouraldeficitscanbeproducedby

differentunderlyingcomputationaldeficits–allcharacterisedbyslower

development–butwhichthenresponddifferentlytointervention.Some

computationaldeficitswillresolve,withmoreexperiencerequiredtodeliverthe

sameamountofbehaviouralchange(suchasareducedlearningrate).Some

computationaldeficitsofferpartialresolution,alteringthekindsofabilitiesthat

canbesupportedbythemechanism(suchasalessdiscriminatingactivation

function).Somecomputationaldeficitscanreachgoodsolutionswithadapted

trainingregimes(reducedconnectivity,supportedbyawiderrangeoftraining

examples).Othercomputationaldeficitswillrestricttheultimatelevelofability

thatcanbereachedbythemechanism,leadingtopersistingdeficits(suchas

fewerhiddenunits).

Second,timingmatters.Agewasrepresentedintwowaysinthemodels

weconsidered.Itcouldbeindexedbyanaccumulationofpreviousexperience.

TheHarm,McCandlessandSeidenberg(2003)readingmodeldemonstrateda

negativeeffectofpriorlearningonthepotentialforintervention,toexplainwhy

orallanguageinterventionswouldhavelimitedsuccessinalleviatingdifficulties

oncethechildhadstartedtoread.Eveniftheorallanguageintervention

alleviatesacoreprobleminphonology,itcannotundopriorlearninglinking

76

orthographytoatypicalphonology.Thesesub-optimalmappingsmustbeover-

writtenbyacomplementaryinterventiontargetingdecoding.Alirezaetal.

(2017)foundasimilareffectintheirmodelofword-findingdifficulties.Laterin

themodel’sdevelopment,improvingstrengths(theinputandoutput

representations)becamelesseffectiveandremediatingweaknessesbecame

moreeffective.Oncepathwayshadcommittedtoutilisingthe(potentiallypoor)

initialrepresentations,maximisingtheperformanceoftheimpairedpathway

throughintensepracticebecamethebestrecourse.Agecouldalsoindex

maturationalchangesinthecomputationalpropertiesofthelearningmechanism.

Inthemodelsimulatingtheeffectsofinsufficientstimulation,lateinterventions

werelesssuccessfulbecausematurationalpruningofconnectivityhad

consolidatedanenvironmentaldisadvantageintoastructuraldeficit.

Researchershavespeculatedaboutthecognitivedomainsinwhichmaturational

constraintsmayhavemostimpactontrainingeffects(Jolles&Crone,2012).

Sensitiveperiodssuggestearlyinterventionisbetter,butthesereducingprofiles

ofplasticitytendtobelimitedtolowerlevelsensoryandmotordomains,rather

thanhigh-levelcognitiveabilities(Huttenlocher,2002).Insomedomains,such

asattention,trainingmayindeedbemoreeffectiveinlaterchildhood–at

youngerages,thetargetsystemsmaybecomputationallyimmature(e.g.,at4

yearsinsteadof6yearsforattentiontraining;Ruedaetal.,2005).Alifespan

perspectivesuggeststhatwhilebehaviourischangeableatallages,behavioural

changesrelyonthebrainsystemsthataremostplasticattheagewhentraining

takesplace(Bengtssonetal.,2005).

Third,thecontentoftheinterventionmatters.Wedrewadistinction

betweenadditionalpracticeonitemsinthechild’snaturalexperienceofthe

77

domainandtheintroductionofnewitemsthathighlightkeyinformationforthe

child,suchasindicatingcompositionalstructure.Weadditionallydistinguished

informationintendedtosupportgeneralisationofimplicitregularitiesofthe

cognitivedomaintonewsituations.Wedistinguishedtasksthatdirectlytargeta

behaviourcomparedtothosethatenhancerepresentationsthatdrivethe

behaviour.Weemphasisedprinciplesderivedfromstatisticallearningtheoryas

candidatestoimprovelearning:therichnessoflearningexperiences,their

variability,theprovisionofnoveltyinfamiliarcontexts,andtheconstructionof

morecomplexrepresentationsfromsimplerones.Theseprincipleswere

caveatedbythepossibilitythatwhatworksinasystemwithtypical

computationallearningconstraintsmaynothavethesameeffectinsystemswith

atypicalconstraints.Lastly,implementationencouragedafocusonthedosage,

duration,andregimeoftraining.Indistributedconnectionistmodels,

modificationofthetrainingsetcancauseinterferencewithpriorestablished

knowledge(so-calledcatastrophicinterference;McCloskey&Cohen,1989).

Interferencecanbereducedbyloweringthedosageofnewinformation,

extendingitsduration,andinterleavingitwithtrainingontheoldinformation.

Twoimportantissuesininterventionsconcernpersistenceof

interventionseffects,andgeneralisationbeyonditemsintheinterventionset.

Withrespecttopersistence,inareviewofpersistenceandfadeoutintheimpacts

ofchildandadolescentinterventions,Baileyetal.(2017)arguedthatimpacts

arelikelytopersistforinterventionsthatbuildskillsinfluencingfuture

development(especiallythatallowtheindividualto‘stayontrack’inhome,

school,orcommunity),andinthecaseofenvironmentsthatsustainthegains.

Skillsmostlikelytoyieldlong-termimpactarethosethatarefundamentalfor

78

success,malleablethroughintervention,andthatwouldnotdevelopeventually

intheabsenceoftheintervention.Thesimulationsweconsideredeither

implementedinterventionasanalterationtothetrainingsetforadiscrete

period,orasapermanentalteration.Thelattercouldbeviewedastheprovision

ofasustainingenvironmentfortheintervention(suchastrainingparentsto

permanentlyalteringtheirinteractionswiththechild,perhapsintheirlevelof

languageinput).Simulationresultspointedtopersistingbenefitsofthe

interventionifthechangetothetrainingsetwaspermanent.Discrete

interventionscouldhavepersistingbenefits,butonlywhenplasticitywas

reducedduringtraining(Davis,2017),notwhenitwasconstantacrosstraining.

Inthelattercase,YangandThomas(2015)foundthatearlyinterventions

showeddissipatingeffectsacrossdevelopmentoncetheinterventionwas

discontinued,withtheexacttypeofinterventionbecominglessrelevant.Inthese

models,therefore,earlydiscreteinterventionshadlong-termbenefitsifthe

consequentgainswereconsolidatedinthestructureofthetargetmechanism.

Thisrevealsthedouble-edgedswordofplasticity:ifplasticityisconsistentacross

age,interventionscanbeappliedatanyage,buttheeffectsofearlydiscrete

interventionswillbelost;ifplasticityreduceswithage,interventionsmustbe

early,buttheireffectswillpersist.

Sincemostofthemodelsconsideredherefocusedonindividual

mechanisms,therewasnotscopetoconsiderthewiderissueoffartransfer/

generalisationoftrainingeffectstodifferentskills.Nevertheless,when

simulatinginterventions,atnotimedidweconsiderimprovementonthe

interventionitemsthemselves–inasense,thiswouldbetrivial,sinceinerror-

correctionnetworks,performanceontheinterventionitemswillalmostalways

79

improve.Weinsteadconsideredtransferfromtheinterventionsettoitems

eitherinthenetwork’susualexperience(thetrainingset)ortopreviously

unencountereditems(thegeneralisationset).Thismightexplaintherelatively

smallsizeoftheinterventioneffectsinanumberofcases(e.g.,seeFigure10).

Resultsalsopointedtotheimportanceofthecompositionoftheinterventionset

insupportingperformanceonthetrainingsetversusgeneralisation.Innetworks

withatypicalcomputationalproperties,generalisation(transfertonovelitems)

neededadditionalsupportfrominterventionitemsselectedtohighlightimplicit

regularitiesinthedomain,regularitiesthattypicalnetworkscouldextractfrom

normalexperience.Atypicalnetworksoftenbestgeneralisedthrough

interpolationratherthanextrapolation,sincetheirpropertiescouldnotsupport

processingofitemsverydifferentfromthosepreviouslyencountered.6

Theideaofcompensationaroseinseveralcontexts,anditisworth

distinguishingthedifferencessensesinwhichitwasused.First,wesawone

principledwaytodefinecompensation,bycontrastingitwithnormalisation

(Yang&Thomas,2015).Innormalisation,theaimofinterventionistoprovide

thefullrangeofabilitiesandknowledgethatanytypicallydevelopingsystem

acquiresthroughexposuretothenormaltrainingset.Inthissenseof

compensation,theaimoftheinterventionistooptimiseasubsetofbehaviours

presentintheoriginaltrainingset.Othermodelsprovidedalternativesensesofa

‘compensated’system.Thesewereforcingasystemtofindapartialsolutionto

6Plaut(1996)foundthatsimulatedrecoveryofreadingfollowingacquireddamagewasbettersupportedbyretrainingonatypicalsemanticcategorymembersthanprototypicalcategory

members.Thiscanbeseenasanexampleofencouragingtrainingtransferbyinterpolation.In

Plaut’simplementation,atypicalcategorymemberssurroundedprototypicalcategorymembers

insemanticspace.Trainingonthesurroundingmemberstransferredtothoselyinginbetween.

80

thecognitivedomainthroughover-training,butleavingresidualdeficits;and

recruitingothermechanismstodeliverthesameorsimilarbehaviour.These

threesenseswouldtranslatetothreedistinctapproachestointerveninguponan

atypicalsystem:(1)selectinganinterventionthattargetsasubsetofthetarget

cognitivedomain;(2)providinggreaterpracticetoforcegreateraccuracyfrom

anatypicalsystem,orsimplyleavingthesystemtoimprovethroughmore

experience;(3)employingexplicitstrategiestoencouragetheuseofalternative

mechanisms.

Modellinglimitations

Akeyaspectofbuildingmodelsissimplification.Weshouldbeclear,then,the

waysinwhichthecomputationalworkwehavereviewedfallsshortwithrespect

tothepracticeofinterventionsfordevelopmentaldisorders.

Onabroaderscale,afocusoncognitivemechanismdoesnotcapturethe

complexityoftheinterventionsituation,whichcandependondynamicsofthe

interactionbetweenthechildandthespeechandlanguagetherapist,andwhere

interventionissometimesaprocessofdiscoveryofwhatworksforindividual

childreninthecontextoftheirfamilyandschoolenvironment.Tosomeextent,

evenfairlymechanism-focusedinterventionsinvolvesubstantialbehavioural

andinteractionalinterchangebetweenthechildrenandthetherapist(and

parent,ifalsocoached),whichmayyieldcollateralbenefits.Simulationsdonot

addresssomeofthecomplexities,suchasdistinguishingtheeffectsofexplicit

instructionfromimplicit,theroleoftheexpertiseofthetherapist,theeffectsof

adaptivevs.non-adaptiveinstruction,thedistinctionbetween1-to-1versus

groupinstruction,thedifferencebetweentherapist-deliveredandparent-

81

deliveredinterventions.Moreover,asBeauchaineetal.argue:‘opponentsof

biologicalapproachestopreventionandinterventionalsoarguethatby

emphasisinggeneticandneurobiologicalprocesses,wedivertattentionand

resourcesawayfromimportantpsychosocialcausesofmaladjustment,suchas

stress,parenting,andfamilyinteractions’(2008,p.748).Workinthe

implementationscienceshasalsopointedtowiderlimiting,enablingand

incentivisingfactorsforchangingbehaviourbeyondcognitivemechanisms,such

asresourcesandpolicy(e.g.,Michie,vanStralen&West,2011).

Onanarrowerscale,ourfocuswasonalimitedsetofcomputational

architecture:associativenetworks.Itispossiblethatotherarchitectures,suchas

self-organisingmapsorattractornetworks,mightprovidedifferentplasticity

conditionsoreffectsofinterventionongeneralisation.Theseremaintobe

explored.Theobservationthatinterventionsfordifferentlanguageskills

requireddifferentlevelsofintensity,duration,andinterleaving(Lindsayetal,

2010)isconsistentwiththeviewthatdifferenttypesofmechanismareinplay.

Speculatively,itmaybethatintensityismoreimportantthandurationtochange

sensoryrepresentations(self-organisingsystems);thatrepeatedshortbursts

overanextendedtimearenecessarytoalteraccesstorepresentations

(associativesystems);andthatanextendeddurationofpracticeisnecessaryto

extractregularitiesincomplexsensori-motorsequences(recurrentnetworks).

Inadditiontodifferentarchitectures,itisnecessarytoconsidercontrolsystems,

mechanismsofexecutivefunctionandreward-basedlearning,inorderto

addresstheoriginandmalleabilityofdeficitsinbehaviouralregulation,suchas

therestrictedrepertoireofinterestsinautism,orattentionaldeficitsinFragileX

syndrome,orimpulsivityinADHD.Lastly,themodelframeworkcaptures

82

developmentintermsofaplasticmechanismexposedtoastructuredlearning

environment.However,thisdoesnotreadilylenditselftoconsideringthe

possibilitythatthedisordermaychangethestructureoflearningenvironment

viaindirectpathways.Forexample,poorreadinglevelsmayreducethechild’s

motivationtospendtimereading,orparentsmayresponddifferentlytochildren

withlearningdisabilitiesthantheywouldtypicallydevelopingchildren.

AswithPlaut’s(1996)influentialconnectionistmodelexamining

relearningfollowingacquireddamage,wetookasimplifyingstepoffirst

adoptingasinglemechanismperspective.However,behaviourisgeneratedby

theinteractionofmultiplemechanisms.Amultiple-mechanismframeworkis

necessarytoconsider,variously,interventionstoencouragealternative

strategies,theuseofexecutivefunctionskillstocompensateforweaknessesin

domain-specificsystems(Johnson,2012),andinterventionsthatmightaddress

deficitsinfunctionalconnectivitybetweenmechanisms(e.g.,assometimes

proposedasadeficitinautism;seeThomasetal.,2016,fordiscussion).TheBest

etal.(2015)modelholdssomepromiseinthisregard,sinceitcapturesseparate

behavioursstemmingfromtheoperationofcomponents(nonwordrepetition,

semanticcategorisation)andfromtheinteractionbetweencomponents(naming,

comprehension),whereeachbehaviourexhibitsitsowndevelopmental

trajectory.Withinsuchamultiple-mechanismframework,itisapparentthata

singlemechanismcanneverthelessserveasalimitingfactoronperformance,

evenifitisnotthesolegeneratorofbehaviour.

Integratingmodelswithdatafromcognitiveneuroscience

83

Neuroanatomicallyconstrainedmodelsofthereadingsystemandthesemantic

systemhaveindicatedhowpayingattentiontoneurosciencedatacanprogress

computationalmodellingandprovideaparadigmforthemodellingofintactand

impairedcognitiveabilities(e.g.,Chen,LambonRalph,&Rogers,2017;Lambon

Ralph,Jefferies,Patterson&Rogers,2017;Uenoetal.,2011).Thisworkbrought

togethermodelsofnormalprocessingoftaskssuchaswordandobjectnaming,

detailedbehaviouralprofilesfromalargecohortofpatients,andfactsaboutthe

natureoftheunderlyingimpairmentthatcouldberelatedtopropertiesthe

computationalmodels,whichtogethercouldexplainawiderangeoffactsabout

deficitpatterns,basesofrecoveryoffunction,andresponsivenessto

intervention.Eachcomponent–modelling,behaviouralevidence,brain

evidence–helpedtobootstraptheother.Themodelssuggestednewwaysof

lookingatbrainandbehaviour,butthebrainevidencealsoconstrainedhowthe

impairmentsweresimulated,yieldingnewtestablepredictions.Thesemodels

incorporatemultiplecomponentsandpathways,andsimulateseveraltarget

behaviours(e.g.,forthereadingmodel,repetition,comprehension,andnaming).

Theyhavebeenappliedtothesimulationofacquireddeficits,suchasaphasias,

semanticdementia,andvisualagnosia,byremovingconnectionsfromcertain

regionsofthemodel,whileretrainingthemodelafterdamagehasthenallowed

investigationofplasticityrelatedrecovery.Modelsofdevelopmentaldeficitsand

interventionsarelesswellprogressed,butideallywoulddevelopinthesame

direction(Woollams,2014).Whatcognitiveneurosciencedatacouldbeusedto

constrainsuchcomputationalmodels?

Thereisafast-growingliteratureidentifyingdifferencesinbrain

structureandfunctioninchildrenwithbehaviourallydefineddevelopmental

84

disorders.Theseincludedifferencesinglobalbrainstructure(e.g.,reduced

globalgreymatterinADHD,Battyetal.,2010;increasedbrainsizeinautism,

Waldie&Saunders,2014);differencesinlocalbrainstructure(e.g.,thinner

cortexintheparsopercularisinADHD,aregioninvolvedininhibitorycontrol,

Battyetal.,2010;smalleramygdalainchildrenwithOppositionalDefiant

Disorder[ODD]andConductDisorder[CD],aregioninvolvedinemotion

processing,Noordermeer,Luman&Oosterlaan,2015);andstructural

connectivity(e.g.,abnormalanatomyoffronto-striatalwhitemattertracts;

Langenetal.,2012).ResearchusingfunctionalMRIindicatesthatindisorders,

activationcanbeeitherreducedorincreasedinrelevantareas,orincreasedin

otherareas.Forexample,indevelopmentaldyslexia,withinthenormalreading

network,thelefttemporo-parietalregionandventraloccipito-temporalregion

areoftenunder-activated,whiletheleftinferiorfrontalgyrusissometimesover-

activatedduetocompensatoryarticulatoryeffort,whilesomestudiesalsoreport

increasedactivationoutsidethereadingnetworkintherighthemisphere

(Barqueroetal.,2014).Functionalconnectivityissensitivebothtoindividual

differences(e.g.,inworkingmemory,inthelinkbetweenthefronto-parietal

networkandvisualareas;Barnesetal.,2016)andtodisorders(e.g.,abnormal

restingstatecorticalconnectivitybetweenfrontalandposteriorregionsin

autism;Waldie&Saunders,2014).

Therearetwokindsofchallengeinadaptingthesenewarchitecturesto

developmentaldisorders.Thefirstchallengeistoidentifytherelevant

computationaldeficittoapplytooneormoreregionsofthearchitecture,inthis

casepriortodevelopmentratherthaninatrainedmodelforacquireddeficits.

Forexample,Seidenberg(2017)identifiedseveralcandidateneuraldeficitsthat

85

mightbeassociatedwithdevelopmentaldyslexia,broadlyfallingundertheview

thatsignalpropagationbetweenandwithinregionsisnoisier(Hancock,Pugh&

Hoeft,2017).Theseincludegreatervariabilityinneuralresponsestostimuli,

consequentreducedfunctionalconnectivitybetweenregions,andslower

learningfromexperiences.Implicatedinnoisiersignallingarepotential

disruptionstomyelination,changestoneuraldynamics(hyperexcitability),and

anomaliesinneuralmigration.Thisisquiteawidesetofcomputational

anomalies,whichinimplementedmodels,couldhavediverseeffectson

developmentanddiverseresponsestointervention.7

Thesecondchallengeistodeterminehowtointerveneonlarger,

interactivearchitectures.Aswehaveseen,inarchitectureswithmultiple

mechanismsandpathways,thereisthescopeforalternateroutestocompensate

foranomaliesinagivencomponent.Thisindeediswhatoccursduring

relearningafterfocalremovalofconnectionstocapturerehabilitation(Uenoet

al.,2011).However,inadevelopmentaldeficit,thesystemispresumedtobe

plasticthroughout,andthequestionarisesastowhysuchcompensationwould

nothavetakenplacealready.Whatinterventionprocedurecouldtrigger

reorganisationinawaythatnaturalexperiencecouldnot?Perhapsitisassimple

asgivingextrapracticeonbehavioursmostcloselylinkedtothosebrainregions

showingreducedactivation,suchasphonologicalawarenesstrainingfor

temporalregionsprocessingphonologyinthecaseofdyslexia.Oncemore,

7Currently,nostraightforwardbehaviouralinterventionstemsfromtheneuralnoisehypothesisofdevelopmentaldyslexia.Hancock,PughandHoeft(2017)arguethehypothesispointsto

interventionsviabrain-stimulationtechniques,suchastranscranialdirectcurrentstimulation

andtranscranialmagneticstimulation,orpharmacologicalagents,toaddressthehypothesised

hyperexcitabilityofneurons.

86

precedingresultscautionusthateveninthissimplecase,theremaybetiming

effects,suchthatunlessanarrowlocusofdevelopmentaldeficitisremediated

early,therestofthesystemmaynotbeabletoadjustwithoutadditional

intervention.Andofcourse,inlargerarchitectures,deficitsneednotbefocal,

theycouldbewidespread,orhavespreadacrossdevelopmentfromaninitially

morerestrictedlocus.

Cognitiveneurosciencecanalsoprovidedataonresponsetointervention.

Inmanycases,behaviouralinterventionleadstoincreasedactivationin

previouslyunder-activatedregionsandchangesinfunctionalconnectivitythat

bringindividualsclosertothepatternsobservedintypicallydevelopingcontrols,

so-callednormalisation(e.g.,indyslexia:Ylinen&Kujala,2015;inautism:

Calderonietal.,2016;Waldie&Saunders,2014).However,sometimes

individualsrespondtointerventionwithdecreasedactivationorcompensatory

recruitmentofdifferentregions,andregionsthatrespondtointerventionare

oftennotlocalisedbutwidespreadacrossthebrain.Itisanareaofactive

researchtouncoverwhethersuchneuralmarkerscanpredicthowindividual

childrenrespondtointervention(Barqueroetal.,2014).Inonestudy,Simosetal.

(2007)foundthatchildrenwhorespondedtointerventionexhibited

normalisationwhilenon-respondersexhibitedcompensation.

Overall,researchfromneurodevelopmentexhibitssimilarthemestothe

computationalmodellingworkdescribedhere–contrastingnormalisationwith

compensation,identifyingindividualdifferencesinresponsetointervention,

distinguishingresolvingfrompersistingdelays,interpretingtheimplicationsof

goodcompensatoryoutcomes.However,theneuroscienceliteratureisalsovery

mixed–inpartduetoheterogeneityinmethods,inpartduetoheterogeneityin

87

participants.Forexample,adifferenceinonedirectionbetweendisorderand

controlgroupinonestudymaybecontrastedbyadifferenceintheopposite

directioninanother(e.g.,inthesizeoftheamygdalainODDandCD,

Noordermeer,Luman&Oosterlaan,2015,forreview;intheactivationofinferior

frontalgyrusindyslexia,Barqueroetal.,2014,forreview).Patternsofbrain

responsestointerventioncanbecomplex.Thelogicoflinkingactivationor

structuretobehaviourisnotalwaysclear:toremediateabehaviouraldeficit,is

moreactivationorlessactivationbetter?Isthinnercortexorthickercortex

better?Ismoreconnectivityorlessconnectivitybetter?Karmiloff-Smith(2010)

arguedthatforbrainimagingtoadvanceourunderstandingofdevelopment,it

hastofocusonmechanismsofchange,ratherthanstaticsnapshotsofstructural

orfunctionalproperties.Thecomputationalmodelswehaveconsideredare

ordersofmagnitudesimplerthanrealneuralsystems.Yettheygeneratea

vocabularytoconsiderhowmechanismsofchangemaycauseatypical

developmentandconstraintresponsetointervention.Aswesawwithattempts

tolinkThomasandKnowland’s(2014)notionsofcapacityandplasticitytobrain

properties,thecontinuingchallengeistodrivecloserlinksbetweencognitive

modelsandbrainsystems.

Conclusion:Theimportanceofnarrowingthegap

Advancesinmechanistic,computationalmodelsofdevelopmentaldisorders

(andmorewidely,individualvariability)setthefoundationforaninvestigation

ofintervention.Implementationcanprovideadriverforadvancesintheory,

althoughquestionsremainaboutwhetherthesimplificationnecessaryfor

modellingomitskeydimensionsoftheinterventionsituation,notablyitsusual

88

basisinsocialinteraction.Itisimportanttonarrowthegapbetweentheoriesof

deficitandtheoriesofintervention,inordertoplaceinterventiononan

evidence-driven,mechanisticbasis.Practice-basedapproachesnaturally

emphasisebehaviouralconsequencesofinterventionandarelessfocusedon

understandingmechanisms:fortheseapproaches,whatisimportantiswhat

worksbehaviourallyandwhatcanenablesuccess.Thisemphasisonproximate

goalisoneofthereasonsforthegap.However,understandingtheactiveagent

underpinningasuccessfulinterventioniskeytounderstandingwhatwillwork

inwhichcontextsforwhatdisorders,aswellastheflexibilityoftheapplication

ofagiventechnique(Lawetal.,2008).AsNathanandAlibali(2010)argue,to

narrowthegap,weneedacombinationofscaling-upfromtheelemental,

mechanisticmodelsofcognitivescienceandscaling-downfromthecomplexity

ofreal-lifeinterventionsituations.Thatinturnrequiresclinicianstobe

interestedinmechanism,despiteitbeinganunderstandablylowerprioritythan

behaviouraloutcomesforthechildrentheytreat.

89

Acknowledgements

Thisresearchwassupportedby:MRCCareerEstablishmentGrantG0300188;

ESRCgrantRES-062-23-2721;aLeverhulmeStudyAbroadFellowshipheldat

theUniversityofChicago;ajointgrantfromtheWellcomeTrustandthe

EducationEndowmentFoundationtotheUniversityofLondonCentrefor

EducationalNeuroscience;aWellcomeTrustInstitutionalStrategicSupport

FundCareerDevelopmentaward;NationalNaturalScienceFoundationofChina

(61402309);BloomsburyCollegesStudentshipawardedtoRD.Wethank

AnnetteKarmiloff-Smithforhersupportandinspirationinthedevelopmentof

thiswork.

90

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Tables

Table1.Keyconcepts

Causeof

disorder

Intervention

outcomes

Interventionsin

developmental

models

Typesof

simulated

interventions

Monogenic(single

cause)

Doesthe

intervention

generalisebeyond

thetreateditems

tootheritemsor

skills?

Doesintervention

improve

performanceon

thetrainingset?

Behavioural(add

newitemsto/

changefrequency

distributionof

trainingset)

Polygenic

(multiplecauses)

Istheir

maintenanceof

gainsafterthe

intervention

ceases?

Doesintervention

improve

performanceon

thegeneralisation

set(novelitems)

Computational

(alterthe

computational

propertiesofthe

learning

mechanisms)

Compensatory

(encourage

alternate

mechanisms/

pathwaysto

acquiretarget

behaviours)

115

Table2.

Computationalparameter Processingrole Effectsizeof

PDvs.RD

comparison

Numberofinternalunits Capacity .031**

Pruningthreshold Capacity/Regressiveevents .021*

Learningalgorithm Capacity/Plasticity .104**

Lexical-semanticlearningrate Plasticity .024**

Unitdiscriminability Plasticity/Signal .025**

Processingnoise Signal .026**

PD=persistingdelay;RD=resolvingdelay

Scoresshowηp2effectsizesfromANOVAcomparingPDandRDgroups(see

Thomas&Knowland,2014,Table2,forparallelanalysesusinglogistic

regressionmethods)

*Effectreliableatp<.05.**Effectreliableatp<.01

116

Table3.Asimulatedinterventionthatproduceddifferenteffectsonpopulation

meanperformanceandstandarddeviations,dependingontimingandtarget

behaviour.Apopulationof1000networkslearningEnglishpasttense

experiencedaninterventioneitherearly(after50epochs)orlate(250epochs)

indevelopment.Duringintervention,differencesintherichnessofthe

environmentbetweenindividualswereremovedandallnetworksgiventhe

mostenrichedtrainingset.Earlyinterventionimprovedthepopulationmeanfor

regularverbsandreducedvariationduetoceilingeffects.Earlyintervention

improvedpopulationmeanforirregularverbsbutdidnotaltervariation–gaps

betweenindividualsdidnotnarrow.Lateinterventionimprovedpopulation

meanforirregularverbs(thoughlesssothanearlyintervention)butincreased

populationvariation–gapsbetweenindividualswidenedafterintervention.

(Stdev=standarddeviation)

Earlyintervention(epoch50)Meanpopulationaccuracyandvariation

Epoch 25 50 55 60 75 100 250 1000

Epochpostintervention: +5 +10 +25 +50 +150 +950

Regularverbs

Untreated Mean 0.47 0.60 0.61 0.62 0.65 0.67 0.73 0.75

Stdev 0.29 0.27 0.27 0.27 0.26 0.26 0.25 0.23

Treated Mean 0.67 0.73 0.81 0.86 0.94 0.97

Stdev 0.22 0.19 0.14 0.11 0.07 0.05

Irregularverbs

Untreated Mean 0.07 0.15 0.17 0.19 0.23 0.27 0.41 0.49

117

Stdev 0.07 0.13 0.14 0.15 0.17 0.19 0.23 0.26

Treated Mean 0.13 0.16 0.24 0.36 0.64 0.80

Stdev 0.13 0.15 0.17 0.20 0.23 0.22

Lateintervention(250epochs)meanpopulationaccuracyandvariation

Irregularverbs

Epoch 250 255 260 275 300 350 500 750 1000

Postintervention: +5 +10 +25 +50 +100 +250 +500 +750

Untreated Mean 0.41 0.41 0.41 0.42 0.43 0.44 0.46 0.48 0.49

Stdev 0.23 0.23 0.23 0.24 0.24 0.24 0.25 0.26 0.26

Treated

Early

Mean 0.64 0.64 0.66 0.67 0.70 0.75 0.79 0.80

Stdev 0.23 0.23 0.23 0.23 0.23 0.23 0.22 0.22

Treated

Late

Mean 0.34 0.34 0.37 0.41 0.46 0.55 0.60 0.63

Stdev 0.24 0.25 0.26 0.27 0.28 0.29 0.30 0.31

118

Table4.Standardisedbetavaluesforlinearregressionspredictingindividual

differencesintreatmenteffectsizesfollowingtwodifferenttypesofintervention,

normalisationandcompensation,insimulatednetworkswithaconnectivity

over-pruningdisorder(Davis,2017).N=790networks(onlythosefromthe

populationshowingabehaviourallyassessedperformancedeficit).Separate

regressionswerecarriedoutforperformanceonthetrainingsetand

generalisationset.Theshadedareashowsparametersrelatedtothepathological

process,elevatedvaluesofthepruningthreshold,permittinglargerconnections

toberemovedfollowingtheonsetofpruning.

Interventiontype

Normalisation Compensation

Parameter Trainingset

performance

Generalisation

performance

Trainingset

performance

Generalisation

performance

Numberofhiddenunits -0.016 0.012 0.011 0.023

Sigmoidtemperature -0.040 -0.001 -0.098 -0.127

Processingnoise 0.028 0.032 0.007 -0.012

Learningrate -0.065 -0.086 -0.053 -0.016

Momentum -0.014 -0.011 -0.013 -0.011

Initialweightvariance -0.015 -0.002 -0.031 -0.023

Architecture -0.110 -0.101 -0.112 -0.092

Learningalgorithm -0.006 -0.059 -0.011 0.010

Responsethreshold -0.055 -0.063 0.000 0.036

Pruningonset 0.022 -0.007 0.057 0.045

119

Pruningrate -0.006 0.006 -0.062 -0.075

*Pruningthreshold 0.014 0.082 0.039 -0.047

Weightdecayrate 0.021 0.007 0.036 0.025

Sparsenessof

connectivity

0.027 0.065 0.049 0.052

Richnessofenvironment -0.030 -0.036 -0.028 -0.028

Boldshowssignificantatp<.05

*Thisparameterwassettoatypicalvaluestoproducethedevelopmental

disorder

120

FigurecaptionsFigure1.Simulationoftypicalandatypicalpasttenseacquisitionpredicting

long-termcompensatedoutcomes.(a)Empiricaldata(percentaccuracy)for

typicallydevelopingchildrenfromThomasetal.(2001)foragroupoftypically

developingchildrenonapasttenseelicitationtaskforregularverbs,irregular

verbs,novelverbs,andover-generalisationerrors;andforagroupofchildren

withDLDfromvanderLelyandUllman(2001),usingthesameelicitationtask.

Errorbarsshowstandarderrorofthemean.(b)SimulationdatafromThomas

(2005)foraconnectionistpasttensemodel,eitherinatypicalconditionoran

atypicalconditionwherethediscriminationofthesimpleprocessingunitswas

reducedbyloweringthe‘temperature’ofthesigmoidactivationfunction

(1=>0.25).Modeldataareshownatapointthatapproximatelymatchedthe

performanceofthechildren(250epochsoftraining).(c)Simulationdataforthe

projected‘adult’outcomeoftypicalandatypicaltrajectories(5000epochsof

training).Theprojectadultmodelreachedceilingonthetrainingsetbut

retainedatypicalgeneralisation.

(Errorbarsshowstandarderrorover10replicationswithdifferentinitial

randomseeds.)

Figure2.Simulationofresolutionofearlydelay.Groupaverageddevelopmental

trajectoriesfor1000simulatedchildreninamodelofEnglishpasttense

formation,assumingapolygenicmodelforlanguagedelay(Thomas&Knowland,

2014).DelaywasdefinedatTime1asnetworkswhoseperformancefellmore

than1standarddeviationbelowthepopulationmean.Networksweredefinedas

havingResolvingdelayiftheirperformancefellwithinthisnormalrangebyTime

121

5;andashavingPersistingdelayiftheirperformanceremainedbelowthenormal

rangebyTime5(SeeThomas&Knowland,2014,forfurtherdetails).Errorbars

showstandarddeviations.

Figure3:Individualdifferencesinresponsetoanenrichmentintervention.Plot

showstherelationshipbetweentreatmenteffects(changeinproportioncorrect

assessedatendoftraining)andthequalityoftheearlyenvironmentforeach

simulatedchild(varyingbetween0and1)for(a)Regularand(b)Irregularverbs.

Poorerfamilylanguageenvironmentpredictedalargertreatmenteffect.This

effectreducedforinterventionslaterindevelopment,andmoresoforirregular

verbs.(Earlyenrichment=50epochs,Late=250epochs,treatmenteffects

assessedat1000epochs.Linearfitsareshownforallconditions.Early

enrichmentforregularverbswasbetterfitbyalogfunction(R2=.87),while

linearfunctionsexplainedmorevariancefortheotherthreeconditions.)

Figure4.Theinteractionofprocessingdeficitswithrichnessofearlylanguage

environment.Theplotdepictspopulationperformanceonregularverbsearlyin

development(50epochs),splitbyindividualsinimpoverishedorenriched

environments,andstratifiedbyindividualswithdifferentunitdiscriminability

(temperaturevalues0.5-1.5).Interactioneffectwasattrendlevel(p=.06).Error

barsshowstandarddeviations.

Figure5.Networkarchitectureandproblemdomainforamodeldesignedto

explorehowbespokeinterventionsetscansupportlearninginsystemswith

atypicalproperties,inthiscasereducedconnectivity:(a)networkarchitecture;

122

(b)examplecategorisationproblem,with10,000datapoints;thenetworkis

requiredtolearnthecategoryboundaries;(c)thetrainingsetgiventothe

network,sufficienttolearntheproblemundertypicalconditions;(d)anexample

interventionsetaddedtothetrainingsettoaiddevelopmentunderatypical

conditions.Networkshad50internalunits(backpropagationnetwork;learning

rate=.1,momentum=.3,temperature=1)

Figure6.Developmentaltrajectoriesandinternalrepresentationsinatypical

case(TD),anatypicalcasewithlowconnectivity(30%,C=0.3)andthesame

atypicalcaseexperiencinganintervention.Toppanel:Developmental

trajectories;interventioncommencedat100epochs.Theinterventionsetwas

addedtothetrainingsetforthedurationoftraining.Verticallinesshowepochs

atwhichsnapshotsweretaken.Lowerpanels:snapshotsoftheactivation

patternoftheunitforoutputcategory2inthethreecases,whichshouldrespond

onlytothecentralband(seeFigure6).Hotcoloursrepresentmoreactivity.

(Fedoretal.,2013).

Figure7.Amodelcomparinginterventionstoremediateweaknessesorto

improvestrengths.(a)Developmentaltrajectoriesfornamingand

comprehensioninamodelacquiringthemeanings(semantics)andwordnames

(phonology)of400vocabularyitems(averagedover3replications).Thetypical

modelshowstheusualcomprehension-productionasymmetry.IntheWord-

FindingDifficulty(WFD)model,therewasarestrictioninthecapacityofthe

pathwaylinkingsemanticstophonology(from175to70hiddenunits),which

impactedonthedevelopmentofnaming,whilecomprehensiontrajectoriesdid

123

notreliablydifferfromnormal.(b)Earlyinterventiontargetingthenaming

pathway(weakness).(c)Earlyinterventiontargetingthedevelopmentofthe

phonologicalrepresentations,thesemanticrepresentations,orboth(strengths).

(d)Aninterventioncombiningtrainingonstrengthsandweakness.Intervention

comprisedtrainingat5timesthefrequencyonacquisitionofthese

representationscomparedtonamingandcomprehension,beginningat100

epochsandlastingfor100epochs,shownbytheshadedarea.(Alireza,Fedor&

Thomas,2017).

Figure8.Abehaviouralinterventiontoaltercomputationalproperties,inthis

case,toprotectagainstover-pruningofconnectivity.(a)Performanceofagroup

of9networkswithadisordercausedbygreater-than-usuallossofconnectivity

(red),comparedtocontrolnetworks(blue).Alsoshownarethedisorder

networksfollowinganearlybehaviouralintervention(green),lastingbetween

epochs30and70.Effectsoftheinterventionsustaintotheendofdevelopment.

(b)Thenumberofnetworkconnectionsforthedisordergroupinuntreatedand

interventionconditions.Theinterventioncausedinitialaccelerationoflossbut

finalpreservationofagreaterproportionofconnections,associatedwith

improvedcomputationalpower.Mid-training=250epochs;Endoftraining=

1000epochs.

Figure9.Differentcomputationaldeficitsproducingthesamebehavioural

impairmentresponddifferentlytointervention.Datashowtreatmenteffectsof

phonologicalversussemanticinterventionsfortheBestetal.(2015)modelof

word-findingdifficulties,whereequivalentbehaviouralimpairmentswere

124

causedbythreedifferentunderlyingcomputationaldeficits.Theatypical

languageprofilesoftwoindividualchildrenweresimulatedandthen

interventionsapplied(heremeasuredinhowmuchnamingdevelopmentwas

advanced).Theprofileofeachchildwassimulatedreducednetworkconnectivity

(DeficitC),reducedhiddenunits(DeficitH),orashallowersigmoidactivation

functionintheartificialneurons(DeficitT).Interventionresponsesdiffered

dependingonhowthedeficitwasimplemented.Errorbarsshowstandarderrors

of10replicationsofeachintervention(SeeBestetal.,2015,forfurtherdetails).

Figure10.Individualdifferencesinresponsetointervention,followingtwotypes

ofintervention.Developmentaldeficitswerecausedbyanover-pruningdisorder

(Davis,2017).X-axisshowstreatmenteffectintermsofchangeinproportion

correct.(a)Distributionforperformanceonthetrainingsetfollowingthe

normalisationorcompensationtreatment;(b)distributionforperformanceon

thegeneralisationsetfollowingeithernormalisationorcompensationtreatment.

[Populationof1000networks,interventionfordurationof40epochsapplied

earlyindevelopment,epoch30outofalifespanof1000,performancetestedat

100epochs]

125

FiguresFigure1

0%

20%

40%

60%

80%

100%

Controls (9;10) gSLI (11;2)

Res

pons

es

Condition

Regular verbs

Irregular verbs

Over-generalisation errors Regularisation of novel forms

0%

20%

40%

60%

80%

100%

Child typical simulation Child atypical simulation

Res

pons

es

Condition

Regular verbs

Irregular verbs

Over-generalisation errors Regularisation of novel forms

0%

20%

40%

60%

80%

100%

Adult typical simulation Adult atypical simulation

Res

pons

es

Condition

Regular verbs

Irregular verbs

Over-generalisation errors Regularisation of novel forms

DLD

(a)Childempiricaldata

(b)Childsimulationdata (c)Projectedadultsimulationdata

126

Figure2

0

0.2

0.4

0.6

0.8

1

Time1 Time2 Time3 Time4 Time5

Prop

or%on

correct

Development

Typicallydeveloping(N=713)

PersisAngdelay(N=118)

Resolvingdelay(N=169)

127

Figure3

y=-0.6933x+0.5748R²=0.73247

y=-0.4975x+0.421R²=0.57513

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1 1.2

Treatm

ente

ffecta

tthe

end

oftraining

Qualityofearlyenvironment

Earlyenrichment

Lateenrichment

Linear(Earlyenrichment)

Linear(Lateenrichment)

y=-0.7023x+0.675R²=0.59999

y=-0.1781x+0.2278R²=0.06446

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1 1.2

Treatm

ente

ffecta

tthe

end

oftraining

Qualityofearlyenvironment

Earlyenrichment

Lateenrichment

Linear(Earlyenrichment)

Linear(Lateenrichment)

Regularverbs

Irregularverbs

(a)

(b)

128

Figure4

0"

0.2"

0.4"

0.6"

0.8"

1"

Impoverish"env" Enriched"env"

Prop

or%o

n'correct'

'

0.5"

0.75"

1"

1.25"

1.5"

Impoverished environment

Enriched environment

129

Figure5

(a)Networkarchitecture(b)Fullproblem(c)Trainingset(d)Interven<onset

130

Figure6

131

Figure7(a)

(b)

(c)

0%

20%

40%

60%

80%

100%

1 10 30 50 70 90 125 175 250 350 450 600 800 1000

Prop

ortio

ncorrect

Development(epochsoftraining)

TypicalComprehension

TypicalNaming

WFDComprehension

WFDNaming

0%

10%

20%

30%

40%

50%

60%

1 10 30 50 70 90 125 175 250 350 450 600 8001000

Prop

ortio

ncorrect

Development(epochsoftraining)

Typicalnaming

Impairednaming

InterventionNamingpathway

0%

10%

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30%

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60%

1 10 30 50 70 90 125 175 250 350 450 600 8001000

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ortio

ncorrect

Development(epochsoftraining)

Typicalnaming

Impairednaming

InterventionPhonology

InterventionSemantics

InterventionPhonology+Semantics

132

(d)

0%

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1 10 30 50 70 90 125 175 250 350 450 600 8001000

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Figure8(a) (b)

0

0.1

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1

Mid-training Endoftraining

Prop

or%on

correct

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Disorder(untreated)

IntervenCon

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log(nu

mbe

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s)

Epochsoftraining

disorder

interven4on

control

134

Figure9

-20

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DeficitC DeficitH DeficitT DeficitC DeficitH DeficitT

Casestudy1 Casestudy2

Advance(in

epo

chs)inre

aching

90%accuracy

Semantictherapy

Phonologicaltherapy

135

Figure10

(a)

(b)

Trainingsetperformance

Generalisa5onsetperformance

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Compensa4oninterven4on

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Compensa5oninterven5on

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