Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing

236 Pages·1993·16 MB·English

by  Axel Cleeremans

MechanismosfI mplicLieta rnign
NeuraNle tworMko delinagn dC onnectionism
JeffrLe.Ey l man, Editor
ConnectiMoondieslti anngdB raiFnu nctiTohneD: e velopIinntge rface
StepheJno sHea nsona ndC arRl .O lsone,d itors
NeuraNle twoDreks igann dt heC omplexoifLt eya rniJn.Sg t epheJnu dd
NeuraNle tworfkosCr o ntrWo.l T homasM illeRirc,h ardS uttoann,d
PaulJ .W erbose,d itors
TheP erceptofiM ounl tipOlbej ecAt Cso:n nectionist Approach
MichaMeolz er
NeuraClo mputatoifPo ant teMront ioMno:d eliSntga goesf M otoin
Analysiints h eP rimaVties uCaolr tMeaxr gareEtu phrasia Sereno
SubsymboNlaitcu aLla nguaPgreo cessAinn Ign:t egrMaotdeedol f
Scripts, LaenxdMi ecmoonr,Riy s toM iikkulainen
Analogy-MakaisnP ge rceptiCoonm:p uAt Meord eMle laniMei tchell
MechanisomfIs m pliLceiatr niCnogn:n ectiMoondieslotsf S equence
ProcessAixneglC leeremans
MechanismosfI mplicLieta rnign
ConnectioMnoidsetl osf S equenPcreo cessing
AxelC leeremans
A BradfoBrodo k
TheM IT Press
CambridgMea,s sachusetts
LondonE,n gland
© 199Ma3s sachusIentsttistu toefT echnology
All righrtess erveNdo. p arotf t hibso okm ayb er eproduinc eandy formb ya nye lectronic
orm echanimceaaln s( includpihnogt ocopyriencgo,r dinogr,i nformatsitoonr aagned
retrievwailt)h opuetr missini ownr itingfr om thep ublisher.
This bookw ass eitn P alatibnoyTh e MITP resasn dw asp rinteandd bound in the United
StatoefsA merica.
LibraorfyC ongreCsast aloging-in-PubliDcaattai on
CleeremaAnxse,l .
Mechanismosfi mpliclietarnin g :c onnectiomnoidsetl osfs equenpcreo cess/i ng
AxelC leeremans.
p. em.- (NeurNaeltw orkm odelianngd c onnectionism)
"AB radfobrodo k,"
Includbeisb liograrpehfiecraeln acnedis n dex.
ISBN0 -262-03205-B
1N.e uranle twor(kCso mputsecri enc2eI.)m pliclietarnin g. 3C.o nnectmiaocnh ines.
IT.i tlIIe..S eries.
QA7.6B.7C541 993
006.3'3-dc20 92-35739
CIP
A I'enfant invisible
Contents
SeriFeosr eworidx
Preface xi
Acknowledgmenxtisii
Chapte1r
ImpliLceiatrn inEgx:p loratiinoB nass iCco gnitio1n
Introduct1i on
EmpiriSctauled sio fI mpliLceiatr nin5g
Modelasn dM echanis1ms9
Chapte2r
The SRN ModelC:o mputatioAnsaple cotfsS equence
Processi3n5g
LearnianF gi nite-GSrtaamtmea r3 9
DiscovearnidnU gs inPga tIhn formati4o9n
Learnin5g8
Encoding NoCnolnotceax6lt3
Discussi7o0n
Chapte3r
SequenLceea rniansga ParadifgomrS tudyiInmgp licit
75
Learning
LearnitnhgSe t ructoufEr veen tS equenc7e6s
Experime1n t8 1
Experime2n t9 1
Simulatoifto hneE xperimenDtaatla 9 5
GenerDails cussi1o0n3
Conclusi1o1n2
Chapte4r
SequenLceea rniFnugr:t hEexrp lorati1o1n3s
SequenLceea rnianngdM emoryD isorde1r1s4
AttentainodnS equenScter uctu1r1e6
ElementSaeryq uenLceea rnianngdt hEeff ecotfsE xpliKcniotw ledg1e2 3
GenerDails cussi1o3n4
viii Contents
5
Chapter
137
EncodiRnegm otCeo ntext
Long-DistCaonnctei ngeanncdiP erse dictionL-eBaarnsiendg1 37
CompariwsiotnOh t heArr chitecftoruS reqeuse ncPer ocess1in3g9
An EmpiriTceaslt 1 44
Experim3e n1t5 7
Discussi1o64n
6
Chapter
169
ExpliSceiqtu enLceea rning
An ExpliPcrietd icTtaisokn1 69
Tranesrtf oN ew Materi1al8 3
7
Chapter
189
GenerDails cussion
Prediction-RaenldCe ovnatnecRxeept r esentat1i9o1n
AttentAiwoanr,e neasnsdt, he R oloef E xpliKcniotw ledg1e9 4
ConsciaonudsU nconsciKonuosw ledg1e97
Tranesraf ndA bstract2i0o0n
On Modelin2g0 5
Conclusi2o0n6
209
Notes
231
References
221
Index
SeriFeosr eword
Theg oaolf t hisse riNeesu,r aNle tworMko delianngd C onnectionism,
ist oi dentainfdyb rintgot hep ubltihceb eswto rki nt hee xcitfiinegl d
ofn euranle tworakn dc onnectiomnoidsetl inTgh.e s eriiensc ludes
monograpbhass eodn d issertaetxitoennsd,re edp orotfws o rkb yl ead­
ersin thef ieledd,i tevdo lumeasn dc ollectoinot nosp iocfss pecial
intermeasjto,rr e ferewnocrek s, uanndde rgradaunadtg er aduate-level
textTsh.ef ieilsdh ighilnyt erdiscipalnidwn oarrkyps,u blishine tdh e
seriweisl tlo ucohn a widev arieotfyt o pircasn gifnrgo ml ow-level
visitoont hep hilosophfiocuanld atiooftn hse oroifer se presentation.
JeffrLe.Ey l manE,d itor
AssociEadtiet ors:
JameAsn dersoBnr,o wnU niversity
Andrew BartUon,i versoifMt ays sachusAemhtetrss,t
GaryD ellU,n iversoiflt lyl inois
JeromFee ldmaUnn,i versoifCt ayl ifornBiear,k eley
StephGerno ssbeBrogs,t oUnn iversity
StephHeann sonP,r inceUtniovne rsity
GeoffrHeiyn toUnn,i versoifTty o ronto
MichaJeolrdaMnf,T
JameMsc ClellaCnadr,n egMieel loUnn iversity
DomenicPoa riIsnis,t itduiPt soi colodgeilCa N R
DaviRdu melhaSrtta,n foUrndi versity
TerrenSceej nowsThkei ,S alIkn stitute
PauSlm olensUknyi,v ersoifCty o lorado
StephPe.nS tihc,R utgeUrnsi versity
DaviTdo uretzCkayr,n egMieel loUnn iversity
DaviZdi pseUrn,i versoifCty a liforSnainDa i,e go
Preface
Il ikteo t hinokft hibso oka sa booka bouetl ementlaerayrnin g pro­
cesseesv,e nt hougshu cahc laitmog eneraliisut nyq uestionoavbe­lry
stateIdn.d eetdh,i bso oki sr ealalbyo uatp henomencoanl liemdp licit
learning-p"rtohcee bsysw hickhn owledagbeo utth er ule-governed
complexiotfti heess timuleunsv ironmaerneat c quiriendd ependently
1989).
ofc onsciaotutse mpttods o s o"( Reber, Butt hinkionfig m plicit
learniansag n e lementaabriyl tiote yx traacntdt op rocesstsr ucture­
rathetrh ana ss omem ysterifoaucsu lftoyrl earninwgi thouetv en
knowinigt -hatsh em eriotf p uttitnhgee mphasoinst hen aturoef
processriantgh tehra onn t hen atureo fkn owledgeT.h ipse rspective
formtsh eb asimco tivatfioortn h ibso okt:o e xplowrhea tk indosf
mechanismmasyb es uffictioae cncto ufnotir m plilceiatr ndiantgaa ,n d
toi nstanttihaetsmeee chanisimnts h ef ormo fc omputatimoondaell s
ofp erformaTnhcieas.p proatcoht hefi elids d ifferfernotmp revious
researicnsh e verwaaly sF.i rsetv,e tnh ougihm plilceiatr nirnegs earch
isr apidglayi niinngc rearseecdo gnittihoefn i,e lidss timlalr rebdy
numeroudse bataebso utth en aturoefk nowledagceq uiriemdp licitly.
Eversyo o ftenne,w s tudieasp peatrh afto rcues t or econsitdheer
validoiftp yr evious methodtooel loigceiixetps l icit knowledge (e.g.,
PerruchGeatl,l eg&o S,a vy1,9 9P1er;r uc&h eAtm orim1,9 9o2rt) h,a t
demonstrtahtaept r eviouessltya blirsehseudld tons o th olvde rwye ll
whenl earninogrt esticnogn ditiaorneas l tereevde ns ligh(tDluyl any,
Carlso&n D,e wey1,9 8P4er;r uc&h ePta ctea1u9,9 P0e)rh.a tphsem ost
enduridnegb athea st od ow itrhe solvtihnefg o llowqiunegs tiHoonw:
muchc onsciaocucse dsosw er ealhlayv teo k nowledtghea tth ee xperi­
mentecrl aimwse acquiriemdp liciTtold ya?t et,h iqsu estihoansn ot
beena nswerseadt isfactIob reilliyet.vh ee riesa v ergyo odr easofno r
thiIsti: st hew ronqgu estitooan s kS.t uditehsar te vemaelt hodological
shortcomianrgeis m portabnuttt, h efya itloh elfpo rmultatheer ight
kinodf r esearscthr ateIgnsy h.o rItb ,e lietvheat th iksin do fa pproach
isn otv eryp roductiIvtme a.y j usbte i mpossitbold ei sentantghlee
contribuotfie oxnpsl iacnidit m plilceiatrn itnogp erformainnac g ei ven
tasks,i mplbye causwee cannottu rn offc onsciousinnen sosr mal
xii Preface
subjec(tNse.ws tudiweist ha mnesiacnsd o tehrb rain-damapgae­d
tiensthso wm uchp romiisneh elpitnogr esolsvoem eo ft heesi ssues,
however.)
Ana pproatchha hta sb eesno mewhamto re succeisnts hfirusel s pect
hasb eetno t rtyo i dentify experimentuanld ecwroh nidciihmt piloincsi t
learninpgr ocesasreems o stl iketlooy p erate-ifnosrt anbcyee ,x plor­
ingp aradigimnws h icohn em ayo btadiins sociabteitwoenesnp erfor­
mancea nda bilittovy e rbaluinzdee sro mec onditibountns o to thers
(e.gB.e,r r&y B roadbe1n9t8,1 49,8T8h)ih.sa sl edt os oliadd vanciens
ourkn owledgoef w hicfha ctoarfsf epcetr formaanncdew hicahf fect
abilittovy e rbalbiuzteo ;fc oursieti, ss tiplols sibtlooe b jetchta tth e
dissociarteisounla trsea reflecmtoiroeno ft hep articucloanrd itions
usedt hano fan actucaolg nitdiivset incbteitwoene ntw o modeso f
learnianngdp rocessIni ntgh.em eantimwee, h avem adev eyr little
progreosns t hen aturoef i mplicpirto cessiitnsge Wlhfa.t a ret he
mechanisimnsv olvCeadn?w ep ropoasd ee taicloemdp utatimoondaell
ofa tl eassotm ea speocfti mplilceiatr nipnegr formatnhcaeit sa blteo
learanss ubjedcota sn dt hauts emse chanissmoes l ementtahraytt h e
complemxa chineorfcy o nsciousdnoeesnsso ta ppeatrob en ecessary?
In thibso okI,p reseanntd e xplosruec ha modeli,n t hec onteoxft
sequence-procteassskiTsnh.ge m odeli sb y no meansa complete
architecftourir mep lilceiatr niinngd;e eidtl, a ckmsa nyf eatutrheast
wouldm akei tg eneraanld i se vend emonstrawbrloyn gi ns ome
instanBcuetsi .ti sa fi rsstt eipn t hed irectoifio dne ntifpylianugs ible
mechanisfmosri mplilceiatrn inpge rforma.n tcIhe intkh aotn cew e
havea repertooifsr uec hm echaniswmes w,i lble i na muchb etter
posititoosn t aarttt acktihnerg e alhlayr dq uestiosnusc,ha sw hatt he
relationissbh eitpw eeinm pliacnidet x plipcriotc esshionwgc ,o nscious
we areo fk nowledagceq uiruendd eri mpliccointd itioornw sh,a t
exacttlhyen aturoefk nowledagceq uiriemdp licmiatylb ye .