1Lan

guag

e

How

canwepossib

lysim

ulate

languag

eab

ilitiesin

neu

ralterm

s?...

•(W

ecan

’t...Butman

yasp

ectsoflan

guag

eare

not“sp

ecial”):

•Justan

other

setofinput/outputpath

s.

•Lev

els:phonem

es/letters,

words,p

hrases,sen

tences,

parag

raphs,an

dbey

ond..

•Hugecombinato

rialpower:

distrib

uted

repsover

time!

2Biological

Substrates

ofLan

guag

e

Broca’s

Wernicke’s

FrontalP

arietal

Occipital

Temporal

-Broca’s

=sp

eechoutput,sy

ntax

,gram

mar

(surface

productio

n):

activemain

tenan

ceofcontex

tto

perfo

rmsyntactic

processin

g

-Wern

icke’s

=sem

antic

compreh

ensio

n+output(deep

):

interco

nnected

overlap

pingdistrib

uted

info

aboutsem

antics

3Trad

itional

view

oflan

guag

e

•Lan

guag

ecompeten

cedefi

ned

byknowled

geofrules

andexceptions

(eg.ibefo

reeexcep

tafter

c)

•Knowled

geab

outwordsissto

redin

acen

tralmen

tallexicon(dictio

nary

)

•Each

word

has

alex

icalrep

resentatio

nthat

islin

ked

to

inform

ationab

outits

orth

ograp

hy,phonology,sem

antics

4Neu

ralnet

/Connectio

nist

View

ofLan

guag

e

•Lan

guag

eisan

other

setofinput-o

utputmap

pings(eg

orth

ograp

hyto

phonology,o

rthograp

hyto

seman

tics)

•These

map

pingsare

trained

upusin

gthesam

elearn

ing

algorith

msused

elsewhere

(e.g.,v

ision)

•Thesam

epath

way

shan

dle

both

rules

andexcep

tions

•Hard

totell

what

is“reg

ular”

vs“ex

ceptio

nal”

–reg

ular:

clown,d

own

–excep

tion:blown..

butblowngoes

with

grown

•Distrib

uted

lexico

n:Knowled

geab

outwordsisem

bodied

in

reciprocal

map

pingsbetw

eenphonology,orth

ograp

hy,

seman

tics–

thereis

nocentral“w

ordrepresentation”

5Questio

ns

•What

gen

eralprocesses

areinvolved

inread

ing,an

dhow

do

these

sometim

esfail

(e.g.,in

dyslex

ia)?

•How

areweab

leto

read“cat”,“y

acht”,an

d“n

ust”?

•Whydokidssay

“Igoed

tosch

ool”

afterfirst

saying“I

wen

t”?

•How

dowordscometo

mean

anything?

•How

dowegobey

ondwordsto

senten

ces?

6Distrib

uted

Lexico

nModel

Orthography

Phonology

Sem

antics

Hidden

Hidden

Hidden

7Distrib

uted

Lexico

nModel

&Read

ing

Orthography

Phonology

Sem

antics

Hidden

Hidden

Hidden

TwoRoutes

•Direct

route:

orth

ograp

hyto

phonology

•Indirect

route:

orth

ograp

hy→

seman

tics→

phonology

8Sim

ulatin

gDifferen

tKindsofDyslex

ia

Orthography

Phonology

Sem

antics

Hidden

Hidden

Hidden

Phonological:

nonwords(“n

ust”)

impaired

.

Deep:

phono+sem

antic

errors

(“dog”as

“cat”)+

visu

alerro

rs(“d

og”as

“dot”)

+more

errors

with

“truth”(ab

stract)than

“chair”

(concrete)

Surface:

nonwordsOK+sem

antic

accessim

paired

+diffi

culty

readingexcep

tionwords(“y

acht”)

+visu

alerro

rs.

9TheModel

tw

y

ad

eg

kn

rs

il

no

pr

sv

lo

rt

ac

eg

pr

st

wa

ei

cd

ef

gh

ln

OS

_Hid

Sem

anticsSP

_Hid

w

st

pr

ln

hk

fg

−d

tw

pr

ln

hk

fg

−d

tw

pr

ln

hk

fg

−d

io

ae

OU

EI

@A

z

tv

rs

np

kl

gj

−d

z

tv

rs

np

kl

gj

−d

z

tv

rs

np

kl

gj

−d

Orthography

OP

_Hid

Phonology

Train

edonall

path

way

s(orth

o⇔

phonoetc)

for40

4-lettermonosyllab

icwords(eg

flag

,star)

Concrete

&ab

stractwordsuse

differen

tpoolsofsem

antic

units

Abstract

wordsactiv

atefew

ersem

antic

units

than

concrete

words

10Corpusan

dSem

antics

Concrete/A

bstract Semantics

Y

X0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

0.0010.00

20.0030.00

40.00

tentflea

lassfacegrin w

avereedloon hare deer

hindtartflan

plea hint plan flaw lackneed

deedtactease past gain

lossfactrole

stay hire loan rent costw

ageropepostlockcase

starcoatflag

seman

ticrep

smad

eupofdistrib

uted

features

(e.g.concrete

=liv

ing;ab

stract=has-d

uratio

n)

11[dyslex

.proj]

12Sem

antic

Path

way

Lesio

ns,In

tactDirect

0.00.2

0.40.6

0.8L

esion Proportion

0 5 10 15 20

Errors

Visual

Vis + Sem

Semantic

Blend

Other

0 5 10 15 20

Errors

0.00.2

0.40.6

0.8L

esion Proportion

Concrete

Abstract

OS_H

id

SP_H

idSP

_Hid

OS_H

id

-visu

alerro

rswith

seman

ticpath

way

lesions;nosem

antic

errs!

-(m

ore

forconcrete

than

abstract..)

13Sem

antic

Path

way

Lesio

ns,In

tactDirect

Surface

Dyslex

ia

0.00.2

0.40.6

0.8L

esion Proportion

0 5 10 15 20

Errors

Visual

Vis + Sem

Semantic

Blend

Other

0 5 10 15 20

Errors

0.00.2

0.40.6

0.8L

esion Proportion

Concrete

Abstract

OS_H

id

SP_H

idSP

_Hid

OS_H

id

-visu

alerro

rswith

seman

ticpath

way

lesions;nosem

antic

errs!

-(m

ore

forconcrete

than

abstract..)

14Sem

antic

Path

way

Lesio

ns,L

esioned

Direct

0.00.2

0.40.6

0.8L

esion Proportion

0 5 10 15 20

Errors

Visual

Vis + Sem

Semantic

Blend

Other

0 5 10 15 20

Errors

0.00.2

0.40.6

0.8L

esion Proportion

Concrete

Abstract

OS_H

id

SP_H

idSP

_Hid

OS_H

id

-multip

leerro

rstypes

-more

abstract

seman

ticerrs

than

concrete

15Sem

antic

Path

way

Lesio

ns,L

esioned

Direct

Deep

Dyslex

ia

0.00.2

0.40.6

0.8L

esion Proportion

0 5 10 15 20

Errors

Visual

Vis + Sem

Semantic

Blend

Other

0 5 10 15 20

Errors

0.00.2

0.40.6

0.8L

esion Proportion

Concrete

Abstract

OS_H

id

SP_H

idSP

_Hid

OS_H

id

-multip

leerro

rstypes

-more

abstract

seman

ticerrs

than

concrete

16Direct

Path

way

Lesio

n

0.00.2

0.40.6

0.8L

esion Proportion

0 5 10 15 20

Errors

Visual

Vis + Sem

Semantic

Blend

Other

0 5 10 15 20

Errors

0.00.2

0.40.6

0.8L

esion Proportion

Concrete

Abstract

Full Sem

Full Sem

No Sem

No Sem

-minordirect

dam

age:

justviserro

rs-more

dam

age:

seman

ticerrs

⇒deep

dyslex

iaev

enwith

FullSem

1718

1920

Abstract

vsConcrete:

Summary

•Sem

antic

path

way

lesionshurtconcrete

wordsmore

than

abstract

words

•Concrete

wordsare

more

strongly

represen

ted(m

ore

units

active)

than

abstract

wordsin

thesem

antic

path

way

•Learn

ingisafunctio

nofactiv

ation,so

thesem

antic

path

way

learnsmore

aboutconcrete

words

•Themore

seman

ticpath

way

learnsab

outconcrete

wordsthe

lessdirect

path

way

learns

•Theless

thedirect

path

way

learns,th

eless

itisab

leto

support

perfo

rman

ceonits

own

•With

fulldirect

path

way

lesions,themodel

mak

esmore

seman

ticerro

rsforab

stractthan

concrete

•Abstract

wordshav

eless

distin

ctivesem

antic

repsthan

concrete

words

•Themodel

ismore

likely

tofall

into

wrongsem

antic

attractor

forab

stractwords

21Read

ing:Distrib

uted

Lexico

nModel

Orthography

Phonology

Sem

antics

Hidden

Hidden

Hidden

•Distrib

uted

reps(notlocalized

toonereg

ion).

•Interactiv

e(notmodules),lead

sto

interestin

gdivisio

nsof

labor.

22Questio

ns

•What

gen

eralprocesses

areinvolved

inread

ing,an

dhow

do

these

sometim

esfail

(e.g.,in

dyslex

ia)?

Distrib

uted

lexico

n(orth

o,p

hono,sem

)

•How

areweab

leto

read“cat”,“y

acht”,an

d“n

ust”?

•Whydokidssay

“Igoed

tosch

ool”

afterfirst

saying“I

wen

t”?

•How

dowordscometo

mean

anything?

•How

dowegobey

ondwordsto

senten

ces?

23Distrib

uted

Lexico

nModel

Orthography

Phonology

Sem

antics

Hidden

Hidden

Hidden

24Reg

ularities

&Excep

tions:A

Contin

uum

Reg

ularities

inpronunciatio

nare

often

partial,co

ntex

tdep

enden

t:

bint

iin

mint,h

int,stin

t,...(reg

ular)

vspint(ex

ceptio

n)

butalso

:mind,fi

nd,h

ind,...

(regular)

mine,fi

ne,d

ine,...

(regular)

Pronunciatio

ndep

endsoncontex

t.

Excep

tionsare

extrem

eofcontex

tdep

enden

t.

Need

aran

geofcontex

tdep

enden

cyforreg

ulars

andexcep

tions.

25

2627

28Phonological

Rep

resentatio

ns

•Sam

e7slo

tvowel-cen

teredrep

resentatio

nsas

befo

re:

–face

=fffA

sss

–grin

=grrin

nn

–star

=sttarrr

–post

=pppOstt

•Excep

tinstead

ofusin

galocalist

repofeach

phonem

e,weuse

adistrib

uted

rep

•Thisallo

wsusto

represen

tthefact

that

phonem

esvary

intheir

similarity

toonean

other...

29

30Read

ingModel

Ortho

Ortho_C

ode

Hidden

Phon

detailed

model

ofthe“d

irect”read

ingpath

way

(orth

o→

phono)

-train

edto

pronounce

largeset

ofreg

ular

&excep

tionwords

-gen

eralizationtestin

g:nonwords(eg

,nust)

3132

Nonword

Perfo

rman

ce

Reg

ularity

tests(G

lush

ko):bint→

/bint/

Pseu

do-homophones

(McC

ann&

Besn

er):

phoyce

→/fYs/

,choyce

→/CYs/

Match

edreg

ularity

/excep

tioncases

(Tarab

an):

Highfreq

:poes

→/pOz/

,goes

→/gOz/

,does

→/dˆz/

Low

freq:mose

→/pOs/

,poes

→/pOz/

,lose

→/lU

z/

Nonword

Set

Model

PMSP

Peo

ple

Glush

koreg

ulars

95.397.7

93.8Glush

koexcep

tionsraw

79.072.1

78.3Glush

koexcep

tionsalt

OK

97.6100.0

95.9McC

ann&

Besn

erctrls

85.985.0

88.6McC

ann&

Besn

erhomoph

92.3n/a

94.3Tarab

an&

McC

lelland

97.9n/a

100.01

33Read

ingSummary

•Onenetw

ork

canlearn

both

regular

pronunciatio

nsan

d

excep

tions,an

ditcan

gen

eralizeproperly

tononwords

•Netw

ork

learnsagoodmix

ofcontex

t-dep

enden

tan

d

contex

t-invarian

trep

resentatio

nsonits

own

34Questio

ns

•What

gen

eralprocesses

areinvolved

inread

ing,an

dhow

do

these

sometim

esfail

(e.g.,in

dyslex

ia)?Distrib

uted

lexico

n

(orth

o,p

hono,sem

)

•How

areweab

leto

read“cat”,“y

acht”,an

d“n

ust”?

Ran

geof

contex

tdep

enden

trep

s&

contin

uum

ofreg

ularity

-excep

tion

•Whydokidssay

“Igoed

tosch

ool”

afterfirst

saying“I

wen

t”?

•How

dowordscometo

mean

anything?

•How

dowegobey

ondwordsto

senten

ces?

35Past

Ten

seSim

ple

model

Input:Someunits

represen

tword

iden

tity,others

inflectio

n

Output:Phonology

Sem

antics

Phonology

pasttense

ed(strong correlation)

36Learn

ingthePast

Ten

seofVerb

sRumelh

art&

McC

lelland(1986)

Child

renexhibitthree

stages

ofdev

elopmen

t:

•Stag

e1:

Small

number

ofverb

sin

past

tense

–Very

highfreq

uen

cy

–Majo

rityare

irregular

–Correct

perfo

rman

ce

–Exam

ples:

came,g

ot,g

ave,lo

oked

,need

ed,took,w

ent

•Stag

e2:

Larg

ernumber

ofverb

s

–Mostly

regular

–Exam

ples:

wiped

,pulled

–Can

gen

eratepast

tense

forinven

tedverb

s(rick

→rick

ed)

–Over-reg

ularize

wordsthat

were

correct

instag

e1(goed

)

•Stag

e3:

Reg

ular

andirreg

ular

form

scoexist

–Reg

ained

use

ofcorrect

irregular

form

s

37Past

Ten

se:U-Shap

edCurve

Thisistheinterestin

gtarg

etdev

elopmen

talphen

omen

on:

23

45

67

89

10Y

ears of Age

0.90

0.92

0.94

0.96

0.98

1.00

1−Overreg Rate

Overregularization in A

damEventualcorrect

performance

assumed

38U-Shap

edHisto

ry

Initially

:explain

edin

termsofsep

arate,overzealo

usrule

system

.

Then

:Rumelh

art&

McC

lelland,U-sh

aped

curvebased

onslo

w

netw

ork

processin

gofreg

ularities.

BUT:train

edirreg

ulars

first,th

enreg

s.

(much

contro

versy

ensu

es)

Later:

Plunkett

etal.,etc,m

anipulate

enviro

ingrad

edway

(contin

uously

addreg

verb

sto

trainingset

instead

ofall

atonce).

39U-Shap

edModel

inLeab

ra

Can

weget

aU-sh

aped

curvewith

outbuild

ingtheexplan

ation

into

theen

viro

nmen

t?

•Key

:all

prev

iousconnectio

nist

accounts

used

feedforw

ard,

back

propnets

–noattracto

rdynam

ics.

•Problem

:back

prop(pure

error-d

riven

learning)lead

sto

steady

decrease

inerro

r;hard

toexplain

increase

inerro

r...

•Interactiv

ity,competitio

n&

Heb

bian

learningproduce

netw

ork

that

isin

dynam

icbalan

cebetw

eenreg

&irreg

map

pings.

•Small

tweak

scan

shift

itoneway

ortheother

(prim

ingmodel).

Strin

gofreg

ular

trialswill

leadto

overreg

ularizatio

n...

40ThePast

Ten

seModel

Sem

antics

Hidden P

honology

41Phonological

Rep

resentatio

ns

•Sam

e7slo

wvowel-cen

teredrep

sas

befo

re:

–face

=fffA

sss

–grin

=grrin

nn

–star

=sttarrr

–post

=pppOstt

•Small

additio

n:8th

slotto

represen

textra

inflectio

n

–started

-sstarttD

–startin

g=sstarttG

42ThePast

Ten

seModel

Inflectio

nReg

sfxReg

ular/

Irregular

exam

ples

Base

–Iwalk

tothesto

redaily.

Igoto

thesto

redaily.

Past

-edIwalk

edto

thesto

reyesterd

ay.Iwen

tto

thesto

reyesterd

ay.3rd

pers

sing

-sShewalk

sto

thesto

redaily.

Shegoes

tothesto

redaily.

Progressiv

e-in

gIam

walk

ingto

thesto

renow.

Iam

goingto

thesto

renow.

Past

particip

le-en

Ihav

ewalk

edto

thesto

rebefo

re.Ihav

egoneto

thesto

renow.

43Past

Ten

seResu

lts

025000

5000075000

Num

ber of Words

0.0

0.2

0.4

0.6

0.8

1.0

1−OR, Responses

Overregularization in B

p

OR

Responses

b)

a)

025000

5000075000

Num

ber of Words

0.0

0.2

0.4

0.6

0.8

1.0

1−OR, Responses

Overregularization in Leabra

OR

Responses

44Past

Ten

seResu

lts

Bp

L H0

L H001

L H005

0 25 50 75

100

Responses

Early C

orrect Responding

To 1st O

RT

o 2nd OR

Bp

L H0

L H001

L H005

0 50

100

150

200

Overregularizations

Total O

verregularizations

45Past

Ten

se:Summary

•Leab

rapast

tense

model

showsthat

youcan

get

U-sh

aped

pattern

from

amodel

with

outman

ipulatin

gthetrain

ing

enviro

nmen

t

•Ach

ieves

asu

bstan

tiallev

elofcorrect

respondingprio

rto

onset

ofoverreg

ularizatio

n

•Overreg

ularizatio

ncontin

ues

atalow,sp

orad

icrate

over

an

exten

ded

perio

doftim

e.(does

thisev

entually

goaw

ay?)

46

Past

Ten

se:Summary

•Thecontro

versy

contin

ues!

•Rules

accountpred

ictsthat

past

tense

acquisitio

nwill

be

sudden

andthat

itwill

beinsen

sitiveto

seman

ticfacto

rs...

•Counterex

ample

from

Ram

scar(2002):

–“frin

k”in

thecontex

tof“d

rink”=fran

k

–“frin

k”in

thecontex

tof“b

link”=frin

ked

Ram

scar(2002)

47Past

Ten

se:Summary

•Thecontro

versy

contin

ues!

•Rules

accountpred

ictsthat

past

tense

acquisitio

nwill

be

sudden

andthat

itwill

beinsen

sitiveto

seman

ticfacto

rs...

•Counterex

ample

from

Ram

scar(2002):

–“frin

k”in

thecontex

tof“d

rink”=fran

k

–“frin

k”in

thecontex

tof“b

link”=frin

ked

•Neu

roim

agingirreg

&reg

past

tenses

show

overlap

pingneu

ralactiv

ation(Jo

anisse

&Seid

enberg

,2005)

–nottw

osystem

s.

•See

alsoMcC

lellandvsPinker

smack

downin

Tren

dsin

Cognitiv

eScien

ces(2002)

48Questio

ns

•What

gen

eralprocesses

areinvolved

inread

ing,an

dhow

do

these

sometim

esfail

(e.g.,in

dyslex

ia)?

Distrib

uted

lexico

n(orth

o,p

hono,sem

)

•How

areweab

leto

read“cat”,“y

acht”,an

d“n

ust”?

Ran

geof

contex

tdep

enden

trep

s&

contin

uum

ofreg

ularity

-excep

tion

•Whydokidssay

“Igoed

tosch

ool”

afterfirst

saying“I

wen

t”?

Dynam

icbalan

cebetw

eenreg

ular

&excep

tionmap

ping

•How

dowordscometo

mean

anything?

•How

dowegobey

ondwordsto

senten

ces?

49How

DoWordsCometo

Mean

Anything?

•What

Gives

WordsTheir

Mean

ing?

•Where

Does

thisMean

ingComeFrom?

50What

Gives

WordsTheir

Mean

ing?:

Distrib

uted

Sem

antics

orthographyphonology

action oriented

tactile

visual

auditory

kinestheticform

color

3−D

telephone

footbrake

kettle

velvet

cloudsthunder

Sem

antics

isdistrib

uted

across

specialized

processin

gareas.

51Where

Does

thisMean

ingComeFrom?:

Correlatio

nal

Sem

antics

Heb

bian

learningen

codes

structu

reofword

co-occu

rrence.

Sam

eidea

as:

•V1recep

tivefield

learning:learn

thestro

ngcorrelatio

ns.

•Sim

ilarto

Laten

tSem

antic

Analy

sis(LSA)

Input

Hidden

52[sem

.proj]

53Multip

le-Choice

Quiz

0.neu

ralactiv

ationfunctio

n5.

attentio

nA

spikingrate

codemem

bran

epoten

tialpt

Acompetitio

ninhibitio

nselectio

nbinding

Binteractiv

ebidirectio

nal

feedforw

ardB

grad

ual

feature

conjunctio

nsp

atialinvarian

ceC

languag

egen

eralizationnonwords

Csp

ikingrate

codemem

bran

epoten

tialpoint

1.tran

sform

ation

6.weig

htbased

prim

ing

Aem

phasizin

gdistin

ctionscollap

singdiffs

Alongterm

chan

ges

learning

Berro

rdriv

enheb

bian

taskmodel

based

Bactiv

emain

tenan

cesh

ortterm

residual

Csp

ikingrate

codemem

bran

epoten

tialpt

Cfast

arbitrary

details

conjunctiv

e2.

bidirectio

nal

connectiv

ity7.

hippocam

puslearn

ing

Aam

plifi

cationpattern

completio

nA

fastarb

itrarydetails

conjunctiv

eB

competitio

ninhibitio

nselectio

nbinding

Bslo

winteg

rationgen

eralstru

cture

Clan

guag

egen

eralizationnonwords

Cerro

rdriv

enheb

bian

taskmodel

based

3.cortex

learning

8.dyslex

iaA

errordriv

entask

based

heb

bian

model

Asu

rfacedeep

phonological

readingproblem

Berro

rdriv

entask

based

Bsp

eechoutputhearin

glan

guag

enonwords

Cgrad

ual

feature

conjunctio

nsp

atialinvar

Ccompetitio

ninhibitio

nselectio

nbinding

4.object

recognitio

n9.

past

tense

Agrad

ual

feature

conjunctio

nsp

atialinvar

Aoverreg

ularizatio

nsh

aped

curve

Berro

rdriv

entask

based

heb

bian

model

Bsp

eechoutputhearin

glan

guag

enonwords

Cam

plifi

cationpattern

completio

nC

fastarb

itrarydetails

conjunctiv

e

54Sen

tences:

Bey

ondjustsem

antics

Trad

itional

approach

:S

(subject)

Art

N

The

boy

VN

P(direct object)

chasesthe cats

NP

VP

Altern

ativeap

proach

:

Distrib

uted

repsofsen

tence

mean

ing:Thesen

tence

Gestalt!

(gestalt

=unified

configuratio

nofelem

ents

that

can’tbedescrib

ed

merely

asasu

mofparts)

55Sen

tence

Compreh

ensio

n

•Wewan

tto

build

anintern

almodel

ofthesitu

ation

•e.g

.,“Theteach

erdran

kPep

siin

theclassro

om”

–Who/what

istheag

ent?

teacher

–What

isthepatien

t(object)?

Pep

si

–What

did

theag

entdo?drin

k

–Where?

classroom

(andso

on)...

•Goal:

Teach

amodel

tounderstan

dsen

tences

•Presen

toneword

atatim

e

•Wan

tthemodel

tobeab

leto

answ

erquestio

ns,

e.g.,W

hoistheag

ent?

(has

tobeab

leto

dothisev

enifag

ent

notcu

rrently

ininput)

56ToyWorld

Peo

ple:

busd

river

(adultmale),teach

er(ad

ultfem

ale),schoolgirl,

andpitch

er(boy).

Actio

ns:eat,d

rink,stir,sp

read,k

iss,give,h

it,throw,d

rive,rise.

Objects:

spot(th

edog),steak

,soup,ice

cream,crack

ers,jelly,iced

tea,koolaid

,spoon,k

nife,fi

nger,ro

se,bat

(anim

al),bat

(baseb

all),

ball,b

all(party

),bus,p

itcher,

andfur

Locatio

ns:kitch

en,liv

ingroom,sh

ed,an

dpark

.

Syntax

:Activ

e&

Passiv

e,phrases.

Someev

ents

more

probab

lethan

others

(egbusd

rivers

eatsteak

more

often

than

teachers)

57Netw

ork

Input

Encode G

estaltG

estalt Context

Decode

Role

Filler

Toan

swer

questio

nsat

theen

dofsen

tence,

net

need

sto

actively

main

taininfo

aboutwordsithas

seen...

SRN

58Train

ing

•Presen

twords&

their

roles,o

neat

atim

e;aftereach

word/role

pair,q

uiz

thenet

onwhat

ithas

seenupto

that

point

•Thebusd

river

stirredKool-A

id

•Presen

t“b

usd

river”

+ag

ent

–Whoistheag

ent?

busd

river

•Presen

t“stirred

”+actio

n

–What

istheactio

n?stirred

–Whoistheag

ent?

busd

river

•Presen

t“K

ool-A

id”+patien

t

–What

isthepatien

t?Kool-A

id

–Whoistheag

ent?

busd

river

–What

istheactio

n?stirred

59Tests

Task

Sen

tence

Role

assignmen

tActiv

esem

antic

Thesch

oolgirl

stirredthekool-aid

with

asp

oon.

Activ

esyntactic

Thebusd

river

gav

etherose

totheteach

er.Passiv

esem

antic

Thejelly

was

spread

bythebusd

river

with

theknife.

Passiv

esyntactic

Theteach

erwas

kissed

bythebusd

river.

(contro

l)Thebusd

river

kissed

theteach

er.Word

ambiguity

Thebusd

river

threw

theball

inthepark

.Theteach

erthrew

theball

intheliv

ingroom.

Concep

tinstan

tiation

Theteach

erkissed

someo

ne(m

ale).Role

elaboratio

nThesch

oolgirl

atecrack

ers(w

ithfinger).

Thesch

oolgirl

ate(so

up).

Onlin

eupdate

Thech

ildate

soupwith

dain

tiness.

(contro

l)Thepitch

erate

soupwith

dain

tiness.

Conflict

Thead

ultdran

kiced

-teain

thekitch

en(liv

ing-ro

om).

60Gestalt

Rep

resentatio

ns

SG G

estalt Patterns

Y

X0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

11.00

12.00

13.00

14.00

15.00

16.00

0.002.00

4.006.00

8.00

sc_at_so.

bu_at_so.

te_at_so.

pi_at_so.

sc_at_st.

bu_at_st.

te_at_st.

pi_at_st.

pi_dr_ic.

sc_dr_ic.

bu_dr_ic.

te_dr_ic.

pi_st_ko.

bu_st_ko.

te_st_ko.

sc_st_ko.

61Problem

swith

thestatistical

approach

?

•Themodel

mak

esmistak

esforinfreq

uen

tan

d/orirreg

ular

senten

ces

•Exam

ple:

busd

river

atesoup;resp

ondswith

steakas

patien

t

•Explan

ation:Net

sawbusd

river

eatingsteak

7xmore

than

soup

•Statistical

model

overrid

esreality...

•Peo

ple

suffer

from

similar

biases!

(How

man

yan

imals

did

Moses

brin

gontheark

?)

62Questio

ns

•What

gen

eralprocesses

areinvolved

inread

ing,an

dhow

do

these

sometim

esfail

(e.g.,in

dyslex

ia)?

•How

areweab

leto

read“cat”,“y

acht”,an

d“n

ust”?

•Whydokidssay

“Igoed

tosch

ool”

afterfirst

saying“I

wen

t”?

•How

dowordscometo

mean

anything?Statistics

ofword

co-occu

rrences.

•How

dowegobey

ondwordsto

senten

ces?Sen

tence

gestalt

63 Applicatio

n:NetT

alk!(Sejn

owksi&

Rosen

berg

,1986)

•Learn

sto

read&

pronounce

english

text

•Inputsare

oneof29

chars

(26letters

+sp

ace,comma,fu

llsto

p)

•7letter

window

(provides

contex

t).total

=29x

7=203

inputs.

•Hidden

layer

of80

units.

•Outputgen

eratesoneof60

phonem

es,rep

resented

by21

articulatio

nunits

and5units

forstress/

syllab

leboundary

info.

64NetT

alk:Resu

lts

•Learn

sreg

ularities

ofen

glish

speech

•Gen

eralizesto

novel

wordsnotin

trainingset

with

78%

accuracy

65NetT

alk:Resu

lts

•Knowled

geisdistrib

uted

:relearn

ingafter

dam

agemuch

faster

than

orig

inal

training

•Distrib

uted

(spaced

)practice

more

effectiveforlongterm

retentio

nthan

massed

practice

66NetT

alk:Im

pressiv

e,But...

•Solves

readingan

dsp

eakingat

once

(unlik

epeo

ple)

•Doesn

’tad

dress

specializatio

nofdifferen

tbrain

areasin

languag

eprocessin

g.

•Uses

biologically

implau

sible

“errorback

propag

ation”meth

od

fortrain

ingweig

hts

•Explicit

“teacher”

provides

correct

inform

ationonoutput

articulato

ryunits

(instead

oftrial

anderro

rlearn

ingthat

we

hav

eto

do)

•Req

uires

man

ypasses

throughexact

sametrain

ingset

(rather

than

natu

rallan

guag

eexperien

ces).