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IA L:
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QUOT
ATION
File:
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C
urrent
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ust 87
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Started
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87
The 1
987
Will
iam Ja
mes L
ectu
res
u
UN
IFIED
T
H EO
RIE S
OF
C OG
NITI
ON
C H A
PTE
R
3
HUM
AN C O
GNI
TIV E
A
RC H
ITEC
TUR
E
D
RA FT
Alien
Newe
ll
4 August 1
98 7
D
epartm
ents
ofCom
puter
Scien
ce and Ps
ychol
ogy
C
arneg
ie Me
llon U
niver
sity
Pitts
burgh
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nnsylv
ania
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3
8/20/2019 Draft-Unified Theories of Cognition-Ch3-Human Cognitive Architecture, 1987-Allen Newell
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Tabl
e of
on
t nts
1
. The hum
an is a
sy
mbol
sy
stem
2
2. Sys
tem Lev
els
4
3. T
he Tim
escale
o f
Hum
an Act
ion
6
4 .
T
he
N
eural
B and
7
5.
Neura
l Cir
cuit Level
8
6.
The R
eal-Ti
m e
ons
traint
on
C
og nitio
n
10
7 .
The Cognitive
Band
12
8.
Level
o
f Simpl
e Op
eratio
ns
16
9
. Lev
el of G
en era
l Op
eratio
ns
1
0 . T
he
I
ntend e
dly Ra
tional
Band
2
1
11. High
er Ba
nds M So
cial
H
istoric
al and E
voluti
onary
2
2
12
. Sum
mary
24
W
J Ch
. 3. H
CA : Prelim
inary dr
aft
of
0
4
A ug
ust 87 17 :
44. Lim
ited distri
bution
D
o n
ot quote .
8/20/2019 Draft-Unified Theories of Cognition-Ch3-Human Cognitive Architecture, 1987-Allen Newell
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List
of Fi
gures
Figure
2-1:
Exp
ansion of
sp a
ce
w
ith
le
vels.
5
Fig
ure 2-2:
Ex
pansion of
space w
ith le
vels.
6
Figure
3-1:
Ti
m escale
o
f human
ac
tion.
7
Figu
re 4-1:
The
neu
ral
level
.
8
Figure
5 -1 :
The neura
l c
ircuit
leve
l.
9
Figure 7 -1:
The
necessar
y phases
o
f delib
eration.
1
3
Figure
7 -2 :
Properties
for
au tomatic
and
contro lled behavior.
14
Figu
re 7-3:
Ex
ample of aut
om atic
an d c
ontrolle
d behav
ior (Shiffri
n & Sch
neider,
1977) .
15
Figur
e 8-1
: The four
levels
of the
cogniti
ve
ban
d.
16
F
igure 9 -
1:
Compre
ssion of
the level sc
ale factor
to s
queeze N+l le v
els
into
N
.
1
7
Figu
re
9 -2
:
Resid
enc e times for
variou
s tasks
(Sim
oN 7 2).
18
Fig
ure
9
-3 :
19
Figu
re 9 -4
:
20
Fig
ure 10-1:
The inte
ndedly
ratio
nal
ban
d M
knowled
ge-level
systems
.
21
F
igure 11 -1
: Hig
her ba n
ds.
22
W
J
Ch
. 3. H CA
: Prelim
inary dr f
t
of
0
4
A ugust
87
17:44
. Limited
d
is tributio
n. D o not quote
.
8/20/2019 Draft-Unified Theories of Cognition-Ch3-Human Cognitive Architecture, 1987-Allen Newell
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Un
ified Theorie
s
of Cognition
Chapte
r 3. Human
Co
gnitive
A
rchitecture
/
In this
lecture w e turn
to the hum an cogn
itive architect.ure.
iS j~lhe last
lecture >
f c provided o u f 3 e , K F S T m J .L h u the
basic concepts necessary
to
understanding
v
>
intelligent
systeiy^
Representation, know ledge,
symbols .and sear
ch
pply
as m uch
to machines and to
hum
ans. H u m a n
s,
of
co urse,
are
sp
ecific in n^ny
ways.
Back
in
Figure
U
TC- CO N STR A T
N TS
we laid ou t ma ny
of the co nstraints
that shape the
human
mi
nd.-k
For example,
d t
they are
co
nstructed of neural te
chnology, tiwithey
arose through ev
olution, and tip*
th
ey m ust
be
high
ly autonomous
.
a
theo
ry
b
rms
t
he
Our ult imate goal is
a unified th eory
of
hu
ma n cognition.
That w ill
be
ex
pressed, we h
ave maintai
n^
of
the architectu
re
of
human cog n
ition hat is, of
the fixed (or slowly
varying)
structure
framework for the
immediate pr o
cesses
of.-c
ognitive
perform an
ce and learning .
Tjbus, w e ne
ed
to set
out th at
Q
\jA<Qi.c
architectur
e. We wil l do so in
phases. In
th^
lec
ture w e will
attempt to derive
some.aspects
o
f
the
human cognit
ive
architectu r
e, at tending only to
the way the human is
situated
in
the
world. This
will lay
the groundw or k
for
proposing in the
fourth lecture
a
sp
ecific architec
ture in
d
etail. (T
hough, ev en there, vty
io uc aspe
cts will re ma in
e
.Alternative^ , the
architecture c
ould
be
laid as
a
total
system. ifclose/£hpp
roximati*l4e
the axi o mat
ic
ideal.
A n
a
dvantage of the
phased description
s, in addition to
whatever didactic vi
rtues it m
ight
ha
ve, is ^ trf separa
tu» w hat
can
be claimed ab o
ut
t
he architecture on t iiKg
y gene
ral grounds, f
rom what
must be justif
ied
by de t
ailed
r i v
f tr
ex pe
rimental da t
a}
/Thu
s,
this
lecture will
be devoted to
a quite gen eral
argument ^Hw^tf
ill start fr om the neural
technolog
y, which is quit
e clearly
th
e techn
ology of the human
cogn itiv
e
ar c
hitecture.
This architectu r
e
m u
st
su pport
mind-like behavi
or. From th e
prior lecture w e alread
y
have a
ch
aracterization of
what that
m
ea ns. W
e
will
add
to thes
e w
hat we will call
the
real
-time constrain
t on human
cognit ion. From this th r
ee constraints,
w e
w
ill
deriv
e
a
number of ke
y
asp
ects
of
th e
cog
nitive architecture.
H
owever, w hatev
er we are able to
derive from them
^te
no
lo nger
o
he ila a
re indeed
rather
generalt
options in
specifying the
details
o
f the h u m a
n architecture.
The wisdom
of
such
a strategy
f a ttempting
to
divide and con quer hould be evident f rom the discussion
in
the last lecture. T here (F igure FC S -A R C H V A R ) w e
saw, thai
large degre
egofj^egdgrn \v£fe
available to
construct arch itectu
res
of
symbolic system
s. W e also
noted th e
the archit
ecture
may
be t t e essential
ly^hidden va
riable
hat there is
n
o way to
determine
wha t'represen
ta t ions^? : whjg gg m
trol structure
s are
u
se d. Since
any general (i.e.,
universal)
architecture can mimic
any other,
the si tuation is
hopeless.^lear
ly
then,
w ha
tever
a
spects of th e
architecture can be
pinned down from the
general situatio
n within whic
h
t
he huW in ar
chitecture is co n
structed and operat
es
uc
h can only
be of g
reat
help
in
m aking
the internal de
tails identifiablksspn
e might have some
doubts
about whether an
ything
c
an be
s
aid, but
as
^ ~
w e
will
seem
im
mediately
that
is
far from
so 7
~-^4 Q
U J »
H er
e is anoth
er
way
to think a b
out the enterprise of this le
cture .
Evolution is th e
designer of th e h um
an cognitive
ar
chitecture. It wil
l pick and choos
e
s
ystems that wi
ll aid
in
th e survival
of the species.
Our pr oblem as sc
ientists is
to guess what des ign evolution has settled for to date. W e know a little about evolution
as
a designer. It never
starts
over, a
lways working w it
h what
i
s available.
In Ja
cob s (1914)
now famous
phrase,
evolution
is
a
tinkerer.
For
inst
ance, once
a
species is
co mm it
ted
to
a k-strategy (heavy
investmen
t
in few
pr oge n
y) evolution will not
shift to
the opp o
site R-strategy (l i
ght investmen t in many
progeny)
t
is
too
hard to
get
fr
om here to
there.
Evolutio
n
picks its
^s fotonJVithin th e d
esign constraints
posed
b
y the situation
in which
the organism finds itself.
If w e ca n
dCL
a
understand th ese
constraints, then we can
so»m
oro
cloatly
tho fimitpjl fi e
ld within which
evolution
must
operate.
T his
lecture
will
b
e
fa i
rly speculative. Any
attempt
to
get at genera l ch
aracteristicsA must
run
t
his
ri sk
. W
e
will
WJ
Ch. 3. HCA:
Prelim
inary draft
of 04 Aug
ust
87
17:
44. L
imited distributio n
.
D o
not
q
uote .
8/20/2019 Draft-Unified Theories of Cognition-Ch3-Human Cognitive Architecture, 1987-Allen Newell
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The
more s
ubstantial argume
nt is
to
ref
lect
on the varie
ty of re
sponse fu
nctions th
at humans
d»
ot
can do,
but
do
. The
fa
ct
is
that
the y
seem
to create new respo
nse fu
nctions
all
th
e,
time.
To
adopt
the w
ell-worn
device of
the
.lio*-. £
Martian biologist,
wh at
would impress
him
most
if
he looked
at
the human species biolog ically
is
'attem
pt to
asses
s
th
at risk
at
the end
of th
e
le
cture, after
the results
are befo
re
us .
Even
if
the spe
culations are
wro
ng,
I
th
ink/they ¥*H be
wro
ng inkitoro o
ting^ays
.
i
c *-
Aju
^-aJUx^S
Tx
H
ere is a preview
of tne resu
lts. Dif
ferent cogn
itive world
s have different
-time
sca
les.
The
different
kin
ds
o
f
cognit
ive worlds
that
we
see
a
re governed
by
the time s
cales on
which
thv occur
he ne
urologica
l, cognitiv
e,
ratio
nal, and social.
Th
ey are wh
atthey are t
o enable
mind-lik
e
beh
av ior
to em
erge o get
to co
mputation
al ,
—————— —
—————— —
— »^
j <
tr»/«*4t
^
symbolic
sy st ems/^
W ithin
the cognitive
werid
there
must be a
distinction
between
au tomatic and controlled
process
es ,
which aga
in
is
a d
istinction
in
le
vel.
In additio
n, the arc
hitecture
has to
be recogn
ition-base
d and
the
re
ha
s-to be a cont
inual
shift to
wards inc
reased re
cognition.
This cor
responds to
continu
al
mov
em ent alon
g
the isoba
r
in Figure FCS-PRE
PD ELffiT
RAD EO
FF, t
owards in
creased p
reparation
.
1 T
he hu m a
n is a
s y m b o
l syste
m
The treatment
in
t f r e last le
cture
wa s abstract
in a
very particu
la r
way. I t
discusse
d
symbol-le
vel system
s and
kn owledg
e- level
system
s as the
general str
ucture that
wa s
nece
ssary to ob
tain gen
eral inte
lligent beh
avior. I d
id not
spec
ifically make
the case tha
t h
umans Jw
erefoiow4
63ge systems a
nd sym
bol sys
tems
hough
it
w
as clear l
y
understo
od that wa s
what we
were after. *3it
we
settled
for underst
anding
the nature
of these
various
s
ys temf
and
what gay^
rise to them,
namely
the need
to
d
eal with
large am
ounts o
f variabilit
y
i
n respo
nse functions
,
i
A
t this
p
oint
we wish t
o
be
ex
plicit that hum
ans
are symbol sy
stems th
at are at least m
odest
appro
ximations
of
knowle
dg e sy
stems.
They might b
e o
ther kinds
of
systems
as w
el l, but at least
they ar
e symb
ol
s
ystems.
The
ground
s for thi
sjargumen
t, as made
clear
by
the entire
previous lec
ture,
is the variety
of resp
onse functi
ons
tha
t the
human use
s. Ifjy|ri£
ty of
response
f
unctions
is
i
mmense
enough, a sy
stem wil
l
be
dri
ven t
o
compose
its
respon
se
functions
b
y
m
eans of
a
ccono
utational
system that
construc
ts
repr
esentation
s b
y me
ans of
co mposed
trans
formation
s and^use
^ symbols
to obtain
distal acces
s. That w
hole a
pparatus ex
ists
be
cause o
f the d
emands of
varie
ty. The groundw
ork
is laid. W e j
ust wi
sh to
be
explicit ab
out its ap pl
ication to
humans.
O
ne weak
argument
is that
human
s can un
doubtedly
emulat
e a
universa
l
madiin
e.
They
migh
t do
it
rat
her
slowly, becaus
e th
ey may ha
ve to sp
end
a
large
amou
nt
of
time m
emorizin
g tflt
new
st
ates.
B ut, if we
wait long
enough, they can
do perf
orm
t
he op
erations of a
univer
sal machin
e. They are
of
course lim
ited in
their
lifetime
s
(measu
red in terms
of total numb
er
o
f
operations
they
ca
n perform)
and ulti
mately
in the
reliab
il ity o
f their me
mory.
B
ut
ne i
ther
of
th
ese
is of the
essenc
e,
j
ust as thejta
re
n
ot
for Q O
j f l r j u t e r s w
hich also
have li
mited lifeti
mes and
reliab
ilities. T
hus, techn
ically they
a
re the
kind of 4beast
vv f
cSncan
be
a
universal
m
achine.
AfclW
th
that
comes
,
^
v
- ag ain
technic
ally, a
ll ther>ther pro
perties. Ho
wever,
the argum
ent is we ak, bec
au se t
he ima
gined type
of verifica
tion
'
^ sp>
oifying
to a hu maJra
spec
ific univ
ersal machine
f C s a y a
Turing
M
achine) a
nd then
observ
ing the pe rson's
executi
on of
it
jn terpretiv
ely
ao to op
cak s ar
tificial, n
is
n
ot
an observ
ation on th
e
ki
nd o
f life
that
hum
ans lead.
TT c
ould b
e the e
xploitatio
n
of a
capab
ility tha
t is actual
ly irrele
vant to
th
e
regul
ar style
with
which th
e
hu
man
interacts
w
ith ^
Environm
ent.
eff l
orescence
of
adaptat
ion
Humans
appear
to go aroun
d simply
creating
opportu
nities
of
all
kinds
to
build
diff
erent resp
onse
function
s.
L
ook
at the varie
ty
o
f jobs in
the
wor
ld. Eac
h one h
as humans do
ing
dif
fer ent
kinds of
4 JkA W n
O
response fun
ctions. Hu
mans invent
games.
They ha
ve a
ll different kin
ds
o
f s
ports. They
n
o soone
r invent one,
than
the y
invent new
ones.
They not onl
y inven
t card game
s, t
hey
collect
them in a book
and publish
thejj* /
(15
0 strong)
(H
oylSO).
Th at
implies that p
eople b
uy
the m so
they
ca
n develo
p
y
et new
respon
se function
s
by^ore
. .
They also
dance, w
rite
bo
ok s,
have c
onversatio
ns. A c
onversati
on
is
not
hing but a
n oppor
tunity to in
teract) w
ith t
he
I
W J C
h 3 H
C A : Prelim
inary draft
o 0
4
A u
gu st
87 17:44 Limit
ed distr
ib ution
D o not q
u ote
8/20/2019 Draft-Unified Theories of Cognition-Ch3-Human Cognitive Architecture, 1987-Allen Newell
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envi
ro nmen t
in a
way th at
w
as
differe
nt th a
n
p
ri or
interac
tions hat
is, t
o
bu
ild
new
r
esponse
fu n
ctions. Think
o
f
the L ib
rary
of
C
ongress
as eviden
ce of th
e varietyof
resp
onse func
tions
t
ha t h
uman h
ave
e
xhibited
by
w riting th
em,
will exhibit
by
read
in g
them
, and
w
ant
to
e
xh ibit by
building
b
uildings
t
o make
them
ava
ilable. To
ac ad
emics
the
mentio
n of bo oks
sugg
ests in
tellectua
l
f
unctions
, as if
all this w a
s perh
aps
a
phenom
en a of
th e
high en
d
of
th e
so
cio-eco
nomic scale.
W hat t
hen of
rapping
? A
cr ea
tion
o
f
the
b
la ck
ghetto ,
rapping
is an in
vention
to produ
ce an
op
portunit
y
fo r
ne w respon
ses
to
a highly dyn
amic en
vironm e
nt. It ser v
es
o
th er
funct
ions
as
w
ell,
bu
t i t
build
s on
t
he hu
m an
pr
oclivity
to inve
nt ne
w form
s of
re s
ponses. Peop
le sa^ £
create
th
ese opp
ortunitie
s. Indeed,
our
M
artian biolo
gist woul
d
not
be
w ro
ng to
conclud
e that
th e
bigg
est biolo
gical pu
zz le abou
t
earthlin
gs is w hy t
hey
hav
e dev
eloped this efflo
re scence
of
adaptat
ion.
Etholo
gy,
lo
oking
at other
orga
nisms
from
d
ig ger
wasps
to herring
gulls,
has pro
perly
b
ec ome
in
trigued
w ith th
e
a
daptatio
ns they ex
hibit. E ach
ad
aptation
is /a
uniq
ue biolo
g ical phe
nomena,
eachis
to
be
understo
od b y
ex p
loring
the
be h
avioral
an
d
phys
io logica
l m
echanis
ms that s
upport it.
Th
ey
ar
e to
cu
rated on
e
by
o
ne . Not so
w
ith huma
ns/
T he
re is n
o
enumer
ating th
eir
adaptat
ions
hey w
il l
inven
t n
ew ada
ptations faster
th a
n they can
be
re
cord^ The
act of recor
di ng
is itself
one m ore
ad ap
tation
r rather a
w ho
le
ge
nerator
of
them,
as the
pr
oblems of
suc
h
a
sc ie
ntific enterp
ri se
unfold
and are
respond
ed
to .
W hat
I am
sa ying
is
not
new,
nor
it
is
supposed
to be
. There are
manyw
ays
o
f
talking abou
t t
he life
o
f h
omo
sapie
ns, so as
to reve
al
what
is th
e life of
the mind.
A lw a
ys
eo
oe ntiolly
t
he
s
ame
gross facts
ar
e to be
describ
ed. Th
e
iss
ue
is
w hat
it
t
akes to
convinc
e ourselve
s
that
hum
ans
deal w
ith sufficie
nt variet
y , so
t
ha t
they m
ust
be
sy m
bol
sys tems
hat no
system
of less powe
r a
nd unive
rsality c
ould suffice.
I
a
m
atte
m pting
a d
escripti
on
th a
t take
s as
th e given
s obser
vations on
the
b
ehavior
of
human
s, to w i
t, o
n the variety
of
th a
t behav
ior.
I
w
ish
to avoid
—
t •ysfr '
ju
dgm ents
on th
e
c
ontent of
th at
beha
vior, fo
r
e
xample,
onits
rationa
li ty
or
o
eg roe of
its
a
daptatio
nfTesp e
cially
w
ish to
avoid
invol
ve m ent
w i
th an y
in te r
nal or
structur
al aspect
s of th
e
hu
man.
M y o
bjective
is to g
ro und
th e
assert
ion that th
e hum
an is a s
ymbolfs
ys tem
on
exte
rn aLasp
ec ts,
so th at
it ca n
serve a
s
a
des i
gn con
s traint f
o r
con
sidering
the
nat
ur e of
th
e internal stru
cture.
~ J
LoJ^oJ
^
A
/
u n J L
To in dicate
th
is
beh
av ioral
ch aracte
r of
humans,
I
use
the ph
rase
un
limited q
ualitative
a
daptatio
n. C l
early,
h
umans a
re not
infinitel
y adaptive
. That i s
easy to
sho
w .
Ju s
t
p
it
two hu
m ans against
each o t h e
B J i n a
compe
tition;
one
w ill
w in,
th e
other
w ill lose. The human
th at
loses clearly was
not
su ff iciently adaptive.
Th^
it is
the variety
of
adap
tations th at
is
a
ss erted
heir ra
nge^nial
it atively spe a
king.
Here th
ejj seem s
to
b
e no
limit wh
atsoever
.
W
hat
m ight
ha
ve seem
ed
a
l imit
, in term
s
of
sp ec
ific sen
sors an
d time-b
ound con
tact, h
umans m
anage
to transce
nd
by
instrum
ents and histor
ie sy
They
e
ven bu ry
t ime c
apsules
so that so
me fa
r-future
h
uman w ill
hav
e
one
ad d
itional
opport
unity
to
beh
avjfcf adapti
vely
with respe
ct to a p
ast he
or
s
he
might
o
therwise
h
ave misse
d.
T
his ar gum e
nt h
as
a s
of t
spot, w hi
ch shoul
d
b
e noted
. I
t is an
as y
mptotic
argumen
t.
T
ha t is
, as
the variety
of
function
s
that
ca n
be ex h
ib ited b
y a
system
increa
se s wi tho
ut
l
imit,
w e know
th
e set of
funct
ions
be
comes
the set of
compu
ta ble func
tions.
Furtherm
ore,
w e know such
a set
ca
n
be
gener
ated only
by a u
niversal
computa
tional
system
s (exac
tly so
,
s
ince th ese
tw o
notions
simply g
o together)
. If w
e
co
nsider
sy s
tems th at
prod
uce an ever
grea
te r
variet
y
o
f fu n
ctions, at
some poi n
t
th ey must
have
the structu
re
of
a
universa
l
s
ystem, i.e.
, of a
symbol
syste
m . A
huma
n is
ca
pable
of p
roducin
g
a
imme
nse variet
y
o
f fu n
ctions and d
oes/so
in
its
everyda
y life. B u
t is
I
his ^enou
gh
variety s
o
th a
t
the structu
re
m
us t be
that of
symbol sy
stem ? Co
m putatio
na l
theory do
es no
t
yet
pro v
ide
any
useful answers
to
this
qu
estion n
part
because
it
hasn
t sough
t therr
i,afike«g
h
in general
such
q
uestions are
hard
to
an s
w er in
use f
ul w
ay s. It w
ould
b
e n
ice to
have som e
theory
like that,
but
I
d
on t kno
w of
any. It is
O
f cours
e,
iijf
ould puzzle
a bo ut it ,
he might a
fortiori
know
th e answe
r- because
th e
art
ian\
would ex hibit
that
s ame efflo
rescence. B
ut
n
o
metaph
or ca n
be
perfect.
n w
e
W J
C
h. 3
. H C A
: Preli
minary d
r ft
of
0
4
Aug u
st
87 17:44. Lim
ited
di str
ibution .
D o
n
ot quote
.
8/20/2019 Draft-Unified Theories of Cognition-Ch3-Human Cognitive Architecture, 1987-Allen Newell
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instructive
to
observe that
computers are
in
th
e sa m e situation,
to
c
ompute any computable
function a computer
has
to have
the
structure
o f
a
symbol sy s
tem.
We co
nstruct computers
so
that^ia
ve this s
tructure.
But
need
we?
I
s the
va riety o f actual
functions that w e want
to compute
such
tha t we c
ould get
by with some st ructure, perhap
s far
removed f
rom
a
symbol system
s.
It
seems hardly li
kely, but there is not the mathem
atical theory to provi
de more
definitive answ
ers.
^^ ^^^^^S^^^E^^ ^
p
li
gsa >
w e will now take
it
as
established that the architecture
of
human cognition
is
a symbol system.
2 System Levels
Let
us tu r
n to
underst
anding the technolo
gy out of which th
e human architecture is co
nstructed. The first point
is
that, o f ne cessity, intelligent
systems
are
built
up of m ultiple leve
ls
of
systems.
A system level is
a
collection
of
comp
onents, which ar
e
lin
ked
t
ogether
in
some arrang
em ent and w
hich interact, t
hus
producing behavior
a t
that
sy stem
level.
Mult
iple levels means
that
the components at
one level are realized
by system s at
the
next
level
belo
w .
We have o f cour
se bee n here before,
as witn
ee eed by
Figure FCS
-C O MPSYSHIERARC
HY , which
showed the
computer
sy stems levels. E mpiri
ca lly, everything
w e understand about engineeri
ng such systems
to
get in telligence
is to build up m ultiple levels. This is one
o f
th e great empirical invariances lthough many different w ay s have
been found
to
c
onstruct informati
on processing s
ystems, they sti
ll
all consist
of
a
hierarchy o f l
evels and indeed
essentia lly the sa m
e hierarchy.
I
wish to maintain
th a
t
the
human
architecture
is
built
up o f
a
hierarchy ha
t
it canno
t be otherwis
e structured
o
that
the
di
scovery of the architectu
re ca n proceed
within this assumption. That eng
in eered computer
systems
see
m
to have a hie
rarchical structure,
as
just
re viewed, c
an
c
ertainly be taken
as
one piller
of support.
A second
pillar
comes from
Herb Simon's analysis fa
r hierarchy (Simo6
2). The
ar
gu m ent there
was
that
stability dictates th
at
systems have to b
e
hier
archical. To build com pl
icated systems witho
ut first building stable
subassemblies
will
al w ays fail
he entire structure wijl'disintegrate
before it
all gets put to gether. If s
table subassemblies
are
cr e
ated,
layer upon lay
er,
th
en each one A
has
a reaso
nable probability
o f being constructed out o f a
few parts. Thus,
there
e
xists a ge neral argument that
stability dictates
the
ex
istence
levels.
Th
e stability argument,
of aefurse can be
taken
to underlay the en
tire hierarchical s
tructure o f matter, from
nucleons, to atoms,
to
m ol
ecules, a
n
on up. So the
levels
hypothesis
may have nothing to
do with intelligent
systems, but simply with
t h e way all systems are
put together.
Al l we need for
the
argument
is that intelligen
t system s will be h
ierarchical. i/lu«.
**
fUL^^J j
^L^-p
^-
J C C M I - M , T ^
Levels are
clearly abstractions, bein
g alternative
ways or descrtmng
the same system , each altern
ative, ignoring
some
o
wha t
is specified at the
level beneath
it. It
is
all in the head o f
the observer. ^
Ffiere is
more
to it th an th
at.
Levels
ca n be stronger or
weaker depending
on how well the behavior
of the sy stem, as described
at
a
le vel,
canjbe
predicted
or explained by
the structure of
the system described at
the same level. In standard t
reatments^ystems
analysis
(SystemAnalysis),
systems are called state determin
ed w
hen
their fu t
ure
beh
av ior is determined wnjfl
th
eir'
2
3
c
urrent s
tate.
That
is what holds for
a strong leve
l. A
level
is weak if co nsiderations
from
lo
w er levels enter
into
de
termining
the
futur
e course.
In
engineered
systems (Figur
e
FCS-COM
PSYSHIEARCHY a
gain), great care
is
taken to make strong levels o seal
off
ea ch level from the on below. When dealing with logic circuits there
is
no
need
to
unde
rstand the continuou
s circuitry underling
them xcept
when things go wron
g.
W he
n dealing
with
programs
there is not need to
under
stand the register-transfer
circuits that realize
the ope rations a
nd the
in t
erpreter
gain, except
when things go
wrong. And so
on.
These
are all very str
ong system levels, as evidenced
by how
sm all
the
failure rates
are
in
commercia
lly successful
systems. Many natural
system levels are also
very strong,
such
the
atomic and molecular
levels. The stronger a
level
is the
more
it forms
a
dis tinct world,
in which
nothing,
m us t be known about lo
w er levels
in
order
to live within it.
However,
aH
natural-sy
stem levels need be stro
ng. In
W J C h 3 H CA:
Preliminary draft of
04 August 87 17
:44 Limited distribut
ion D o not quote
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Figure
2-2: Expansion of space with levels.
with
new
properties. It
can't/be
done with two or three components and it
can
be done with
just
a couple
of
system
times. Novel behavior cantbuild up that
rapidly.
On the other
hand,
there are
lots
of cases where
less than
100
or
1000 components or component-times suffice. Thus, we
can
take a factor of 10 to be the minimum characteristic
factor to get
from
one level to the next. This factor is highly approximate, of course. It might be only 3 in
some
C M i r e m c
cases,
it
might be
as
many as 30.
Let
us
use
0
s a special notation
to
indicate
such
very approximate
numbers. 2
The
Timescale of Human Action
Let us now consider Jhe timescale at
which
human action occurs,
as shown in
Figure HCA -TIMESCALE. At
the
o
left
time
is
measure^n seconds, using
a
log
scale.
The next
column
to
the
right
names the
time units, m illiseconds
(ms) to sec/ to mki to hours. The units themselves are, of course, geometrically related to each
other. The
next
n
system with that
characteristic operation
time.
Time is
the useful
measure of system
level,
for us^^ /
space. But of course
the characteristic physical
size
increases correspondingly.
characterized by different theories, and
these
are
shown
in the
right
hand
column.
Different levels are
at
the
bottom, there is
the
neural b a n e } of three
eurons;
and neural circuits,
a factor
of
ten
up .
This figure provides an overview of where we
are going. Startin
\ evels eurons: organelles, which are
a
factor of
ten
dpwjvfroL _-__ _,
_._
____ _ __
.
.
•-
•
Ak/ 4J
jK« «M
<
«•*
TT/t
tiff
A s
we'll*
see, neurons
have
a
characteristic operation
time ofaoout a ms,
and neuraTcircuits
have a
characteristic of
*+**
a***) ^s^ J>
r\
about
10 ms.
Thpii^here is the
cognitive
band.
Here
the levels are unfamiliar
.r ^ve
called them deliberate acts,
cognitive operajions and unit
tasks.
Each of them
takes
about ten times as long as the
level
beneath. It
will
be the
main
task of
this
lecture to establish these levels
and
something of their criaract^^rx ve^ecpjmitivej?and
lies-the-
the rational band, ^which is of
the
order of
minutes
to
hours.
All of these levels are given
the
same
label,
namely
/as£s.
W e
will see why that is
appropriate. Finally, even
higher
up
thej^ lies
something^alled
the social
band.
which we will
have only a little to say about ts
L
f tm^C?
£*
One striking feature of Figure HCA-TIMESCALE
is
that
each level
is
only the
minimal
factor
of
ten
above its
y v
components.
This
is
evident directly
from
what
we
know empirically about
the
levels
of
the neural band.
Each
new
level
occurs, so
to speak, iarf as
soon
as
it can
in terms
of
components.
3 This justification of the
minimal
property
1 0
rather
than
-10 because we
want to preserve
-10 to
indicate the
usual
degrees
of
approximation. In the
lectures we
used
~
special
character is
not
available
here. 10 is
a place holder for a
special notation.
3 N<ne: This needs work. One
interesting
aspect is
that
what makes for larger
steps
is aridity, as in sys tem plains. On the
contrary,
if one is
trying to get« much complication as possible, then one wants levels as soon as possible.
V
W J C h 3 HC
A:
Preliminary
draft of 04
August
87
17:44 Limited distribution Do not quote
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25
Refe
rences
Fodor, J. A .
The
M odula
ri ty
o M in
d. C a m b
ridge,
M
A :
Brad
ford Books
, M I
T Press ,198
3 .
K ol
ers,
P.
A .
M e
m orial c
onseque
nces
of
automat
iz ed encod
ing. J
ournal o Expe
rimenta
l
P sycholo
gy: Hum
an
le a
rning and
m e m or
y,
1975 ,
1,
689-7 01
.
N ew
ell, A .
Simon, H .
A .
H uman
Pro
blem Sol
ving.
E nglew
ood
C l
iffs:
Prenti
ce-Hall , 197
2.
Posner , M
.
I.
S
nyder, C.
R. R.
Facil i
ta t ion an
d inhibitio
n
in
th e
proc
essing
o
f sig n
als. In
R ab
it t,
P.
M . A .
Dornic
,
S. (E
d.),
Attent
io n
an d
P e
rforman
ce
V .
N ew
Y o
rk :
A cadem
ic Pre
ss , 1975.
Sc
hneider,
W
. Sh
iffrin, R . M .
C ontroll
ed
a
nd auto
matic hum
an in
formatio
n process
ing: I.
Detect i
on ,
se
arch,
and
a
ttention.
Psych
ological
Review
191
1 84 1-
190.
Sh eph
erd.
The Syn
o pt ic Orga
nization
o
the
Brain, 2n d Ed
. N
ew
Y
ork :
Oxfo
rd Unive
rs i ty Pr
ess ,
1
979.
Shiffr
in, R. M .
, Sc
hneider, W .
C o
ntrolled and
autom
atic hum
an info
rmation
process
in g:
II .
Percept
ual learning
,
automat i
c attend
in g, and a
general th e
ory. Psycho
lo gical R
eview,
1977
,
84
, 127 -19
0 .
Si
mon, H .
A .
T he arc
hitectur
e
of
compl
exity. Proce
ed ings
o th e
Am
erican
Philoso
ph ical
Soc
iety,
196
2,
26,
4
67-482 .
W
J C h
. 3. H C
A : Prelim
in ary
draft o
f 04
A
ugust
87 17
:44. Limited
distrib
ution
D o
not
q
uote