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Percep
tion
&A
ttentio
n
Percep
tion
iseffo
rtlessb
ut
itsu
nd
erlyin
gm
echan
isms
arein
credib
ly
sop
histicated
.
•B
iolo
gy
of
the
visu
alsy
stem
•R
epresen
tation
sin
prim
aryv
isual
cortex
and
Heb
bian
learnin
g
•O
bject
recog
nitio
n
•A
ttentio
n:
Interactio
ns
betw
eensy
stems
inv
olv
edin
ob
jectreco
gn
ition
and
spatial
pro
cessing
Percep
tion
&A
ttentio
n
Percep
tion
&A
ttentio
n
So
me
mo
tivatin
gq
uestio
ns:
1.W
hy
do
esp
rimary
visu
alco
rtexen
cod
eo
riented
bars
of
ligh
t?
Percep
tion
&A
ttentio
n
So
me
mo
tivatin
gq
uestio
ns:
1.W
hy
do
esp
rimary
visu
alco
rtexen
cod
eo
riented
bars
of
ligh
t?
2.W
hy
isv
isual
system
split
into
wh
at/w
here
path
way
s?
Percep
tion
&A
ttentio
n
So
me
mo
tivatin
gq
uestio
ns:
1.W
hy
do
esp
rimary
visu
alco
rtexen
cod
eo
riented
bars
of
ligh
t?
2.W
hy
isv
isual
system
split
into
wh
at/w
here
path
way
s?
3.W
hy
do
esp
arietald
amag
ecau
seatten
tion
pro
blem
s(n
eglect)?
Percep
tion
&A
ttentio
n
So
me
mo
tivatin
gq
uestio
ns:
1.W
hy
do
esp
rimary
visu
alco
rtexen
cod
eo
riented
bars
of
ligh
t?
2.W
hy
isv
isual
system
split
into
wh
at/w
here
path
way
s?
3.W
hy
do
esp
arietald
amag
ecau
seatten
tion
pro
blem
s(n
eglect)?
4.H
ow
do
we
recog
nize
ob
jects(acro
sslo
cation
s,sizes,rotatio
ns
with
wild
lyd
ifferent
retinal
imag
es)?
Ov
erview
of
the
Visu
alS
ystem
Hierarch
ieso
fsp
ecializedv
isual
path
way
s,starting
inretin
a,toL
GN
(thalam
us),to
V1
&u
p:
Tw
oS
treams:
Ven
tral“w
hat”
vs.
Do
rsal“w
here”
V1
V2V
3V
4T
EO
TF
TE
PO
V3A
MT
FS
T
MS
T
VIP
PGP
G
TE
V1
p
mm
d
Th
eR
etina
Retin
ais
no
ta
passiv
e“cam
era”
Key
prin
ciple:
con
trasten
han
cemen
tth
atem
ph
asizesch
ang
eso
ver
space
&
time.
retinal
ou
tpu
tg
ang
lion
cells
LG
No
fth
eT
halam
us
A“relay
station
”,bu
tso
mu
chm
ore.
•O
rgan
izesd
ifferent
typ
eso
fin
form
ation
into
differen
tlay
ers.
•P
erform
sd
yn
amic
pro
cessing
:m
agn
ocellu
larm
otio
np
rocessin
gcells,
attentio
nal
pro
cessing
.
•O
n-
and
off-cen
terin
form
ation
from
retina
isp
reserved
inL
GN
Prim
aryV
isual
Co
rtex(V
1):E
dg
eD
etectors
V1
com
bin
esL
GN
(thalam
us)
min
pu
tsin
too
riented
edg
ed
etectors:
•E
dg
esd
ifferin
orien
tation
,size
(spatial
frequ
ency
),and
po
sition
.
•F
or
coh
erent
visio
n,
need
tod
etectv
aryin
gd
egrees
of
allth
ese.
Ex
amp
leV
1ed
ge
detecto
rHu
bel
&W
ieselN
ob
elP
rize
Prim
aryV
isual
Co
rtex(V
1):T
op
og
raph
y
Hy
perco
lum
n:
Fu
llset
of
cod
ing
for
eachp
ositio
n
Prim
aryV
isual
Co
rtex(V
1):T
op
og
raph
y
Hy
perco
lum
n:
Fu
llset
of
cod
ing
for
eachp
ositio
n
Pin
wh
eelcan
arisefro
mlearn
ing
and
lateralco
nn
ectivity
:n
ot
hard
-wired
!
Rero
utin
go
fV
isual
Info
toA
ud
itory
Co
rtex
•S
harm
a,An
gelu
cci&
Su
r(2000),N
ature
Rero
uted
fib
ersfro
mR
etina→
aud
itory
thalam
us
(MG
N)→
A1
•If
visu
alp
rop
ertiesare
learned
,they
sho
uld
dev
elop
inA
1.
Rero
utin
go
fV
isual
Orien
tation
Mo
du
lesin
A1
Ba-d
:O
rientatio
nm
aps,d
ark-
hig
hact
for
giv
eno
rientatio
n(b
otto
mrig
ht).
C:co
mp
osite
map
of
orien
tation
preferen
ces
D:red
do
ts=
pin
wh
eelcen
ters
Visu
alB
ehav
ior
After
Rero
utin
gR
igh
tV
isual
Field
vo
nM
elchn
er,Pallas
&S
ur
(2000)
Visu
alA
cuity
After
Rero
utin
g
Visu
alA
cuity
After
Rero
utin
g
→S
olearn
ing
isp
ow
erful,b
ut
sois
evo
lutio
n!
AQ
uestio
n
Wh
atm
akes
visu
alco
rtexv
isual
cortex
?W
hy
do
esit
represen
to
riented
bars
of
ligh
t?
Prim
aryV
isual
Rep
resentatio
ns
Key
idea:
Orien
teded
ge
detecto
rscan
dev
elop
from
Heb
bian
correlatio
nal
learnin
gb
asedo
nn
atural
visu
alscen
es.
Th
eM
od
el:S
imu
lating
on
eH
yp
ercolu
mn
•N
atural
visu
alscen
esare
prep
rocessed
by
passin
gth
em(sep
arately)
thro
ug
hlay
erso
fo
n-cen
teran
do
ff-center
inp
uts
•H
idd
enlay
er:ed
ge
detecto
rsseen
inlay
ers2/
3o
fV
1;Lay
er4
(inp
ut)
just
represen
ts
un
orien
tedo
n/
off
inp
uts
like
LG
N(b
ut
canb
em
od
ulated
by
attentio
n)
Th
eM
od
el:S
imu
lating
on
eH
yp
ercolu
mn
•H
ebb
ianlearn
ing
on
ly
•F
FF
Bin
hib
com
petitio
nfo
rsp
ecialization
(seeC
h4)
[v1rf.p
roj]
Th
eR
eceptiv
eF
ields
01
23
45
67
89
1011
1213
Red
=o
n-cen
ter>
off-cen
ter,B
lue
=o
ff-center
>o
n-cen
ter
So
me
differen
ces,b
ut
pin
wh
eelsstill
emerg
e
Percep
tion
and
Atten
tion
1.W
hy
do
esp
rimary
visu
alco
rtexen
cod
eo
riented
bars
of
ligh
t?
Co
rrelation
allearn
ing
based
on
natu
ralv
isual
scenes.
Refl
ectsreliab
lep
resence
of
edg
esin
natu
ralim
ages,w
hich
vary
insize,
po
sition
,o
rientatio
nan
dp
olarity.
→m
od
elsh
ow
sh
ow
do
cum
ented
V1
pro
perties
canresu
ltfro
m
interactio
ns
betw
eenlearn
ing
,architectu
re(co
nn
ectivity
),an
dstru
cture
of
env
iron
men
t.
Percep
tion
and
Atten
tion
1.W
hy
do
esp
rimary
visu
alco
rtexen
cod
eo
riented
bars
of
ligh
t?
Co
rrelation
allearn
ing
based
on
natu
ralv
isual
scenes.
2.H
ow
do
we
recog
nize
ob
jects(acro
sslo
cation
s,sizes,rotatio
ns
with
wild
lyd
ifferent
retinal
imag
es)?
3.W
hy
isv
isual
system
split
into
wh
at/w
here
path
way
s?
4.W
hy
do
esp
arietald
amag
ecau
seatten
tion
pro
blem
s(n
eglect)?
Th
eO
bject
Reco
gn
ition
Pro
blem
Pro
blem
:R
ecog
nize
ob
jectreg
ardless
of:
locatio
n,size,ro
tation
.S
ame
Diff
Th
isis
hard
becau
sed
ifferent
pattern
sin
same
locatio
ncan
ov
erlapa
lot,
wh
ileth
esam
ep
atterns
ind
ifferent
locatio
ns/
sizes/ro
tation
scan
no
t
ov
erlapat
all!
Grad
ual
Inv
ariance
Tran
sform
ation
s(F
uk
ush
ima,’80)
Grad
ual
Inv
ariance
Tran
sform
ation
s(F
uk
ush
ima,’80)
Increasin
grecep
tive
field
sizeen
ables:
Co
nju
nctio
no
ffeatu
res(to
form
mo
reco
mp
lexo
bjects);an
d
Co
llapsin
go
ver
locatio
nin
form
ation
(“spatial
inv
ariance”)
Grad
ual
Inv
ariance
Tran
sform
ation
s(F
uk
ush
ima,’80)
ifd
idsp
atialin
varian
cein
on
efell
swo
op
:b
ind
ing
pro
blem
-can
’ttell
Tfro
mL
Grad
ual
Inv
ariance
Tran
sform
ation
s(F
uk
ush
ima,’80)
Go
al:U
nits
atth
eto
po
fth
eh
ierarchy
sho
uld
represen
tco
mp
lexo
bject
features
ina
locatio
nan
dsize
inv
ariant
fashio
n
Grad
ual
Inv
ariance
Tran
sform
ation
s(F
uk
ush
ima,’80)
Go
al:U
nits
atth
eto
po
fth
eh
ierarchy
sho
uld
represen
tco
mp
lexo
bject
features
ina
locatio
nan
dsize
inv
ariant
fashio
n
(alsow
ant
ben
efits
of
top
-do
wn
amp
lificatio
n,
pattern
com
pletio
n,
distrib
uted
reps
etc)
“Co
nv
olu
tion
alN
eural
Netw
ork
s”
•v
eryp
op
ular
for
“deep
learnin
g”
inm
achin
elearn
ing
,Yan
LeC
un
,Hin
ton
etc.
Th
eM
od
el:co
mb
inin
gF
uk
ush
ima
with
con
vo
lutio
nal
neu
raln
ets,bid
irection
alco
nn
ectivity
and
learnin
g!
V1
=o
riented
line
(edg
e)d
etectors,
hard
-cod
edV
4u
nits
enco
de
con
jun
ction
so
fV
1ed
ges
across
asu
bset
of
space
Each
ITu
nit
pay
satten
tion
toall
of
V4
(V2
om
ittedh
ere,imp
ortan
tfo
rfi
gu
re-gro
un
detc)
Th
eO
bjects
Each
ob
jectis
presen
tedat
mu
ltiple
locatio
ns,sizes
Netw
ork
’sjo
bis
toactiv
ateth
eap
pro
priate
Ou
tpu
tu
nit
(0-19)fo
reach
ob
ject,regard
lesso
flo
cation
and
size
[ob
jrec.pro
j]
Activ
ation
-Based
Recep
tive
Field
s
•H
ow
do
we
plo
trecep
tive
field
sfo
rV
4?
•R
eceivin
gw
eigh
tssh
ow
wh
ichV
1u
nits
aV
4u
nit
respo
nd
sto
,bu
tth
ey
do
n’t
sho
ww
hat
thin
gin
the
wo
rldth
eu
nit
respo
nd
sto
•S
olu
tion
:S
ho
wth
en
etwo
rklo
tso
fin
pu
tp
atterns.
•T
hen
,d
isplay
aco
mp
osite
of
allth
ein
pu
tp
atterns
that
activate
the
un
it.
V4
Recep
tive
Field
s
•S
om
eV
4u
nits
cod
efo
rlo
cation
-specifi
cco
nju
nctio
ns
of
V1
features
–T
his
will
sho
wu
pas
ash
arprecep
tive
field
for
Imag
ein
pu
t
V4
Recep
tive
Field
s
•S
om
eV
4u
nits
cod
efo
rsim
ple
features
ina
locatio
nin
varian
tw
ay
–T
his
will
sho
wu
pas
smeary
parallel
lines
inIm
age
inp
ut
V4
Recep
tive
Field
sfo
rO
utp
ut
•C
analso
loo
kat
wh
ichO
utp
ut
un
itsten
dto
get
active
for
any
giv
enV
4u
nit
–G
enerally
ag
iven
V4
un
itis
associated
with
mu
ltiple
ob
jects
Gen
eralization
•C
anth
en
etwo
rkg
eneralize
tou
nseen
view
so
fstu
died
ob
jects?
•In
oth
erw
ord
s:D
oes
trainin
gth
en
etto
recog
nize
aset
of
ob
jectsin
a
size/lo
cation
inv
ariant
fashio
nh
elpit
recog
nize
new
ob
jectsin
a
size/lo
cation
inv
ariant
fashio
n?
•P
roced
ure:
–T
ake
an
ettrain
edo
n18
ob
jects
–T
rainw
ith2
new
ob
jectsin
on
lyso
me
locatio
ns/
sizes
–T
estth
en
etw
ithn
on
stud
ied“v
iews”
(sizes/lo
cation
s)o
fn
ew
ob
jects
Gen
eralization
•C
anth
en
etwo
rkg
eneralize
tou
nseen
view
so
fstu
died
ob
jects?y
es
•A
pp
rox
.90%
correct
on
no
vel
view
sfo
llow
ing
trainin
go
nju
st6%
of
po
ssible
sizes/lo
cation
s
Gen
eralization
•C
anth
en
etwo
rkg
eneralize
tou
nseen
view
so
fstu
died
ob
jects?y
es
•A
pp
rox
.90%
correct
on
no
vel
view
sfo
llow
ing
trainin
go
nju
st6%
of
po
ssible
sizes/lo
cation
s
Ex
plan
ation
:D
istribu
tedrep
resentatio
ns
and
Heb
blearn
ing
!
•V
4rep
resents
ob
jectfe
atu
res
ina
locatio
n/
sizein
varian
tw
ay
•E
acho
bject
activates
ad
istribu
tedp
atterno
fth
esein
varian
tfeatu
re
detecto
rs
Yeah
,bu
tth
eseo
bjects
arereg
ularly
shap
ed,straig
ht
lines...
wh
atab
ou
treal
ob
jects?
“Em
er”th
ero
bo
treco
gn
izing
ob
jects..
Vid
eoD
emo
:em
erd
emo
1.mo
v
O’R
eilly,C
og
Sci
2009
Big
ger
netw
ork
mo
del,b
idirectio
nal
dy
nam
ics
Wy
atteet
al.,2012;O’R
eillyet
al.,2013
Bid
irection
alD
yn
amics
O’R
eillyet
al.,2013;W
yatte
etal.,2012
AC
hallen
ge
Deep
Pred
ictive
Learn
ing
:W
hat-W
here-In
tegratio
n
•u
sesp
redictiv
elearn
ing
via
recurren
tfeed
back
bu
tn
osu
perv
isedtarg
etlab
els!O
’Reilly
etal,
2017arX
iv
Still
missin
g...
Mo
tion
:m
otio
nn
oise.m
p4
Still
missin
g...
Mo
tion
:m
otio
nn
oise.m
p4
•N
euro
ns
inarea
MT
very
sensitiv
eto
mo
tion
•L
ots
of
wo
rko
nh
ow
do
wn
streamareas
integ
ratem
otio
nsig
nals
across
time
tod
etectco
heren
ce(e.g
.S
had
len,N
ewso
me,
etc)
•T
ho
mas
Serre
has
sho
wn
that
mo
tion
sign
alsv
eryreliab
lefo
r
discrim
inatin
gb
etween
particu
laractio
ns
(egth
row
ing
ab
aseball)
Still
missin
g...
Mo
tion
:m
otio
nn
oise.m
p4
•N
euro
ns
inarea
MT
very
sensitiv
eto
mo
tion
•L
ots
of
wo
rko
nh
ow
do
wn
streamareas
integ
ratem
otio
nsig
nals
across
time
tod
etectco
heren
ce(e.g
.S
had
len,N
ewso
me,
etc)
•T
ho
mas
Serre
has
sho
wn
that
mo
tion
sign
alsv
eryreliab
lefo
r
discrim
inatin
gb
etween
particu
laractio
ns
(egth
row
ing
ab
aseball)
•S
ho
uld
be
able
toso
lve
pro
blem
via
bid
irection
alin
flu
ence
of
mo
tion
integ
ration
sign
als,ob
jectreco
gn
ition
,an
dsp
atialatten
tion
(nex
t)....
Percep
tion
and
Atten
tion
1.W
hy
do
esp
rimary
visu
alco
rtexen
cod
eo
riented
bars
of
ligh
t?
Co
rrelation
allearn
ing
based
on
natu
ralv
isual
scenes.
2.H
ow
do
we
recog
nize
ob
jects(acro
sslo
cation
s,sizes,rotatio
ns
with
wild
lyd
ifferent
retinal
imag
es)?T
ransfo
rmatio
ns:
increasin
gly
com
plex
featural
enco
din
gs,in
creasing
levels
of
spatial
inv
ariance;
Distrib
uted
represen
tation
s.
3.W
hy
isv
isual
system
split
into
wh
at/w
here
path
way
s?
4.W
hy
do
esp
arietald
amag
ecau
seatten
tion
pro
blem
s(n
eglect)?
Sp
atialA
ttentio
n:
Un
ilateralN
eglect
Patien
tco
py
ing
ascen
e:
neg
lect.mp
4
Self
po
rtrait,co
py
ing
,lin
eb
isection
tasks:
Inall
cases,patien
tsw
ithp
arietal/tem
po
rallesio
ns
seemto
forg
etab
ou
t
1/2
of
space!
bu
tth
eystill
seeit!
Effects
of
Parietal
Lesio
ns
on
Po
sner
Task
40−
60−
80−
100−
120−01
2
Intact
Lesioned
Neutral
Valid
Invalid
•P
atients
perfo
rmn
orm
allyin
the
“neu
tral”(n
ocu
e)co
nd
ition
,
regard
lesso
fw
here
the
target
isp
resented
•P
atients
ben
efit
just
asm
uch
asco
ntro
lsfro
mv
alidcu
es
•P
atients
areh
urt
mo
reth
anco
ntro
lsb
yin
valid
cues
Po
ssible
Mo
dels
+
Alert
Interrupt
Localize
Disengage
Move
Engage
Inhibit
Atten
tion
emerg
esfro
mb
idirectio
nal
con
straint
satisfaction
&in
hib
itory
com
petitio
n.
Sim
ple
Mo
del
Input
V1
Spat1
Spat2
Obj1
Obj2
targ
cue
Output
Object 1 (C
ue)
Object 2 (T
arget)
[attnsim
ple.p
roj]
Po
sner
Task
Data
Valid
Inv
alidD
iffA
du
ltN
orm
al350
39040
Eld
erlyN
orm
al540
60060
Patien
ts640
760120
Eld
erlyn
orm
alized(*.65)
350390
40P
atients
no
rmalized
(*.55)350
41868
Po
sner
Task
Sim
s
•T
he
mo
del
exp
lains
the
basic
fin
din
gth
atv
alidcu
essp
eedtarg
et
pro
cessing
,w
hile
inv
alidcu
esh
urt
•A
lsoex
plain
sfi
nd
ing
that
patien
tsw
ithsm
allu
nilateral
parietal
lesion
s
ben
efit
no
rmally
from
valid
cues
inip
silateralfi
eldb
ut
are
disp
rop
ortio
nately
hu
rtb
yin
valid
cues.
•N
on
eedto
po
sit“d
iseng
age”
mo
du
le!
Po
sner
Task
Sim
s
•T
he
mo
del
exp
lains
the
basic
fin
din
gth
atv
alidcu
essp
eedtarg
et
pro
cessing
,w
hile
inv
alidcu
esh
urt
•A
lsoex
plain
sfi
nd
ing
that
patien
tsw
ithsm
allu
nilateral
parietal
lesion
s
ben
efit
no
rmally
from
valid
cues
inip
silateralfi
eldb
ut
are
disp
rop
ortio
nately
hu
rtb
yin
valid
cues.
•N
on
eedto
po
sit“d
iseng
age”
mo
du
le!
•A
lsoex
plain
sfi
nd
ing
of
ne
gle
ct
of
con
tralateralv
isual
field
afterlarg
e,
un
ilateralp
arietallesio
ns
wh
enso
me
stimu
lus
isp
resent
inip
silateral
field
(“extin
ction
”)
Mo
reP
osn
erL
esion
Fu
n
•R
eturn
ing
top
atient
with
leftp
arietallesio
n...
•W
hat
hap
pen
sif
cues
arep
resented
inc
on
trala
tera
l(affected
)
hem
ifield
?(“R
everse
Po
sner”)
Mo
reP
osn
erL
esion
Fu
n
Retu
rnin
gto
patien
tw
ithleft
parietal
lesion
...
•W
hat
hap
pen
sif
cues
arep
resented
inc
on
trala
tera
l(affected
)
hem
ifield
?
Pred
iction
s:
•S
maller
ben
efit
for
valid
cues
•P
atients
sho
uld
be
hu
rtless
than
con
trols
by
inv
alidcu
es.
[attnsim
ple.p
roj]
Inh
ibitio
no
fR
eturn
•T
yp
ically,targ
etd
etection
isfaster
on
trialsw
ithv
alidv
sin
valid
cues
•H
ow
ev
er,if
the
cue
isp
resented
for
alo
ng
ertim
e(eg
.500
ms),
perfo
rman
ceis
fastero
nin
va
lidv
sv
alidtrials
•C
anex
plain
interm
so
fa
cc
om
mo
da
tion
(neu
ralfatig
ue)
[attnsim
ple.p
roj]
Sim
ple
mo
del:
too
simp
le?
•H
asu
niq
ue
on
e-to-o
ne
map
pin
gs
betw
eenlo
w-lev
elv
isual
features
and
ob
jectrep
resentatio
ns
(no
trealistic)
•D
oes
no
tad
dress
issue
of
spatial
attentio
nw
hen
tryin
gto
perceiv
e
mu
ltiple
ob
jectssim
ultan
eou
sly
Sim
ple
mo
del:
too
simp
le?
•H
asu
niq
ue
on
e-to-o
ne
map
pin
gs
betw
eenlo
w-lev
elv
isual
features
and
ob
jectrep
resentatio
ns
(no
trealistic)
•D
oes
no
tad
dress
issue
of
spatial
attentio
nw
hen
tryin
gto
perceiv
e
mu
ltiple
ob
jectssim
ultan
eou
sly
•“C
om
plex
”m
od
elco
mb
ines
mo
rerealistic
mo
del
of
ob
jectreco
gn
ition
(starting
from
LG
N)
with
simp
leatten
tion
mo
del
→C
anu
sesp
atialatten
tion
torestrict
ob
jectp
rocessin
gp
athw
ayto
on
eo
bject
ata
time,en
ablin
git
toseq
uen
tiallyp
rocess
mu
ltiple
ob
jects.
•L
esion
so
fen
tiresp
atialp
athw
aycau
sesim
ultan
agn
osia:
inab
ilityto
con
curren
tlyreco
gn
izetw
oo
bjects
Co
mp
lexM
od
el
LGN
_On
LGN
_Off
V1
V2
V4
/IT Output
Spat1
Spat2
Target
Sp
at1h
asrecu
rrent
pro
jns
toen
cou
rage
focu
so
no
ne
regio
no
fsp
ace
Co
mp
lexM
od
el
LGN
_On
LGN
_Off
V1
V2
V4
/IT Output
Spat1
Spat2
Target
Sp
at1h
asrecu
rrent
pro
jns
toen
cou
rage
focu
so
no
ne
regio
no
fsp
ace
Bu
to
nly
mech
anism
for
switch
ing
isacco
mm
od
ation
...
Percep
tion
and
Atten
tion
1.W
hy
do
esp
rimary
visu
alco
rtexen
cod
eo
riented
bars
of
ligh
t?
Co
rrelation
allearn
ing
based
on
natu
ralv
isual
scenes.
2.H
ow
do
we
recog
nize
ob
jects(acro
sslo
cation
s,sizes,rotatio
ns
with
wild
lyd
ifferent
retinal
imag
es)?T
ransfo
rmatio
ns:
increasin
gly
com
plex
featural
enco
din
gs,in
creasing
levels
of
spatial
inv
ariance;
Distrib
uted
represen
tation
s.
3.W
hy
isv
isual
system
split
into
wh
at/w
here
path
way
s?
Tran
sform
ation
s:em
ph
asizing
and
collap
sing
across
differen
tty
pes
of
relevan
td
istinctio
ns;atten
tion
4.W
hy
do
esp
arietald
amag
ecau
seatten
tion
pro
blem
s(n
eglect)?
Atten
tion
asan
emerg
ent
pro
perty
of
com
petitio
n
Gen
eralIssu
esin
Atten
tion
Atten
tion
:
•P
rioritizes
pro
cessing
.
•C
oo
rdin
atesp
rocessin
gacro
ssd
ifferent
areas.
•S
olv
esb
ind
ing
pro
blem
sv
iaco
ord
inatio
n.
Gen
eralIssu
esin
Atten
tion
Atten
tion
:
•P
rioritizes
pro
cessing
.
•C
oo
rdin
atesp
rocessin
gacro
ssd
ifferent
areas.
•S
olv
esb
ind
ing
pro
blem
sv
iaco
ord
inatio
n.
Bu
tatten
tion
sho
uld
be
mu
chm
ore
flex
ible
than
just
spatial
bias!
Later:
ho
wto
inco
rpo
rateg
oals,rein
forcem
ent
pro
bab
ility,in
toatten
tion
al
allocatio
n