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THEORIE DE LA DETECTION DU SIGNAL
La Théorie de la Détection du Signal (TDS) est à la fois un instrument de mesure de la performance et un cadre théorique dans lequel cette performance est interprétée du point de vue des mécanismes sous-jacents. En tant que telle, la TDS peut être regardée comme étant à la base de la psychophysique moderne. Le cours développera les bases de la TDS, discutera sa relation avec les concepts de seuil sensoriel et de décision et exemplifiera son application dans des situations expérimentales allant depuis le sensoriel jusqu'au cognitif.
SIGNAL DETECTION THEORY AND PSYCHOPHYSICS
David M. Green & John A. Swets
1st published in 1966 by Wiley & Sons, Inc., New York
Reprint with corrections in 1974
Copyright © 1966 by Wiley & Sons, Inc.
Revision Copyright © 1988 by D.M. Green & J. A. Swets
1988 reprint edition published by Peninsula Publishing, Los Altos, CA 94023, USA
CONTINUUM SENSORIEL
PR
OB
AB
AB
ILIT
É
1
R
FA
Hits
p(‘yes’│N) = p(RN>c)
p(‘yes’│S) = p(RS>c)
p(N) p(S)STIMULUS
RÉ
PO
NS
E
Bruit Signal
Oui FA Hit
Non CR Omiss.c
R
LE MODEL STANDARD :LE MODEL STANDARD :THEORIE de la DETECTION du SIGNALTHEORIE de la DETECTION du SIGNAL
CR
Miss
0
S Rd' z(Hits) z(FA)
Bc .5 z(H) z(FA) ; c z FA
-
xz
Score zOu « score normal » ou « score standard »
-4.0 -3.0 -2.0 -1.0 0 +1.0 +2.0 +3.0 +4.0Z scores
LE MODEL STANDARD :LE MODEL STANDARD :THEORIE de la DETECTION du SIGNALTHEORIE de la DETECTION du SIGNAL
CONTINUUM SENSORIEL
f R:
PR
OB
AB
AB
ILIT
É
FA
Hits
The likelihood ratioLe rapport de vraisemblance
c R
cf
cf
NcRp
ScRp)X(L
N
S
R
R
-4 -3 -2 -1 0 1 2 3 4 5 6
Sensory Continuum (z)
0
0,1
0,2
0,3
0,4
0,5
P(z
)
N S
d’=1
cA = c + d’/2 = 1.60 =cA
c = 1.10c
LE MODEL STANDARD :LE MODEL STANDARD :THEORIE de la DETECTION du SIGNALTHEORIE de la DETECTION du SIGNAL
p(S) = .25
c
3
p
p
czp
czp
S
N
N
S
pN(z
=c
)
pS (z=
c)
-zFA
c0
= 1
p(S) = . 50
c = 0
z(FA)z(FA)
-z
1 - p > .5p < .5
1 - p > .5
+z
p > .51 - p < .5
p > .51 - p > .5
+z +z
z(H)z(H)
-z +zd’ = z(H) – [-z(FA)]
+z +zd’ = z(H) – z(FA)
d’ d’
z(.5) = 0z(p < .5) < 0z(p > .5) > 0z(1 - p) = -z(p)
d’ computation with Excel
d’ = LOI.NORMALE.STANDARD.INVERSE[p(H)] - LOI.NORMALE.STANDARD.INVERSE[p(FA)]
c0 = -0.5*{LOI.NORMALE.STANDARD.INVERSE[p(H)] + LOI.NORMALE.STANDARD.INVERSE[p(FA)]}
cA = -LOI.NORMALE.STANDARD.INVERSE[p(FA)]
= LOI.NORMALE(cA;d’;1;FAUX)/LOI.NORMALE(cA;0;1;FAUX)
HITS FA HITS FA HITS FA HITS FAn 70 30 90 10 83 5 60 20
out of 100 100 100 100 100 100 100 100
N_Tot
d'
c0ca 1.0000
0.3453 0.29410.5244 1.2816 1.6449 0.8416
200 200 200 200
1.0000 2.4536 1.3800
1.09502.59902.56311.0488
0.0000 0.0000
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d' = 0
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 0
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d' = 0
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 0
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d' = 0
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 0
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d' = 0
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 0
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d' = 0
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 0
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d' = 1
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 1
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d' = 1
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 1
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d' = 1
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 1
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d' = 1
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 1
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d' = 1
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 1
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d' = 1
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 1
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d' = 1
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 1
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d'= 2
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 2
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d'= 2
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 2
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d'= 2
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 2
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d'= 2
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 2
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d'= 2
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 2
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d'= 2
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 2
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d'= 2
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 2
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d'= 3
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 3
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d'= 3
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 3
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d'= 3
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 3
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d'= 3
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 3
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d'= 3
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 3
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d'= 3
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 3
0
0.2
0.4
-8 -4 0 4 8
Sensory Continuum (z)
P(z
d'= 3
0
0.2
0.4
0.6
0.8
1
0.0 0.2 0.4 0.6 0.8 1.0
pFAp
H
d' = 3
d’d’
d’
Receiving Operating Characteristics (ROC)Receiving Operating Characteristics (ROC)
ROC & le Rating experimentROC & le Rating experiment1. Faire le choix entre Oui et Non;
375
375
Tot.
28590N
157218S
NonOui
2. Donner son niveau de certitude de 1 (peu sûr) à (par ex.) 3 (très sûr); l’on calcule les p pour chaque case en divisant sa fréq. par le no. total
1.00
1.00
Tot.
N, FA
S, H
*
.301
.059
“3”
.301
.200
“2”
.160
.160
“1”
Non
.200.251.131
.120.099.021
“1”“2”“3”
Oui
3. Pr chaque case, l’on calcule les p cumulés de gauche à droite, les scores z, les d’ et c
FA
H
1.00
1.00
“1”
.701
.942
“2”
.400
.742
“3”
.582.382.131
.240.120.021
“4”“5”“6”
Oui
-1.050-0.1980.2500.7381.579c
1.0460.9020.9130.8740.916d’
zFA
zH
0.527
1.573
-0.253
0.6490.207-0.301-1.121
-0.706-1.175-2.037
-4 -3 -2 -1 0 1 2 3 4 5 6Sensory Continuum (z)
0
0.1
0.2
0.3
0.4
0.5
P(z
)
N S
d’=.91
c
.202-(-.706) = .908d’
zH-zFA
-.706
zFA
-.5(.202 - .706) =.252c
-.5(zH + zFA)
90/375 = .24
.202218/375
= .58
pFAzHpH
2. Calculer le pH, pFA; puis d’ & c.
*Notez que l’échelle « Oui/Non » ci-dessus est équivalente à une échelle «Oui» avec 6 graduations, du plus sûr («6») au moins sûr («1»):
Tot.“1”“2”“3”“4”“5”“6”
Oui
ROC & le Rating experimentROC & le Rating experiment
-4 -3 -2 -1 0 1 2 3 4 5 6
Sensory Continuum (z)
0
0.1
0.2
0.3
0.4
0,5
P(z
)
N S
d’=.91
p z p z p z p z p z p zH 0.131 -1.122 0.382 -0.300 0.582 0.207 0.742 0.650 0.942 1.572 1.000 ######
FA 0.021 -2.034 0.120 -1.175 0.240 -0.706 0.400 -0.253 0.701 0.527 1.000 ######d'c0
OUI NON
#NOMBRE!1.0450.9030.9130.8750.912
"2""3"
#NOMBRE!-1.050-0.1980.2500.7381.578
"3""2""1""1"
Note that a symmetrical ROC indicates that d’ is independent of c0
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
pFA
pH
ROC & le Rating experimentROC & le Rating experiment
p z p z p z p z p z p zH 0.131 -1.122 0.382 -0.300 0.582 0.207 0.742 0.650 0.942 1.572 1.000 ######
FA 0.021 -2.034 0.120 -1.175 0.240 -0.706 0.400 -0.253 0.701 0.527 1.000 ######d'c0
OUI NON
#NOMBRE!1.0450.9030.9130.8750.912
"2""3"
#NOMBRE!-1.050-0.1980.2500.7381.578
"3""2""1""1"
ROC in z units
-2.5
-1.5
-0.5
0.5
1.5
-2.5 -0.5 1.5z(FA)
z(H
)
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
pFA
pH
d’ with unequal variancesd’ with unequal variances
CONTINUUM SENSORIEL
PR
OB
AB
AB
ILIT
É
FA
Hits
c
?'d
1
2
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
pFA
pH
d’ ?
0
0.2
0.4
0.6
0.8
1
pH
d’
-2
-1
0
1
2
-2 -1 0 1 2
zFA
zH
in N units
'1d
'2d in S units
'N/Yd
d’a
d’a
-2
-1
0
1
2
zH
'2d
in N units
in S units
'1d
d’2 < d’a < d’1
d’
wit
h u
neq
ual
var
ian
ces
d’
wit
h u
neq
ual
var
ian
ces
The index da has the properties we want.
1. It is intermediate in size between d1 and d‘2.2. It is equivalent to d' when the ROC slope is 1,
for then the perpendicular line of length DY/N coincides with the minor diagonal, and the two lines of length da coincide with the d'1, and d'2 segments.
3. It turns out to be equivalent to the difference between the means in units of the root-mean-square (rms) standard deviation, a kind of average equal to the square root of the mean of the squares of the standard deviations of S1 and S2.
22
21
'N/Y
'a /Rd2d
To find da from an ROC that is linear in z coordinates, it is easiest to first estimate d’1, d’2 and the slope s = d’1/d’2. Because the standard deviation of the S1 distribution is s times as large as that of S2, we can set the standard deviation of S1 to s and that of S2 to 1.
To find da directly from one point on the ROC once s is known:
-2
-1
0
1
2
-2 -1 0 1 2
zFA
zH
in N units
'1d
'2d in S units
'N/Yd
d’a
d’a
d’
wit
h u
neq
ual
var
ian
ces
d’
wit
h u
neq
ual
var
ian
ces
5.
'25.
'2'
a ²s1
2d
²s15.
dd
5.
'a ²s1
2FszHzd
Sometimes a measure of performance expressed as a proportion is preferred to one expressed as a distance. The index Az, is such a proportion and is simply DYN transformed by the cumulative normal distribution function , i.e. (DYN):
Az is the area under the normal-model ROC curve, which increases from .5 at zero sensitivity to 1.0 for perfect performance. It is basically equal to %Correct in a 2AFC experiment. It is a truly nonparametric index of sensitivity.
d’
wit
h u
neq
ual
var
ian
ces
d’
wit
h u
neq
ual
var
ian
ces
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
pFA
pH
d’
2dDAz,ROCunderArea a
YN
High-Threshold
Sum Absolute Difference (SAD)
Max Absolute Difference (MAD)
Schematic of a single trial used for the set size and the target number experiments. In the orientation and spatial frequency experiments, the colored squares were replaced with Gabor patches.
Wilken, P. & Ma, W.J (2004). A detection theory account of change detection. J. Vision 4, 1120-1135.
ROCs obtenus avec une méthode de « rating » sur une échelle de 1 à 4. Different symbols represent performance at different set sizes: set-size 2 for color, orientation and SF – red stars; set-size 4 for color and SF, set-size 3 for orientation – green triangles; set-size 6 for color and SF, set-size 4 for orientation – blue circles; set-size 8 for color and SF, set-size 5 for orientation – purple squares.
Hits & FA
HT
SAD
MAD
H
FA
H
FA
H
FA
LA RELATION ENTRE Y/N & 2AFC
See MacMillan & Creelman (2005, 2nd ed.) pp 166-170.
0 S
1 2
S 0
1 2
A = « 1 » - « 2 » -S
B = « 1 » - « 2 » S22SD
S2BA
'N/Y
'AFC2 d2
2S
2
S2d
LA RELATION ENTRE Y/N & 2AFC
d’
d’
d’2
RéponseCorrectIncorrectDroite
IncorrectCorrectGauche
DroiteGauche
Stimulus
%Correct Hits%Incorrect FA
d’ 2AFC
d’2AFC = 2d’Oui/Non
D
0
G
Yes/No
Yes
/No
See MacMillan & Creelman (2005, 2nd ed.) pp 166-170.
So, objects may be “invisible” for only 2 + 1 reasons;
because the internal activity they yield upon presentation:
because Sj’s report on the presence/absence of the stimulus is solicited within a time delay beyond his/her mnemonic capacity (the case of change blindness).
is below Sj’s criterion the objet is ignored, or
0
0,1
0,2
0,3
0,4
0,5
-4 -3 -2 -1 0 1 2 3 4 5 6
P(z
)
is undistinguishable from noise (null sensitivity) d' = 0,
0
0,1
0,2
0,3
0,4
0,5
-4 -3 -2 -1 0 1 2 3 4 5 6
P(z
)
0
0,1
0,2
0,3
0,4
0,5
-4 -3 -2 -1 0 1 2 3 4 5 6
P(z
)
d' = 3
c =1.5
Reasons for “invisibility”Reasons for “invisibility”
Failure of discriminating Signal from Noise has a triple edge:
Stimuli may be out of the standard window of visibility [] (Signal Internal noise)
e.g. ultraviolet light, SF > 40 c/deg, TF > 50 Hz reduced window of visibility (experimental or pathological –
e.g. blindsight)
Stimuli may be "masked" by external noise [] so that the S/N ratio is decreased by means of:
increasing N (external noise impinges on the detecting mechanism the classical pedestal effect, or increases spatial/temporal uncertainty e.g. the "mud-splash effect")
decreasing S (external noise activates an inhibitory
mechanism lateral masking/metacontrast) [] Change of the transducer [](experimentally or neurologically
induced gain-control effects, normalization failure …)
SENSITIVITYSENSITIVITY 0
0,1
0,2
0,3
0,4
0,5
-4 -3 -2 -1 0 1 2 3 4 5 6
P(z
)
can be manipulated in a number of ways: probability pay-off
can be affected by experience in general
and, possibly, by some neurological conditions blindsight neglect / extinction attentional deficit syndromes
CRITERIONCRITERION
0
0,1
0,2
0,3
0,4
0,5
-4 -3 -2 -1 0 1 2 3 4 5 6
P(z
)
-4 -3 -2 -1 0 1 2 3 4 5 6
Criterion (zFA)
% Y
ES
d' = 3
49.4%50%
0
0,1
0,2
0,3
0,4
0,5
-4 -3 -2 -1 0 1 2 3 4 5 6
P(z
)
-4 -3 -2 -1 0 1 2 3 4 5 6
Criterion (zFA)
% Y
ES
d' = 3
98.7%
0
0,1
0,2
0,3
0,4
0,5
-4 -3 -2 -1 0 1 2 3 4 5 6
P(z
)
-4 -3 -2 -1 0 1 2 3 4 5 6
Criterion (zFA)
% Y
ES
d' = 3
91.5%
0
0,1
0,2
0,3
0,4
0,5
-4 -3 -2 -1 0 1 2 3 4 5 6
P(z
)
-4 -3 -2 -1 0 1 2 3 4 5 6
Criterion (zFA)
% Y
ES
d' = 3
73.9%
0
0,1
0,2
0,3
0,4
0,5
-4 -3 -2 -1 0 1 2 3 4 5 6
P(z
)
-4 -3 -2 -1 0 1 2 3 4 5 6
Criterion (zFA)
% Y
ES
d' = 3
56.1%
0
0,1
0,2
0,3
0,4
0,5
-4 -3 -2 -1 0 1 2 3 4 5 6
P(z
)
-4 -3 -2 -1 0 1 2 3 4 5 6
Criterion (zFA)
% Y
ES
d' = 3
49.4%50%
0
0,1
0,2
0,3
0,4
0,5
-4 -3 -2 -1 0 1 2 3 4 5 6
P(z
)
-4 -3 -2 -1 0 1 2 3 4 5 6
Criterion (zFA)
% Y
ES
d' = 3
42.5%
0
0,1
0,2
0,3
0,4
0,5
-4 -3 -2 -1 0 1 2 3 4 5 6
P(z
)
-4 -3 -2 -1 0 1 2 3 4 5 6
Criterion (zFA)
% Y
ES
d' = 3
24.1%
0
0,1
0,2
0,3
0,4
0,5
-4 -3 -2 -1 0 1 2 3 4 5 6
P(z
)
-4 -3 -2 -1 0 1 2 3 4 5 6
Criterion (zFA)
% Y
ES
d' = 3
7.3%
The response criterion can be assimilated to response bias (and everything that goes with it, i.e. predisposition, context effects, etc.)
AN EXAMPLE: CHANGE BLINDNESSAN EXAMPLE: CHANGE BLINDNESS
Dinner
Money
Airplane
CANAUX, BRUIT
et
THÉORIE DE LA DÉTECTION DU SIGNAL
« Qualité » ()Intensité ou « qualité » du StimulusRé
po
ns
e d
’un
fil
tre
(s
/s o
u a
utr
e)
Le principe de l’UNIVARIANCE
Un Canal,Filtre.
Chp Récep.
R V
Snn
n
max RI
IRR
I = stimulus intensityR = responseRmax = max resp.N = max slopeS = semi-saturation cst.Rs = spontaneous R
Un autreR
ép
on
se
d’u
n f
iltr
e
Normalization works (here) by
dividing each output
by the sum of all outputs.
Heeger, D.J. (1992). Normalization of cell responses in cat striate cortex. Visual Neuroscience, 9, 181-197.
RESPONSE NORMALIZATION
Divisive inhibition
R V
RÉPONSE
CANAUX & PROBABILITÉ DE RÉPONSEIntensity Discrimination
p(s/s)
RE
PO
NS
E (
e.g
. s
/s)
pmin
pMax
pmin
pMax
p(s/s)
R V
RÉPONSE
CANAUX & PROBABILITÉ DE RÉPONSEWithin & Across Channels Discrimination
p(s/s)
p(s/s)
RE
PO
NS
E (
e.g
. s
/s)
pmin
pMax
pmin
pMax
CANAUX & PROBABILITÉ DE RÉPONSE« OFF-LOOKING » CHANNEL
RÉPONSE
R Vp(s/s)
RE
PO
NS
E (
e.g
. s
/s)
p(s/s)R
EP
ON
SE
(e
.g.
s/s
)R V
I0 I1
kIR = cst.
kI
.)cstiff;at(kR
IR
IR
log (I)lo
g (I
)
slope
= 1
NO Weber ’s LawI = k
k
Input
R =
kI
p(R)
II1
R
II0
R
TRANSDUCTION
Input
R =
log
(I)
I0 I1
II1
R
II0
R
)Fechner()Ilog(R = cst.
p(R)log (I)
log
(I
)
slope
= 1
Weber ’s LawI = kI
)Laws'Weber(I'kI
.)cstiff;at(kRI
IR
)Fechner()Ilog(R
TRANSDUCTION
Input
R =
log
(I)
I0 I1
)0βiffLaws'Weber(IlogkII
)Rσ;θat(RRI
IR
)Fechner(IlogR
βθ
ββθ
II0
R
p(R)
R
II1
log (I)lo
g (I
)
slop
e >
1
Weber ’s LawI = kI
)Fechner(IlogR R
TRANSDUCTION
VISUAL SEARCH
and
SIGNAL DETECTION THEORY
T
emp
s. R
éact
ion
No. Éléments
ORIENTATION
Parallèle (?!)
Tem
ps.
Réa
ctio
n
No. Éléments
Sériel (?!)
ORI + COL
Le paradigme type
Treisman A.M. & Gelade G. (1980)
LE MODÉLE STANDARD : LA THÉORIE DE LA DÉTECTION DU SIGNAL
« COULEUR »
L
R V
RE
SP
ON
SE
RE
SP
ON
SE
CONTINUUM SENSORIEL (R-V)
PR
OB
AB
.
R
d’ n’est pas mesurable
« COULEUR »
L
R V
RE
SP
ON
SE
CONTINUUM SENSORIEL (R-V)
PR
OB
AB
.
R
Rd’=
T
emp
s. R
éact
ion
No. Éléments
ORIENTATION
Parallèle (?!)
Tem
ps.
Réa
ctio
n
No. Éléments
Sériel (?!)
Procs. Coopératifs
Tem
ps.
Réa
ctio
n
No. Éléments
COULEUR
Parallèle (?!)
Tem
ps.
Réa
ctio
nNo. Éléments
Sériel (?!)
Procs. Coopératifs