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Rescorla-Wagner (1972) Theory of Classical Conditioning. Rescorla-Wagner Theory (1972). Organisms only learn when events violate their expectations (like Kamin’s surprise hypothesis) Expectations are built up when ‘significant’ events follow a stimulus complex - PowerPoint PPT Presentation
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Rescorla-Wagner (1972) Theory of Classical
Conditioning
Rescorla-Wagner Theory (1972)
• Organisms only learn when events violate their expectations (like Kamin’s surprise hypothesis)
• Expectations are built up when ‘significant’ events follow a stimulus complex
• These expectations are only modified when consequent events disagree with the composite expectation
Rescorla-Wagner Theory
• These concepts were incorporated into a mathematical formula:– Change in the associative strength of a stimulus
depends on the existing associative strength of that stimulus and all others present
– If existing associative strength is low, then potential change is high; If existing associative strength is high, then very little change occurs
– The speed and asymptotic level of learning is determined by the strength of the CS and UCS
Rescorla-Wagner Mathematical Formula
∆Vcs = c (Vmax – Vall)
• V = associative strength• ∆ = change (the amount of change)• c = learning rate parameter• Vmax = the maximum amount of associative strength
that the UCS can support• Vall = total amount of associative strength for all
stimuli present• Vcs = associative strength to the CS
Before conditioning begins:
• Vmax = 100 (number is arbitrary & based on the strength of the UCS)
• Vall = 0 (because no conditioning has occurred)
• Vcs = 0 (no conditioning has occurred yet)• c = .5 (c must be a number between 0 and
1.0 and is a result of multiplying the CS intensity by the UCS intensity)
First Conditioning TrialTrial c (Vmax - Vall) = ∆Vcs 1
.5 * 100 - 0 = 50
0
50
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8
Trials
Ass
oci
ati
ve S
trength
(V
)
Vall
Second Conditioning Trial
Trial c (Vmax - Vall) = ∆Vcs 2 .5 * 100 - 50 = 25
0
50
75
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8
Trials
Ass
oci
ati
ve S
tren
gth
(V
)
Vall
Third Conditioning Trial
Trial c (Vmax - Vall) = ∆Vcs 3 .5 * 100 - 75 = 12.5
0
50
75
87.5
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8
Trials
Ass
oci
ati
ve S
tren
gth
(V
)
Vall
4th Conditioning Trial
Trial c (Vmax - Vall) = ∆Vcs 4 .5 * 100 - 87.5 = 6.25
0
50
75
87.593.75
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8
Trials
Ass
oci
ati
ve S
trength
(V
)
Vall
5th Conditioning Trial
Trial c (Vmax - Vall) = ∆Vcs 5 .5 * 100 - 93.75 = 3.125
0
50
75
87.593.7596.88
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8
Trials
Ass
oci
ati
ve S
trength
(V
)
Vall
6th Conditioning Trial
Trial c (Vmax - Vall) = ∆Vcs 6 .5 * 100 - 96.88 = 1.56
0
50
75
87.593.7596.8898.44
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8
Trials
Ass
oci
ati
ve S
trength
(V
)
Vall
7th Conditioning Trial
Trial c (Vmax - Vall) = ∆Vcs 7 .5 * 100 - 98.44 = .78
0
50
75
87.593.7596.8898.4499.22
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8
Trials
Ass
oci
ati
ve S
trength
(V
)
Vall
8th Conditioning Trial
Trial c (Vmax - Vall) = ∆Vcs 8 .5 * 100 - 99.22 = .39
0
50
75
87.593.7596.8898.4499.2299.61
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8
Trials
Ass
oci
ati
ve S
trength
(V
)
Vall
1st Extinction Trial
Trial c (Vmax - Vall) = ∆Vcs 1 .5 * 0 - 99.61 = -49.8
Acquisition
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8
Trials
Ass
oci
ati
ve S
trength
(V
)
Vall
Extinction
99.61
49.8
0
20
40
60
80
100
0 1 2 3 4 5 6
Trials
Ass
oci
ati
ve S
tren
gth
(V
)
Vall
2nd Extinction Trial
Trial c (Vmax - Vall) = ∆Vcs 2 .5 * 0 - 49.8 = -24.9
0
50
75
87.593.75 96.88 98.44 99.22 99.61
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8
Trials
Asso
ciativ
e St
reng
th (V
)
Vall
Acquisition
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8
Trials
Asso
ciativ
e St
reng
th (V
)
Vall
Extinction
99.61
49.8
24.9
0
20
40
60
80
100
0 1 2 3 4 5 6
Trials
Ass
oci
ati
ve S
tren
gth
(V
)Vall
Extinction Trials
Trial c (Vmax - Vall) = ∆Vcs 3 .5 * 0 - 12.45 = -12.46
Trial c (Vmax - Vall) = ∆Vcs 4 .5 * 0 - 6.23 = -6.23
Trial c (Vmax - Vall) = ∆Vcs 5 .5 * 0 - 3.11 = -3.11
Trial c (Vmax - Vall) = ∆Vcs 6 .5 * 0 - 1.56 = -1.56
Hypothetical Acquisition & Extinction Curves with c=.5 and
Vmax = 100Extinction
99.61
49.8
24.9
12.456.23 3.11 1.560
20
40
60
80
100
0 1 2 3 4 5 6
Trials
Asso
ciat
ive
Stre
ngth
(V)
Vall
Acquisition
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8
Trials
Asso
ciativ
e St
reng
th (V
)
Vall
Acquisition & Extinction Curves with c=.5 vs. c=.2 (Vmax = 100)
Acquisition
0
20
40
60
80
100
120
0 1 2 3 4 5 6 7 8
Trials
Ass
oci
ati
ve S
trength
(V
)
c=.5c=.2
Extinction
0
20
40
60
80
100
120
0 1 2 3 4 5 6
Trials
Ass
oci
ati
ve S
trength
(V
)
c=.5c=.2
Theory Handles other Phenomena
• Overshadowing– Whenever there are multiple stimuli or a compound
stimulus, then Vall = Vcs1 + Vcs2
• Trial 1:– ∆Vnoise = .2 (100 – 0) = (.2)(100) = 20– ∆Vlight = .3 (100 – 0) = (.3)(100) = 30– Total Vall = current Vall + ∆Vnoise + ∆Vlight = 0 +20 +30
=50
• Trial 2:– ∆Vnoise = .2 (100 – 50) = (.2)(50) = 10– ∆Vlight = .3 (100 – 50) = (.3)(50) = 15– Total Vall = current Vall + ∆Vnoise + ∆Vlight = 50+10+15=75
Theory Handles other Phenomena
• Blocking– Clearly, the first 16 trials in Phase 1 will result in most
of the Vmax accruing to the first CS, leaving very little Vmax available to the second CS in Phase 2
• Overexpectation Effect– When CSs trained separately (where both are close to
Vmax) are then presented together you’ll actually get a decrease in associative strength
Rescorla-Wagner Model
• The theory is not perfect:– Can’t handle configural learning without a little
tweaking– Can’t handle latent inhibition
• But, it has been the “best” theory of Classical Conditioning
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