 Instructor: David A. Townsend
 Email:
 Class Web Page:
RESCORLA-WAGNER THEORY:
BACKGROUND
 Research by Rescorla and Kamin requires a
change in our conception of conditioning.
...
RESCORLA-WAGNER THEORY:
BACKGROUND
 This view of Pavlovian conditioning makes
conditioning process appear much more
compl...
RESCORLA-WAGNER THEORY:
BACKGROUND
 In last 30 years, many different theories have
been advanced to explain rich details ...
RESCORLA-WAGNER THEORY:
PRELUDE
 Kamin’s blocking effect set stage for RescorlaWagner model.
 Blocking effect suggested ...
RESCORLA-WAGNER THEORY:
OVERVIEW
 Rescorla-Wagner theory explains complex
contingency analysis in terms of simple
associa...
RESCORLA-WAGNER THEORY:
TUTORIALS
If you need help, then please consult:
http://psy.uq.oz.au/~landcp/PY269/rwmodel/index...
RESCORLA-WAGNER THEORY:
ACQUISITION
 Standard conditioning curves are negatively
accelerated.
 Changes in conditioning s...
RESCORLA-WAGNER THEORY:
ACQUISITION
 Negatively accelerated learning curve suggests
that organism does not profit equally...
RESCORLA-WAGNER THEORY:
ACQUISITION
 Rather than learning a fixed amount with each
trial, one learns a fixed proportion o...
RESCORLA-WAGNER THEORY:
ACQUISITION
 Here is the equation:

∆Vn = K( — Vn-1)

 V, associative strength, is measure of l...
RESCORLA-WAGNER THEORY:
ACQUISITION
 ∆Vn = K( — Vn-1)

 K reflects salience of CS.
 K can vary between 0 and 1 (0 ≤ K ≤...
RESCORLA-WAGNER THEORY:
ACQUISITION
 ∆Vn = K( — Vn-1)
 indicates that different USs support different
maximum levels of ...
RESCORLA-WAGNER THEORY:
ACQUISITION
 ∆Vn = K( — Vn-1)

 Change in strength on Trial n (∆Vn) is
proportional to differenc...
∆Vn = K( — Vn-1)
∆ (change in) Delta
 V, associative strength, is measure of learning
 K reflects salience of CS.
 ( L...
Acquisition Trials
First conditioning trial:

Light (CS) is paired with shock (US)

∆Vn= light (CS)=0
k = .20
= associated...
∆Vn = K( — Vn-1)

Acquisition Trials
First conditioning trial:

Light (CS) is paired with shock (US)

∆Vtotal= light (CS)=...
∆Vn = K( — Vn-1)

Acquisition Trials
First conditioning trial:

Light (CS) is paired with shock (US)

∆Vtotal= light (CS)=...
∆Vn = K( — Vn-1)

Acquisition Trials
First conditioning trial:

Light (CS) is paired with shock (US)

∆Vtotal= light (CS)=...
Rescorla-Wagner Model
 Calculations from
the RescorlaWagner model
show a
mathematical
relationship to the
process of
cond...
Rescorla-Wagner Theory (1972)
 Organisms only learn when
events violate their expectations
(like Kamin’s surprise hypothe...
First Conditioning Trial
Trial
1

K ( - Vn-1 )
.5 * 100 -

=
0

∆Vn
=
50

Associative Strength (V)

100
80

∆Vn = K( — Vn-...
Second Conditioning Trial
K ( - Vn-1 )
.5 * 100 -50

∆Vn
25

=
=

100

Associative Strength (V)

Trial
2

80

75

60
50
40...
Third Conditioning Trial
K ( - Vn-1 )
.5 * 100 -75

∆Vn
12.5

=
=

100

Associative Strength (V)

Trial
3

87.5
80

75

60...
4th Conditioning Trial
K ( - Vn-1 )
c (Vmax .5 * 100
-

87.5

80

∆Vn
∆Vcs
6.25

=
=
=

Vall)
87.5

100

Associative Stren...
5th Conditioning Trial
K ( - Vn-1 )
.5 * 10 - 93.75
100

Associative Strength (V)

Trial
5

87.5

80

=
=

96.88
93.75

75...
6th Conditioning Trial
K ( - Vn-1 )
.5 * 100 - 96.88
100

Associative Strength (V)

Trial
6

87.5

80

=
=

96.8898.44
93....
7th Conditioning Trial
Trial
7

K ( - Vn-1 )
.5 * 100 - 98.44

Associative Strength (V)

100
87.5

80

=
=

96.8898.4499.2...
8th Conditioning Trial
K ( - Vn-1 )
.5 * 1 - 99.22
100

Associative Strength (V)

Trial
8

87.5

80

∆Vn
.39

=
=

93.75

...
1st Extinction Trial
Trial
1

K ( - Vn-1 )
.5 * 0 -99.61

∆Vn
-49.8

=
=
Extinction

100
80
60
40

Vall

20
0
0

1

2

3

...
2nd Extinction Trial
K ( - Vn-1 )
.5 *
0 -49.8
Acquisition

60
40

93.75

87.5

80 100
Associative Strength (V)

Associati...
Extinction Trials
Trial
3

K ( Vn-1 )
.5 *
0
12.45
Less and Less surprising

=
=

∆Vn
-12.46

4

.5 *

0

-

6.23

=

-6.2...
Hypothetical Acquisition & Extinction
Curves with K=.5 and = 100

100

Extinction

Associative Strength (V)

Associative S...
Acquisition & Extinction Curves with
c=.5 vs. c=.2 ( = 100)
Extinction

120

Associative Strength (V)

Associative Strengt...
RESCORLA-WAGNER THEORY:
COMPETITION
 Key feature of Rescorla-Wagner model is how it
explains conditioning with compound s...
∆Vn = K( — Vn-1)
 ∆ (change in) Delta
 V, associative strength, is measure of learning
 K reflects salience of CS.

( ...
RESCORLA-WAGNER THEORY:
OVERSHADOWING
 VAX = VA + VX
 How can theory account for overshadowing?
 With equally salient s...
Eyeblink Conditioning: OVERSHADOWING

Training: Tone/light + Shock
Tone = Eyeblink  CR
Light = ?

No CR to light

Corn...
Overshadowing:
 Overshadowing
 Whenever there are multiple stimuli
or a compound stimulus,
then ∆Vn = Vcs1 (K) + Vcs2 (M...
Overshadowing
Trial 1: VA = .40(100 – 0) = 40
Vx = .10(100 – 0) = 10
Trial 2: VA = .40(100 – 50) = 20
Vx = .10(100 – 50)...
RESCORLA-WAGNER THEORY:
BLOCKING
 VAX = VA + VX
 How would theory account for blocking?
 With equally salient stimuli, ...
Blocking
Group

Phase 1

Experimental
A
Group (blocking)
Control
Group

US

Nothing

Phase 2

Phase 3

AB

US

Test B

AB
...
RESCORLA-WAGNER THEORY:
BLOCKING

A

Associative Strength (V)

Acquisition
100
80
60
40

X

20
0
0

1

2

3

4
Trials

5

...
The Rescorla-Wagner associative model of conditioning is based upon
four assumptions that refer to the process by which th...
RESCORLA-WAGNER THEORY:
CONTINGENCY
 Theory can also explain animals’ ability to detect
different degrees of contingency ...
RESCORLA-WAGNER THEORY:
CONTINGENCY
 To explain contingency
sensitivity, Rescorla-Wagner
theory makes use of
background o...
RESCORLA-WAGNER THEORY:
CONTINGENCY
 Random training can be seen to represent blocking with two kinds of
trials:
 A (con...
RESCORLA-WAGNER THEORY:
CONTINGENCY
 So, blocking is basic to effect of random CS and
US presentations.
 Contextual cues...
RESCORLA-WAGNER THEORY:
INHIBITION
 In Chapter 4, we saw that conditioning can be
either excitatory or inhibitory.
 At f...
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Behavior Models

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Behavior Models

  1. 1.  Instructor: David A. Townsend  Email:  Class Web Page:
  2. 2. RESCORLA-WAGNER THEORY: BACKGROUND  Research by Rescorla and Kamin requires a change in our conception of conditioning.  Animals do not evaluate CS-US pairings in isolation.  Evaluation occurs against a background that includes: unpaired presentations of CS and US, or presentations of other CSs, or general experimental context.  Conditioning is most likely to occur when evaluation of entire situation reveals that a CS is best available predictor of US.
  3. 3. RESCORLA-WAGNER THEORY: BACKGROUND  This view of Pavlovian conditioning makes conditioning process appear much more complex than it had seemed.  How do animals do it?  By what means do animals keep track of CSs and USs, estimate probabilities, compute probability differences, and make CRs to CS?  If such calculations do occur, then animals are likely to make them automatically.  We need an account of the mechanism by which such automatic calculation might occur.
  4. 4. RESCORLA-WAGNER THEORY: BACKGROUND  In last 30 years, many different theories have been advanced to explain rich details of Pavlovian conditioning.  Most influential account was that of Robert A. Rescorla and Allan R. Wagner in 1972.  All later theories have been responses to shortcomings of Rescorla-Wagner account.  So, we will focus on Rescorla-Wagner theory.
  5. 5. RESCORLA-WAGNER THEORY: PRELUDE  Kamin’s blocking effect set stage for RescorlaWagner model.  Blocking effect suggested to Kamin that USs were only effective when they were SURPRISING or unpredicted by CSs.  However, USs were not effective when they were unsurprising or predicted by CSs.  Added CS was not associated with any change in US.
  6. 6. RESCORLA-WAGNER THEORY: OVERVIEW  Rescorla-Wagner theory explains complex contingency analysis in terms of simple associations of the sort Pavlov envisioned.  It can account for most standard conditioning phenomena in Chapter 3 as well as many newer phenomena in Chapter 4.  Theory is also precise; specified in such clear detail that one can derive predictions about behavior in untested experimental situations. Mathematically
  7. 7. RESCORLA-WAGNER THEORY: TUTORIALS If you need help, then please consult: http://psy.uq.oz.au/~landcp/PY269/rwmodel/index.html http://www.biols.susx.ac.uk/home/Martin_ Yeomans/Learning/Lecture6.html http://www.psych.ualberta.ca/~msnyder/Ac ademic/Psych_281/C5/Ch5page.html
  8. 8. RESCORLA-WAGNER THEORY: ACQUISITION  Standard conditioning curves are negatively accelerated.  Changes in conditioning strength are very substantial early in training.  But, as training proceeds, a leveling-off point, or asymptote, is approached.  Generally speaking, changes in strength of conditioning get smaller with each trial.
  9. 9. RESCORLA-WAGNER THEORY: ACQUISITION  Negatively accelerated learning curve suggests that organism does not profit equally from each training trial.  How much one profits depends on how much one already knows: When one knows nothing, profits are substantial (US surprise is high). When one knows a great deal, profits from further trials are small (US surprise is low).
  10. 10. RESCORLA-WAGNER THEORY: ACQUISITION  Rather than learning a fixed amount with each trial, one learns a fixed proportion of difference between one’s present level of learning and maximum possible.  As difference gets smaller (as one learns more), amount of new learning produced by further trials gets smaller.  Rescorla-Wagner model simply builds in a mathematical expression that conforms to negatively accelerated learning function.
  11. 11. RESCORLA-WAGNER THEORY: ACQUISITION  Here is the equation: ∆Vn = K( — Vn-1)  V, associative strength, is measure of learning.  It is a theoretical quantity.  It is not equivalent to magnitude or probability of any particular CR.  But, it is assumed to be closely related to such measures of conditioned responding.
  12. 12. RESCORLA-WAGNER THEORY: ACQUISITION  ∆Vn = K( — Vn-1)  K reflects salience of CS.  K can vary between 0 and 1 (0 ≤ K ≤ 1).  Bigger K, bigger change in V on any given trial.  Thus, salient stimuli mean large Ks, which mean large ∆Vs, which mean large changes in association from trial to trial. Intensity Sensory modality Organism Types of US’s employed ( belongingness)
  13. 13. RESCORLA-WAGNER THEORY: ACQUISITION  ∆Vn = K( — Vn-1)  indicates that different USs support different maximum levels of conditioning.  Asymptote of conditioning will vary with US; different asymptotes are reflected by different s.  More intense US, higher asymptote of conditioning, and higher .  is always equal to or greater than 0 ( ≥ 0).
  14. 14. RESCORLA-WAGNER THEORY: ACQUISITION  ∆Vn = K( — Vn-1)  Change in strength on Trial n (∆Vn) is proportional to difference between and prior associative strength Vn-1.  Because V grows from trial to trial, quantity ( Vn-1) gets smaller and smaller, so ∆Vn also gets smaller and smaller, generating a negatively accelerated learning curve.  Eventually, V will equal , so that ( - Vn-1) will be 0, and conditioning will be complete (asymptote will be reached).
  15. 15. ∆Vn = K( — Vn-1) ∆ (change in) Delta  V, associative strength, is measure of learning  K reflects salience of CS.  ( Lambda) indicates that different USs support different maximum levels of conditioning. Change in strength on Trial n (∆Vn) is proportional to difference between and Vn-1 prior associative strength.
  16. 16. Acquisition Trials First conditioning trial: Light (CS) is paired with shock (US) ∆Vn= light (CS)=0 k = .20 = associated strength of shock = 100 ∆Vn = K( — Vn-1) ∆ (change in) Delta V, associative strength, is measure of learning K reflects salience of CS and US. ( Lambda) indicates that different USs support different maximum levels of conditioning. Change in strength on Trial n (∆Vn) is proportional to difference between and Vn-1 prior associative strength. First Trial ∆Vn=k( -Vn-1) ∆Vn= .20(100-0) ∆Vn = .20 units
  17. 17. ∆Vn = K( — Vn-1) Acquisition Trials First conditioning trial: Light (CS) is paired with shock (US) ∆Vtotal= light (CS)=0 k = .20 = associated strength of shock = 100 ∆ (change in) Delta V, associative strength, is measure of learning K reflects salience of CS and US. ( Lambda) indicates that different USs support different maximum levels of conditioning. Change in strength on Trial n (∆Vn) is proportional to difference between and Vn-1 prior associative strength. Second Trial ∆Vn=k( -Vn-1) ∆Vn= .20(100-20) ∆Vn = .16 units
  18. 18. ∆Vn = K( — Vn-1) Acquisition Trials First conditioning trial: Light (CS) is paired with shock (US) ∆Vtotal= light (CS)=0 k = .20 = associated strength of shock = 100 ∆ (change in) Delta V, associative strength, is measure of learning K reflects salience of CS and US. ( Lambda) indicates that different USs support different maximum levels of conditioning. Change in strength on Trial n (∆Vn) is proportional to difference between and Vn-1 prior associative strength. Third Trial ∆Vn=k( -Vn-1) ∆Vn= .20(100-36) ∆Vn = .12.8 units
  19. 19. ∆Vn = K( — Vn-1) Acquisition Trials First conditioning trial: Light (CS) is paired with shock (US) ∆Vtotal= light (CS)=0 k = .20 = associated strength of shock = 100 ∆ (change in) Delta V, associative strength, is measure of learning K reflects salience of CS and US. ( Lambda) indicates that different USs support different maximum levels of conditioning. Change in strength on Trial n (∆Vn) is proportional to difference between and Vn-1 prior associative strength. Forth Trial ∆Vn=k( -Vn-1) ∆Vn= .20(100-48.8) ∆Vn = .10.2 units N= trial4, N-1= trial 3
  20. 20. Rescorla-Wagner Model  Calculations from the RescorlaWagner model show a mathematical relationship to the process of conditioning 60 50 40 30 Vtotal 20 10 0 trial 0 trial 2 trail 4
  21. 21. 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  Surprise
  22. 22. First Conditioning Trial Trial 1 K ( - Vn-1 ) .5 * 100 - = 0 ∆Vn = 50 Associative Strength (V) 100 80 ∆Vn = K( — Vn-1) 60 50 40 20 0 0 0 1 2 3 4 Trials 5 6 7 8 ∆ (change in) Delta V, associative strength, is measure of learning K reflects salience of CS and US. ( Lambda) indicates that different USs support different maximum levels of conditioning. Change in strength on Trial n (∆Vn) is proportional to difference between and Vn-1 prior associative strength.
  23. 23. Second Conditioning Trial K ( - Vn-1 ) .5 * 100 -50 ∆Vn 25 = = 100 Associative Strength (V) Trial 2 80 75 60 50 40 20 0 0 0 1 2 3 4 Trials 5 6 7 8
  24. 24. Third Conditioning Trial K ( - Vn-1 ) .5 * 100 -75 ∆Vn 12.5 = = 100 Associative Strength (V) Trial 3 87.5 80 75 60 50 40 20 0 0 0 1 2 3 4 Trials 5 6 7 8
  25. 25. 4th Conditioning Trial K ( - Vn-1 ) c (Vmax .5 * 100 - 87.5 80 ∆Vn ∆Vcs 6.25 = = = Vall) 87.5 100 Associative Strength (V) Trial Trial 4 93.75 75 60 ∆Vcs = c (Vmax – Vall) 50 40 Vall 20 0 0 0 1 2 3 4 Trials 5 6 7 8
  26. 26. 5th Conditioning Trial K ( - Vn-1 ) .5 * 10 - 93.75 100 Associative Strength (V) Trial 5 87.5 80 = = 96.88 93.75 75 60 50 40 20 0 0 0 1 2 3 4 Trials 5 6 7 8 ∆Vn 3.125
  27. 27. 6th Conditioning Trial K ( - Vn-1 ) .5 * 100 - 96.88 100 Associative Strength (V) Trial 6 87.5 80 = = 96.8898.44 93.75 75 60 50 40 20 0 0 0 1 2 3 4 Trials 5 6 7 8 ∆Vn 1.56
  28. 28. 7th Conditioning Trial Trial 7 K ( - Vn-1 ) .5 * 100 - 98.44 Associative Strength (V) 100 87.5 80 = = 96.8898.4499.22 93.75 ∆Vn = K( — Vn-1) 75 60 50 40 20 0 0 0 1 2 ∆Vn .78 3 4 Trials 5 6 7 8 ∆ (change in) Delta V, associative strength, is measure of learning K reflects salience of CS and US. ( Lambda) indicates that different USs support different maximum levels of conditioning. Change in strength on Trial n (∆Vn) is proportional to difference between and Vn-1 prior associative strength.
  29. 29. 8th Conditioning Trial K ( - Vn-1 ) .5 * 1 - 99.22 100 Associative Strength (V) Trial 8 87.5 80 ∆Vn .39 = = 93.75 96.8898.44 99.22 99.61 75 60 50 40 20 0 0 0 1 2 3 4 Trials 5 6 7 8
  30. 30. 1st Extinction Trial Trial 1 K ( - Vn-1 ) .5 * 0 -99.61 ∆Vn -49.8 = = Extinction 100 80 60 40 Vall 20 0 0 1 2 3 4 Trials 5 6 7 8 Associative Strength (V) Associative Strength (V) Acquisition 100 99.61 80 60 49.8 40 20 0 0 1 2 3 Trials 4 5 6
  31. 31. 2nd Extinction Trial K ( - Vn-1 ) .5 * 0 -49.8 Acquisition 60 40 93.75 87.5 80 100 Associative Strength (V) Associative Strength (V) 100 75 80 60 50 40 Vall 20 20 0 0 0 0 0 1 1 2 2 3 3 4 4 Trials Trials 5 5 6 6 7 7 8 8 ∆Vn -24.9 = = Extinction 96.88 98.44 99.22 99.61 Associative Strength (V) Trial 2 100 99.61 80 60 49.8 40 24.9 20 0 0 1 2 3 Trials 4 5 6
  32. 32. Extinction Trials Trial 3 K ( Vn-1 ) .5 * 0 12.45 Less and Less surprising = = ∆Vn -12.46 4 .5 * 0 - 6.23 = -6.23 5 .5 * 0 - 3.11 = -3.11 6 .5 * 0 - 1.56 = -1.56
  33. 33. Hypothetical Acquisition & Extinction Curves with K=.5 and = 100 100 Extinction Associative Strength (V) Associative Strength (V) Acquisition 80 60 40 20 100 99.61 80 60 49.8 40 24.9 20 12.45 0 0 0 1 2 3 4 Trials 5 6 7 8 6.23 0 1 2 3 Trials 4 3.11 5 1.56 6
  34. 34. Acquisition & Extinction Curves with c=.5 vs. c=.2 ( = 100) Extinction 120 Associative Strength (V) Associative Strength (V) Acquisition 100 80 60 40 20 0 0 1 2 3 4 Trials 5 6 7 8 120 100 80 c=.5 60 c=.2 40 c=.5 c=.2 20 0 0 1 2 3 Trials 4 5 6
  35. 35. RESCORLA-WAGNER THEORY: COMPETITION  Key feature of Rescorla-Wagner model is how it explains conditioning with compound stimuli comprising two or more elements.  Associative strength of a compound stimulus is assumed to equal the sum of associative strengths of elements.  VAX = VA + VX  Here, A and X may have different saliences, K and M, respectively.
  36. 36. ∆Vn = K( — Vn-1)  ∆ (change in) Delta  V, associative strength, is measure of learning  K reflects salience of CS.  ( Lambda) indicates that different USs support different maximum levels of conditioning.  Change in strength on Trial n (∆Vn) is proportional to difference between and  Vn-1 prior associative strength. A,X symbols used for multiple CS’s Salience with multiple CS’s: A=K, X=M
  37. 37. RESCORLA-WAGNER THEORY: OVERSHADOWING  VAX = VA + VX  How can theory account for overshadowing?  With equally salient stimuli, VX would attain only .50 rather than 1.00 if X alone were trained-mutual overshadowing.  Increases in salience of A would further reduce VX from .50 toward .00.  If salience of A (K) is very high and salience of X (M) is very low, then overshadowing should be complete.
  38. 38. Eyeblink Conditioning: OVERSHADOWING Training: Tone/light + Shock Tone = Eyeblink  CR Light = ? No CR to light Corneal Air Puff Elicits Eyeblink Response Corneal Air Puff Given with Tone Tone Given Alone Elicits Eyeblink Response
  39. 39. Overshadowing:  Overshadowing  Whenever there are multiple stimuli or a compound stimulus, then ∆Vn = Vcs1 (K) + Vcs2 (M) ∆Vn = K( — Vn-1)  Trial 1: ∆Vnoise = .2 (100 – 0) = (.2)(100) = 20 ∆Vlight = .3 (100 – 0) = (.3)(100) = 30 Total ∆Vn = ∆ (K)Vnoise + ∆Vlight = 0 +20 +30 =50  Trial 2: Noise= 30 ∆Vnoise = .2 (100 – 50) = (.2)(50) = 10 ∆Vlight = .3 (100 – 50) = (.3)(50) = 15 Light= 45 Total ∆Vn = Vn-1 + ∆Vnoise + ∆Vlight = 50+10+15=75
  40. 40. Overshadowing Trial 1: VA = .40(100 – 0) = 40 Vx = .10(100 – 0) = 10 Trial 2: VA = .40(100 – 50) = 20 Vx = .10(100 – 50) = 5 T2: A=60 X=15
  41. 41. RESCORLA-WAGNER THEORY: BLOCKING  VAX = VA + VX  How would theory account for blocking?  With equally salient stimuli, VX would be only .50 rather than 1.00 if AX only were trained.  Prior training with A would further reduce VX, because VA would already be substantial before AX trials were introduced.  Extensive training with A should lead to complete blocking of X.
  42. 42. Blocking Group Phase 1 Experimental A Group (blocking) Control Group US Nothing Phase 2 Phase 3 AB US Test B AB US Test B Same # trials Contiguity Contingency
  43. 43. RESCORLA-WAGNER THEORY: BLOCKING A Associative Strength (V) Acquisition 100 80 60 40 X 20 0 0 1 2 3 4 Trials 5 6 7 8 ?
  44. 44. The Rescorla-Wagner associative model of conditioning is based upon four assumptions that refer to the process by which the CS and UC gain associative strength  (1) a particular US can only support a specific level of conditioning,  (2) associative strength increases with each reinforced trial, but depends upon prior conditioning,  (3) particular CSs and US can support different rates of conditioning and  (4) when two or more stimuli are paired with the UC, the stimuli compete for the associative strength available for conditioning.
  45. 45. RESCORLA-WAGNER THEORY: CONTINGENCY  Theory can also explain animals’ ability to detect different degrees of contingency between CS and US.  Recall that fear of a CS for shock is a direct function of contingency between CS and US.  When contingency between events is zero, no learning of fear to CS occurs.  But, does a rat really compute probabilities to form a judgment of contingency?  Not according to Rescorla-Wagner model.
  46. 46. RESCORLA-WAGNER THEORY: CONTINGENCY  To explain contingency sensitivity, Rescorla-Wagner theory makes use of background or contextual stimuli as Pavlovian predictors. (Context = A discrete CS)  Such contextual stimuli themselves can compete with CSs for association with USs.  Case of random presentations of CS and US provides a useful illustration.
  47. 47. RESCORLA-WAGNER THEORY: CONTINGENCY  Random training can be seen to represent blocking with two kinds of trials:  A (context)-US [relatively frequent]  AX (context plus CS)-US [relatively infrequent]  As animal receives frequent A-US pairings, VA (and hence VAX) approaches asymptote.  As VAX approaches asymptote from frequent A-US pairings, VX can receive no further increments and little responding to X will be observed despite occasional AX-US pairings. CS unpaired US 0.5 s time
  48. 48. RESCORLA-WAGNER THEORY: CONTINGENCY  So, blocking is basic to effect of random CS and US presentations.  Contextual cues are present whenever US occurs in absence of CS; contextual cues thus acquire excitatory strength.  On trials when CS is paired with US by chance, contextual cues are present as well.  So, context replaces Stimulus A in blocking example and randomly presented CS replaces Stimulus X.
  49. 49. RESCORLA-WAGNER THEORY: INHIBITION  In Chapter 4, we saw that conditioning can be either excitatory or inhibitory.  At first glance, it is not obvious that RescorlaWagner theory can explain inhibition.  Inhibition requires a V that is less than zero; but, none of the variables in the equation can ever be less than zero. How can V become negative when none of the terms contributing to V can be negative?
  50. 50. Time to Leave:
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