1. Rescorla-Wagner (1972) Theory of Classical Conditioning

2. 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

3. 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

4. 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

5. 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)

6. First Conditioning Trial Trial c (Vmax - Vall) =∆Vcs 1.5 * 100 - 0=50

7. Second Conditioning Trial Trial c (Vmax - Vall) =∆Vcs 2.5* 100 - 50=25

8. Third Conditioning Trial Trial c (Vmax - Vall) =∆Vcs 3.5 * 100 - 75=12.5

9. 4th Conditioning Trial Trial c (Vmax - Vall) =∆Vcs 4.5 * 100 - 87.5=6.25

10. 5th Conditioning Trial Trial c (Vmax - Vall) =∆Vcs 5.5 * 100 - 93.75=3.125

11. 6th Conditioning Trial Trial c (Vmax - Vall) =∆Vcs 6.5 * 100 - 96.88=1.56

12. 7th Conditioning Trial Trial c (Vmax - Vall) =∆Vcs 7.5 * 100 - 98.44=.78

13. 8th Conditioning Trial Trial c (Vmax - Vall) =∆Vcs 8.5 * 100 - 99.22=.39

14. 1st Extinction Trial Trial c (Vmax - Vall) =∆Vcs 1.5 * 0 - 99.61=-49.8

15. 2nd Extinction Trial Trial c (Vmax - Vall) =∆Vcs 2.5 * 0 - 49.8=-24.9

16. 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

17. Hypothetical Acquisition & Extinction Curves with c=.5 and Vmax = 100

18. Acquisition & Extinction Curves with c=.5 vs. c=.2 (Vmax = 100)

19. Theory Handles other Phenomena Overshadowing –Whenever there are multiple stimuli or a compound stimulus, then Vall = Vcs 1 + Vcs 2 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

20. 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

21. 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