Classical Conditioning (also Pavlovian / Respondent Conditioning)

1. Classical Conditioning(also Pavlovian / Respondent Conditioning)

3. PAVLOVIAN PARADIGM unconditional stimulus unconditional response elicits UCR UCS elicits CR CS conditional stimulus conditional response But what does mean?

4. CS CS CS CS US US US US Temporal Relations and Conditioning Delay Conditioning Trace Conditioning Simultaneous Conditioning Backward Conditioning

5. BASIC PHENOMENA • ACQUISITION • EXTINCTION • STIMULUS CONTROL

6. Acquisition

7. Extinction

8. Spontaneous Recovery

9. Stimulus Control

10. Is key element S-S (CS – UCS) or S-R (CS – UCR) relationship? • Sensory preconditioning • Idea of stimulus substitution

11. Is key element S-S (CS – UCS) or S-R (CS – UCR) relationship? • Sensory preconditioning • Idea of stimulus substitution

12. What are the necessary and sufficient conditions for Pavlovian conditioning to occur? • Response Class • Temporal Relations • Contingency

13. Respondent Contingencies • Standard Procedure • P(UCS|CS) = 1 ; P(UCS|~CS) = 0 • Partial Reinforcement • 0 < P(UCS|CS) < 1 ; P(UCS|~CS) = 0 • Random Control • 0 < P(UCS|CS) = P(UCS|~CS) • Inhibitory CS • 0 < P(UCS|CS) < P(UCS|~CS) Pavlovian Conditioning

14. Contingency Table UCS ~UCS #UCSCS = A # CS = A + B P(UCS|CS) = A / (A+B) CS B A+B A ~CS D C+D C A+C B+D N |AD - BC| (A+B)(C+D)(A+C)(B+D)  = Pavlovian Conditioning

15. Staddon’s Data Pavlovian Conditioning

16. Contingencies and Staddon’s Data SH ~SH = 20/30 = 2/3 = 10/30 = 1/3 = 10/30 = 1/3 = 20/30 = 2/3 P(S) P(~S) P(SH) P(~SH) 10 20 S 10 012 011 10 ~S 10 0 022 021 30 10 20 Pavlovian Conditioning

17. Contingencies and Staddon’s Data If S and SH were independent (“random control”): P (SH|S) = P (SH) or P (SH  S) = P (SH) P(S) By definition: P (SH|S) = P (SH  S) = #(SH and S) P(S) #S = 10/20 = 1/2 But: P (SH) = 10/30 = 1/3 So: P (SH|S) ≠ P (SH). Also, P (SH  S) ≠ P (SH) P(S) 10/30 = 1/3 ≠ (1/3)(2/3) = 2/9 Pavlovian Conditioning

18. = X²1df= │011022 – 012021│ • N (011 + 012)(021 + 022)(011 + 021)(012 +022) Recall X² test for independence in contingency table with observed frequencies 0ij rc i=1 j=1  Where the Eij’s are the Expected Frequencies X²1df = (0ij – Eij) ² Eij  For a 2 x 2 Table X²1df = N │011022 – 012021│² (011 + 012)(021 + 022)(011 + 021)(012 +022) Pavlovian Conditioning

19. For a 2 x 2 Table Χ²1df = N │011022 – 012021│² (011 + 012)(021 + 022)(011 + 021)(012 +022) SH ~SH E11 = (011 + 012)(011 + 021) N E12 = (011 + 012)(012 + 022) N E21 = (021 + 022)(011 + 021) N E22 = (021 + 022)(012 + 022) N S 011 + 012 012 011 021 022 021 + 022 ~S N 012 + 022 011 + 021 Pavlovian Conditioning

20. X1² = (13.33 – 10)² + (10 - 6.67)² 13.33 6.67 + (10 – 6.67)² + (3.33 -0)² 6.67 3.33 = 7.486 ≈ 7.5 X².95 = 3.84 1df  = │(10(0) – (10)(10) │= 100 = 0.5 (20)(10)(20)(10) (20)(10) ² = 0.25 = X1² / 30, so X1² = 7.5 as above. S and Shock are not independent • For Staddon’s Data, the table is: 011 = 10 E11 = 13.33 012 = 10 E12 = 6.67 20 022 = 0 E22 = 3.33 021 = 10 E21 = 6.67 10 30 10 20 Pavlovian Conditioning

21. Operant Conditioning

22. cs S = Rcs Rcs+ Rcs cs cs S = 0.0 S = 0.5 Pavlovian Conditioning

23. P(UCS|CS) = P(UCS|~CS) = P(UCS~CS) = # (UCS~CS) P(~CS) # ~CS [P(CS) > 0] ~CS E UCSCS UCS~CS ~CS = E – CS = Context P(UCS|CS) = 1- P(~UCS|CS) P(UCS|~CS) = 1- P(~UCS|~CS) ~UCS Pavlovian Conditioning

24. RESCORLA-WAGNER MODEL

25. What are some characteristics of a good model? Variables well-described and manipulatable. Accounts for known results and able to predict non-trivial results of new experiments. Dependent variable(s) predicted in at least relative magnitude and direction. Parsimonious (i.e., minimum assumptions for maximum effectiveness).

26. STEPS IN MODEL BUILDING • IDENTIFICATION: WHAT’S THE QUESTION? • ASSUMPTIONS: WHAT’S IMPORTANT; WHAT’S NOT? • CONSTRUCTION: MATHEMATICAL FORMULATION • ANALYSIS: SOLUTIONS • INTERPRETATION: WHAT DOES IT MEAN? • VALIDATION: DOES IT ACCORD WITH KNOWN DATA? • IMPLEMENTATION: CAN IT PREDICT NEW DATA?

27. PRINCIPAL THEORETICAL VARIABLE: ASSOCIATIVE STRENGTH, V

28. RESCORLA-WAGNER MODEL

29. ASSUMPTIONS 1. When a CS is presented its associative strength, Vcs, may increase (CS+), decrease (CS-), or remain unchanged. 2. The asymptotic strength () of association depends on the magnitude (I) of the UCS:  = f (UCSI). 3. A given UCS can support only a certain level of associative strength, . 4. In a stimulus compound, the total associative strength is the algebraic sum of the associative strength of the components. [ex. T: tone, L: light. VT+L =VT + VL] 5. The change in associative strength, V, on any trial is proportional to the difference between the present associative strength, Vcs, and the asymptotic associative strength, .

31. Contiguity in R-W Model If P(UCS|CS) = P(UCS|~CS), we really have CS = CS + CTX and ~CS = CTX. Then: P(UCS|CS + CTX) = P(UCS|CTX) V (CS + CTX) = VCS + VCTX (R-W axiom) V CS = V (CS +CTX) = VCS + VCTX but: V CTX = V ~CS so: V (CS + CTX) = V CS + V~CS = 0  No significant conditioning occurs to the CS Pavlovian Conditioning