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RESCORLA-WAGNER MODEL

RESCORLA-WAGNER MODEL. 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.

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RESCORLA-WAGNER MODEL

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  1. RESCORLA-WAGNER MODEL

  2. 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).

  3. 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?

  4. PRINCIPAL THEORETICAL VARIABLE: ASSOCIATIVE STRENGTH, V

  5. 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, .

  6. Accounting for Known Data K

  7. Overshadowing k= .1, .6

  8. Accounting for Known Data

  9. Accounting for Known Data • Un-Blocking • What value can we change to allow associative strength to accrue to CS2?

  10. Accounting for Known Data V1= 90 V2= 0 k= .2

  11. Novel Predictions • Overexpectation effect • CS1+UCS… • CS2+UCS… • What happens when you present CS1+CS2?

  12. Weaknesses of Models • Errors of commission • Errors of omission

  13. Weaknesses of the R-W Model • Extinction curve not mirror image of Acquisition • Facilitated re-acquistion • What about time? • Spontaneous recovery • CS pre-exposure effect (latent inhibition) (see review article Miller et al. 1995)

  14. Importance of Context • UCS pre-exposure effect • Present UCS by itself • Then pair UCS with CS • Explained by R-W, how?

  15. Probability Space Random Control: 0 < P(UCS|CS) = P(UCS|~CS) Inhibitory CS: 0 < P(UCS|CS) < P(UCS|~CS) Conditioning: 0 < P(UCS|~CS) < P(UCS|CS)

  16. Combining Conditional Probability and Rescorla-Wagner • How can R-W account for no conditioning to the CS in the following: • 0 < P(UCS|CS) = P(UCS|~CS) • Hint: CS is a compound (CS + CTX) • ΔVCS= ΔVCS +ΔVCTX

  17. 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

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