T.4: Rescorla-Wagner Model

Question 1

Define V

Answer 1

The associative strength between CS and US

The amount you know

Question 2

What does a higher value of V mean?

Answer 2

The stronger the associative learning between CS-US

Question 3

Define delta V

Answer 3

Change in V - how much has been learnt in each trial

Question 4

What is the R-W model equation?

Answer 4

alpha x beta x (lamda - sigmaV) = deltaV

Question 5

Define alpha

Answer 5

Salience of the CS

Question 6

Define beta

Answer 6

Salience of the US

is always valued as 1 in this course

Question 7

What are the salience factors of the equation?

Answer 7

alpha x beta

Question 8

What represents the prediction error term in the equation?

Answer 8

(lamda - sigmaV)

Question 9

What is the prediction error term quantifying?

Answer 9

The notion of surprise

Question 10

What is the prediction error term?

Answer 10

The discrepancy between the expectation and what actually happens in the trial

Question 11

Define lamda

Answer 11

Maximum amount of learning that can occur

Magnitude of the outcome

Question 12

Define sigmaV

Answer 12

Expectancy of the CS-US pairing

Question 13

What are the 3 steps used when applying this model to a trial of learning?

Answer 13

Step 1: calculate sigmaV before the pairing

Step 2: figuring out how much learning occurs when CS is paired with the US - work out deltaV

Step 3: update the value of V - add original V to deltaV to work out updated V

Question 14

What happens to the value of deltaV with ongoing trials?

Answer 14

It decreases because learning decreases as the animal is becoming less surprised

The error term has become smaller

Question 15

What effect does higher salience have on learning?

Answer 15

Increases the rate of learning

Question 16

What happens to V across trials?

Answer 16

Approaches 1 - expectation of the outcome is being learned

The discrepancy is getting smally

Question 17

What does delta V approach across trials?

Answer 17

Approaches 0 as the outcome becomes less surprising to the animal

Less learning happening on each progressive trial

Question 18

What happens to V when you apply the R-W model to extinction?

Answer 18

V gradually approaches 0 - learning is decreasing and losing its associative strength

Question 19

What happens when a CS is more salient during extinction trials?

Answer 19

The learning decreases faster

Rate of extinction increases

Question 20

What does deltaV approach when the R-W model is applied to extinction?

Answer 20

Approaches 0

Question 21

What happens to V when you apply the R-W model to over expectation?

Answer 21

V decreases from 1 to a new asymptote - this is inhibitory learning

Question 22

What happens to deltaV when you apply the R-W model to over expectation?

Answer 22

The sign (+ or-) of the error term dictates the direction of learning (not just the presence vs absence of the US)

DeltaV approaches 0 on graph example in the slides?