T4 R-W model Flashcards

1
Q

Define V

A

The associative strength between CS and US

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2
Q

What does a higher value of V mean?

A

The stronger the associative learning between CS-US

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3
Q

Define delta V

A

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

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4
Q

What is the R-W model equation?

A

alpha X beta X (lambda - sigmaV) = deltaV

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5
Q

Define alpha

A

Salience of the CS

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6
Q

Define Beta

A

Salience of the US, always 1 in this course

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7
Q

What are the salience factors of the equation?

A

alpha and beta

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8
Q

What represents the prediction error term in the equation?

A

lambda - sigmaV

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9
Q

What is the prediction error term quantifying?

A

The notion of surprise

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10
Q

What is the prediction error term?

A

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

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11
Q

Define Lambda

A

Maximum amount of learning that can occur

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12
Q

Define SigmaV

A

Expectancy of the CS-US pairing

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13
Q

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

A

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

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14
Q

What happens to the value of deltaV with ongoing trials

A

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

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15
Q

What effect does higher salience have on learning

A

Increases the rate of learning

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16
Q

What happens to V across trails

A

Approaches 1 - expectation of the outcome is being learned

17
Q

What does delta V approach across trials

A

0

18
Q

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

A

approaches 0

19
Q

What happens when a CS is salient during extinction trials?

A

approaches 0 faster

20
Q

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

A

0

21
Q

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

A

approaches 0.5

22
Q

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

A

becomes 0