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PSYC 2330 (214)
Lecture 6

# Tuesday, Sept 25/2012 - Lecture 6 Notes

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School
Department
Psychology
Course
PSYC 2330
Professor
Francesco Leri
Semester
Fall

Description
Tuesday, September 25 2012 PSYC 2330 Lecture 6 The essence of classical conditioning • Keep track of what the logic of classical conditioning is • The emphasis in classical conditioning is on finding predictors for things that are important in your life • What is important in your life is the unconditional stimulus • The idea is that the subject is looking in their environment for predictors of important events in their life • We've evolved to search out biologically meaningful stimuli • Classical conditioning describes the laws that govern how we create links between predictors and biologically relevant stimuli • Conditioned stimuli are effective predictors of biologically relevant stimuli. Conditioned stimuli that have the negative symbol in the subscript describe predictors that the biologically relevent stimuli will not be received • Aversive stimuli are just as important Contingency • The CS must not only be contiguous with a US ◦ How close together the CS and US occur • It must also be an accurate predictor of the occurance of the US ◦ Random contingency does not cause classical conditioning to occur ▪ In this case, the CS does not accurately predict the US ◦ Perfect predictor causes strong classical conditioning to occur ▪ In this case, the CS accurately and consistently predicts the US ◦ Partial predictor causes classical conditioning to occur, but not as strongly ▪ In this case, the CS does predict the US, but not consistently • Contingency is the reason animals develop classical conditioning Contingency can be calculated as a difference between two probabilities • 1. Probability that a US will occur in the presence of the CS = p(US | CS) • 2. Probability that a US will occur in the absense of the CS = p(US | no CS) • Phi = p(US | CS) – p (US | no CS) ◦ the value is given between 0 and 1 (it is a probability) • In the random contingency, phi = 0 because the US is as likely to occur in the presence of the CS as not. • In the perfect predictor, phi = 1 because the US only occurs in the presence of the CS • In partial predictor situations, phi varies depending on the accuracy of the CS's ability to predict the US Contingency is calculated as a difference between: a is correct (Question is available on course link) Positive Contingency: US more probable when CS is on Negative Contingency: US less probable when CS is on • Transfer-of-control experiment (Pavlovian-instrumental transfer) • Positive contingency group = shock contingent upon presence of tone ◦ tone = shock ◦ CS training increases the strength of avoidance behaviour ◦ The rat will eventually relax back down as their habitual behaviour takes over the lingering effects of the CS • Random contingency group = no contingecy between shock & tone ◦ CS is meaningless ◦ CS training does not influence the avoidance behaviour of these rats • Negative contingency group = no shock contingent upon the presence of tone ◦ tone = for sure no shock ◦ CS training reduces the strength of avoidance behaviour ◦ The rat will eventually speed back up as their habitual behaviour takes over the lingering effects of the CS “Surprisingness” of the US • The model is a mathematical expression of surprise: ◦ Learning will occur only when the subject is surprised – that is, when what happens is different from what the subject expected to happen • The first time the event occurs, it is surprising, and each time after that there is less surprise each time because the predictor has prepared you • When something unexpected happens to you that is meaningful, you will look for predictors for when that event will happen again • When something expected that is meaningful doesn't happen, you will also look for predictors • Studied by Rescola and Wagner • When we are surprised, we want to learn why things didn't go the way we expected • Mathematical models allow us to make predictions that are then tested through experimentation Blocking (Leon Kamin)
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