The conditioned stimulus

1. The conditioned stimulus • Generalization: A range of stimuli varying on a particular dimension will produce responses in proportion to how similar they are to the trained CS. • The generalization gradient is the graph of the strength of the CR to stimuli varying above and below the CS.

2. A generalization gradient

3. Discrimination learning • If stimuli other than the CS are presented, but never paired with the US, the generalized responses to similar stimuli will diminish, so that the individual discriminates between CS and non-CS. • Examples of classically-conditioned discrimination learning abound in education: colors, shapes, letters...The color or shape or letter is CS, the teacher’s word for it is the US.

4. Results of discrimination learning Generalization around a light CS 40 30 20 Eyeblinks per minute 10 0 470 480 490 500 510 520 530 Wavelength of light, nm (Color)

5. The nature of the CR • The CR is always different from the UR. • Sometimes the CR differs from the UR only in magnitude, duration, or latency. • But sometimes, the CR is opposite to the UR: Relaxation and slowed heart rate as a CR to a CS for shock, but agitation and increased heart rate as a UR to the actual shock US.

6. Opponent Process Theory • Opponent processes are bodily reactions opposite to the effect of a US, in order to preserve physiological balance. • If a CS is conditioned to a US, the opponent process rather than the UR becomes the CR. • Wagner (1981) argued that opponent processes are conditioned as CRs only for URs which provoke compensatory reactions, but not for monophasic URs: SOP.

7. Applications of SOP • Drug tolerance • CS (setting for drug taking) + US (drug) ----> CR (tolerance) • US (drug) without CS (setting) ----> Overdose • Spousal boredom • CS (setting: Usual routine) + US (spouse) ----> CR (boredom) • US (spouse) without CS (new setting) ----> Excitement

8. Contiguity vs. contingency • The S-S and S-R theories we have studied operate on the associationist principle of contiguity: Research on CS-US intervals. • A competing view is called contingency: differential prediction of the US by the CS, not mere co-occurrence. • In a contingent relationship, p(US | CS) > p(US | not-CS)

9. Rescorla and contingency learning • The greater the differential contingency, the greater the learning: (Correct legend p.59) • The suppression ratio: (A - P)/(A + P) • A is the response rate in the Absence of the CS • P is the response rate in the Presence of the CS • If A = P, there is no suppression, and the ratio = 0 • If A > P, there is suppression, expressed in the ratio. • Thus, the higher the ratio, the greater the suppression, and the greater the learning.

10. Contiguity or contingency: Rescorla (1968) • Notice that the greater the difference between the pUS|CS and the pUS|CS, the greater the suppression ratio: Contingency.

11. Occasion setting and conditioned inhibition • Conditioned inhibition: Summation paradigm • Separately condition CS+ to US and CS- to not-US • Test CS+ and CS- together • Amount of reduction of CR shows strength of CS- • Conditioned inhibition: Retardation paradigm • First condition CS- to not-US • Then, keeping CS-, try to condition CS+ to US • Amount of retardation of conditioning shows strength of CS- • Occasion setting • Clocks and kisses

12. Preparedness and associative bias: Garcia • Shock US conditions better to light/tone CS than to taste CS • Nausea-inducing US drug conditions better to taste CS than to light/tone CS • Possible conclusion: Associations are reasonable inferences

13. Stimulus combinations • Kamin (1968): Informativeness, blocking, and unblocking Group 1: CS1 + CS2 ---> US (eight trials) Group 2: CS1 ---> US (sixteen trials) CS1 + CS2 ---> US (eight trials) Group difference? • Configural cue learning: not individual cue learning: A + B but not A or B; A or B but not A + B; [A+C and B+D] but not [A + C or B + D]

14. Rescorla-Wagner theory • The theory has an equation with three parts: • V, the amount of learning acquired • a , the rate of learning • l , the maximum amount of learning that can happen to the particular US. • Together, DV = a ( l - V) • The equation is applied once for each learning trial, to see how much learning will happen on each trial.

15. Example of Rescorla-Wagner computations • DV = a ( l - V) • If a = .20 and l = 100, work through 4 trials: • Trial 1: .20 ( 100 - 0) = 20 • Trial 2: .20 ( 100 - 20) = 16 • Trial 3: .20 ( 100 - 36) = 12.8 • Trial 4: .20 ( 100 - 48.8) = 10.24 • Note the diminishing returns with repeated trials. Will learning ever stop?

16. Another example • DV = a ( l - V) • If a = .10 and l = 200, work through 5 trials: • Trial 1: .10 ( 200 - 0) = 20 • Trial 2: .10 ( 200 - 20) = 18 • Trial 3: .10 ( 200 - 38) = 16.2 • Trial 4: .10 ( 200 - 54.2) = 14.58 • Trial 5: .10 ( 200 - 68.78) = 13.12 • In a relative sense, learning is happening more slowly here.

17. A learning curve from Rescorla-Wagner theory

18. Rescorla-Wagner and compound stimuli • Competitive learning: The total learning available, l , must be shared by each stimulus in a compound. Thus, the total amount of learning to each stimulus is less in a compound than if that stimulus is alone. • Rescorla-Wagner predicts blocking and conditioned inhibition accurately.

19. Problems with Rescorla-Wagner • CS preexposure produces slower conditioning to CS later, a phenomenon known as latent inhibition. For example, displaying the letters of the alphabet around the classroom before they are learned is CS preexposure. • Latent inhibition is not predicted by Rescorla-Wagner, unless you assume that preexposure lowers the learning rate (a) by lowering salience.

20. Connectionism: A Neural model • Learning may involve forming patterns of synaptic connections. • ABCD --> CR1, ie US1 • ABCF --> CR2, ie US2 • The rule for connection formation is the delta rule, based on Rescorla-Wagner • CSs are inputs; USs are outputs.

21. Applications of classical conditioning • Classroom structure • Make salient what is to be learned • Eliminate redundant CSs • Establish differential contingency • Extinction • Counterconditioning • Desensitization • Flooding