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Configural learning Learning about holistic stimulus representations

Configural learning Learning about holistic stimulus representations. no food. food. Structural discriminations George Ward-Robinson & Pearce, 2001. food. no food. Structural discriminations George Ward-Robinson & Pearce, 2001. Can this be solved in terms of simple associations?

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Configural learning Learning about holistic stimulus representations

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  1. Configural learning Learning about holistic stimulus representations

  2. no food food

  3. Structural discriminations George Ward-Robinson & Pearce, 2001 food no food

  4. Structural discriminations George Ward-Robinson & Pearce, 2001

  5. Can this be solved in terms of simple associations? Can it be solved with conditional learning? food no food

  6. If green: red-left + red-right - If blue: red-left - red-right + If green: blue-right + blue-left - food no food

  7. If green: red-left + red-right - If blue: red-left - red-right + If green: blue-right + blue-left - relies on use of compound cues - red-left etc food no food

  8. so why not use fact these stimuli are all unique? red-left&green-right+ red-right&green-left - food no food

  9. Some types of learning associative theory cannot explain. Last week we saw how conditional learning can explain some of these Today we consider an alternative approach - configural learning Can associative theory adapt by changing the way in which the stimulus is represented?

  10. So far have assumed that a compound stimulus is equivalent to the sum of its parts: A --> food B--> food A --> cr B --> cr AB --> CR Predict SUMMATION

  11. Feature negative discrimination A --> food AB --> no food CR cr

  12. VA=  (  - V ) Learning stops when (=V ) A --> food AB --> no food VA = 1 VA + VB = 0

  13. VA= (  - V ) Learning stops when ( = V ) A --> food AB --> no food VA = 1 VA + VB = 0 A becomes excitatory: V = +1 B becomes inhibitory: V = -1 thus A alone predicts food, whereas A+B is neutral

  14. Feature positive discrimination A --> no food AB --> food cr CR

  15. VA=  (  - V ) Learning stops when (  = V ) A --> no food AB --> food VA = 0 VA + VB = 1

  16. VA=  (  - V ) Learning stops when ( = V ) A --> no food AB --> food VA = 0 VA + VB = 1 B becomes excitatory: V = +1 A eventually becomes neutral: V = 0 Thus A alone predicts nothing, but when B is present food is expected

  17. Performance on feature negative and feature positive discriminations can be explained by the Rescorla-Wagner equation If you condition to asymptote, it predicts perfect performance But how about.......

  18. Positive patterning discrimination: A --> no food B --> no food AB --> food cr cr CR

  19. VA=  (  - V ) Learning stops when (=V ) A --> no food B --> no food AB --> food VA = 0 VB = 0 VA + VB = 1

  20. A --> no food B --> no food AB --> food VA = 0 VB = 0 VA + VB = 1 This one is insoluble - you can never reach asymptote: what is gained on AB trials is lost on A and B trials

  21. A --> no food B --> no food AB --> food But associative theory can explain accurate performance Both A and B acquire associative strength on compound trials, and lose some on element trials Animals respond more on AB trials (when two signals for food are present) than on A or B trials (when there is only one) But it doesn't predict perfect performance

  22. Negative patterning discrimination A --> food B --> food AB --> no food CRCR cr

  23. VA =  (  - V ) Learning stops when (= V ) A --> food B --> food AB --> no food VA = 1 VB = 1 VA + VB = 0

  24. Simple associative theory can never predict accurate performance here A --> food B --> food AB --> no food If A and B have enough associative strength to elicit responding, then the compound of A and B must elicit more responding, not less -- violates summation principle So can animals learn nonlinear discriminations of this type?

  25. Wagner (1971) and Rescorla (1972) suggested the unique stimulus account: A stimulus compound should be treated as the combination of its elements... + A B

  26. A stimulus compound should be treated as the combination of its elements... PLUS a further stimulus that is generated only when those elements are presented together: + A B ab

  27. A stimulus compound should be treated as the combination of its elements... PLUS a further stimulus that is generated only when those elements are presented together: + A B configural stimulus not very salient; so only learned about when absolutely "forced" ab

  28. Now the negative patterning discrimination looks like this: A --> food B --> food AB --> no food

  29. Now the negative patterning discrimination looks like this: A --> food B --> food AB ab --> no food

  30. Now the negative patterning discrimination looks like this: A --> food B --> food AB ab --> no food VA = 1 VB = 1 VA + VB+ Vab = 0

  31. A --> food B --> food AB ab --> no food VA = 1 VB = 1 VA + VB+ Vab = 0 B becomes excitatory: V = +1 A becomes excitatory: V = +1 ab becomes inhibitory: V = -2 ...and the discrimination is solved...

  32. Rescorla tested this interpretation with the following experiment: A + B + AB - AB + A ? B ? A + B + C - AB + A ? B ? Which group will respond more in the test?

  33. Stage 1 Stage 2 Test A + B + AB ab - AB ab + A ? B ?

  34. Stage 1 Stage 2 Test A + B + AB ab - AB ab + A ? B ? In Stage 1 A and B become excitatory and ab inhibitory; the combination of A, B and ab should therefore be neutral

  35. Stage 1 Stage 2 Test A + B + AB ab - AB ab +A ? B ? In Stage 1 A and B become excitatory and ab inhibitory; the combination of A, B and ab should therefore be neutral In Stage 2 the neutral AB ab is paired with food; the food is surprising, and A, B and ab all gain associative strength

  36. Stage 1 Stage 2 Test A + B + AB ab - AB ab + A ? B ? In Stage 1 A and B become excitatory and ab inhibitory; the combination of A, B and ab should therefore be neutral In Stage 2 the neutral AB ab is paired with food; the food is surprising, and A, B and ab all gain associative strength In the Test A and B now have more associative strength than they started with

  37. Stage 1 Stage 2 Test A + B + C - AB + A ? B ?

  38. Stage 1 Stage 2 Test A + B + C - AB + A ? B ? In Stage 1 A and B become excitatory

  39. Stage 1 Stage 2 Test A + B + C -AB + A ? B ? In Stage 1 A and B become excitatory In Stage 2 the excitatory A and B both predict food -- thus two foods are predicted, but only one happens; this produces inhibitory learning, and the strength of A and B drops...

  40. Stage 1 Stage 2 Test A + B + C - AB + A ? B ? In Stage 1 A and B become excitatory. In Stage 2 the excitatory A and B both predict food -- thus two foods are predicted, but only one happens; this produces inhibitory learning, and the strength of A and B drops... In the Test A and B now have less associative strength than they started with

  41. Responding to A and B

  42. So.. can Rescorla & Wagner explain everything? Not quite: consider the following discriminations: Discrimination 1: A+ AB- Discrimination 2: AC+ ABC- In the second case a common element C has been added on both reinforced and nonreinforced trials; this should make the discrimination harder...

  43. So.. can Rescorla & Wagner explain everything? Not quite: consider the following discriminations: Discrimination 1: A+ AB- Discrimination 2: AC+ABC- In the second case a common element C has been added on both reinforced and nonreinforced trials; this should make the discrimination harder...

  44. BUT Rescorla & Wagner's theory predicts that the AC+ ABC- discrimination will be learned most easily Because AC has more elements than A, it will acquire associative strength faster Discrimination 1: A+ AB- Discrimination 2: AC+ABC-

  45. BUT Rescorla & Wagner's theory predicts that the AC+ ABC- discrimination will be learned most easily Because AC has more elements than A, it will acquire associative strength faster Discrimination 1: A+ AB- Discrimination 2: AC+ABC-

  46. on first trial VA=  (  - V ) =( - 0 ) Vc= (  - V ) =  (- 0 ) So AC will have twice as much strength as A after trial 1 Faster EXCITATORY learning Discrimination 1: A+ AB- Discrimination 2: AC+ABC-

  47. And the more AC predicts food, the greater the surprise on ABC- trials, and so the faster B will become inhibitory Faster INHIBITORY learning Discrimination 1: A+ AB- Discrimination 2: AC+ABC-

  48. The faster the excitatory and inhibitory learning is acquired, the faster the discrimination is acquired oops!

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