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Unit 7: Base-Level Activation

Unit 7: Base-Level Activation. February 25, 2003. Activation Revisited. Both latency and probability of recall depend on activation Activation Equation. Base-level activation. Spreading activation. Partial matching. Noise. (sgp :bll t). Base-Level Activation.

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Unit 7: Base-Level Activation

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  1. Unit 7: Base-Level Activation February 25, 2003

  2. Activation Revisited • Both latency and probability of recall depend on activation • Activation Equation Base-level activation Spreading activation Partial matching Noise (sgp :bll t) Unit 7

  3. Base-Level Activation • Base-level activation depends on the history of usage of a chunk • Memory strength depends on • How recently you used it in the past • How much you practiced it Unit 7

  4. Time now 0 Pres 1 Pres 2 Pres k Pres n Base-Level Learning Unit 7

  5. Time now 0 Pres 1 Pres 2 Pres k Pres n Base-Level Learning time since the k-th presentation of the chunk i Mathematically transforming the ages to conform to the functions optimal in the environment decay parameter (sgp :bll 0.5) Unit 7

  6. Power Law of Forgetting • Strength of memories decreases with time E.g. -Speed to recognize a sentence at various delays • Number of paired associates that subjects recall • People’s ability to recognize the name of a TV show for varying numbers of years after it’s been canceled • More and more delay produces smaller and smaller losses • This is the idea that individual events are forgotten according to a power function Unit 7

  7. p=probability • p is a decreasing function of retention time • p/(1-p) is a power function of retention time with exponent d • ln(p/(1-p)) is a linear function of ln(retentiontime)-d • Accounts for the fact that each event age (tk) decays at rate d Unit 7

  8. Power Law of Learning Memory improves with practice; recall often gets close to perfection, but speed increases with practice even after that: This is the idea that the accumulating sum of events is also a power function. Proof omitted shows that this holds true for evenly spaced presentations Unit 7

  9. p= need probability n=number of occurrences • p is a linear function of n • p/(1-p) is approximately a power function of n • ln(p/(1-p)) is a linear function of ln (n) • Accounts for the sum of all event ages (tks) contributing Unit 7

  10. How many tks are there at time 40, 10, and 100. What are they? Unit 7

  11. What Is a Event Presentation? • Creating a new chunk (p my-production =goal> isa associate term1 vanilla term2 3  +goal> isa associate) • Re-creating an old chunk • Retrieving and harvesting a chunk Unit 7

  12. Optimized Learning • At each moment when chunk i could be potentially retrieved, ACT-R needs to compute new  n computations; for each chunk ACT-R needs to store the presentations • Optimized learning is a fast approximation  1 operation per potential retrieval • (sgp :ol t) Unit 7

  13. Optimized Learning Equation Optimized learning works when the n presentations are spaced approximately evenly Time now 0 Pres 1 Pres 2 Pres k Pres n Unit 7

  14. Unit 7

  15. Paired-Associates Example Study and recall pairs word-digit: vanilla 3 Each digit was used as a response twice. 20 paired associates; 8 trials Unit 7

  16. Paired Associates: Results Accuracy: items get under retrieval threshold if not rehearsed soon Latency: power law of learning Unit 7

  17. Homework: Zbrodoff’s Experiment True or false? A + 3 = D (true) G + 2 = H (false) • Possible addends: 2, 3 or 4 • Frequency manipulation: • Control -- each problem x 2 • Standard – 2-add x 3, 3-add x 2, 4-add x 1 • Reverse -- 2-add X 1, 3-add X 2, 4-add x 3 • 3 Blocks Unit 7

  18. Zbrodoff’s Data Control Two Three Four Block 1 1.840 2.460 2.820 Block 2 1.210 1.450 1.420 Block 3 1.140 1.420 1.170 Standard Group (smaller problems more frequent) Two Three Four Block 1 1.840 2.650 3.550 Block 2 1.080 1.450 1.920 Block 3 0.910 1.080 1.430 Reverse Group (larger problems more frequent) Two Three Four Block 1 2.250 2.530 2.420 Block 2 1.470 1.460 1.110 Block 3 1.240 1.120 0.870 Unit 7

  19. Tips • Compute the addition result when it’s not available for retrieval • May add extra effort to the productions that make the computation (articulation) (spp myproduction :effort .1) • (setallbaselevels <n> <T>) • (spp :ga 0 :pm nil) • Change retrieval threshold, latency factor, noise Unit 7

  20. Activation, Latency, and Recall Activation Probability of Retrieval Base Level Retrieval Latency Back Unit 7

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