ACT-R and the basic-level activation

1. ACT-R and the basic-level activation 2009-5-22 노홍찬

2. Content • ACT-R 5.0: An Integrated Theory of the Mind • J.R. Anderson et al., Psychological Review, 2004, 633 • ACT-R architecture overview • The perceptual-Motor System • The Goal Module • The Declarative Memory Module • Procedural Memory • Reflections of the Environment in memory • J.R. Anderson et al., Psychological Science, 1991, 374 • Form of the memory functions • Environmental Explanation • Formulating the Effects of Practice and Retention

3. Why ACT-R? • ACT-R • Adaptive Control of Thought-Rational, first proposed in 1998 • Motivation of ACT-R • An image of the mind as a disconnected set of mental specialties. • “how is it all put back together?” • Goals • Producing a theory • that is capable of attacking real-world problems • that is capable of integrating the mass of data from cognitive neuroscience methods like brain imaging

4. ACT-R 5.0 Architecture • visual module • for identifying objects in the visual field • manual module • for controlling the hands • goal module • keeping track of current goals and intentions. • production module • coordination in the behavior of other modules • only respond to a limited amount of information in the buffers • recognize patterns in these buffers and make changes to these buffers • serial vs parallel processing • the content of any buffer is limited to a single declarative unit of knowledge, called a chunk • only a single production is selected at each cycle to fire. In this

5. The perceptual-Motor System • Two separate modules for the visual module • Visual-location module with a buffer • Where is the object? • Visual-object module with a buffer • What is the object? • Object identifying process • Request for where system with a series of constraints • Where system returns a chunk representing a location meeting the constraints • Request for what system with the chunk representing the visual location • What system shifts attention to the location • What system generates a declarative memory chunk representing the object • Dual tasks for motor modules • Ex) visual motor, manual motor, … • For the same modules, serial execution • For the different modules, parallel execution

6. Goal Module • Responsibility • in the absence of supporting external stimuli keeping track of what intentions are • Keeping a representation of a set of subgoals • Keeping track of problem state

7. The Declarative Memory Module • Cognitive core of ACT-R along with procedural system • Activation of a chunk (Ai) • Base level activation (Bi) • Reflect general the memory’s usefulness in the past • Associative activation (∑WjSji) • Reflect its relevance to the current context • Wj reflect the attentional weighting of each element (elem j) of current goal • Sji are the strengths of association from each element (elem j) to chunk I • Retrieval probability and the latency is determined by Ai

8. Base level activation (Bi) • Base level activation Bi of chunk i • reflects the log odds an item will reoccur as a function of how it has appeared in the past. • where • frequency of the retrieval of the chunk is n • jth retrieval of the chunk represents jth element of the series • tj represents the elapsed time since the jth retrieval of the chun • many applications suggests the value of the parameter d as 0.5 • Bi has been suggested by the author’s previous work • The most successfully and frequently used part of the ACT–R theory.

9. Associative activation (∑WjSji) • The attentional weighting Wj • Wj is 1/n • where n is the number of elements consisting chunk i • The strength of association Sji • Sji is S – ln(fanj) • Where • fanj is the number of chunks associated to element j • S is a parameter, which is estimated as about 2 in many applications • Ex) A hippie was in the park • Each oval is a chunk • Each element has the same attentional weight as 1/3 • Hippie in park • Wj is 1 or 3 for each element

10. Retrieval probability & latency • Retrieval probability • Almost the same as Ai • just transformed by sigmoid function • Where • ζ is the threshold that is the minimum Bi for the retrieval to begin • S is a parameter whose role is the transform noise • 0.4 for many applications • Latency of the retrieval • just the same as the value of Ai without log function • F is the latency factor

11. Hippie experiment for Activation theory • Activation level calculated by the activation equation • Real retrieval time vs estimated retrieval time • The retrieval time is estimated by the activation level presented above • The correlation between the real and estimated time • 0.986 • nearly no dependency with the parameters

12. Procedural memory • The production system • can detect the patterns that appear in these buffers and decide what to do next • Because of the seriality in production rule execution, only one can be selected • the one with the highest utility • where • Pi is an estimate of the probability that if production iis chosen the current goal will be achieved, • G is the value of that current goal, • Ci is an estimate of the cost • Pi and Ciare learned from experience with that production rule.

13. Base-level activation in more detail • Anderson et al, Reflections of the Environment in memory, 1991 • Gave the foundation of base-level learning equation to ACT-R theory • 가정 • 인간의 기억 메커니즘은 인간의 진화과정을 통해 환경적인 조건에 최적으로 반응하도록 적응해 왔다. • 기존의 연구들은 사람에 대해 적절한 입력을 주고 사람들의 행동을 관찰함으로써 기억의 메커니즘을 찾아내려고 시도함 • 이와는 반대로 인간의 행동이 환경적인 조건에 최적으로 적응하도록 진화해왔다는 가정하에, 환경적인 조건들을 관찰함으로써 인간의 기억 메커니즘을 밝히려는 시도를 수행함 • 기존의 연구들은 retention function과 practice function, spacing effect에 대해서 부분적인 설명이 가능할 뿐, 모두를 다 설명하지 못함 • Anderson은 환경적인 조건을 관찰하고 이를 인간의 기억 메커니즘을 설계하는데 적용함으로써 위 3가지 effect를 모두 성공적으로 설명할 수 있는 theory를 주장

14. Retention function • Exponential function

15. Retention function • Power function

16. Practice Function

17. Spacing effects

18. Environmental Explanation • Odds • If the probability of event i’s happening is p • the odds is defined as p/(1-p) • Ranging from 0 to infinity • 환경에서의needs odds가 과거의 기억 인출 기록에 의해서 어떻게 영향을 받는지 알아봄 • 3 environmental sets • New york times headlines • 1986101 ~ 19871231 • Checked each word occurrence • Every time a word appears in the text, it’s a request for the reader to retrieve the word • CHILDES database • Related to children’s verbal interactions • Every time someone says a word to a child, it’s a request on the child to retrieve the word’s meaning • Mail messages of the author • 198503 ~ 198912 • Every time the author receives a message a certain person, it’s a request for the author to retrieve the memory of the sender

19. Environmental Explanation • Recency effect

20. Environmental Explanation • Frequency effect

21. Environmental Explanation • Spacing effect

22. The provided mathematical formula • Basic assumptions from the environmental experiments • The strengths from individual presentations sum to produce a total strength (frequency effect) • Strengths of individual presentations decay as a power function of the time (recency effect) • The exponent of the power function for decay of each presentation decreases as a function of time since previous presentation (spacing effect)

23. Comparison between estimation and real one

24. Discussion • Base-level activation vsCache algorithms • Base-level activation • Frequency effect와 recency effect의 결합 • Power function의 활용 • Cache algorithms • Need odds vs cache replacement policy • LRU, LFU, LRFU (1999) • LRU: only recency effect 고려 • LFU: only frequency effect 고려 • LRFU: recency effect + frequency effect • not with power function

25. Discussion • Chunking using ACT-R vs association rule mining • Association rule mining • Offline algorithm • Chunking with ACT-R • Online algorithm