1. Probabilistic inference in human semantic memory Mark Steyvers, Tomas L. Griffiths, and Simon Dennis 소프트컴퓨팅연구실오근현 TRENDS in Cognitive Sciences vol. 10, no. 7, 2006

2. Overview Relational models of memory The Retrieving Effectively from Memory(REM) Model High-dimensional semantic spaces Probabilistic topic models Modeling semantic memory at the sentence level Conclusion 1

3. Relational Models of Memory Encoding much knowledge stored in memory using language –Most of the stimuli used in memory experience are linguistic –How the statistics of language influence human memory? Rational analysis –A framework for developing computational models of cognition –Making the working assumption human cognition approximates an optimal response to the computational problems posed by the environmental The role of probabilistic inference in memory –A history factor The occurrence pattern of the item over time( Recent or frequent ) –A context factor The association between items (Similar) 2

4. The Retrieving Effectively from Memory(REM) Stronger assumptions –Encoding process –Representation of information Emphasizing the role of probabilistic inference in explaining human memory Recognition memory task(Application) –Discriminating between old items and new items –Assumptions Words : Vectors of Features(noisy and incomplete) –A calculation of the likelihood ratio Balancing the evidence for and ‘old’ decision against a ‘new’ decision Degree of match between the memory probe and contents of memory 3

5. Example of Comparing a Test 5 Features Green box : Matches Red box : Mismatches Empty Positions : Missing Features 3 rd memory trace : the most evidence that test item is old Data ‘D’ : Each trace 4

6. Posterior odds Posterior Odds –Posterior : 증거 (Evidence) 이후 –Odds : 두 배반적 가설의 확률 비 Prior odds is set at 1  # of old == # of new An old decision –Exceeding some criterion(usually set at 1) 5 An example distribution of log posterior odds

7. Mirror Effects Increase in hit rate –Hit rate : correctly recognizing an old item as old decrease in false alarm rates –False alarm rates : falsely recognizing a new item as old Example –Low frequency words –More rare features are stored for low frequency words relative to high frequency words –How encoding conditions that improves the diagnosticity of feature matches for low frequency target items –lower the probability of chance matches by low frequency distractor items 6

8. The Hyperspace Analog to Language(HAL) Each word by a vector where each element of the vector corresponds to a weighted co-occurrence value of that word with some other word 7

9. The Latent Semantic Analysis(LSA) The co-occurrence information between words and passages 8 Applying matrix decomposition techniques –to reduce the dimensionality of the original matrix to a much smaller size –preserving as much as possible the covariation structure of words and documents

10. The Word Association Space(WAS) Input a set of association norms –Formed by asking subjects to produce the first word that comes to mind in response to a given cue 9

11. Probabilistic Topic Models The Focus on prediction as a central problem of memory Emphasizing the role of context in guiding predictions The latent structure of words using topics –Documents : mixtures of topics –A topic : A Probability distributions over words Topic Model is a generative model for documents 10

12. Distributions for Topics Different weight to thematically related words P(z) : The distribution over topics z in a particular document P(W|z) : The probability distribution over words w given topic z P( =j) : the probability that the ‘j’th topic was sampled ‘i’th word P( | =j) : the probability of word under topic j T : The number of topics 11

13. Using Bayes’ Rule The problem is to predict the conditional probability of word (the response word) given the cue word Applying Bayes’s rule 12

14. The same word with two different meaning 13

15. Predictions about which words are likely to appear next in a document or conversation, based on the previous words A rational model of how context should influence memory –The focus on the role of history in earlier models –The effects of context being appropriately modulated by the statistics of the environment 14

16. The Syntagmatic Paradigmatic(SP) Model Modeling Semantic Memory at the sentence level Specifying a simple probabilistic model of knowledge and allowing representational content –to emerge response to the structure of the environment Syntagmatic Associations –Capturing structural and propositional knowledge between words that follow each other –Structural traces that correspond to individual sentence Paradigmatic Associations –between words that fit in the same slots across sentences –combined to form relational(or propositional) traces that correspond to the same sentences 15

17. Sampling structural and relational exemplars Mapping the two sentences to each other in a one to one fashion Change : Sampras-Kuerten / Agassi-Roddick Match : defeated Delete : Pete – ㅡ (symbol of deletion, empty words) Priority –Match > change > insertion or deletion 16

18. The Architecture of SP Model 4 and 5 traces in Sequential LTM # containing the pattern {Roddick, Costa} {Sampras} defeat {Agassi}  Agassi need to align ‘#’ symbol The pattern {Agassi, Roddick, Costa} 17

19. Conclusion Probabilistic inference –A natural way to address problems of reasoning under uncertainty A search for richer representations of the structure of linguistic stimuli Contribution to the question –How linguistic stimuli might be represented in memory Rational Analysis –A natural framework to understand the tight coupling between behavior and environmental staticstics –Linguistic stimuli form an excellent testing ground for rational model of memory 18