lec6-2010 - 1 Lecture 6 Long-Term Memory, Schemas, and...

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Unformatted text preview: 1 Lecture 6 Long-Term Memory, Schemas, and Models 2 Outline for today Models of Memory MyLifeBits http:// research.microsoft.com/barc/mediapresence/MyLifeB Frames and Schemas Eyewitness Testimony Scientific American Frontiers http://www.pbs.org/saf/1402/ 3 Questions to ask How can we model memory? (Chap 6) Symbolic vs. distributive approaches Are there tools to help with memory? MyLifeBits Why are memories distorted? (Chap 7) Schemas and scripts What do we know about real-life memories? (Chap 7) Eyewitness testimony, flashbulb memories, and recovered memories 4 When I was younger I could remember anything, whether it happened or not.--Mark Twain 5 Outline for today Models of Memory MyLifeBits http:// research.microsoft.com/barc/mediapresence/MyLifeB Frames and Schemas Eyewitness Testimony Scientific American Frontiers http://www.pbs.org/saf/1402/ 6 How to model memory ACT-R, Anderson, CMU PDP, McClelland & Rumelhart See http:// rumpole.psych.purdue.edu/models/index http://www2.psy.uq.edu.au/CogPsych/Noetica/O 7 ACT Theory Propositions Smallest units that can be T or F Flower Is Pretty Agent Relation 8 ACT Theory Type-token distinction Type is category X Is Blue Agent Relation Flower Is-a 9 ACT Theory, continued Activation spreads through network Learn The lawyer paid the barber The dentist fought the janitor The lawyer fought the doctor Test 1. The barber fought the dentist new 2. The lawyer paid the barber old [2 1] Fan Effect More links, the slower the response 10 Parallel Distributed Processing Representation is distributed over fixed set of nodes and links (+1 1 1 +1)-> (-1 1 +1 +1) 0+0-.5-.5=-1 +1-1-1 +1 11 IN1 OUT1 ( 29 ( 29 ( 29 ( 29 ( 29 ( 29 ( 29 ( 29 ( 29 ( 29 ( 29 ( 29 ) 1 1 1 1 ( 1 ) 1 .( 5 . 1 5 . 1 5 . 1 ) 5 . 1 .( 1 1 1 ) 1 ( 5 . 1 5 . 1 5 . 1 ) 5 . 1 ( 1 1 5 . 5 . 5 . 5 . 5 . 5 . 5 . 5 . ) 1 1 1 1 ( + +-- = + + - +- - + + + + +- - + - + + + + + - + - +- + +- + + - + - + + = ---- +-- + 12 IN2 OUT2 (-1 +1 1 +1) x weigh ts = (-1 +1 +1 1) Knowledge does not reside in any one link, but is distributed across the weights The same weights can encode multiple bits of knowledge 13 14 Results Pairs INPUT OUTPUT PAIR3 (+1 +1 +1 1) (+1 +1 +1 +1) PAIR1 (+1 1 1 +1) (-1 1.5 +66 +.5) instead of (-1 1 +1 +1) PAIR2 (-1 +1 1 +1) (-1 +.5 +66 -1.5) instead of (-1 +1 +1 1) As PAIR3 is not orthogonal to other two pairs. PAIR1 and PAIR2 are orthogonal. 15 Orthogonal Vectors In the 2-D shown in Fig 6.13, Vector h and f are orthogonal, while f and g point in similar directions Vectors f and g should be used for similar concepts (dog and cat) Vectors f and h can be used for independent concepts (dog and snake) Input: Animal Output: Action Other Models...
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lec6-2010 - 1 Lecture 6 Long-Term Memory, Schemas, and...

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