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Distributed pdp representafons why parallel

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Unformatted text preview: idence Fan effects A=Agent R=RelaFon O=Object O –  Ge}ng the “gist” •  Tara dated Sam •  = Tara went out with Sam •  ≠ Tara hated Sam –  Nurse primes doctor –  Fan effects RelaFon chalkboard A R Crazy girl Jason A R O R Tara A O Sam A dated R O Dawn R A O Daphne Fans: Dated: 6 Jason: 3 Sam: 2 Sookie: 1 A R Maude]e < ­ ­ a fan Sookie O Vampire Bill 8 2/10/13 < ­ ­ a fan Did Sookie date Bill? A=Agent R=RelaFon O=Object O Jason R R Tara O Sam A O dated R R O Sookie A Tara Vampire Bill O A Sam O R Dawn dated R R A O O Sookie Daphne So the more facts you know, the harder it is to access one. But aren’t we faster to recall more about what we know a lot about? If based on plausibility, more facts leads to faster response. = A R O R Daphne A=Agent R=RelaFon O=Object Jason Maude]e Dawn Fans: Dated: 6 Jason: 3 Sam: 2 Sookie: 1 = A R A A O O A R Maude]e A A=Agent R=RelaFon O=Object Crazy girl A O = A R Crazy girl Fans: Dated: 6 Jason: 3 Sam: 2 Sookie: 1 Did Jason date Dawn? Vampire Bill Fan effects & plausibility •  Reder & Ross (1983) –  Learn facts with different fan sizes –  Then test either •  Strict [email protected] or •  Plausibility “Recognize?” “Plausible?” long long (Typical fan effect) RT RT short short Li]le fan Big fan One more model… Faster with more facts when judging plausibility Li]le fan Big fan Parallel Distributed Processing (PDP) •  E.g. McClelland & Rumelhart (1986) •  Very different approach than ACT –  RepresentaFons •  Localist (ACT) vs. distributed (PDP) –  Combines episodic and semanFc •  Episodes “add up to” semanFcs –...
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