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Goals of paper

Goals of paper. Create a neural network which simulates story comprehension Determine what parts of the network are damaged to produce schizophrenic behavior. Steps to understanding a story. Identify each word (lexical access) Determine role in sentence of each word Who does what to whom?

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Goals of paper

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  1. Goals of paper • Create a neural network which simulates story comprehension • Determine what parts of the network are damaged to produce schizophrenic behavior

  2. Steps to understanding a story • Identify each word (lexical access) • Determine role in sentence of each word • Who does what to whom? • Relate sentence to the rest of the story • Use scripts and schemas to fill in gaps and make inferences • Summarize key points of story

  3. Two example “stories” I was a doctor I worked in New-York I liked my job I was good doctor Tony was a gangster Tony worked in Chicago Tony hated his job Tony was a bad gangster

  4. Model for story comprehension

  5. Comparing performance of model to unimpaired humans

  6. Symptoms of schizophrenia • Disorganized thought processes • Attributing acts to others or oneself incorrectly • Dysfunctional executive disorder

  7. Agent slotting error: Claiming incorrectly that an agent had a role in an event. eg1. The girl gave the old man the flowers is wrong. correct: The old man gave the old man the flowers. eg2. The cop arrested me for speeding. correct: The cop arrested Vince for speeding. Lexical misfire: incorrect words used with different meaning from story. eg. “wispy old man”  “whispering man” Derailment: entire clause of meaning is different from the story. Eg. A girl was sitting on the bus and he noticed her looking at his eyes.

  8. Conclusions • Computational models can be used to specify what parts of brain network break down during disorders • Hyperlearningpredicted schizophrenic behavior the best. • Exaggerated backpropagation prediction error signaling leads to over correction, and reduces the separation between stories.

  9. Limitations • Only part of story memory process simulated • The network’s memory is too good! (over 95% accuracy) • Cannot simulate unimpaired performance • Only simulates some schizophrenic behavior • Will it scale up to encode more information?

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