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Neural Network Model for Story Comprehension: Understanding Schizophrenic Behavior and Comparison to Human Performance

This paper aims to create a neural network model that simulates story comprehension and identifies parts of the network that contribute to schizophrenic behavior. By analyzing lexical access, sentence structure, and narrative coherence, the model generates inferences and summaries of key story points. Two example stories are used for comparison to unimpaired human performance, highlighting symptoms of schizophrenia such as disorganized thought processes and attribution errors. The study also explores errors like agent slotting, lexical misfires, and derailments to understand the breakdown in brain networks during disorders. Conclusions suggest hyperlearning as a key predictor of schizophrenic behavior, with limitations including partial simulation of story memory and challenges in scaling up information encoding.

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Neural Network Model for Story Comprehension: Understanding Schizophrenic Behavior and Comparison to Human Performance

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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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