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

7. Comparing performance of model to unimpaired humans

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

9. 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.

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

12. 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?