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?

• Relate sentence to the rest of the story– Use scripts and schemas to fill in gaps and make

inferences– Summarize key points of story

Two example “stories”

I was a doctorI worked in New-YorkI liked my jobI was good doctor

Tony was a gangsterTony worked in ChicagoTony hated his jobTony was a bad gangster

Model for story comprehension

Comparing performance of model to unimpaired humans

Symptoms of schizophrenia

• Disorganized thought processes• Attributing acts to others or oneself

incorrectly• Dysfunctional executive disorder

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.

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.

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?