Schizophrenia is one of the most infamous and mysterious mental disorders. Attempting to get to the root of the problem, scientists recently came up with an extraordinary solution: They built a schizophrenic computer. In a study published in the online version of Biological Psychiatry in March, researchers altered an artificial neural network capable of learning language and stories, to the point where it started "acting" schizophrenic.
People who suffer from schizophrenia often have difficulty thinking logically or discerning what is real or not real in their lives. "It is characterized by delusions or disassociation of language, often with hallucinations of spoken speech," says psychiatrist Ralph Hoffman of Yale University, coauthor of the study along with computer scientist Risto Miikkulainen of the University of Texas, Austin. Because of that language disassociation, struggling to recount stories correctly is one of the early signs of schizophrenia. So to gain new insight into how schizophrenia might affect the human brain, Hoffman and Miikkulainen tried to give a storytelling computer system the same problem.
To make a schizophrenic computer, they began with an artificial neural network called DISCERN that Miikkulainen has been working on intermittently since the 1990s. DISCERN is a computer model designed to function as a biological neural network—like a brain. Instead of neurons, DISCERN has small sections of isolated computer code that form networks called modules. These modules are then linked together to form a still larger, more complicated network. Unlike a computer program, where the computer is explicitly told what an output should be for a given input, DISCERN is designed to learn from the inputs what the correct output should be.
Hoffman and his colleagues then started to tell simple stories to the computer. This presented a big challenge: First you have to teach the computer how to understand the complexities of language (to grasp how difficult this is, ask the IBM team that spent years building the Watson supercomputer to parse (Jeopardy) clues). The computer has to learn what the words mean, what their purpose is in a sentence, then understand how to put those concepts together to create scenes, and put the scenes together into stories. Each of those activities takes place in an individual module before being sent on to the next.
In DISCERN, the process begins in a module called the sentence parser, which examines each sentence one word at a time. The module assigns each word a grammatical function, such as noun, predicate or modifier. Then the sentence module sends along its analysis to the next module, a story parser. The story parser places the sentences in scripts or patterns, analyzing the group of sentences for plot, setting and character details. The scripts are then recorded in the memory encoder and sent for storage in the episodic memory module.
Once the computer system could "understand" a story, the researchers began to feed stories to it. They were simple tales—the team used only 159 individual words to create 28 different stories, 14 told in the first person and 14 told in the third person. The first person stories were realistic, involving themes of going to work, attending a wedding, or eating at a restaurant. The third person stories were more like action movies, featuring gangsters and terrorists.
Then, it was DISCERN's job to repeat the tales back to the scientists. To do this, it would take the memory from the episodic memory module, and reverse the process in a module called a story generator, essentially repeating the story based on what it understood from the inputs. After numerous repetitions of the story set, DISCERN learned how to understand and repeat a story, just like an ordinary human adult.
To make DISCERN start acting like a schizophrenic brain, however, researchers changed these modules to mimic different kinds of brain damage. Miikkulainen and Uli Grasemann reprogrammed the memory encoder so that it would learn at an accelerated rate, forcing the computer to remember story details that it would have normally dismissed as irrelevant. Instead of learning the stories faster, DISCERN got confused, mixing up stories with entirely different plot lines. When they did the same thing to the story generator, DISCERN began inserting itself into the third person stories, even claiming at one point that it planted a bomb. (The bomb scene was part of a terrorist story, but with the memory encoder on hyperdrive, DISCERN thought that the story was in the first person and placed itself as the terrorist in the story.)
The computer's mix-ups closely resemble symptoms of schizophrenia called derailment and delusion. In fact, it showed similar rates of derailment and delusion as schizophrenic people whom the team tested with the same stories. This led the researchers to conclude that the accelerated learning rate—where suddenly all memories are important—might be a cause of schizophrenia. They have dubbed this the "hyperlearning" hypothesis.
Hoffman's hypothesis is so novel that many other scientists are still working on how to respond. Deanna Barch, a professor of psychology at Washington University in St. Louis, has also studied schizophrenia using computer models. "It's so new, it's too early to ask the question of whether or not people agree or disagree," she says. According to Barch, the hyperlearning hypothesis is closely related to the "aberrant salience" hypothesis, which attributes imbalances in dopamine levels to symptoms of schizophrenia. Indeed, a flood of dopamine released into the human brain could potentially cause hyperlearning, Hoffman says. But while many researchers believe that abnormal levels of dopamine are connected to schizophrenia, they disagree about exactly how—it's not clear, for instance, whether those levels could be a cause of schizophrenia or a symptom.
"It does challenge conventional wisdom about schizophrenia," Hoffman says of the study, though there's still a long way to go to prove whether or not what they've learned in a computer model applies to humans. Miikkulainen and Hoffman plan to investigate the human aspect with fMRI and medication studies, and also develop more advanced models of neural networks. If the team can give a computer schizophrenia, they may eventually be able to model disorders like mania, psychosis and depression, offering new insights into those diseases as well.