Program mimics human creativity

A cognitive science professor and his team say they’ve applied a new psychology theory to create a computer program that can mimic creative human problem solving.

“As a psychological theory, this theory pushes forward the field of research on creative problem solving and offers an explanation of the human mind and how we solve problems creatively,” says Ron Sun of the Rensselaer Polytechnic Institute.

“But this model can also be used as the basis for creating future artificial intelligence programs that are good at solving problems creatively.”

Explicit-Implicit Interaction theory is based on ‘stage decomposition’, the widely-supported theory that humans go through four stages – preparation, incubation, insight and verification – in solving problems creatively.

In building upon this theory, several competing ideas have been developed. But Sun says his model integrates a number of these into a larger equation.

And it seems to work.

The team created a computer program, Clarion, based on the model, and found that its performance on a problem requiring creative thinking to solve mimicked that of human beings very closely.

Subjects were given a situation in which a coin dealer is offered a coin stamped with an emperor’s head on one side and the date 544BC on the other – and calls the police. They were then asked why this might be.

A dealer in antique coins gets an offer to buy a beautiful bronze coin. The coin has an emperor’s head on one side and the date “544 B.C.” stamped on the other. The dealer examines the coin, but instead of buying it, he calls the police. Why?

Human subjects were given a chance to respond after being interrupted either to discuss their thought process or to work on an unrelated task – the incubation period of the model. The team found that 35.6 percent of participants answered correctly after discussing their thinking, while 45.8 percent of participants answered correctly after working on another task.

In 5,000 runs of the Clarion program, it answered correctly 35.3 percent of the time in the first instance, and 45.3 percent of the time in the second instance.

“The simulation data matches the human data very well,” said Sun. The team believes the model could be an important boost to artificial intelligence programs.

(The answer, by the way, is that the coin is a fake – Arabic numerals didn’t exist then, and nobody knew it was ‘Before Christ’ at the time.)