PHYSICAL & LIFE SCIENCE TECHNOLOGIES
Psychology Modeling the mind
Researchers have developed a computer model to simulate theory of mind
Humans can posit the intentions and predict the actions of others through observation of movements and behaviors. This cognitive ability, known as theory of mind, has important implications in personality development and social interaction. Despite its significance, however, scientists are only beginning to understand how people use motion information to perceive intentions. For instance, recent studies have demonstrated that a simple anima- tion of moving shapes is sufficient to convey intentions. David Pautler at the A*STAR Institute of High Performance
Computing and co-workers have now developed an artificial intelligence system that models how people recognize the intentions of others1
. The system could help computer scientists
to develop computerized systems that detect and recognize human actions. Pautler’s group built on a technique called incremental chart
parsing, developed by computer scientists and linguists to analyze the structure of text, and modified it to develop a system that analyzes the movements of shapes and ascribes to them inten- tions, based on small sets of rules. They then created animations using Adobe Flash software —
in one, object X moves away from V and towards Y and Z, but as it nears Y it suddenly arcs around Y and continues until stopping near Z. This is ambiguous early on but resolves in an “A-ha!” moment with a single explanation at the end. Three of the explanations attributed intentions to X: it intends to move further away from V, closer to Y, or closer to Z. The other two attribute the movements to physical forces that either repel it from V or attract it to Z. The model was designed to guess any of these explanations at any time, whenever the visual cues suggest them. The researchers recruited 38 volunteers and asked them to
watch the animation, confirming that they perceive the animation as predicted by the model. Thirty-five others watched another animation, in which object X moves in a straight line at a constant speed. Far fewer ascribed any intention to the object. Experiments such as these provide a guide as to what kinds of explanations the model needs, and the types of visual cues that trigger each kind of explanation. Pautler and his team are now
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Time-lapse view of a video intended to show a fight between a blue and a green object
developing the system further by formulating more rules to refine the process by which it makes predictions. “Computer vision systems can detect a person’s presence and
track him, but these systems don’t explain what the person is doing or why he might be doing it,” says Pautler. “If we could combine computer vision capabilities with an ability to attribute intentions to what is seen, that would be useful in surveillance, robot assistance for the elderly and many other applications.” ■
1. Pautler, D., Koenig, B. L., Quek, B.–K. & Ortony, A. Using modified incremental chart parsing to ascribe intentions to animated geometric figures. Behavior Research Methods 43, 643–665 (2012).
A*STAR RESEARCH OCTOBER 2011– MARCH 2012
© David Pautler and Bryan Koenig
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