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I will describe research using fMRI to track the learning of mathematics with a computer-based algebra tutor. I will describe the methodological challenges in studying such a complex task and how we use cognitive models in the ACT-R architecture to interpret imaging data. I wll also describe how we can use the imaging data to identify mental states as the student is engaged in algebraic problems solving.
Advances in neurophysiological and cognitive science research have fueled a surge of research aimed at more effectively combining human and machine capabilities. In this talk we will give and overview of progress and prospects for four current thrusts of technology development resulting from this research: brain-machine interfaces, robotic prostheses and orthotics, cognitive and sensory prostheses,...
In recent years, the Semantic Web has seen rapid growth in size (many billions of facts and rules are now available) and increasing adoption in many sectors (government, publishing industry, media). This success has brought with it a whole new set of problems: storage, querying and reasoning with billions of facts and rules that are distributed across different locations. The Large Knowledge Collider...
Midazolam is a benzodiazepine commonly used as an anxiolytic in surgery. A useful attribute of this drug is that it creates temporary, reversible, anterograde amnesia. Studies involving healthy subjects given midazolam in one session and saline in another, in a double-blind, cross-over design, provide insights into memory function. Several experiments will be described to illustrate the potential...
After more than 60 years of development, the operation speed of computer is up to several hundred thousand billion (1014 ) times, but its intelligence level is extremely low. Studying machine which combines high performance and the people’s high intelligence together becomes the effective way with high capacity and efficiency of exploring information processing. It will bring the important impetus...
Machine learning tries to improve the performance of the system automatically by learning from experiences, e.g., objects or events given to the system as training samples. Generally, each object is represented by an instance (or feature vector) and is associated with a class label indicating the semantic meaning of that object. For ambiguous objects which have multiple semantic meanings, traditional...
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