The Experts below are selected from a list of 14742 Experts worldwide ranked by ideXlab platform
Kenneth R Koedinger - One of the best experts on this subject based on the ideXlab platform.
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learning to perceive Two Dimensional Displays using probabilistic grammars
European conference on Machine Learning, 2012Co-Authors: William W Cohen, Kenneth R KoedingerAbstract:People learn to read and understand various Displays (e.g., tables on webpages and software user interfaces) every day. How do humans learn to process such Displays? Can computers be efficiently taught to understand and use such Displays? In this paper, we use statistical learning to model how humans learn to perceive visual Displays. We extend an existing probabilistic context-free grammar learner to support learning within a Two-Dimensional space by incorporating spatial and temporal information. Experimental results in both synthetic domains and real world domains show that the proposed learning algorithm is effective in acquiring user interface layout. Furthermore, we evaluate the effectiveness of the proposed algorithm within an intelligent tutoring agent, SimStudent, by integrating the learned display representation into the agent. Experimental results in learning complex problem solving skills in three domains show that the learned display representation is as good as one created by a human expert, in that skill learning using the learned representation is as effective as using a manually created representation.
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ECML/PKDD (2) - Learning to perceive Two-Dimensional Displays using probabilistic grammars
Machine Learning and Knowledge Discovery in Databases, 2012Co-Authors: William W Cohen, Kenneth R KoedingerAbstract:People learn to read and understand various Displays (e.g., tables on webpages and software user interfaces) every day. How do humans learn to process such Displays? Can computers be efficiently taught to understand and use such Displays? In this paper, we use statistical learning to model how humans learn to perceive visual Displays. We extend an existing probabilistic context-free grammar learner to support learning within a Two-Dimensional space by incorporating spatial and temporal information. Experimental results in both synthetic domains and real world domains show that the proposed learning algorithm is effective in acquiring user interface layout. Furthermore, we evaluate the effectiveness of the proposed algorithm within an intelligent tutoring agent, SimStudent, by integrating the learned display representation into the agent. Experimental results in learning complex problem solving skills in three domains show that the learned display representation is as good as one created by a human expert, in that skill learning using the learned representation is as effective as using a manually created representation.
William W Cohen - One of the best experts on this subject based on the ideXlab platform.
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learning to perceive Two Dimensional Displays using probabilistic grammars
European conference on Machine Learning, 2012Co-Authors: William W Cohen, Kenneth R KoedingerAbstract:People learn to read and understand various Displays (e.g., tables on webpages and software user interfaces) every day. How do humans learn to process such Displays? Can computers be efficiently taught to understand and use such Displays? In this paper, we use statistical learning to model how humans learn to perceive visual Displays. We extend an existing probabilistic context-free grammar learner to support learning within a Two-Dimensional space by incorporating spatial and temporal information. Experimental results in both synthetic domains and real world domains show that the proposed learning algorithm is effective in acquiring user interface layout. Furthermore, we evaluate the effectiveness of the proposed algorithm within an intelligent tutoring agent, SimStudent, by integrating the learned display representation into the agent. Experimental results in learning complex problem solving skills in three domains show that the learned display representation is as good as one created by a human expert, in that skill learning using the learned representation is as effective as using a manually created representation.
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ECML/PKDD (2) - Learning to perceive Two-Dimensional Displays using probabilistic grammars
Machine Learning and Knowledge Discovery in Databases, 2012Co-Authors: William W Cohen, Kenneth R KoedingerAbstract:People learn to read and understand various Displays (e.g., tables on webpages and software user interfaces) every day. How do humans learn to process such Displays? Can computers be efficiently taught to understand and use such Displays? In this paper, we use statistical learning to model how humans learn to perceive visual Displays. We extend an existing probabilistic context-free grammar learner to support learning within a Two-Dimensional space by incorporating spatial and temporal information. Experimental results in both synthetic domains and real world domains show that the proposed learning algorithm is effective in acquiring user interface layout. Furthermore, we evaluate the effectiveness of the proposed algorithm within an intelligent tutoring agent, SimStudent, by integrating the learned display representation into the agent. Experimental results in learning complex problem solving skills in three domains show that the learned display representation is as good as one created by a human expert, in that skill learning using the learned representation is as effective as using a manually created representation.
Nick Donnelly - One of the best experts on this subject based on the ideXlab platform.
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Experience with searching in Displays containing depth improves search performance by training participants to search more exhaustively
Acta psychologica, 2020Co-Authors: Hayward J. Godwin, Tamaryn Menneer, Simon Paul Liversedge, Kyle R. Cave, Nick Holliman, Nick DonnellyAbstract:In a typical visual search task, participants search for single targets amongst Displays containing non-overlapping objects that are presented on a single depth plane. Recent work has begun to examine Displays containing overlapping objects that are presented on different depth planes to one another. It has been found that searching Displays containing depth improves response accuracy by making participants more likely to fixate targets and to identify targets after fixating them. Here we extended this previous research by seeking first of all to replicate the previous pattern of results, and then to determine whether extensive training using depth in search transfers to Two-Dimensional Displays. We provided participants with sixteen sessions of training with Displays containing transparent overlapping objects presented in depth, and found a similar pattern of results to our previous study. We also found evidence that some performance improvements from the depth training transferred to search of Two-Dimensional Displays that did not contain depth. Further examinations revealed that participants learn to search more exhaustively (i.e., search for longer) in Displays containing depth. We conclude that depth does influence search performance but the influences depend very much on the stimuli and the degree of overlap within them.
Albert De Roos - One of the best experts on this subject based on the ideXlab platform.
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magnetic resonance measurement of velocity and flow technique validation and cardiovascular applications
American Heart Journal, 1993Co-Authors: Sidney A Rebergen, Ernst E Van Der Wall, Joost Doornbos, Albert De RoosAbstract:Abstract With a newly developed magnetic resonance (MR) technique for blood flow measurements, qualitative and quantitative information on both flow volume and flow velocity in the great vessels can be obtained. MR flow quantitation is performed with a gradientecho MR sequence with high temporal resolution enabling measurements at frequent intervals throughout the cardiac cycle. MR flow quantitation uses the phase rather than the amplitude of the MR signal to reconstruct the images. These images, often referred to as MR velocity maps or velocity-encoded cine MR images, are Two-Dimensional Displays of flow velocity. From these velocity maps, velocity and volume flow data can be obtained. Previous validation experiments have demonstrated the accuracy of MR velocity mapping, and this technique is now being applied successfully in several clinical fields. MR velocity mapping may be of considerable value when Doppler echocardiography results are unsatisfactory or equivocal, particularly because MR is suited for the analysis of volumetric flow and complex flow patterns. Among the vastly growing number of clinical cardiovascular applications that have been reported are the great arteries and veins, coronary vessels, valvular disease, and the abdominal and peripheral vessels. These items are reviewed, and some aspects of the technique that need improvement are discussed.
Hayward J. Godwin - One of the best experts on this subject based on the ideXlab platform.
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Experience with searching in Displays containing depth improves search performance by training participants to search more exhaustively
Acta psychologica, 2020Co-Authors: Hayward J. Godwin, Tamaryn Menneer, Simon Paul Liversedge, Kyle R. Cave, Nick Holliman, Nick DonnellyAbstract:In a typical visual search task, participants search for single targets amongst Displays containing non-overlapping objects that are presented on a single depth plane. Recent work has begun to examine Displays containing overlapping objects that are presented on different depth planes to one another. It has been found that searching Displays containing depth improves response accuracy by making participants more likely to fixate targets and to identify targets after fixating them. Here we extended this previous research by seeking first of all to replicate the previous pattern of results, and then to determine whether extensive training using depth in search transfers to Two-Dimensional Displays. We provided participants with sixteen sessions of training with Displays containing transparent overlapping objects presented in depth, and found a similar pattern of results to our previous study. We also found evidence that some performance improvements from the depth training transferred to search of Two-Dimensional Displays that did not contain depth. Further examinations revealed that participants learn to search more exhaustively (i.e., search for longer) in Displays containing depth. We conclude that depth does influence search performance but the influences depend very much on the stimuli and the degree of overlap within them.
