Building a Computational Model for Graph Comprehension Using BiSoar
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2009
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=osu1243999359 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-osu12439993592021-08-03T05:56:21Z Building a Computational Model for Graph Comprehension Using BiSoar Lele, Omkar M. Artificial Intelligence graph comprehension biSoar computational cognitive model for graph comprehension unification imagination in graph comprehension <p>Graphs of various kinds are important in modern culture. Humans can understand and draw inferences from large amounts of data represented in a graphical format more easily than the same data represented in textual form. Even though graphs seem to be very effective, a badly designed graph may affect the accuracy or speed in comprehending information represented in a graph. Graph designers, instead of just depending on just their intuitions about what makes a good graph, need guidance based on a set of scientific principles. </p><p>The relevant science involves understanding the various cognitive processes involved in graph comprehension. This has led many scientists to study graph comprehension as a cognitive task. However, most of the models proposed by the various scientists are qualitative descriptions of aspects of the graph comprehension process. Though these models are consistent with a computational approach, they were neither expressed as computer programs, nor were they sufficiently detailed to support implementation as a computer program. Computational models are more useful than descriptive models, since they can be run on a computer to predict behavioral details such as time taken to perform tasks under varying assumptions. </p><p>Our goal in this work is to build a computational cognitive model for graph comprehension that unifies the multiplicity of models proposed by the various researchers in our survey. Instead of multiple models, we will have one model that exhibits the multiplicity of identified phenomena under appropriate conditions. We should be able to explain how background knowledge, the attention mechanism, visual activities such as scanning and anchoring, and mental imagery – all features of a general architecture – are deployed opportunistically in the specific graph comprehension task in response to the specifics of the task and agent's situation.</p><p>The thesis describes a set of models for a range of graph comprehension tasks that together provide the unification that we seek.</p> 2009-09-08 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1243999359 http://rave.ohiolink.edu/etdc/view?acc_num=osu1243999359 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
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NDLTD |
| language |
English |
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NDLTD |
| topic |
Artificial Intelligence graph comprehension biSoar computational cognitive model for graph comprehension unification imagination in graph comprehension |
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Artificial Intelligence graph comprehension biSoar computational cognitive model for graph comprehension unification imagination in graph comprehension Lele, Omkar M. Building a Computational Model for Graph Comprehension Using BiSoar |
| author |
Lele, Omkar M. |
| author_facet |
Lele, Omkar M. |
| author_sort |
Lele, Omkar M. |
| title |
Building a Computational Model for Graph Comprehension Using BiSoar |
| title_short |
Building a Computational Model for Graph Comprehension Using BiSoar |
| title_full |
Building a Computational Model for Graph Comprehension Using BiSoar |
| title_fullStr |
Building a Computational Model for Graph Comprehension Using BiSoar |
| title_full_unstemmed |
Building a Computational Model for Graph Comprehension Using BiSoar |
| title_sort |
building a computational model for graph comprehension using bisoar |
| publisher |
The Ohio State University / OhioLINK |
| publishDate |
2009 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=osu1243999359 |
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