The Generalizability of Cognitive Modeling Parameters
| dc.contributor.advisor | Byrne, Michael | en_US |
| dc.contributor.committeeMember | O'Malley, Marcia | en_US |
| dc.contributor.committeeMember | Kortum, Philip | en_US |
| dc.creator | Howie, Nicole | en_US |
| dc.date.accessioned | 2016-01-07T22:30:52Z | en_US |
| dc.date.available | 2016-01-07T22:30:52Z | en_US |
| dc.date.created | 2015-05 | en_US |
| dc.date.issued | 2015-07-06 | en_US |
| dc.date.submitted | May 2015 | en_US |
| dc.date.updated | 2016-01-07T22:30:52Z | en_US |
| dc.description.abstract | Increased awareness of the importance of usability has stemmed from the realization that customer satisfaction and revenue generation are affected by usability. One method that can be used to evaluate usability is cognitive modeling, which can make quantitative predictions about human performance across different tasks and devices. However, it is unclear if cognitive models can accurately predict older adult performance. Trewin et al. (2012) proposed that parameters used in models for older adults may have to be specific to a task and/or device. The purpose of this research was to determine if task and/or device type must be accounted for in parameters used to model older adult performance in a cognitive architecture. Jastrzembski and Charness (2007) estimated architectural parameters for older adults and were able to successfully predict performance of tasks performed with a mobile phone. The current study investigated if these parameters generalize to different tasks and devices. In the experiment, older (70 years and above) and younger (18 - 39 years old) adults performed two tasks with two different devices. Overall, the results from the behavioral data showed that older adults performed more slowly than younger adults, however older adult’s performance varied across task. Performance differences between older and younger adults due to device were caused by strategy differences. Cognitive models of each task with one device were created using modeling parameters that represented older and younger adults. Then the behavioral data were compared to the models. The models were mainly slower than older and younger adults across each task. The results helped provide evidence that task and/or device type are important and should be incorporated into modeling parameters. However, strategy must be accounted for as well in order to accurately model older adult performance. | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Howie, Nicole. "The Generalizability of Cognitive Modeling Parameters." (2015) Diss., Rice University. <a href="https://hdl.handle.net/1911/87797">https://hdl.handle.net/1911/87797</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/87797 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | cognitive modeling | en_US |
| dc.title | The Generalizability of Cognitive Modeling Parameters | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Psychology | en_US |
| thesis.degree.discipline | Social Sciences | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy | en_US |
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