Wikipedia

MindModeling@Home

MindModeling@Home[2] is an inactive non-profit, volunteer computing research project for the advancement of cognitive science. MindModeling@Home is hosted by Wright State University and the University of Dayton in Dayton, Ohio.

MindModeling@Home
interactive screensaver
Initial releaseMarch 17, 2007 (2007-03-17)
Development statusInactive
Operating systemCross-platform
PlatformBOINC
Average performance0 GFLOPS,[1]
Active users0
Total users0
Active hosts0
Total hosts0
Websitemindmodeling.org

In BOINC, it is in the area of Cognitive Science and category called Cognitive science and artificial intelligence.[3] It can only operate on a 64-bit operating system, preferably on a computer with multiple cores, running a Microsoft Windows, Mac OS X, or Linux operating system. This project is not compatible with mobile devices, unlike other projects on BOINC.

Research focus

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  • N-2 Repetition: understanding how people have a harder time returning to a task from another one
  • Observing how people read through their eye movement for the purpose of helping people reduce eye strain and processing what they read better and faster.
  • Modeling decision-making: resolving around decisions made from visual processing (focus and filtering)
  • Integrated Learning Models (ILM) to create algorithms based on how people learn and make decisions
  • How the brain performs tasks sequentially and simultaneously by measuring its blood flow[4]

Problems

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  • Its status is inactive.[5] However, it is "not down or closed,"[6] as its servers are still running.[7]
  • The projects are long; prolonged amounts of computing time can overheat a computer. The solution is to stop work on the project until the computer cools down.[8]
  • It is subject to power outages, as seen on October 7, 2018[9]
  • When the website will be out of beta mode is unknown, as it has been in beta since 2007[10]

Scientific results

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  1. Godwin H.J., Walenchok S. et al. Faster than the speed of rejection: Object identification processes during visual search for multiple targets. J Exp Psychol Hum Percept Perform. 41-4, (2016).[11]
  2. Moore L. R., Gunzelmann G. An interpolation approach for fitting computationally intensive models. Cognitive Systems Research 19, (2014).[12]
  3. Moore L.R. Cognitive model exploration and optimization: a new challenge for computational science. Comput Math Organ Theory 17, 296–313. (2011).[13]
  4. Moore L.R., Kopala M., Mielke T. et al. Simultaneous performance exploration and optimized search with volunteer computing. 19th ACM International Symposium on High Performance Distributed Computing, (2010).[14]
  5. Harris J., Gluck K.A., Moore L.R. MindModeling@Home. . . and Anywhere Else You Have Idle Processors. 9th International Conference on Cognitive Modelling, (2009).[15]
  6. Gluck K., Scheutz M. Combinatorics meets processing power: Large-scale computational resources for BRIMS. 16th Conference on Behavior Representation in Modeling and Simulation, BRIMS. 1. 73-83. (2007).[16]

See also

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References

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  1. ^ de Zutter W. "MindModeling@Home: Credit overview". boincstats.com. Archived from the original on 2022-03-01. Retrieved 2023-06-18.
  2. ^ Moore, L. Richard; Kopala, Matthew; Mielke, Thomas; Krusmark, Michael; Gluck, Kevin A. (2010-06-21). "Simultaneous performance exploration and optimized search with volunteer computing". Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing. HPDC '10. New York, NY, USA: Association for Computing Machinery. pp. 312–315. doi:10.1145/1851476.1851518. ISBN 978-1-60558-942-8. S2CID 18679055. Archived from the original on 2022-09-05. Retrieved 2022-08-14.
  3. ^ "Choosing BOINC projects". boinc.berkeley.edu. Archived from the original on 2018-01-03. Retrieved 2019-07-13.
  4. ^ "Projects Overview". mindmodeling.org. Archived from the original on 2019-07-12. Retrieved 2019-07-13.
  5. ^ "MindModeling@Home - BOINC". boinc.berkeley.edu. Archived from the original on 2019-03-06. Retrieved 2019-07-13.
  6. ^ "Hails and Farewells". mindmodeling.org. Archived from the original on 2018-08-17. Retrieved 2019-07-13.
  7. ^ "Project status". mindmodeling.org. Archived from the original on 2019-07-13. Retrieved 2019-07-13.
  8. ^ "Read our rules and policies". mindmodeling.org. Archived from the original on 2019-07-13. Retrieved 2019-07-13.
  9. ^ "MindModeling@Home (Beta)". mindmodeling.org. Archived from the original on 2019-07-13. Retrieved 2019-07-13.
  10. ^ "When will mindmodeling@home be out of beta". mindmodeling.org. Archived from the original on 2018-08-27. Retrieved 2019-07-13.
  11. ^ Godwin, Hayward J.; Walenchok, Stephen C.; Houpt, Joseph W.; Hout, Michael C.; Goldinger, Stephen D. (August 2015). "Faster than the speed of rejection: Object identification processes during visual search for multiple targets". Journal of Experimental Psychology: Human Perception and Performance. 41 (4): 1007–1020. doi:10.1037/xhp0000036. ISSN 1939-1277. PMC 4516661. PMID 25938253.
  12. ^ Richard Moore, L.; Gunzelmann, Glenn (2014-09-01). "An interpolation approach for fitting computationally intensive models". Cognitive Systems Research. 29–30: 53–65. doi:10.1016/j.cogsys.2013.09.001. ISSN 1389-0417. S2CID 26656979.
  13. ^ Moore, L. Richard (2011-09-01). "Cognitive model exploration and optimization: a new challenge for computational science". Computational and Mathematical Organization Theory. 17 (3): 296–313. doi:10.1007/s10588-011-9092-8. ISSN 1572-9346. S2CID 7767242.
  14. ^ Moore, L. Richard; Kopala, Matthew; Mielke, Thomas; Krusmark, Michael; Gluck, Kevin A. (2010-06-21). "Simultaneous performance exploration and optimized search with volunteer computing". Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing. HPDC '10. New York, NY, USA: Association for Computing Machinery. pp. 312–315. doi:10.1145/1851476.1851518. ISBN 978-1-60558-942-8. S2CID 18679055.
  15. ^ "Mindmodeling@Home. . . and Anywhere Else You Have Idle Processors". {{cite journal}}: Cite journal requires |journal= (help)
  16. ^ "ACT-R » Publications » Combinatorics meets processing power: larger-scale computational resources for BRIMS". Retrieved 2022-10-09.
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