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 release	March 17, 2007 (2007-03-17)
Development status	Inactive
Operating system	Cross-platform
Platform	BOINC
Average performance	0 GFLOPS,[1]
Active users	0
Total users	0
Active hosts	0
Total hosts	0
Website	mindmodeling.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

• 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

• 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

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

• List of volunteer computing projects