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A cognitive model is an approximation to animal cognitive processes (predominantly human) for the purposes of comprehension and prediction. Cognitive models can be developed within or without a cognitive architecture, though the two are not always easily distinguishable.
In contrast to cognitive architectures, cognitive models tend to be focused on a single cognitive phenomenon or process (e.g., list learning), how two or more processes interact (e.g., visual search and decision making), or to make behavioral predictions for a specific task or tool (e.g., how instituting a new software package will affect productivity). Cognitive architectures tend to be focused on the structural properties of the modeled system, and help constrain the development of cognitive models within the architecture. Likewise, model development helps to inform limitations and shortcomings of the architecture. Some of the most popular architectures for cognitive modeling include ACT-R and Soar.
Cognitive modeling historically developed within cognitive psychology / cognitive science (including human factors), and has received contributions from the fields of machine learning and artificial intelligence to name a few.
There are many types of cognitive models, and they can range from box-and-arrow diagrams to a set of equations to software programs that interact with the same tools that humans use to complete tasks (e.g., computer mouse and keyboard).
- Cognitive modeling at CMU
- Cognitive modeling at RPI (HCI)
- Cognitive modeling at RPI (CLARION)
- Cognitive modeling at the University of Memphis
- A Cognitive Model of Learning
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