A cognitive architecture is a theoretical framework that aims to explain how the human mind works. When creating a new cognitive architecture, cognitive scientists consider multiple aspects, including:
- Representation: How information is represented in the architecture, such as symbolic or sub-symbolic representations.
- Processing: How the architecture processes information, such as through rule-based or connectionist processes.
- Adaptivity: The ability of the architecture to adapt to new situations, such as through learning and self-organization.
- Interaction: How the architecture interacts with the environment, including perception, action, and communication.
- Integration: How the architecture integrates information from different sources, such as from perception, memory, and reasoning.
- Development: How the architecture develops over time, including both innate abilities and learned abilities
- Explainability: How well the architecture can be understood and explained in terms of its underlying mechanisms and processes.
In addition to these general considerations, there are also specific issues to be addressed depending on the domain of application of the cognitive architecture. For example, if the architecture is being designed for use in a robotics or artificial intelligence application, it may need to incorporate mechanisms for dealing with uncertainty, or for handling noisy or incomplete information.
Overall, creating a new cognitive architecture requires a deep understanding of the current state of knowledge about the mind and brain, as well as the ability to integrate this knowledge into a coherent and testable framework. It also requires a clear understanding of the specific requirements and constraints of the domain of application, and the ability to evaluate the architecture through rigorous experimental and empirical testing.