Cognitive Architecture - Characterization

Characterization

Common among researchers on cognitive architectures is the belief that understanding (human, animal or machine) cognitive processes means being able to implement them in a working system, though opinions differ as to what form such a system can have: some researchers assume that it will necessarily be a symbolic computational system whereas others argue for alternative models such as connectionist systems or dynamical systems. Cognitive architectures can be characterized by certain properties or goals, as follows, though there is not general agreement on all aspects:

1. Implementation of not just various different aspects of cognitive behavior but of cognition as a whole (Holism, e.g. Unified theory of cognition). This is in contrast to cognitive models, which focus on a particular competence, such as a kind of problem solving or a kind of learning.
2. The architecture often tries to reproduce the behavior of the modelled system (human), in a way that timely behavior (reaction times) of the architecture and modelled cognitive systems can be compared in detail. Other cognitive limitations are often modeled as well, e.g. limited working memory, attention or issues due to cognitive load.
3. Robust behavior in the face of error, the unexpected, and the unknown. (see Graceful degradation).
4. Learning (not for all cognitive architectures)
5. Parameter-free: The system does not depend on parameter tuning (in contrast to Artificial neural networks) (not for all cognitive architectures)
6. Some early theories such as Soar and ACT-R originally focused only on the 'internal' information processing of an intelligent agent, including tasks like reasoning, planning, solving problems, learning concepts. More recently many architectures (including Soar, ACT-R, PreAct, ICARUS, CLARION), FORR have expanded to include perception, action, and also affective states and processes including motivation, attitudes, and emotions.
7. On some theories the architecture may be composed of different kinds of sub-architectures (often described as 'layers' or 'levels') where the layers may be distinguished by types of function, types of mechanism and representation used, types of information manipulated, or possibly evolutionary origin. These are hybrid architectures (e.g., CLARION).
8. Some theories allow different architectural components to be active concurrently, whereas others assume a switching mechanism that selects one component or module at a time, depending on the current task. Concurrency is normally required for an architecture for an animal or robot that has multiple sensors and effectors in a complex and dynamic environment, but not in all robotic paradigms.
9. Most theories assume that an architecture is fixed and only the information stored in various subsystems can change over time (e.g. Langley et al., below), whereas others allow architectures to grow, e.g. by acquiring new subsystems or new links between subsystems (e.g. Minsky and Sloman, below).

It is important to note that cognitive architectures don't have to follow a top-down approach to cognition (cf. Top-down and bottom-up design).