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Cognitive architecture - Characterization

Cognitive architecture - Characterization: Encyclopedia II - Cognitive architecture - Characterization

Common to cognitive architecture is the belief that understanding (human) cognitive processing means being able to implement them on a computational level. Cognitive architectures can be characterized by certain properties or goals that are as follows: 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. The architecture often tries to reproduce the behavior of the modelled system (human ...

See also:

Cognitive architecture, Cognitive architecture - Characterization, Cognitive architecture - Distinctions, Cognitive architecture - Some famous cognitive architectures

Cognitive architecture, Cognitive architecture - Characterization, Cognitive architecture - Distinctions, Cognitive architecture - Some famous cognitive architectures, Cognitive science, Strong AI, Artificial consciousness, Autonomous agent, production system, unified theory of cognition

Cognitive architecture: Encyclopedia II - Cognitive architecture - Characterization



Cognitive architecture - Characterization

Common to cognitive architecture is the belief that understanding (human) cognitive processing means being able to implement them on a computational level. Cognitive architectures can be characterized by certain properties or goals that are as follows:

  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.
  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.
  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)

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).

Other related archives

1980s, ACT, ACT-R, AI, Allen Newell, Artificial consciousness, Artificial neural networks, Autonomous agent, Bamberg, Biologically-inspired computing, Boicho Kokinov, Carnegie Mellon University, Cognitive science, Copycat, DUAL, Dietrich Dörner, Douglas Hofstadter, Germany, Graceful degradation, Holism, Indiana University, Bloomington, Information Processing Language, Learning, NASA Ames Research Center, Otto-Friedrich University, Parameter, Robust behavior, Rodney Brooks, SOAR, Strong AI, Subsumption architectures, Top-down_and_bottom-up_design, Unified theory of cognition, University of Michigan, atomism, behavior, bottom-up, centralized, cognitive models, complex systems, complexity, computational, connectionism, connectionist, creator, decentralised, decentralized, emerges, generic rules, holistic, hybrid, intelligence, intelligent, knowledge representation, modular, neural networks, nodes, parallel distributed processing, processor, production system, reaction times, symbolic, unified theory of cognition



Adapted from the Wikipedia article "Characterization", under the G.N U Free Docmentation License. Please also see http://en.wikipedia.org/wiki

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