In this blog post, I will provide an high-fly overview of some of the most popular cognitive architectures.
ACT-R (Adaptive Control of Thought-Rational) is a symbolic, rule-based architecture that models human cognition as the interaction between declarative memory, production rules, and working memory. ACT-R has been used to model a wide range of cognitive tasks, including reading, decision-making, and problem-solving. It has been widely used in both psychology and artificial intelligence, and has been applied to the study of human-computer interaction, cognitive aging, and education.
SOAR (State, Operator, and Reference) is a production system architecture that models human problem-solving and decision-making as the interaction between declarative knowledge, production rules, and working memory. SOAR has been used to model a wide range of cognitive tasks, including chess playing, scientific reasoning, and decision-making.
EPIC (Executive Process Interactive Control) is a hybrid architecture that combines symbolic and connectionist models. EPIC models the interaction between declarative knowledge, production rules, and working memory, as well as the interaction between the rule-based system and the connectionist system. EPIC has been used to model a wide range of cognitive tasks, including reading, decision-making, and problem-solving.
The LIDA (Learning Intelligent Distribution Agent) model is a theory of consciousness and cognition based on the Global Workspace theory. LIDA is designed to be a general-purpose architecture that can be applied to a wide range of cognitive tasks, including perception, attention, and decision-making. It incorporates both bottom-up and top-down processing, and emphasizes the role of attention in shaping perception and guiding action.
The Cognitive Architecture with Learning and Incremental Organizational Networks (CLARION) is a theory of cognition and emotion that is based on the idea of incremental learning and the self-organization of cognitive structures. CLARION has been used to model a wide range of cognitive and emotional processes, including perception, attention, decision-making, and motivation.
Neural networks (haha, no link here, just google it ^^) are connectionist models inspired by the structure and function of the human brain. They are designed to learn from experience, and have been used to model a wide range of cognitive tasks, including image and speech recognition, natural language processing, and decision-making.
Bayesian networks are probabilistic graphical models that represent the causal relationships between variables. They have been used to model a wide range of cognitive tasks, including perception, decision-making, and causal reasoning.
These are just a few of the most popular cognitive architectures used in the study of the human mind and cognition. By exploring the different approaches and models, we can gain a deeper understanding of the human mind and develop more advanced artificial systems that can perform human-like tasks. If you wonder why I, as an obvious fan, do not list PSI Theory here: well, I imagine that this theory is going to take up plenty of space on my website, just be a bit patient… 😉