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CHREST (Chunk Hierarchy and REtrieval STru … CHREST (Chunk Hierarchy and REtrieval STructures) is a symbolic cognitive architecture based on the concepts of limited attention, limited short-term memories, and chunking. The architecture takes into low-level aspects of cognition such as reference perception, long and short term memory stores, and methodology of problem-solving and high-level aspects such as the use of strategies. Learning, which is essential in the architecture, is modelled as the development of a network of nodes (chunks) which are connected in various ways. This can be contrasted with Soar and ACT-R, two other cognitive architectures, which use productions for representing knowledge. CHREST has often been used to model learning using large corpora of stimuli representative of the domain, such as chess games for the simulation of chess expertise or child-directed speech for the simulation of children's development of language. In this respect, the simulations carried out with CHREST have a flavour closer to those carried out with connectionist models than with traditional symbolic models. CHREST stores its memories in a chunking network, a tree-like structure that connects and stores knowledge and information acquired, allowing for greater efficiency in information processing. Figure 1 highlights the links between perceived knowledge, memory, and acquired experiences that are formed based on “familiar patterns” between new and old information. CHREST is developed by Fernand Gobet at Brunel University and Peter C. Lane at the University of Hertfordshire. It is the successor of EPAM, a cognitive model originally developed by Herbert A. Simon and Edward Feigenbaum.by Herbert A. Simon and Edward Feigenbaum.
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CHREST (Chunk Hierarchy and REtrieval STru … CHREST (Chunk Hierarchy and REtrieval STructures) is a symbolic cognitive architecture based on the concepts of limited attention, limited short-term memories, and chunking. The architecture takes into low-level aspects of cognition such as reference perception, long and short term memory stores, and methodology of problem-solving and high-level aspects such as the use of strategies. Learning, which is essential in the architecture, is modelled as the development of a network of nodes (chunks) which are connected in various ways. This can be contrasted with Soar and ACT-R, two other cognitive architectures, which use productions for representing knowledge. CHREST has often been used to model learning using large corpora of stimuli representative of the domain, such as chess games for the f the domain, such as chess games for the
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CHREST
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