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.
Architecture
The architecture contains a number of capacity parameters (e.g., capacity of visual short-term memory, set at three chunks) and time parameters (e.g., time to learn a chunk or time to put information into short-term memory). This makes it possible to derive precise and quantitative predictions about human behaviour.
The model includes interaction with elements in the external world, short-term and long-term memory stores, in particular visual and verbal memory storage, and the individual's mechanisms with problem-solving. Chunks in CHREST are referenced in short-term memory while being held in long-term memory, often recognised through neural categorial perception involving discrimination. In much similarity to EPAM, chunks in cognition learning in long-term memory are acquired as a “network of nodes”, and are interconnected by the similarity of their contents and are depicted as a discrimination network, storing and sorting chunks in the network. Chunks are essentially “clusters of information that can be used as units of perception”, thus when applied in situations of chess play, fragments and sections of chess positions will be used as the stimuli fed to the system. According to Gobet et al. and Smith et al., cognitive templates, or better known as schemas, form when chunks adapt based on recurring environmental patterns and structures. Templates are cognitive structures that represent environmental perception, allowing for cognitive organisation, recall, behavioural guidance, situational prediction and overall understanding. Each template has slots where values can be “slotted in”, which allows for faster understanding when faced with similar information already existing in the template.
Simulations are carried out by allowing the model to acquire knowledge by receiving stimuli representative of the domain under study. For example, during the learning phase of the chess simulations, the program incrementally acquires chunks and templates by scanning a large database of positions taken from master-level games. This makes it possible to create networks of various sizes, and so to simulate the behaviour of players of different skill levels. Taken together with the presence of time and capacity parameters, this enables CHREST to make unambiguous and quantitative predictions.
CHREST's notability lies in the significance placed on the perception process. The procedure of perception and information processing is passive, leading to complex emergent behaviour where the secondary acquisition process is led and directed by pre-existing knowledge. This phenomenon is closely observed in chess experiments, where perception and eye movements are closely associated, while also being proportionate to attention span. This process is governed by the chunks held in heuristics and memory . In the case of chess experiments, perception is equated with eye movements (which are approximately correspondent to attention), which are directed by chunks held in memory and heuristics .