A cognitive architecture implements a theory of how the human mind works as a computer program, and is used to develop models of behaviour in individual tasks. The performance of the architecture can be directly compared with that of humans.
CHREST - Chunk Hierarchy and REtrieval STructure - is a cognitive architecture developed by Fernand Gobet in the 1990s by combining his template theory with the existing EPAM - Elementary Perceiver and Memoriser - architecture created by the late Herbert Simon and colleagues. The distinctive characteristic of CHREST is that long-term memory is modelled as a set of chunks and templates, which can be efficiently retrieved through a discrimination net. More information on CHREST is available at: http://chrest.info.
I first started working on CHREST in a post-doc position in 1998, and since then have continued to work with Fernand Gobet on various aspects and applications of the theory.
Some general publications on CHREST, chunks and computational modelling:
- F. Gobet, M. Lloyd-Kelly and P.C.R.Lane, ‘Computational models of expertise’, in Hambrick, D.Z., Campitelli, G., and Macnamara, B.N. (Eds.), The science of expertise, (New York: Psychology Press), pp.347-364, 2017. Web page.
- F. Gobet, M. Lloyd-Kelly and P.C.R. Lane, ‘What’s in a name? The Multiple Meanings of “Chunk” and “Chunking”’, Frontiers in Psychology, 7(102), 2016. Web page.
- P.C.R. Lane and F. Gobet, ‘Perception in chess and beyond: Commentary on Linhares and Freitas (2010)‘, New Ideas in Psychology, 29:156-61, 2011. Web page.
- F. Gobet, P.C.R. Lane, S. Croker, P.C-H. Cheng, G. Jones, I. Oliver, and J.M. Pine, ‘Chunking mechanisms in human learning,’ Trends in Cognitive Sciences, 5:236-243, 2001. Web page.
Implicit / Language Learning
Implicit learning means the kind of learning that occurs without conscious intention or awareness. Classic examples of such learning are word segmentation, or recognising that a sequence of sounds or symbols falls within a familar pattern.
Using CHREST, we have constructed models of this learning process which are comparable to human performance and other kinds of model (e.g. ACT-R).
- M. Lloyd-Kelly, F. Gobet and P.C.R. Lane, ‘Under pressure: How time-limited cognition explains statistical learning by 8-month old infants’, in Papafragou, A., Grodner, D., Mirman, D., and Trueswell, J.C. (Eds.), Proceedings of the 38th Annual Meeting of the Cognitive Science Society, pp.1475-80, 2016.
- P.C.R. Lane and F. Gobet, ‘CHREST models of implicit learning and board game interpretation’, in J.Bach, B.Goertzel and M.Ikle (Eds.), Proceedings of the Fifth Conference on Artificial General Intelligence, LNAI 7716, pp. 148-157, 2012. (Berlin, Heidelberg: Springer-Verlag) Download.
The CHREST architecture is a good model of intuitive, pattern-matching decisions. CHREST is a good fit to what is often called “System 1” forms of thinking in the psychological literature. By combining CHREST with a “System 2” model of analytical problem-solving, we have created and tested how models of such Dual Process theories perform when controlling agents in a simulated environment. In particular, we were looking at properties of the environment which might lead one or other system of thinking to be more or less important.
- M. Lloyd-Kelly, F. Gobet and P.C.R. Lane, ‘A question of balance: The benefits of pattern-recognition when solving problems in a complex domain,’ LNCS Transactions on Computational Collective Intelligence XX, pp.224-258, 2015.
Learning from Diagrams
During my post-doc at the University of Nottingham, I extended CHREST to work with diagrams. The aim was to capture how humans learnt to work with AVOW diagrams to compute properties of electric circuits. This work was directed by Prof. Fernand Gobet and Prof. Peter Cheng.
- P.C.R. Lane, P.C-H. Cheng and F. Gobet, ‘Learning perceptual chunks for problem decomposition’, in Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, pp. 528-33, 2001. Proceedings.
- P.C.R. Lane, P.C-H. Cheng and F. Gobet, ‘CHREST+: Investigating how humans learn to solve problems with diagrams,’ AISB Quarterly, 103:24-30, 2000.
- P.C.R. Lane, P.C-H. Cheng and F. Gobet, ‘Learning perceptual schemas to avoid the utility problem’, in M. Bramer, A. Macintosh, and F. Coenen (Eds.) Research and Development in Intelligent Systems XVI: Proceedings of ES99, the Nineteenth SGES International Conference on Knowledge-Based Systems and Artificial Intelligence, (Springer-Verlag) pp. 72-82, 1999.