Book

The following book describes CHREST and detailed comparisons of chess players’ eye movements with the model:

Cover of book Perception and Memory in Chess

  • A.D. De Groot and F. Gobet, Perception and Memory in Chess: Heuristics of the Professional Eye, Van Gorcum (1996). [This book is currently out of print, but copies may be available: contact Fernand Gobet for details.]

The general theory / architecture

  1. F. Gobet and P.C.R. Lane, ‘Constructing a standard model: Lessons from CHREST’, in Proceedings of the AAAI Fall Symposium on A Standard Model of the Mind, 2017.
  2. 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.
  3. 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.
  4. F. Gobet, P.C.R. Lane and M. Lloyd-Kelly, ‘Chunks, schemata and retrieval structures: Past and current computational models’, Frontiers in Psychology, 6(1785), 2015. Web page.
  5. 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.

CHREST : Various projects

  1. 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.
  2. 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.
  3. M. Lloyd-Kelly, F. Gobet and P.C.R. Lane, ‘Piece of mind: Long-term memory structure in ACT-R and CHREST’, in Noelle, D. C., Dale, R., Warlaumont, A. S., Yoshimi, J., Matlock, T., Jennings, C. D., & Maglio, P. P. (Eds.), Proceedings of the 37th Annual Meeting of the Cognitive Science Society, (Austin, TX: Cognitive Science Society) pp.1422-27, 2015. Web page.
  4. M. Lloyd-Kelly, F. Gobet and P.C.R. Lane, ‘The art of balance: Problem-solving vs pattern-recognition,’ in Proceedings of the Seventh International Conference on Agents and Artificial Intelligence, 2015.
  5. M. Lloyd-Kelly, P.C.R. Lane and F. Gobet, ‘The effects of bounding rationality on the performance and learning of CHREST agents in Tileworld’, in M.Bramer and M.Petridis (Eds.) Research and Development in Intelligent Systems XXXI: Proceedings of AI-2014, The Thirty-Fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp.149-162, 2014. (London, UK: Springer-Verlag)
  6. 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.
  7. P.C.R. Lane and F. Gobet, ‘Using chunks to categorise chess positions’, in M.Bramer and M.Petridis (Eds.) Research and Development in Intelligent Systems XXX: Proceedings of AI-2012, The Thirty-Second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp. 93-106, 2012. (London, UK: Springer-Verlag)
  8. T. Bossomaier, J. Traish, F. Gobet and P.C.R. Lane, ‘Neuro-cognitive model of move location in the game of Go’, in the 2012 International Joint Conference on Neural Networks (IJCNN 2012). Web page.
  9. F. Gobet and P.C.R. Lane, ‘The CHREST architecture of cognition: The role of perception in general intelligence’, in M. Hutter, E.B. Baum and E. Kitzelmann (Eds). Proceedings of the Third conference on Artificial General Intelligence, pp. 7-12, 2010. (Paris, France: Atlantis Press)
  10. R. Ll. Smith, F. Gobet and P.C.R. Lane, ‘Checking chess checks with chunks: A model of simple check detection’, in A. Howes, D. Peebles, R. Cooper (Eds.), Proceedings of the Ninth International Conference on Cognitive Modelling, 2009.
  11. P.C.R. Lane, F. Gobet and R. Ll. Smith, ‘Attention mechanisms in the CHREST cognitive architecture’, in L. Paletta and J. K. Tsotsos (eds), Proceedings of the Fifth International Workshop on Attention in Cognitive Systems, (Springer-Verlag) LNCS 5395, pp. 183-196, 2009.
  12. R. Ll. Smith, P.C.R. Lane and F. Gobet, ‘Modelling the relationship between visual short-term memory capacity and recall ability’, in The Second UKSim European Symposium on Computer Modelling and Simulation (IEEE Computer Society), pp. 99-104, 2008.
  13. R. Ll. Smith, F. Gobet and P.C.R. Lane, ‘An investigation into the effect of ageing on expert memory with CHREST’, in Proceedings of The Seventh UK Workshop on Computational Intelligence, 2007.
  14. P.C.R. Lane, A. K. Sykes, and F. Gobet, ‘Combining low-level perception with expectations in CHREST’, in F. Schmalhofer, R. M. Young and G. Katz (eds.), Proceedings of EuroCogSci’03, (Mahwah, NJ: Lawrence Erlbaum Associates) pp. 205-210, 2003.
  15. 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.

EPAM-VOC: Learning new words

  1. Jones, G., Gobet, F., & Pine, J. M. (2007). Linking working memory and long-term memory: A computational model of the learning of new words. Developmental Science, 10, 853-873.
  2. Jones, G., Gobet, F., & Pine, J. (2005). Modelling vocabulary acquisition: An explanation of the link between the phonological loop and long-term memory. Journal of Artificial Intelligence and Simulation of Behaviour, 1, 509-522.

MOSAIC : Language acquisition

  1. Gobet, F., Pine, J. M., and Freudenthal, D. (2007). Towards a unified model of language acquisition. Proceedings of the European Cognitive Science Conference 2007.
  2. Freudenthal, D., Pine, J. M., and Gobet, F. (in press). Explaining quantitative variation in the rate of Optional Infinitive errors across languages: A comparison of MOSAIC and the Variational Learning Model. Journal of Child Language.
  3. Freudenthal, D., Pine, J. M., and Gobet, F. (2009). Comparing MOSAIC and the Variational Learning model of the optional infinitive stage in early child language. In N.A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st Annual Meeting of the Cognitive Science Society. Cognitive Science Society.
  4. Freudenthal, D., Pine, J. M., and Gobet, F. (2009). Simulating the referential properties of Dutch, German and English Root Infinitives in MOSAIC. Language Learning and Development, 5, 1-29.