CHREST (Chunk Hierarchy and REtrieval STructures) is a complete computational architecture implementing processes
of learning and perception. CHREST models have successfully simulated human data in a variety of domains, such as the acquisition of syntactic categories, expertise in programming and in chess, concept formation, implicit learning, and the acquisition of multiple representations in physics for
problem solving. In this tutorial, we describe the learning, perception and attention mechanisms within CHREST as well
as key empirical data captured by CHREST models. Apart from the theoretical material, this tutorial also introduces participants to an implementation of CHREST and its use in a variety of domains. Material and examples are provided so participants can adapt and extend the CHREST architecture

Gobet, F.

Lane, P.C.R.

English

University of Hertfordshire Research Archive

Building Models of Learning and Expertise with CHRESTPeter C. R. Lane (peter.lane@bcs.org.uk)School of Computer Science, University of Hertfordshire,College Lane, Hatfield AL10 9AB, UKFernand Gobet (fernand.gobet@brunel.ac.uk)Department of Psychology, Brunel UniversityCleveland Road, Uxbridge, Middlesex, UB8 3PH, UKKeywords: CHREST; EPAM; MOSAIC; cognitive modelling;chunking; expertise; learningDeveloping detailed process models of cognitive phenomenais important to the development of cognitive science as onlythen can cognitive theories be used to generate quantitativepredictions for complex phenomena. The history of compu-tational modelling includes many diverse approaches, frommodels of single phenomena (such as Sternberg’s model ofSTM; Sternberg, 1966), to integrated models covering a widerange of different phenomena (such as Soar (Newell, 1990)and ACT-R (Anderson, 1998)), to over-arching principles,which guide the development of models in disparate domains(such as connectionist approaches, or embodied cognition).The CHREST system, which forms the heart of this tuto-rial, was developed from the earlier EPAM model. EPAMprovided the impetus to develop the chunking theory, whichhas been an important component of theories of human cogni-tion ever since. Focussing on learning phenomena, CHRESTplaces a great emphasis on how the model’s informationis learnt through interactions with an external environment.Thus, CHREST models are typically developed from largequantities of naturalistic input. For example, in modellingexpert perception of chess players, actual chess games areused. Similarly, in modelling the acquisition of syntax, largecorpora of mother-child interactions are employed to developthe model’s long-term memory.Objectives and ScopeCHREST (Chunk Hierarchy and REtrieval STructures) is acomplete computational architecture implementing processesof learning and perception. CHREST models have success-fully simulated human data in a variety of domains, suchas the acquisition of syntactic categories, expertise in pro-gramming and in chess, concept formation, implicit learning,and the acquisition of multiple representations in physics forproblem solving. In this tutorial, we describe the learning,perception and attention mechanisms within CHREST as wellas key empirical data captured by CHREST models. Apartfrom the theoretical material, this tutorial also introduces par-ticipants to an implementation of CHREST and its use in avariety of domains. Material and examples are provided soparticipants can adapt and extend the CHREST architecture.We begin by providing an introduction to CHREST. As anarchitecture, CHREST has three basic modules:• Input/output module, which is responsible for feature ex-traction, passing features to the long-term memory for sort-ing, and guiding eye movements;• Long-term memory, which holds information in the form adiscrimination network; and• Short-term memories, which hold pointers to nodes in thelong-term memory.Historically, CHREST is derived from the EPAM (Elemen-tary Perceiver and Memorizer) model of Feigenbaum and Si-mon (1984). In both models, learning occurs through the cre-ation and elaboration of a discrimination network. In addi-tion, CHREST has mechanisms for the automatic construc-tion of templates (a form of slotted schema) and for learning‘lateral links’, which can be used to create elementary pro-ductions or semantic links. CHREST can thus be situatedbetween production systems such as Soar and connectionistsystems. Just as EPAM was the computational embodimentof the key aspects of the chunking theory (Chase & Simon,1973), CHREST implements the essential aspects of the tem-plate theory (Gobet & Simon, 2000).All the mechanisms within CHREST will be covered, in-cluding relevant aspects of the earlier EPAM model. Thesemechanisms include:1. constructing a discrimination network2. formation of templates3. perceptual and attention mechanisms4. the short-term memoryWe will illustrate the mechanisms, individually and incombination, using domains including:1. categorisation2. verbal and language learning3. implicit learning4. chess expertiseOne element which distinguishes modelling with CHRESTfrom modelling in ACT-R or Soar is the emphasis on learning.CHREST’s long-term memory is indexed through a discrim-ination network which must be constructed from experiencein the domain of interest. For domains with high levels ofhuman expertise, these networks become large: in the chessdomain, networks comprise 100,000 to 300,000 nodes to sim-ulate the highest levels of performance. Similar figures holdin language learning. In other domains, where experimentalparticipants learn a task within a single session or over sev-eral days, the networks will be smaller, perhaps no more thana few hundred nodes, but even here, the networks are con-structed by CHREST using its learning mechanisms. Thus,unlike ACT-R or Soar, CHREST is not ‘programmed’ withinformation about a task, but is presented with data (such aschess positions), and its behaviour is then observed. Indeed,this technique makes CHREST more akin to standard prac-tice in connectionist modelling, where networks are trained.However CHREST operates at the level of meaningful sym-bols.CHREST SoftwareThe latest CHREST implementation will be demonstratedand described. CHREST is implemented in Java and comescomplete with a flexible graphical interface. The graphicalenvironment enables users to build CHREST models fromdata provided within an input data file. The implementa-tion also supports the construction of more complex or novelexperimental setups through a programmable interface: anyJava-aware language may be used to develop models, and sev-eral complete examples are provided with the distribution.Within the tutorial we will introduce participants to thegraphical environment, walk them through a number of pro-vided examples which will illustrate the workings of the ar-chitecture and some samples of successful applications, andfinally describe the input data format for applying the envi-ronment to new domains. Documentation and examples areavailable for participants wanting to look at the code and useit as a library for writing more complex models.Target AudienceThe tutorial is designed to introduce the area of symboliccomputational modelling using the chunking/template theo-ries, as implemented in CHREST. We have designed the ma-terial to be suitable for newcomers to the fields of either cog-nitive science or cognitive modelling. We assume some un-derstanding of human psychology and learning, plus experi-mental methods, appropriate to graduate students in this area.As the domains covered by CHREST are not frequently tack-led by other symbolic cognitive models, we would expect thelevel to also be appropriate for more advanced researcherswho have not previously studied EPAM or CHREST.Participants are recommended to bring a laptop with thesoftware installed (only Java 6 is required), to follow alongthe demonstration parts of the tutorial.Further MaterialsParticipants receive a CD-ROM containing copies of the soft-ware, lecture slides and supporting papers. The latest ver-sion of the software and further links are available fromhttp://chrest.infoSchedule of Events1st half (80 minutes)Introduction to tutorial 5 minOverview of CHREST 20 minLearning mechanisms in CHREST 15 minDemonstration of CHREST shell andlearning processes, by tracing a working model 15 minKey experimental results (e.g. verbal-learningphenomena, MOSAIC, EPAM-VOC) 25 minCoffee break 20 min2nd half (80 minutes)Key experimental data about expertise 15 minPerceptual mechanisms in CHREST 15 minDemonstration of CHREST models of perceptualexpertise, using key experiments in chess. 20 minDescription of input format, so participants candevelop CHREST models for their own domains. 15 minQuestions 10 minConclusion 5 minReferencesAnderson, J. R., & Lebie`re, C. (Eds.). (1998). The atomiccomponents of thought. Mahwah, NJ: Lawrence Erlbaum.Chase, W. G., & Simon, H. A. (1973). Perception in chess.Cognitive Psychology, 4, 55-81.Feigenbaum, E. A., & Simon, H. A. (1984). EPAM-likemodels of recognition and learning. Cognitive Science, 8,305–336.Freudenthal, D., Pine, J. M., Aguado-Orea, J., & Gobet, F.(2007). Modelling the developmental patterning of finite-ness marking in English, Dutch, German and Spanish usingMOSAIC. Cognitive Science, 31, 311–341.Gobet, F., Lane, P. C. R., Croker, S. J., Cheng, P. C.-H.,Jones, G., Oliver, I., et al. (2001). Chunking mechanisms inhuman learning. Trends in Cognitive Sciences, 5, 236–243.Gobet, F., & Simon, H. A. (2000). Five seconds or sixty?Presentation time in expert memory. Cognitive Science,24, 651–82.Jones, G. A., Gobet, F., & Pine, J. M. (2007). Linkingworking memory and long-term memory: A computationalmodel of the learning of new words. Developmental Sci-ence, 10, 853–873.Lane, P. C. R., Gobet, F., & Ll. Smith, R. (2009). Atten-tion mechanisms in the CHREST cognitive architecture. InL. Paletta & J. K. Tsotsos (Eds.), Proceedings of the fifthinternational workshop on attention in cognitive science(Vol. LNAI 5395, pp. 183–196). Berlin: Springer-Verlag,GmbH.Newell, A. (1990). Unified theories of cognition. Cambridge,MA: Harvard University Press.Sternberg, S. (1966). High speed scanning in human memory.Science, 153, 652–654.

Attention mechanisms in the CHREST cognitive architecture. In

Chunking mechanisms in human learning.

EPAM-like models of recognition and learning.

Five seconds or sixty? Presentation time in expert memory.

Highspeed scanning inhuman memory.

Linking working memory and long-term memory: A computational model of the learning of new words.

Modelling the developmental patterning of ﬁniteness marking in English, Dutch, German and Spanish using MOSAIC.

Perception in chess.

The atomic components of thought.

Uniﬁed theories of cognition.

Building models of learning and expertise with CHREST

Building models of learning and expertise with CHREST

Abstract

Similar works

Full text

Available Versions

University of Hertfordshire Research Archive