Generating  quantitative  predictions  for  complex  cognitive phenomena requires precise implementations of the underlying cognitive theory. This  tutorial  focuses  on  the  EPAM/CHREST  tradition, which has been providing signiﬁcant models of human behaviour for 50 years

Gobet, F

Lane, PCR

English

Brunel University Research Archive

EPAM/CHREST Tutorial: Fifty Years of Simulating Human LearningPeter 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)School of Social Sciences, Brunel University, Cleveland Road, Uxbridge, Middlesex, UB8 3PH, UKKeywords: CHREST; EPAM; cognitive modelling; chunkingOverviewGenerating quantitative predictions for complex cognitivephenomena requires precise implementations of the underly-ing cognitive theory. The history of computational modellingincludes many diverse approaches to this problem of creatingprocess models. These include models of single phenomena(such as Sternberg’s model of STM; (Sternberg, 1966)), to in-tegrated models covering a wide range of different phenom-ena (such as Soar (Newell, 1990) and ACT-R (Anderson &Lebie`re, 1998)), to over-arching principles, which guide thedevelopment of models in disparate domains (such as con-nectionist approaches (McLeod, Plunkett, & Rolls, 1998), orembodied cognition (Pfeifer & Scheier, 1999)).This tutorial focuses on the EPAM/CHREST tradition,which has been providing significant models of human be-haviour for 50 years. The first implementation of EPAM (El-ementary Perceiver and Memoriser) was developed by Ed-ward Feigenbaum in 1959. Early models of EPAM pro-vided the impetus to develop the chunking theory (Chase& Simon, 1973; Gobet et al., 2001), which has been animportant component of theories of human cognition eversince. With an emphasis on learning phenomena, EPAMand CHREST (Chunk Hierarchy and REtrieval STructures)are sensitive to the ways in which a model’s information isbuilt up through interactions with an external environment.Thus, EPAM/CHREST models are typically developed fromlarge quantities of naturalistic input. For example, in mod-elling expert perception of chess players, actual chess gamesare used (Gobet & Simon, 2000). Similarly, in modelling theacquisition of syntax, large corpora of mother-child interac-tions are employed to develop the model’s long-term mem-ory (Freudenthal, Pine, Aguado-Orea, & Gobet, 2007).The tutorial is structured so that participants will:1. Acquire a complete understanding of the EPAM andCHREST approach to computational modelling, and theirrelation to the chunking and template theories of cognition;2. Explore some key learning phenomena supporting thechunking theory, based around experiments in verbal-learning, categorisation and the acquisition of expertise;3. Be introduced to an implementation of CHREST whichcan be used for constructing models of their own data.Further information about CHREST, supporting publicationsand implementations can be found at: http://chrest.infoChunking and Template TheoriesA chunk is a ‘familiar pattern’, an item stored in long-termmemory. Chunks collect together more basic elements whichhave strong associations with each other, but weak associ-ations with other elements (Chase & Simon, 1973; Cowan,2001). The chunking theory is based on Miller’s observa-tion (Miller, 1956) that short-term memory typically containsa limited number of pieces of information, but that the sizeof these pieces varies with context. Chase and Simon (1973)confirmed the presence of chunks in the recall of chess po-sitions, and the EPAM model provides a means of learning,storing and retrieving such chunks.The template theory (Gobet & Simon, 1996, 2000) extendsthe chunking theory by adding mechanisms to create retrievalstructures, using specific retrieval cues to store and obtain in-formation rapidly. The template is a form of slotted schema,containing a core, of stable information, and slots, contain-ing variable information. Where the chunking theory cap-tures much of how the average person learns in tasks such asverbal-learning, the template theory further captures the wayin which highly-trained human experts perceive and identifypatterns in their domain of expertise.A more detailed overview of the chunking and templatetheories is contained in Gobet et al. (2001).ImplementationCHREST comprises three basic modules:• Input/output module, which is responsible for feature ex-traction, passing the features to the long-term memory forsorting, and guiding the 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.The key feature which distinguishes EPAM/CHRESTmodels is the discrimination network for storing and retriev-ing information in long-term memory. Information is as-sumed to form a list of subobjects, each of which is eithera further list of subobjects or else a primitive. Tests in thediscrimination network check for the presence of individualprimitive objects, or a list of subobjects. The discriminationnetwork is trained by exposing CHREST to a large set of nat-uralistic data. Typical sizes of network for an expert in a com-plex domain is of the order of 100,000 nodes.In both EPAM and CHREST, chunks are stored as nodeswithin the discrimination network. CHREST also includesmechanisms to bring together chunks when an internal nodemeets specific criteria relating to its connections with othernodes within memory. A template is then formed from thecommon information in the linked chunks, with slots createdfor the variable information. Just as EPAM was the compu-tational embodiment of key aspects of the chunking theory,CHREST implements essential aspects of the template the-ory.Input can be provided to CHREST in one of two ways.As a single pattern, which is assumed to be perceived in asingle glance. These patterns are input to the network andstored directly. The second way is to use the in-built atten-tional mechanism, by which CHREST scans an input array,such as a chess board, and stores parts of the input array intomemory. Short-term memory will then hold a set of chunks,each of which may hold information about a different part ofthe chess board, and collectively holding information aboutmost of the board. The attention mechanism in CHREST isdescribed in Lane, Gobet, and Ll. Smith (2009).CHREST is implemented in Lisp, and uses Tk to providea graphical interface. A graphical environment enables usersto create simple CHREST models by providing data withinan input data file. The implementation also supports morecomplex tailored models which may be developed by writingspecial-purpose code using the packages within CHREST.Within the tutorial we will introduce participants to the graph-ical environment, walk them through a number of providedexamples which will illustrate the workings of the architec-ture and some samples of successful applications, and finallydescribe the input data format for applying the environmentto new domains. A library and manual is provided to assistusers wishing to write more complex models.ApplicationsA variety of experimental data will be covered to illustrate thetheory and processes. The EPAM learning system itself wasinitially developed as a simulation of human verbal learningprocesses. EPAM’s memory structure is constructed throughan interlinked set of learning operations which alternately ex-tend and elaborate information in the network. We illustratethese key learning processes using applications from verballearning (Feigenbaum, 1959; Feigenbaum & Simon, 1984).Further properties of the chunking network will be describedwith reference to results from categorisation (Gobet, Rich-man, Staszewski, & Simon, 1997), implicit learning and lan-guage learning (Freudenthal et al., 2007; Jones, Gobet, &Pine, 2007).More elaborate models of expertise require an understand-ing of how information is retrieved from the external environ-ment. To illustrate this aspect of the theory, we focus on chessexpertise, particularly the challenge of the recall task. Thisapplication is used to describe CHREST’s attention mecha-nisms (Lane et al., 2009) and how they relate to training thediscrimination network.ReferencesAnderson, 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.Cowan, N. (2001). The magical number 4 in short-term mem-ory: A reconsideration of mental storage capacity. Behav-ioral and Brain Sciences, 24, 87–114.Feigenbaum, E. A. (1959). An information processing theoryof verbal learning. The RAND Corporation MathematicsDivision, P-1817.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., Richman, H., Staszewski, J., & Simon, H. A.(1997). Goals, representations, and strategies in a con-cept attainment task: The EPAM model. The Psychologyof Learning and Motivation, 37, 265–290.Gobet, F., & Simon, H. A. (1996). Templates in chess mem-ory: A mechanism for recalling several boards. CognitivePsychology, 31, 1–40.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.McLeod, P., Plunkett, K., & Rolls, E. T. (1998). Introductionto connectionist modelling of cognitive processes. Oxford,UK: Oxford University Press.Miller, G. A. (1956). The magical number seven, plus orminus two: Some limits on our capacity for processing in-formation. Psychological Review, 63, 81–97.Newell, A. (1990). Unified theories of cognition. Cambridge,MA: Harvard University Press.Pfeifer, R., & Scheier, C. (1999). Understanding intelligence.MIT Press.Sternberg, S. (1966). High speed scanning in human memory.Science, 153, 652–4.

An information processing theory of verbal learning.

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.

Goals, representations, and strategies in a concept attainment task: The EPAM model.

High speed scanning in human memory.

Highspeed scanning inhuman memory.

Introduction to connectionist modelling of cognitive processes.

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.

Templates in chess memory: A mechanism for recalling several boards.

The atomic components of thought.

The magical number 4 in short-term memory: A reconsideration of mental storage capacity.

The magical number seven, plus or minus two: Some limits on our capacity for processing information.

Themagical number 4inshort-termmemory: A reconsideration of mental storage capacity.

Understanding intelligence.

Uniﬁed theories of cognition.

EPAM/CHREST tutorial: Fifty years of simulating human learning

EPAM/CHREST tutorial: Fifty years of simulating human learning

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