This article discusses how CHREST's mechanisms
lead to the implicit learning of a large number
of chunks, which underpin (expert) behaviour in a
number of domains. Results from chess research are discussed

Gobet, F

English

Brunel University Research Archive

8       AISB QuarterlyMuch of what we know about expertise comesfrom research into chess by de Groot in the fortiesand Chase and Simon in the seventies.1,2 Twoclassic de Groot results demonstrated theimportance of perception in expert behaviour.First, even though grandmasters found bettermoves than strong amateurs in a problem-solvingtask, there were few differences in their searchbehaviour. In particular, all players were selectiveand visited only about one hundred positions.Second, chess masters performed almost perfectlyin the recall of game positions (see Figure 1)presented for a few seconds. To explain theseresults, Chase and Simon developed the ‘chunkingtheory’ that proposed mechanisms specifying howknowledge is implicitly acquired during practice.Expertise is seen as the acquisition of a largenumber of perceptual chunks (groups of featuresthat can be used as units), that give access torelevant information (e.g., what move to play).Over the last decade, my research has aimedto flesh out these mechanisms computationallyand to test them empirically. The computationalwork has led to the development of CHREST(Chunk Hierarchy and REtrieval STructures), whichmodels expertise as the growth of a discriminationnet. Each node (chunk) in the net containsinformation about the location of pieces, as wellas pointers to possible (sequences of) moves.Provision for eye-movement mechanisms enablesa close interaction between perception andmemory. Finally, high-level schemas are createdautomatically. The empirical work has investigatedexpert perception and problem solving using verbalprotocols, eye movements, and—more recently—brain imaging. I have also manipulated severalvariables in recall experiments, such as time ofpresentation, level of position distortion, and levelof position randomisation. In general, CHREST,serving as a subject‘in silico’, models thememory experimentswell. Here, I focus onthe recall of randompositions.As documented inpsychology textbooks,Chase and Simonfound no skillImplicit learning ofexpert chess knowledgedifference in the recall of random positions (seeFigure 1). However, CHREST predicts a smalldifference, as chunks are more likely to berecognized serendipitously in random positionswith large nets than with small ones. Re-analysisof the literature, as well as the collection of newdata, supported this prediction.3Random positions are typically created byshuffling the piece locations of a game position.Vicente and Wang4 noted that these positions arenot really random, as they still contain informationabout the distribution of pieces (e.g., only onewhite King is allowed). They raised the questionas to whether skill differences would remain if‘truly-random’ positions were used, where boththe location and the distribution of pieces arerandomised (see Figure 1). CHREST predicts thatthis would be the case. An experiment with 36players ranging from weak amateurs tograndmasters confirmed CHREST’s prediction: withtruly-random positions, there was a statisticallyreliable correlation between skill and recallperformance.5 This difference remained whenvariables such as age and visual memory werepartialled out.Current work with Andrew Waters furtherexplores the role of perception in expert memory.We created positions where the pieces lie at theintersection, rather than the middle, of squares(see Figure 2). Results indicate that overallperformance drops drastically. While masters stillmaintain some superiority with game positions,they do not perform better with random andtruly-random positions. CHREST simulates theseresults by assuming that players need to ‘re-centre’ the pieces in their mind’s eye in order tofacilitate the recognition of chunks. This takestime and thus lowers performance.Beyond chess, the chunking mechanismsembodied in CHREST have explained empiricaldata in other domains.6 Within expertise research,they have accounted for computer programmers’memory and the learning of multiplerepresentations in physics. Beyond this, they havehelped model how children acquire the syntacticcategories of their native language, and howhumans combine information from different inputmodalities (see Peter Lane’s contribution on page7). Overall, CHREST shows that simple mechanismsleading to the implicit learning of a large numberof chunks may underpin (expert) behaviour in anumber of domains.Fernand GobetDepartment of Human SciencesBrunel UniversityUxbridge, Middlesex, UKE-mail: fernand.gobet@brunel.ac.ukhttp://www.psychology.nottingham.ac.uk/staff/frg/References1. A.D. de Groot and F.Gobet, Perception andmemory in chess, VanGorcum, 1996.2. F. Gobet, A.J. de Voogtand J. Retschitzki, Movesin mind: The psychologyof board games,Psychology Press, in press.3. F. Gobet and H.A.Simon, Five seconds orsixty? Presentation time inexpert memory, CognitiveScience, 24, pp. 651-682,2000.4. K.J. Vicente and J.H.Wang, An ecological theoryof expertise effects inmemory recall,Psychological Review105, pp. 33-57, 1998.5. F. Gobet and A.J.Waters, The role ofconstraints in expertmemory. Journal ofExperimentalPsychology: Learning,Memory & Cognition, inpress.6. F. Gobet, P.C.R. Lane,S. Croker, P.C.H. Cheng, G.Jones, I. Oliver and J. M.Pine, Chunking mechanismsin human learning, Trendsin Cognitive Sciences 5,pp. 236-243, 2001.Figure 1. A game position(left), random position(middle), and ‘trulyrandom’ position (right).Figure 2. Positions wherethe pieces have beenplaced at the intersectionof squares: a gameposition (left); and a trulyrandom position (right).

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Implicit learning of expert chess knowledge

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