Chunk hierarchies and retrieval structures: Comments on Saariluoma and Laine

The empirical results of Saariluoma and Laine are discussed and their computer simulations are compared with CHREST, a computational model of perception, memory and learning in chess. Mathematical functions such as power functions and logarithmic functions account for Saariluoma and Laine’s correlation heuristic and for CHREST very well. However, these functions fit human data well only with game positions, not with random positions. As CHREST, which learns using spatial proximity, accounts for the human data as well as Saariluoma and Laine’s correlation heuristic, their conclusion that frequency-based heuristics match the data better than proximity-based heuristics is questioned. The idea of flat chunk organisation and its relation to retrieval structures is discussed. In the conclusion, emphasis is given to the need for detailed empirical data, including information about chunk structure and types of errors, for discriminating between various learning algorithms.

[1]  P. Saariluoma.  Chess players' recall of auditorily presented chess positions , 1989 .

[2]  K. A. Ericsson,et al.  Skill and Working Memory , 1982 .

[3]  H. Simon,et al.  A simulation of memory for chess positions. , 1973 .

[4]  Herbert A. Simon,et al.  Templates in Chess Memory: A Mechanism for Recalling Several Boards , 1996, Cognitive Psychology.

[5]  H. Simon,et al.  STM capacity for Chinese words and idioms: Chunking and acoustical loop hypotheses , 1985, Memory & cognition.

[6]  Allen and Rosenbloom Paul S. Newell,et al.  Mechanisms of Skill Acquisition and the Law of Practice , 1993 .

[7]  H. Simon,et al.  Recall of random and distorted chess positions: Implications for the theory of expertise , 1996, Memory & cognition.

[8]  H. Simon,et al.  Perception in chess , 1973 .

[9]  Walter Kintsch,et al.  Comprehension: A Paradigm for Cognition , 1998 .

[10]  H. Simon,et al.  The mind's eye in chess. , 1973 .

[11]  A. D. Fisk,et al.  The Role of Stimulus-to-Rule Consistency in Learning Rapid Application of Spatial Rules , 1988, Human factors.

[12]  Allen Newell,et al.  Human Problem Solving. , 1973 .

[13]  Fernand Gobet,et al.  Perception and memory in chess: Heuristics of the professional eye , 1996 .

[14]  Hans Gruber,et al.  Expertise and hierarchical knowledge representation in chess , 1992 .

[15]  H. Simon,et al.  Expert chess memory: revisiting the chunking hypothesis. , 1998, Memory.

[16]  D. Holding.  The Psychology of Chess Skill , 1985 .

[17]  K. A. Ericsson,et al.  Long-term working memory. , 1995, Psychological review.

[18]  Fernand Gobet,et al.  Memory for the meaningless: How chunks help , 1998 .

[19]  M. Chi.  Knowledge structures and memory development. , 1978 .

[20]  H A Simon,et al.  Information-processing analysis of perceptual processes in problem solving. , 1969, Psychological review.

[21]  Heikki Hyötyniemi,et al.  Foundations of Simulative Theorizing , 1998, ECAI.

[22]  H. Simon,et al.  Recall of rapidly presented random chess positions is a function of skill , 1996, Psychonomic bulletin & review.

[23]  Herbert A. Simon,et al.  Five seconds or sixty? Presentation time in expert memory , 2000 .

[24]  P. Saariluoma,et al.  Novice construction of chess memory. , 2001, Scandinavian journal of psychology.

[25]  K. A. Ericsson,et al.  Expert chess memory without chess knowledge-a training study , 1990 .

[26]  L. Reder,et al.  The Strategy-Specific Nature of Improvement: The Power Law Applies by Strategy Within Task , 1998 .

[27]  Nancy J. Cooke,et al.  Role of high-level knowledge in memory for chess positions , 1993 .

[28]  Fernand Gobet.  A computer model of chess memory , 1993 .

[29]  H. Simon,et al.  EPAM-like Models of Recognition and Learning , 1984, Cogn. Sci..

[30]  H B Richman,et al.  Simulation of expert memory using EPAM IV. , 1995, Psychological review.

[31]  H. Hyötyniemi,et al.  Simulating chess players' recall: how many chunks and what kind can they be? , 1998 .