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Title: Memory for the meaningless: How chunks help
Authors: Gobet, F
Keywords: chess;CHREST;chunking;computer modeling;game positions;random positions;presentation time;EPAM;memory;task;skill;expertise;short-term memory capacity;STM capacity
Issue Date: 1998
Publisher: Erlbaum
Citation: Gobet, F. (1998). Memory for the meaningless: How chunks help. Proceedings of the 20th Meeting of the Cognitive Science Society. (pp. 398-403). Mahwah, NJ: Erlbaum.
Abstract: It is a classic result in cognitive science that chess masters can recall briefly presented positions better than weaker players when these positions are meaningful, but that their superiority disappears with random positions. However, Gobet and Simon (1996a) have recently shown that there is a skill effect with random chess positions as well. The impact of this result for theories of expert memory is discussed. CHREST, a computational, chunking model of chess expertise based on EPAM (Feigenbaum & Simon, 1984) accounts for this skill difference. The model is also compared with human data from an experiment where the role of presentation time for random positions was systematically varied from 1 second to 60 seconds. Simulations show that the model captures the main features of the human data, thus adding support to the EPAM theory. They also corroborate earlier estimates that visual short-term memory may contain three or four chunks.
Appears in Collections:Psychology
Dept of Life Sciences Research Papers

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