Decision-making performance and self-generated knowledge in handball-defense patterns: a case of representational redescription
In sport games, perceptual-cognitive skills are discussed as a decisive aspect of players` expertise. However, an understanding of the relationship between these skills and actual game performance is limited, particularly, regarding the role of pattern identification and situational-probability estimation in performance. The present study thus aimed to examine how identification of teammates` defensive qualities relates to decision-making performance in a 3:3 virtual-reality defensive task. Examining data collected in two previously published studies, we analyzed the relationship between explicit pattern detection and response correctness, and also as a function of players` experience. Experience was operationalized as either expertise level (Experiment 1) or task-specific experience (Experiment 2). As expected, the explicit detection of a game-specific pattern was found to be facilitated by experience. However, the results imply that it is accumulated experience that enhances decision-making performance rather than the degree of self-generated explicit knowledge. This finding supports the notion of "representational redescription" as introduced by Karmiloff-Smith (1994). For sports practice, this suggests that the pattern identification demonstrated by skilled athletes should not be overestimated as a predictor of game performance, while the explicit provision of knowledge might be beneficial for less-skilled athletes, particularly in situations of high uncertainty.
© Copyright 2023 German Journal of Exercise and Sport Research. Springer. All rights reserved.
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| Notations: | technical and natural sciences social sciences sport games |
| Published in: | German Journal of Exercise and Sport Research |
| Language: | English |
| Published: |
2023
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| Online Access: | https://doi.org/10.1007/s12662-022-00868-1 |
| Volume: | 53 |
| Issue: | 2 |
| Pages: | 217-224 |
| Document types: | article |
| Level: | advanced |