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Psychological Research PRPF

, 73:644 | Cite as

Planning paths to multiple targets: memory involvement and planning heuristics in spatial problem solving

  • J. M. Wiener
  • N. N. Ehbauer
  • H. A. Mallot
Original Article

Abstract

For large numbers of targets, path planning is a complex and computationally expensive task. Humans, however, usually solve such tasks quickly and efficiently. We present experiments studying human path planning performance and the cognitive processes and heuristics involved. Twenty-five places were arranged on a regular grid in a large room. Participants were repeatedly asked to solve traveling salesman problems (TSP), i.e., to find the shortest closed loop connecting a start location with multiple target locations. In Experiment 1, we tested whether humans employed the nearest neighbor (NN) strategy when solving the TSP. Results showed that subjects outperform the NN-strategy, suggesting that it is not sufficient to explain human route planning behavior. As a second possible strategy we tested a hierarchical planning heuristic in Experiment 2, demonstrating that participants first plan a coarse route on the region level that is refined during navigation. To test for the relevance of spatial working memory (SWM) and spatial long-term memory (LTM) for planning performance and the planning heuristics applied, we varied the memory demands between conditions in Experiment 2. In one condition the target locations were directly marked, such that no memory was required; a second condition required participants to memorize the target locations during path planning (SWM); in a third condition, additionally, the locations of targets had to retrieved from LTM (SWM and LTM). Results showed that navigation performance decreased with increasing memory demands while the dependence on the hierarchical planning heuristic increased.

Keywords

Target Location Path Planning Travel Salesman Problem Near Neighbor Navigation Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by the DFG (Deutsche Forschungsgemeinschaft, WI 2729/1-1) and the VW-Foundation. Special thanks to Dominik Seffer for his help carrying out the experiments.

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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Cognitive NeuroscienceUniversity of TübingenTübingenGermany
  2. 2.Center for Cognitive ScienceUniversity of FreiburgFreiburgGermany

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