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

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.

The integration of spatial information across different viewpoints

“…Between presentations, participants either moved and came back to the original location (same viewpoint condition) or they moved to a new location (different viewpoint condition). After the second presentation, participants were asked to plan and navigate the shortest path to visit all targets (for similar planning tasks, see Wiener, Ehbauer, & Mallot, 2009). Planning an efficient path requires integration (i.e., considering all locations together).…”

“…This planning task (cf. Wiener et al, 2009) required participants to integrate the six target locations that were perceived at different (1) Spatial integration happens within a single reference frame (2) Transforming misaligned spatial representations into a common reference frame requires mental processing, which may result in memory errors or increased latencies relative to an appropriate baseline (i.e., integrating already-aligned representations). (3) Spatial information acquired from a novel viewpoint 2 can be integrated with existing spatial memory acquired from a viewpoint 1 using the following: (a) the already-established reference frame corresponding to viewpoint 1 (cf.…”

Route planning with transportation network maps: an eye-tracking study

“…Indeed, we found that search time was not affected by the task difficulty while the planning and selection time increased with the increasing task complexity. This result suggests that with the increasing task complexity more processes, Processes required during the Planning and Selection Phase, such as encoding the exact locations of connecting stations or routes in short term memory and manipulating this information, is also more cognitively demanding than a pure visual search (Gärling et al, 1986, Wiener, Ehbauer & Mallot, 2009. Such increases in cognitive demands are also known to result in shorter saccades (Just & Carpenter, 1976;Pomplun et al, 2001).…”

Single-destination navigation in a multiple-destination environment: a new “later-destination attractor” bias in route choice

“…Participants tend to choose a path that has a long initial segment or the fewest turns (Bailenson, Shum, & Uttal, 1998, Bailenson, Shum, & Uttal, 2000Christenfeld, 1995), and, most often, deviate the least from a straight line from start to destination (least angle; Hochmair & Karlsson, 2005). In each case, participants seem to prefer the routes that require the least cognitive effort, even if the path is longer in the end (Wiener, Ehbauer, & Mallot, 2009).…”

“…In still other cases, participants choose to visit the nearest cluster of locations first, then the next nearest cluster, and so forth (hierarchical clustering; Graham, Joshi, & Pizlo, 2000). Finally, when there are marked regions on a map or in a virtual neighborhood and the starting and ending locations are in different regions, participants prefer the path that gets them to the edge of the first region the quickest, even if other routes are shorter in distance to the end location (Wiener et al, 2009). This regionalization bias especially reflects a conservation of cognitive effort: just get to the next region quickly, and then worry about the location of the destination(s) within the second region.…”