Figure 1.
The virtual foraging environment, resource distributions, and representative paths.
A. Participants’ perspective during the task. One of the global landmarks (a mountain) is visible in the distance. The number in the lower left hand corner is the number of resources collected so far. B. The resource distribution in the dispersed environment with a path generated by one participant. C. The resource distribution in the patchy environment with a path generated by one participant.
Figure 2.
Rank/frequency plots of aggregated and individual data along with model fits on logarithmic axes.
Black circles are movement lengths ≥ x. The four model fits are power-law (blue-straight line), bounded power-law (curved blue-dashed line), unbounded exponential (curved red line), and bounded exponential (curved red-dashed line). A. The aggregated data for the dispersed condition. The inset shows the results of logarithmic binning with best fitting power-law. B. The aggregated data for the patched condition. The inset shows the results of logarithmic binning with best fitting power-law. C. Data for each individual in the dispersed condition. D. Data for each individual in the patched condition.
Table 1.
Model comparisons for aggregated data.
Table 2.
Model comparisons for individual data.
Figure 3.
Turning angle as a function of distance after item encounter for the empirical data (“Experiment dispersed” and “Experiment patched”) and for random locations along the trajectories (“Random dispersed” and “Random patched”).
Participants in the patched condition significantly increased turning in response to resource encounters relative to both the dispersed condition (F(1,30) = 5.31, P = .03, repeated measures analysis of variance) and ‘random’ baseline turning (F(1,15) = 5.71, P = .03, repeated measures analysis of variance). Turning angles in the dispersed condition were not different from the ‘random’ baseline turning (F(1,15) = 1.68, P = .21, repeated measures analysis of variance). Data show mean±sem.
Figure 4.
Comparing path performance across environments.
We compared path performance by randomly simulating paths from the alternative environment using 100 simulated versions of each observed path in the alternative resource distribution. Paths from the patched condition simulated in the dispersed environment performed as well as dispersed paths in the dispersed environment (t(15) = 0.05, P = .97, two-tailed t-test). However, paths from the dispersed environment simulated in the patchy environment were outperformed by the original paths from the patchy environment (t(14) = −3.91, P = .002, two-tailed t-test).