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Foraging as an evidence accumulation process

Fig 4

Different foraging environments with associated patch decision strategies.

Shown are simulation results with the density-adaptive and robust-counting strategies in two different foraging environments. (A,D) illustrates the foraging environment for a given case, (B,E) shows average energy and patch residence time when a particular strategy is used in that environment along with the MVT-optimal energy energy (E*) and patch residence time (T*), and (C,F) shows simulation results compared to MVT-optimal strategies in each environment. All simulations use a noise level of and a patch size of A = 5, and the robust counting strategy is implemented by setting α = −0.2ρ₀. (A-C) Uncertainty in patch food density. Patches have a Gaussian distribution for initial food density with mean of and a standard deviation of , and rewards are received continuously (c = 0). Travel time between patches is constant at T_tr = 5. The solid line in (C) shows an approximate analytical solution (Methods, Eq 25) for small changes in ρ₀ about . (D-F) Scattered patches with discrete rewards. Food reward is received in discrete chunks (c = 8) and each patch has the same initial food density of ρ₀ = 9.439. Travel time between patches is drawn from an exponential distribution with mean . (F) shows a histogram of simulation results for how many food chunks were taken before leaving the patch, for both the density adaptive strategy (left) and the robust counting strategy (right).

doi: https://doi.org/10.1371/journal.pcbi.1007060.g004