Foraging as an evidence accumulation process

PLoS Comput Biol. 2019 Jul 24;15(7):e1007060. doi: 10.1371/journal.pcbi.1007060. eCollection 2019 Jul.


The patch-leaving problem is a canonical foraging task, in which a forager must decide to leave a current resource in search for another. Theoretical work has derived optimal strategies for when to leave a patch, and experiments have tested for conditions where animals do or do not follow an optimal strategy. Nevertheless, models of patch-leaving decisions do not consider the imperfect and noisy sampling process through which an animal gathers information, and how this process is constrained by neurobiological mechanisms. In this theoretical study, we formulate an evidence accumulation model of patch-leaving decisions where the animal averages over noisy measurements to estimate the state of the current patch and the overall environment. We solve the model for conditions where foraging decisions are optimal and equivalent to the marginal value theorem, and perform simulations to analyze deviations from optimal when these conditions are not met. By adjusting the drift rate and decision threshold, the model can represent different "strategies", for example an incremental, decremental, or counting strategy. These strategies yield identical decisions in the limiting case but differ in how patch residence times adapt when the foraging environment is uncertain. To describe sub-optimal decisions, we introduce an energy-dependent marginal utility function that predicts longer than optimal patch residence times when food is plentiful. Our model provides a quantitative connection between ecological models of foraging behavior and evidence accumulation models of decision making. Moreover, it provides a theoretical framework for potential experiments which seek to identify neural circuits underlying patch-leaving decisions.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Decision Making
  • Feeding Behavior*
  • Models, Theoretical*

Grants and funding

This work was partially supported by the DFG Centre of Excellence 2117 “Centre for the Advanced Study of Collective Behaviour" (ID: 422037984), and by the Office of Naval Research, grant numbers N00014-09-1-1074 and N00014-14-1-0635. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.