Abstract
SummaryForaging is a common decision problem in natural environments. When new exploitable sites are always available, a simple optimal strategy is to leave a current site when its return falls below a single average reward rate. Here, we examined foraging in a more structured environment, with a limited number of sites that replenished at different rates and had to be revisited. When participants could choose sites, they visited fast-replenishing sites more often, left sites at higher levels of reward, and achieved a higher net reward rate. Decisions to exploit-or-leave a site were best explained with a computational model that included both the average reward rate for the environment and reward information about the unattended sites. This suggests that unattended sites influence leave decisions, in foraging environments where sites can be revisited.
Highlights
Decision making requires anticipating how our choices will influence our fortunes in the future
When new exploitable sites are always available, a simple optimal strategy is to leave a current site when its return falls below a single average reward rate
Decisions to exploit-or-leave a site were best explained with a computational model that included both the average reward rate for the environment and reward information about the unattended sites
Summary
Decision making requires anticipating how our choices will influence our fortunes in the future. Much work in neuroscience has focused on how decision making can be understood as an optimization problem based on a Markov Decision Process (MDP) formalism, in which future environmental states and rewards are under partial control of the decision maker (Sutton and Barto, 1998) This approach can account for behavior in complex environments, where planning is needed to form an optimal policy (Huys et al, 2012, 2015; Kurth-Nelson et al, 2016), and can explain neural activity found in dopaminergic areas during value prediction (Schultz et al, 1997). Canonical Optimal Foraging Theory (OFT) considers a simplified version of this problem, in which patches within an environment are encountered at fixed rates (Charnov, 1976) Under these conditions, the average reward rate in an environment is the sole variable needed to anticipate future reward outcomes for deciding to leave. Evidence has suggested that the unique structure of foraging decisions might be solved using neural substrates that are at least partially distinct from those involved in other reward-based decisions (Kolling et al, 2012; Rushworth et al, 2011), with the anterior cingulate cortex serving a critical role in regulating leave decisions (Fouragnan et al, 2019; Hayden et al, 2011; Wittmann et al, 2016, see Kolling et al, 2016; Mobbs et al, 2018; Rushworth et al, 2011 for reviews)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
