@article {70145,
	title = {Foraging as an ethological framework for neuroscience.},
	journal = {Trends Neurosci},
	year = {2025},
	month = {2025 Oct 06},
	abstract = {<p>The study of foraging is central to a renewed interest in naturalistic behavior in neuroscience. Applying a foraging framework grounded in behavioral ecology has enabled probing of the mechanisms underlying cognitive processes such as decision-making within a more ecological context. Yet, foraging also involves myriad other aspects, including navigation of complex environments, sensory processing, and social interactions. Here, we first provide a brief overview of the neuroscience of foraging decisions, and then combine insights from behavioral ecology and neuroscience to review the role of these additional dimensions of foraging. We conclude by highlighting four opportunities for the continued development of foraging as an ethological framework for neuroscience: integrating normative and implementation-level models, developing new tools, enabling cross-species comparisons, and fostering interdisciplinary collaboration.</p>
},
	issn = {1878-108X},
	doi = {10.1016/j.tins.2025.08.006},
	url = {https://doi.org/10.1016/j.tins.2025.08.006},
	author = {Grima, Laura L and Haberkern, Hannah and Mohanta, Rishika and Morimoto, Mai M and Rajagopalan, Adithya E and Scholey, Emma V}
}
@article {69659,
	title = {A global dopaminergic learning rate enables adaptive foraging across many options},
	journal = {bioRxiv},
	year = {2024},
	month = {2024 Nov 05},
	abstract = {<p>In natural environments, animals must efficiently allocate their choices across multiple concurrently available resources when foraging, a complex decision-making process not fully captured by existing models. To understand how rodents learn to navigate this challenge we developed a novel paradigm in which untrained, water-restricted mice were free to sample from six options rewarded at a range of deterministic intervals and positioned around the walls of a large ( 2m) arena. Mice exhibited rapid learning, matching their choices to integrated reward ratios across six options within the first session. A reinforcement learning model with separate states for staying or leaving an option and a dynamic, global learning rate was able to accurately reproduce mouse learning and decision-making. Fiber photometry recordings revealed that dopamine in the nucleus accumbens core (NAcC), but not dorsomedial striatum (DMS), more closely reflected the global learning rate than local error-based updating. Altogether, our results provide insight into the neural substrate of a learning algorithm that allows mice to rapidly exploit multiple options when foraging in large spatial environments.</p>
},
	doi = {10.1101/2024.11.04.621923},
	url = {https://doi.org/10.1101/2024.11.04.621923},
	author = {Grima, Laura L and Guo, Yipei and Narayan, Lakshmi and Hermundstad, Ann M and Dudman, Joshua T}
}
