@article {69515, title = {Recent experience and internal state shape local search strategies in flies}, journal = {Curr Biol}, year = {2026}, month = {2026 Mar 18}, abstract = {

After finding food, a foraging animal must decide whether to continue feeding or to explore the environment for potentially better options. One strategy to negotiate this tradeoff is to perform local searches around the food while repeatedly returning to feed. We studied this behavior in flies and used genetic tools to uncover the underlying behavioral strategies. Over time, flies gradually expand their search, shifting from primarily exploiting food sources to exploring the environment, a change likely driven by increased satiety. We found that flies{\textquoteright} search patterns preserve these dynamics even as the overall range of the search is modulated by starvation. In contrast, search induced by optogenetic activation of sugar-sensing neurons does not show these dynamics. We asked what navigational strategies underlie local search. Using a generative model, we found that a change in locomotor pattern after food consumption could account for repeated returns to the food, but not the relatively direct return trajectories that flies make even from far away. Such trajectories likely rely on alternative strategies, such as path integration or sensory taxis. We tested this by individually silencing their likely neural components, the compass system, olfaction, and hygrosensation. The only substantial effect was from perturbing hygrosensation, which reduced the number of long exploratory trips with subsequent return to the food. Our study illustrates that local search comprises multiple behavioral features that evolve over time based on both internal and external factors, providing a path toward uncovering the underlying neural mechanisms.

}, doi = {10.1016/j.cub.2026.02.037}, url = {https://https://doi.org/10.1016/j.cub.2026.02.037/content/early/2024/06/28/2024.06.26.600764}, author = {Goldschmidt, Dennis and Guo, Yipei and Chitnis, Shivam S and Christoforou, Christina and Turner-Evans, Dan and Ribeiro, Carlos and Hermundstad, Ann M and Jayaraman, Vivek and Haberkern, Hannah} } @article {70017, title = {Efficient planning and implementation of optimal foraging strategies under energetic constraints}, journal = {PNAS Nexus}, year = {2025}, month = {2026 Jan 17}, abstract = {

To successfully forage for food, animals must balance the energetic cost of searching for food sources with the energetic benefit of exploiting those sources. While the Marginal Value Theorem provides one normative account of this balance by specifying that a forager should leave a food patch when its energetic yield falls below the average yield of other patches in the environment, it assumes the presence of other readily reachable patches. In natural settings, however, a forager does not know whether it will encounter additional food patches, and it must balance potential energetic costs and benefits accordingly. Upon first encountering a patch of food, it faces a decision of whether and when to leave the patch in search of better options, and when to return if no better options are found. Here, we explore how a forager should structure its search for new food patches when the existence of those patches is unknown, and when searching for those patches requires energy that can only be harvested from a single known food patch. We identify conditions under which it is more favorable to explore the environment in several successive trips rather than in a single long exploration, and we show how the optimal sequence of trips depends on the forager{\textquoteright}s beliefs about the distribution and nutritional content of food patches in the environment. This optimal strategy is well approximated by a local decision that can be implemented by a simple neural circuit architecture. Together, this work highlights how energetic constraints and prior beliefs shape optimal foraging strategies, and how such strategies can be approximated by simple neural networks that implement local decision rules.

}, issn = {2692-8205}, doi = {10.1093/pnasnexus/pgag009}, url = {https://doi.org/10.1093/pnasnexus/pgag009}, author = {Guo, Yipei and Hermundstad, Ann M} } @article {70223, title = {Environmental dynamics impact whether matching is optimal}, journal = {PNAS Nexus}, year = {2025}, month = {2025 Dec 17}, pages = {pgaf392}, abstract = {

Foraging animals often sample options that yield rewards with different probabilities. In such scenarios, many animals exhibit {\textquotedblleft}matching{\textquotedblright}, whereby they allocate their choices such that the fraction of rewarded samples is equal across options. While matching can be optimal in environments with diminishing returns, this condition alone is not sufficient to determine optimality. Moreover, diminishing returns arise when resources deplete and replenish over time, but their form depends on the temporal structure and statistics of replenishment. Here, we investigate how these environmental properties influence whether matching is optimal. We consider an agent that samples options at fixed rates and derive the resulting reward probabilities across different types of environments. This allows us to analytically determine conditions under which the optimal policy exhibits matching. When all options share the same replenishment dynamics, matching emerges as optimal across a wide range of environments. However, when dynamics differ across options, optimal policies can deviate from matching. In such cases, the rank-ordering of observed reward probabilities depends only on the qualitative nature of the replenishment process, and not on the specific replenishment rates. As a result, the optimal policy can exhibit under- or over-matching depending on which options are more rewarding. We use this result to identify environments where performance differs substantially between matching and optimality. Finally, we show that fluctuations in replenishment rates{\textemdash}representing environmental stochasticity or internal uncertainty{\textemdash}can amplify deviations from matching. These findings deepen our understanding of the relationship between environmental variability and behavioral optimality, and provide testable predictions across diverse settings.

}, issn = {2752-6542}, doi = {10.1093/pnasnexus/pgaf392}, url = {https://doi.org/10.1093/pnasnexus/pgaf392}, author = {Guo, Yipei and Hermundstad, Ann M} } @article {69659, title = {A global dopaminergic learning rate enables adaptive foraging across many options}, journal = {bioRxiv}, year = {2024}, month = {2024 Nov 05}, abstract = {

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.

}, 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} }