ArticleCognitive, affective & behavioral neuroscience2026
Human foraging strategies flexibly adapt to resource distribution and time constraints.
Article in Cognitive, affective & behavioral neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Using computer games to explore foraging-predation trade-offs and spatial learning in humans.Royal Society open science · 2026Article
- Continuous dynamics of cooperation and competition in social decision-making.Communications psychology · 2025Article
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Authors and funding
4 authors.
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Abstract
Foraging is a crucial activity, yet the extent to which humans employ flexible versus rigid strategies remains unclear. This study investigates how individuals adapt their foraging strategies in response to resource distribution and foraging time constraints. For this, we designed a video-game-like foraging task that requires participants to navigate a four-area environment to collect coins from treasure boxes within a limited time. This task engages multiple cognitive abilities, such as navigation, learning, and memorization of treasure box locations. Findings indicate that participants adjust their foraging strategies - encompassing both stay-or-leave decisions, such as the number of boxes opened in initial areas and behavioral aspects, such as the time to navigate from box to box - depending on both resource distribution and foraging time. Additionally, they improved their performance over time by reducing the uncertainty about resource locations and distributions, demonstrating enhancements in both foraging strategies and navigation skills. They also adapted their strategies within trials based on their uncertainty, leaving areas more quickly when they resolved that other areas offered better foraging opportunities, and more slowly when they became certain that the alternatives were poorer. Finally, participants' performance was initially distant from the reward-maximizing performance of optimal agents due to the learning process humans undergo. However, it approximated the optimal agent's performance towards the end of the task, without fully reaching it. These results highlight the flexibility of human foraging behavior and underscore the importance of employing optimality models and ecologically rich scenarios to study foraging.
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