人类觅食策略灵活适应资源分配和时间限制

Valeria Simonelli, Davide Nuzzi, Gian Luca Lancia, Giovanni Pezzulo
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引用次数: 0

摘要

觅食是一项至关重要的活动,但人类在多大程度上采用灵活而非刻板的策略仍不清楚。本研究探讨了个体如何根据资源分配和觅食时间限制来调整自己的觅食策略。为此,我们设计了一个类似于视频游戏的觅食任务,要求参与者在有限的时间内浏览四个区域的环境,从宝箱中收集硬币。这项任务涉及多种认知能力,如导航、学习和记忆宝箱位置。研究结果表明,参与者会根据资源分配和觅宝时间来调整他们的觅宝策略,包括停留或离开决策(例如在初始区域打开宝箱的数量)和行为方面(例如从一个宝箱导航到另一个宝箱的时间)。此外,随着时间的推移,他们的表现也在不断提高,这既是导航技能提高的结果,也是觅食策略调整的结果。最后,由于人类的学习过程,参与者的表现最初与最优代理的奖励最大化表现相去甚远;然而,在任务接近尾声时,参与者的表现接近最优代理的表现,但并未完全达到最优代理的表现。这些结果突显了人类觅食行为的灵活性,并强调了采用最优性模型和丰富的生态情景来研究觅食行为的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human foraging strategies flexibly adapt to resource distribution and time constraints
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-areas 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 as an effect of both enhanced navigation skills and adaptation of foraging strategies. 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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