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Updated: Jan 12, 2026

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Foraging Path-length Protocol for Drosophila melanogaster Larvae
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在结构化食环境中基于模型的规划
Thea R Zalabak1, Laura A Bustamante1, Wouter Kool1
1Department of Psychological & Brain Sciences, Washington University in St. Louis, 1 Brookings Drive CB 1125, St. Louis, MO 63130, USA.
Cognition
|November 5, 2025
概括
人类平衡寻求奖励和计划更好的奖励. 一项新的任务显示,人们在决定留下或离开时使用环境结构并考虑替代方案,特别是目标导向的个人.
科学领域:
- 决策和行为经济学
- 认知心理学 认知心理学
- 计算神经科学是一种计算神经科学.
背景情况:
- 为了最大限度地获得奖励,需要平衡利用当前的选择和探索更好的选择.
- 最佳食理论的边际价值定理 (MVT) 提供了一个简单的规则,用于基于预期与经验回报的停留/离开决策.
- MVT在食决策中没有考虑环境结构或环境规划.
研究的目的:
- 研究个人如何将环境结构和规划纳入食决策.
- 探索人们在多大程度上使用任务结构的内部模型来选择停留/离开.
- 检查目标导向对食战略的影响.
主要方法:
- 开发一种新的结构化食任务.
- 参与者停留/离开决策的行为分析.
- 计算建模以评估结合替代选项信息的好处.
主要成果:
- 参与者的行为通常与MVT保持一致,但包含了关于替代奖励选择的信息.
- 考虑替代方案的倾向在目标导向的个体中更为明显.
- 计算模型表明,使用替代信息是有益的,由选择随机性调节.
结论:
- 这项研究引入了研究结构化环境中的决策的新范式.
- 食决策受到即时奖励反和战略规划的影响.
- 这些发现对理解食行为与目标导向规划之间的相互作用有影响.
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