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Foraging optimally in social neuroscience: computations and methodological considerations
Social Cognitive and Affective Neuroscience ( IF 3.9 ) Pub Date : 2020-03-30 , DOI: 10.1093/scan/nsaa037
Anthony S Gabay 1, 2 , Matthew A J Apps 1, 2, 3
Affiliation  

Abstract
Research in social neuroscience has increasingly begun to use the tools of computational neuroscience to better understand behaviour. Such approaches have proven fruitful for probing underlying neural mechanisms. However, little attention has been paid to how the structure of experimental tasks relates to real-world decisions, and the problems that brains have evolved to solve. To go significantly beyond current understanding, we must begin to use paradigms and mathematical models from behavioural ecology, which offer insights into the decisions animals must make successfully in order to survive. One highly influential theory—marginal value theorem (MVT)—precisely characterises and provides an optimal solution to a vital foraging decision that most species must make: the patch-leaving problem. Animals must decide when to leave collecting rewards in a current patch (location) and travel somewhere else. We propose that many questions posed in social neuroscience can be approached as patch-leaving problems. A richer understanding of the neural mechanisms underlying social behaviour will be obtained by using MVT. In this ‘tools of the trade’ article, we outline the patch-leaving problem, the computations of MVT and discuss the application to social neuroscience. Furthermore, we consider the practical challenges and offer solutions for designing paradigms probing patch leaving, both behaviourally and when using neuroimaging techniques.


中文翻译:

社会神经科学中的最佳觅食:计算和方法论考虑

摘要
社会神经科学的研究越来越多地开始使用计算神经科学的工具来更好地理解行为。事实证明,这种方法对于探索潜在的神经机制是卓有成效的。然而,很少有人关注实验任务的结构如何与现实世界的决策相关,以及大脑已经进化来解决的问题。为了大大超越目前的理解,我们必须开始使用行为生态学的范式和数学模型,这些模型可以深入了解动物为了生存而必须成功做出的决定。一个极具影响力的理论——边际价值定理 (MVT)——精确地描述了大多数物种必须做出的重要觅食决定并提供了最佳解决方案:斑块离开问题。动物必须决定何时离开收集当前补丁(位置)的奖励并前往其他地方。我们建议社会神经科学中提出的许多问题都可以作为补丁遗留问题来解决。使用 MVT 可以更深入地了解社会行为背后的神经机制。在这篇“交易工具”文章中,我们概述了补丁遗留问题、MVT 的计算并讨论了它在社会神经科学中的应用。此外,我们考虑了实际挑战并提供了解决方案,以在行为上和使用神经成像技术时设计探测补丁离开的范式。使用 MVT 可以更深入地了解社会行为背后的神经机制。在这篇“交易工具”文章中,我们概述了补丁遗留问题、MVT 的计算并讨论了它在社会神经科学中的应用。此外,我们考虑了实际挑战并提供了解决方案,以在行为上和使用神经成像技术时设计探测补丁离开的范式。使用 MVT 可以更深入地了解社会行为背后的神经机制。在这篇“交易工具”文章中,我们概述了补丁遗留问题、MVT 的计算并讨论了它在社会神经科学中的应用。此外,我们考虑了实际挑战并提供了解决方案,以在行为上和使用神经成像技术时设计探测补丁离开的范式。
更新日期:2020-03-30
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