Breaking human social decision making into multiple components and then putting them together again
Breaking human social decision making into multiple components and then putting them together again
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DOI：
10.31234/osf.io/hmbue
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发表时间：
2020-01
期刊：
影响因子：
3.6
通讯作者：
Shinsuke Suzuki;J. O’Doherty
中科院分区：
文献类型：
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作者：
Shinsuke Suzuki;J. O’Doherty
作为人类，我们醒着的大部分时间都花在与其他人互动上。为了在这种社会环境中做出正确的决定，通常有必要对他人的内部状态、特征和意图做出推断。最近，通过将功能磁共振神经成像（fMRI）与行为计算模型相结合，在揭示人类社会决策背后的神经计算方面取得了一些进展。行为数据建模使我们能够识别社会决策所需的关键计算，并确定如何集成这些计算。此外，通过将这些变量与神经影像数据相关联，有可能阐明大脑中各种计算的执行位置。在这里，我们回顾了社会计算神经科学领域的知识现状。迄今为止的研究结果强调，社会决策是由并行进行的多个计算驱动的，并在不同的大脑区域中实施。我们建议，进一步的进展将取决于确定如何以及在何处整合这些变量，以便产生连贯的行为输出。
Most of our waking time as human beings is spent interacting with other individuals. In order to make good decisions in this social milieu, it is often necessary to make inferences about the internal states, traits and intentions of others. Recently, some progress has been made toward uncovering the neural computations underlying human social decision-making by combining functional magnetic resonance neuroimaging (fMRI) with computational modeling of behavior. Modeling of behavioral data allows us to identify the key computations necessary for social decision-making and to determine how these computations are integrated. Furthermore, by correlating these variables against neuroimaging data, it has become possible to elucidate where in the brain various computations are implemented. Here we review the current state of knowledge in the domain of social computational neuroscience. Findings to date have emphasized that social decisions are driven by multiple computations conducted in parallel, and implemented in distinct brain regions. We suggest that further progress is going to depend on identifying how and where such variables get integrated in order to yield a coherent behavioral output.