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.

中文翻译：

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将人类的社会决策分为多个组成部分，然后将它们重新组合在一起。

人类大部分的清醒时间都花在与其他人的互动上。为了在这个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，特质和意图进行推断。最近，通过将功能磁共振神经成像（fMRI）与行为的计算模型相结合，在揭示人类社会决策基础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，并确定如何将这些计算集成在一起。此外，通过将这些变量与神经影像数据相关联，有可能阐明在大脑中何处进行各种计算。在这里，我们回顾了社会计算神经科学领域的当前知识状态。迄今为止的发现强调，社会决策是由并行进行的多种计算驱动的，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整合和整合这些变量，以产生一致的行为输出。