Suzuki Shinsuke, O'Doherty John P
Brain, Mind and Markets Laboratory, Department of Finance, Faculty of Business and Economics, The University of Melbourne, Parkville, Australia; Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai, Japan.
Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, USA; Computation and Neural Systems, California Institute of Technology, Pasadena, USA.
Cortex. 2020 Jun;127:221-230. doi: 10.1016/j.cortex.2020.02.014. Epub 2020 Mar 9.
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.
作为人类，我们醒着的大部分时间都用于与他人互动。为了在这种社会环境中做出明智的决策，通常有必要对他人的内心状态、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，并确定这些计算是如何整合的。此外，通过将这些变量与神经成像数据相关联，已经能够阐明大脑中各种计算在何处得以实现。在此，我们回顾社会计算神经科学领域的当前知识状态。迄今为止的研究结果强调，社会决策是由并行进行的多种计算驱动的，并在不同的脑区中得以实现。我们认为，进一步的进展将取决于确定这些变量如何以及在何处进行整合，以便产生连贯的行为输出。
