Patzelt Edward H, Hartley Catherine A, Gershman Samuel J
Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA.
Department of Psychology and Center for Neural Science, New York University, New York, NY, USA.
Personal Neurosci. 2018 Oct 18;1:e18. doi: 10.1017/pen.2018.14. eCollection 2018.
This paper reviews progress in the application of computational models to personality, developmental, and clinical neuroscience. We first describe the concept of a computational phenotype, a collection of parameters derived from computational models fit to behavioral and neural data. This approach represents individuals as points in a continuous parameter space, complementing traditional trait and symptom measures. One key advantage of this representation is that it is mechanistic: The parameters have interpretations in terms of cognitive processes, which can be translated into quantitative predictions about future behavior and brain activity. We illustrate with several examples how this approach has led to new scientific insights into individual differences, developmental trajectories, and psychopathology. We then survey some of the challenges that lay ahead.
本文回顾了计算模型在人格、发展和临床神经科学中的应用进展。我们首先描述计算表型的概念，即从拟合行为和神经数据的计算模型中得出的一组参数。这种方法将个体表示为连续参数空间中的点，对传统的特质和症状测量起到补充作用。这种表示的一个关键优势在于它是机械性的：这些参数可以根据认知过程进行解释，进而转化为对未来行为和大脑活动的定量预测。我们通过几个例子来说明这种方法如何为个体差异、发展轨迹和精神病理学带来新的科学见解。然后，我们探讨了未来面临的一些挑战。
