Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany; Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands; International Max Planck Research School LIFE, Berlin, Germany.
Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany; International Max Planck Research School LIFE, Berlin, Germany.
Dev Cogn Neurosci. 2018 Oct;33:42-53. doi: 10.1016/j.dcn.2017.09.008. Epub 2017 Oct 13.
In recent years, the application of computational modeling in studies on age-related changes in decision making and learning has gained in popularity. One advantage of computational models is that they provide access to latent variables that cannot be directly observed from behavior. In combination with experimental manipulations, these latent variables can help to test hypotheses about age-related changes in behavioral and neurobiological measures at a level of specificity that is not achievable with descriptive analysis approaches alone. This level of specificity can in turn be beneficial to establish the identity of the corresponding behavioral and neurobiological mechanisms. In this paper, we will illustrate applications of computational methods using examples of lifespan research on risk taking, strategy selection and reinforcement learning. We will elaborate on problems that can occur when computational neuroscience methods are applied to data of different age groups. Finally, we will discuss potential targets for future applications and outline general shortcomings of computational neuroscience methods for research on human lifespan development.
近年来，计算建模在研究与年龄相关的决策和学习变化中的应用越来越受欢迎。计算模型的一个优势是，它们可以访问无法从行为中直接观察到的潜在变量。与实验操作相结合，这些潜在变量可以帮助在仅通过描述性分析方法无法实现的特定水平上测试关于行为和神经生物学测量的与年龄相关的变化的假设。这种特异性水平反过来又有助于确定相应的行为和神经生物学机制的身份。在本文中，我们将使用风险承担、策略选择和强化学习的终身研究示例来说明计算方法的应用。我们将详细说明将计算神经科学方法应用于不同年龄组数据时可能出现的问题。最后，我们将讨论未来应用的潜在目标，并概述计算神经科学方法在人类寿命发展研究中的一般缺点。
