Schaaf Jessica V, Miletić Steven, van Duijvenvoorde Anna C K, Huizenga Hilde M
Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands.
Cognitive Psychology Unit, Institute of Psychology, Leiden University, the Netherlands; Integrative Model-Based Cognitive Neuroscience Unit, Department of Psychology, University of Amsterdam, the Netherlands.
Dev Cogn Neurosci. 2025 Apr;72:101512. doi: 10.1016/j.dcn.2025.101512. Epub 2025 Jan 16.
Computational neuroscience offers a valuable opportunity to understand the neural mechanisms underlying behavior. However, interpreting individual differences in these mechanisms, such as developmental differences, is less straightforward. We illustrate this challenge through studies that examine individual differences in reinforcement learning. In these studies, a computational model generates an individual-specific prediction error regressor to model activity in a brain region of interest. Individual differences in the resulting regression weight are typically interpreted as individual differences in neural coding. We first demonstrate that the absence of individual differences in neural coding is not problematic, as such differences are already captured in the individual specific regressor. We then review that the presence of individual differences is typically interpreted as individual differences in the use of brain resources. However, through simulations, we illustrate that these differences could also stem from other factors such as the standardization of the prediction error, individual differences in brain networks outside the region of interest, individual differences in the duration of the prediction error response, individual differences in outcome valuation, and in overlooked individual differences in computational model parameters or the type of computational model. To clarify these interpretations, we provide several recommendations. In this manner we aim to advance the understanding and interpretation of individual differences in computational neuroscience.
计算神经科学为理解行为背后的神经机制提供了宝贵的机会。然而，解释这些机制中的个体差异，如发育差异，就不那么直接了。我们通过研究强化学习中的个体差异来说明这一挑战。在这些研究中，一个计算模型生成一个个体特异性的预测误差回归量，以模拟感兴趣脑区的活动。所得回归权重的个体差异通常被解释为神经编码的个体差异。我们首先证明，神经编码中个体差异的不存在并非问题，因为这些差异已经在个体特异性回归量中得到体现。然后我们回顾，个体差异的存在通常被解释为大脑资源使用的个体差异。然而，通过模拟我们表明，这些差异也可能源于其他因素，如预测误差的标准化、感兴趣区域之外脑网络的个体差异、预测误差反应持续时间的个体差异、结果估值的个体差异，以及计算模型参数或计算模型类型中被忽视的个体差异。为了澄清这些解释，我们提供了一些建议。通过这种方式，我们旨在推进对计算神经科学中个体差异的理解和解释。
