Simmering Vanessa R, Schutte Anne R, Spencer John P
Department of Psychology, University of Iowa, USA.
Brain Res. 2008 Apr 2;1202:68-86. doi: 10.1016/j.brainres.2007.06.081. Epub 2007 Jul 26.
Within cognitive neuroscience, computational models are designed to provide insights into the organization of behavior while adhering to neural principles. These models should provide sufficient specificity to generate novel predictions while maintaining the generality needed to capture behavior across tasks and/or time scales. This paper presents one such model, the dynamic field theory (DFT) of spatial cognition, showing new simulations that provide a demonstration proof that the theory generalizes across developmental changes in performance in four tasks-the Piagetian A-not-B task, a sandbox version of the A-not-B task, a canonical spatial recall task, and a position discrimination task. Model simulations demonstrate that the DFT can accomplish both specificity-generating novel, testable predictions-and generality-spanning multiple tasks across development with a relatively simple developmental hypothesis. Critically, the DFT achieves generality across tasks and time scales with no modification to its basic structure and with a strong commitment to neural principles. The only change necessary to capture development in the model was an increase in the precision of the tuning of receptive fields as well as an increase in the precision of local excitatory interactions among neurons in the model. These small quantitative changes were sufficient to move the model through a set of quantitative and qualitative behavioral changes that span the age range from 8 months to 6 years and into adulthood. We conclude by considering how the DFT is positioned in the literature, the challenges on the horizon for our framework, and how a dynamic field approach can yield new insights into development from a computational cognitive neuroscience perspective.
在认知神经科学领域，计算模型旨在在遵循神经原理的同时，深入洞察行为的组织方式。这些模型应具备足够的特异性以产生新颖的预测，同时保持跨任务和/或时间尺度捕捉行为所需的通用性。本文介绍了这样一种模型，即空间认知的动态场理论（DFT），展示了新的模拟结果，这些模拟结果提供了一个论证证明，该理论能够推广到四项任务（皮亚杰A非B任务、A非B任务的沙盒版本、典型空间回忆任务和位置辨别任务）中表现的发展变化。模型模拟表明，DFT既能实现特异性（产生新颖、可测试的预测），又能实现通用性（跨越发展过程中的多个任务），且只需一个相对简单的发展假设。至关重要的是，DFT在不改变其基本结构且坚定遵循神经原理的情况下，实现了跨任务和时间尺度的通用性。在模型中捕捉发展所需的唯一变化是感受野调谐精度的提高以及模型中神经元之间局部兴奋性相互作用精度的提高。这些微小的定量变化足以使模型经历一系列跨越从8个月到6岁直至成年的年龄范围的定量和定性行为变化。我们通过思考DFT在文献中的定位、我们框架面临的挑战以及动态场方法如何从计算认知神经科学的角度为发展带来新的见解来结束本文。
