Model-Development in Neuroscience: Simplicity and Generalizability in Mechanistic Explanations

神经科学中的解释通常是由机制模型提供的。然而，许多大脑机制包含大量不同的组件，并跨越几个组织层次。因此，神经科学家经常面临一个选择问题：模型中应该包含哪些组件和级别？或者简单地说：在一组相互竞争的多层次机械模型中，哪一个模型是最好的？为了解决这样的选择问题，神经科学家使用或多或少明确的简单性和概括性措施，以及其他标准。简单性的目标问题，如：为了机械地解释一个特定的行为或认知现象，应该包括多少个层次？应该如何准确地描述这些不同层次的组成部分和相互作用？这种简单性的考虑与经典的曲线拟合过程有很大的不同。可推广性涉及的问题包括：一个特定的模型应该适用于多少种现象，以及它应该推广到多少个个体和物种？平衡简单性和可概括性这两个有时相互冲突的双重标准在发展多层次脑机制模型时是至关重要的，本项目的总体研究问题涉及将简单性和可概括性作为神经科学中多层次机制模型发展的标准的规范和实践。 通过几个深入的案例研究的基础上确定这些标准是如何，应该，在当前的神经科学研究中应用，该项目旨在澄清如何开发和选择最好的多层次机制解释。因此，它不仅有助于改善神经科学中机械解释的哲学概念，而且还为当前的科学研究提供规范性指导方针。更具体地说，本项目的目标有四个方面：（1）总体而言，发展一种改进的神经科学多层次机制解释的哲学解释，强调简单性和概括性作为解释充分性的标准。(2)阐明一个简单的帐户作为评估多层次的机械解释的解释充分性的标准。(3)阐明在机械解释实践中对概括性的改进理解。(4)最后，一个科学的目标是阐明规范的指导方针，模型开发和模型选择，用于未来的神经科学研究。

Explanations in neuroscience are often delivered by models of mechanisms. However, many brain mechanisms contain a large number of distinct components and span several levels of organization. As a consequence, neuroscientists often face a choice problem: Which components and levels should be included in a model? Or simply: Which model is the best one among a set of rival multi-level mechanistic models? In order to resolve such choice problems, neuroscientists use more or less explicit simplicity and generalizability measures, among other criteria. Simplicity targets questions such as: How many levels should be included in order to mechanistically explain a particular behavioral or cognitive phenomenon? How precisely should the components and interactions of these various levels be described? Simplicity considerations of this kind are of a very different nature than classical curve-fitting procedures. Generalizability concerns questions such as: To how many phenomena should a particular model apply, and over how many individuals and species should it generalize? Balancing the sometimes conflicting dual criteria of simplicity and generalizability is of paramount importance when developing models of multi-level brain mechanisms.The overall research question of this project concerns the norms and practices by which simplicity and generalizability are applied as criteria for the development of models of multi-level mechanisms in neuroscience. By determining on the basis of several in-depth case studies how these criteria are, and should be, applied in current neuroscientific research, this project aims to clarify how the best multi-level mechanistic explanations are developed and selected. Thus, it is expected to not only contribute to an improved philosophical conception of mechanistic explanation in neuroscience, but to also deliver normative guidelines for current scientific research. More concretely, the aims of this project are fourfold: (1) Generally, to develop an improved philosophical account of multi-level mechanistic explanation in neuroscience, with an emphasis on simplicity and generalizability as criteria of explanatory adequacy. (2) To articulate an account of simplicity as a criterion for assessing the explanatory adequacy of multi-level mechanistic explanations. (3) To articulate an improved understanding of generalizability in mechanistic explanatory practice. (4) Finally, a scientific objective is to articulate normative guidelines for model-development and model-selection to be used in future neuroscientific research.