The Applied Neurophysiology Laboratory, Rambam Health Care Campus, Haifa, Israel.
Comput Intell Neurosci. 2021 Nov 28;2021:5074913. doi: 10.1155/2021/5074913. eCollection 2021.
Scientists rely more and more upon computerized data mining and artificial intelligence to analyze data sets and identify association rules, which serve as the basis of evolving theories. This tendency is likely to expand, and computerized intelligence is likely to take a leading role in scientific theorizing. While the ever-advancing technology could be of great benefit, scientists with expertise in many research fields do not necessarily understand thoroughly enough the various assumptions, which underlie different data mining methods and which pose significant limitations on the association rules that could be identified in the first place. There seems to be a need for a comprehensive framework, which should present the various possible technological aids in the context of our neurocognitive process of theorizing and identifying association rules. Such a framework can be hopefully used to understand, identify, and overcome the limitations of the currently fragmented processes of technology-based theorizing and the formation of association rules in any research field. In order to meet this end, we divide theorizing into underlying neurocognitive components, describe their current technological expansions and limitations, and offer a possible comprehensive computational framework for each such component and their combination.
科学家越来越依赖计算机化的数据挖掘和人工智能来分析数据集和识别关联规则，这些规则是不断发展的理论的基础。这种趋势可能会扩大，计算机智能可能会在科学理论化中发挥主导作用。虽然不断进步的技术可能会带来巨大的好处，但在许多研究领域拥有专业知识的科学家并不一定非常透彻地理解不同的数据挖掘方法所依据的各种假设，这些假设首先对可以识别的关联规则构成了重大限制。似乎需要一个全面的框架，该框架应在我们的理论化和识别关联规则的神经认知过程的背景下呈现各种可能的技术辅助手段。这样的框架有望用于理解、识别和克服当前基于技术的理论化过程以及任何研究领域中关联规则形成的局限性。为了达到这个目的，我们将理论化分为潜在的神经认知成分，描述它们当前的技术扩展和局限性，并为每个这样的成分及其组合提供一个可能的全面计算框架。
