A Theoretical Comprehensive Framework for the Process of Theories Formation

Area of Science:

• Cognitive Science
• Computer Science
• Scientific Methodology

Background:

• Scientific theorizing increasingly relies on computational tools like data mining and artificial intelligence (AI).
• These technologies identify association rules, forming the basis for scientific theories.
• A gap exists in understanding the neurocognitive underpinnings and limitations of AI-driven theorizing.

Purpose of the Study:

• To propose a comprehensive computational framework for technology-based scientific theorizing.
• To contextualize technological aids within the neurocognitive processes of theorizing and association rule identification.
• To address limitations in current fragmented approaches to AI-assisted scientific discovery.

Main Methods:

• Decomposition of the theorizing process into underlying neurocognitive components.
• Analysis of current technological expansions and limitations for each component.
• Development of a computational framework integrating these components.

Main Results:

• Identification of key neurocognitive components involved in scientific theorizing.
• Evaluation of the benefits and limitations of existing data mining and AI methods.
• Proposal of a unified computational framework to enhance AI-assisted theorizing.

Conclusions:

• A comprehensive framework is needed to bridge neurocognitive processes and computational tools in scientific theorizing.
• Understanding the assumptions and limitations of AI is crucial for reliable association rule discovery.
• The proposed framework aims to improve the integration and effectiveness of AI in scientific discovery.