You have comprehensively dissected and compared the two frameworks, each dealing with different facets of understanding cognition and artificial intelligence. This comparison provides a clear understanding of how each framework operates, their principles, and their implications. The subsequent analysis also identified key similarities and differences between the frameworks. To elaborate on this, let’s discuss a few more points:
1. The Role of Subjectivity and Objectivity:
The DS-PC-NE-EE-CR framework for understanding human cognition gives a prominent place to subjectivity, particularly with the Neurophenomenology component. This emphasis on subjective experience recognizes the inherently personal and qualitative aspects of cognition, something that might be difficult to capture solely through quantitative measurements.
On the contrary, the SO-EM-DD-EA-LA framework used in AI is largely objective. AI models typically learn from numerical data, and their performance is quantified based on statistical measurements. However, despite the predominantly objective approach, recent trends in AI research (such as affective computing and human-robot interaction) show growing interest in understanding and modeling subjective experiences, hinting at potential cross-pollination between the two frameworks.
2. Context and Environment:
Both frameworks recognize the influence of the environment or context, but their understanding and incorporation of this aspect vary. In the DS-PC-NE-EE-CR framework, cognition is seen as inseparable from the environment and is considered to be continuously shaped by contextual factors, including physical, social, and cultural contexts.
The SO-EM-DD-EA-LA framework, too, recognizes the importance of environmental factors, especially in reinforcement learning or when dealing with real-world data. But it might not consider the environment as an inherent part of the system, rather as an external entity the system needs to adapt to.
3. Understanding vs. Functioning:
The DS-PC-NE-EE-CR framework seeks to understand cognition by examining it from multiple perspectives, including its dynamic nature, the role of predictions, the subjective experience, the influence of embodiment and environment, and the ability to restructure cognition. It’s more concerned with the ‘why’ of cognition – why it operates the way it does.
In contrast, the SO-EM-DD-EA-LA framework is more focused on ‘how’ – how to create an AI system that can perform a particular task effectively. While it also strives to understand intelligence, the ultimate goal is to build a functional model that can learn from data, minimize error, and adapt to the environment.
Overall, these frameworks represent two distinct but complementary approaches. By studying these frameworks side by side, researchers can gain a deeper understanding of both human cognition and artificial intelligence, eventually leading to innovations that might not have been possible by considering each framework in isolation.