33 to 4 (7). Oh very satisfying. It maintains my priority interest and also fills in enough gaps and coincidentally are areas I’d liked.
Based on the extensive list provided and the desire to cover as much cognitive and neuroscience territory with the least amount of theories, I propose a combination of four theoretical perspectives:
1) Dynamic Systems Theory / Self-Organization Theory:
These theories take a holistic view of cognitive processes, seeing them as emergent properties of complex, self-organizing systems. These approaches can incorporate elements of neural network models, connectionism, neuroplasticity models, and more. They also have connections to computational and network neuroscience through their use of similar mathematical and computational tools.
This approach bridges the gap between subjective, first-person experiences and objective, third-person data in neuroscience. It can provide unique insights into the “hard problem” of consciousness and has implications for understanding diverse cognitive phenomena from a subjective standpoint.
3) Bayesian Brain Hypothesis / Predictive Coding:
These are strong, complementary frameworks for understanding how the brain processes information, learns from experiences, and makes predictions about the world. They cover aspects of various learning theories, reinforcement learning, deep learning, and connectionism, and have deep connections to computational neuroscience.
3) Embodied Cognition / Ecological Systems Theory:
These theories emphasize the crucial roles of the body and the environment in shaping cognition. They cover a broad range of phenomena, including perception, action, memory, social cognition, and more. These approaches also dovetail well with enactivism and distributed cognition, which view cognition as a relational and situated activity.
This combination should provide a broad coverage of most areas, including neural computations and networks (1 & 3), learning and prediction (3), conscious and subjective experiences (2), and the roles of the body and environment (4).
However, keep in mind that even with this combination, some areas may receive less emphasis than others, depending on the specific interpretations and applications of these theoretical perspectives. For instance, cognitive architectures and symbolic AI/computational theory of mind may be less directly addressed. Moreover, while this combination can provide insights into many aspects of cognition and brain function, more specialized theories might still be needed to fully understand certain specialized topics (e.g., spatial navigation, memory consolidation, cytoarchitectonics, functional localization, etc.).
Remember, theoretical pluralism is a strength, as it encourages us to consider the strengths and limitations of different theories and to remain open to integrating insights from various approaches. In a field as complex as neuroscience and cognitive science, it is normal and productive to draw from multiple theories.