Philosopher Mazviita Chirimuuta discusses simplification in neuroscience, highlighting the use of models, math, and analogies to understand the complex brain. She explores the intersection of neuroscience and philosophy, delves into simplification strategies in science, and emphasizes the interplay of technology and scientific understanding. The discussion touches on the challenges of interpreting scientific results, the limitations of reductionism, and the importance of maintaining a critical mindset in scientific pursuits.
Computational models in neuroscience serve as simplifying strategies, not direct representations of brain processes.
Analogies and metaphors aid in interpreting complex systems like the brain by simplifying key relationships and concepts.
The historical role of technology preceding science highlights the importance of simplification and abstraction in scientific research.
Deep dives
Simplifying Strategies in Neuroscience
The podcast episode explores the need for caution in interpreting the results of neuroscience, focusing on the computational model of perception as a convenient simplifying strategy rather than a direct representation of brain processes. It emphasizes the role of technology as a stepping stone in understanding complex systems, highlighting the importance of abstraction and simplification in scientific research.
Analogies and Metaphors in Science
Analogies and metaphors play a critical role in scientific interpretation, particularly in relating complex biological systems like the brain to simpler, well-understood technologies such as computers. The use of analogies as simplifying strategies allows scientists to highlight key relationships and concepts, even though the analogies themselves may not fully capture the complexity of the system being studied.
Theoretical Frameworks and Technology in Science
The discussion delves into the interplay between theoretical frameworks and technological artifacts in scientific research, highlighting how understanding often emerges from the study of simplified systems or technologies. The historical context of technology preceding science is explored, underscoring the role of controlled systems in advancing scientific knowledge across various disciplines.
Complexity and Context Sensitivity in Biological Systems
The episode delves into the complexity and context sensitivity of biological systems like plants, emphasizing how factors like behavioral plasticity and environmental variability impact scientific study. It poses questions about the adaptability of organisms in unconstrained ecologies compared to controlled laboratory environments, showcasing the unique challenges presented by complex systems in scientific research.
Understanding Dynamical Systems Theory in Neuroscience
The podcast episode delves into the modern boom of dynamical systems theory in neuroscience, highlighting its importation from physics. The speaker expresses interest in a more holistic, process-based perspective in studying brain systems, indicating uncertainty about whether dynamical systems theory fully aligns with this approach.
Limitations of Inductive Knowledge in Studying Brain Complexity
The episode explores the challenges of studying the brain's complex and ever-changing nature. It discusses how the brain's continual adaptation to experiences makes it difficult to predict future outcomes based on past data. The conversation touches on the limitations of inductive knowledge in understanding changeable, complex systems, emphasizing the importance of acknowledging the brain's inherent variability and responsiveness.
She largely argues that when we try to understand something complex, like the brain, using models, and math, and analogies, for example - we should keep in mind these are all ways of simplifying and abstracting away details to give us something we actually can understand. And, when we do science, every tool we use and perspective we bring, every way we try to attack a problem, these are all both necessary to do the science and limit the interpretation we can claim from our results. She does all this and more by exploring many topics in neuroscience and philosophy throughout the book, many of which we discuss today.
0:00 - Intro
5:28 - Neuroscience to philosophy
13:39 - Big themes of the book
27:44 - Simplifying by mathematics
32:19 - Simplifying by reduction
42:55 - Simplification by analogy
46:33 - Technology precedes science
55:04 - Theory, technology, and understanding
58:04 - Cross-disciplinary progress
58:45 - Complex vs. simple(r) systems
1:08:07 - Is science bound to study stability?
1:13:20 - 4E for philosophy but not neuroscience?
1:28:50 - ANNs as models
1:38:38 - Study of mind
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