Dr. S-J,
I am doing some deep historical and anatomical work into the muscle sense, I am a bodyworker, historian and theorist. I would love a chance to consult with you. Below is a recent presentation on perceptual bias and anatomy for your reference. I am currently working on the kinesiology of proprioception and would love to get your feedback.
Reading Roger Smith’s article “Kinaesthesia and a Feeling for Relations” I am struck by the idea that there may be others like myself that may understand the importance of the body and movement and who may see it as our most valuable adaptive resource in these very challenging times.
I believe there is something that needs to be said (quite clearly and loudly) today and it needs to be spoken by the body somehow. I am writing because I think that it is timely in this situation for like minds to seek each other but also because recent developments in complexity theory, neuroscience and anatomy make a kinaesthetic approach practical and comprehensible.
Professor Kelso mentioned you at an earlier stage of my research. I hope he is well. My research has shifted significantly since I discovered the work of Charles Sherrington, someone historian Roger Smith has written a great deal about. Sherrington’s (1906) Integrative Action of the Nervous System was as revolutionary for the somatic tradition as Einstein (1905) and Planck (1900) were for the mathematical tradition.
Sherrington’s work nearly completely cracks cybernetics wide open to the felt sense. Schrodinger named Sherrington his great example in the “honest search” for the sensations “lacking in a world model from which we have removed our own mental person” ed (1992 p. 119). We are now able to identify the simple anatomy and intuitive kinesiology of those sensations.
Perhaps the solution to the hard problem has always been the felt sense. What has happened since then is that on the one hand our grasp of mathematical theory now affords us artificial intelligence, while on the other hand our grasp of anatomy promises to also afford us access to our own “little ai”- our own inner animal intelligence.
Sherrington has been formative for contemporary neuroscience and psychology and his original perceptual model now plays a central role in the new field of perceptual or Bayesian “mechanics”, a set of mathematical first principles for living systems. I understand none of the math. However if we contrast Karl Friston’s theory of perceptual mechanics with classical or quantum mechanics we see that it offers a powerful and organic framework for reintroducing kinaesthesia to the model.
Both Sherrington and Friston emphasize the action/perception loop. Action is where we can maneuver in updating the theory. The model is a perceptual model, made of three overarching sensory-motor fields. The model goes back to Sherrington’s “neural architecture of the animal as a whole, but needs updating. The muscle sense is the “deep field“ or core of this model, which further divides the body into interoception and exteroception.
By “restoring kinaesthesia to the model” we simply mean to clarify and identify its bony landmarks and intuitive kinesiology. Biomechanically it is the postural system, and was named proprioception by Sherrington to infer active self awareness. Self-awareness is the system’s primary property, just as “other awareness” is the primary function of exteroception.
What would it mean to name something like that for ourselves? What would it mean to return the felt sense to the model? What would it mean when it is integrated over time and becomes common sense?
There are more people now than ever before that might be able to understand what that means, in terms of the felt sense. There is so much interest in a somatic solution and so much global experimentation in movement and perception.
I thought you might have some reflections for me.
I am attaching a recent presentation on the topic and I hope that I hear back from you soon.
Jim Freda
Jim’s talk on Exteroceptive bias at the 2025 Symposium YouTube account
Manual Therapy Panel on Active Inference:

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