How Science Got Sound Wrong

I didn’t even have squeeze your head that time, glubby. Good show!

artemus_5,

"BTW @atdavid. Have you REALLY posted 367 times since Oct 30., 19? That may be a record."

Not even close to a record.

In that same period (October 30, 2019 until now) geoffkait has posted 418 times.

This is worse than watching Fox News, CNN or MSNBC. Get a life! 

Frankly I think the author might be onto something and I’m only judging by what he wrote in some other articles I located somewhere in cyberspace, including this excerpt from one of them. It just sounds right. 

“I’m fascinated by sound baths not just for the quiet, subtle thrills that pure tones give to any breathing human. As a neuroscientist and biophysicist, I spent my career understanding the mathematical theory of how fluid brains interact with vibrating bodies, with the most relevant discoveries published in three research papers (see here, here and here). It turns out that coherent sound patterns can help nervous systems “tune” themselves, in the same general way tuning-forks help experts tune pianos or harps.”

It just sounds right.

Sensory Metrics of Neuromechanical Trust
William Softky
wsoftky@stanford.edu
Bioengineering Deptartment, Stanford University, Menlo Park, CA 94025, U.S.A.
Criscillia Benford
criscillia.benford.gmail.com
Continuing Studies, Stanford University, Menlo Park, CA 94025, U.S.A.
Today digital sources supply a historically unprecedented component of human sensorimotor data, the consumption of which is correlated with poorly understood maladies such as Internet addiction disorder and In- ternet gaming disorder. Because both natural and digital sensorimotor data share common mathematical descriptions, one can quantify our in- formational sensorimotor needs using the signal processing metrics of entropy, noise, dimensionality, continuity, latency, and bandwidth. Such metrics describe in neutral terms the informational diet human brains re- quire to self-calibrate, allowing individuals to maintain trusting relation- ships. With these metrics, we define the trust humans experience using the mathematical language of computational models, that is, as a prim- itive statistical algorithm processing finely grained sensorimotor data from neuromechanical interaction. This definition of neuromechanical trust implies that artificial sensorimotor inputs and interactions that at- tract low-level attention through frequent discontinuities and enhanced coherence will decalibrate a brain’s representation of its world over the long term by violating the implicit statistical contract for which self- calibration evolved. Our hypersimplified mathematical understanding of human sensorimotor processing as multiscale, continuous-time vibratory interaction allows equally broad-brush descriptions of failure modes and solutions. For example, we model addiction in general as the result of homeostatic regulation gone awry in novel environments (sign reversal) and digital dependency as a sub-case in which the decalibration caused by digital sensorimotor data spurs yet more consumption of them.