How Science Got Sound Wrong

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.

Exhibit A - Aerospace Engineering, Wikipedia page excerpt

Some of the elements of aerospace engineering are:[16][17]

Abbreviated list for uh, brevity

• Radar cross-section – the study of vehicle signature apparent to remote sensing by radar.
• Fluid mechanics – the study of fluid flow around objects. Specifically aerodynamics concerning the flow of air over bodies such as wings or through objects such as wind tunnels(see also lift and aeronautics).
• Astrodynamics – the study of orbital mechanics including prediction of orbital elements when given a select few variables. While few schools in the United States teach this at the undergraduate level, several have graduate programs covering this topic (usually in conjunction with the Physics department of said college or university).
• Statics and Dynamics (engineering mechanics) – the study of movement, forces, moments in mechanical systems.
• Mathematics – in particular, calculus, differential equations, and linear algebra.
• Electrotechnology – the study of electronics within engineering.
• Propulsion – the energy to move a vehicle through the air (or in outer space) is provided by internal combustion engines, jet engines and turbomachinery, or rockets (see also propeller and spacecraft propulsion).

🔜The basis of most of these elements lies in theoretical physics, such as fluid dynamics for aerodynamics or the equations of motion for flight dynamics. 🔙 There is also a large empirical component. Historically, this empirical component was derived from testing of scale models and prototypes, either in wind tunnels or in the free atmosphere. More recently, advances in computing have enabled the use of computational fluid dynamics to simulate the behavior of the fluid, reducing time and expense spent on wind-tunnel testing. Those studying hydrodynamics or hydroacoustics often obtain degrees in aerospace engineering.

Isn't it a little early for you to be sitting on your face, glubsong?