Sensory Metrics of Neuromechanical Trust
William Softky, Criscillia Benford

TL;DR
This paper develops a mathematical framework to understand human sensorimotor processing and trust, linking digital media consumption to potential long-term brain decalibration and proposing metrics to improve well-being and social bonding.
Contribution
It introduces a novel multiscale, continuous-time vibratory model of neuromechanical trust using signal processing metrics, connecting digital dependency to brain decalibration and social effects.
Findings
Digital media affects sensorimotor trust through entropy, noise, and bandwidth metrics.
Decalibration from digital data can lead to addiction and maladaptive behaviors.
Low-latency, multisensory interactions promote bonding and well-being.
Abstract
Today digital sources supply an unprecedented component of human sensorimotor data, the consumption of which is correlated with poorly understood maladies such as Internet Addiction Disorder and Internet Gaming Disorder. This paper offers a mathematical understanding of human sensorimotor processing as multiscale, continuous-time vibratory interaction. We quantify human informational needs using the signal processing metrics of entropy, noise, dimensionality, continuity, latency, and bandwidth. Using these metrics, we define the trust humans experience as a primitive statistical algorithm processing finely grained sensorimotor data from neuromechanical interaction. This definition of neuromechanical trust implies that artificial sensorimotor inputs and interactions that attract low-level attention through frequent discontinuities and enhanced coherence will decalibrate a brain's…
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