The adaptability of physiological systems optimizes performance: new directions in augmentation

TL;DR

This paper critiques traditional augmented cognition models and introduces complex systems and physiology concepts to better understand and optimize human performance in human-machine interfaces.

Contribution

It introduces a multidimensional fitness landscape model and a four-step approach for characterizing complex physiological and behavioral systems.

Findings

Comparison of Gaussian and fitness landscape models

Proposal of a four-step model for multivariate systems

Advocacy for an alternative design approach in human-machine systems

Abstract

This paper contributes to the human-machine interface community in two ways: as a critique of the closed-loop AC (augmented cognition) approach, and as a way to introduce concepts from complex systems and systems physiology into the field. Of particular relevance is a comparison of the inverted-U (or Gaussian) model of optimal performance and multidimensional fitness landscape model. Hypothetical examples will be given from human physiology and learning and memory. In particular, a four-step model will be introduced that is proposed as a better means to characterize multivariate systems during behavioral processes with complex dynamics such as learning. Finally, the alternate approach presented herein is considered as a preferable design alternate in human-machine systems. It is within this context that future directions are discussed.