The adaptability of physiological systems optimizes performance: new directions in augmentation
| dc.creator | Alicea, Bradly | |
| dc.date | 2008-10-27 | |
| dc.date | 2008-11-11 | |
| dc.date.accessioned | 2026-07-07T10:17:02Z | |
| dc.date.available | 2026-07-07T10:17:02Z | |
| dc.description | 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. | |
| dc.description | 12 pages, 7 figures, 1 table (review, theoretical overview paper) | |
| dc.identifier | https://arxiv.org/abs/0810.4884 | |
| dc.identifier | http://arxiv.org/abs/0810.4884 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/173715 | |
| dc.subject | Human-Computer Interaction | |
| dc.subject | Neural and Evolutionary Computing | |
| dc.title | The adaptability of physiological systems optimizes performance: new directions in augmentation | |
| dc.type | text |