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Digital control stations such as airliner cockpits, power stations, and intensive care units must manage large amounts of data. Excessive display information can be distracting and even disorienting for users ultimately nullifying the human-machine interface (HMI). Without effective working memory triggers placed in locations congruent to methods of loci based on personality types, busy systems users must adapt before reacting (Goldstein, 2018).
It may be possible to utilize the “Bayesian inference” to improve the display clutter based on personality requirements (Goldstein, p. 76, 2018). By integrating prior knowledge added to the mental chronometry of encoding, it may be possible to determine retrieval, which could then be factored by the misleading postevent information (MPI), in order to then reach a task event-related potential that would determine the best placement of icons that would serve as umbrella portals for major components (Goldstein, pp. 134, 244, 300). Using the idea of concentric circles and focused attention through the balance of internal and external sustained attention, the system could work on a rolling focus parameter rather than a voxelated platform.
Due to operator response challenges, situational awareness is challenged. In organizations that service high-level functioning data processing such as intensive care units, this is a concern for human factors professionals. Disadvantages of allowing operator-focus-driven design include possible lack of synergy and human error situations leading to terminal situations. This can be considered using the human error views as discussed by Dr. Johan Bergström (2017) whereby errors in daily life can be considered as factors in managing high-risk processes.
“Although SA [situational awareness] is sometimes derived through a conscious deliberative process to form an understanding of what is going on, it is also often based on a highly automatic process of situation recognition, using [the] schema of prototypical situations, that is dynamic and ongoing, whereas sensemaking is characterized as primarily of the conscious deliberative type” (Endsley, 2015).
By creating interferences that result in distractions, users are less likely to reach the full potential of task event relation as time is a factor that limits the availability of information at influx thus, in turn, functionally affecting the expected versus actual output. In the example of an intensive care unit, a nurse focused on a computer patient file loading is less interactive with the reason why the patient requires care. Streamlining these digital control stations would enhance the human-centric design by allowing the focus to create a space for personalized mindfulness lending to a betterment for the species as a whole. A nurse focusing on a patient instead of a rolling computer system would be more apt to respond efficiently to patient needs on a human-to-human level which lends to what makes humanism unique: the ability to share “love.”
References
Bergström, D. J. (2017). Two Views on Human Error. Lund University – Human Factors and Systems Safety. Retrieved December 4, 2022, from https://www.youtube.com/watch?v=rHeukoWWtQ8&t=4s.
Endsley, M. R. (2015). Situation Awareness Misconceptions and Misunderstandings. Journal of Cognitive Engineering and Decision Making, 9(1), 4–32. https://doi-org.ezproxy.libproxy.db.erau.edu/10.1177/1555343415572631
Goldstein, E. B. (2018). Cognitive psychology: connecting mind research and everyday experience (5th ed.).76, 134, 233-300. Wadsworth Cengage Learning.
Human Centric Designs 