In Rasmussens paper on ‘Skills, Rules, and Knowledge; Signals, Signs, and Symbols, and Other Distinctions in Human Performance Models’ the author starts by discussing how automation may not seem to rely on human interference for its day to day activities but it is largely dependent on human support as it serves them. He has described the nature of humans to be teleological and explains how humans act as per the purpose they want to achieve which he further proves in his paper.
The author further goes on to discuss how human behavior and performance is categorized as per skill, rule and knowledge. The skill-based behavior is used for situations that require slow and accurate movements and is explained using a few examples on how actions in this scenario are a result of continuous integration. Next, he explains the rule-based behavior as higher in level compared to skill-based behavior such that it is controlled by a procedure which is either a part of instructions or has been formed as a result of certain planning.
The main difference between skill and rule based is that the human is aware of the conscious decisions he makes in the latter one. The third is the knowledge-based which is of the highest order among the three and consists of situations one hasn’t faced before. The goal is formed considering it has been previously tested and the effects have been analyzed thoroughly.
Then the author explains the role of signals, signs and symbols where signals being a part of skill-based behavior are just time-space data without any significance.
Signs being a part of rule-based data, are situational based and are what humans perceive as goals. In order to take higher level action like perform prediction; symbols are used where higher understanding and action is required and they cannot be converted to signs considering their complexity. Information is a conglomeration of all, and it is necessary to keep the data in signal format for better man-machine reliability.
Moving forward he discusses the ways to process mental data such as aggregation, abstraction and analogies and has shown interest in how abstract data can hierarchically be categorized from physical form to functional purpose. What I like specifically about this part is that he has successfully explained how changes along the lower and higher level of models of abstraction can be achieved. He states that for everyday purpose the human generally opts for the top-down approach rather than bottom-up providing a strong ground for his initial statement which states that humans perceive a goal-oriented thinking.
He finally concludes the paper by differentiating between qualitative and quantitative models in which the former is used when the person is facing a knowledge-based situation and the latter when the surrounding is familiar to the human being. I believe the writer has done justice to his findings by proving how man-machine interface can be improved by obtaining a conceptual understanding of humans and their goal-oriented strategy rather than just analyzing data.
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