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  • Application of SRK Framework to Game Mechanics

    [06.17.10]
    - Paul Goodman

  • Previous Applications of SRK Framework

    Since its introduction, SRK Framework has been used by several different industries as a method in which to measure human cognitive level and to assist in determining both how errors occur and measure human performance. It has also been used as a basis for the development of new work models and user interfaces with the goals of decreasing errors and increasing performance.

    Primarily, it has been used by Rasmussen in regards to the potential design and development of new interface systems. In his article "Skills, Rules, and Knowledge; Signals, Signs, and Symbols, and Other Distinctions in Human Performance Models" (1987), Rasmussen discussed the basics of the SRK Framework and the information processing characteristics of each level. In skill-based behaviors, collected information takes shape in the form of signals, or raw information in the form of "direct physical time-space data" (Rasmussen, 1987, p. 294). Signals assist in ensuring that the automated patterns of behavior that take place on a skill-based level, such as riding a bike, progress smoothly, by providing information about the environment. Signs refer to information perceived at the rule-based level, and serve to "activate or modify predetermined actions or manipulations" (Rasmussen, 1987, p.294), as rule-based behavior is commonly guided by procedures that have worked for an individual before. However, signs are limited to the selection or modification of rules controlling a rule-based process; they don't provide for problem-solving, rule development or the performance tasks at a knowledge-based level. Symbols, on the other hand, accomplish this as information perceived at the knowledge-based level; as "abstract constructs related to and defined by a formal structure of relations and processes" (Rasmussen, 1987, p.295), they are used in the problem solving and mental reasoning processes done when encountering an unfamiliar situation. In regards to interface design, Rasmussen proposes that a cognitive model based off of the SRK Taxonomy and how individuals process information through signals, signs and symbols would be beneficial to the development of new interface systems, as such a model would allow for the matching of performance categories to situation types (Rasmussen, 1987, p.296). The method in which information is presented from a complex system's interface needs to be designed in accordance with the tasks needed to be performed by a user, and what cognitive level those tasks may fall on.

    Research into the development of interfaces and cognitively beneficial systems has also further expanded with Rasmussen's collaboration with Kim Vicente in regards to Ecological Interface Design (EID), a "theoretical framework for interface design for complex human-machine systems" (Rasmussen & Vicente, 1992, p. 589). Rasmussen's SRK Taxonomy was as a standard for describing the methods in which people process information, with the levels of the SRK being split into two separate groups. The first of which is analytical problem solving and knowledge-based behavior. The second group is centered on perceptual processing, where skills and rules-based behavior are included. The authors put forth that users of a system built for a complex work domain will have extensive experience and training, and that interface design for complex systems is highly specific into what information an application needs to convey and what it needs to be able to accomplish (Rasmussen & Vicente, 1992, p. 595). Therefore, individuals operating a complex work domain system will engage in skill and rules-based behavior based on their familiarity with the system, and to design interfaces with the goal of taking advantage of an experienced individuals perceptual processing would be beneficial. Analytical problem solving under knowledge-based behaviors can be implemented to deal with problems or events that occur outside of the complex work domain system and are not confined to "specific conditions (e.g., frequently encountered scenarios)" (Rasmussen & Vicente, 1992, p. 599). EID focuses on designing interfaces to boost an experienced individual's ability to complete familiar tasks efficiently at the skills and rules-based level while still maintaining the applicability of knowledge-based behavior should a situation require it.

    SRK Framework has also been used in studies around several different types of industries to determine common errors and human performance. A two-stage study conducted of the aircraft maintenance industry by Hobbs and Williamson (2002) was based off of the hypothesis that a better understanding of the cognitive processing and work routines commonly employed by mechanics and workers in the field of aircraft repair and maintenance will lead to reduced risks to injury and maintenance errors. Using real-time observations of maintenance workers and mechanics, Hobbs and Williamson sought to determine the most common types of errors and accidents that could occur during a normal work day using SRK Framework. Participants were asked by observers at set intervals throughout a work day to briefly describe the activity they were working on, and then rate it according to a familiarity scale:

    How would you rate the task you are doing right now?

    1. The task is very routine. It is proceeding at a normal rate without any problems.
    2. The task is routine, but there are some familiar problems to sort out.
    3. The task is slightly different to normal, or slightly unfamiliar problems are slowing things down a little.
    4. The task is unfamiliar to me, or an unusual problem is slowing down work. (Hobbs & Williamson, 2002, p. 295)

    The individual survey plus the observer's findings resulted in an approximation of what level on the SRK framework the particular activity lay, and if it possibly included behaviors from more than one SRK category. Additional interviews with participants about previous accidents and maintenance errors were also completed as the second stage of the study, breaking down each incident into three separate categories; behavioral, environmental, or equipment related (Williamson & Hobbs, 2002, p. 297). Overall, the information gathered allowed Hobbs and Williamson to determine the percentage of time committed to commonly completed tasks that were carried out on a day to day basis versus a monthly or yearly basis. This also led to their findings that those primarily rule-based and knowledge-based behaviors employed by the average maintenance mechanics could result in "more than twice as many rule-based errors and nearly three times as many knowledge-based errors as skill-based errors" (Williamson & Hobbs, 2002, p. 304). SRK Framework was helpful in this study in showing that focusing on reducing errors that may arise at the rule-based and knowledge-based level may help reduce accidents and incidents in the aircraft maintenance industry, as they appear to have been more common than skill-based errors.

    Similar studies on human error and accidents in workplace environments have also been conducted, with the goal of using SRK Taxonomy as a basis for further models of error-type classification or for proposing organizational improvements. Saurin et al. developed an algorithm (in the form of a detailed flow chart) for classifying the errors and incidents encountered by front-line workers in industrial settings, using SRK Framework as its core concept (Saurin et al., 2008). Two field studies were conducted, one at an oil distribution plant, the other at a manufacturing facility that produced heavy machinery. Information on approximately 40 incidents out of a few hundred documented were gathered and summarized, before being evaluated using the SRK-based algorithm. The data collected suggested that the algorithm could be useful in the development and implementation of specific safety management strategies in an effort to reduce accidents and errors. The SRK Taxonomy could also be used potentially in the development of safety regulations made in response to certain kinds of errors, as "preventive measures will...have different emphasis whether they are focusing skill, rule or knowledge- based failures" (Saurin et al., 2008).

    Overall, the SRK framework has certainly proven to be a versatile tool, however; like any method of measuring human performance it also has its drawbacks, especially regarding its implementation. One obvious issue with the use of SRK taxonomy is the apparent ease of assigning particular levels of cognition contained within the framework to specific activities, without thoroughly supporting the assignment with evidence or hypotheses as to why. Defining the act of typing on a keyboard as a skill based activity without clarifying the criteria used to make that determination, or not providing examples/studies as to why that activity could be classified that way will put the method at risk for a construct validity threat.

    A good example of an assumption lacking support could be found in one of the previous studies mentioned. A small study by Bracco et al. (2008) used SRK Taxonomy as a basis for maintaining resilience in a system of operation. Citing a need for a more proactive organizational system with a focus on safety, Bracco et al. proposed a cognitively resilient model that would "dynamically maintain the circulation in the SRK framework", (Bracco et al., 2008) enabling operators to operate within rules-based behaviors by avoiding automatic, skill-based behaviors when possible. These proposed Skill-based behaviors in the proposed resilience model are best initially passed over in favor of developing rules and knowledge based behavior, which offer more experience and versatility that could possibly be shared with team members. The study conducted by Bracco and their colleagues on emergency room operations (2008) to support their proposal, while giving an example of how SRK Framework can be used to develop new organizational policies for a non-industrial field such as medical care; their study was limited to a single emergency room in a small Italian hospital with a staff of about 5 individuals (1 doctor, 3 nurses and one assistant). Only one emergency room case was observed, regarding the admittance of a 43-year-old Moroccan male with chest pain following a visit to a local police station regarding a fight (Bracco et al., 2008). Each step and the actions taken by the hospital staff are detailed out in their analysis; from the patient's admittance to the ER, to several echocardiograms, physical examinations (which revealed lesions) and blood tests and finally the unusual diagnosis of 'thoracic trauma' and transfer to an intensive care unit (Bracco et al., 2008). The actions of the staff are then broken down into different SRK categories:

    The stretcher-bearers' description and the superficial decision of the hospital assistant contributed to set the SRK profile of the team at the S level, entering a cognitive tunnel that led them to treat this as a routine case... Nobody, including the doctor, moved from their cognitive level at the S stage, since they did not recognize either the ECG or the blood data as signs of an infarction, which required a move to the R level... (Bracco et al., 2008)

    Bracco et al. felt that the hospital staff for the most part reacted at a skill-based level, only moving up to a rules-based level only when medical tests showed that the patient's case was not routine. However, if a researcher is using SRK taxonomy to measure human behavior and performance, additional observations, such as the ones conducted in Hobbs and Williamson's aircraft maintenance study (2002) which involved two separate airlines and at least four times as many participants, are needed to support the categorization of the actions and behaviors observed. While an explanation was offered that the activities of the hospital staff were at the skills-based level due to how "it was quite common to see Moroccans with lesions due to brawls" (Bracco et al, 2008, p. 6) in that particular ER unit, that must be the explanation for the actions of the hospital staff. Yet, since no additional cases are included in the study, the weight of that argument and Bracco et al.'s proposed organizational policies is lessened.

    To summarize, if SRK Framework is going to be used as a method of measuring cognitive behavior, multiple examples or a larger study than the one conducted by Bracco et al. is needed for better support. Also, more clearly defined guidelines as to how one is determining an observed behavior would fall into what level of the SRK Taxonomy is needed.

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