Knowledge, skills, and mental representations

It can be confounding that there are not necessarily similar specific attributes of research byproducts that achieve a successful R2O transition and become routinely useful in an operational environment. While there are certain elements common to the R2O process and how a product is presented that increase the likelihood of a successful transition, there is no common strategy to pursue to guarantee a winner. Transitioned research byproducts are comprised of scientific knowledge such that they present new information in a way that serves as a basis for making, or at least altering, operational decisions. Knowledge and information alone are a small piece of the “puzzle” for the practitioner. Retention of knowledge and information is difficult, even in the short-term, but it becomes even more challenging if the practitioner has to reason about how to apply it – that is, finding out how it “fits”.

This is abstract because it is quite primitive in that it relates to how humans learn and think. The application of that information requires the practitioner to possess a certain degree of skill with regards to making the relevant decision. In demonstrating that skill, the practitioner will have a certain mental representation of the components and factors relevant to the operational task that he or she is performing. This mental representation is the “puzzle”. Not all practitioners will have the same mental representations. These mental representations will likely vary, at least by experience, education, or the time of entry into the field. This is because scientific fields are continually evolving and the demands for practitioners to perform certain tasks as part of their duties change over time. One major forcing of change to mental representations recently is the substantial increase in data.

For this reason, there should be no expectation that the mental representations of the more experienced or more recently educated are better in some way, or that R2O projects should be focused on practitioners with less experience in order to “improve” them. Experience alone (that is, the cumulative amount of time doing something) is not necessarily proportional to the quality of the tasks that a practitioner performs if that individual has not attempted to improve. While in the modern workplace, there tends to be some deference toward experience, most people have probably encountered that stale colleague that is resistant to change and does a fairly mediocre job, even without some metric to assess whether an experienced colleague is “better” than an ambitious young professional a few years out of school.

While perhaps the best transition projects in R2O are attractive to a wide range of practitioners with varying mental representations, certain projects may be more appealing to those practitioners with mental representations that have gaps, or there is a research byproduct that in some way simplifies the mental representation of practitioners to remove a degree of uncertainty or add clarity. The very best transition projects add value to the expert mental representations because those are the representations that push the functioning boundaries of the field or enterprise. A practitioner with a mental representation that already has a sufficient amount of information to make a confident decision needs little further developing or additional inputs.

Mental representations of tasks, particularly in scientific fields, are built around information from data and research byproducts. And how practitioners use that information in arriving at a particular operational decision (through the practitioner’s mental representation) is not necessarily straightforward, especially when the data space is crowded with many competing pieces of information and no clear way to sort or weigh one thing relative to something else. R2O transition processes must take care to assess and carefully attempt to quantify how the operational decision is changing as a result of a new input. It is not enough to expect additional data or a new research byproduct to lead to an improvement, even if it is more precise or novel in some way compared to existing sources. More information may simply prove onerous to incorporate with certain operational constraints (cost or time among them).

Just as there is great emphasis on creating meaningful research byproducts for practitioners based on scientific work, R2O initiatives should focus on improving the mental representations and skills of practitioners for certain operational tasks that are under scrutiny. This is not a matter of a starting point that is “good” or “bad”, but instead a place where there is an opportunity to be better based on the state of the science. For those expert practitioners, this means becoming the best, and expanding the horizons of the entire enterprise in the process.

Further reading for those interested: There is a book, “Peak: Secrets from the New Science of Expertise”, by Anders Ericsson and Robert Pool, that discusses how deliberate practice builds expertise and how expertise is the result of superior mental representations, almost exclusive of the discipline.

Jordan Gerth

Jordan Gerth

is a research meteorologist with a decade of R2O experience, interacting with academia, the federal government, and the private sector on weather satellite and software projects.
Jordan Gerth

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