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Part of a Model 1 series


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ES is a compound function in Model 1, created by the combined activity of Ne->Si or "Ne in service to Si." ES has the same general attitudes as Se (E+S), but is coming from Ne-Si's metabolism. ES is the sub-primary function of the NeFi and NeTi types and is present in all Alpha and Delta types.


See also: Inter-Function Dynamics (Model 1)

The ES compound is produced when Ne's exploration, as a Pe function, is directed towards correlating a series of Si recalls in real-time. What results is an environmental savviness which is nonetheless coming from the rapid access of prior Si catalogs, rather than a truly real-time intake of the uniquely vivid moment. When Ne directs its attention to the navigation of Si's information recall, the Ne user will be able to navigate discrete information in real-time, but only within the bounds of what has already been mapped by Si. For example, Ne->Si may lead to an excellent athletic navigation of reality, which requires real-time adaptability (Pe), using the rapid recall from Si to output the moves of the game. However, this dynamic navigation will tend to recycle the moves already known, and respond to the moves already cataloged, rather than meet the literal present as a completely fresh situation with a generalized approach the way Se does.

Behavioral Differences between ES and Se

The following are behaviors commonly manifested by Ne in service to Si.

ES: Real-Time Anecdotal Recall

When Ne is put in service to the recall of Si, the literal attitude of Si is retained, not Ne's associative attitude. Thus, only Pe metabolism is added to Si, giving us a real-time exploration of Si's literal and discrete information landscape. Ne's exploration of literality must pass through Si first, making it so Ne needs to first catalog literal details before it can "surf" them effectively. Thus, Ne will not be very good at the real-time navigation of a literal situation for the first time. However, after enough practice, Si will have stored the necessary information for Pe to navigate the real-time context while not making any miss-steps. This learned expertise can be applied to any domain including music, dance, people reading, sports or any rapid, information-rich activity. However, as soon as the Ne user steps into unknown territory, only Ne is left to explore it, causing them to resort to divergent exploration. Within their domain of knowledge, the ES user can flow and play fluidly in the present moment. But as soon as the ES user reaches the edge of their Si map, Ne's improvisations take over, causing them to try things in random combinations to see what sticks. This can lead to miscalculations that seem out of character with their expertise, because their expertise is specific (Si) rather than generalized (Se).

Of course, Se users also gain expertise with practice, but for them this happens with far more broad application. Ni's thematic data sotrage is able to help Se generalize its approach in such a way that the knowledge gained from one literal proficiency can transfer over to other non-identical disciplines with comparably less loss. With ES, Si's expertise is far more domain-restricted, causing the learning curve to be steeper upon jumping to a new territory, even if a largely adjacent one.

ES: (pending)


ES: (pending)