The INFJ has a desire to truly understand. It’s not enough to just learn by rote, we are divergent creatures by nature and need to know how all the pieces of a given problem fit together and what those connections mean within the context of the whole.
Failing frequently, without apparent improvement is not a particularly efficient way to learn. Before the journey of edification has properly gotten underway, repeated failures could signal to both the individual and the teacher imparting the knowledge, a lack of aptitude and potential. But, the fault may not necessarily be found in the failure itself. This could be a result of not being able to comprehend a given concept, rather than a failure to understand it.
To not understand generally means that one has not grasped what the final result should be, but to not comprehend is a lack of understanding of how or why those results have or should be achieved.
In order to progress effectively the INFJ learner needs to be able to build up a broad perspective by taking several metaphorical bites of the ‘apple’, so to speak, from various locations to get a proper sense of what ‘appleness’ can potentially be, before fully comprehending what an apple actually is.
A rudimentary association is investigated from the inside out. Seeds, core, starchy flesh, skin, leafy stalk, branch, tree, wood, desk, teacher, apple…
This happens in the background, but can often be sensed in an abstract way. The information is perceived as a feeling rather than a thought.
As the data is processed it’s compared and contrasted with previous experiences.
After adequate scrutiny of the phenomena has taken place it’s constituent parts are threaded together into a larger network, like data nodes in a constellation, primed for further integration.
Less intuitive sensing types usually have an inclination towards a step by step sequential mode of filtering information. INFJ’s on the other hand tend to bounce around from one point to another in a non-linear fashion testing options and possibilities before arriving at fixed conclusions. This can prove problematic in the short term as this approach is less energy efficient and can be much slower.
One advantage the system does have, is that it’s always on, chugging along in the background meticulously sifting through the data and scanning for patterns. →
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