Transformational Learning

Building on the ideas in The Suffering of Expectations, I want to look more closely at the expectation gradients. These are predictions of future experiences, and they can have negative or positive values. Negative values indicate that I expect a future that is worse for me than it is now, and positive values are the opposite: I expect the future to be better than the present. The steeper the slope, the more dramatic the future outlook. Worse outcomes look catastrophic, and better outcomes promise pure bliss. The gentler slope leads to a slightly worse or slightly better future. The way I like to imagine it is gauging how quickly a murky lake gets deep as I wade into it.

Expectation gradients are shaped by my previous experiences. My mind subconsciously sifts all of my past experiences, finds–or synthesizes!–the best match and this match now becomes the expectation gradient. So, if I had a really terrible experience and the situation appears to match the beginning of that experience, I will feel a steep negative expectation gradient — whoa, that lake bed is dropping away fast! Conversely, if the present appears to match the start of a mild or pleasant experience, I will feel a gentle or upward-sloping gradient.

Sifting through all past experiences can be expensive, and I am not blessed with a source of infinite energy, so there’s an optimization process at play that relies on prediction errors. Each prediction is compared with the actual outcome, and a prediction error is computed. Prediction errors are a signal to organize my past experiences. Lower prediction errors reinforce the value of the experience used to make the prediction. Higher errors weaken that value.  This continuous process fine-tunes how my mind makes predictions. Higher-valued past experiences are looked at first, as they are more likely to repeat. The experiences with the lower value are gently pushed to the bottom. This process of ranking allows my mind to work more efficiently: skim the top hits, and ignore the rest. Energy saved! Another word for this optimization process is informational learning: every bit of new information is incorporated to improve my ability to make accurate predictions.

At this point, I want to introduce the concept of prediction confidence. I continue to have experiences, and they fuel the learning. Ideally, this process results in effective predictions: a clear winner of a prediction for every situation. A less comfortable situation happens when there does not seem to be a clear winner. Here, matching past experiences to the present produces not one, but multiple predictions that vary in their slope. Expanding the wading-into-lake analogy, it feels like even though I took the same exact path, the lake bed had a different shape at times. Most times, it had a nice and gentle slope, but every so often, the same exact bed somehow felt steeper. I swear, it’s like the lake bed had shifted! Now that’s a puzzler.

To reflect on the nature of prediction confidence, consider the framing of complexity of the environment. If the environment is simple, then my experiential journey quickly produces a perfect map of this environment and I am able to make exact, 100% confidence predictions. If this then that — bam! In a simple environment, the list of my experiences wading into the lake has only one item, because it repeats every time with clockwork precision.

The more complex the environment, the more fuzzy the prediction matching. If the environment is highly complex, I may find myself in a situation where I have near-zero confidence predictions: every situation might as well be brand new, because I can’t seem to find a match that isn’t the whole set of my experiences. Walking into the lake is a total surprise. Each time, I find a seemingly differently-shaped lake bed. What the heck is going on?

In such an environment, energy-saving optimizations no longer seem to work, and if anything, hinder the progress. It’s clear that something is amiss, but the existing machinery just keeps chugging away trying to build that stack rank — and failing. How can the stupid lake bed be so different… Every. Fricking. Time?!

This crisis of confidence is an indicator that it’s time for change, for another kind of learning. Unlike informational learning, which is all about improving my ability to make predictions within a situation, the process of transformational learning is about uncovering a different way to see the situation. The outcome of transformational learning is a profound reevaluation of how I perceive the environment. It is by definition mind-boggling. Transformational learning feels like discovering that all this time, when I was feeling the lake bed shift under my feet, I was actually only perceiving my own movement along one axis. I was assuming a two-dimensional space, unaware that there’s another dimension! Whoa. So the lake bed isn’t moving. Instead, I wasn’t accounting for my own movements across the shore. If I incorporate the “lake shore” axis, all of these past experiences suddenly snap into a static, three-dimensional map of a lake bed.

Transformational learning is a rearrangement of my past experiences into a new structure, a new way to organize them and produce a whole different set of predictions. Also necessarily, the letting go of the old way and the acceptance of the uncertainty that comes with that. A three-dimensional map of the lake bed represents the environment more usefully, but it is also more complex, allowing for more degrees of freedom and requiring more energy to operate. Another long journey of informational learning awaits to optimize my prediction-making machinery and turn this novel perspective into a familiar surrounding — until the next transformation time.

Whenever I get that sense of the shape-shifting lake bed, in these “what the heck just happened, this is wrong!” moments, I take comfort in the notion that transformational learning awaits. Though it might not offer immediate insight right then and there, this movement of the surface, a seemingly exogenous change is a signal. It tells me that I am approaching yet another edge of my current understanding of the environment, and a new perspective beckons to be revealed.

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