Escaping the Herd with Cognitive Load Theory

Issue #16 October 28 2019

It’s Monday and that means it is time to invest in our cognitive capacity so that we can win the day, week, and finish the month of October strong as we round the corner to the final 60 days of 2019.

Every Algorithm needs a Humarithm

One of my favorite futurists/thought leaders Gerd Leonhard coined the word humarithm and centers a bunch of his work and research around the future of technology vs humanity. Humarithms differ from algorithms in Gerd’s summary slide below.

Algorithms and machine learning are intended to assist in the reduction of our cognitive load in sensory and working memory processing, but left unchecked without conscious intention (development of our humarithms) we can forget to use this increased bandwidth to actually “think” or learn new things. This leads to us becoming too reliant/addicted to the feed and our existing biases/schemas. As an investor, this can cause us to miss the arbitrage opportunities and as someone seeking alpha returns from the market in life and business, it can also make it harder to “escape the herd”.

Learning Faster with Cognitive Load Theory

Cognitive Load Theory (CLT) is a toolkit developed and published over 30 years ago by John Sweller, et al in 1988, that you can use to reduce your cognitive load and increase your learning power to balance out the impact of our present-day sensory overload reality and absorb new ideas and context more fluidly.

CLT helps you design a personalized rapid-learning method that reduces the demands on your working memory so that they learn more effectively. Working memory is the place where your brain works with a variety of new sensory inputs and decides whether to discard them immediately or move them into a bundle/schema that can begin to be stored in long-term memory through the activity of repetition. You can apply the concept of cognitive load to learning and training in several ways.

1) Measure expertise and adapt the presentation of new information-It’s ok to be “dumb” despite what we were conditioned to believe through 14,000 hours of our K-12 education. Learning requires us to move through unconscious incompetence (we don’t know what we don’t know) to unconscious competence (we can do without thinking).. Schemas (or paradigms) are packets of complexity that our mind has distilled into a unit of “1” piece of “knowing” that reduces the load on our working memory so that we can ingest the new thing (unknown) and contextualize or connect the dots to things we already “know”.

Sometimes at this phase, we have zero prior knowledge (or schemas) to work with so measuring our expertise (or lack thereof) in an area provides us a good baseline of how much further we need to go back into the basics to establish the proper schemas (context) to ingest the new idea. As an example, that is timely, if you don’t understand or have schemas related to how fractional reserve banking or the current monetary system was set up, is administered, and etc, then it is difficult to fully understand the power and possibility of a decentralized monetary system like Bitcoin. Therefore you should test your current understanding of the existing monetary system as you learn about the potential new scenarios.

“Complexity is the enemy of execution” ~ Tony Robbins

2) Reduce the problem space- with complex problems/systems (like the financial markets or global economy today), the learner needs to work backward from their desired goal to the present state. Doing this requires you to hold a lot of information in your working memory simultaneously. Focusing on the goal also takes attention away from the information being learned, which makes learning less effective.

A better approach is to break the problem down into parts (reverse engineer backward from the goal to learn and build schemas for each layer of complexity) as a way to find simplicity on the other side of this complexity. Reducing the problem space lightens the cognitive load, making learning and the building of new schemas (i.e. cognitive short cuts) more effective.

3) Reduce the Split-Attention effect-When learning new things the load on working memory is harmed when you have two or more visual or audio stimuli to contend with at once. For example. If you are looking at a diagram or chart, then having a narrative over top of it will actually be better than trying to read a manual about it in parallel. Vice-Versa if you are listening to an audio podcast, you will want to reduce related background noise (traffic noise, side-conversations, etc) to preserve cognitive load. Failure to reduce these avoidable cognitive loads makes it more difficult to create new schemas as rapidly.

4) Use auditory and visual channels simultaneously to extend working memory capacity- You can overcome the drain on cognitive load from split-attention theory to combine audio and visual channels because each has its own working memory capacity.

In a 1998 study by Mayer and Moreno, for example, students were found to learn most effectively when they were shown an animation that was accompanied by narration, rather than the using same animation with added on-screen text.

Learners (not the learn-ed) will inherit the future

Your and my ability to design rapid-learning practices using proven frameworks like CLT to design our learning architecture is a requirement for the faster future we are living and investing into each day. Be willing to embrace our constraints versus resist them. Be willing to leverage CLT as a tool to hack those constraints. Be willing and excited to be temporarily “dumb” and be motivated that all of this effort will help you escape the herd that blindly follows the status quo into a financial slaughter.