Non-Conscious Knowledge Affects Our Learning And Performance
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How Non-Conscious Knowledge Affects Our Learning And Performance

Here’s a clue: Most of our thinking and behavior is automated. Automated means it’s non-conscious or in other words, on autopilot. It’s extremely difficult to change automated and non-conscious thinking and behavior because we cannot directly access it.

For example, when driving to work, we often get there without even thinking. Automating behavior makes sense as it reduces the effort required. But it also means we can end up at work when we meant to go somewhere else.

What DO We Know?

Being on autopilot has a lot of implications for learning and performance. Recently, Guy Wallace (@guywwallace on Twitter) posted about experts having difficulties figuring out what people must learn to perform a task. But experts often unintentionally leave things out. Their performance is highly automated so they no longer have conscious access to exactly what they are doing.

Automated and non-conscious prior knowledge is stored in long-term memory. An expert’s deep prior knowledge makes them far more capable of solving difficult problems in their area of expertise. But because it’s automated and non-conscious, they’re often unaware of exactly what they are doing.

Some guy pointed me to Richard Clark’s article, The Impact of Non-Conscious Knowledge on Educational Technology Research and Design. And this article turned out to be a goldmine of important information. Experts, research finds, tend to be conscious of the physical actions they take, as well as the knowledge they use. But they are much more unaware of the mental activities used to perform tasks and solve problems.

Why Are Mental Processes Automated?

Clark points out that we have overwhelming evidence that non-conscious processes guide a great deal of our learning and performance. He says that as adults, we are only consciously aware of about 30% of the workings of our thinking and knowledge. The rest, he tells us, is largely non-conscious. Much of what we know and use becomes automated and non-conscious over time.

The reason that our usable knowledge is largely non-conscious and automated is that type of knowledge makes it easier to perform well. Working memory has considerable limitations but must process information from the environment and from long-term memory. If we had to process everything we think about and do (through working memory), working memory would be overloaded a lot of the time. Automated and non-conscious knowledge doesn’t use this limited resource. Which frees it up for other thinking and doing that does use working memory, such as learning.

Implications Of Largely Hidden (Non-Conscious) And Automated Knowledge

Here are a few of the major implications of having much of our long-term knowledge base largely non-conscious and automated:

Implication Description
Attitudes And Beliefs We unavoidably have non-conscious beliefs and biases, and they heavily influence how we see things and the decisions we make. You may think you know what your beliefs and biases are, but your real ones are non-conscious.
Misconceptions Misconceptions are hard to change and they inhibit learning and understanding. They are largely automated and non-conscious which makes them difficult to change. We don’t “unlearn” because most of what we know is non-conscious.
Analyzing Work Typical ways to analyze jobs, skills, and tasks fail when mental processes are largely non-conscious and automated.
Performance We have mental models about the way things work and when systems do not operate this way, work is effortful and slower. This is one of the reasons why people are so resistant to major changes in the applications they regularly use.

Our non-conscious beliefs and biases affect all aspects of learning, including how we feel about what we’re learning and how much effort we’re willing to put in. My previous articles discuss how important effort is to outcomes.

Prior knowledge in long-term memory helps us perform, but it can have errors that wreak havoc. These errors damage both performance and future learning. For example, a person may have mistaken knowledge about the need to save money (for unexpected bills, retirement, etc.). And these errors may cause them to reject strategies that would result in better financial security. New information about financial well-being is filtered through existing biases.

Intentionally Creating Automated Knowledge

We realize that automated knowledge makes us perform more fluidly and with less effort. How can we use this knowledge to create automated knowledge when teaching?

An evidence-based method for automating thinking and doing is overlearning. Instead of practicing until the task is simply learned, the practice continues until the skill can be performed with little effort. The point is this: More effort while learning means less effort while doing. For example, a dancer may practice overlearning in order to perform flawlessly and with less effort in front of an audience.

Overlearning has been extensively studied in physical skills. Alaa Ahmed, from the University of Colorado’s physiology department, studied overlearning with repetitive arm movements. Her team showed that effort continued to decrease as participants overlearned the movements. Ahmed, like other researchers, showed that overlearning reduces energy needs for a specific task. These efficiencies free up energy that can be used elsewhere.

Overlearning hasn’t been heavily tested yet with cognitive skills. But many researchers expect overlearning to work as well or even better for cognitive skills. When using physical skills, repetition commits them to muscle memory, which automates the movements. Learning scientists tell us that there is cognitive muscle memory as well.

Since non-conscious and automated knowledge makes it hard for us to capture the exact processes needed to perform, how can we capture these processes to design good instruction? Clark recommends using Cognitive Task Analysis (CTA) to understand jobs and tasks. See the Schraagen reference at the end for a recommended book on CTA. Guy Wallace recommends Facilitated Group Process (FGP).

We need to use evidence-based tactics when designing and implementing workplace learning because these tactics take into consideration the attributes and limitations of how our mental processes work. For example, we know that we should design to automate thinking and doing. That’s because it helps people perform without having to think through all the details. This is the opposite of many (misguided) notions that we can look everything up or provide content alone to help people gain needed skills.

References:

Anderson, J. R. (1996). ACT: A simple theory of complex cognition. American Psychologist, 51, 355-365.

Anderson, J. R., & Lebiere, C. (Eds.). (1998). Atomic components of thought. Mahwah, NJ: Lawrence Erlbaum Associates.

Bargh, J. A., & Chartrand, T. L. (1 999). The unbearable automaticity of being. American Psychologist, 54, 462-479.

Clark, R. E. (2011). The impact of non-conscious knowledge on educational technology research and design, Educational Technology, 51(4), 3-11.

Cowan, N. (2001 ). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87-1 1.

Driskell, J. E., Willis, R. P., & Copper, C. (1992). Effect of overlearning on retention. Journal of Applied Psychology. 77 (5): 615–622.

Hoffrage, U., Hertwig, R., & Gigerenzer, G. (2000). Hindsight bias: A byproduct of knowledge updating? Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 566-58

Huang, H. J., Kram, R. & Ahmed, A. (2012). Reduction of metabolic cost during motor learning of arm reaching dynamics, Journal of Neuroscience, 32 (6) 2182-2190.

Schraagen, J. M., Chipman, S. F., & Shalin, V. L. (2000). Cognitive task analysis. Mahwah, NJ: Lawrence Erlbaum Associates.

Shibata, K., Sasaki, Y., Bang, J. W., Walsh, E. G., Machizawa, M. G., Tamaki, M., Chang, L., Watanabe, T. (2017). Overlearning hyperstabilizes a skill by rapidly making neurochemical processing inhibitory-dominant, Nature Neuroscience, 20, 470–475.

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