Source | elearningindustry.com | Patti Shank
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:
|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.