“Human Factors” are design principles devoted to understanding and applying how humans interact with products or processes. There are universal human factors that help guide good design, such as:
- Sensory Perception
- Pre-attentive Processing
- Integration of Prior Knowledge (topic of today’s blog)
- Metacognition
- Working Memory
The best thing that a UX designer can do is to leverage as much prior learning as possible in order to cut down on the user’s cognitive load.
Whenever we come in contact with new information, we have to integrate it into the framework of our mind. We do this through assimilation, where new information is merged within a pre-existing structure (schema), or accommodation, where we create a new schema because the information does not really match anything we have already learned.
The brain is comprised of schemas: mental maps which can have simple or complex structures, depending on the level of expertise a person has in that area. Each schema is comprised of linked concepts, where the link is strengthened by frequency and recency of use. For example, the link between “roses” and “flowers” would be strong if you just planted a rosebush in your yard.
Schemas can also be scripts, which are devised from an action sequence that is well-practiced and works together as a unit. For example, a script for brushing our teeth would involve picking up the toothbrush and the toothpaste, squeezing toothpaste onto the brush, wetting it under the sink, etc.
As we learn information, our schemas can change, which is known as assimilation. We can delete, add, and re-organize information as we learn. A little kid may brush his teeth rapidly at first, until his dad teaches him to count out ten strokes back and forth on the top left, the bottom left, top right, and bottom right. The boy would add this information into his script for teeth brushing.
Sometimes there is not enough pre-existing knowledge to match with new incoming information. In this case, we have to create new schemas, which is known as accommodation. This process is not as efficient and it demands more work, therefore our brain always tries to assimilate information first.
When processing information, the schema becomes activated, and that activation spreads to related networks. The activated networks are considered short-term memory, and anything not activated is stored in long-term memory.
When a person is using a new product, parts of the brain will activate as they try to match the product to their prior learning experiences.
Designers can prepare a user by ensuring the right parts of the brain are activated in order to use the product correctly. For example, if someone is using a digital SLR camera for the first time, the camera schema in their brain would activate and their past experience would help guide the initial interaction with the digital SLR. A UX designer should ensure the digital SLR icons look similar to the icons on other types of cameras and that placement of the shutter button is consistent with their prior camera experience (right side of the camera in the US).
As mentioned earlier, experts have more complex mental maps – maybe three or more dimensions – whereas novices will have rudimentary schemas with fewer links. There is also a middle ground, someone who is familiar with something but not an expert.
Each level of expertise requires a different approach to design. If you are designing for novices, embedded learning and intuitive design is crucial. This is also extremely important when developing an innovative design, because people will not have existing mental models for innovative products. Games like Candy Crush provide a great example of embedded learning: a user is not exposed to all the rules and features right away, but gradually as they move up in levels. They are also required to demonstrate their understanding of a rule before beginning that level.
Screenshot of Candy Crush Game with embedded learning built in to new levels.
In order to design for people who are familiar with a product, a good UX designer must leverage previous learning to lighten the load for the user. For example, if you have spent some time learning a Canon digital SLR camera, each version that comes out should have similar set-up of dials and features. For example, if 80% of the experience has changed, the user has to relearn that entire 80% by investing time into the new version. A more consistent design would only make incremental changes to the experience, so that a user would not have to re-invest all of that time and energy they spent on the first camera to read the manual and practice using each feature.
This increases the likelihood that a user (1) will invest in your brand long-term, (2) will learn how to use the new model correctly (3) reduces the possibility that a user becomes frustrated.
Side by side comparison of Canon EOS 5D and Canon EOS 1200 to demonstrate very different feature set-up within same brand and type of camera.
However, when designing for experts, an interface can be information-heavy and demanding, because experts are able to process information more efficiently. However, experts are also more likely to exhibit strong bias based on their experience. Because of their expertise, they may lean more towards specific brands or features when deciding on a product.
The biggest lesson of the blog post today is that we must make the learning process as easy as possible for users. Our lives today are filled with things that demand so much of our attention that we do not have the brain space to invest large amounts of time in a new product. A good UX designer will simplify the learning process so that people will have a positive user experience, rather than getting frustrated and giving up.
Photo Credits:
http://www.digitalversus.com/digital-camera/canon-eos-1200d-p19454/test.html
http://www.the-digital-picture.com/Reviews/Canon-EOS-5D-Mark-II-DSLR-Digital-Camera-Review.aspx
For more information on this topic, please visit links below:
Glaser, R. (1984). Education and thinking: The role of knowledge. American psychologist, 39(2), 93.
Johnson‐Laird, P. N. (1980). Mental models in cognitive science. Cognitive science, 4(1), 71-115.