I love my educational neuroscience class. It’s fantastic and every week I come away from class just blown away and so energized with ideas. Professor Rose is a phenomenally engaging lecturer and I love the way the class is structured and our learning is scaffolding–truly an example of someone who practices what they preach.
This week, we went over the neuroscience of perception and the mechanics of visual information processing. Here are the main takeaways from this week’s material (I skipped the technical stuff on how the retina actually processes information and jumped to the practical implications):
1. Novelty vs. familiarity. The brain gets a LOT of visual stimulus and must make sense of it really quickly and really accurately all the time. It can’t spend all its energy on every single visual input, and so it relies on a dual system of the familiar and the novel. Once it recognizes a visual stimuli as familiar, it doesn’t bother with processing it all over again. Instead, when something is novel or new, it flags down the rest of the brain to pay attention to it. In short: once something hits the visual field more than once, you stop processing it. Your brain only processes change (whether movement or new stimuli) and relies on prior knowledge to construct the rest of your perceived reality.
2. The brain is intrinsically goal-driven. Your goals drive attention, perception, and inevitably, learning. What your goal is determines where you’re going to deploy your attention. We did a couple exercises that showed that quite literally, if you are focused solidarily on a singular goal, you flat out will not perceive (literally will not visually register) other competing stimuli. In another vein, he used the example of a kid working on a fifth grade gingerbread house building exercise. From the outside perspective, it looked like Nathan was not goal-oriented because his house looked very un-houselike (more of a gingerbread fort than a house) and instead had as much candy as possible attached to it. Upon closer investigation, we find that Nathan asked his teacher if they could take the leftover candy home and the teacher said “no, you can only take home the candy that’s on your house.” Nathan’s goal then immediately shifted to maximizing the amount of candy he could get on his house. While that goal was perhaps not what the teacher intended, it was still goal-driven behavior. And the idea that any kid isn’t goal-driven is poppy-cock–just because you don’t know what goals are driving the kid’s behavior, doesn’t mean they don’t have any. So, the important take away from this is that we need to figure out what goals are driving our students’ behavior–don’t just reprimand them for not following your expected goal, but find out what their goals are. How are their goals driving their attention and focus to certain behaviors?
3. Multiple representations of content matter. To effectively teach an abstract idea, you need to give your brain multiple representations of the idea for it to figure out the pattern regularity. Introduce novel representations (to catch its attention), with similar underlying theories, so that it can pick up on the pattern and turn those cues into familiar signals. This seems key for transfer to occur as well–if you just show the brain one representation of a concept, when it later encounters a new situation (with that underlying concept) it’s not going to automatically register the underlying concept as a familiar pattern but rather, the visual signal as novel. For transfer to happen, you need to develop that pattern recognition across varying contexts.
4. Contextually relevant examples positively change how you’re perceiving information. Yes, it seems like common knowledge that having relevant examples make ideas stick more effectively, but it’s actually pretty cool when we examine why and how that happens. Say you have a math word problem: “Olya goes into a hardware store. She finds six wrenches, three screws…” For a student who has never been in a hardware store and doesn’t really have a frame of reference for that context, more time and energy is spent decoding the context than the actual math problem part that we want them to focus on. Instead, if you have contextually relevant examples, it limits the irrelevant novelty of inputs and allows the student to focus on the actual task at hand. Having those relevant reference points help you see past the clutter of unnecessary information and address the relevant cues.
It was a fascinating lecture and he so seamlessly interwove the elements of perception (as it pertains to visual stimuli processing and more generally) within the context of learning and understanding. These takeaways don’t do it justice but are hopefully interesting, nonetheless!