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Labels and Fables: How Our Brains Learn

One of the most remarkable capabilities of the human brain is its ability to categorize objects, even those that have little visual resemblance to one another. It’s easier to see that visually similar objects, like different trees, fit into a category and it’s a skill that non-human animals also possess. For example, dogs show distinct behaviors in the presence of other dogs compared to their interactions with humans, demonstrating that they can differentiate the two even if they don’t have names for them.

A fascinating study explored whether infants are able to form categories for different looking objects. Researchers presented ten-month-old infants with a variety of dissimilar objects, ranging from animal-like toys to cylinders adorned with colorful beads and rectangles covered in foam flowers, each accompanied by a unique, made-up name like “wug” or “dak.” Despite the objects’ visual diversity, the infants demonstrated an ability to discern patterns. When presented with objects sharing the same made-up name, regardless of their appearance, infants expected a consistent sound. Conversely, objects with different names were expected to produce different sounds. This remarkable cognitive feat in infants highlights the ability of our brains to use words as a label to categorize objects and concepts beyond visual cues. 

Our ability to use words as labels comes in very handy to progressively build more abstract concepts. We know that our brains look for certain patterns (that mimic a story structure) when deciding what information is useful to store in memory. Imagine that the brain is like a database table where each row captures a unique experience (let’s call it a fable). By adding additional labels to each row we make the database more powerful. 

As an example, let’s suppose that you read a story to your toddler every night before bed. This time you are reading, “The Little Red Hen.” As you read the story, your child’s cortisol level rises a bit as she imagines the challenges that Little Red Hen faces when no one helps her; and as the situation resolves she feels a sense of relief. This makes it an ideal learning unit to store into her database for future reference. The story ends with the morals of working hard and helping others, so she is now able to add these labels  to this row in her database. As she reads more stories, she starts labeling more rows with words like “honesty” or “courage”, abstract concepts that have no basis in physical reality. Over time, with a sufficient number of examples in her database for each concept, she has an “understanding” of what that particular concept means. Few days later when you are having a conversation with her at breakfast and the concept of “helping others” comes up, she can proudly rattle off the anecdote from the Little Red Hen. 

In other words, attaching labels not only allowed her to build a sense of an abstract concept, it also made it more efficient for her brain to search for relevant examples in the database. The figure above shows a conceptual view, as a database table, of how we store useful information in our brains. The rows correspond to a unit of learning — a fable — that captures how a problem was solved in the past (through direct experience or vicariously). A problem doesn’t even have to be big – a simple gap in existing knowledge can trigger a feeling of discomfort that the brain then tries to plug. The columns in the table capture all the data that might be relevant to the situation including context, internal states and of course, labels. 

Labels also play a role in emotional regulation. When children are taught more nuanced emotional words, like “annoyed” or “irritated” instead of just “angry”, they have better emotional responses. Research shows that adolescents with low emotional granularity are more prone to mental health issues like depression. One possible reason is that when you have accurately labeled rows you are able to choose actions that are more appropriate for the situation. If you only have a single label “anger” then your brain might choose an action out of proportion for a situation that is merely annoying. 

At a fundamental level, barring any disability, we are very similar to each other – we have the same type of sensors, the same circuitry that allows us to predict incoming information or the same mechanisms to create entries in the table. What makes us different from each other is simply our unique set of labels and fables. 

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What Neuroscience Tells Us About Learning

Students today spend more time on academic learning than generations before. They cover more ground – learning things like programming or environmental science that their parents didn’t have to fret about – and spend more hours doing homework after school. One study found that in the sixteen-year period from 1981 to 1997, there was a 25% decrease in time spent playing outside and a 145% increase in time spent doing homework. 

As our society advances even more, students will have to cover more and more content, not just during their K1-2 school years but throughout their careers. By some estimates, students growing up today will have to learn entirely new domains and reinvent their careers every few years. Learning is no longer limited to younger ages but is becoming a lifelong journey. 

What does this really imply?

Students have to learn to learn –  acquire knowledge and master concepts faster – without which they will find it harder to stay abreast of new developments coming their way. But it’s not just about superficially memorizing things. Students will have to understand how to apply their newfound knowledge to problem-solving. In other words, learning has to become a more efficient process in terms of speed, depth, and understanding.

Thankfully, advances in neuroscience are giving us clues on how to make learning more efficient. Understanding how the brain processes information can help students take charge of their own learning, not just in their student years but throughout their life.

Neuroscience Of How Our Brain Learns

At a high level, we can view learning as a three-step process. When we encounter any new information, our brain first encodes this information and places it in short-term memory. For example, if you come across a new fact, say learning about a new breed of dog, the information first goes into your short-term memory. The next day, you might recall that your childhood friend had a similar-looking dog, and now you start to remember other details about the dog – how friendly it was, how it played, and so on.

At this stage, your memory is in long-term storage; it continuously consolidates other pieces of information that you already had. Over time you might add more connections to this piece of information, maybe a joke you heard about it, and it starts to get more and more enmeshed with other pieces of memory. 

After a few days, you might forget the name of the breed and try to recall it. You struggle a bit and then remember your friend’s dog, the joke, and other bits of memory that were tied to it. And then the name suddenly comes back to you, and you get a sudden burst of relief! 

A few days later, as you share a story about your childhood friend, her dog and the name of the breed come to your mind effortlessly, and you marvel at how well you remember this now. 

The picture above encapsulates how our memory works. Once we consolidate information into our long-term memory, subsequent retrieval and reconsolidation help to strengthen the memory traces and make it easier to recall information in the future. 

Forgetting Is The Path To Learning

Over the last couple of decades, neuroscientists have discovered interesting things about how our memory works, and counterintuitive as it sounds, forgetting information is an important aspect of remembering! Our brain is constantly pruning information that it thinks it doesn’t need so that it can serve the really important bits of information faster. 

Imagine if your brain stored every little nugget of information that it receives – the color of the shirt a passenger wore in the subway, or the name of the street your friend in another state lives on – it would make it much harder to find the useful information that you really need. So if you don’t need any piece of information, its retrieval strength starts to get weaker. However, when you try to recall something that you have forgotten, i.e., when you have to struggle a bit to remember it, that’s when the brain gets a cue that this particular memory is important and might be needed again.

So, with the process of retrieval, it starts to reconsolidate the information – find newer connections to other traces of memory so the memory is stored more strongly. As a result, this process of forgetting and remembering actually helps you learn better. 

Neuroscience-based memory models give us clues on how to structure our learning for maximum effectiveness. Here are three ways to boost your learning.

Repurpose Failure

When students don’t remember or don’t apply concepts correctly, it’s a sign that the information has been stored weakly in the brain. However, instead of feeling that they are ‘not cut out’ for this kind of work, students need to understand that their failure is simply a sign for their brain to reconfigure and become more efficient. Human brains are designed to learn through mistakes, so it makes sense to reframe forgetting as what it really is – a trigger that tells us that we need to take additional steps to ensure learning is complete. Students should use the opportunity to review concepts again and try to reconcile the mistakes so their understanding of the subject increases.  

Adopt Active Learning Strategies & Neuroscience

Adding some challenge to the learning process that taps our brain’s natural mechanisms to process, store and understand information can significantly boost learning. Such challenges are ‘desirable difficulties’ because they make learning more efficient. Here are a few strategies that students and teachers can adopt: 

  • Retrieval Practice: When learning new information, periodically quiz yourself about the central ideas and new terms encountered without looking at the text. This forces your brain to fetch the answers from long-term memory, and repeated retrieval is going to strengthen your memory.
  • Spaced Learning: To add more desirable difficulty to learning, practice retrievals after a period of time. When you start forgetting, you exert more effort in trying to remember, which then cues the brain to store the information more deeply. The gap between learning and retrieving can be anything from a day to a week – the key is that the gap should allow for some forgetting to happen.
  • Interleaving: Instead of waiting to thoroughly master one concept before moving on to the next, try mixing up different kinds of problems or concepts once you feel you have gained sufficient understanding in one. Not only does this make good use of spacing, but it also allows you to spot connections or differences between different kinds of problems. 

Research studies show that such strategies can be very effective in the classroom. In one study, students who practiced math problems in three sessions spaced apart by a week performed twice as well on the final test compared to students who did all the practice problems in one session.  In another study, students performed significantly better on their science exam when a practice quiz one month before the exam interleaved concepts on the quiz. 

Associative Learning & Neuroscience

Another useful strategy in learning is to connect the information you are learning to other pieces of knowledge you already possess. If retrieval practice creates deep roots, then associative learning creates more branches that help anchor the information better. To build associative learning

  • Find an analogy: Ask yourself if the new concept is similar to any other piece of information that you already possess. As an example, you might make a connection between gravity and magnetism as both involve a force that they can’t see and attract objects. 
  • Find a personal connection: In some cases, your personal experience can be helpful in finding connections about what you are learning. For example, while learning about the ice age, you might remember an earlier trip to Grand Coulee, where they saw how the Missoula Floods carved out a massive canyon in a very short time. The scale and impact of the event will give you an enhanced perspective of the topic and deepen your understanding. 

Conclusion

By understanding the neuroscience behind learning, students can take charge of their own learning. The key to efficient learning is to add and embrace the right kind of challenges that push our brains to reconfigure themselves. Unless students lack relevant background or specific skills to make sense of the concept in front of them, such challenges should be welcomed instead of dread. 

With a deeper understanding of the learning process, students can try different approaches and customize them to their needs. As an example, for some students, one day of spacing might be enough, whereas, for others, it might be a week. For the latter set, practicing a skill every day might not be as effective because they haven’t forgotten enough for reconsolidation to take place. With some trial and error, students can identify strategies that work best for them and become smart learners. 

This article first appeared on edCircuit

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How Rewards Impact Learning And Motivation

In an interesting study to understand the relationship between motivation and learning, researchers gave elementary students a reading comprehension task. One group was explicitly told that they were going to be tested and graded on what they learned at the end of the activity, while the others were not.

The results of the experiment revealed a lot about the interplay between learning, motivation and rewards. Students who were told that they would be tested and graded, found the reading task less interesting and felt more stress compared to the others. Their assessment afterwards also showed an interesting pattern. They performed as well as the other groups, but only when limited to rote information. Conceptual integration of the material was poorer than the other groups. In addition, one week after the experiment, they had forgotten more information compared to other groups! As the researchers concluded, “It is not unreasonably speculative to argue that grades as traditionally used in schools often result in the perception of an external locus of causality, produce pressure, and result in force-fed, poorly integrated and maintained learning.”

So how does learning get affected by motivation and rewards, like grades?

Learning can happen in multiple ways. Autonomous learning, where there is no directive to learn something specific, happens all the time and might even be the biggest source of learning. This type of learning, also called undirected learning, is triggered by curiosity and interest and is associated with lower negative emotional states. However, since this type of learning can’t be managed, we’ll focus on directed learning, where there is a specific set of material that needs to be learned and assimilated.

Students can be directed to learn in two ways:

  • Controlling, where the control comes from external mechanisms like grades or evaluations.
  • Noncontrolling, which uses approaches that tap into students’ need for autonomy and self-determination.

The issue with the controlling approach is that it leads to inferior learning outcomes compared to the noncontrolling approach. The reason behind this is better explained through achievement goal theory of motivation.

According to the achievement goal theory, people expend different levels and quality of cognitive self-regulation depending on the purpose of the goal. Cognitive self-regulation refers to how deliberate one is in the learning process and includes using different strategies, or planning and using resources effectively. What determines the level of cognitive self-regulation is the purpose behind the goal, which could be performance or learning based.

Performance Goals

Performance goals, also known as ego-goals, are driven primarily by a need to outperform others in order to increase one’s status. Performance goals are positively associated with more superficial, rote learning and not with deep learning. Performance orientation further comes in two flavors – performance/approach and performance/avoidance. Performance/approach is when students are aiming to outperform their peers. Students with this orientation do end up spending considerable effort and using superior study strategies. Performance/avoidance students want to avoid failure so as not to look less competent compared to their peers, and therefore put in less effort and avoid challenging work.

This is where class incentives or rewards, like grades, also come into play. When rewards are scarce, like when only the top few students get the highest grade, it creates a competitive environment where the focus changes from learning a concept to finding ways to outperform other students.

Students in the performance/avoidance orientation fare the worst since the incentive structure does not give them any reason to learn. Instead, they use strategies like procrastination which provides an explanation of their poor performance without being perceived incompetent (if the student only studies on the last day, they are not expected to do well and it isn’t a reflection of their ability).

Learning Goals

Learning goals, also known as mastery goals, are driven by a need to improve one’s competency irrespective of how others are doing. Related to this is the growth mindset, or the belief that one can learn and become smarter by putting in effort. Learning orientation is positively associated with deep-level processing, higher cognitive self-regulation, and pride and satisfaction in success.

Research has shown some promising directions to change grades and reward structure to create a better learning environment. This includes permitting students to work for any grade they want by accomplishing more, and using mastery based grading which focuses on whether one finally mastered a concept regardless of failures along the way.

 

Our current educational system has often been compared to a factory model where students are expected to learn the same content at the same pace as others in their age group. However, there is an additional dimension – extrinsic-focused scarce rewards – that makes the educational system mirror a corporate environment. Unfortunately, such rewards encourage performance goals in both systems leading to poorer learning, higher stress and less satisfaction.

Extrinsic rewards and performance goals work can be effective in limited ways where the task is simple or algorithmic. For more complex and creative work, a learning orientation becomes critical. However, nurturing a learning and growth mindset cannot happen in a vacuum – it needs a supportive environment to go with it. A poorly designed environment can push people from a learning orientation to that of a ego-focused performance mindset, while a well designed one could enable deep learning, growth and positive emotional well-being.

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Creativity Is Learning

One of the most famous psychologist and epistemologist of all times, Jean Piaget, developed the material for one of his most noted books in an unusual way. The subjects of his book, “The Origins of Intelligence in Children” were his own three children, whom he observed from infancy to about 2 years of age, over a period of several years. Piaget made detailed recordings several times a day, of at least one of his children, constantly for 3,000 days!

The result of these detailed observations led him to his theory of learning, providing the underpinnings of the constructivist theory of learning in more recent times. Piaget explained learning in terms of schemas (basic units of knowledge) and the process of adaptation. When a new information comes along, it can either be assimilated into an existing schema but if not, it triggers the process of accommodation where new schemas and organization takes place. A process of equilibrium in a child occurs when most new information can be incorporated through assimilation.

It is easy to see how Piaget’s theories tie into the constructivist model of learning. The fundamental tenet of constructivism is that learning is a meaning-making process and “each learner individually (and socially) constructs meaning as he or she learns.” From a pedagogical perspective, constructivism implies putting the learner in the center of the learning process, providing them with experiences and opportunities to construct meaning for themselves. As Prof. Hein further explains, “The crucial action of constructing meaning is mental: it happens in the mind. Physical actions, hands-on experience may be necessary for learning, especially for children, but it is not sufficient; we need to provide activities which engage the mind as well as the hands.”

Piaget’s concept of schema is intimately tied to the associative nature of our brain. Daniel Kahneman, illustrates the concept of ideas and how they are related to each other in our brain. He is uses the analogy of nodes in a network, where each node is an idea and the vast network is our associative memory. He explains, “There are different types of links: causes are lined to their effects (virus -> cold); things to their properties (lime -> green); things to the categories to which they belong (banana -> fruit).” When an idea is invoked, it brings to mind other connected ideas in turn. For instance, if you hear the word “Strawberry”, you might then think of a smoothie if the link between strawberry and smoothie happens to be  particularly strong in your brain.

Learning something new in the associative model implies creating new nodes and relationships, between ideas. Psychologists have found that human associative learning results from conscious reasoning efforts. In their expanded model, propositions connect ideas and “learning is not separate from other cognitive processes of attention, memory, and reasoning, but is the consequence of the operation of these processes working in concert. There is, therefore, no automatic mechanism that forms links between mental representations. Humans learn the causal structure of their environment as a consequence of reasoning about the events they observe.”

In essence, both Piaget’s model (and constructivism by extension) and associative learning provide similar definitions of what learning means –  the building of ideas and relationships that are continually updated to incorporate new information. But how does this relate to Creativity?

Creativity is coming up with ideas (or building products) that are both novel and useful. Looking through the lens of learning, novelty implies that the existing structures (ideas and relationships) aren’t enough to represent the new idea, and some form of accommodation is needed to incorporate the creative idea. So, the process of creative thinking forces the learner to expand his existing structures, thereby improving his ability to assimilate future new information.

In other words, creativity isn’t just about making new things – it is learning in itself.

 

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Building Creativity Through Integrative Learning

Integrative learning, or the concept of combining multiple subjects or educational strategies, is not new. In the early 1800s, Johann Herbart, a German philosopher, psychologist and educator believed that only large units of subject matter are able to arouse curiosity and keep a young mind engaged in deep learning. Even when teaching a particular subject, he proposed teachers support the learning by correlating with and integrating other subject areas.

While his ideas gained ground in the US and other countries, social and economical changes in the early twentieth century led to a different pedagogical approach of teaching subjects independently of each other. Professors Mathison and Freeman write, “Industrial efficiency studies and scientific thinking characterized by objective, quantifiable measurement has led to the assumption “that complex tasks become more manageable (i.e. easier) once broken down into their so-called basic parts”” This approach of simplification-by-isolation soon became the predominant approach in teaching.

However, interest in integrative learning is rising once again in response to the more complex educational challenges of the 21st century. Professor Julie Klein, lists the three catalysts that are driving the trend back towards integrative learning. The first is “knowledge explosion” that over the last few decades has resulted in new areas of specialties like machine learning that didn’t exist before. The second is the complexity of problems we face today that require pulling solutions from multiple domains. Finally, the focus on educational reform is linking the two concepts with complementary pedagogies.

Our project based learning modules use an integrative and interdisciplinary approach to make for a more wholesome educational experience. Here are three things we typically do in each module:

Integration with Arts

Integrating arts into the regular curriculum has been found to improve test scores and reduce the academic achievement gap for economically disadvantaged students. In most of our sessions we typically use theater and improv exercises as warm-up games. Some of the improv games build the same cognitive thinking patterns that underlie creative thinking, which is likely why improv artists come up with more (and better) product design ideas than professional product designers.

Interdisciplinary

Our projects also integrate multiple subject areas like science and humanities. In our latest module, Imaginary Worlds, students are diving deeper into topics like natural and man-made habitats (architecture and geography), social hierarchy and norms (anthropology and anthrozoology) and mathematical symbols and operations (mathematics), as they work towards developing their own fantasy worlds.

Blended Learning

While students use the online platform during the module, they never spend the entire lesson on the computer. Each lesson also incorporates group activities or discussions, time for each student to think and work independently and also collaborate in groups.

 

We find that using the above approaches gives us a more well-rounded and engaging approach to teaching different concepts, including areas in STEM that some students find intimidating.