Learning involves a complicated interplay of factors. Chapter 2 discussed the importance of focusing on the cultural factors that influence learning. The committee explained new ways of understanding what culture is and the complex ways it influences development and learning. In this chapter, we examine different types of learning in order to understand the variety of complex processes involved. We then discuss brain development through the life span and changes in the brain that both support learning and occur as a result.
In this discussion, we draw on research in education and in social, cultural, and cognitive neuroscience. We build on what was discussed in HPL I1 and other reports that have contributed to a neurobiological account of how brains develop. These sources have explored how both experience and supportive environments can fundamentally alter developmental trajectories—both normative and maladaptive—across the life span.
It may seem obvious to say that there are many types of learning, but researchers have explored this multifaceted construct from a variety of angles. People learn many different kinds of things and use different learning strategies and brain processes in doing so. Consider three scenarios that highlight the wide range of activities and accomplishments that all can be called “learning.”
In scenario 1, Kayla is learning about the Pythagorean theorem in her geometry class. Her immediate motivation is to do well on a math exam, but she may have other motivations, such as impressing her parents, teachers, and friends, or at least not losing face; maintaining the grade-point average needed for a competitive college application; appreciating that this material is a prerequisite for learning advanced topics in math and science; seeing the application of the Pythagorean theorem to her interests in computer graphics and game programming; and seeing beauty and timelessness in the elegant and definitive proofs of the theorem.
As she works, Kayla is likely to engage in several types and applications of learning. She will probably learn both key terms and rules: for she will learn that “hypotenuse” is the term for the longest side of a right triangle and how to find the length of any hypotenuse using a formula. She will encode the formula in words or a picture so that she can later retrieve the rule for a test. She may learn to create and transform a spatial model that provides an intuitively compelling justification for the theorem. She may learn to link the spatial model to algebraic notation, and she may learn procedures to manipulate this symbolic notation to provide a formal proof of the theorem. She will learn to apply the Pythagorean theorem to closely related problems like finding the distance between two coordinates on a computer screen. She may even learn how to transfer the bigger concept to other contexts such as analyzing a communication network (Metcalfe, 2013).2
In scenario 2, Martina is developing her abilities on the guitar. Her motivations are very different from Kayla’s. She began playing the instrument so that she could accompany her own singing, but after some years of experience, she has become interested in learning more sophisticated skills, such as using new chord progressions and picking styles to better reproduce her favorite musicians’ performances and craft her own compositions. She has engaged in motor learning to improve her finger work, perceptual learning to pick out chord progressions from recordings, and observational learning by watching others’ live and recorded performances. Practice and regimentation figure prominently in her training. Her playing has improved considerably with individual lessons and her accompanying efforts to use both verbal and example-based instruction to improve.
The third scenario is Foldit,3 a computer-based game in which players learn to find solutions to the notoriously difficult problem of protein folding. (Figure 3-1 is an illustration of what a Foldit learner-player sees.) Foldit is an
2 According to Metcalfe’s law, the usefulness of a communications network increases proportionally to the square of the number of connected users because each person can connect to each of the other users (Metcalfe, 2013).
example of a “serious game”: one designed not only to entertain but also to educate or train users to solve real-world problems (Mayer, 2014). Foldit challenges its players to fold proteins into as low an energy state as possible, a difficult problem even for the most sophisticated artificial intelligence systems available (Cooper et al., 2010). Scientists can analyze the best solutions found by players to determine whether they can be applied to understanding or manipulating proteins in the real world. For example, in 2011, Foldit players, who include retirees and citizens of more than 13 countries, as well as science students, uncovered the crystal structure of a virus that causes AIDS in monkeys, producing a solution that had eluded professional scientists for 15 years (Khatib et al., 2011).
In 2012, using a version of the game that allows for the creation of new proteins, game players constructed an enzyme that can speed up a biosynthetic reaction used in a variety of drugs, including cholesterol medications, by 2, 000 percent (Hersher, 2012). Khatib and colleagues (2011) studied the strategies that 57, 000 Foldit players used to achieve these successes and found
that a key to these players’ results is that they create new tools, in this case computer software “recipes.” They also learn collaboratively by forming teams, sharing specific solutions and general software recipes, distributing tasks among the team members, and regularly updating one another on their failures and successes.
These scenarios give a sense of the range of functions and processes involved in learning; they illustrate the complexity of learning to solve even fairly straightforward challenges. Contexts matter, as do the variety of factors that influence learners’ motivations and approaches and the range of strategies and processes learners can recruit. We explore these issues further in this and later chapters.
We will return to these three scenarios to illustrate some of the basic universal types of learning researchers have investigated. We emphasize that these are not discrete functions that operate independently but are aspects of complex, interactive learning processes.
There are many types of learning, and as the scenarios illustrated, they often operate in concert. In this section, we describe several important types, chosen to acquaint the reader with the range, diversity, and dynamic nature of learning, rather than to provide a comprehensive taxonomy of learning types. We begin with forms of learning that may be considered “knowledge lean” such as the learning of habits and patterns and move toward more complex, “knowledge-rich” forms of learning such as inferential learning. The knowledge-rich types may be implicit, occurring outside the learners’ conscious awareness and requiring limited verbal mediation. More explicit learning would include learning with models and learning executed with the learner’s intention.
Research on types of learning is often conducted in laboratory settings where an effort is made to simplify the learning task and “strip away” nuances that reflect specific contexts. Often, participants in these studies are from cultures that are Western, educated, industrialized, rich, and democratic, which may limit the generalizability of findings to people who live in different cultural contexts (see Chapter 1 and Appendix C on the WEIRD problem). In the real world, learning situations almost always involve multiple learning processes and always are influenced by context and by the learner’s own characteristics and preferences.
Habit Formation and Conditioning
Habits are behaviors and thought patterns that become engrained and feel fluent in particular contexts (Wood et al., 2002). Habits can be positive (e.g.,
making healthy snack choices or double-checking one’s math homework), or they can be harmful (e.g., skipping meals and instead grabbing a candy bar from the vending machine, or giving up when one’s math homework seems difficult). Both learning and unlearning of habits occur gradually and usually unconsciously, though one can become aware of one’s habits and work to reinforce or change them mindfully. Habits tend to be self-reinforcing; because they achieve some short-term goal and are enacted relatively automatically, bad habits especially are notoriously hard to unlearn. Good habits, once established, can grow into rich patterns of behavior that help the learner succeed.
The gradual learning and unlearning of habits follows principles of conditioning, a nonconscious form of learning in which one automatically adjusts one’s decisions and behaviors when particular and familiar contextual cues or triggers are present. These decisions and behaviors can be strengthened when they are closely followed by rewards; for example, when the candy bar tastes good and gives an energy rush (even if the rush is followed by an energy crash) or the homework-checking habit reveals a careless error. The rewards might be external, but they can also be generated by the learner, as when Martina, the guitar student, realizes that her playing has improved because she has made a habit of practicing every day before bed.
The probability and time horizon of rewards also matters. For example, Martina may not notice any difference in her playing right away after she starts practicing regularly, and she may be tempted to give up before she experiences the reward. Or, the diligent student checking her math homework may not perceive the reward for her extra effort if homework is graded for completion so careless errors do not count. It might be thought that habits will become strongest when the behavior is always rewarded—when Martina’s progress is steady and the math student always earns praise—but predictable rewards actually reduce the durability of habits. That is, bad habits are often harder to extinguish when they are only intermittently rewarded, and the benefits of good habits may seem unclear when one takes the reward for granted. For example, if a child’s tantrums are occasionally rewarded by a parent who “caves in,” then the tantrum habit may resist extinction. The child learns that she might possibly be rewarded for a tantrum and so becomes more persistent. Similarly, though Martina may need to push herself to continue practicing nightly, on the night when she suddenly makes a breakthrough, the effort she put in will make the reward feel even sweeter.
People often think that they are in rational control of their behaviors and that they act the way they do because they have made a conscious decision. However, the prevalence of habit-driven acts shows that much of our behavior is not consciously chosen. Both negative habits such as obsessively checking one’s cell phone for messages and positive habits such as morning exercises are frequently initiated without a conscious decision to engage in the activity: one begins before fully realizing a habit is being formed. This means
that establishing a new, good habit might initially take effort and significant application of will power. As Martina works on her guitar playing, she develops good habits for holding the guitar with the neck pointed up rather than down, sitting with a straight back, and holding the pick loosely enough for it to have some play, habits that are critical for her growth in skill. Over time these behaviors need to become automatic, rather than deliberate, if she is to have sufficient mental resources left over to learn new pieces and techniques.
It is easy to be impatient with learners who have not yet instilled successful learning habits, such as listening attentively, creating outlines before writing, or periodically summarizing material that is read, and jump to the conclusion that they are not trying hard to learn. But these habits of learning take effort initially and only gain momentum over time. Once acquired, they can become second nature to the learner, freeing up attentional resources for other, more cognitively demanding aspects of a task.
There are many ways to establish a habit, such as classical conditioning.4 Ivan Pavlov’s research on classical conditioning is so well known that it appears in cartoons: Pavlov noticed that a dog automatically salivates when it is presented with food. Cleverly, he began playing a bell whenever he presented the dog with food. Soon he observed that the dog salivated when it heard the bell, even when no food was present. Classical conditioning such as this can be viewed as a form of adaptation to the environment, in the sense that salivation aids the digestion of food.
Although conditioning is an adaptive learning process, sometimes it can lead to undesirable consequences, as in some acquired taste aversions, or in the case of abused children who learn antisocial strategies for protecting themselves. For example, cancer patients who become nauseated from chemotherapy drugs may begin to feel nauseated even when thinking about the drugs or when eating a food they had previously eaten before a treatment (Bernstein et al., 1982).
Conditioned learning is so basic to survival and adaptation that it extends beyond just mental processing to also include adaptive patterns of processing in the body. For example, there is evidence that the immune system is subject to classical conditioning. Researchers have found that reactions of the immune system can be suppressed or enhanced as a learned response to a taste stimulus (Ader et al., 2001; Schedlowski et al., 2015). This work has given rise to the new interdisciplinary field known as psychoneuroimmunology, which explores possibilities for using conditioning of the immune system to fight disease. For our purposes, it highlights that learning is a fundamental
4 One of the characteristics of habit learning is that it is gradual. However, classical conditioning is not always gradual. Even a single exposure to a taste that later results in a stomach ache may result in avoidance of that flavor (García et al., 1955). We nonetheless include classical conditioning in this section on habit formation because it is one of the major mechanisms through which habits are formed.
property of humans and of all animals. It is not only our minds that are shaped by experience; even our bodies are.
People also learn by observing and modeling others’ behavior, attitudes, or emotional expressions, with or without actually imitating the behavior or skill. Humans’ talent, rare among animals, for observational learning has been called “no-trial learning” (Bandura, 1965) because it is even faster than the one-trial learning observed in animals that have a strong built-in tendency to form certain associations (e.g., between the taste of a food and a subsequent stomach ache). Learning by observation allows the learner to add new behaviors to his repertoire while minimizing the costs of trial-and-error learning, and it often can proceed without any explicit feedback.
Learning by observation is a sophisticated skill requiring advanced cognitive capacities for imitation, interpretation, and inference (Blackmore, 2000). It requires the learner to observe something that may not be immediately visible (such as an attitude or recipe), and figure out how to reproduce what she has observed. Martina likely learns about how to improve aspects of her guitar playing through watching and listening carefully as her teacher plays, even if neither she nor the teacher could describe in words every aspect of what she is learning.
The human penchant for learning by observation underscores the importance of the social milieu of the learner, a connection that has long been established. Studies by Bandura and colleagues beginning in the 1960s established the role of observational learning and social modeling in learning and motivation (Bandura, 1989; Bandura et al., 1961, 1963). The researchers found that for modeling to be a successful learning method, learners must not only pay attention to the critical components of the modeled behavior but also ignore irrelevant features of the behavior or skill; they must also be able to remember and replicate what they have observed. The Foldit players in our third learning scenario benefit from observational learning as they follow both general strategies and particular solutions they see their peers do. They organize teams, online forums, and recipe repositories specifically to promote their own observational learning.
Various factors may influence observational learning. For example, an individual’s perception of his own potential role and goal with respect to the behavior being observed influences how well he reproduces the learning behavior (Lozano et al., 2006; Zacks et al., 2001). But, it has long been known that people readily take cues for how to behave from others, particularly from authority figures such as teachers or parents but also from peers (Schultz et al., 2007). Peer observation is a key source of information about descriptive norms: standards for conduct among socially related people, which are ac-
quired by seeing how peers actually do behave. By contrast, injunctive norms describe how people should behave and are traditionally provided by higher authorities. Both descriptive and injunctive norms contribute to learning in social settings.
Descriptive norms are especially influential to learning (Cialdini, 2007). For example, people are more likely to litter when they observe a lot of other litter on the ground, even though they know that littering is against the official rules. Messages such as “Many people litter. Don’t be one of them!” may have the paradoxical effect of increasing littering because it suggests a descriptive norm that littering is commonly tolerated (Cialdini et al., 1990). Teachers and parents frequently lament that students seem to pay more attention to what their peers do than to advice given by more authoritative voices. However, this tendency to favor descriptive norms has been harnessed by the “peer learning” approach, which encourages learners to interact with and teach each other (Crouch and Mazur, 2001; Slavin, 2016). Understanding of descriptive norms highlights the need to establish classroom cultures that promote high-quality peer learning, especially through descriptive norms (Hurley and Chater, 2005).
Empirical studies also illustrate cultural differences in observational learning. For example, working with pairs of American and Mayan children ages 5 to 11, Correa-Chávez and Rogoff (2009) showed one child how to construct a novel toy while the other child was nearby doing a similar activity independently, without explicit instruction. They then asked the second child to attempt the task in the structured teaching situation. The researchers found that the children who first worked independently had learned from observing the other children. They also noted that the children’s observational learning differed, depending on their cultural community as well as their degree of exposure to Western schooling (in the case of the Guatemalans). In this study, the Mayan children were more likely to watch intently as the other child was given instruction, while the American children, and the Mayan children with more exposure to Western education, were more likely to focus exclusively on their own task rather than watching. The children who learned the most during the waiting period were from families with the most traditional Mayan practices.
Implicit Pattern Learning
Observational learning is not the only way a person can learn without receiving external feedback or rewards. Implicit pattern learning, also called statistical learning, involves the learning of regular patterns in a particular environment without actively intending to do so. This kind of learning requires extended exposure to a pattern sufficient for unconscious recognition of regularities in an otherwise irregular context, without conscious attention and reflection (Willingham et al., 1989). Statistical learning is observed in
many species and across age groups in humans, and it is relatively unrelated to IQ; even infants can do it (Cleeremans, 1996). In a 1996 study, researchers exposed 8-month-old infants to a 2-minute, continuous, monotone stream of speech that was random except for a repeated pattern of several nonsense words made up of three syllables (e.g., “bi-da-ku”) (Saffran et al., 1996). Even though there was no gap between the words, the infants showed a novelty preference after this exposure, listening longer to new nonsense words than the nonsense words they had already encountered.
Language learning is a good example of statistical learning because people spontaneously and without conscious effort use the regularities that language contains to produce their own utterances (Bybee and McClelland, 2005). Imagine hearing a new verb, “sniding,” which means, “to try to humiliate somebody with a disparaging remark.” To use the verb in the past tense you might say, “he snided his cousin,” applying the regular “+ed” way of forming a past tense, or, “he snid his cousin,” basing your verb form on other similar irregular verbs such as “hide→hid,” “slide→slid” and “bite→bit.” You might even say, “he snode his cousin,” but you probably would not say “snood,” “snade,” or “snud” because without realizing it you have learned the rules for indicating past tense in English.
Learning patterns without feedback generally requires extended experience with an environment and is gradual. The regularities learned in this fashion may not be easily verbalized because they are not the result of explicit hypothesis formation and testing. Figure 3-2 shows how a learner can extract patterns from an environment without a teacher or parent providing feedback. In this environment, 80 circles varying in size and color are distributed in distinctive clusters. Even if none of the circles is categorized or given a label, it is possible to see that they fall into four clumps. Many real-world categories are clumpy in exactly this way. For example, the category bird encompasses several properties that are correlated with each other, such as nesting in trees, laying eggs, flying, singing, and eating insects. Other categories such as snakes and fish have different constellations of correlated properties (Rosch and Mervis, 1975). Learners often come to recognize which attributes define categories simply through observation over time; even very young children recognize, for example, that it would be a strange, improbable animal that borrows hissing and scales from snakes but feathers and chirping from birds.
Perceptual and Motor Learning
We have seen that some types of learning are unconscious and some require deliberate intention. Perceptual and motor learning are ways that an individual learns skills primarily through sensory experiences. This type of learning may take place without the learner being able to put into words how it occurred, but it may be deliberately pursued. Learning to hear the difference
between major and minor chords, practicing a golf or tennis swing, improving one’s skill at smoothly maneuvering a car, or learning (as a dermatologist) to distinguish between benign and malignant skin growths are all examples of this type of learning. Skills learned this way gradually increase over a protracted course of years, or decades, of practice. Different training regimes may accelerate skill training, but there is usually no simple shortcut that will yield skilled performance without long hours of practice; it is doing the activity, not being explicitly instructed, that brings the gains (Ericsson, 1996).
Motor learning, such as learning how to swim, ride a bicycle, or play a guitar chord without a buzzing sound, is often highly specific. That is, if a person who has learned to play guitar is asked to switch which hand strums and which hand fingers the chords, she will suddenly regress to a nearly novice level (Gilbert et al., 2001). This high degree of specificity has been associated with changes to brain areas that are activated rapidly after an object is shown and are specialized for perception. It is easy to forget how dramatically people’s
perceptions and actions can be changed by experience because once they have changed, the individual no longer has access to the earlier perception.
People learn from the world through their senses, but these same senses are changed by that learning. Both perceptual and motor learning can lead to surprisingly robust changes in the perceptual system. A striking demonstration of this is a phenomenon known as the McCollough Effect (McCollough, 1965), in which a very brief exposure to some objects can have a relatively long-lasting influence on the continued experience of other objects.
As an example, look at the pattern in Figure 3-3 and confirm that the vertical and horizontal striped quadrants appear black and white. Then, alternate between looking at the red and green stripe patterns in Figure 3-4 for 3 minutes, looking at each pattern for 2 to 3 seconds at a time. Now look back at the pattern of four quadrants in Figure 3-3. The quadrants with the vertical lines should appear red-tinged, and the quadrants with the horizontal lines should appear green-tinged. Celeste McCollough’s explanation, which continues to receive empirical confirmation, is that there is adaptation in early stages of visual processing in the brain to combinations of orientation and
color. This adaptation, which creates orientation-specific reference points to which subsequent colored bars are compared, is surprisingly robust. As little as 15 minutes of exposure to the red and green stripes can make people see color differences in the quadrants lasting for 3.5 months (Jones and Holding, 1975). If you followed the viewing instructions above, your experience of the world in just 3 minutes has had a durable and hard-to-suppress influence on how you see it.
Figures 3-5 and 3-6 show another example of how a very brief experience can rapidly alter future perceptions. Look at Figure 3-5 first, before you view Figure 3-6. If, like most people, you are not able to identify all four of the objects in the images shown in Figure 3-5, you may experience the frustrating but gripping phenomenon of not being able to form a coherent interpretation of your visual world. Now look at Figure 3-6. The images in this figure provide hints that will make the images in Figure 3-5 readily interpretable. If you now go back to view the images in Figure 3-5, you will most likely not be able to return to your naïve state of incomprehension. The striking difference between how the images in Figure 3-5 appeared to you before and after the clarifying experience of seeing Figure 3-6 provides a compelling, rapid analog for the greater, often gradually accumulated, power of experience to change what we see.
Perceptual-motor learning can also play a large role in the development of academic knowledge. Not only does it support abilities to see and discriminate letters for reading, it also supports what Goodwin (1994) called “professional vision.” Goodwin described the ways in which training in archeology involves changes to how one perceptually organizes objects of inquiry, such as the texture and color of dirt found at an excavation site.
It is possible to organize instructional experiences that maximize people’s abilities to leverage perceptual learning. Kellman and colleagues (2010) developed brief online modules to support perceptual learning in mathematics. Students using the modules make quick decisions for 120 problems. For instance, they have to decide which of three equations, all using similar num-
bers but differing in operators (e.g., 3X + 5 versus –3x + 5), goes with a given graph and which of three graphs goes with a given equation. After choosing an answer, students simply see the correct answer without explanation. The goal is to have the students see the structure, not explain it. The juxtapositions of the similar equations and similar graphs create contrasting cases as in wine tasting, exploring near contrasts helps people learn to perceive the distinctive features. Twelfth-grade students who completed the module nearly tripled their abilities to translate between graphs and equations, even though they had previously completed algebra.
The importance of perceptual learning for academic topics can easily be
underestimated. One reason is that experts may not realize how much of their understanding stems from perceptual learning. As mentioned previously, once one has learned how to see something, it is hard to remember what it looked like when one was a novice. Experts may not realize that novices cannot see what they themselves see because it seems so self-apparent to their perception.
Learning of Facts
Humans have many reasons to learn facts and information, such as the elements of the periodic table or the factors that ushered in the industrial revolution, and they may do so intentionally or without realizing it. A single exposure to a striking fact, such as that human and koala fingerprints are highly similar, could be sufficient for a listener to remember and subsequently recall it, though he may forget when and where he learned it.
Although fact learning may seem mundane and highly restrictive in what it can mobilize a learner to do, it is a kind of learning at which humans excel, compared to other animals. It allows educators to impart information efficiently to learners by harnessing the power of language. The power and convenience of being able to simply say something to somebody and have it change their behavior is undeniable. A naturalist who tells a hiker about the likely consequences of eating the mushroom Amanita phalloides conveys information that would be impractical, if not deadly, for the hiker to learn from experience.
Although a fact might be learned in a single exposure or from being told, it is important to note that this apparent efficiency and directness can be misleading. Facts are rarely learned in a single instance, and accurate generalizations are rarely learned from a single example. It is generally only in cases where learners have substantial background knowledge already that one example or one instance of exposure can suffice (e.g., the hiker would need to already know a lot about poison and mushrooms to appreciate the information about Amanita phalloides). Moreover, a considerable body of research on memory shows that repeated opportunities to retrieve facts strengthen memory, particularly if they are spread over time, location, and learning contexts (Benjamin and Tullis, 2010; see Chapter 6).
Fact learning need not be rote: It is promoted when learners elaborate by connecting the information to be learned with other knowledge they already have (Craik and Tulving, 1975). One could simply try to memorize that Christopher Columbus was born in 1451, or one could connect this fact to others, such as that the Eastern Roman Empire (Byzantium) fell 2 years after Columbus was born (with the fall of Constantinople in 1453), a connection that adds meaning to both facts. Organizing items to be remembered into related groups makes them easier to retain (Bower et al., 1969), as does forming strong mental images of the information (Sadoski and Paivio, 2001). Taxi
drivers have better memory for street names when they are part of a continuous route than if the street names are presented in random order (Kalakoski and Saariluoma, 2001). All of these results are unified by the notion that facts that are placed into a rich structure are easier to remember than isolated or disconnected ones.
Learning by Making Inferences
To make sense of their world, people often have to make inferences that while not certain to be correct, are necessary to move forward. The philosopher Charles Sanders Peirce used the term “abductive reasoning” to describe this type of inference. He described it as forming a possible explanation for a set of observations. As an example of this type of reasoning, John Couch Adams and Urbain Jean Joseph Leverrier inferred that a previously undetected planet of a particular mass must be located beyond Uranus, based on observations of Uranus’ deviations from its predicted orbit. Following up on this prediction, Johann Gottfried Galle discovered Neptune in 1846.
Chemistry students inferring that substances are “acid” or “base” and hypothesizing possible electrostatic interactions between them is another example of abduction (Cooper et al., 2016). However, abduction is not only practiced by scientists. The dog owner who sees dog footprints on the dining room tablecloth, a spilled glass of wine, and an empty hotdog bun is using abduction when she assumes the worst. Even modern machine-learning systems have shown that abductive inference is important for making efficient learning possible. Such systems can inspect their world and infer in human-like ways which processes created the objects they see. When they use abductive reasoning, they can learn more from less data and better generalize what they have learned to new situations (see Figure 3-7; Lake et al., 2015, 2017; Tenenbaum et al., 2011).
Model building is an important special case of abductive inference that people use when seeking to understand complex phenomena. Educators and others often use models to teach and explain. A three-dimensional pictorial, diagrammatic, or animated model of the Earth, Moon, and Sun can help students grasp how night-day, tidal, and seasonal cycles are generated. Adults may often rely on established models such as the circle of fifths in music theory, but people also develop their own models in many circumstances, for example to try to understand the most economical way to manage their home heating system. Models are powerful tools for making inferences in novel situations, but almost all models can yield incorrect predictions in circumstances that do not fit, so it is important to consider the purposes for which they are used. For example, the Newtonian laws of physics are adequate for predicting the movement of planets in the solar system, but they fall short of accurately
predicting black holes (which are much more massive than anything Newton knew) or subatomic particles.
The primary advantage of model-based learning is that the learner who is equipped with an apt model can make good predictions about new situations that go well beyond the originally experienced situations. For example, if a learner has a model of water as being composed of molecules whose random movements increase with the water’s temperature, then he might be able to predict that a drop of food coloring will diffuse faster in hot water than cold; a bit of experimentation will reveal that he is correct (Chi et al., 2012).
Overcoming model-based misconceptions is a major goal in formal education (Clement, 2000). Figure 3-8 illustrates how students may reconcile their visual experience of the Earth as flat with their teacher’s instruction that the Earth is spherical, by concluding that Earth is shaped like a pancake (or disc): people do not fall off the round (flat) Earth because they live on the top portion of the pancake! A typical strategy for addressing this sort of misconception is to first understand what the students’ model is (Osbourne
Because the models that people use to help them reason and act are often implicit, children and adults rarely critique their own models. People may only discover that alternative models for a situation are even possible when they encounter one. For example, two common but incompatible models for home heat control are the “valve model” and the “feedback model” (Kempton, 1986). According to the valve model, the temperature at which the thermostat is set determines how hard the furnace works to produce heat. That is, higher temperature settings make the furnace run harder, much as further depressing a gas pedal on a car makes the engine rev up more and more. According to the feedback model, the thermostat sets the threshold below which the furnace turns on, but the furnace runs at a constant rate.
These different models drive very different home heating behaviors. If two people come home to a 55 °F home and would like it to be 65 °F, the valve theorist might set the thermostat to 75 °F because she wants the house to warm up quickly, whereas the feedback theorist would set it to exactly 65 °F, realizing that setting the thermostat higher than 65 °F will not make the house warm up to 65 °F any faster. Applying the common but inaccurate valve model wastes both energy and money.
In other cases, different models exist not because some people are wrong but because of culture differences. What is considered rude behavior in a business meeting, which direction to push or pull a saw, and conceptions of time reflect varying models that are neither correct nor incorrect. A study that illustrates this point examined views of the future among U.S. residents and members of the Aymara people of the Andes region (Núñez and Cooperrider, 2013). The researchers found that whereas the U.S. residents tended to conceive of the future as spatially in front of them, the Aymara participants conceived of it as spatially behind them (perhaps because it is invisible). Such differences can cause misunderstanding and miscommunications when a member of one culture comes to a new culture; these problems occur not because of weak cognitive capabilities but because of a cultural mismatch of models. Learners and instructors may not recognize the extent to which their models are not shared (Pronin et al., 2002).
Despite the potential for misunderstanding, it is difficult to imagine an area of advanced human creative or scholarly pursuit that does not involve models: the artist’s model of complementary and analogous colors, the medical model of blood sugar–insulin regulation, the historian’s use of Marxist accounts of class struggles, the double helix model of DNA, and the physicist’s model of atomic and subatomic particles are just a few examples. The power of model-based learning in education has been showcased in the Next Genera-
tion Science Standards and Common Core Mathematics standards5 because models make it easier for learners to describe, organize, explain, predict, and communicate to others what they are learning.
While experts in virtually all domains see the value of hypothesizing models because they are trying to organize a wealth of observations, sometimes early learners are not as convinced of the value of models because they may seem speculative, indirect, and invisible. This student resistance can be reduced by facilitating better learning of and with models through use of spatial representations, diagrams, animations, and interactive computer simulations (see Chapter 6).
Creating models for themselves, rather than simply using models suggested by others, can be a beneficial activity for learners (VanLehn et al., 2016). The value of constructing models for understanding and organizing material has been associated with specific learning approaches, including discovery learning, inquiry-based learning, problem-based learning, learning by invention, learning by doing, and constructivism. In each of these approaches learners are encouraged to either discover for themselves or explore with guidance the applicable rules, patterns, or principles underlying a phenomenon (Bruner, 1961). Foldit players demonstrate remarkable learning by creating models when they program (code) new computer algorithms to help in their efforts to fold proteins, sometimes learning how to program just so that they can create tools to help them play the game better (Khatib et al., 2011). Likewise, Schwartz and colleagues (2005) showed that if children are prompted by a teacher to use mathematics, they could use their mathematical knowledge to model the complex causal relationship between distance and weight to determine balance on a scale.
Inferential learning is likely most effective when the learner receives some guidance. For example, someone making yogurt for the first time might want to determine experimentally how the fat content of milk affects the firmness, acidity, and smoothness of the yogurt. In a pure case of discovery learning, this cook would develop the question, experimental methods, measures, and analyses. However, without some guidance, beginning learners may not know enough to ask good questions or identify critical variables, and they may become frustrated because of lack of progress (Mayer, 2004; Spencer, 1999). Research has shown that allowing learners to experiment on their own, with no guidance (unassisted discovery), does not improve learning outcomes (Alfieri et al., 2011).
Guided, or assisted, discovery learning is an approach in which the educator provides a level of guidance tailored so that the task is at a level of difficulty that fits the learner. (This approach builds on the notion of the
“zone of proximal development,” or “sweet spot,” proposed by Vygotsky in the 1930s). Ways to do this include providing just-in-time access to critical knowledge, worked-out examples, assistance with hypothesis generation, and advice as needed. This approach allows learners to take ownership of the construction of their own knowledge. Evidence suggests that learners who engage with these types of learning resources, rather than learning by rote, are more likely to retain the knowledge beyond the original context of instruction (Lee and Anderson, 2013).
Most learning experiences involve multiple types of learning, not just one. For example, collaborative learning and problem solving in teams would engender learning by observation, feedback, facts, rules, and models, as well as possibly other types of learning. At the same time, research supports the principle that different situations and pedagogical strategies promote different types of learning. Before a teacher or learner can design an ideal learning situation, she has to decide what kind of learning she is trying to achieve. For example, one generalization that has emerged from decades of research is that promoting memory for specific facts requires different learning experiences than promoting knowledge that is transferable to new situation (Koedinger et al., 2013). Techniques focused on improving memory include spacing practice over time, rather than massing all practice at a single time; practicing retrieval of memorized information, rather than just studying the information again; and exposing learners to materials in different settings. By contrast, techniques focused on promoting transfer to new situations include comparing and contrasting multiple instances of concepts; having students reflect on why a phenomenon is or is not found; and spending time developing powerful models, rather than asking learners to simply repeat back what they are told. Chapter 5 discusses in more detail techniques for supporting different types of learning.
One of the most striking advances in learning sciences in the past 15 years has been in understanding the protracted course of brain development, which begins in utero and continues well into adulthood. Several reports have examined the research on brain development and the implications for learning. From Neurons to Neighborhoods: The Science of Early Childhood Development (National Research Council and Institute of Medicine, 2000) drew attention to evidence that infants are born able and ready to learn, that early childhood
experiences and relationships are critical to development, and that individual biology and social experiences are equally influential in determining developmental outcomes. Transforming the Workforce for Children Birth Through Age 8: A Unifying Foundation (Institute of Medicine and National Research Council, 2015) and a review of the literature by Leisman and colleagues (2015) identified key findings from recent research on early brain development as it affects lifelong learning. Among these findings are the following:
- Experience and genetics both contribute to observed variability in human development.
- The human brain develops from conception through the early 20s and beyond in an orderly progression. Vital and autonomic functions develop first, then cognitive, motor, sensory, and perceptual processes, with complex integrative processes and value-driven and long-term decision making developing last.
- Early adversity can have important short- and long-term effects on the brain’s development and other essential functions.
The prenatal period is marked by an astounding rate of formation of new neurons, synapses, and myelinated axons—with the result that the brain has more of these structural elements than it needs. This development continues after birth: the brain increases fourfold in size during the preschool years and reaches approximately 90 percent of adult brain volume by age 6 (Lenroot and Giedd, 2006). Beginning in early childhood, this explosion in growth, which continues until adolescence, is the result of the dramatic increase in synaptic connections among neurons (gray matter) and in the myelination of nerve fibers (white matter) (Craik and Bialystok, 2006).
Although vigorous growth continues, the synapses and neurons are also pruned, a process that continues until after puberty. This pruning occurs in a specific way: the synapses that are continually used during this period are retained, while those that are not used are eliminated (see Low and Cheng, 2006, for more on synaptic pruning). The removal of unnecessary or unused synapses and neurons improves the “networking” capacity of the brain and the efficiency of the cortex (Chechik et al., 1999). Because this pruning is influenced by environmental factors, the developing child’s experiences determine which synapses will be strengthened and which will not, laying a critical foundation for future development and learning (see Box 3-1). Just as strategic placement and pruning of plants yields a healthy garden, a balance between strengthening of some connections and pruning of others fosters healthy brain development: having more neurons left alive is not a better outcome.
Environmental stimulation and training can affect brain development throughout the life span (Andersen, 2003; Diamond et al., 1964; Leisman, 2011). The organization of cortical and subcortical signaling circuits, which are integrated into networks with similar functions, also occurs during this period. In other words, as the learner acquires new knowledge, regions of the cortex develop specialization of function. This is known as experience-dependent learning (see Andersen, 2003; Greenough et al., 1987; Leisman et al., 2014). These structures and associated circuits underlie the neural systems for complex cognitive and socioemotional functions such as learning and memory, self-regulatory control, and social relatedness, as discussed in a 2009 National Academies report (National Research Council and Institute of Medicine, 2009).
Beginning in the fourth decade of life, changes occur in both cortical thickness and connectivity that seem to be the start of the cognitive decline often observed in aging adults. These changes occur after a period during which the parts of the brain that support learning seem to be stable with respect to gross physiological features. The changes are illustrated in Figure 3-9, in which warm colors (red, orange, yellow) indicate greater cortical thickness. As the
figure shows, the brains of healthy middle-aged adults (40–60 years) have less cortical thickness compared to the brains of healthy individuals under 40 years of age, though it is not clear whether this is the result of decreases in brain tissue or, for example, lower hydration levels. These effects are found across the cortex, although they are larger in some areas (e.g., the prefrontal cortex) than others (e.g., anterior cingulate; see Fjell et al., 2009).
The brain operates as a complex interconnected system, rather than as a collection of discrete processors (Bassett et al., 2011; Medaglia et al., 2015). Different parts of the brain do not act in isolation but instead interact with one another, exchanging information through extraordinarily complex networks (Sporns, 2011). There is no learned skill that uses only one part of the brain, and there is no one part of the brain with a singular function. Instead, the brain systems that support learning and academic skills are the same brain systems that are integral to personhood—that is, to social, cognitive, emotional, and cultural functioning and even to health and physiological survival (Farah, 2010; Immordino-Yang and Gotlieb, 2017).
Moreover, learners dynamically and actively construct their own brain’s networks as they navigate through social, cognitive, and physical contexts. It has been assumed that brain development always leads the way in cognitive development and learning, but in fact the brain both shapes and is shaped by experience, including opportunities the individual has for cognitive development and social interaction. The reciprocal interactions in learning between the dynamically changing brain and culturally situated experience form a fascinating developmental dance, the nuances of which are not yet fully understood. A person’s brain will develop differently depending on her experiences, interpretations, needs, culture, and thought patterns (Hackman and Farah, 2009; Immordino-Yang and Fischer, 2010; Kitayama and Park, 2010). In addition, features internal to the brain’s development and structure will constrain the way a person engages with the world.
The brain has remarkable capacity to adapt to phenomena that are new, such as cultural innovations or new challenges. Researchers continue to develop new insights in this area, but one particularly intriguing finding is that adaptation can take place in a time frame far shorter than has been traditionally associated with evolution. Written language and written, symbolic mathematics are two classes of skill with which the human species has not collectively had long experience. Numerous archeological artifacts for both written language and mathematics date back to the Sumerians of Mesopotamia, but it is likely that neither has existed for more than 6, 000 years. Despite this relatively short history, specific neural regions are implicated in reading and
Sharing and Recycling of Neural Tissue
First, people solve new cognitive tasks by reusing brain regions and circuits that likely originally evolved for other purposes (Anderson, 2015a; Bates, 1979). Research has shown that just as multiple types of learning blend in practice, circuits in the brain also combine in diverse ways in different types of learning. One might expect that different types of learning depend on different neural mechanisms, but seemingly very different types of learning behavior share brain circuitry. For example, the hippocampus is heavily involved in fact and rule learning as well as spatial navigation, but it is also centrally important for statistical learning (see section above on “Implicit Pattern Learning”; also see Schapiro and Turk-Browne, 2015). This finding may seem surprising, but it is consistent with the fact that the hippocampus is involved whenever learning requires that different events or features be bound together into a single representation (see Chapter 4). This possibility for combining and recombining circuits is key to adaptation.
Research on the way blind people use the visual cortex, which normally processes visual inputs, offers a striking illustration of this circuit adaptability. In one study, for example, blind research subjects were able to recruit a particular subregion of the visual cortex—a portion associated with constructing spatial representations and relations for hearing and touch—when they were performing spatial tasks like reporting where in space they heard a sound (Renier et al., 2010). Other research has shown that activity in the spatial reasoning part of the visual cortex increased with blind study subjects’ accuracy in solving auditory and tactile spatial tasks. Likewise, when sighted adults are taught to read braille, the brain regions that normally process visual, not tactile, information undergo the most significant reorganization (Siuda-Krzywicka et al., 2016). This research suggests that spatial reasoning, whether it is visual, auditory, or tactile, shares basic attributes, so parts of the brain that are normally responsible for visual tasks can be effectively reused for nonvisual spatial tasks if they are not being used for vision. Brain organization through learning is therefore more about the character or logic of thought than it is about the modality, such as visual or tactile (Bates, 1979; Immordino-Yang and Damasio, 2007).
“Tuning” to New Requirements
Second, the brain is sufficiently adaptive that its parts become “tuned,” over an individual’s life span, in response to needs and experiences. Neuroscientists use the term “tuning” to describe their observation that neural
responses are strongest when the stimulation is at an ideal level, as the tones produced by the strings of a musical instrument correspond to their tautness and the position and angle at which they are struck. Neurons become tuned over time to respond in particular ways, based on the kinds of stimuli that have arrived and on how the learner has engaged with these stimuli to build experiences and skills.
Neural tuning, which occurs in response to experience, is part of the reason that individual learners’ brains are organized differently. For example, the brains of people who can read show greater specialization for words than those of illiterate individuals, and learning to read as an adult engages a broader set of brain regions than does learning when young (Dehaene et al., 2010). In another striking example, Elbert and colleagues (1995) measured brain activity in the sensory cortex of violinists as their fingers were lightly touched and found greater activity in the sensory cortex for the left hand than the right hand. This is logical because a violinist needs to control each of the fingers on his left hand individually, whereas the job of the right hand, bowing, does not require manipulation of the individual fingers.
Varying Time Frames for Adaptation
The explanation of how brains come to effectively accommodate new cultural requirements intertwines three temporal scales of adaptation: (1) the slow evolution of bodies, including brains, in response to challenges to survive and reproduce; (2) the creation over human evolution of cultural innovations like stone tools, pencils, calculators, and online tutoring systems; and (3) the adaptation of an individual’s brain over a lifetime to meet the demands of one’s culture and one’s particular role within that culture.
The slow evolution of the human brain in comparison with the faster pace of cultural changes suggests that humans’ distant evolutionary past may provide hints as to what can be learned with efficiency. Humans seem to be born with certain biases,6 such as for learning human faces and voices (Cohen-Kadosh and Johnson, 2007) or attending to objects that have long evolutionary histories of being dangerous, such as snakes and spiders. (Newer objects such as guns and electrical outlets, whose risks are culturally specific, do not elicit comparable reactions) (LoBue, 2014; Öhman and Mineka, 2001; Thrasher and LoBue, 2016). Because of these evolutionary biases, situating material to be learned in relation to the kinds of objects and contexts to which our brains have evolved to attend, such as food, reproduction, and social interactions, may improve learning outcomes.
The ability of cultural innovations to change to better fit human capabili-
ties suggests another leverage point for learning: adapting technologies to better fit how people naturally learn. For example, technologies for immersing individuals in three-dimensional interactive worlds leverage people’s naturally strong memories for objects encountered during first-person navigation, such as finding one’s way to one’s office (Barab et al., 2005; Dunleavy and Dede, 2013). Likewise, some computer-based dialogue tutoring systems are designed to recreate the kinds of interaction that a human student and teacher would naturally have, leveraging humans’ proclivity to seek desired information from perceived experts (Graesser et al., 2014; Tomasello, 2008).
The final leverage point for change is the individual’s ability to change in response to a cultural context. This ability underlies the sometimes striking differences that can be observed in learning trajectories across different cultures. For example, whereas 11-month-old Efe children living in the Ituri rainforest of the Democratic Republic of Congo can safely use a machete, middle-class 8-year-old children in America are rarely trusted with sharp knives (Rogoff, 2003). Learning trajectories are often massively influenced by the expectations and training practices within a community. Individuals are not infinitely adaptive, but the extent to which they can rise to cultural expectations when provided with opportunities and support is impressive.
The finding that dramatic brain reorganization takes place throughout early childhood and adolescence clearly has implications for education, but linking developmental neuroscience and human behavior research directly to instructional practice and to education policy is complex (Leisman et al., 2015). Nevertheless, educators may be able to use some developmental neuroscience findings to improve instructional practice. For example, research suggests that middle and secondary school students may benefit from instruction that takes advantage of abilities (such as multitasking and planning, self-awareness, and social cognitive skills) that are controlled by the parts of the brain that undergo the most change during adolescence (Blakemore, 2010).
The sequence of cortical maturation in childhood seems to parallel developmental milestones and is reflected in behavior, with motor and sensory systems maturing earliest (Keunen et al., 2017; Lyall et al., 2016; Stiles and Jernigan, 2010). After a pre-pubertal period of cortical thickening (i.e., an increase in the number of neurons and thus the density of gray matter), there is a post-pubertal period of cortical thinning. In general, these processes are the physiological ways in which children’s and adults’ relationships and opportunities—including learning opportunities—and habits of mind directly shape the anatomy and connectivity of the brain. Current developmental neuroscience is largely focused on understanding how networks of communica-
tion and regulation are formed and maintained and how they subtly change with age and experience. In humans, for example, cultural experiences with particular kinds of social values and interactions shape the networks of key regions of the brain involved in social emotional and cognitive processing (see, e.g., Kitayama et al., 2017). Social engagement and cognitive activity help even elderly adults maintain a healthy brain and mind (see Chapter 8).
These facts about how the brain develops throughout life have important implications. First, the processes of brain development persist beyond the first 3 years of age and well into the second decade of life and beyond—that is, throughout the period of formal schooling for most Americans. At the same time, extensive research has revealed that the brain continues to undergo structural changes as a function of learning and experience (e.g., Draganski et al., 2004), and these changes continue into old age (e.g., Lövdén et al., 2010). This research emphasizes that a core mechanism of learning—the brain’s ability to modify its connections on the basis of new experiences—functions effectively throughout the life span (see Box 3-2).
Since HPL I was released, scientists have learned much more about how brain development constrains and supports behavior and learning and about how opportunities to learn in turn influence brain development. For example, research with rats has shown that effects of environmental enrichment can be observed even in mature rats and that they persist well after the adult rats are returned to less-stimulating environments (Briones et al., 2004).
Most of the research regarding the effects of opportunities to learn on changes in brain structure has been conducted in rodents because conducting such studies with humans is obviously more challenging. However, limited research with humans indicates similar effects. To examine how absence of
experience (i.e., a lack of opportunity to learn) influences brain development (and therefore learning), researchers have studied the effects of early deprivation experienced by children exposed to institutional rearing. Neuroimaging studies show that early deprivation of learning opportunities of specific kinds (psychosocial, linguistic, sensory, etc.) leads to a dramatic reduction in overall brain volume (both gray and white matter) and to a reduction in electrical activity (Nelson et al., 2009). However, these researchers found that when children who were reared in deprived circumstances were placed in high-quality foster care before the age of 2, their IQs increased significantly (Nelson et al., 2007).
Consistent with the important role of culture and context underscored in Chapter 2, research has demonstrated both culturally unique and culturally universal neurological structures and functions (Ambady and Bharucha, 2009; Kitayama and Uskul, 2011). It is now known that repeated engagement in cultural practices reinforces neural pathways involved in completing such tasks, ultimately leading to changes in neural structure and function (Kitayama and Tompson, 2010).
The use of an abacus for arithmetic operations, a tool-using capability found primarily in Asian cultures, illustrates this point. Even before HPL I, research in psychology had suggested that abacus experts use a mental image of an abacus to remember and manipulate large numbers while solving problems (Hatta and Ikeda, 1988). Hanakawa and colleagues (2003) examined the neural correlates underlying mental calculations in abacus experts and found that these experts do in fact recruit different brain areas for mental operations tasks than do non-experts. Another example is long-term engagement in culturally embedded behavioral practices such as meditation, which leads to long-lasting changes in neural structure and function and may in some cases offset age-related cortical thinning (Braboszcz et al., 2013; Creswell and Lindsay, 2014; Davidson and Lutz, 2008; Lazar et al., 2005).
Different models have been developed to describe the conditions under which older adults recruit additional resources (see Table 3-1). Although the neural processes that underlie the observed patterns of compensatory neural recruitment are still being actively investigated, these models all emphasize that even in older age there can be flexibility in how neural networks work together and that task demands can influence the nature of those network connections. Moreover, this research emphasizes the fact that earlier life experiences can set the stage for the ability to compensate effectively (Cabeza, 2002; Kensinger, 2016; Park and Reuter-Lorenz, 2009; Reuter-Lorenz and Cappell, 2008). For example, becoming bilingual when young seems to be associated with more robust cognitive development (Bialystok, 2017) and increased cognitive resilience into old age (Bialystok et al., 2016). The lifelong, persistent demand involved in handling two language systems pushes the cognitive
|Hemispheric Symmetry Reduction in Older Adults (Cabeza, 2002)||
|Compensation-Related Utilization of Neural Circuits Hypothesis (Reuter-Lorenz and Cappell, 2008)||
|Scaffolding Theory of Aging and Cognition (Park and Reuter-Lorenz, 2009)||
|SOURCE: Kensinger (2016).|
Although changes in brain structures have not been directly linked to learning throughout the life span, we note several points from this research. First, although the brain is able to change and adapt throughout the life span, environmental influences in the early years lay the neural scaffolding for later learning and development (Amedi et al., 2007; Keuroghlian and Knudsen, 2007). Second, many (though not all) of the age-related changes in brain structure are gradual effects that occur throughout middle age and older adulthood. That is, not all age-related changes in brain structure are linear effects of age (e.g., Raz et al., 2005, 2010), and changes in structure can begin well before older age (e.g., Bendlin et al., 2010; de Frias et al., 2007). We also note
that the age-related changes in brain structure do not affect all brain regions equally: some regions and networks of the brain are affected more substantially by age than others.
Finally, although cortical thickness, mass, and connectivity do appear to decrease with age, older adults are able to compensate for declines in some abilities by recruiting different or additional neural mechanisms. Neural plasticity, which is the ability of the brain to reorganize itself physically and functionally across the life span in response to the environment, individual behavior, thinking, and emotions—in effect, what is colloquially called “wisdom” (Sternberg, 2004)—may partly explain how older adults are able to compensate (see, e.g., Reuter-Lorenz and Cappell, 2008). Even the earliest studies comparing young and older adults’ neural activation during task performance (e.g., Grady et al., 1994) revealed that older adults recruited different regions than young adults did while performing tasks. Indeed, there are few studies that have found reduced levels of neural activity generally in older adults; most studies have found reduced levels of activity in some regions but increased activity in others (Kensinger, 2016).
In this chapter, we examined some of the diverse types of learning that humans must orchestrate in response to the complex social and cultural environments in which they develop. We emphasized that these types of learning are not discrete functions that operate independently but aspects of a complex, interactive process. The learner shapes that process through decisions and capacities to orchestrate his learning, but many aspects of learning occur below the level of consciousness. Different situations, contexts, and pedagogical strategies promote different types of learning. We saw that many kinds of learning are promoted when the learner engages actively rather than passively, by developing her own models, for example, or deliberately developing a habit or modeling an observed behavior. We saw that learning is predicated on learners’ understanding and adopting the learning goal.
In addition, we have explored structural changes that occur in the brain in response to learning and experience throughout life, as well as the processes characteristic of different life stages. We have noted that environmental influences in the early developmental years lay the foundation for later learning and development, that synaptic pruning and other neurological developments through adolescence shape and are shaped by the learner’s experiences, and that the brain adapts to age-related declines in some functions by recruiting other mechanisms.
We have shown that the relation between brain development and learning is reciprocal: learning occurs through interdependent neural networks at the same time that learning and development involve the continuous shaping
and reshaping of neural connections in response to stimuli and demands. Development of the brain influences behavior and learning, and in turn, learning influences brain development and brain health. We highlight three broad conclusions from this work.
CONCLUSION 3-1: The individual learner constantly integrates many types of learning, both deliberately and unconsciously, in response to the challenges and circumstances he encounters. The way a learner integrates learning functions is shaped by his social and physical environment but also shapes his future learning.
CONCLUSION 3-2: The brain develops throughout life, following a trajectory that is broadly consistent for humans but is also individualized by every learner’s environment and experiences. It gradually matures to become capable of a vast array of complex cognitive functions and is also malleable in adapting to challenges at a neurological level.
CONCLUSION 3-3: The relationship between brain development and learning is reciprocal: learning occurs through interdependent neural networks, and at the same time learning and development involves the continuous shaping and reshaping of neural connections in response to stimuli and demands. Development of the brain influences behavior and learning, and in turn, learning influences brain development and brain health.