Supporting current pedagogical approaches with neuroscience research.

In this article we attempt to appropriate relevant neuroscience research findings and draw possible implications to learning and instruction. In such an attempt, we also complement findings from the cognitive and learning sciences with relevancy from social-cultural perspectives of the mind. In essence, although we recognize that direct links or bridges from neuroscience to learning may still be difficult, we conjecture that current pedagogical approaches such as problem-based learning and case-based reasoning are congruent to neuroscience findings. These approaches have roots in theories such as situated cognition where context, problems, activities, emotions, and cognition are interwoven. Thus, the aim of this article is not primarily to draw new implications to educational practice from neuroscience research but rather to support current and recent pedagogical approaches.

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Neuroscience has provided fascinating glimpses into the human brain's development and function (Ratey, 2001) and educators could take advantage of these findings. For example, many educators have known intuitively that children's capacities develop in tandem, and the findings of neuroscience seem to support the educational researchers' pleas for integrated, contextual instruction in mathematics, reading, spelling, and science (e.g., Duffy & Cunningham, 1996). However, the bridge between neuroscience and education is still "unsteady" but the integrated disciplines of cognitive science, learning sciences, and other disciplines related to human functioning and behavior could provide us with possible frameworks for learning and instruction (Bruer, 1997). In other words, neuroscience has yet to make significant progress in its research findings before education can translate or transform these findings into policies into areas such as mathematics and science. Moreover, the present findings in neuroscience appear to suggest possible trends towards recent pedagogical approaches such as problem-based learning, situated learning, discovery learning, case-based instruction, and similar others (Goldberg, 2001; also see the working report of Blakemore & Frith, 2000). Thus, the aim of this article is not to draw new implications to educational practice from neuroscience research but rather to support the more current and recent pedagogical approaches as previously mentioned.

To reiterate, the objectives of this article are to: (a) synthesize some of the major neuroscience research findings and draw relevancy to learning processes; (b) discuss social-cultural factors influencing learning in the brain; (c) put in context some of the types of learning in terms of neuroscience research, and (d) discuss some of the recent pedagogical approaches such as problem-based learning with support from neuroscience findings. Before we begin to discuss neuroscience findings, we find it useful to describe the recent notions of the brain as a biological rather than as a mechanistic computer-like entity (inherited from the information processing paradigm of cognition).

THE BIOLOGICAL BRAIN

The mechanistic worldview and the information processing computer metaphor appear to have dominated our conception of the brain (Newell, 1990). Such a mechanistic worldview had resulted in many explanations of the brain being like a computer and that each part of the brain does a particular function. Predominately, brain imaging techniques (such as Magnetic Resonance Imaging [MRI], Positron Emission Tomography [PET], etc.) are also largely dependent on attempting to find which parts of the brain are activated in the event of certain actions (Squire & Kosslyn, 2000). To date, we could attempt to isolate certain brain areas more dedicated to particular operations, but we have to emphasize that these more focused areas are interconnected to neurons all over the brain (which are not necessarily picked up by brain imaging techniques). Current brain imaging techniques have advanced neuroscientists' work but many of the findings are yet subjected to different interpretations (Pinker, 1999). With all the imaging techniques trying to understand language use (for example), the answer should come from the local circuitry that actually does the computing. "Methods such as aphasiology and neuroimaging are a bit like using bomb craters and blurred satellite photos to understand the long-distance telephone networks" (Pinker, 1999, p. 127).

Edelman (1992) argued that the mechanistic worldview is an inappropriate model because the computer is preprogrammed and automated by an external force--in other words, a prior (see also Clancey, 1997). Furthermore, the powerful role that emotion plays in regulating brain activity, and the preponderance of parallel (rather than linear) processing in our brains, suggested to Edelman that a useful model for our brain must come out of biology, not technology (Sylwester, 1995). Edelman discovered that the immune system does not operate through a rule-based instruction through the memory model but rather through evolutionary natural-selection procedures. Edelman (1989, 1992) subsequently studied the brain to see if it also operates principally on natural selection, rather than on "programmed instruction." His controversial theory (Neuronal Darwinism) argued that our brains do operate on the basis of natural selection--or at least that natural selection is the process that explains learning. Edelman's theory currently appears to be the most completely developed biological brain theory (see Edelman, 1992).

Edelman's (1992) model suggested that the electrochemical dynamics of the brain's development and operation resembled the rich, layered ecology of a jungle environment metaphor. A jungle has no external developer or predetermined goals. It is a messy place characterized more by organic excess than by goal-directed objectives and efficiency. Such views are congruent with the notions of situated cognition where memory should be seen as a process-memory rather than as a storehouse (Clancey, 1997). Each recollection is a re-construction of the past memory, activity, or event rather than a retrieval (Clancey, 1997). The jungle environment does not instruct organisms how to behave in an ecologically appropriate manner, for example, by teaching trees how to position their branches and roots to get sunlight and soil nutrients (Sylwester, 1995). It is more a matter of natural selection. The vast network of highly interconnected neural networks could be seen as the neural equivalent of the complex set of jungle organisms that respond variously to environmental challenges. The natural selection processes that shape a jungle over periods of time also shape the human brain over time--the brain's neural networks. Just as each type of immune antibody responds to a specific environmental antigen, so each sensory network processes a specific element of the external world. Various interconnected combinations of these neural networks process complicated but related phenomena--from sounds to words, to actions, and so forth. Thus, we have an interconnected brain, in that a relatively small number of standard, nonthinking components combine their information to create an amazing complex cognitive structure (Edelman, 1989, 1992). Edelman's theories cannot be "proven," but they provide a conceptual basis that is worth considering in contrast to the computational and information processing world-view.

The theory of neural Darwinism (Edelman, 1989) argued that genetic processes that evolved over time created a generic human brain that is fully equipped at birth with the basic sensory and motor components a human needs to hardwire its basic survival networks (e.g., circulation, respiration, reflexes), but humans also need the flexibility of adaptable or "softwired" networks to be able to respond to specific environmental challenges (e.g., to learn a new language). Thus, one implication is that learning becomes a delicate but powerful dialogue between genetics and the environment (Bateson, 1979). The human brain is powerfully shaped by genetics, development, and experience--but it also then actively shapes the nature of our own experience and of the culture in which we live (Ratey, 2001). Stimulating experiences create complex reciprocal connections among neural structures. Instruction may be perceived more like facilitating the learners' interaction with appropriate environmental and social contexts and teachers. These notions are similar to the pedagogies advocated by situated cognitivists (Bredo, 1994). Situated cognition emphasized the contextual dimensions of knowledge where meanings are considered inseparable from its relations among situations and verbal or gestural actions (Bredo, 1994; Brown, Collins, & Duguid, 1989). In other words, meanings are perceived as inseparable from interpretation, and knowledge is linked to the relations of which it is a product (Clancey & Roschelle, 1991; Dewey, 1910/1981). According to Brown, Collins, and Duguid (1989), knowing, and not just learning is inextricably situated in the physical and social context of its acquisition and use. It cannot be extracted from these without being irretrievably transformed. In other words, one key notion of situated cognition is the emphasis of context including social relationships.

At the most general level, it would appear by implication that behavioral, cognitive, motivational, and emotional systems are designed by natural selection to achieve some level of control over the environment (Sylwester, 1995). There is a crucial distinction, however, between biologically primary and secondary cognitive abilities. It appears that children are prepared emotionally, motivationally, cognitively, and neurobiologically to acquire biologically primary competencies (van der Veer & Valsiner, 1994). Examples of such primary competencies are the acquisition of languages and forms of social cognition, and gauging the physical and biological environment. Learning develops more fully as children are exposed to their environment. Children are biologically motivated to seek out situations, often through play, that help develop their primary competencies such as language and social skills (Vygotsky, 1994). But much of what children learn in school is biologically secondary. Children do not appear to be compelled by biology to learn what they need to learn to function in a technologically complex society like ours. They are primed to acquire language and basic skills, but not to learn to read and solve complex arithmetic problems. Strong cultural support is needed to help children learn those secondary skills, for example, the process of scientific inquiry. These notions are congruent to Vygotsky's thoughts on the genetic laws of cultural development and higher order thinking (Vygotsky, 1981). Vygotsky posits that cognition is grounded on two planes--the biological and the social. The basic biological functions--which are genetically endowed to humans such as language--are innate but they can be developed subsequently at the social-cultural plane. Such a cultural development applies to the disposition of scientific inquiry and thinking (Hung, 1999).

PLASTICITY OF THE HUMAN BRAIN

The (young) brain is very flexible, sensitive, and plastic; possesses the predispositions for learning in certain domains (e.g., language and social interaction), and is influenced by events in the outside world. Sensory areas of the brain develop optimally when the environment contains a variety of sensory stimuli--visual stimuli, textures, and sounds. Although infants' brains undergo a large amount of change in the first few years of life, the human brain continues to develop well into adolescence and adulthood (Bruer, 1999).

To investigate developmental "plasticity" (also see Blakemore & Frith, 2000), Wiesel and Hubel (1965) temporarily covered one eye of newborn kittens. After about three months, the eye was uncovered and the researchers studied the connections between the two eyes and the brain. They found that this early visual deprivation led to a severe deterioration of neuronal connections in the visual areas of the brain and to virtual blindness. This is because the brain had received no stimulation from the deprived eye and it had wired itself to receive information only from the other, open eye. The kittens remained blind in the initially deprived eye. The irreversible consequences of early visual deprivation are often cited as evidence for the importance of early childhood education. However, subsequent research has suggested that some recovery of function is possible depending on the specific period of deprivation and the circumstances following deprivation. The shorter the period of deprivation the more recovery of function is possible. This is enhanced if the animal is trained to use the initially deprived eye after it is uncovered (Blakemore & Frith, 2000; Mitchell, 1989).

LEARNING AND DEVELOPMENT

Neuroscientists have learned that the brain's "wiring diagram" starts to develop early in gestation (Bruer, 1997, 1999). All parts of the brain develop in an integrated fashion over time and an infant comes into the world with a nearly adult-sized brain that has most of its mental circuitry already in place. The task remaining is to "solder" the neural connections linking the cerebral structures. That process takes place at a rapid pace in the first five or so years of life, with the appearance of first steps, first words, and other developmental milestones taking place within a well-established time frame. Most neuroscientists do agree that certain forms of learning are acquired with ease during various periods in an infant's development. Language is one such faculty. Early experiences, without a doubt, help shape the brain (Blakemore & Frith, 2000). The brain's "wiring" and thus its potential for future learning are influenced by the sounds, sights, and touch of the first few years of life (Bruer, 1997).

Most neuroscientists now believe that "critical periods" for developing certain cognitive functions are not rigid and inflexible. Rather, most interpret them as "sensitive" periods comprising subtle changes in the brain's ability to be shaped and changed by experiences that occur over a lifetime (Bruer, 1997, 1999). Higher cognitive capacities, such as language, have several sensitive periods, many of which continue into adulthood, including second language learning (Blakemore & Frith, 2000). Neuroscience research findings and common sense in early childhood care go hand in hand: it is important that parents and teachers rapidly identify and, if possible, treat children's sensory problems, such as visual and hearing difficulties, so that they can regain normal function. They also suggest that recovery can occur and that learning can take place later in life. This may be a different kind of learning and may be facilitated by different kinds of teaching. Whether sensitive periods exist for culturally transmitted knowledge systems, such as those responsible for reading and arithmetic, is currently unknown (Bruer, 1997, 1999). Some of the brain's primary functions, such as vision, (may most probably) have critical developmental periods, which, if missed, will never occur. Whether critical periods exist for other functions such as language is not clear, but there at least may be sensitive periods--windows of opportunity when learning is easier. Neuroscience has not yet established whether secondary (higher-order) skills such as reading and math have critical or sensitive periods for acquisition (Blakemore & Frith, 2000).

LANGUAGE LEARNING AND CULTURAL INFLUENCES

Up to now, the results with PET and functional MRI have not appreciably advanced our understanding of the neural mechanisms that underlie language ability beyond what we already knew through other approaches to the study of the functional organization of the human brain (Squire & Kosslyn, 2000). It would take more than technological advancements in imaging techniques for us to make significant progress in understanding the neural bases for language (Pinker, 1999). We will need highly detailed processing theories of language that will provide the functional pieces that map onto neural events. Current frameworks that advocate notions such as "reading center" are meaningless, since reading is assumed to be the product of a vast network of perceptual/cognitive/motor mechanisms involving many areas of the left and right hemisphere. There is no single brain center for reading, writing, or comprehension. There are only networks of highly specific mechanisms dedicated to the individual operations that comprise a complex task (Pinker, 1999).

However, what we know is that language and its components--sounds, vocabulary, and grammar--are mastered in early childhood using powerful (possibly innate) learning mechanisms that help children learn a vast amount by listening to and interacting with adults and siblings. Because children acquire language without any explicit instruction well before they enter formal education, it has been suggested that humans have a predisposition to learn and generalize the rules of language (Pinker, 1994). Newborn babies are able to distinguish between all speech sounds. Sound organization is determined by the sounds in a infant's environment in the first 12 months of life (Kuhl, 1998). There is evidence that learning the sounds of one's own language begins in the mother's womb (Mehler et al., 1988). Again, it is likely that some dedicated neural mechanisms for processing speech are present in the human brain. A group of researchers recently used MRI to obtain brain scans from children of ages 3 to 15 years. The researchers scanned the children's brains at intervals ranging from two weeks to four years, which allowed them to follow changes in their brains and construct "growth maps" of the children's brain development (Thompson et al, 2000). They found that the children's brains develop in a specific pattern, with a spurt of growth that starts in the front of the brain from ages 3 to 6. Between the ages 6 and 13, the researchers found that the pattern of rapid growth moves from the front to the back, toward the areas of the brain that are specialized for language skills. The researchers found that there is a sharp cut-off in the growth of the language areas of the brain after age 13. Here is some evidence that language should be better acquired in earlier years (Blakemore & Frith, 2000).

We also know from cognitive psychology and language relating to studies in Japan, Russia, Sweden, Finland, and the United States that babies are born with a keen ability to distinguish among language sounds (Kuhl, 1998). Adults, on the other hand, cannot readily separate similar sounds in a foreign language. That is why adult native Japanese speakers have great difficulty learning the difference between R and L and native English speakers cannot tell a B sound from a V sound in Spanish. In contrast babies brought up in the USA at the same age become even better at hearing this distinction because they are exposed to these sounds in their language. Similarly, before about 12 months of age babies brought up in the USA can detect the difference between certain sounds common in the Hindi language, which after 12 months they cannot distinguish (Kuhl, 1998).

Infants learn to categorize their languages' specific vowel sounds simply by listening to their parents' speech. At six months, even before they can produce and understand words, infants' perceptual systems are configured to acquire their native languages. They have developed an adult ability to ignore fine differences in instances of the sounds and lump them into a single category. This "wiring" must be quite extensive after a year of exposure to the language. Thus it seems that wiring or "perceptual maps," accounts for the indelible accents that signal national and regional origins.

These findings have important implications for educators. First, they underscore the role of the parents or caretakers, who provides essential early input during the crucial birth to age three timeframe. Second, the practice of teaching foreign languages could possibly start as early as possible. Perhaps the ideal situation would be to learn two languages from infancy in a household with a native speaker of each.

NUMBER-CONCEPT LEARNING AND CULTURAL INFLUENCES

For the adult brain, lesion and brain-imaging studies indicate that the left and right intraparietal area, which is involved in visuo-spatial processing, is associated with knowledge of numbers and their relations ("number sense"). The parietal lobe is activated by arithmetic (Dehaene, Spelke, Pinel, Stanescu, & Tsivkin, 1999). This fits with the notion that calculation contains a spatial element. It is believed that this quantity representation system is present, at least in a rudimentary form, very early on in development and even in evolution because behavioral studies have revealed number perception, discrimination, and elementary calculation abilities in infants (Spelke, 1994) and animals. Fluency in arithmetic in adults is likely to depend on a constant interplay between quantity, visual, and verbal representations of numbers. For instance, rote calculations are performed largely using the verbal system, while approximations of calculations are made using the quantity system. Many studies have shown that there are gender differences in mathematical ability. However, if they exist, these differences are clearly not purely biological. There are variations in the size of the gender effect between cultures. Procedures, such as steps taken to accomplish a goal, are often linked to culture. Counting, for example, is a simple numeric procedure for which a Papua New Guinea tribe has developed a system based on the human body. Their counting system consists of 27 body parts starting from the thumb of one hand, progressing up around the upper body to the little finger of the opposite hand. The tribe has no number words, so they gesture toward the body part representing that number--the bicep is nine, for example (Saxe, 1996).

In contrast, English-speaking peoples have evolved a very different way to count (Saxe, 1996). The system involves using words to describe numbers; the English language has many number words that children by age two or three use to count objects. Children learn many other procedures, such as how to multiply, divide, and do fractions. They are also taught conceptual operations that give mathematical meaning to the procedures they learn. These conceptual operations are really a form of sense-making, and their organization shifts as they develop. If elementary school students are asked to write a fraction showing how much of the drawing is gray, some children will write 1. Those children are interpreting the fraction as a whole number. Other children will write 1/6, meaning they understand fractions as the relationship among parts. Still others will view it as a part/whole relationship, writing the number 1/7, not appreciating the square as a continuous quantity. Each of those ways of making sense of this fraction has some internal coherence. This demonstration makes it clear that procedures can be quite separate from conceptual operations, and developmental psychologists are trying to understand how the organization of mathematical thinking shifts over time (Saxe, 1996).

LEARNING AND MEMORY

Neuroscientists do not completely understand the process of memory, but they believe that attention, thought, and memory emerge out of synchronized patterns of neural network activity. Memories are composed of changing and constant elements that vary in their ease of recall. The information stored by synaptic changes in neural networks (the memory engram) is only a small part of a much larger system that involves such processes as perception and emotion, synthesis and analysis, speech, and writing. Memory is a function of the entire system, not just synaptic changes. Whereas a tape replays exactly what has been stored, each memory recall is a fresh reconstruction of the past activity (Clancey, 1997).

Thus human memory is less precise than machine memory, and it is more adaptive and inventive over time as experience adds depth and breadth to the variability of our memories. Unfortunately much of our schools testing program requires our students' very inventive biological memory system to exhibit high-level technological machine precision. A memory is a neural representation of an object or event that occurs in a specific context, and emotionally important contexts can create powerful memories. When objects and events are registered by several senses (e.g., seeing, hearing, touching, tasting), they are registered in several interrelated memory networks. A memory stored in this way becomes more accessible and powerful than a memory stored in just one sensory area, because each sensory memory checks and extends the others.

We probably all agree that learning is part of memory. Learning is what happens when information is presented; memory is the gradual process of manipulating that information into a form it will maintain over time so it can be consciously retrieved, applied, and transferred to other contexts. If there is no learning, there is no memory, and therefore, no literacy. Humans have multiple memory systems: the declarative system, which makes possible conscious memory; the ability to pull information out of the memory banks and apply it in flexible ways; an emotional system, which in some ways operates independently but can influence operation of the declarative system; and systems that deal with learning motor skills. All are important in developing understanding. No structure in the brain is an island. Each performs its function as part of a system.

The brain's multiple memory systems processes and act on information in different ways. For example, short-term memory is formed in one part of the brain but must be transferred to another for long-term storage and retrieval. Different memory systems contribute differently to physical, intellectual, and emotional activities (Ratey, 2001). Long-term memory relies on many parts of the brain. For example, spatial knowledge, object knowledge, and word knowledge are stored in different parts of the cerebral cortex. Short-term memory, considered the cornerstone of cognition, shows a high correlation with intelligence, although we do not yet know whether individual differences in achievement or problem-solving strategies reflect biological variations in brain function. These findings in neuroscience have immediate implications for higher-level thinking skills--abstract problem solving, inference, deduction, and so on--the very issues we are being called on to address in education reform. We need to ask whether or not agility in rote memory acquisition and conventional IQ tests are the only standards of excellence. We also need to develop measures that accommodate creative thinkers and conceptualizers and consider individual variations in learning style.

The short-term memory process appears to function through temporary synchronized firing patterns that emerge between related networks in the thalamus (the current situation) and the cortex (related memories). The more rapid firing, synchronized thalamus-cortex networks become foreground (attentional) information, and the less active neural networks become background (or context). Because short-term memory space is limited to perhaps a half-dozen units of foreground information at any time, we must rapidly combine or chunk related bits of foreground information into larger units by identifying similarities, differences, and patterns that can simplify and consolidate an otherwise confusing sensory field. Thus, we see a person as a unit, not just the individual components of the body. Our conscious brain monitors the total sensory field while it simultaneously searches for and focuses on familiar, interesting, and important elements--separating foreground from background.

The development of a long-term memory emerges out of an ill-understood, often conscious decision that elements of the current situation are emotionally significant, and will probably reoccur. If the situation does indeed reoccur after the memory is formed, sensory and perceptual processes will represent it in the thalamus. The cortical memory of the previous experience, resonating with the current thalamic perception, will often create an attentional state, and help determine the response--current behavior influenced by past experience.

Declarative long-term memories are factual and label-and-location memories. The principal brain mechanisms involved in processing declarative memories are the hippocampus (in the limbic system) and the cortex (especially the temporal lobes). Episodic declarative memories are very personal--intimately tied to a specific context whereas semantic declarative memories are more abstract--context-free and often represented by symbols, such as those used in language and mathematics. Procedural long-term memories are automatic skill sequences. The principal brain mechanism involved in processing procedural memories are the amygdala (brain's emotional center, located in the limbic system), the cerebellum (located at the lower part of the brain), and the automatic nervous system (which regulates circulation and respiration)--but procedural memories also involve altered muscle systems. Episodic memories (memories of episodes) are stored in different brain areas from semantic memories (memories of facts). Thus, people with profound amnesia cannot remember episodes (what has happened to them a few minutes ago) but can retain their semantic knowledge and can still talk. Different regions of the prefrontal cortex are used in memory encoding (left prefrontal cortex) and memory retrieval (right prefrontal cortex) (Fletcher, Shallice, & Dolan, 1998).

A biological memory system differs from a mere physical information-storage device by virtue of the system's inherent capability of using the information in the service of its own survival--any biological system's first priority. The key process of memory is retrieval. The storage or engram alone, in the absence of retrieval, is no better than no storage and no engram at all. If you know something, or if you have stored information about an event from the distant past, and never use that information, never think of it, your brain is functionally equivalent to that of that does not contain that information. Such a thought is profound for education. If students do not find an application for the knowledge they may learn in schools (because it is thought to be feasible to cramp as much knowledge into the students' heads), such knowledge becomes unproductive and useless (Wertheimer, 1961). Another basic principle in neuronal structuring in the brain is: "neurons that fire together wire together" (Ratey, 2001, p. 31). In other words, through application and experience, students strengthen the "wiring" of the neurons that pertain to a particular knowledge gained. Through use and application, the neurons take a particular form and structure. Through not using that formed neuronal structure, the neurons pertaining to that structure may weaken and gradually "break up." In other words, if we do not apply knowledge gained, we soon loose it. The axiom is: "use it or loose it" (Ratey, 2001, p. 31). If students learn certain knowledge such as historical dates of kings in certain eras, and do not retrieve it or apply it anywhere for years to come, all the efforts in trying to memorize those facts would be seemingly put to waste!

Neuroscientists are compelled to conclude that certain physical-chemical changes must exist in the nervous tissue that correspond to the storage of information for remembering. However, if the engram is a kind of an entity that manifests itself only in activity, or retrieval, then we might conjecture that the physical changes resulting from an experience do not exist as an engram in the absence of that activity. Remembering is a completely emergent, biological-psychological process of the brain. In other words, recall and memory are intricately related to the activity, context, or situation through which that "chemical" or neuronal-memory change occurred. Again, these thoughts are congruent with situated cognition (Clancey, 1997). The implications for learning in schools are crucial in that students must be able to make sense of the knowledge learned, and often the context or situation through which knowledge is applied aids in the sense-making process. Rote learning methods often times present a seemingly context-poor situation, and as a result, remembering knowledge through such pedagogical methods seems to be an "unnatural" way of knowledge acquisition.

MODELING AND IMITATING

Human infants appear to be born with the ability and the disposition to imitate the communicative gestures of those around them. They will copy facial expressions when only a few days old, and by 10 weeks mimic such basic emotions as happiness and anger (Harris, 1989). This early imitation is likely to have a different brain basis from later imitation. When human subjects observe someone making a movement, without making any movements themselves, a component of their brain's motor system is activated (Iacoboni et al., 1999). Imitation might play a role in understanding other people's intentions and desires from their motor actions. Polanyi (1964) emphasized that in imitation hidden "rules of the art" (including beliefs) are being appropriated.

The traditional apprenticeship model of learning would require a distinct use of modeling processes. Learning through apprenticeship was initially assumed by educators to be concrete, mechanical, rote, imitative, and neither creative nor innovative (Hung, 1999). However, these views of apprenticeship learning have changed, and the resurgence of it's interest has been supported by the recent claims that cognition is context dependent and that the community plays an important role (Jonassen & Rohrer-Murphy, 1999; Lave & Wenger, 1991). In the same vein, cognitive apprenticeship methods (Collins, Brown, & Newman, 1989) seek to enculturate learners into authentic practices through activity and social interaction. Such methods support learning in a domain by enabling students to acquire, develop, and use conceptual tools in authentic domain activity, just as craft apprenticeship enables apprentices to acquire and develop the tools and skills of their craft through membership of the trade.

"Modeling is the process of offering behavior for imitation" (Tharp & Gallimore, 1988, p. 47). To Bruner (1996), modeling functions which includes "showing" or "telling" were as humanly universal as "speaking" (p. 20). Modeling is a powerful means of assisting performance, one that continues its effectiveness into adult years. In the educational setting, both expert teachers and peer models are highly important sources of assisted performance, for children and adults alike (Gallimore & Tharp, 1990). Modeling is usually complemented with notions such as coaching. Coaching is the situated responses and feedback running through the entire learning or apprenticeship experience (Collins, 1996; Laffey, Tupper, Musser, & Wedman, 1999). The teacher or master coaches the apprentice student through a "wide range of activities," for example choosing the task, providing hints, evaluating his or her progress, giving encouragement and feedback (including instructing and question), structuring more scaffolds if necessary, and working to improve the student's skills. Modeling coupled with feedback (which is coaching) is a powerful means of learning. Feedback is such an integral part of normal life that it goes unnoticed; without feedback to the self, no correction is possible (Collins, 1996; Gallimore & Tharp, 1990). Questioning, in contrast with instructing, explicitly calls for an active linguistic and cognitive response-it provokes the student to think (Hung, 1999).

SKILLS LEARNING

Related to apprenticeship learning is skills learning. Learning a skill, that is procedural learning, differs from the acquisition of declarative knowledge (Bechara et al., 1995). It is well known that amnesic patients who have hippocampal damage have severely impaired declarative memory--they are unable to learn and retain new facts. However, they are able to learn new skills and they retain skills they acquired before their brain damage. For example, a severely amnesic patient who used to be a pianist has no memory for events that happened more than five minutes earlier, but is nonetheless able to play the piano as perfectly as he could before damage to his hippocampus occurred. Amnesic patients are also able to acquire new skills, despite not explicitly remembering being taught the skill. It is proposed that such patients' basal ganglia, which are intact, are capable of procedural learning and execution and maintaining previously acquired skills. Patients with Parkinson's disease, whose basal ganglia function abnormally, have good memory for episodes and facts, but they are unable to learn new skills. In other words, there appears to be a dissociation between declarative and procedural learning, confirmed by recent functional imaging studies (Gabrieli, Brewer, & Poldrack, 1998). For purposes of teaching it might be important to know that learning facts, such as mathematical equations and historical dates, relies on different brain regions than learning to play a sport or a musical instrument. A possible research question is whether or not the two kinds of learning can occur in parallel rather than each having to be taught separately. Another point is that when a skill to be learned becomes mastered, students can free up their cognition for other parallel actions. When learning a skill, the cortex can initially increase, but when the skill becomes automatic, the neurons are delegated to other parts of the brain lower in the chain of command. The control center--the cerebral cortex reverts back to its original size, freeing up neurons to learn other things (Ratey, 2001).

IMPLICIT LEARNING

Briefly, research on implicit learning has shown that the brain processes information that is neither attended to nor noticed. This tendency of the brain to do things "behind one's back" is pervasive and might have repercussions on theories of teaching. It has been shown by psychologists and neuroscientists that learning can be implicit and explicit, and these different types of learning take place in different areas of the brain. Implicit memory is typically seen when we experience a vague sense of familiarity. Moreover, objects identified as familiar are also preferred. We will briefly highlight some of this research, and its possible implications for education. In the future it should be possible to establish a systematic program of research in which different types of learning at different ages and the effects of different types of teaching are made visible in the brain.

Many years of research on implicit learning have shown that people are able to learn information in the absence of awareness. People can learn complex rules by being exposed to sequences that adhere to the rules, without having any explicit notion of the rules or having learned them (Berns, Cohen, & Mintun, 1997). Berns et al., (1997) mapped brain regions responsive to novelty without awareness using brain imaging techniques (PET). Subjects performed a simple reaction-time task in which all stimuli were equally likely; but unknown to them, they actually followed a complex sequence. Subjects' behavioral performance indicated that they learned the sequences even though they were unaware of the existence of any order. It is clear that we know tacitly something about things before we can talk about them (Polanyi, 1964).

Teaching often involves making procedural knowledge declarative. How do teachers know when to make rules explicit? Does a reciprocal dialectic between implicit learning and explicit teaching aid learning? Can explicit teaching replace missing implicit learning? Is a degree of prior implicit learning always helpful?

PROBLEM SPECIFIC LEARNING MECHANISMS

We tend to think of learning as information we have access to, but that is not always so. Research has shown that patterns of activity persist in the brain, even when a behavior such as fear is no longer being expressed (Gallistel, 1999). When learning increases activity in one cell, it also increases the relationships among cells, forming "cell assemblies," which can be seen as memories. These cell assemblies may fire spontaneously, without receiving a stimulus. In other words, the circuit initiates its own activity, which may shed light on phobias and other unconscious behaviors. These patterns of "hard-wired" activity appear to relate to the phenomenon of learning position by dead reckoning, which is a ubiquitous process in animal navigation. It is well-developed in insects and birds (Gallistel, 1999).

As with all learning mechanisms, the ant (or bird) most likely has to "compute" and "store" the value(s) of navigational variables. In this case, the mechanism computes the values of the variables that represent the animal's position relative to its nest or home base. The computation is equivalent to integrating velocity with respect to time. Such examples of nonassociative learning mechanisms have led scientists to argue that there is no unitary learning process at the computational level of analysis. There are many different learning mechanisms or modules. They can be called these different problem-specific learning mechanisms "instincts to learn." Each such mechanism has a structure that enables it to compute certain facts about the world. In other words, learning is made possible in humans and animals, by computationally complex task-specific learning modules. To understand these modules neurobiologically, we have to understand how neural tissue represents the values of variables, how it stores and retrieves those values, and how it implements the presumably small set of basic computational operation, such as addition and multiplication, on which all the modules depend. Presently, we are not close to understanding any of these things.

LEARNING AND EMOTIONS

If the emotional setting in which a memory originally occurred is tied to the memory, recreating the original emotional setting enhances the recall of that memory and related memories. Emotional, multisensory school activities such as games, role playing, simulations, and arts experiences can create powerful memories. Our task as educators is to help students begin to find relationships between the somewhat random, often trivial fact-filled experiences of everyday life and the fewer enduring principles that define life--and help create and constantly test the memory networks that solidify those relationships. It seems that one of the best school vehicles for this search for relationships is storytelling as a broad concept that includes elements such as conversations, debates, role playing, simulations, songs, games, films, and novels. The best students are the ones who always have their hand in the air to expand the discussion through stories about their own experiences--to maintain and extend their own memory networks through active recall (Schank, Berman, & Macpherson, 1999).

Learning is also related to imagery. Four empirical findings are particularly relevant to understanding the relationship between imagery and emotion. First, neuroimaging studies have revealed that at least two-thirds of the same brain areas are activated during visual imagery and visual perception, even when the stimuli and task are very different (Mellet et al., 2000). Second, people differ dramatically in their abilities to form and use imagery. Moreover, these differences are specific to individual aspects of imagery. For example, the ability to imagine objects rotating is not necessarily related to the ability to scan objects in images or to the ability to form vivid images. Third, visualizing aversive stimuli (such as horror pictures) causes skin conductance changes and changes in heart rate. Thus, mental images affect the body. Fourth, visualizing aversive stimuli activates some brain areas more than visualizing neutral stimuli (such as everyday objects) (Kosslyn et al., 1996).

It is known that negative emotional events are better remembered than nonemotional events. Both animal and human investigations have shown that the amygdala, an important part of the brain's limbic system, is involved with the formation of enhanced long-term memory associated with events arousing fear and sadness. For example, people were scanned while viewing and remembering emotionally arousing films (Cahill et al., 1996). Activity in the amygdala while viewing the emotional films was highly correlated with the number of emotional (but not the number of neutral) films recalled. This suggested that the amygdala is crucial for memory of emotionally salient events, which are better recalled than neutral events. The same group of researchers has shown that the amygdala interacts with the hippocampus, the part of the brain that stores nonemotional memories. It appears that the amygdala is particularly involved in fear conditioning, which may be a type of one-trial learning. In contrast the hippocampus is responsible for remembering neutral events.

Using activities that provide emotional context. Memory is contextual, and the context of an experience often triggers emotion. The reoccurrence of the emotional state in which memory was formed can thus trigger the recall of the memory. Emotional-laden classroom activities such as simulations, role playing, and cooperative projects can provide the important contextual memory prompts that a student may need to recall the information during a closely related event in the world outside the school. Doing worksheets in school probably prepares a student emotionally to do worksheets in life (Sylwester, 1995).

EMOTION AND ATTENTION

Damasio (1994) differentiated between primary and secondary emotions. Primary emotions included innate responses to stimuli that had a high potential for danger--loud noises, sudden looming shadows, and so forth. The principal brain structures appeared to be the limbic system (especially the amygdala) for primary emotions, and specialized cortical networks in the right hemisphere, and frontal lobes for secondary emotions and for modulating the more primal emotional responses of the limbic system. Our emotions allow us to assemble life-saving information very quickly, and thus to bypass the extended conscious and rational deliberation of a potential threat. Most incoming sensory information is sent first to the thalamus, and then it is relayed to the sensory and frontal lobes (in the cortex) for detailed analysis and response (Goldberg, 2001). A second, quicker pathway also sends any emotionally laden information from the thalamus to the amygdala (in the limbic system), which uses primitive general categorizations of the limited sensory information it has received to activate an immediate aggressive or defensive response, if the stimulus is sufficiently strong. Emotions can be controlled if time if given to insert rationalizations into the process of actions and thought.

Attention has always been a central concern of educators. The brain's ability to focus and maintain its attention on objects and events is critical to learning and memory, and attention is a basic element on classroom motivation and management. Attention deficit disorder and dyslexia are two educationally significant attentional maladies. An effective attentional system must be able to (a) quickly identify and focus on the most important items in a complex environment, (b) sustain attention on its focus while monitoring related information and ignoring other stimuli, (c) access memories that are not currently active, but that could be relevant to the current focus, and (d) shift attention quickly when important new information arrives. The formation and recall of each memory are influenced by mood, attention, surroundings, and gestalt at the time the memory is formed or retrieved. That is why the same event can be remembered differently by different people (Ratey, 2001).

A math relay game is an active, emotional, and fun-filled complement to the tedious task of mastering the multiplication tables. Such games artificially enhance attention-getting excitement through rapid action, and teachers have intuitively used them to adapt their instruction to the processing realities of their students' stable attentional mechanisms. Discussions, debates, and storytelling activities force students to hold bits of information in their mind so that they can communicate with others on the same subject. Collaborative learning activities create opportunities for students to attend to others' contributions as well as their own. Simulations, role-playing, and games require students to compare the real world with a created world. Metaphoric stories and dramas provide only the outlines of the study, forcing students to fill in the details. And metacognitive discussions about attention compel students to confront their own thought processes. Good simple ideas will easily emerge in an exploratory environment.

LEARNING AND CONTEXTUAL EXPERIENCES

The individual person and individual brain cannot be studied in isolation (Bateson, 1979). Educators and psychologists have a long and distinguished track record in illuminating this question. However, neuroscience can also offer insights about how context affects learning. Context includes biological as well as social factors, and factors that are intrinsic to the task being learned (Goldberg, 2001).

An exciting area of research has been that addressing the conditions under which processing capacity is dynamically reallocated, resulting in fluctuations in sensitivity to sensory stimuli. The characteristics of sensitivity changes are many and varied, but all serve to optimize acquisition in a world in which environmental features and behavioral goals are constantly in flux. The form of these changes may be broad in scope or highly stimulus-specific and task-dependent (Goldberg, 2001). Changes may be instantaneous, or they may come about gradually through exposure to specific environmental features. Finally, sensitivity changes differ greatly in the degree to which they are influenced by stored information about the environment and that they are under voluntary control.

Learning and memory appear to be rich experience-dependent processes involving active synapse formation and modification in response to contextual situations. Thus contextual experiences update the organization of the brain on the basis of the learner's experience. Play is an important element in a child's neural development (Sylwester, 1995; Vygotsky, 1994).

CURRENT PEDAGOGICAL APPROACHES

Edelman's (1989, 1992) model of the brain as a rich, layered, messy, unplanned jungle ecosystem is especially intriguing, however, because it suggests that a junglelike brain might thrive best in a junglelike classroom that includes many sensory, cultural, and problem layers that are closely related to the real-world environment in which we live--the environment that best stimulates the neural networks that are genetically tuned to it. The classroom of the future might focus on drawing out existing abilities than on precisely measuring one's success with imposed skills, encourage the personal construction of categories rather than impose existing categorical systems, and emphasize the individual, personal solutions of an environment challenge (even if inefficient) over the efficient group manipulation of the symbols that merely represent the solution. Integrated activities, problems, and real-life authenticity would evoke engagement in tasks with the possibilities of discovering concepts and productive knowledge and understanding.

Recent pedagogical notions such as problem-based learning, case-based reasoning, authentic tasks, and interdisciplinary project work seem to find support from neuroscience literature and findings. One must be careful drawing implications from neuroscience into pedagogy; however, it would seem to suggest that some of these pedagogical approaches are seemingly in the appropriate direction.

Problem-based learning (PBL) starts primarily with a focus on problems, that is, real-life problems and activities, rather than intense disciplinary knowledge. The approach attempts to move students towards the acquisition of knowledge and skills through a staged sequence (serving as a scaffolding process) of problems presented in context, together with associated learning materials and support from necessary sources, for example, teachers and experts (Hung, 2002a). Neuroscience appears to advocate contextual activities and tasks for learning, whereas from a cognitive psychology perspective, the learner is to construct knowledge meaningfully, which leads to engagement of personal sense and emotion.

Case-based reasoning capitalizes on narratives and stories in instruction. These stories provide a context and meaning, which relate to persons and emotions, thus supporting multi-neuronal activations in the brain and episodic memories. Moreover, as the world and human brain are characterized by multi-layered, interconnectedness (see Edelman's model of the brain), the recent notions of interdisciplinary project work also appear to model the inter-relationships between knowledge and understanding. These attempts of making inter-relationships are no more from a perceptual point of view are mechanisms, which assist learners in constructing more holistic meanings in their understanding.

Authenticity in school curricula can be supported by investigations that are open-ended, answers that are not predefined by any particular perspective or discipline, and that students can be engaged in the social construction process of knowledge. Project work can be seen as a form of open-ended contextual activity-based learning and instructional process that places great emphasis on student problem solving as a collaborative effort carried out over historical period of time. Blumenfeld et al. (1991), described project based learning as centered on relatively long-term, problem-focused, meaningful units of instruction that integrate concepts from a number of disciplines or fields of study. However, learning which begins in rich situated contexts can be "transferred" if intentions to decontextualize or generalize the concepts or knowledge constructions across situations are made (Bereiter & Scardamalia, 1989). Authenticity and generalizibility or abstraction of meanings should be seen as complimentary. The human brain appears to have "designated" locations for different forms of memories and knowledge--episodic, procedural, and declarative. On the whole, because the brain is a biological entity, it would appear that John Dewey's (1910/1981) notion of learning by doing--through which knowledge is translated into action and being--is congruent with neuroscience research findings (see also Schank, Berman, & Macpherson, 1999). Experience, thoughts, actions, and emotions actually change the structure of our brains. Learning is a continuous, unending process. From a neuronal perspective, axons and dendrites, and their connections, can be modified up to a point, strengthened, and perhaps even regrown.

ISSUES RELATING TO PRACTICE--THE WAY AHEAD

To date, neuroscientists admit that the findings of neuroscience have not yet been able to inform educational practices in a resounding way (Blakemore & Frith, 2000). The gap between neuroscience and educational research is still large (Bruer, 1997). Nevertheless we have sufficient evidence that the way ahead in terms of pedagogy and learning is along the paths of meaningful contextualized learning and no longer with a predominam emphasis on knowledge reception per se. The previously mentioned accounts and studies of brain research and neuroscience are aimed at providing the reader with a greater depth of research findings; however, we reiterate that many of the findings have yet to find implications to practice. It is thus not the aim of this article to draw implications from the detailed neuroscience findings, but rather to point the way ahead for educational practice that recent pedagogical approaches of problem-based learning and theories of situated cognition are worth pursuing. The way ahead for pedagogical practices such as problem- and case-based learning can be synthesized along three principles as described next.

First, although not specifically neural in perspective, Brown and Duguid (2000) emphasized that learning is demand-driven, which conjures the notions of problem-specific, skills-based, and attentional learning. The demand drivenness denotes the sense of intentional and voluntary learning based on specific and regulatory demands and personal needs. The demand driven principle springs from the contextual and implicit demands of the cultural and social context, which imbues meaning to the learning process (Brown & Duguid, 2000).

Second, many recent educators are also advocating that learning is a process of doing through a social act (e.g., Brown & Duguid, 2000; Schank, Berman, & Macpherson, 1999). Engaging in cognitive acts is seen as a process of "cognitive doing" through social-cognitive actions such as cognitive modeling and imitating. Methods such as cognitive and skills based apprenticeships have recently been advocated in schools. Such notions attempt to bring the community of practice into schools (Hung, 2002b). Moreover, researchers such as Barab, Squire, and Dueber (2000) have also recently espoused the idea of bringing schools into the community of practice such that authenticity and situated practices are preserved. All of these attempts are efforts to focus on authenticity and skills relevant to the real-world. Through such practices, it is hoped that students will acquire the dispositions and beliefs of practitioners in the real-world (Hung, 1999).

Finally, neuroscience is also revealing to us that learning is a complex process and that traditional forms of pedagogy such as rote learning has to be fundamentally refocused. Brain functions are not fixed and permanently localized and that the brain is fundamentally dynamic. Such a reorientation also informs educational practices in ways that encourage multiple forms of intelligences and instructional methods. Ultimately learning is with a goal of developing dispositions, and abilities that spans multiple, contextualized, and interdisciplinary knowledge and skills.

These principles are congruent with educational goals that are different from the traditional conceptions of learning and instruction. The principles are aligned with constructivist practices of learning and instruction (Duffy & Cunningham, 1996) connoting active forms of cognition where the construction of meanings are orchestrated by the prefrontal lobes (Goldberg, 2001). The prefrontal lobes as the brain's orchestrater and indexer of meaning are a vital "processor" of the brain without which meaningful retrieval is inhibited. Where learning is demand-driven, doing-focused, contextual, and interdisciplinary in nature, the frontal lobes index knowledge and meanings based on the rich implicit and explicit cues derived from authentic problems and situations (Goldberg, 2001).

Thus far, we can only draw generic applications to practice and not specific relations to language, mathematics, and the arts and sciences. Due to the plasticity of the brain, many of the former theories that were formulated were not able to accurately inform practice. Thus, we have chosen at this stage of the research findings to only draw relevance to generic and recent educational practices, rather than specific frameworks of how to instruct in terms of language acquisition and other disciplines.

CONCLUSION

Despite the remarkable progress, brain research has not yet found significant application in theory or practice of education except in providing us with conjectures of whether or not the pedagogical approaches and policies are headed in the appropriate directions or orientations. However, we have still deemed it appropriate to present to the reader the findings as described. Misconceptions about neuroscience, what neuroscientists are interested in, and how far neuroscience can extend in terms of its application to education, are only too easy to foster. Learning experiments conducted by cognitive psychologists have taught us a lot about how we process information, about different ways of encoding information, about how to make encoding more efficient, and about the application of skills that enhance learning, such as mnemonics and imitation. Results of these experiments as well as studies related to social-cultural dimensions of the mind have implications for education as well as for neuroscience research.

We strongly believe that to continue discovering how the brain learns and how to facilitate this learning, an interdisciplinary "learning science" is needed. This would allow a common vocabulary to emerge and research questions to be discussed and elaborated through which future learning technologies and pedagogies can emerge.

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DAVID HUNG

National Institute of Education, Nanyang

Singapore

[e-mail]