Science of Learning: History

A workshop held at NSF, 4-5 October 2012

Submitted by the Steering Committee: David Lightfoot (PI), Ralph Etienne-Cummings, Morton Gernsbacher, Eric Hamilton, Barbara Landau, Elissa Newport and David Poeppel

Science of Learning Workshop: History

1. IntroductionFor a long time NSF has supported work on learning, through regular programs in SBE, CISE and EHR and also through special large initiatives like the Learning

and Intelligent Systems component of the two-year Foundation-wide program in Knowledge and Distributed Intelligence (KDI) in the late 1990’s. In its support, NSF has responded to ground-breaking shifts in our understanding of learning as thinking moved beyond B. F. Skinner’s long-dominant behaviorist paradigm, from a single associationist approach toward an appreciation of the complexity – and potential multiplicity – of learning mechanisms. As one example, the 1973 Nobel Prize in Physiology (to Konrad Lorenz, Niko Tinbergen and Karl von Frisch) marked the discovery and description of what were originally called “innate releasing mechanisms” in ethology: an external triggering stimulus releases a developmental program that allows the organism to learn highly specific actions or representations; it requires a well-articulated, genetically specified scaffold that is triggered by input. Appendix 1 provides a brief bibliography of some of the influential ground-breaking work that brought about the shifts in our understanding of learning processes.

NSF has construed learning broadly, dealing with the cognitive and neural basis of human learning, learning in other animals and computer models of learning. In 2003 it established the Science of Learning Centers (SLC) program. The goal was to stimulate and integrate research in the science of learning, dealing with the cognitive and neural bases of learning (as distinct from the more education-driven “learning sciences”); to connect the research to scientific, technological, educational and workforce challenges; and to enable research communities to capitalize on new opportunities and discoveries. The thinking was that the complexity of these goals required expertise from various disciplines and integrative research agendas that were beyond the capabilities of individual investigators or small groups. The longer durations of funding and the stable environments of centers would provide incentives for committed, long-term interactions among researchers to reconceptualize their thinking beyond the paradigms of traditional disciplines. The first solicitation is at SLC Solicitation and the six centers are listed in Appendix 2.

The SLC Program has represented a big investment in the human sciences broadly and in the multidisciplinary science of learning involving several of the NSF directorates. As the centers begin to phase down toward the expiration of NSF support after ten years, the time has come to think about the future of the science of learning and, to this end, two two-day workshops are being held, The Science of Learning: History and Prospects. This report covers the first workshop, held at NSF on 4-5 October 2012 and dealing with what has been achieved over recent decades in the science of learning, particularly in the last ten years. The second workshop, to be held at NSF on 28 February and 1 March 2013, will consider what we can look forward to over the next ten years in terms of opportunities and threats and will be a forum to brainstorm mechanisms for how work on the science of learning might be supported and funded over the coming decade, outlining strategies and objectives.

2. Organization of the workshops

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A Steering Committee of leading figures in work on learning is guiding the organization of both workshops and is writing the reports: Ralph Etienne-Cummings (Johns Hopkins), Eric Hamilton (Pepperdine), Elissa Newport (Rochester and now Georgetown), David Poeppel (NYU and recent member of the SBE Advisory Committee), and current members of the SBE AC, Morton Gernsbacher (Wisconsin) and Barbara Landau (Johns Hopkins).

For the first workshop, six speakers were invited to address topics in the science of learning: Michael Stryker from UC San Francisco on neural plasticity, Nitin Gogtay from NIMH on abnormal and normal brain development, Ranu Jung from Florida International on motor control learning linked to rehabilitation, Sharon Goldwater from Edinburgh University on computational modeling and large-scale data-mining, Linda Smith from Indiana University on cognitive development, and David Andrews from Johns Hopkins on learning and education. Soo-Siang Lim from NSF was invited to discuss infrastructure developed by the SLC Program through the six centers and six representatives from the centers were asked to speak about achievements and challenges in the focal area of their center: Nora Newcombe from SILC on spatial learning, Pat Kuhl from LIFE on social foundations of learning, Ken Koedinger from PSLC on computational models and robust learning, Barbara Shinn-Cunningham from CELEST on brain-inspired technologies, Gary Cottrell from TDLC on timing elements in learning, and Laura-Ann Petitto from VL2 on visual learning and signed languages. All speakers were invited to identify two signal achievements and two challenges in the areas they were addressing.

Presenters all sent in one-pagers in advance of the meeting, listing their main points and providing links to publications (Appendix 5). There was extensive discussion: ten minutes after each presentation, half an hour at the end of the first day, and then structured discussion for the whole of the morning on the second day. The list of participants is in Appendix 3 and the program in Appendix 4 (with links to the one-pagers).

3. The science of learning The presentations from invited speakers and from representatives of the existing Science of Learning Centers covered broad territory, raising the question of what we mean by “learning” and what has been discovered about its processes and mechanisms. One relatively broad idea, provided by Michael Stryker's contribution, is that learning consists of some ‘reasonably specific set of changes in neural connections corresponding to the thing learned.’ It is notable that this idea does not constrain learning to changes that depend on experience per se. For example, formation of structure in the developing visual system occurs as a consequence of both spontaneous neural activity and exposure to structured patterns of information available to the organism from the environment. A slightly narrower idea is that learning encompasses experience-dependent change. Even here, the range of changes that are consequent upon experience, the kinds of experience that create change, and the

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timetable on which these changes can occur constitutes vast territory. As a consequence, it is likely that the mechanisms underlying learning could be quite varied. Consider, for example, the infant who learns to reach and grasp objects; the toddler who learns to talk and understand; the child who learns to count or to read; the adolescent who learns to drive; the adult who learns to re-use his or her limbs after stroke. Moving into the realm of machine learning, consider the machine that learns to translate an unknown language, learns to diagnose a tumor type on the basis of brain images, or learns to play Jeopardy, and compete with human experts.

The vast territory that comprises human learning can be organized to some degree by considering evolutionary foundations, the specific domains of learning, and likely mechanisms underlying learning in a given domain. Evolutionary foundations suggest that some aspects of human learning are likely to be continuous with other species (e.g. development of visual-motor coordination, tool use, number, navigation), while others are likely to be distinct from that of other species (e.g. human language, formal use of symbol systems). Still others will likely be hybrids, in which some foundational aspects of the system are shared by many species while other accomplishments require formal tutoring only available to humans. Number constitutes a good example: while fundamental aspects of numerical sensitivity are shared by other species, only humans master algebra (Dehaene 1997).

Domain-specific structures vary considerably, suggesting that some domains may engage distinct learning mechanisms. For example, navigation in all species requires that the organism keep track of its current location as it moves through space; for many species, this in turn depends at least partly on the mechanism of dead-reckoning, which allows the animal to keep track of its changing location as it moves (Gallistel 1990) and this supports the ability to form a map of the environment. The distinctly different case of language acquisition has been subject to intense controversy, with solid evidence now showing that aspects of the learning problem may depend on quite general statistical learning mechanisms (e.g. parsing the speech stream, Saffran, Aslin & Newport 1996) but other aspects of learning in syntax and semantics are still unexplained by such general mechanisms. It has been discovered (i) that language acquirers can entertain multiple representations of a syntactic string and (ii) that the representations entertained sometimes go against the statistics of the input: that is, learners entertain highly constrained options that are only in part driven by properties of the input. In addition, learning mechanisms may vary depending on the knowledge domain and, therefore, the computational problem to be solved. Learning mechanisms have also been categorized by scientists at a more macro level, into those that appear to require explicit (conscious) learning (as in learning a list of new word pairs by reading them out loud) or implicit (unconscious) learning (as in learning the properties of "outdoor vs. indoor scenes" by passively observing many exemplars, and constructing summaries of their statistical structure).

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These basic organizational cuts are surely inadequate to capture the full richness of learning. Moreover, they may leave aside many kinds of change that - although they might not be part of the natural kind "learning" - will likely shed light on the breadth of changes that any science of learning will want to capture. These include such cases as the changes to the visual system underlying the development of binocular vision; changes to the developing brain that occur as information is recruited, manipulated and stored; changes to memory during the life-span and in the diseased brain; and changes that occur during rehabilitation after brain injury. The vast territory of learning requires not just a single science of learning, but, more likely, multiple sciences of learning.

4. Some historyThe last heyday of learning theory was during the 1940’s and 1950’s, when the study of learning was dominated by associationist theories that proposed a few general principles that would explain all types of learning, across domains and species. That optimistic view waned with two striking findings. First, the seminal work of John Garcia showed that, even in rodents and birds, simple principles of conditioning were invaded by species-specific biases and innate constraints on what could be readily learned. Second, Noam Chomsky profoundly altered our understanding of cognition, suggesting that there are abstract universal principles of human language and arguing that language learning (and other types of learning) is made possible by constraints on the types of patterns that can be learned and processed. Together these lines of work, and others that followed in fields from psychology to computer science, have suggested that learning systems operate successfully by being quick to acquire certain types of information - and correspondingly slow or entirely failing to acquire other types.

Surprisingly, for a few decades after these claims appeared, the study of learning continued within linguistics and computer science but, without a search for general principles, languished within psychology. Departments of psychology that had always offered courses on ‘learning’ and had programs of graduate study focused on animal learning ceased to offer these specialties. But in more recent years, several important findings have revitalized interest in the study of learning, which is now one of the cutting edge fields within cognitive science. First, while the Chomskyan analysis of specialized learning modules has become richer and deeper, challenges have come from the study of neural networks, and the controversies surrounding this work, both from supporters and critics, have helped to put the study of learning back in the center of the cognitive and neuro- sciences. Second, discoveries within neuroscience of some of the cellular-molecular and systems-level underpinnings of learning – from LTP and NMDA receptors to studies of the hippocampus and other memory systems – have begun to shed light on the mechanisms by which experience alters the brain. Third, the field of infancy has provided remarkable findings of very early human cognitive capacities and also very early capacities for learning, even including

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prenatal learning. Fourth, the field of machine learning has undergone revitalization, providing a wealth of computational models for how human (and non-human) learning might in principle work.

Among the many important discoveries of recent years are the following:

Developmental and adult plasticity : We have learned not only that the brain is particularly plastic and susceptible to environmental influences early in life, but also that it is still, to some degree, plastic even in adulthood. Adult plasticity is reduced as compared with early development, but some of the same mechanisms for plastic change are still present in the adult brain – and new findings show even how to re-open critical periods for plastic change in mature organisms.

Cross-species comparisons of learning and the evolution of learning mechanisms: We have also learned that many mechanisms of learning are shared across species, and have begun to understand as well the arenas in which learning differs or has evolved differently across species and domains. An excellent summary of work on non-human animals is Gallistel et al.’s 1991 landmark review. Evidence now abounds that, for many important learning problems, most species begin life equipped with structures and mechanisms that guide learning. Examples include the barn owl, equipped with a specialized learning mechanism that calibrates its sound localization circuitry as it grows; migratory songbirds that are capable of representing the spatial arrangement of the stars in order to direct their initial flights; and ducks that compute the relative distribution of foods so that they can select the optimal location for foraging.

Mechanisms of learning, integrating from cells to behavior: In several important systems in animals – particularly in the sensory and motor systems – there has been remarkable progress in understanding both the effects of experience and the cellular-molecular changes that mediate them in shaping neural circuits.

o From early seminal work in vision, we know two important principles. The early findings of Hubel & Wiesel (1962) show that early visual input to the two eyes in cats can permanently alter the size of the neural regions devoted to each eye, and also their relative dominance in binocular vision (a critical period effect of input on neural circuits). We also know that the broader organization of visual cortex, as well as other sensory and motor cortices, is fundamentally “topical,” with a consistent mapping of the receptor surface (e.g. from left to right in the visual system, from low to high pitch in the auditory system) onto the corresponding layout of the primary cortical areas.

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o From the work of Knudsen (1999, 2004), Carr & Konishi (1988) and others on barn owls, we have learned how early auditory experience can alter sound localization; the mechanisms by which sound localization is mediated, through cleverly evolved simple neural circuits (delay lines); and the rich ways in which these mechanisms can and cannot be altered throughout life, by experience with flight and localization of prey.

o We have learned, from the work of Merzenich et al. (1983), about reorganization of somatosensory cortex in primates that can occur with experience using the hands, even in adulthood.

o While these matters are much more difficult to investigate in humans – and links between cellular circuitry and behavior are at present out of reach - the study of language is one prominent arena in human cognitive science in which critical periods and plasticity early versus late in life has been the subject of important and sophisticated investigation.

Types of learning and memory : As interest in fundamental principles of learning has been revived in basic behavioral research, a diversification of types of learning has been explored. Some cognitive scientists distinguish procedural and declarative learning, the learning of procedures (such as how to ride a bicycle or compute a square root) versus the learning of information (such as the capital of Brazil or the color fuchsia). Other scientists distinguish between short-term learning (including the maintenance of knowledge in so-called working memory) and longer-term learning. Engle et al. 1999 demonstrated a strong correlation between the ability to quickly store and accurately retrieve recently learned information and standardized assessments of fluid intelligence.  Still other scientists distinguish between implicit learning, obtained without conscious awareness or notable effort, and explicit learning, which requires effortful encoding and rehearsal.

There are many different types of learning. Domains of knowledge such as language, space, number and, likely, social interaction, are well-structured, but quite different from each other, and learning in each domain depends on having some initial structural biases. The biases for each domain are qualitatively different and there is no necessary reason for the mechanisms underlying our ability to produce and understand complex sentences to be identical to the mechanisms underlying our ability to navigate through space or decide whether a con-specific should be trusted. Of course, the question of whether domain-general mechanisms also play an important role in learning in every domain, and how these mechanisms interface with domain-specific mechanisms, is still very much under debate.

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We have also learned that many cases of learning are subserved by anatomical regions that support different types of operations. For example, the functional organization of primary visual cortex (V1) includes both ocular dominance columns and orientation-selective pinwheels. The same function can also be implemented by different neural mechanisms that support different algorithms: consider the different neural mechanisms for sound localization in different animals (Grothe 2003). Learning also involves strong feedback and feed-forward mechanisms, with no clear sequential processing from simple to complex function. Areas previously thought to perform very low-level processing are also modulated by high-level information (e.g. V1 is modulated by online language processing and linguistic experience; see Dikker et al. 2010).

Machine learning and learning in silicon: Alan Turing’s proposition in 1950 that ‘the only way to determine if a machine can actually learn is if we communicate with it and cannot distinguish it from another human’ remains the open challenge to the machine learning community. The recent IBM JeopardyTM player, “Watson”, made some progress towards responding to this challenge, however, a major gap still exists between what humans and other living organisms can learn, and learning capabilities of machines.

Some landmark developments followed Turing’s proposition: o In 1952 Arthur Samuel (IBM) wrote the first learning-based game-

playing program, for checkers, to achieve sufficient skill to challenge a world champion. This lead to the ELIZA system in the early 60’s, which simulated a psychotherapist by using tricks like string substitution and canned responses based on keywords. When the original ELIZA first appeared, some people actually mistook her for a human.

o In 1957 Frank Rosenblatt invented the Perceptron, a simple linear classifier, which, when configured into networks, could solve hard problems. Minsky and others in the 1960s challenged the effectiveness of the approach by showing that simple problems could disrupt its functionality (Minksy & Papert 1969). Nonetheless, researchers continued to improve the efficacy of perceptrons, leading to today’s Deep Learning Architectures (Hinton et al. 2006) and Support Vector Machines (Vapnik et al. 1997).

o Since the late 1980s, engineers have been modeling the nervous system in silicon using integrated circuits (Hopfield 1987, Mead & Mahowald 1988). More recently, learning silicon synapses, using Spike-Timing-Dependent Plasticity (STDP), have also been included in these so-called neuromorphs (Indiveri et al. 2006). We are now witnessing the advent of ultra-large scale models of the nervous

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system in silicon, models that use billions of neurons and trillions of learning synapses (Ananthanarayanan et al. 2009).

Sleep and consolidation: There is now overwhelming evidence that sleep enhances memory consolidation - that is, it strengthens memories and makes them resistant to disruption. Conversely, sleep deprivation results in memory deficits. The exact mechanisms underlying these results are not fully understood, but theory and empirical evidence suggest that this is at least partly due to the "replay" of experiences during the sleep cycle, with the hippocampus playing a significant role. A review by Ellenbogen, Payne & Stickgold (2006) concludes ‘that sleep leads to improved performance in memory recall; that sleep renders memories resistant to subsequent interference; that the resistance to interference lasts throughout the subsequent waking period; that certain stages of sleep correlate with performance improvements on certain tasks; that the hippocampus replays information during sleep; and that the behavioral improvements correlate with hippocampal re-activation. Given this evidence, we believe the most parsimonious conclusion is that there are specific, sleep-dependent, neurobiological processes that directly lead to the consolidation of declarative memories.’

Statistical learning, Bayesian hierarchical models, information theoretic approaches to learning: Surprising discoveries have been made about the abilities of human learners to acquire and use complex probabilistic information - about the organization and sequencing of linguistic elements, as well as the causal structure of the world (Saffran, Aslin & Newport 1996, Tenenbaum et al. 2011). Earlier approaches assumed that human learners could not compute or retain such complex information about speech corpora or causal scenes; and, in the absence of such information from the environment, theories would need to assume more complex innate structure or more limited abilities to learn. However, recent research, some using Bayesian models, has shown that both infants and adults can implicitly and rapidly compute rich information about the structure of the environments to which they are exposed, and has stimulated the development of old and new types of information theoretic approaches to learning. Domain-general probabilistic learning mechanisms are critical but they interact with biological constraints and research focuses on the interplay between constraints and learning.

Number, language acquisition, and space as domains of development and learning: Following the seminal work of Tinbergen (1951) and Chomsky (1959, 1965), generalist theories of learning and development were challenged; theoretical frameworks developed in the second half of the 20th century stressed that different domains of knowledge

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require the construction of qualitatively different representations and associated learning mechanisms (Gallistel et al. 1991 for review). Remarkable empirical discoveries within number, space, language and social interaction illustrate that domain-specific foundations of human knowledge can be observed even quite early in development. For example:o Numerical cognition is now known to be rooted in a species-general

ability to use the approximate number system, which permits infants, children and adults to estimate small and large numerosities. This system engages specific brain circuits in humans. Later acquisition of numerical competences involving large exact numbers is thought to be unique to humans. These ideas have generated an active search for mechanisms underlying the varieties of numerical knowledge (Dehaene 1997).

o Spatial representation, especially navigation, has long been known to have a specific neural foundation, with the hippocampus and related structures playing a crucial role. Computational sub-divisions of the navigation problem have revealed a range of structural components, with the problem of re-orientation revealing both solid contributions available across species and in humans as young as infants, as well as learned contributions that change navigation behavior over development (O'Keefe & Nadel 1978, Gallistel 1990, Hermer & Spelke 1996, Newcombe & Ratliff 2007).

o The full acquisition of language requires the construction of highly specific knowledge; there is on-going debate about the relative roles of domain-general processes (such as statistical learning) and other learning mechanisms in acquisition. Problems such as parsing of the speech stream and formation of basic categories engage powerful statistical learning mechanisms even in infancy, whereas problems such as the acquisition of complex syntax and semantics remain challenges to existing proposed learning mechanisms and require postulating biological constraints on what is learnable through variable environmental input.

o The role of social interaction in learning is now known to be a powerful force in early development, with recognition that the human ability to imitate and to explicitly teach each other may separate humans from other species, resulting in learning systems that go beyond those of other species (Hermann et al. 2009).

5. Tools for investigating learningThe past two decades have also witnessed great advances in the tools that scientists can use to explore the mechanisms underlying and promoting human learning. Foremost has been the advent of non-invasive brain imaging. Prior to these advances, explorations of the neural bases of cognitive processes as ubiquitous as language, memory or attention, were dependent on inferences drawn from accidents of nature: persons who had experienced brain damage.

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Functional magnetic resonance imaging (fMRI), as well as the increasing developments of other non-invasive brain imaging techniques such as NIRS (Near-Infrared Spectroscopy), allow us to watch the intact brain at work and to compare function across groups or within an individual across time in children as young as preschool age. These methods complement the use of EEG (Electroencephalography), which is becoming increasingly more sophisticated in its applications, and MEG (Magnetoencephalography).

TMS (Transcranial Magnetic Stimulation), which stimulates neurons electrically as a means of eliciting, modifying and inhibiting responses, and TDCS (Transcranial Direct Current Stimulation) are also employed increasingly as tools for exploring the neural bases of learning. Putatively non-invasive, these techniques have been heralded as improving learning (of video games), decreasing training time for pilots and improving plasticity following brain injury.

Advances in structural brain imaging have also aided our understanding of the brain bases of learning. In addition to identifying attributes of volume and shape, tools such as DTI (diffusion tensor imaging) allow visualization, in three dimensions, of white matter tracks and fibers. For example, a recent DTI study of elite gymnasts identified the ‘neuroanatomical adaptations and plastic changes [that] occur in gymnasts' brain anatomical networks either in response to long-term intensive gymnastic training or as an innate predisposition or both’ (Wang et al. 2012).

The Internet is also providing tools for investigating learning. Amassing large data sets, such as those contained in CHILDES (the Child Language Data Exchange System), TalkBank and the Linguistic Data Consortium, is aided by researchers’ ability to upload, store and download through cloud computing. Internet-based data collection, through platforms such as Mechanical Turk and Label Me, is revolutionizing the breadth of research participant samples. And Internet sites, such as NeuroSynth.org, which automatically extracts and syntheses fMRI data from published articles, are allowing researchers to compare studies and findings in a fraction of the time required previously.

6. Interdisciplinarity The construct of learning was once covetously contained within its own discipline. In past decades it has attracted multiple disciplines, often working together. The field is replete with examples but we will illustrate it by considering a marriage of engineering and cognitive psychology.

New cyberinfrastructure has had transformative consequences throughout scientific research, as is well known. The transformative consequences for work on learning have taken two quite different forms: providing models of learning and tools for learning, notably search tools.

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Machine learning and their silicon implementation counterparts have very different roots. Machine learning algorithms emerged primarily out of mathematics, and their goal has been to perform a task, regardless of the similarity of the methodology to biological learning. On the other hand, silicon models of learning have been primarily driven by the desire to mimic biological learning in silicon. The two approaches started to converge in the 1980s, when Sejnowski and Rosenberg developed a parallel computer that learned to read and pronounce text, and Hopfield presented his associative memory network of resistors and comparators (Sejnowski & Rosenberg 1987, Hopfield 1987). In both cases, deeply mathematical constructs were readily implemented with digital (former) and analog (latter) electronic circuits, and both systems purported to have some relevance to biological learning. Machine learning, in the form of perceptron learning, continued to develop mainly along mathematical and computer science lines (e.g. support vector machines and deep belief networks), however, the silicon implementation side of the equation continued to look to neuroscience for inspiration.

The fields converged further with the discovery of spike-timing-dependent-plasticity (STDP) by Markram and colleagues (1997) and Long-Term Depression and Protentiation (LTD & LTP) by Feldman (1999). These results, which provided a mechanism for strengthening and weakening of biological synapses, also correlated with the biological learning theory proposed by psychologist Donald Hebb in the 1940s. Furthermore, the elegance of STDP, and the ease with which it can be implemented with digital or analog integrated circuits, has led to a large number of silicon implementations of learning in artificial neural systems (Ananthanarayanan et al. 2009).

While STDP explained how individual connections between individual neurons can be modified, statistical learning, e.g. hierarchical Bayesian networks, provided a means for inferring complex relationships between high dimensional input data streams and output actions at much higher levels than the individual neurons (Lee & Mumford 2003). Again this represented a further conversion of cognitive science and mathematics to attempt to explain how learning occurs in cortex. What is evident in these developments is that the study of machine learning and/or their implementation in electronic hardware requires the interaction of myriad fields: mathematics, computer science and engineering, neuroscience, psychology and cognitive science play central roles in developing theory, models, algorithms and practical implementations.

Machine learning is playing increasingly indispensable roles in our everyday lives. We routinely use search engines on the web to find information and get recommendations (e.g. Google, Bing, Netflix). We use voice recognition/translation software to interface with our computers or to interact with customer service agents (e.g. Siri, Android, Google translate, Dragon). Nowadays, machine learning is even used to automate the grading of standardized exams. Furthermore, such algorithms are also becoming more

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prominent in healthcare for mining databases, for bioinformatics and imaging retrieval and processing, and for correlating tests results with pathologies. In general security systems, they are used to identify credentialed users and anomalies in their actions that may indicate potential threats to the systems. Machine learning also plays a significant role in the financial sector, predicting trends, identifying risks and suggesting investment strategies. With further development, improved accuracy and faster execution, they will become even more integrated in society so that machines will become even more indistinguishable from other humans, thus meeting the challenge that Turing posed for the community in 1950.

7. Education and societyWe have been describing and dissecting learning as it is situated within an individual organism, but the process of learning plays a powerful role across individuals into groups and societies at various levels. Learning shapes and is shaped by social dynamic and some aspects of learning require attention to the socio-cultural context.

Many of our society’s most precious institutions – economic livelihood, scientific achievement and productive international relations – rely on a learned citizenry. One of the most obvious applications of the science of learning is the education system. However, although the science of learning, as a discipline, draws scientists from a wide range of fields – cognitive psychology, educational psychology, computer science, engineering, neuroscience, sociology, anthropology and education – the interface between educators and scientists has not yet reached equilibrium. The applications of science to formal learning in school environments are understood at only primitive levels, and scientific findings about connections between learning and usable strategies in education are continually evolving.

Nonetheless, part of the mandate of the centers was to connect the research to scientific, technological, educational and workforce challenges and the centers as a group have worked with educators and schools; Pittsburgh’s LearnLab, in particular, has provided a basis for experimental work and other centers have generated innovative forces in school systems. In addition, there is a scientific basis for certain principles ripe for application in the schools, including the following principles that have animated education researchers in recent years and have been summarized by Sawyer (2008, p58):

- Acquiring deeper conceptual understanding is more valuable than memorizing superficial facts and procedures.

- Learning connected and coherent information is more valuable than learning information compartmentalized into distinct subjects.

- Gaining knowledge that is authentic to its context is more valuable than executing “decontextualized classroom exercises.”

- Learning in collaboration is often superior to learning in isolation.

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Sawyer further proposes that learning environments that have the following characteristics will be most effective:

- Each learner receives a customized learning experience.- Learners can acquire knowledge whenever they need it from a variety of

sources: books, websites and experts around the globe.- Students learn together as they work collaboratively on authentic,

inquiry-oriented projects.- Tests should evaluate the students’ deeper conceptual understanding,

the extent to which their knowledge is integrated, coherent and contextualized.

When viewed from this perspective, learning extends beyond the bounds of a classroom to community environments such as museums and extra-curricular activities, and the process of education expands beyond the traditional structure of schools. Moreover, learning is situated beyond the standard transmission of information from a teacher to a student, expanding the societal contributions to education.

8. The role of the centersThe science of learning has been a vibrant field over the last decades, as shown by the recent conceptual shifts (sections 1 and 3) and new tools and new cyberinfrastructure (section 5). We understand more about how learning takes place and, as a result, we learn more about the very nature of language, audition, vision, memory and other mental faculties by considering how they change over time and through experience. Many important discoveries have been made and section 4 and the first six one-pagers provide a sample (Appendix 5).

In addition, while much has been accomplished in work on learning generally, the centers themselves have been productive. Each representative at the workshop provided two achievements (see the one-pagers in Appendix 5); one presentation (Soo-Siang Lim) described new infrastructure that the centers have generated. In their most recent reports, SLC’s have identified approximately fifty important findings, as well as reconceptualizations of commonly held assumptions in the field, new research communities, new tools, and training student researchers with fresh patterns of inquiry. A brief synopsis of these self-reported center accomplishments appears in Appendix 6, with links to fuller reports.

The synergistic focus of the SLC cooperative agreements tasked each center with the challenges of collaboration. SLC researchers were pressed to cross boundaries and to seek insights that extended beyond the perspective of individual research communities or teams. The boundaries that SLC researchers have crossed are varied and include, for example, temporal scales (e.g. microsecond activation patterns to small grained to more extended task performance), spatial scales (e.g. cellular to structural to regional to functional to extended task performance), and scientific domains (e.g. from neuroscience to

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cognitive psychology, to data-mining methods, to impairment therapies and education research more broadly). While the term “translation” often is associated with a notion of making research on learning applicable in contexts such as education (i.e. from basic research to practice), a primary task of the SLCs has been to build translation between basic research communities and broader ranges of activity, including industry and business, partly to generate new ways of viewing research problems. This has been based at least in part on an expectation that researchers collaborating across boundaries would create different and more advanced landscapes for the entire field.

Cross-boundary endeavors were often expressed thematically by individual SLC emphases. SILC, for example, has sought to connect the cellular and functional aspects of the malleability of spatial cognition with scientific learning. In so doing, it views itself as founding a new research community emphasizing spatial cognition’s role in STEM education. TDLC examines the relationships between finely grained neural event sequencing and signal systems and higher level functioning in learning and cognitive therapies. The LIFE Center has made significant breakthroughs in areas such as early childhood functional brain imaging and in tracing connections between social patterns and language acquisition, while seeking to advance a broader and cross-disciplinary conceptualization of informal human learning. In collaboration with TDLC, the LIFE Center has charted principles to guide a multi-tiered and cross-disciplinary science (or sciences) of learning. Cross-boundary research programs especially characterize CELEST, the SLC most heavily focused on cognitive neuroscience. Its research on memory structures, for example, has made significant strides in connecting brain activation patterns to the cognitive functions that organize and process visual and auditory stimuli. These research threads are part of a fuller body of inquiry in human and animal learning that explores connections between regions or functions of the brain. LearnLab at PSLC has approached integrative research in part by building what is the world’s largest repository of cross-boundary research related to in situ classroom learning. LearnLab’s own research studies are consistently marked by analysis of fine-grained data in service of developing larger grained models and theories that seek to test causal mechanisms of human learning processes and related socio-affective dynamics.

While collaborative multidisciplinary research is given privileged standing in the SLC network, each SLC has also carried out significant “within-discipline” research. Reports for the SLCs highlight breadth enhanced by crossing boundaries at various levels, but also depth associated with the pursuit of questions within specific fields. At its best, the SLC network operated on the principle that highly specific, smaller-grained research both informs and is informed by boundary-crossing research. Intellectual management of the SLCs entailed finding (and then explaining to NSF as a cooperative agreement partner) an authentically balanced and adjustable mix of research teams and

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studies. This management task in itself was seen by NSF as advancing how the different fields in the science(s) of learning situate themselves in a changing landscape. The SLC researchers affirmed the intrinsic value to the (benignly) coerced “between and within” integrative forces that characterized their performance agreements with NSF.

The SLCs have also promoted a new generation of students aimed at interdisciplinary work, who are gaining traction and success in their research and publications. The publishing success of these early career researchers suggests an important capacity to formulate and situate strategically valuable questions and to size up their within-boundary and across-boundary dimensions. The (former) students present at the workshop spoke enthusiastically about the center mode and believe that they have been well equipped to pursue interdisciplinary studies for the rest of their careers.

The centers are large entities, each incorporating many researchers from many institutions and that scale brings with it a certain unwieldiness. They also represent large investments by NSF and that requires monitoring and management that can be time-consuming, cumbersome, and expensive. Center representatives expressed frustration at the time and money required for the annual site visits and other reporting requirements, which were not always very productive. Much of this is inevitable, given the general requirements for NSF centers and cooperative agreements.

While the centers report a large number of publications and peer-reviewed research conference presentations through SLC support, it also needs to be said that the they have not always been as visible in some of the most prominent meetings and journals in several very successful areas of the science of learning, such as language acquisition and visual development. Furthermore, some of their valuable achievements deal not with learning itself and but with the properties of cognitive or neural functions regardless of strict matters of development and learning. Again that reflects the large scale of the centers and how they are assembled in the course of submitting a proposal to NSF, drawing together colleagues with a broad range and then struggling to generate coherent collaboration on matters of learning. Some of the most important work in the field of learning has not been included within the centers, due in part to these issues.

In addition, for all the emphasis on integrative approaches, it is not clear that much has been achieved in defining a general science of learning with its own general principles that cross the domains and different levels of analysis in STEM learning, language acquisition, development of spatial cognition, etc, each of which has its own distinct principles. This makes the science of learning more like the umbrella of computer science than the apparently more cohesive physics or chemistry. It has often been suggested that referring to the “sciences of

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learning” rather that the singular science of learning might give a more accurate feel for the field.

A 2009 COV found that the SLC program through its six centers had succeeded in fostering a scientific community in the science of learning, construed in this broad fashion. This happened partly through the scale of the centers and through creating a learning network, going beyond the individual centers. The community has been internationalized; the OECD held a workshop in January 2012 on the science of learning, where the NSF initiative played a central role, and now the centers model is being followed and funded in other countries. However, workshop participants argued that, while the centers have clearly been productive and have succeeded in generating new research, future work in the science of learning will benefit from a more diverse set of funding mechanisms, rather than simply funding a set of new centers. It will be important to continue to foster the beneficial aspects of the productive interdisciplinarity that has been cultivated through the centers but to do it in a way that diversifies the avenues for advancing the field and that reduces the unwieldy research management problems that often characterize large center funding.

That will be the subject matter of the second workshop.

9. ChallengesOne challenge for the future will be to remedy the absence of critical elements of learning from the current Science of Learning Centers program. Four areas where there has been good work that has not been a focus for the centers and will benefit from future nourishment are the following.

Social/emotional/attentional factors and individual differences : Current models of learning tend to consider only the informational content of the input to a generic learner.  A challenge for the future is to understand how to model additional psychological factors that are known to affect learning, such as social situation, emotion and attention, as well as individual differences in learner performance.  One difficulty is in determining whether these factors are simply regulating information uptake (e.g. an inattentive learner just “misses” some of the data) or whether they qualitatively change the computations performed.  New findings will also have implications for machine learning: can social/emotional factors be effectively replaced by more or different kinds of data that are easily available to machines, or will we need to simulate emotions and social interaction for effective machine learning to occur?

Childhood acquisition of parametric variation in language capacities: There has been a great deal of productive work on how children acquire systematic properties of their native language that differ from what is found in other languages, which differ around the world, both in syntax and phonology. We are now well positioned to focus more intensely on

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the acquisition and “learnability” of the parametric variation under normal childhood conditions.

How groups and societies learn: sometimes societies from university departments to nations undergo structural shifts in attitudes and political perspectives. A striking example came around 1990, when many countries shifted from authoritarian and totalitarian regimes to systems where individual citizens had more power. This is a kind of learning and much insightful work has been done, while mysteries remain.

Cultural influences on learning : Although learning, by its vary nature, is a culturally shaped process, as many learning scientists note, ‘the learning sciences have not yet adequately addressed the ways that culture is integral to learning’ (Nasir, Rosebery, Warren & Lee 2005). Thus, the influence and impact of culture and its variations will be as important to identify as the impact and influence of other environmental features and variations.

10. Second workshop The second workshop will take place in February-March of 2013 and will consider, in light of the history described in this report, the prospects for future work in the science of learning and how it can be best organized in terms of funding possibilities. Speakers have been invited to address prospects for work on memory, genetics, brain plasticity, language and cognitive development; one or two other areas will be added. In addition, speakers will be invited to discuss the strengths and weaknesses of various funding mechanisms, including a national synthesis center and possible support from foundations and other agencies, given the goal of continuing to foster interdisciplinary approaches to learning and young researchers at the graduate, postdoc and early career stages.

Submitted by the Steering Committee:David W. Lightfoot, PI, Georgetown URalph Etienne-Cummings, Johns Hopkins UMorton Gernsbacher, U WisconsinEric Hamilton, Pepperdine UBarbara Landau, Johns Hopkins UElissa Newport, Georgetown UDavid Poeppel, New York U

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