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. Introduction
For 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 educationdriven “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
2
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
A Steering Committee of leading figures in work on learning is guiding the
organization of both workshops and is writing the reports: Ralph EtienneCummings (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 largescale 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
3
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
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
4
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).
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 history
The 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 neuro5
sciences. Second, discoveries within neuroscience of some of the cellularmolecular 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
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 reopen 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
6
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.
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 wellstructured, 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
7
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.
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 feedforward mechanisms, with no clear sequential processing from simple
to complex function. Areas previously thought to perform very lowlevel 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 gameplaying 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 &
8
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
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 reactivation. 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
9
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
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 subdivisions 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 learning
The past two decades have also witnessed great advances in the tools that
scientists can use to explore the mechanisms underlying and promoting human
10
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.
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.
11
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.
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-dependentplasticity (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
12
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
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 society
We 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.
13
- 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.
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 centers
The 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 selfreported 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
14
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
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
15
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
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
16
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
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. Challenges
One 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?
17



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
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 U
Ralph Etienne-Cummings, Johns Hopkins U
Morton Gernsbacher, U Wisconsin
Eric Hamilton, Pepperdine U
Barbara Landau, Johns Hopkins U
Elissa Newport, Georgetown U
David Poeppel, New York U
18
References
Ananthanarayanan, R., S.K. Esser, H.D. Simon & D.S. Modha 2009. The cat is out
of the bag: Cortical simulations with 109 neurons, 1013 synapses. Proceedings of
the Conference on High Performance Computing Networking, Storage and
Analysis, p.63. ACM.
Carr, C. & M. Konishi 1988. Axonal delay lines for time measurement in the owl’s
brainstem. PNAS 85: 8311-8315.
Chomsky, N. 1959. Review of B. F. Skinner Verbal Behavior. Language 35.1: 2658.
Chomsky, N. 1965. Aspects of the theory of syntax. MIT Press.
Dehaene, S. 1997. The number sense. Oxford UP.
Dikker, S., H. Rabagliati, T.A. Farmer & L. Pylkkänen 2010. Early occipital
sensitivity to syntactic category is based on form typicality. Psychological
Science 21: 293-321.
Ellenbogen, J.M., J.D. Payne & R. Stickgold 2006. The role of sleep in declarative
memory consolidation: Passive, permissive, active or none? Current Opinion in
Neurobiology 16: 1-7.
Engle, R.W., S.W. Tuholski, J.E. Laughlin & A.R.A. Conway 1999. Working memory,
short-term memory, and general fluid intelligence: A latent-variable approach.
Journal of Experimental Psychology: General 128: 309-331.
Feldman, D.E. 1999. LTP and LTD induced by action-potential-epsp pairing at
vertical inputs to layer II/III pyramids in rat somatosensory cortex. Soc.
Neurosci. Abst. 25:223.
Gallistel, C.R. 1990. The organization of learning. MIT Press.
Gallistel, C.R., A.L. Brown, S. Carey, R. Gelman, & F.C. Keil 1991. Lessons from
animal learning for the study of cognitive development. In S. Carey & R. Gelman,
eds. The epigenesis of mind: Essays on biology and cognition. Lawrence Erlbaum.
Grothe, B. 2003. New roles for synaptic inhibition in sound localization. Nature
Reviews Neuroscience 4: 540-550.
Hermann, E., J. Call, M.V. Hernandez-Lloredia, B. Hare & M. Tomasello 2009.
Humans have evolved specialized skills of social cognition: The cultural
intelligence hypothesis. Science 317: 1360-1366.
Hermer, L & E. Spelke 1996. Modularity and development: the case of spatial
19
reorientation. Cognition 61: 195-232.
Hinton, G.E., S. Osindero & Y.-W. Teli 2006. A fast learning algorithm for deep
belief nets. Neural Computation 18.7: 1527-1154.
Hopfield, J. J. 1987. Learning algorithms and probability distributions in feedforward and feed-back networks. PNAS 84.23: 8429-8433.
Hubel D. & T. Wiesel 1962. Receptive fields, binocular interaction and functional
architecture in the cat's visual cortex. Journal of Physiology 160: 106-154.
Indiveri, G., E. Chicca & R. Douglas 2006. VLSI array of low-power spiking
neurons and bistable synapses with spike-timing dependent plasticity. IEEE
Transactions on Neural Networks 17.1: 211-221.
Knudsen, E. I. 1999. Mechanisms of experience-dependent plasticity in the
auditory localization pathway of the barn owl. Journal of Comparative Physiology
185: 305–321.
Knudsen, E. 2004. Sensitive periods in the development of brain and behavior. J.
Cognitive Neuroscience 16: 1412-1425.
Lee, T.S. & D. Mumford 2003. Hierarchical Bayesian inference in the visual
cortex. J. Opt. Soc. Am. A. 20.7, July 20.
Markram H., J. Lubke, M. Frotscher & B. Sakmann 1997. Regulation of synaptic
efficacy by coincidence of postsynaptic APs and EPSPs. Science 275: 213-215.
Mead C. & M.A. Mahowald 1988.
Pergamon.
A silicon model of early visual processing.
Merzenich M.M., J.H. Kaas, J. Wall, R.J. Nelson, M. Sur & D. Felleman 1983.
Topographic reorganization of somatosensory cortical areas 3b and 1 in adult
monkeys following restricted deafferentation. Neuroscience 8.1: 33-55.
Minsky, M. & S. Papert 1969. Perceptrons. MIT Press.
Nasir, N.S., A.S. Rosebery, B. Warren & C.D. Lee 2005. Learning as a cultural
process: Achieving equity through diversity. In R.K. Sawyer, ed. The Cambridge
Handbook of the Learning Sciences. Cambridge UP.
Newcombe, N.S. & K.R. Ratliff 2007. Explaining the development of spatial
reorientation: Modularity-plus-language versus the emergence of adaptive
combination. In J. Plumert & J. Spencer, eds. The emerging spatial mind. Oxford
UP. Pp53-76.
20
O'Keefe, J. & L. Nadel 1978. The hippocampus as a cognitive map. Oxford UP.
Saffran, J.R., R.N. Aslin & E.L. Newport 1996. Statistical learning by 8-month-old
infants. Science 274: 1926–1928.
Sawyer, R.K. 2008. Optimizing learning: Implications of learning sciences
research. From Innovating to learn, learning to innovate (pp45-61). Centre for
Educational Research and Innovation.
Sejnowski, T. & C. Rosenberg 1987 Parallel networks that learn to pronounce
English text. Complex Systems 1: 145-168.
Tenenbaum, J.B., C. Kemp, T.L. Griffiths & N.D. Goodman 2011. How to grow a
mind: Statistics, structure, and abstraction. Science 331.6022: 1279-1285.
Tinbergen, N. 1951. The study of instinct. Oxford UP, Clarendon.
Vapnik, V., S.E. Golowich & A. Smola 1997. Support vector method for function
approximation, regression estimation, and signal processing. Adv. Neural
Information Processing Systems 9:281-287.
Wang B, Y. Fan, M. Lu, S. Li, Z. Song, X. Peng, R. Zhang, Q. Lin, Y. He, J. Wang & R.
Huang 2012. Brain anatomical networks in world class gymnasts: A DTI
tractography study. Neuroimage. doi: 10.1016/j.neuroimage.2012.10.007. [Epub
ahead of print]
21