1. EP1.
Learning
Elena Pasquinelli
Educa3on, cogni3on, cerveau
Cogmaster 2010‐2011

2. Op3miza3on of educa3on
• “Considera3ons on the op3miza3on of
educa3onal strategies should take into
account knowledge on brain development and
learning mechanisms that has been
accumulated by neurobiological research over
the past decades.” (Singer, in BaKro, Fischer &
Léna, 2008, p. 97)

3. BIOLOGICAL DEFINITION OF LEARNING
RELATIONSHIP BETWEEN LEARNING, EVOLUTION,
DEVELOPMENT

4. Defini3on of learning
• Learning = modifica3on of stored • “any learning, i.e. the
knowledge and of computa3onal
programs
modificaFon of
computaFonal
• Which takes place through the programs and of stored
modifica3on of the brain knowledge, must occur
func3onal architecture through las$ng
changes in their
• Learning = long‐las3ng change in func$onal
the func3onal architecture of the
brain
architecture.” (Singer,
2008, p. 98)

5. Defini3on of knowledge
• Knowledge is the product of • « there is no dichotomy between
biological processes, which hard‐ and soXware in the brain.
determine or modify the The way in which brains operate
func3onal architecture of the is fully determined by the
brain integra3ve proper3es of the
• Learning is one of these individual nerve cells and the way
processes in which they are interconnected.
It is the func3onal architecture,
the blueprint of connec3ons and
their respec3ve weight, that
determines how brains perceive,
decide, and act.
• … all the knowledge that a brain
possesses reside in its func3onal
architecture. » (Singer, 2008, p.
98)

6. Modifica3on of the brain’s func3onal
architecture: 3 processes
• 3 different processes are “Such changes can be obtained
responsible of the by altering the integraFve properFes of
individual neurons,
specifica3on/modifica3on by changing the anatomical connecFvity
of the brain’s func3onal paPerns,
architecture (and thus, to and by modifying the efficacy of
knowledge acquisi3on): excitatory and/or inhibitory
connecFons. …”(Singer, 2008, p. 98)
“Evolu3on,
Ontogene3c development,
And learning.” (Singer, 2008, p. 98)

7. a. Learning and evolu3on
• “The architectures of the brain have evolved according
1 Evolu3on has selected both to the same principles of trial, error and selec3on as all
the other components of organisms. …Through this
learning mechanisms and process of selec3on, informa3on about useful
knowledge contents: computa3onal opera3ons was implemented in the
– Ex.: “Fire together, wire brain architectures and stored in the genes. Every 3me
together”: les neurones qui an organism develops, this informa3on is transmiKed
sont ac3fs en même temps from the genes through a complicated developmental
tendent à créer des process into specific brain architectures which the
connexions (appren3ssage translate this knowledge into well adapted
associa3f) behavior.” (Singer, 2008, p. 98‐99)
– Ex. How humans interpret • “… computa3onal strategies, as for example the
sensory signals learning mechanisms that associates temporally
conFngent signals, have remained virtually unchanged
2 The brain stores knowledge throughout evolu3on.” (Singer, 2008, p. 99)
even before making • “Thus, an enormous amount of informaFon is stored
experiences: it’s not a in the funcFonal architecture of highly evolved brains,
and one of the sources of this informaFon is
tabula rasa. evoluFonary selecFon.”
– EducaFon cannot be • “Inborn knowledge defines how we perceive and
considered as the task of interpret sensory signals, evaluate regulari3es and
filling a hollow box derive rules, associate signals with one another and
iden3fy causal rela3ons, aKach emo3onal connota3ons
to sensory signals, and finally how we reason.” (Singer,
2008, p. 99)

8. b. Learning and development
• Neural circuits are formed and • “… this process of circuit forma3on
selected during the development of and selec3on according to func3onal
the brain (from birth to the end of criteria persists un3l the end of
puberty) puberty – but it occurs within
– Development includes 3me window, or precisely 3med windows that differ
expects certain s3muli at specific for different structures.”
periods of the life of the animal in order
to implementcertain func3ons • “Once the respec3ve developmental
windows close, neurons stop forming
• Development and learning cross their new connec3ons and exis3ng
paths, but aXer puberty neural connec3ons cannot be removed.”
circuits and the structural
architecture of the brain are • “The only way to induce further
(apparently) mi‐ostly stabilized modifica3ons in the now cristallized
architecture is to change the efficacy
• Adult learning: Func3onal of the exisFng connecFons. » These
modifica3ons (strenght of the func3onal modifica3ons are assumed
connec3ons) are the main to be the basis of adult learning and
mechanisms for the modifica3on of aXer puberty are constrained by the
the func3onal architecture of the the invariant anatomical
brain architectures.” (Singer, 2008, p. 101)

9. The role of experience
• In addic3on to gene3c mechanisms, the • “The drama3c effects that deprivaFon has
brain is modified by experience on the matura3on of brain architectures
– At the level of epigenesis and raise the ques3on why nature has
development
– At the level of learning implemented developmental mechanisms
• Contraints to what can be learnt: that expose the maturing brain to the
hazards of sensory experience.
– Certain mechanisms protect the brain
from adap3g‐ng to any new informa3on • Through epigene3c shaping of the brain’s
coming from the environment func3onal architecture the organisms can
adapt their neuronal architectures to the
• The brain at birth is s3ll immature: neurons environment in which they happen to be
are in place, basic distant connec3ons born, and this economizes greatly the
between neurons are formed, but not the
most part of the neurons of the cortex computa3onal resources that have to be
• During development connec3ons are formed invested in order to cope with the specific
and tested “fire together‐wire together”: challenges of the respec3ve
those connec3ons, which have a high environments.” (Singer, 2008, p. 102‐103)
probability of being ac3vated simultaneously
are consolidated, those which have a low
probability are discarded.
• AXer birth, this networking ac3vity is
influenced by individual experience of the
environment and sensory signals

10. c. Learning (adult) = func3onal modifica3ons of
brain’s func3onal architecture
• Learning does not modify the • “… adult learning relies on changes in
architecture of the brain at a the efficacy of excitatory and/or
structural level (mostly): inhibitory connec3ons. The
• it produces func3onal modifica3ons mechanisms that mediate these
that affect the strength of the learning‐induced changes in the
connec3ons between neurons coupling strength among neurons
(synapses) = closely resemble those which
• Func3onal plas3city mediate the ac3vity dependent
circuit changes during experience‐
dependent development.
– The defini3on raises the issue of the
defini3on of plas3city, and the • The only major difference is that in
rela3onship between plas3city and the adult, weakening of connec3ons
learning is not followed by removal and that
no new connec3ons are
formed.” (Singer, 2008, p. 108)

11. CAN HUMANS LEARN ALL LIFE LONG?
‐ THE PROBLEM OF CRITICAL PERIODS
‐ THE ROLE OF EXPERIENCE
‐ THE FORMS OF PLASTICITY

12. Cri3cal (sensi3ve) periods for learning
• Cri3cal periods = 3me‐window • “Several brain researchers have
opportuni3es hypothesized that humans’ brains are
• Development of vision preprogrammed to learn certain kinds
– Hubel & Wiesel, 1970: monocular of knowledge during a limited window
depriva3on reduces the number of cells of 3me known as cri3cal period.
responding to the ac3vity of the • But the latest brain science is
deprived eye beginning to ques3on this simplis3c
– monocular depriva3on has different developmental no3on. For example,
effects at different ages
new brain research shows that the
• Development of language 3ming of cri3cal periods differ
significantly in the visual, auditory and
language systems. Even within
different systems, there is emerging
evidence that the brain is much more
plas3c that herefore
assumed…” (Bransford, et al, in Sawyer,
2009, p. 21)

13. The myth of the first three years
• The no3on of cri3cal periods has been • “ Neuroscien3sts now understand that cri3cal
domina3ng the world of educa3on and has periods and synaptogenesis/synap3c pruning
given birth to myth of the first three years are related. Neural systems, par3cularly highly
• Bruer, 1997 describes this myth as a typical acute systems like vision, have evolved to
case of bad transla3on from neuroscien3fic depend on the presence of ubiquitous
data to educa3onal applica3ons environmental s3muli to fine‐tune their neural
circuitry.
• Bruer, 1997 cri3cizes the iden3fica3on of • Neuroscien3sts also know that that there are
learning with synaptogenesis: different cri3cal periods for specific func3ons.
… For example, within the visual system, there
– Different systems have different sensi3ve are different cri3cal periods for ocular
periods, in the sense that they do not develop
at the same rate (including within the visual dominance, visual acuity, binocular func3on,
system) and stereopsis. … The human language
– Human cri3cal periods are not necessarily the func3on also seems to have several cri3cal
same as animals periods … In contrast to phonology and syntax
– The brain is more plasFc than accorded there is no cri3cal period for learning the
before lexicon.
– Learning cannot be reduced to • … they now tend to interpret cri3cal periods in
synaptogenesis terms of subtle, possibly gradual, changes in
brain plas3city – changes in the brain’s ability
to be shaped and changed by experience that
occurs during the life3me of the
animal.” (Bruer, 1997, p. 8)

14. general rule for neuroeduca3on
• “ In reviewing this work, readers outside the field • Bruer has used the myth of the first three years for
should be aware of its complexity and the showing that neuroscience is s3ll a bridge too far
methodological issues involved.” (Bruer, 1997, p. 6) from educa3on, and can give rise to neuromyths
• “Whatever the 3me course of synaptogenesis in and misapplica3ons
humans, if it has relevance for child development • i.e. Generaliza3on of considera3ons that are
and educa3on, we must be able to associate this extracted from
neurodevelopmental change with changes in – Animal experiments
infants’ behavior and cogni3ve capaci3es …These – Data on specific func3ons
exemples are all significant developmental • i.e. Genraliza3on of brain facts into behavioral
milestones that no doubt depend on brain phenomena
development. We do know that these milestones – E.g. in the case of synaptogenesis, cri3cal periods and
are correlated with synaptogenesis (at least in the leanring
visual cortex)… Educators should note two things
however. First in all these examples, increases in
synap3c density are correlated with the ini3al
emergence of skills and capaci3es. These skills and • Nonetheless,
capaci3es con3nue to improve aXer synap3c – Neuroscience does not reduce learning to
densi3es begin to regress to adult, mature levels. … synaptogenensis and synap3c selec3on, …
Thus the most we can say is that synaptogenesis
may be necessary for the emergence of these
abili3es and behaviors, but it cannot account
en3rely for their con3nued refinement. ” (Bruer,
1997, p. 6)

15. From cri3cal periods to different forms
of plas3city
• The brain is interested by
experience in two ways: as an • “ informa3on storage refers to incorpora3on of environmental
informa3on that is ubiquitous in the environment and
expecta3on or as a dependent common to all species members, such as the basic elements of
paKern percep3on. Experience expectant processes appear to
variable for modifica3on have evolved as a neural prepara3on for incorporing specific
informa3on: in many sensory systems, synap3c connec3ons
• Those of Experience‐ between nerve cells are overproduced, and a subsequent
selec3on process occurs in which aspects of sensory
expectant and of Experience‐ experience determine the paKern of connec3ons that remains.
dependent modifica3ons are • Experience‐dependent informa3on storage refers to
incorpora3on of environmental informa3on that is
an alterna3ve to the concepts idiosyncra3c, or unique to the individual, such as learning
about one’s specific physical environment or vocabulary. The
of cri3cal or sensi3ve period neural basis of experience‐dependent processes appear to
involve ac3ve forma3on of new synap3c connec3ons in
• The two no3ons point to response to the events providing the informa3on to be stored.
different neural mechanisms of • Although these processes probably do not occur en3rely
independently of one another in development, the categories
plas3city (advantage in offer a new view more in accord with neural mechanisms than
were terms like “cri3cal” or “sensi3ve period”. (Greenough,
comparison to the no3on of Black & Wallace, 1987)
cri3cal period)

16. • Experience‐expectant plas3city:
• “We propose that mammalian brain development
– Selected by evolu3on relies upon two different categories of plas3city for the
– Concerns sensory motor func3ons storage of environmentally origina3ng informa3on.
• The first of these probably underlies many sensi3ve or
– Allows to fine‐tune the sensory motor systems in cri3cal period phenomena. This process, which we
term experience expectant , is designed to u3lize the
rela3onship to the environment sort of environmental informa3on that is ubiquitous
and has been so throughout much of the evolu3onary
– Through the selec3on of synapses that have been history of the subject.
generated in excess • An important component of the neural processes
underlying experience expectant informa3on storage
– Defines the s3muli that should be found in the appears to be the intrinsically governed genera3on of
environment for the func3on to develop in a certain way an excess of synap3c connec3ons among neurons, with
experien3al input subsequently determining which of
– Experiences are very general and concern s3muli, which them survive.
• The second type of plas3city, which we call experience
are normally present in the environment dependent, is involved in the storage of informa3on
that is unique to the individual. Mammals in par3cular
have evolved nervous systems that can take advantage
of such informa3on…
• Experience‐dependent plas3city: • An important aspect of the mechanism underlying
experience dependent informa3on storage appears to
– Does not depend on mechanisms that have been selected be the genera3on of new synap3c connec3ons in
by evolu3on according to a precise 3ming response to the occurrence of a to‐be‐remembered
event.” (Greenough, Black & Wallace, 1987)
– Evolu3on has selected a capacity to learn from experience
in general
– Through the genera3on of synapses, and the modifica3on
of the strength of the synapses

17. 3 mechanisms for func3onal and
structural plas3city
• Plas3city is the basis of learning from • « The most fascina3ng and important
experience, and learning modifies property of mammalian brain is its
future thought, behavior, feeling remarkable plas3city, which can be
• 3 mechanisms: thought of as the ability of
– Synap3c plas3city = change in strength experience to modify neural circuitry
or efficacy of synap3c transmission and thereby to modify future
– Synaptogenesis & synap3c pruning thought, behavior, feeling. Thinking
– Excitability proper3es of single neurons simplis3cally, neural ac3vity can
modify the behavior of neural circuits
• Synap3c plas3city can be transient by one of three mechanisms: (a) by
(short term phenomena such as modifying the strength or efficacy of
synap3c transmission at preexis3ng
short‐term adapta3on to sensory synapses, (b) by elici3ng the growth
inputs) – depends on modula3on of
transmiKer release of new synap3c connec3ons or the
pruning away of exis3ng ones, (c) by
• Or long las3ng: long‐term form of modula3ng the excitability proper3es
memory of individual neurons. Synap3c
– LTP/LTD (long‐term poten3a3on/long‐ plas3city refers to the first of these
term depression) mechanisms mechanisms …» (Malenka, 2002, p.
147)

18. LTP
• LTP: repe33ve ac3va3on of excitatory synapses in the • “During the last decade, there was enormous interest in
hyppocampus causes an increase in synap3c strength that can elucida3ng the mechanisms responsible for ac3vity‐
last for hours dependent long‐las3ng modifica3ons in synap3c strength.
• LTP is hypothesized to be involved in the forma3on of The great interest in this topic is largely based on the simple
memories and more generally in informa3on storing, hence in idea that external and internal events are represented in the
learning in general, because LTP and learning considered at brain as complex spa3otemporal paKerns of neuronal ac3vity,
the behavioral level share some proper3es: the proper3es of which result from the paKern of synap3c
– LTP can be generated rapidly and is prolonged and weights at the connec3ons made between the neurons that
strengthened by repe33on are contribu3ng to this ac3vity. The corollary to this
– It is input specific (it is elicited at the ac3vated synapses and hypothesis is that new informa3on is stored (i.e., memories
not at adjacent synapses of the same neuron) are generated) when ac3vity in a circuit causes a long‐las3ng
change in the paKern of synap3c weights.
– It’s long‐las3ng
• How? Modifica3on of dendri3c spines? Growth of spines? • …support for such a process was lacking un3l the early 1970s,
Genera3on of new synapses as a consequence of the splirng or when it was demonstrated that repe33ve ac3va3on of
duplica3on of exis3ng spines? excitatory synapses in the hippocampus caused an increase in
• Incorpora3ng structural changes into the mechanisms of long‐
term synap3c plas3city provides means by which the ac3vity synap3c strength that could last for hours or even days
generated by experience can cause long‐las3ng modifica3ons of (12,13). This long‐las3ng synap3c enhancement, LTP, has
neural circuitry been the object of intense inves3ga3on because it is widely
believed that LTP provides an important key to understanding
the molecular mechanisms by which memories are formed
(14,15) and, more generally, by which experience modifies
behavior. Furthermore, the ac3vity‐ and experience‐
dependent refinement of neural circuitry that occurs during
development shares features with learning, and thus a role
for LTP in this process has been proposed” (Malenka, 2002, p.
148)

19. More structural plas3city
• Experience dependent • “ Un3l rela3vely recently, it was widely assumed that, except for
plas3city would certain cases of response to brain damage, the brain acquired all of
depend on the dynamic the synapses it was going to have during development, and that
further plas3c change was probably accomplished through
genera3on of synapses
modifica3ons of the strength of preexis3ng connec3ons.
(or the dynamic
• … it has now become quite clear that new connec3ons may arise
modifica3on of the as a result of of differen3al housing condi3ons and other
strength of synapses) manipula3ons throughout much, if not all, the life of the rat…
rather than on a • There has not yet been a specific demonstra3on of what might be
mechanism of chronic represented by the changes in synap3c connec3ons brought about
overproduc3on of by differen3al environmental complexity, nor are the details of the
synapses, which are rela3onship between brain structure and behavioral
successively selected performance.” (Greenough, Black & Wallace, 1987, p. 547‐548)
by experience • “However, there are a few excep3ons. Over the past years,
• Chronic overproduc3on evidence has become available that in a few dis3nct brain region,
parts of the hippocampus and the olfactory bulb neurons con3nue
and selec3on would be
to be generated throughout life, and these neurons form new
the mechanisms connec3ons and become integrated in exis3ng circuitry.”
behind experience • “Thus in these dis3nct areas of the brain, developmental processes
expectant, early, 3me persist throughout life…” (Singer, 2008, p. 108)
framed learning

20. Structural plas3city in the adult brain
• “MRI of licensed London taxi drivers were analyzed and
compared with those of control subjects who did not drive
taxis.
• The posterior hippocampi of taxi drivers were significantly
larger rela3ve to those of control subjects.
• Structural plas3city
• Hippocampal volume correlated with the amount of 3me (produc3on of
spent as a taxi driver (posi3vely in the posterior and
nega3vely in the anterior hippocampus). synapses and of
• These data are in accordance with the idea that the posterior neurons) seems to
hippocampus stores a spa3al representa3on of the
environment and can expand regionally to accommodate
elabora3on of this representa3on in people with a high
con3nue in certain
dependence on naviga3onal skills. parts of the brain
• It seems that there is a capacity for local plas3c change in all life long
the structure of the healthy adult human brain in response
to environmental demands” (Maguire, et al.,2000)

21. CAN HUMANS LEARN ANYTHING?
‐ BIOLOGICAL CONSTRAINTS
‐ THE ROLE OF EDUCATION

22. The role of educa3on
• 3 possible views:
– One can learn everything, and learns it from scratch
– What we learn depends on past experiences and is
constructed star3ng from these experiences, but one can
learn everything
– The way brain has been shaped by selec3on strongly
constrains what can be learnt
• (Posner & Rothbart, 2007)

23. Can we learn anything? Constraints and biases
• “Kuhl’s recent neuropsychological and brain imaging work
• Learning experiences sculpt suggests that language acquisi3on involves the development
of neural networks that focus on and code specific proper3es
the brain and cons3tute a
of the speech signals heard in early infancy, resul3ng in neural
3ssue that is dedicated to the analysis of these learned
paKerns. Kuhl claims that early neural commitment to
framework for future learned paKerns can also constrain future learning; neural
networks dedicated to na3ve‐language paKerns do not detect
non‐na3ve paKerns, and may actually interfere with their
learning •
analysis (… Kuhl, 2004…).
If the ini3al coding of na3ve‐language paKerns interferes with
the learning of non‐na3ve paKerns, because they do not
• E. g. According to Kuhl conform to the established “mental filter”, then early learning
of one’s primary language may limit second language
learning. By this argument, the “cri3cal period” depends on
(2004) mother language experience as much as 3me, and is a process rather than a
strictly 3med window of opportunity that is opened and
learning builds a mental
closed by matura3on.
• The general point is that learning produces neural
commitment to the proper3es of the s3muli we see and
filter that limits second hear. Exposure to a specific data set alters the brain by
establishing neural connec3ons that commit the brain to
processing informa3on in an ideal way for a par3cular input. ..
language learning Neural commitment func3ons as a filter that affects future
processing…
• In adulthood, second language learners have to overcome
commiKed brains to develop new networks.” (Bransford, et al,
in Sawyer, 2009, p. 21‐22)

24. Can we learn anything? Evolu3on and selec3on
• «… I have oXen observed that educators hold an implicit model of brain as a tabula
rasa or blank slate (Pinker, 2002), ready to be filled through educa3on and classroom
prac3ce. In this view, the capacity of the human brain to be educated, unique in the
human kingdom, relies upon an extended range of cor3cal plas3city unique to
humans. The human brain would be special in its capacity to accommodate an
almost infinite range of new func3ons through learning.
• In this view, then, knowledge of the brain is of no help in designing educa3onal
policies.
• …. Much of current classroom content, so the reasoning goes, consists in recent
cultural inven3ons, such as the symbols we use in wri3ng or mathema3cs. Those
cultural tools are far too recent to have exerted any evolu3onary pressure on brain
evolu3on. … Thus, it is logically impossible that there exist dedicated brain
mechanisms evolved for reading or symbolic arithme3c. They have to be learned,
just like myriads of other facts and skills in geography, history, grammar, philosophy
… The fact that our children can learn those materials implies that the brain is
nothing but a powerful universal learning machine. » (Dehaene, in BaKro, Fischer, &
Léna, 2008, p. 233).

25. Biology and culture
• Implica3on of the idea • «… While such a learning‐based theory might explain the vast range
of tabula rasa: each of human cultural abili3es, it also implies that the brain
learner is radically implementa3on of those abili3es should be highly variable across
different from other individuals. Depending on an individual’s learning history, the same
learners, and the brain regions might become involved in various func3ons. … Thus,
same cerebral areas one would not expect to find reproducible cerebral substrates for
can be affected to recent cultural ac3vi3es such as reading and arithme3c.
different func3ons • … a wealth of recent neuroimaging and neuropsychological findings
shed light on the ability of the human brain to acquire novel cultural
objects such as reading and arithme3c. Those data go against the
hypothesis of an unbiased tabula rasa.
• … Small cor3cal regions, which occupy reproducible loca3ons in
different individuals, are recruited by tehse tasks.They accomplish
thier func3on automa3cally and oXen without awareness.
Furthermore, the leasion of these regions can lead to specific
reading or calcula3on impariments. In brief, the evidence seems to
support the existence of dis3nct, reproducible, and rather specific
bases for reading and arithme3c …

26. Neural recycling
• Neural • «… Close examina3on of the func3ons of those brain areas in evolu3on
Recycling suggests a possible resolu3on of this paradox. It is not the case that those
hypothesis: areas acquire an en3rely dis3nct, culturally arbitrary new func3on. Rather,
biology and they appear to possess, in other primates, a prior func3on closely related to
culture have a the one that they will eventually have in humans. … rela3vely small changes
reciprocal may suffice to adapt them to their new cultural domain.
influence • « neural recycling hypothesis », according to which the human capacity for
• The example of cultural learning relies on a process of pre‐emp3ng or recycling pre‐exis3ng
mathema3cs brain circuitry.
• In my opinion, this view implies that an understanding of the child’s brain
organiza3on is essn3al to educa3on.
• … It postulates that, although Arabic digits and verbal numerals are culturally
arbitrary and specific to humans, the sense of numerical quan3ty is not. This
« number sense » is present in very young infants and in animals. We learn
to give meaning to our symbols and calcula3on by connec3ong them to this
pre‐exis3ng quan3ty representa3on. …
• Animals and infants cannot discriminate two neighboring numbers such as 36
and 37, but only have an approximate feeling of numerosity which gets
progressively coarser as the numbers get larger. » (Dehaene, in BaKro,
Fischer, & Léna, 2008, p. 234).

27. Neural recycling
• Neural • « Tanaka and colleagues (Tanaka, 1996) have studies the minimal features of
Recycling objects that make monkey occipito‐temporal neurons discharge. To this end,
hypothesis: they have used a procedure of progressive simplifica3on. First, a large set of
biology and objects is presented un3l one is found that reliably causes a given neuron to
culture discharge. The the shape of the object is simplified while trying to maintain an
have a op3mal neuronal response. When the shape cannot be simplified further
reciprocal without loosing the neuronal discharge, it is thought that one has discovered the
influence simplest feature to which the neuron responds. Remarkably, many of these
• The shapes resemble our leKers: some nerons respond to tow bars shapen in a T,
example of others to a circle or to two superimposed circles forming a figure 8, etc.
reading Obviously, those shapes have not been learned as leKers. Rather, they have
emerged in the course of ontogeny and/or phylogeny as a simple repertoire of
shapes….
• In summary, reading, just like arithme3c, does not rely only on domain‐general
mechanisms of learning. Rather, learning to read is possible because our visual
system already possesses exquisite mechanisms for invariant shape recogni3on,
as well as the appropriate connec3ons to link those recognized shapes to toher
areas involved in auditory and abstract seman3c representa3ons of objects.
• Learning is also possible because evolu3on has endowed the system with a high
degree of plas3city. Although we are not born with leKer detectors, leKers are
sufficiently close to the normal repertoire of shapes in the inferotemporal
regions to be easily acquired and mapped onto sounds. We pre‐empt part of this
system while learning to read, rather than crea3ng a « reading area » de novo.
» (Dehaene, in BaKro, Fischer, & Léna, 2008, p. 241‐242).

28. PRACTICAL ISSUES RELATED TO PLASTICITY AND LEARNING
‐ LONG LASTING LEARNING
‐ TRANSFERABLE LEARNING
‐ BIASES IN THE EVALUATION OF LEARNING EFFECTS

29. From theory to prac3ce
• “Learning and brain plas3city are fundamental proper3es of the
• How can we generate successful nervous system, and they hold considerable promise when it
interven3ons for promo3ng comes to learning a second language faster, maintaining our
relevant learning ? perceptual and cogni3ve skills as we age, or recovering lost
– How do we pass from theory to func3ons aXer brain injury. Learning is cri3cally dependent on
prac3ce? experience and the environment that the learner has to face. A
– Which kind of theory and central ques3on then concerns the types of experience that
evidence do we need? favor learning and brain plas3city. Exis3ng research iden3fies
three main challenges in the field. First, not all improvements in
– What is relevant learning? performance are durable enough to be relevant. Second, the
– Learning that is long‐las3ng and condi3ons that op3mize learning during the acquisi3on phase
transferable are not necessarily those that op3mize reten3on. Third, learning
is typically highly specific, showing liKle transfer from the trained
– How do we promote learning task to even closely related tasks.
that is long‐lasFng and • While individuals trained on a task will improve on that very task,
transferable? other tasks, even closely related ones, oXen show liKle or no
improvement.
• … brain plas3city … can also be maladap3ve … as when expert
string musicians suffer from dystonia or motor weakness in their
fingers as a result of extensive prac3ce with theirinstruments.
• Finally, … we are s3ll missing the recipe for successful brain
plas3city interven3on at the prac3cal level.” (Bavelier, et al., in
Gazzaniga, 2009, p. 153)

30. Training & Relevant learning
• “it is well documented that individuals who have an
• Studies on the effects of training on learning should ac3ve interest taken in their performance tend to
prove that the effects are long‐las3ng and that there improve more than individuals who have no such
is a causal rela3onship between the kind of training interest taken…
and the learning effect
• This effect can lead to powerful improvements in
– The placebo effect of learning: mo3va3onal factors performance that have liKle to do with the specific
influence performance, but they are not part of the
learning experience being evaluated cogni3ve training regimen being studied, but instead
– The popula3on effect: causal links are not the same reflect social and mo3va3onal factors that influence
than correla3ons, since correla3on could depend performance.
from external factors • Inherent differences in abili3es may lead to to the
differences in the ac3vi3es experienced, rather than the
other way round. For example, individuals born with
superior hand‐eye coordina3on may be quite successful
at baseball and thus preferen3lly tend to play baseball, …
• The effects of training should be measured at least a full
day aXer comple3on of training…
• Training studies should include a groupe that controls for
test‐retest effects … and, just importantly, for
psychological and mo3va3onal effects.
• Finally, evalua3on of the efficacy of training cri3cally
depends on the choice of outcome measures. Outcome
measures closely related to the training experience are
more likely to show robust improvement … Yet it is
cri3cal to show transfer to new tasks within the same
domain …” (Bavelier, et al., in Gazzaniga, 2009, p.
154‐155)

31. Learning as reusable
• “Learning involves acquiring new informa3on • Learning is supposed to be re‐usable
and uFlizing it later when necessary. Thus, – An example: Imagine a motor therapy which
any kind of learning implies generalizaFon of induces the learning of new movements, but
the originally acquired informa3on: to new these movements can only be accomplished
occasions, new loca3ons, new objects, new in the therapy room
contexts, etc. However, any piece of new
informa3on that an organism perceives is
episodic and par3cular: it involves a single
3me, a specific loca3on and context, and
par3cular objects).” (Gergely & Csibra, 2009,
p. 3)
• “The ques3on of how one can learn (i.e.,
acquire general knowledge) from bits of
episodic informa3on is known as the
inducFon problem and has been tackled by
various theories of learning. These usually
rely on sta3s3cal procedures that involve
sampling mul3ple episodes of experience to
form the basis of generaliza3on to novel
instances.” (Gergely & Csibra, p. 3)

32. Learning is long‐las3ng
The neuromyth of the Mozart effect
• In many cases, training produces effects that • “Many types of transient effect may indeed
cannot be considered as relevant learning, be causally related to the training
because: interven3on; however, they are not
considered true learning effects because they
– It is not sufficiently generalized learning: an last only a few minutes following the
effect on learning that is bound to the trained cessa3on of training.
task is barely interes3ng • An excellent example is the so‐called Mozart
– It is not long‐las3ng learning : an effect on effect, where listenint to only 10 minutes of a
learning is not proved by experiments that Mozart sonata was reported to lead to
evaluate short‐term effects (e.g.: violent significant performance increases on the
effects of violent video games)
Stanford Binet IQ spa3al reasoning task …
– Other variables than the the learning
experience produce an effect, but are not • Unfortunately, in addi3on to proving difficult
controlled for and evaluated to replicate consistently, … the validity of this
enhancement as true learning effect has
been ques3oned, as any posi3ve effects last
only a few minutes.” (Bavelier, et al., in
• The Mozart effect, a classic case of Gazzaniga, 2009, p. 153)
performance enhancement that is NOT a
form of learning, because it does not last
• … and a classic neuromyth

33. Learning is long‐las3ng
The lack of evidence about violent
video games
• Violent video games seem to produce effects • “studies that have examined the impact of
on physiological arousal, verbal violence, but playing violent viode games on aggressive
these effects are only tested few minutes behavior may suffer from the same
aXer the exposi3on. weakness, as the tests used to assess
changes in the dependent variables of
interest (behavior, cogni3on, affect, etc;) are
typically given within minutes of the end of
exposure to the violent video game. Given
that violent video games are known to
trigger a host of transient physiological
changes associated with increased arousal
and stress (i.e. fight or flight responses) it is
important to demonstrate that any changes
in behavior or cogni3on are noy likewise
transient in nature.” (Bavelier, et al., in
Gazzaniga, 2009, p. 154)

34. Learning generalizable and
transferable
• Learning shows a strong specificity: transfer • “In the field of learning, transfer of learning
to even near domains is rare from the trained task to even other very
similar task is generally the excep3on rather
than the rule.
• For isntance, Pashler and Baylis (1991)
trained subjects to associate one of three
keys with visually presented symbols (leX key
= P or 2, middle key = V or 8, right key = K or
7). Over the course of mul3ple training
blocks, par3cipants reac3on 3me decreased
significantly. However, when new symbols
were added that needed to be mapped to
the same keys in addic3on to the learned
symbols … no evidence of transfer was
evident.” (Bavelier, et al., in Gazzaniga, 2009,
p. 153‐154)