EP1. Learning Elena Pasquinelli Educa3on, cogni3on, cerveau Cogmaster 2010‐2011
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)
BIOLOGICAL DEFINITION OF LEARNING RELATIONSHIP BETWEEN LEARNING, EVOLUTION, DEVELOPMENT
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)
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)
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)
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)
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)
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
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)
CAN HUMANS LEARN ALL LIFE LONG? ‐ THE PROBLEM OF CRITICAL PERIODS ‐ THE ROLE OF EXPERIENCE ‐ THE FORMS OF PLASTICITY
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)
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)
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)
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)
• 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
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)
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)
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
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)
CAN HUMANS LEARN ANYTHING? ‐ BIOLOGICAL CONSTRAINTS ‐ THE ROLE OF EDUCATION
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)
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)
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).
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 …
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).
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).
PRACTICAL ISSUES RELATED TO PLASTICITY AND LEARNING ‐ LONG LASTING LEARNING ‐ TRANSFERABLE LEARNING ‐ BIASES IN THE EVALUATION OF LEARNING EFFECTS
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)
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)
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)
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
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)
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)
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