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How We Learn: The New Science of Education and the Brain

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In today's technological society, with an unprecedented amount of information at our fingertips, learning plays a more central role than ever. In How We Learn, Stanislas Dehaene decodes its biological mechanisms, delving into the neuronal, synaptic, and molecular processes taking place in the brain. He explains why youth is such a sensitive period, during which brain plast In today's technological society, with an unprecedented amount of information at our fingertips, learning plays a more central role than ever. In How We Learn, Stanislas Dehaene decodes its biological mechanisms, delving into the neuronal, synaptic, and molecular processes taking place in the brain. He explains why youth is such a sensitive period, during which brain plasticity is maximal, but also assures us that our abilities continue into adulthood, and that we can enhance our learning and memory at any age. We can all "learn to learn" by taking maximal advantage of the four pillars of the brain's learning algorithm: attention, active engagement, error feedback, and consolidation. The human brain is an extraordinary machine. Its ability to process information and adapt to circumstances by reprogramming itself is unparalleled, and it remains the best source of inspiration for recent developments in artificial intelligence. The exciting advancements in A.I. of the last twenty years reveal just as much about our remarkable abilities as they do about the potential of machines. How We Learn finds the boundary of computer science, neurobiology, and cognitive psychology to explain how learning really works and how to make the best use of the brain's learning algorithms, in our schools and universities as well as in everyday life.


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In today's technological society, with an unprecedented amount of information at our fingertips, learning plays a more central role than ever. In How We Learn, Stanislas Dehaene decodes its biological mechanisms, delving into the neuronal, synaptic, and molecular processes taking place in the brain. He explains why youth is such a sensitive period, during which brain plast In today's technological society, with an unprecedented amount of information at our fingertips, learning plays a more central role than ever. In How We Learn, Stanislas Dehaene decodes its biological mechanisms, delving into the neuronal, synaptic, and molecular processes taking place in the brain. He explains why youth is such a sensitive period, during which brain plasticity is maximal, but also assures us that our abilities continue into adulthood, and that we can enhance our learning and memory at any age. We can all "learn to learn" by taking maximal advantage of the four pillars of the brain's learning algorithm: attention, active engagement, error feedback, and consolidation. The human brain is an extraordinary machine. Its ability to process information and adapt to circumstances by reprogramming itself is unparalleled, and it remains the best source of inspiration for recent developments in artificial intelligence. The exciting advancements in A.I. of the last twenty years reveal just as much about our remarkable abilities as they do about the potential of machines. How We Learn finds the boundary of computer science, neurobiology, and cognitive psychology to explain how learning really works and how to make the best use of the brain's learning algorithms, in our schools and universities as well as in everyday life.

30 review for How We Learn: The New Science of Education and the Brain

  1. 5 out of 5

    Toni

    Written in a very captivating manner, easy to follow and understand, How We Learn discusses one of the most important abilities that dstinguishes humans form the rest of the world- our ability to learn in a conscious way. Stanislas Dehaene is a neuroscientist who has written a number of books to help the general public understand better how our brains use information in order to learn and create. The amount of information available to us nowadays is staggering. Our ability to select and process i Written in a very captivating manner, easy to follow and understand, How We Learn discusses one of the most important abilities that dstinguishes humans form the rest of the world- our ability to learn in a conscious way. Stanislas Dehaene is a neuroscientist who has written a number of books to help the general public understand better how our brains use information in order to learn and create. The amount of information available to us nowadays is staggering. Our ability to select and process input in order to adapt to and enhance our environment as well as using feedback as the most important learning tool are the reasons why we keep surviving and advancing our knowledge. Stanislas Dehaene looks in detail at the biological processes that happen in our brain, and discusses the issue of neuroplasticity and learning at different ages. As we live in a very technological society, it is not surprising that our interest in the science of learning is partly driven by our desire to develop 'smarter' machines and artificial intelligence. But it is not the only reason why we should keep trying to understand our learning process better. Our life is already calling for a life-long - 360° learner able to cope with the speed with which our world is changing. This is why this thought-provoking book is so important and timely. Thank you to Edelweiss and Viking for the ARC provided in exchange for an honest opinion.

  2. 4 out of 5

    Morgan Blackledge

    I took cognitive science at the end of my psychology degree. It was AMAZING. The findings and insights of cognitive science are all about how people learn. In other words, when you learn cognitive science, you learn how to learn. In a crazy kind of Möbius strip, life imitating art type lived irony, you are literally applying the findings that you are learning, as you are learning them, and they help you learn the things your learning, which is essentially how to learn. Much of the findings are co I took cognitive science at the end of my psychology degree. It was AMAZING. The findings and insights of cognitive science are all about how people learn. In other words, when you learn cognitive science, you learn how to learn. In a crazy kind of Möbius strip, life imitating art type lived irony, you are literally applying the findings that you are learning, as you are learning them, and they help you learn the things your learning, which is essentially how to learn. Much of the findings are counter intuitive, and slaughtered many of the sacred cows of liberal education. Like learning styles for instance. Not really a real thing. But there’s more. Common college truisms like, underlining, re-reading, long study sessions and cramming were all debunked as ridiculously inefficient and ineffective. Other WAY less intuitive techniques were found to be WAY more helpful including, short study sessions distributed over longer periods of time, systematic self testing, plenty of sleep, exercise, healthy diet, no booze, location based mnemonics, and more. It was an extremely difficult class, but I took a leap of faith and broke my old study habits in order to apply what I was learning in the class, and BAM! I CRUSHED the exams. KILLED the course. I did significantly better on the difficult, highly technical material, and it took WAY less time. The time I did spend studying was better quality, and more effortful, but none the less...less. Less (but better) time spent got me better results. All because I was studying the correct way, for the FIRST TIME!!! It was UNREAL. I remember talking to the instructor and reflecting that it was strange that learning how to learn wasn’t part of every curriculum at every stage of every educational path. I reflected this same course should have been offered in 9th grade, because it was literally the blueprint for academic success. I went on to reflect that, at minimum, the course should have come at the very beginning of my college career, not at the end of a masters degree. Of course the instructor just smiled and said “you’re preaching to the choir dude”. What was perhaps even more terrifying, was that I was working as an adjunct professor for undergraduates in psychology at that very time, and I had (until that point) received ZERO training in pedagogy, until that very course. Thats right, I was a college instructor, and I was learning how to learn at close to the end of my career as a college instructor. WTF!!! And it gets worse. The real reason no one ever offered you or I that education in education (call it a meta-education) was that they didn’t get it either. Most educators, including school teachers and college professors don’t get any specific training in cognitive pedagogy. I think this is the issue that Stanislas Dehaene is addressing in this book. And he’s really brilliant, and a really good writer and educator, so...you guessed it...the book is really really good. You’re not gonna get tons of study tips. It’s not that kind of book (thank god). It’s much more meta-level than that. But it’s easy to apply Dehaene’s talking points to whatever field of study that you’re engaged in, at whatever level. So whether you’re a teacher or a student, or just a human with the brain, I highly recommend that you get this book, and continue to pursue a basic education in cognitive science. It will pay dividends in terms of better results and less anxiety regarding learning, for yourself and for who ever will listen to you (unfortunately this probably excludes your kids). Great Book 📚

  3. 4 out of 5

    Gretchen Rubin

    A friend recommended Dehaene's work, and I found this book so interesting that I immediately got my hands on his other work. A friend recommended Dehaene's work, and I found this book so interesting that I immediately got my hands on his other work.

  4. 4 out of 5

    Debjeet

    HOW WE LEARN • The brain draws its knowledge from its environment. • Nerve cells posses a remarkable ability to constantly adjust their synapses to signal they perceive. • Our 23 pair of chromosomes contain 3 billion pairs of the letters A,C,G,T -the molecules adenine,cytosine,guanine and thymine. Information is contained in bits and DNA contains 6 billion bits. • There are 86 bn neurons and a thousand trillion connections • The capacity of brain is on the order of 100 terabytes • Animal that possess HOW WE LEARN • The brain draws its knowledge from its environment. • Nerve cells posses a remarkable ability to constantly adjust their synapses to signal they perceive. • Our 23 pair of chromosomes contain 3 billion pairs of the letters A,C,G,T -the molecules adenine,cytosine,guanine and thymine. Information is contained in bits and DNA contains 6 billion bits. • There are 86 bn neurons and a thousand trillion connections • The capacity of brain is on the order of 100 terabytes • Animal that possessed even a rudimentary capacity to learn had a better chance of surviving than those with fixed behaviors & they were more likely pass their genome to next generation. • Most of what we know know about the world was not given to us by our genes: we had to learn it from our environment or from those around us. • From making fire & designing tools to agi,exploration etc is the story of humanity of constant self exploration. • The brain has an extra ordinary ability to formulate hypothesis & select those that fit with our environment. • The active verification of knowledge, rejection of simple heresay & the personal construct of meanings are essential filter to protect us. • Mindset plays an important role in learning. Having a deeply entrenched view that anyone can progress is, itself , a source of progress. • retrieval practice- it is one of the most effective educational strategies. The mere act of putting your memory to test makes it stronger. • self awareness or meta memory is useful b/c it allows learner to focus harder on difficult items. • to get information into long term memory ,it is essential to study the material ,then test yourself.Self testing is one of best strategy. • space out learning- the rule of thumb is to review the info. at intervals of app. 20% of desired memory durations-for instance , rehearse after 2 mnths if you want memory to last about 10 mnths. • to keep information in memory as long as possible,it is best to gradually increase the time intervals. Start with rehearsels everyday,then review info. after weeks, a month,then a year. This strategy guarantees optimal memory at all points. • 4 pillars of learning- 1 Focussed attention- it allocates resource to info. it considers most essential. Attention makes relevant info. sensitive & it increases influence on brain. attention system • alerting-when to attend • orienting-what to attend to • exec. attention-how to process attended info. • selective attention-select the relevant ,ignore irrelevant. multitasking:- • our brain can process only one piece of info. at a time . • if 2 simple task given simultaneously . 1st one might get an attention & 2nd one might get delayed or forgotten altogether. How can we multitask? • Automization frees the conscious workspace by routinizing an activity, we can execute it unconsciously, without tying up brains central resources. 2 active engagement-learning requires an active generation of hypothesis with motivation and curiosity. • motivation is an essential : we learn well only if we have clear goal and we fully commit to reaching it. • to better digest new concepts, rephrase them into words or thoughts of your own. • curiosity- fundamental driver of an organism: it pushes us to act, it encourages us to explore • the degree of curiosity corelates with activity of nucleus accumbens and ventral tegmental areas, 2 essential regions of dopamine brain circuit. • the more curious you are about something, the more likely you are to remember it. • laughing or positivity seems to increase curiosity and enhance subsequent memory. • curiosity occurs whenever our brain detect a gap b/w what we already know & what we would like to know- a potential learning area • to maximise what we learn,we have to constantly enrich our env, with new objects that are stimulating enough to not be discouraging. • as learning progresses ,the expected learning gain shrinks: the better we master a field , the more we reach the limits of what it can offer & less interested we are in it. • to maintain curiosity, one must always provide brain with stimulants that match their intelligence. • one can restore desire to learn by offering them stimulating problems carefully tailored to their current levels. • the neuroscience of motivation is clear: desire to do action x must be associated with an expected reward,be it material or cognitive. 3 error feedback- whenever we are surprised because world violates our expectation , error signal spreads throughout our brain.They correct our mental models,eliminate inappropriate hypothesis ad stabilise the most accurate ones. • Every error offers an opportunity to learn. • the quality and accuracy of feedback we receive determines how quickly we learn. • surprise is one of the fundamental drivers of the learning.No learning is possible without an error signal. organisms can only learn when events violate their expectations. No surprise, no learning. • being immersed in fear free,stimulating env. can re-open synaptic plasticity. 4 daily rehearsel and nightly consolidation- our brain compiles what it has acquired and transfer it to long term memory, thus freeing neural resources for further learning.Repetition plays an important role in consolidation process. • learning is more efficient when done at regular intervals, rather cramming an entire session in 1 day, we are better off spreading out the learning. The reason is simple: every night our brain consolidates what it has learned during the day. • while we sleep,our brain remains active, it runs a specific algorithm that replays the important events it recorded during previous days & gradually transfer them into more efficient compartment of memory • durng sleep,the neural circuits that we used during preceding day gets reactivated. • experiments indicate that information you learned is better consolidated the next morning if you had slept while being exposed to another smell. • If we smell an aroma while we take on new knowledge & then sleep next to source to that same odor, we will find it easier to recall info. at later date. • practice during the day and sleep during night to reactivate and consolidate what we acquired. • By constantly attending to probabilities and uncertainties, brain optimises its ability to learn. • To learn is to progressively form an internal model of the outside world. • At its core ,intelligence is a process that converts unstructured information to useful and actionable knowledge. • Through learning ,raw data that strikes our senses turned into refined idea,abstract enough to be reused in a new context. • Behind the scenes,our sensory areas ceaselessly compute with probabilities & only most likely model makes it to our consciousness. • It is the brains projection that ultimately give meaning to flow of data that reaches us from our senses.In the absense of an internal model,raw sensory inputs would remain meaningless. • Learning allows our brain to grasp a fragment of reality that it had previosly missed & to use it to build a new model of the world. It can be part of external reality as when we learn from history,science etc but also brain learns to map the reality internals to our bodies, as we learn to play musical instrument etc. • 7 definition of what leaning means 1. learning is adjusting the parameters of mental models-it boils down to searching among internal models that best correspond to the external model 2 learning is exploiting a combinatorial explosion • combinatorial explosion-the exponential increase that occurs when you combine even a small no of possibilities. • In our brain ,there are 86 bn neurons, each with about 10k synaptic connections whose strength can vary.The space of mental representation that opens up is practically infinite. • Human bran breaks down the problems of learning by creating a hierarchical,multilevel model 3 Learning is minimising errors • Brain observe the errors & use them to adjust internal state in directions that they feel is best able to reduce error 4 learning is exploring space of possibilities- random exploration,random curosity & noisy firing plays an essential role in learning . • characteristic of human species is relentless search for abstract rules, high level conclusion that are extracted from a specific situation & subsequently tested on a new observation 5 learning is optimising a reward function- The network can correct itself by calculating the diff. b/w its response and correct answer. This procedure is known as “supervised learning”. • reinforcement learning • act & self evaluate • actor-critic combination is one of the most effective strategies of contemp. AI, when backed by hierarchical neural network , it works wonders. • bootstrapping-a neural network can become a world champion simply by playing againsti itself. • adversarial learning- consists of training 2 opponent system-one that learns to become an expert & other whose sole goal is to make 1st one fail. • metacognition-the ability to know oneself,to self evaluate,to mentally stimulate what would happen if we acted this way or that way-plays a fundamental role in human learning. • meta rules allows to search for a possible meaning among object around them, rather tha treating word as verb or adjective. 6 learning is restricting a search space- the more parameter the internal model has, the more difficult it is to find the best way to adjust it. • curse of dimensionality- having too many free parameter can be curse while system learns ,it is unable to generalise a new situation.The ability to generalise is key to learning. • one of the most effective interventions ,which can both accelerate learning and improve generalisation is to simplify the models. • what is learned in one place can be reused everywhere else. • the system has to tune only a single filter that it applies everywhere ,rather than a plethora of diff. connections for each locations. 7 learning is projecting a priori hypothesis • exploitation of innate knowledge- convolutional neural network learns better & faster than other type of neural network b/c they donot learn everything. • what I learn in 1 place can be generalised anywhere else. • rather than learning from scratch ,it is more effective to rely on prior assumption that clearly describe the basic laws of domain that must be explored & integerate this into very architecture of system. • to learn is to eliminate. why our brain learn better than current machines? 1. unlike a computer, we posses the ability to question our beliefs & refocus our attention on those aspects of image that dont fit with our 1st impression 2. human learning is not just setting a pattern recognition filter, but also forming an abstract model of the world. 3. social learning- we learn a lot from our fellow human through language. • what human do exceedingly well is to integerate new information with an existing network of knowledge. 4 the ability to discover a general laws that lie behind specific cases- • the ability to make infinite use of finite means characterises human thought. • humans can integerate piecewise knowledge into single formula & integerated pattern of activity happens in “Brocas area”. How scientific reasoning work? 1 researchers apply a simple logic-they state several theories, unravel the web of ensuing predictions and eliminate the theories whose predictions are invalidated by experiments and observations 2 In each of our mind,ignorance is gradually erased as our brain successfully formulates increasingly accurate theories of the outside world through observations. 3 learning is reasoning like detective-going to hidden cause of phenomenon, in order to deduce the most plausible models that govern them. 3 bayesian approach - • To learn is to be able to draw as many inference as possible from each observations • tracing every observation to its most plausible cause allows us to return to foundation of logic. • the bayesian theory allows us to travel from observation to causes.The theory explains how to update our belief after each observations. • the brain should calculate an error signal : the diff. b/w what the model predicted and what has been observed. The bayesian algorithm indicates how to use this error signal to modify internal models of the world. • when you eliminate the impossible,whatever remains ,however improbable, must be the truth. 4 our adult judgement combines 2 levels of insights-innate knowledge of species and our personal experiences. 5 learning is most effective when we have vast spaces of hypothesis, a set of mental model with myriad settings to choose from & sophisticated algorithm that adjust those settings a/c to data received. self organisation 1 self organisation is ubiquitous in the developing brain. 2 grid cells are ‘GPS of brain’- it forms a network of triangle which grouped together to form a network. 3 hexagon being frequently produced from giraffe skin to beehives • hexagon is common b/c it is a shape that fills a plane with equal size units and leaves no wasted space.It is most mechanically stable • brain contains a neuronal map with hexagonal symmetry. 4 foundation of our core brain circuits arise through self organisations by bootstrapping themselves from database generated inside the system. 5 the knowledge of mathematical thing is almost innate in us. For people who are illiterate also know how to count and reckon. neuroplasticity • in addition to reinforcement of pre established pathways,new pathways are created by the progressive growth of terminal dendritic and axonal process. • neurons, synapses and microcircuits that they form are material hardware of brain plasticity, they adjust each time we learn. • as we learn, all elements can change: the presence ,number and strength of synapses and neurons. • our brain contains about 100 trillions of synapses & synapses are genuine nanoprocessors of the brain. • hubbs rule-when 2 neurons are activated at same time or in short successions,their connections strengthens • emotional circuits of our brain considered most significant. • when we learn, the explosion of new synapses forces the neurons to grow additional branches. • to make & break a few million synapses/second , it requires a balanced diet,oxygenation & physical exercises. • vitamin B1 which contains thiamine is imp. for brains health. • the acquisition of novel skill doesnt require a radical rewriting of cortical circuits but a repurposing of existing org. memory 1 episodic memory- records the episodes of our daily lives • neurons in hippocampus memorize the context of each event • they encode where,when,how and with whom things happened • hippocampus is involved in all kinds of rapid learning. 2 semantic memory-memory donot stay in hippocampus forever. At night,brain plays them back and moves them to a new location within cortex.There,they are transformed into permanant knowledge. 3 procedural memory- when we repeat the same activity over and over again .Neurons gets strengthened and information flows better in future.

  5. 4 out of 5

    picoas picoas

    If you're into stuff like this, you can read the full review. Act of Abstraction: "How We Learn - Why Brains Learn Better Than Any Machine . . . for Now" by Stanislas Dehaene A machine whatever it does is programmed by humans. So how on earth can it possible be more creative some people like to say? Answer: Our children are created and taught by us; how could they ever be more creative? It asks a wider question of humanity. Individuals can be creative and able to compute information, ideas and emot If you're into stuff like this, you can read the full review. Act of Abstraction: "How We Learn - Why Brains Learn Better Than Any Machine . . . for Now" by Stanislas Dehaene A machine whatever it does is programmed by humans. So how on earth can it possible be more creative some people like to say? Answer: Our children are created and taught by us; how could they ever be more creative? It asks a wider question of humanity. Individuals can be creative and able to compute information, ideas and emotions entirely uniquely - but that's not many individuals.

  6. 4 out of 5

    Emre Sevinç

    This book strikes a special chord with me: I started my formal cognitive science training and research activities almost 20 years ago, and reading the cognition-related developments in brain sciences that occurred in the last 20-25 years brings a unique type of excitement. I haven't been involved with academic research for a long time, alas, but professionally I'm under daily pressure to learn new aspects of technology and apply them in various settings, and on top of that, I'm trying to teach s This book strikes a special chord with me: I started my formal cognitive science training and research activities almost 20 years ago, and reading the cognition-related developments in brain sciences that occurred in the last 20-25 years brings a unique type of excitement. I haven't been involved with academic research for a long time, alas, but professionally I'm under daily pressure to learn new aspects of technology and apply them in various settings, and on top of that, I'm trying to teach some of the things I know to my two sons, while observing some of their developmental struggle with the complexities of our world. Therefore, reading this book was a delight, because it not only summarizes the state-of-the-art of learning and teaching, but also sets the evidence-based path for future learners and teachers, that is, us. Even if you're not into the scientific aspects of developmental neuropsychology, or how some of the cutting-edge research in machine learning and artificial intelligence are inspired by the neurological mechanisms in the brain, you'll probably get something useful and practical out of this book because some sections will force you to think very consciously about the basic and critical mechanisms of attention, memory, engagement with a topic, giving and receiving feedback during learning something new, and other relevant aspects of your life. Needless to say, the message of the book is even more important for actual teachers, trainers and young pupils, as well as the administrators responsible for shaping the future of education, and I strongly recommend reading this book with a critical perspective if you're professionally involved with such activities. I take 1 star, and give it 4 stars, because of the author's over-enthusiasm and exaggerated analogies with modern, artificial deep-learning systems. This topic deserves more nuance, and subtlety, not TED-like simplification and sometimes outright misleading phrases. I'm sure Prof. Dehaene is very well aware of dangers of forcing such analogies, and I don't think he'd be keen on claiming strong correspondence between the intricacies of human minds/brains and over-hyped AI systems (yes, that attitude gets a little bit on my nerves, not only emotionally but also philosophically). Long story short, if you want to learn about our best and current understanding of learning mechanisms that happen in the brain, especially starting from birth, and scientifically validated ways of learning and teaching better, you can't go wrong with this highly readable book.

  7. 4 out of 5

    Sabin

    This one’s a hidden gem. Ok, the first third is a bit underwhelming. In the beginning, the author talks about general mechanisms by which we encode information about the world and how machine learning computer algorithms cannot match the human brain because they do not employ these mechanisms. Yet. He does point out that they can't do this stuff yet. But the repetition is tedious. And we’re talking about machine learning. So even though I think I listened to an updated version of the book (2020) This one’s a hidden gem. Ok, the first third is a bit underwhelming. In the beginning, the author talks about general mechanisms by which we encode information about the world and how machine learning computer algorithms cannot match the human brain because they do not employ these mechanisms. Yet. He does point out that they can't do this stuff yet. But the repetition is tedious. And we’re talking about machine learning. So even though I think I listened to an updated version of the book (2020), at the current rate of AI research some content is bound to be outdated in one or two years. The beauty and importance of the book lies in its up-to-date descriptions and theories about how humans learn and what new information recent neural imaging studies bring to this topic. His conclusions focus on human learning and development. The book discusses language acquisition, mathematical reasoning, brain plasticity and the developmental stages of the brain. He debunks myths about learning while sleeping and discovery learning (letting children stumble upon truths by themselves, unaided) while at the same time championing a 4-pronged learning approach which consists of attention, active engagement, error correction and consolidation. And yes, flashcards do work. But if you find them tedious, then you’re going about learning in the wrong way. A great resource for parents and educators. Could be worth a shot even for machine learning engineers who want to prove the author wrong or at least force him to revise the book on one account.

  8. 5 out of 5

    Virginia MD

    A must read for teachers, parents, and students of all ages I was eager to read the latest book by neuroscientist Stanislas Dehaene because I found “Reading in the Brain" and “Consciousness and the Brain" to be quite compelling. In his new book Dr. Dehaene explores the latest research about how our brains learn. More importantly, he explains how these principles can be applied in the real world of education. Babies are incredible “learning machines” but they are not born as blank slates. Thus, Deh A must read for teachers, parents, and students of all ages I was eager to read the latest book by neuroscientist Stanislas Dehaene because I found “Reading in the Brain" and “Consciousness and the Brain" to be quite compelling. In his new book Dr. Dehaene explores the latest research about how our brains learn. More importantly, he explains how these principles can be applied in the real world of education. Babies are incredible “learning machines” but they are not born as blank slates. Thus, Dehaene argues that learning is “never nature or nurture, but always both.” His discussion of how human learning differs from the so-called “deep learning” of current computers will be of special interest to anyone who wonders whether we are about to be surpassed by Artificial Intelligence (AI). After describing the fascinating science of learning Dehaene shares what he calls “the four pillars of learning,” which are the key principles he feels must be used to improve our educational efforts. These four pillars are Attention, Active Engagement, Error Feedback, and Consolidation. Dehaene gives a clear explanation of each pillar so that they can be applied by educational leaders, parents, teachers, and even individuals who strive to be life-long learners. I just posted an interview with Dr. Dehaene at brainsciencepodcast.com. You can listen on my website or in your favorite podcasting app.

  9. 5 out of 5

    Thanh Tuyen NT

    A book that brilliantly takes stock of the state of science in terms of learning both about the human brain and artificial intelligence. In a very fluid way, the author makes a parallel between the human brain and the artificial intelligence and especially how the research of the former influenced the technical advances of the latter. We learn about how our brain works and how the engineers in artificial intelligence tried to apply the latest discoveries in neuroscience, for example, error feedb A book that brilliantly takes stock of the state of science in terms of learning both about the human brain and artificial intelligence. In a very fluid way, the author makes a parallel between the human brain and the artificial intelligence and especially how the research of the former influenced the technical advances of the latter. We learn about how our brain works and how the engineers in artificial intelligence tried to apply the latest discoveries in neuroscience, for example, error feedback or alternating phases of "sleep" and active learning. But today, our brain is still much more capable than computer's brain and the brain of our child can still do much more amazing things. If you can read it in French, please do so, this is a must read not only for people who are interested in learning, parents, teachers, educators, tutors... but also for everybody else because each of us has to be a lifelong learner in order to thrive in our ever-changing world.

  10. 5 out of 5

    Steve

    Maybe 3.5 stars. Interesting stuff about how the learning process works within the brain, with some contrast/compare of computer learning algorithms. Some suggestions about how schools should restructure educational techniques in light of knowledge about how learning works. I felt disappointed that there was very little discussion about how what is learned is actually encoded in the brain for future retrieval. The simple fact is that scientists just don’t know how this works, but I would have lov Maybe 3.5 stars. Interesting stuff about how the learning process works within the brain, with some contrast/compare of computer learning algorithms. Some suggestions about how schools should restructure educational techniques in light of knowledge about how learning works. I felt disappointed that there was very little discussion about how what is learned is actually encoded in the brain for future retrieval. The simple fact is that scientists just don’t know how this works, but I would have loved to hear some intelligent informed speculation. Not sure if this is the cautious scientist sticking to scientific facts, or if the mysteries of how knowledge is stored is still so unfathomable that it’s just not worth speculating about. But I guess I expect that a book about how the mind learns would addressthis situation, if at least to say that nobody has any real idea how information is encoded in the brain.

  11. 5 out of 5

    Alja

    The book provides an accessible review of current neuroscience findings on how our brains learn various skills. Throughout the book, the author also compares our "learning algorithms" to state of the art machine learning approaches. Through the comparison, it becomes clearer why machines can't (yet) learn as efficiently as we do, but the author also hints at how newer, more efficient algorithms are becoming better at imitating our biological learning software. In the final third of the book, the The book provides an accessible review of current neuroscience findings on how our brains learn various skills. Throughout the book, the author also compares our "learning algorithms" to state of the art machine learning approaches. Through the comparison, it becomes clearer why machines can't (yet) learn as efficiently as we do, but the author also hints at how newer, more efficient algorithms are becoming better at imitating our biological learning software. In the final third of the book, the author explores the four pillars of learning – attention, active engagement, error feedback, and consolidation –, which offer practical guidance on how to apply the presented neuroscience findings to teaching and learning. Overall, the weakest part of the book is the introductory chapter. Luckily, the rest of the book is well-argued, rich in resources from diverse fields of research, and the final evidence-based recommendations for parents, teachers, and designers of educational systems pave the way to valuable real-life applications of the science of learning.

  12. 4 out of 5

    Nick Clark

    Very clear and highly readable book on the nature of learning. A (surprisingly) clear exposition of ways in which the brain is organised in handling and structuring information, with actionable recommendations for how to learn, pedagogy and teaching. Dehaene's four pillars of the human learning algorithm are attention, active engagement, error feedback, and consolidation. I really like him calling out the relationship between making errors and learning, and how our current school systems in most Very clear and highly readable book on the nature of learning. A (surprisingly) clear exposition of ways in which the brain is organised in handling and structuring information, with actionable recommendations for how to learn, pedagogy and teaching. Dehaene's four pillars of the human learning algorithm are attention, active engagement, error feedback, and consolidation. I really like him calling out the relationship between making errors and learning, and how our current school systems in most countries does not sufficiently accommodate for the variation in proficiency and curiosity across pupils, and the risk of stigmatisation caused by the current usage of grading in schools. The role of sleep as an active consolidator of knowledge was an eye-opener. Great read. Give it a try. There is something here for everyone.

  13. 4 out of 5

    George Hipp

    Well researched and written book about how humans have evolved to learn, parallels with ML (machine learning), importance of education for children and how everyone can use the tools our physical brain provides to learn effectively. This is not a book about how to cheat time to learn or shortcuts to learning, more of information about what we know about our brain and theories around how we can optimize learning by understanding how it works. The relationship between theories around learning and Well researched and written book about how humans have evolved to learn, parallels with ML (machine learning), importance of education for children and how everyone can use the tools our physical brain provides to learn effectively. This is not a book about how to cheat time to learn or shortcuts to learning, more of information about what we know about our brain and theories around how we can optimize learning by understanding how it works. The relationship between theories around learning and experiments with ML is also fascinating. Approachable book, good for anyone interested in learning and even more so for people who teach.

  14. 5 out of 5

    Paige McLoughlin

    I read this in the early part of 2020. I had been exploring concepts in machine learning and developments in Artificial intelligence and I have always had an interest in human cognition and learning. This book combined both topics and compares and contrasts the functioning down to some real nuts and bolts of both human and machine learning. Humans and machine learning via neural networks (which oddly enough are modeled somewhat on animal neural systems) have a lot of similarities. It seems logic I read this in the early part of 2020. I had been exploring concepts in machine learning and developments in Artificial intelligence and I have always had an interest in human cognition and learning. This book combined both topics and compares and contrasts the functioning down to some real nuts and bolts of both human and machine learning. Humans and machine learning via neural networks (which oddly enough are modeled somewhat on animal neural systems) have a lot of similarities. It seems logical categorical processing systems are great expert systems and good algorithms for expert and bureaucratic decision-making bodies developed in the early days of AI research are very alien to the way humans think. Do things precisely and efficiently way beyond human capacity but needed strict rules and had a hard time with novelty and ambiguity or not well-formed problems to tackle. The newer neural nets took a long time to get off the ground but can do amazing things at visual or audio tasks of distinguishing objects that are novel and not labeled in a clear-cut manner. It is also good at cluster messy data and fitting it into digestible graphical layouts and clusters. It also much like people require large amounts of training sets and use feedback in performance to "learn" and be evaluate performance before it is unleashed on novel data. The book gets down to the nuts and bolts of such systems and where they look like human learning and cognition and places where they diverge from it. Definitely will hit this one again in the near future.

  15. 5 out of 5

    Kris Muir

    Dehaene is a well-respected neuroscientist and his expertise is obvious from his intricate explanation of the neuronal scaffolding of the brain. For my own personal teaching, I appreciated his framework around the 4 pillars of learning (attention, active engagement, error feedback, consolidation). I found his chapter on error feedback very interesting, particularly the research by Roediger on memory retention being stronger when students tested themselves (and studied less) vs the students that Dehaene is a well-respected neuroscientist and his expertise is obvious from his intricate explanation of the neuronal scaffolding of the brain. For my own personal teaching, I appreciated his framework around the 4 pillars of learning (attention, active engagement, error feedback, consolidation). I found his chapter on error feedback very interesting, particularly the research by Roediger on memory retention being stronger when students tested themselves (and studied less) vs the students that actually spent more overall time studying. I'm also very curious about the notion of the spacing effect and what an ideal timeline might look like for something you're trying to keep for a lifetime (e.g. knowledge of neuroscience!). Dehaene references intervals of 20% of how long you want to remember something, but he doesn't give any specifics in terms of timeline. If I want to learn X topic and still be able to remember it 5 years from now, how often do I need to schedule retrieval practice? Is it every week, then every month, then every 6 months, etc.? On balance, I liked the Dehane book but I felt that it lacked strong practical approaches to applying neuroscience in teaching and learning. Advice for learners (and maybe teachers): -build in more low-stakes tests (as simple as brain dumps on a white sheet of paper) -do the hard thinking involved in retrieval practice (set a timer and see how much you remember) -leverage the spacing effect (plan ahead and schedule study sessions over many days instead of cramming) -consider cascading your study sessions (20% reviewing old material, 40% new material) Inspired by this book, I attended a wonderful workshop on the neuroscience of learning given by neuroscientist Kristi Rudenga at Notre Dame. Her main insights were: -“learning changes the physical structure of the brain” -“repetition strengthens synapses” -“richer networks = stronger learning” -“stress short-circuits the brain” Happy Reading!

  16. 5 out of 5

    Peter Gelfan

    This is an important and fascinating book for parents, teachers, education administrators and bureaucrats, and anyone who wants to learn anything, including children, teens, college students, people starting a new profession, and retirees hoping to master new skills. For much of his career, the author, a brain scientist, has researched learning and education over the full spectrum from brain imaging and function to educational techniques and results. The book covers the same span. Dehaene punctur This is an important and fascinating book for parents, teachers, education administrators and bureaucrats, and anyone who wants to learn anything, including children, teens, college students, people starting a new profession, and retirees hoping to master new skills. For much of his career, the author, a brain scientist, has researched learning and education over the full spectrum from brain imaging and function to educational techniques and results. The book covers the same span. Dehaene punctures a number of long-standing myths about learning and education. He explains the marvelous acquisitiveness and plasticity of the baby brain, which is far more active than our pared down and more settled adult brains. Sleep turns out to be vital throughout life but especially for young people, because while we sleep, parts of our brains rev up to consolidate and streamline the takeaway from the events and newly acquired knowledge of the day. Near the end, the book outlines a number of thoroughly tested educational principles and techniques that any school, student, or parent can use to great advantage—and don’t worry, it’s nothing weird, the best teachers you ever had already used some of these methods. The lively, clear prose is easy to read and free of academese. Fittingly, the author makes it easy for us to learn about learning.

  17. 5 out of 5

    Matt Hutson

    How We Learn by Stanislas Dehaene is one of the best books about learning that I have read. I imagine, as a teacher myself, that this book is most helpful for those in the education industry. However, if you are an entreprenuer, business owner, or any conceivable field of work, if you want to become better at learning this book can help you to do that. The core of the book is based on The Four Pillars of Learning: 1. Attention 2. Active Engagement 3. Error Feedback 4. Consolidation These four pill How We Learn by Stanislas Dehaene is one of the best books about learning that I have read. I imagine, as a teacher myself, that this book is most helpful for those in the education industry. However, if you are an entreprenuer, business owner, or any conceivable field of work, if you want to become better at learning this book can help you to do that. The core of the book is based on The Four Pillars of Learning: 1. Attention 2. Active Engagement 3. Error Feedback 4. Consolidation These four pillars can be used both in child learning and adult learning. As a creator of an online course about how to become a better reader having a framework like the one above turns out to be extremely useful. And you, the reader of this review, can use this framework in your own learning. I would highly recommend this book to anyone who is looking to improve the way they learn and to understand how they can teach others how to learn better as well.

  18. 4 out of 5

    Mihai Cosareanu

    Amazing book about how we learn. It contains an overview about all the research available related to learning presented in a well summarized and structured way. This has become my go to recommendation for anyone who asks "how can I improve my learning?". Before this I would have recommended the "How We Learn" course on Coursera, and indeed it contains one concept that wasn't touched in this book, the concept of Chunking (you can google to understand what it means). Apart from this concept that's Amazing book about how we learn. It contains an overview about all the research available related to learning presented in a well summarized and structured way. This has become my go to recommendation for anyone who asks "how can I improve my learning?". Before this I would have recommended the "How We Learn" course on Coursera, and indeed it contains one concept that wasn't touched in this book, the concept of Chunking (you can google to understand what it means). Apart from this concept that's missing, I totally loved the book and the insights that came from it (tiny spoiler: newborns don't have a blank state, they already can assess objects, quantities and probabilities, I loved the research on this :) )

  19. 5 out of 5

    Nadine

    This book brings research on learning and neuroscience up to date and dispels a number of myths circulating in education and parenting circles. Written clearly and in an engaging way.

  20. 5 out of 5

    Megan S

    Wordy and sometimes just plain inaccurate. You can just read the conclusion to get all the important information from this book and none of it was new to me.

  21. 5 out of 5

    Brandon

    I was expecting this book to be just another think piece about learning and education, but I ended up being really surprised by how good it is. Dehaene does for the most part an amazing job at weaving lessons from neuroscience, cognitive science, and even artificial intelligence together to create a compelling narrative about how we learn and how we can take advantage of our understanding of how the brain learns to improve education. I'll start off with some quotes I want to remember: "Take a new I was expecting this book to be just another think piece about learning and education, but I ended up being really surprised by how good it is. Dehaene does for the most part an amazing job at weaving lessons from neuroscience, cognitive science, and even artificial intelligence together to create a compelling narrative about how we learn and how we can take advantage of our understanding of how the brain learns to improve education. I'll start off with some quotes I want to remember: "Take a new group of kindergartners and put them into the passive, receptive pedagogical mode. All you have to do is give them the object while saying, “Look, let me show you my toy. This is what it does . . .” and then play the music box, for instance. One might think that this would stimulate the children’s curiosity . . . but it has the opposite effect: exploration massively decreases following this kind of introduction. Children seem to make the (often correct) assumption that the teacher is trying to help them as much as possible, and that he has therefore introduced them to all the interesting functions of the device. In this context, there is no need to search: curiosity is inhibited." I wonder how many parents have accidentally turned their kids off of their field of specialty by doing this exact thing. Reminder to self - NEVER DO THIS TO YOUR KIDS!!!! Another surprising point I want to remember: "The myth of learning styles: According to this idea, each student has his or her own preferred learning style—some are primarily visual learners, others auditory, yet others learn better from hands-on experience, and so on. Education should therefore be tailored to each student’s favorite mode of knowledge acquisition. This is also patently false: as amazing as it may seem, there is no research supporting the notion that children differ radically in their preferred learning modality." I was always told that people have different learning styles. But Dehaene really tries to emphasize that almost all children have very similar cognitive circuits, and learning techniques that work for one will work for another. I think that he pushes his myth-busting a little too far though - does he remember being a kid? There are alsl sorts of personalities and brains out there, and even if in general what works for one kid should work for all, there is undeniably a huge amount of mental diversity out there, not just in ability but in inclinations. The book is a whole divided into three parts. The first part, which explains how modern AI works and how it falls short of human cognition, was the worst in my opinion. I almost gave up on the book actually, because it was extremely boring to me. I happen to have taken a course in exactly the sort of AI he was talking about (Bayesian models of cognition), and Part 1 was basically a pop-sci version of the course I took. Plus, it went deep enough into AI techniques that I expect it will lose a lot of readers who aren't familiar with AI, while not going deep enough to interest readers who know AI well. But he still makes some interesting points. First, he gives nine different definitions of learning: - adjusting the parameters of a model - exploiting a combinatorial explosion - minimizing errors - exploring the space of possibilities - optimizing a reward function - restricting a search space - projecting a prior hypotheses - inferring the grammar of a domain - reasoning as a scientist I was impressed by this list - I think it does a pretty good job giving a lot of different AI-inspired ways of thinking about learning - but it's also a very weird presentation of these concepts, and the different definitions have a huge amount of overlap. The next list was also interesting, a list of functions that modern AI is lacking: - learning abstract concepts - data-efficient learning - one-trial learning - social learning (the ability to use cues from other agents to speed learning) - systematicity and language of thought (the ability to learn general laws governing an example) - composition I found the next part, about neuroscience, to be much more interesting, maybe because I hardly know anything about neuroscience. What struck me the most is that apparently neuroscientists have identified many brain circuits that are devoted to specific tasks, some of them much more specific and powerful than I would have imagined. For example, I already knew that there are specific areas of the brain for processing faces and language. But I didn't know that there are grid cells in the brain that are arranged in hexagons that keep track of our location in 2D space, or that there is a line of neurons in our brain that we use as a number line to compare quantities. Another theme that Dehaene really hammers home is that literally everything we learn has a physical representation in our brains, and that we have specialized circuits for virtually everything we do. He advocates for his theory of "neuronal recycling", where, in order to learn a new task, we repurpose the most relevant specialized neural circuit to learn the task. For example, humans evolved to speak and listen, but not to write and read. So when we learn to write and read, we end up repurposing parts of our language system for writing and reading (but at no cost to language). Interestingly, literate adults are much better at many mental tasks than illiterate adults - "not only are [illiterate adults] incapable of recognizing letters, but they also have difficulties recognizing shapes and distinguishing mirror images, paying attention to a part of a face, and memorizing and distinguishing spoken words". However, I'm a little bit suspicious of this line of research. I don't know how you can fairly compare literate and illiterate adults (maybe the papers talk about this more), and maybe with more effort it would be possible to identify tasks that the illiterate adults are better at. So when Dehaene writes, "The myth of the illiterate bard who effortlessly musters immense powers of memory is just that: a myth", I have to call him out on his bullshit. Those "illiterate" bards were trained to memorize epic poems from childhood and definitely had greatly enhanced "neural "circuits" that were probably at least as impressive as our literate circuits. Dehaene also talks about interesting results about how math is represented in the brain. We apparently upcycle our primitive built-in math circuitry to learn arithmetic and continue to repurpose those circuits to understand more and more advanced math. Even professional mathematicians rely on those same circuits to think of abstract concepts. This is actually really interesting because it shows that most people probably think of concepts, even abstract mathematical concepts, in similar ways, because those concepts are tied into the same brain circuitry. "parity, negative numbers, fractions . . . all these concepts are demonstrably grounded in the representation of quantities that we inherit from evolution.36 Unlike a digital computer, we are unable to manipulate symbols in the abstract: we always grind them in concrete and often approximate quantities." Just by examining the brain's responses to things like phonemes, letters, or numbers, you can tell how that person was raised. The brain of someone born in China and adopted to the US, who knows no Chinese, will still activate slightly more when exposed to Chinese phonemes. The brain of someone who learned to read as a child will respond to letters differently than someone who learned to read as an adult. The brain of someone taught to read musical notes as a child responds differently to sheet music than someone who learned to read music later in life. Etc. The last and most interesting section of the book is finally on how we learn, or the four pillars of learning: "Attention, active engagement, error feedback, and consolidation. Four slogans effectively summarize them: “Fully concentrate,” “participate in class,” “learn from your mistakes,” and “practice every day, take advantage of every night.” These are very simple messages that we should all heed." The "four pillars" sounds like some vapid self-help catch-phrase (the "four agreements"), but they were actually really interesting to learn about. Regarding attention, Dehaene talks about Posner's three types of attention: "Alerting, which indicates when to attend, and adapts our level of vigilance. Orienting, which signals what to attend to, and amplifies any object of interest. Executive attention, which decides how to process the attended information, selects the processes that are relevant to a given task, and controls their execution." It is really interesting how each of these types of attention have been carefully studied, and we know pretty well how they work. "Engaging all three types of attention" sounds technical, but at its finest, attention translates into passion - so when Dehaene says that we understand how attention works, in a way, it means that we have some understanding of how passion works. And, unsurprisingly, passion is crucial for learning. "Alerting" and "orienting" can be encouraged, and "executive attention" which is basically concentration or self-control can be trained (apparently playing music from a young age helps a lot). Concentration is also linked very closely with fluid intelligence (it affects how well we can hold and manipulate objects in our working memory), and fluid intelligence is closely linked to IQ - which is Dehaene's explanation for why every year of schooling seems to raise IQ. One other interesting aspect of attention of its social element - children seem to have a built-in 'pedagogical stance', where they recognize when an adult is trying to teach them something and then pay very close attention to the adult to intuit what they are trying to teach. "Parents and teachers, always keep this crucial fact in mind: your attitude and your gaze mean everything for a child. Getting a child’s attention through visual and verbal contact ensures that she shares your attention and increases the chance that she will retain the information you are trying to convey." This information is sad to learn in our age of Zoom education. Activate engagement translates to curiosity - Dehaene uses terminology that is suspiciously similar to the terminology used in "curiosity-driven reinforcement learning agents" in this section, and I'm not sure which came first, AI curiosity or neuroscience curiosity. But it's interesting either way. According to Dehaene, curiosity is the difference between what we expect (what our mental model predicts) and what we end up observing. There is crucially a sweet spot for learning. If there isn't enough stimulation/surprise, then we grow bored and our brains stop learning. If there is too much, we become overwhelmed and our brains also stop learning. Interestingly, the way that a teacher presents the material can have a huge effect on how "curious" we perceive the subject to be: "take a new group of kindergartners and put them into the passive, receptive pedagogical mode. All you have to do is give them the object while saying, “Look, let me show you my toy. This is what it does . . .” and then play the music box, for instance. One might think that this would stimulate the children’s curiosity . . . but it has the opposite effect: exploration massively decreases following this kind of introduction. Children seem to make the (often correct) assumption that the teacher is trying to help them as much as possible, and that he has therefore introduced them to all the interesting functions of the device. In this context, there is no need to search: curiosity is inhibited." (yes I think it's worth quoting this twice). Error feedback is also surprisingly interesting. The concept is obvious - we learn more when we have the opportunity to make mistakes, or even the opportunity to possibly make mistakes - and when we receive frequent, informative, and positive feedback of what we did wrong or right. But what is interesting is that Dehaene makes the case that we shouldn't have tests and grades for measuring performance - they are not constructive. Of course, tests are still a useful pedagogical tool, but only to motivate students and get them to participate in a form of error feedback. But not as a way to punish students for not knowing the material. One way to get around this would be to have more frequent tests where the student is allowed to retake the test, receiving feedback each time, until they get all the answers right. As someone who has taken about 1 million tests over the course of my life, that makes a lot of sense to me. Consolidation is ALSO surprisingly interesting! Dehaene talks mainly about two things - spaced repetition learning and sleep. Spaced repetition learning is great, and I need to use Anki more to memorize and practice things. And sleep is even more important than I thought - Dehaene really believes that our brains are generative models, and that sleep is used to sample experiences from our generative model. He seems to think that sleep's main function is to help us learn, because during the night our brain can sample experiences from its generative model much more rapidly than we can experience things during the day, allowing us to learn and commit things to memory as we sleep.

  22. 4 out of 5

    Leonardo Longo

    In this amazing book Dehaene explains that human brains are more efficient learners than computers because they are skilled in reasoning about probabilities and extracting abstract principles from observations, so the author goes through four critical elements of learning: attention, active engagement, error feedback, and consolidation. With the right balance of theory and examples, the author reviews evidence showing that babies are born with evolutionarily programmed knowledge, explores how sch In this amazing book Dehaene explains that human brains are more efficient learners than computers because they are skilled in reasoning about probabilities and extracting abstract principles from observations, so the author goes through four critical elements of learning: attention, active engagement, error feedback, and consolidation. With the right balance of theory and examples, the author reviews evidence showing that babies are born with evolutionarily programmed knowledge, explores how schools should use active learning and the relevance of sleep. Definitely a must book for anyone who likes neuroscience and artificial intelligence

  23. 5 out of 5

    Shubrashankh Chatterjee

    This review has been hidden because it contains spoilers. To view it, click here. I started this book and first 25% felt ,man this is just another wannabe book about Human intelligence with nothing to add.I was almost ready to dump it but somehow I gave this one another few hours of my life to try and spellbound me, and it actually was not that bad . The book starts of slowly trying to explain why the current state of artificial intelligence is no where near the human ability to perceive, react and learn from world at large.The short answer that the author has is that is we ar I started this book and first 25% felt ,man this is just another wannabe book about Human intelligence with nothing to add.I was almost ready to dump it but somehow I gave this one another few hours of my life to try and spellbound me, and it actually was not that bad . The book starts of slowly trying to explain why the current state of artificial intelligence is no where near the human ability to perceive, react and learn from world at large.The short answer that the author has is that is we are pre-wired within our genetic code for some of the most crucial functions(audio, vision, locomotion) which are in turn function of thousands of years of evolution. while the present day ANN(Artificial neural networks) are spending huge compute hours trying to better the humans at these functions ,they always seem to fail at edge cases(cases which are in the grey zone of decision making ) . The second part of the book is around core principles of learning .The author tries to explain it within attention, active engagement, error feedback and consolidation.Although none of these were new to me but few of pointers that the author throws are quite interesting.For example, while explaining teaching style he debunks the myth around free form learning and allowing kids to learn without any guidance with just their curiosity at hand being a good form of learning .This is so as it would take a huge amount of time for a newbie to figure out the best practices or behaviors needed to learn well and in a more concrete way a particular life skill. Hence, instruction based learning is extremely important and is needed to channel a child's curiosity and willingness of learning in the correct direction. The book seems to do a decent job in explaining how learning works but if you have already read few of the others in the same genre(*best I have read is "A mind for numbers" by Barbara Oakley ,if you like it you might also like the MOOC "How we learn " by her) ,you can happily avoid it without a fear of missing out on any conceptual gem .

  24. 5 out of 5

    Scott Wozniak

    This book started very technical on the biology of brains. Learning isn't just a metaphysical process, a physical brain change occurs every time. I like learning the neuroscience, but it did drag a little at first. Then he began to unpack the different theories of learning from history and work our way up to what we currently understand. So the book got better and better as we went along. The ending was worth the entire book. Some great ideas in there, including: Attention is required for learnin This book started very technical on the biology of brains. Learning isn't just a metaphysical process, a physical brain change occurs every time. I like learning the neuroscience, but it did drag a little at first. Then he began to unpack the different theories of learning from history and work our way up to what we currently understand. So the book got better and better as we went along. The ending was worth the entire book. Some great ideas in there, including: Attention is required for learning. To increase every other part of the learning process, find out how to capture (and keep) the attention of the learners. The human brain is hard-wired to learn certain things (languages) and to respond to certain stimuli (eye contact from another human face). We learn from other humans interacting with us, far better than we learn from any other source saying the same info. Spreading out your learning into multiple sessions is much better for recall than one big chunk. Testing yourself all the time is really good for recall. However, we have to avoid shaming people for failure. It turns out that all learning comes after we realize we have made an error. Making mistakes is the learning process, not a failure of the process.

  25. 5 out of 5

    William Schram

    Stanislas Dehaene explores learning in the brain and compares it with neural net processors. I read Reading In the Brain several years ago and enjoyed it a lot. So I bought this book hoping for the same quality of writing. Computers beat humans in games like Checkers, Chess, and Go. You may think that humans aren't capable anymore. Professor Dehaene argues that this is far from the truth. Dehaene examines preconceived notions about learning. Piaget is a big name in early childhood development, but Stanislas Dehaene explores learning in the brain and compares it with neural net processors. I read Reading In the Brain several years ago and enjoyed it a lot. So I bought this book hoping for the same quality of writing. Computers beat humans in games like Checkers, Chess, and Go. You may think that humans aren't capable anymore. Professor Dehaene argues that this is far from the truth. Dehaene examines preconceived notions about learning. Piaget is a big name in early childhood development, but while he made proper observations, he drew the wrong conclusions from them. For example, babies are not blank slates waiting for knowledge. Infants come pre-equipped with neuronal circuits attuned to numbers, probabilities, language acquisition, and other tasks. Another myth that Dehaene shatters is; "babies don't have object permanence." They do. It's just that infants don't have the cognitive control to manage their observations. I think it's more impressive that someone managed to devise an experiment to determine this fact. I had an MRI before, and you have to stay perfectly still. Have you ever seen a baby stay perfectly still? I haven't. Beyond that, the human brain is far more efficient than any computer. Finally, Dehaene covers the four pillars of learning and how they work together in the brain. Attention, Active Engagement, Error Feedback, and Consolidation all contribute to learning. He goes over how to utilize your brain to the best of its ability and offers sound advice. How We Learn is one of those rare books that I regret having to put down. Thanks for reading my review, and see you next time.

  26. 5 out of 5

    Chris

    I have mixed feelings about this book. On one hand, it's a very engaging synthesis of the cognitive science of learning, in a general sense. Dehaene dispels the myth of tabula rasa by showing that babies come into the world with pre-wired expectations and learning algorithms (and this is why machine learning has a lot to live up to, and there is a lot of comparison with artificial intelligence). A good example of this is Chomsky's idea of the language acquisition device and how infants engage in I have mixed feelings about this book. On one hand, it's a very engaging synthesis of the cognitive science of learning, in a general sense. Dehaene dispels the myth of tabula rasa by showing that babies come into the world with pre-wired expectations and learning algorithms (and this is why machine learning has a lot to live up to, and there is a lot of comparison with artificial intelligence). A good example of this is Chomsky's idea of the language acquisition device and how infants engage in shared attention. Dehaene also deals with the nurture side of the debate, discussing brain plasticity, sensitive periods, and his neuronal recycling hypothesis in reading development. All fascinating stuff. On the other hand, I feel that the book's subtitle ('the new science of education') is misleading; much of the work discussed here isn't strictly 'new', and much of it isn't directly relevant to education. For instance, the final section discussing the 'four pillars of learning', much of the work relates to animal studies or infants, with only a few light pepperings of education research. To some extent this reflects the dearth of scientific education research, something Dehaene acknowledges in the conclusion, but I wonder how some teachers would react to his 13 key take away messages such as 'enrich the environment', 'accept and correct mistakes', and 'set clear learning objectives'. Not exactly revolutionary. (I note how this subtitle has been changed from 'why brains learn better than any machine, for now' in the hardback edition - much more appropriate). In conclusion, I think this book is best enjoyed with its original subtitle. Don't expect in-depth application of cognitive science to education, and just enjoy the neuroscience!

  27. 5 out of 5

    Fromeggtodragon

    This will probably be one of my favorite books of the year ! It brilliantly takes on the link between education science and neurology as well as cognitive science. Many myths about learning are dismantled in a captivating and clever way. Although I wasn't really interested by the part about machines and their ability to learn in the first part, it ended up being fairly fascinating and most importantly linked up well with the rest of the book. I would recommend this non-fiction to everyone, espec This will probably be one of my favorite books of the year ! It brilliantly takes on the link between education science and neurology as well as cognitive science. Many myths about learning are dismantled in a captivating and clever way. Although I wasn't really interested by the part about machines and their ability to learn in the first part, it ended up being fairly fascinating and most importantly linked up well with the rest of the book. I would recommend this non-fiction to everyone, especially parents, teachers and doctors because it truly highlights in a very easy manner the basics (and new finds) about how children learn most effectively. Truly a masterpiece !

  28. 5 out of 5

    Nick

    This is an excellent current primer on what we know about learning from the POV of neuroscience. Tests are good, lectures are bad, and sleep is essential -- and lots, lots more. The four pillars of learning alone are worth the book, because they let you know what needs to be in place for learning to succeed. The saddest thing I learned from reading this splendid book is that language plasticity, while it does technically last your lifetime, is effectively over at a very early age. Learning that This is an excellent current primer on what we know about learning from the POV of neuroscience. Tests are good, lectures are bad, and sleep is essential -- and lots, lots more. The four pillars of learning alone are worth the book, because they let you know what needs to be in place for learning to succeed. The saddest thing I learned from reading this splendid book is that language plasticity, while it does technically last your lifetime, is effectively over at a very early age. Learning that second language is very hard if you don't start young! That makes my efforts to learn Italian in time for a long-delayed trip there rather puny, I'm afraid. The happiest thing I learned from the book is the importance of making mistakes, really, to surprise your brain into learning new things. If you don't get it wrong, your brain responds with "been there, done that," and doesn't pay much attention. But if you make a mistake, the brain says, "Oh, the world didn't respond like I expected; I'd better pay attention and learn something new." Therein lies learning gold.

  29. 5 out of 5

    Michiel Mennen

    A wonderful read. Part homage to the unbelievable capacity of the human brain, compared to what is possible with AI and Machine Learning algorithms. Part specific, hands-on guidance for educators, trainers and people in general on how learning actually works and how to get the most out of learning experiences. From the nitty gritty detail of the inner workings of the brain to the more general conceptual pillars of a solid learning strategy. The beginning may be a bit of a tough read, but worth fo A wonderful read. Part homage to the unbelievable capacity of the human brain, compared to what is possible with AI and Machine Learning algorithms. Part specific, hands-on guidance for educators, trainers and people in general on how learning actually works and how to get the most out of learning experiences. From the nitty gritty detail of the inner workings of the brain to the more general conceptual pillars of a solid learning strategy. The beginning may be a bit of a tough read, but worth following through! And getting a good night sleep after every reading session :). Recommended!

  30. 5 out of 5

    Axel Jantsch

    The book describes the mechanisms of human learning and is informed as much by neuroscience and cognitive science as by computer science. It is in fact surprising how much of Dehaene's explanations and elaborations are supported by the way machine learning algorithms work. I suspect the reason for this is how we study mammalian brains, what is experimentally accessible and what is not. Two directions of research have a relatively long history and have brought key insights: the study of nerve cells at t The book describes the mechanisms of human learning and is informed as much by neuroscience and cognitive science as by computer science. It is in fact surprising how much of Dehaene's explanations and elaborations are supported by the way machine learning algorithms work. I suspect the reason for this is how we study mammalian brains, what is experimentally accessible and what is not. Two directions of research have a relatively long history and have brought key insights: the study of nerve cells at the microscopic level and behavioral experiments that reveal macro-level mechanisms of our brain. The study of neurons at the cellular level started in earnest with the work of Santiago Ramón y Cajal who earned a Nobel Prize in 1905 for his discovery that the brain was built up of neuronal cells. Equipped with a microscope and an artistic talent he produced numerous drawings that shape our perception of the cellular organization of our brain up to today. Cajal revealed the daunting complexity and variety of nerve cells and discovered that they consist of dendrites, a cell body and an axon, and he speculated about the direction of information flow by adding arrows to his diagrams: from the dendritic tree to the cell body to the axon. Cajal not only discovered that the brain tissue was made up of distinct neural cells but also that they come in contact with each other at points that we today call synapses. How a message (a "spike") traverses the syanpitc gap from one cell's axon to the next cell's dendrite was studied and explained by Thomas Südhof, and others, many years later. Synapses play a key role in learning because they adapt to the activities of information transfer. Their growth, strengthening, weakening and disappearing are key mechanisms of information storage and learning. They adapt in time spans of minutes, hours, days, weeks and months and keep changing during lifetime. A basic rule of learning, put forward by Donald Hebb, is "if neurons fire together they wire together", meaning that synaptic connections are strengthened when both the pre-synaptic and the post-synaptic neuron is active at the same time. Due to many studies of synaptic adaptations and anatomical changes of neurons in response to neuronal activities, today we understand fairly well the cellular mechanisms underlying learning, memorization and recall. When neurons, that react to the image of a particular face, are often activated at the same time as neurons representing a name, the connections between these two groups of neurons are strengthened and we associate the face with a the name. A huge number of clever behavioral experiments have given us detailed insights into a great variety of mental capabilities of the human and mammalian brain. For instance babies are born with an innate capability to count and do approximate arithmetic; they have a number sense. Within the sight of the baby objects are moved behind a curtain, then the curtain is removed and the baby's reaction is observed. If you move one ball and then another ball behind the curtain, and if there are two balls seen after removal of the curtain, the baby shows no surprise. If there is only one ball or three ball, the baby is very surprised. The same works with larger numbers like 5+5 balls with the expectation of seeing 10 balls. It works for counting, addition and subtracting. If you move 10 balls behind the curtain, then subsequently remove 5 balls, then the baby is surprised if there are still 8 balls behind the curtain. Many experiments have confirmed, that it is the abstract numbers, that babies consider, not other physical properties like the shape, color or size of objects. This innate number sense has also been confirmed in many other mammals. For instance experimenters went to great length to make sure that newborn chickens have not seen any object before the experiments, and they still can already reason about numbers. Behavioral experiments have given us insight about innate capabilities like a number sense, a sense of probabilities, learning languages and recognizing faces. They have shown how we build up episodic memories, how we interact with other people, how we balance long term against short term desires, etc. While these two lines of investigation have given us an extensive and detailed understanding of the human brain, the level in between seems to be harder to decipher. How do we abstract from 100 pictures of flowers to the concept of flower? How does the neural circuitry correctly predict the trajectory of a ball and direct our body to catch it in flight? How do we estimate that it is safe to cross the street right before a moving care? What is the language of thought, what is its grammar and how do we determine its vocabulary? These and many other feats involve the coordinated activity of huge numbers of neurons in different regions of the brain, the communication of large amounts of information across data highways between brain modules. They require the integration of several senses and specialized brain circuitry into a unified assessment. They require the coordination over periods of time and the building and usage of symbols and concepts at the right abstraction level, not too low and not too high. Many of these mechanisms are still poorly understood and therefore a theory about how they may work can greatly structure the investigation and search for experimental evidence for confirmation and disapproval of hypotheses. For learning, the rapidly developing field of machine learning in computer science is inspiring a framework for generating hypotheses about how learning in the brain may work. Therefore, I suspect, Dehaene is drawing heavily on the parallels to deep artificial neural networks, a particularly successful branch of machine learning, to contemplate and explain features of learning in the human brain. Ironically, artificial neural networks have been inspired by natural neural networks more than 60 years ago, but have only in the last ten years emerged as a highly successful branch in machine learning in the form of Deep Neural Networks (DNN). DNNs, as alluded to in the figure here taken from the book, consist of tens or even hundreds of layers of neurons. Each layer's output is the input of the next layer. The "deep" in DNN refers to the large number of layers. DNNs have been shown to outperform humans in a wide area of tasks like object detection, face recognition, sensor data analysis, medical image analysis, games, transforming of images and videos into fakes, etc. They have a number of features that certainly also play key roles in human learning. For instance, error correction, is equally important in DNNs as in human learning. In DNNs an input (a handwritten "2" in the picture) is processed sequentially by all the layers and a response is generated, which is the recognition of a number in this case. During training of the DNN, the correct answer is then presented to the DNN, and the error is propagated back from the output all the way to the input of the network. During this back-propagation, parameters are adjusted such, that the same image would be more correctly processed the next time. After this training procedure is repeated hundreds or thousands of times with different images, the parameters in the DNN are adjusted such, that it produces correct output for all images in the training set. Moreover, it also performs very well for images that are sufficiently similar to the images in the training set. Hence, the DNN in fact abstracts from the individual images. The larger the training set and the deeper the DNN the more powerful it becomes in terms of abstraction and object recognition. If you are unhappy with the performance of your 50-layer network, simply use a 200-layer network, increase the training set tenfold, run the training and the results will most likely astonish you. Eight Definitions of Learning The book starts out with eight definitions of learning: 1) To learn is to form an internal model of the external world 2) Learning is adjusting the parameters of a mental model 3) Learning is exploiting a combinatorial explosion 4) Learning is minimizing errors 5) Learning is exploring the space of possibilities 6) Learning is optimizing a reward function 7) Learning is restricting a search space 8) Learning is projecting A Priori Hypotheses These definitions seem to be almost exclusively inspired by computer science, and exhibit little trace from neuroscience or psychology. However, they seem to apply exceedingly well to what is actually happening in the human brain, which lends support to the idea that a computer science inspired theoretical framework of learning can be very fruitful in researching the brain's capabilities and mechanisms. However, Dehaene is quick to point out, that there are a number of routine accomplishments of the human brain, that machine learning algorithms cannot replicate. For example, DNNs need thousands or, better, hundreds of thousands of examples in the training session, while humans can learn very effectively from few, or even only one given example. DNNs are most effective when the correct answer for the training set is known, which is called supervised learning. Human learning is mostly unsupervised, and very effective in that. Humans learn often by analogy, transferring rules and patterns from one domain to another. If you play a lot of chess, your chess game strategies may inform you when planing your career. When you do a lot of high effort hiking, you may take its lessons about ups and downs, effort and sweat, planning and perseverance into other domains like starting a business. Machine learning algorithms cannot do this today and are not close to it. Also, humans are good in penetrating new territory and learning the regularities of a new domain even if they know nothing or little about it at the outset. Human "learning is inferring the grammar of a domain", writes Dehaene. Again, computer science have yet to come up with algorithms that can do that. As a result, research in artificial intelligence and cognitive science is mutually inspiring. Computer scientists see in the example of the brain, what is possible, using them to find ways to accomplish similar behavior. Neuroscientists can hypothesize and test if mechanisms developed successfully for machine learning also are at work in the human brain. Memory in the Brain Dehaene describes our current understanding of memory structures in the brain as follows. Working memory consists of activity patterns of neurons. There is no permanent, physical change in the brain underlying working memory. If the current pattern of activity changes, the current content of active memory is lost. The amount of information in working memory is very limited; only few symbols can be stored there. The duration is also limited and after a few seconds the content in working memory is fading away. Episodic memory is a weird thing. Episodic memory gives us identity, history and stability over time. It seems highly efficient. Streams of events, images and scenarios are continuously recorded without effort. It seems also highly effective giving priorities to important events that are remembered years later while unimportant events are quickly fading into oblivion. The hippocampus, a brain module below the cortex, is the gate keeper to episodic memory recording the unfolding episodes of our lives. Neurons in the hippocampus seem to memorize the context of each event: they encode where, when, how, and with whom things happened. They store each episode through synaptic changes, so we can remember it later. The famous patient H.M., whose hippocampi in both hemispheres had been obliterated by surgery, could no longer remember anything: he lived in an eternal present, unable to add the slightest new memory to his mental biography. (p 91) But H.M. could still remember and recall his life before the surgery. So while the hippocampus records episodic memory, it is not necessarily stored there. Also, H.M. could still learn new skills which he could use later on. But he did not remember that, when and how he has acquired a new skill. Semantic memory While the hippocampus is key in recording new memories, they are stored throughout the brain. At night, the brain plays them back and moves them to a new location within the cortex. There, they are transformed into permanent knowledge: our brain extracts the information present in the experiences we lived through, generalizes it, and integrates it into our vast library of knowledge of the world. After a few days, we can still remember the name of the president, without having the slightest memory of where or when we first heard it: from episodic, the memory has now become semantic. What was initially just a single episode was transformed into long-lasting knowledge and its neural code moved from the hippocampus to the relevant cortical circuits. (p 91) Procedural memory is about skills like tying shoes, reciting a poem, calculating, juggling, playing the violin, or cycling. When we repeat the same activity over and over again [...] neurons in the cortex and other subcortical circuits eventually modify themselves so that information flows better in the future. Neuronal firing becomes more efficient and reproducible, pruned of any parasitic activity, unfolding unerringly and as precisely as clockwork. This is procedural memory: the compact, unconscious recording of patterns of routine activity. (p 91 ff) The hippocampus plays no or only a minor role; that is why H.M. could learn new skills like writing backwards while looking at his hand in a mirror. An important host of procedural memory is a set of subcortical neural circuits called "basal ganglia". Learning in the Brain Cells, dentrites, synapses At the neural level learning happens foremost through adapting connections between neurons by strengthening and weakening existing synapses, building new and removing unnecessary synapses. Also, the dendrite tree of neurons can grow and shrink. During the growth of the embryo, the baby and the child the neural architecture is built according to a relatively fixed scheme. The brain has an innate architecture with many specialized modules, e.g. for the visual pathways, the auditory sensors, motor control, emotion management, face recognition, language understanding and production. During this growth period the neural circuits adapt to specific needs and conditions. When some neural circuits are damaged, others can jump in and assume their task to some extent. The book relates the story of the child Nico that lacked almost the entire right hemisphere and the neurons in the intact left hemisphere assumed many of the tasks that were usually located in the right hemisphere, to the extent that the Nico developed artistic talents in drawing and got a university diploma in IT. Hence, during growth the brain shows great capacity to adapt, but it is best during the sensitive period of the corresponding brain module, and is greatly reduced afterwards. in many brain regions, plasticity is maximal only during a limited time interval, which is called "sensitive period." It opens up in early childhood, peaks, and then gradually decreases as we age. The entire process takes several years and varies across brain regions: sensory areas reach their peak around the age of one or two years old, while higher order regions such as the prefrontal cortex peak much later in childhood or even early adolescence. What is certain, however, is that as we age, plasticity decreases, and learning, while not completely frozen, becomes more and more difficult. (p 103) Four pillars of learning Then Dehaene covers the four pillars of learning: Attention, Active engagement, Error feedback and Consolidation, from which he also draws specific pedagogic conclusions for teaching and training. Stanislas Dehaene is a cognitive neuroscientist with a training in mathematics and experimental psychology. He is a professor at the Collège de France and, since 1989, the director of INSERM Unit 562, "Cognitive Neuroimaging". He has written several other popular science books that are worth reading: "The number sense", "Reading in the brain" and "Consciousness and the Brain".

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