How We Learn Why Brains Learn Better Than Any Machine . . . for Now - Stanislas Dehaene

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Here is a summary of the book sections mentioned in the passage:

• Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts - This book explores how the brain codes our thoughts and represents consciousness.

• Reading in the Brain: The New Science of How We Read - This book examines the latest neuroscience research on how the brain processes reading.

• The Number Sense: How the Mind Creates Mathematics - This book investigates how the human mind develops mathematical concepts and abilities.

So in summary, the listed books by Dehaene cover topics related to consciousness, reading, and mathematical cognition from a neuroscience perspective. They examine the brain mechanisms underlying complex cognitive functions like thinking, reading, and mathematical reasoning.

The passage discusses why learning evolved and why it is important and unique in humans. It argues that completely pre-wiring the brain with all necessary knowledge is impossible given the limited information contained in our DNA. Instead, learning allows organisms to adapt rapidly to unpredictable environments. Even simple creatures like worms can learn behaviors, demonstrating the evolutionary advantages of this ability.

For humans, learning is particularly important and extensive. We spend many more years developing skills compared to other mammals. Our abilities with language and mathematics allow us to explore an immense number of hypotheses. Learning has enabled humanity to radically change our environment and accomplishments like fire, tools, agriculture, exploration and technology.

The key ability driving all this is our brain’s power to form hypotheses and select those that fit our surroundings. Billions of brain parameters are free to adapt through learning. Evolution selected which areas should be pre-wired versus left open for environmental influence. For humans, the domain of learning is especially large. We have also augmented our learning through institutions like schools, making pedagogy unique to our species. In summary, learning is humanity’s defining strength.

• Education vastly increases human brain potential by systematically teaching knowledge and skills to children through schools and universities. This refinement of neural circuits through education has accelerated the development of modern complex societies.

• Recent research has helped elucidate the algorithms and brain mechanisms underlying human learning. Understanding these principles of how we learn can help optimize learning at all ages. Factors like metacognition, attention, engagement, error feedback, and consolidation through sleep are important pillars of the human learning process.

• Machine learning algorithms are challenging human intelligence by mimicking some initial unconscious visual and linguistic processing abilities. However, they still lack higher-level human capacities for reasoning, inference, flexibility and learning from very little data.

• The human brain remains superior to machines in its optimal use of scarce data through mechanisms like attention, and its ability to synthesize learning through sleep consolidation. Understanding these learning tricks could help develop more human-like machine intelligence in the future. Overall, education and a growing scientific understanding of learning are helping maximize human brain potential.

• Learning can be defined as forming an internal model of the external world by gaining knowledge and understanding through experience. This captures experiences and allows them to be reused in new contexts.

• Both brains and machine learning algorithms build internal models by searching for optimal combinations of “parameters” or settings that define the model. In the brain these are synapse strengths, in machines they are weights or probabilities.

• Comparing brain learning to machine learning gives insights into learning at the neurological level. While machines haven’t replicated all of human intelligence yet, they are helping uncover a theory of optimal learning based on probabilistic reasoning.

• This Bayesian theory views the brain as a statistician that sets up hypotheses about the world and uses experience to select the most accurate ones. It specifies distinct roles for nature and nurture - genetics sets up hypothesis spaces while experience selects among them.

• Understanding how learning is implemented in the brain involves examining psychology and neuroscience, especially studying infant learning abilities which show innate statistical reasoning abilities from birth.

• Four key mechanisms enhance human learning ability: attention, active engagement, error feedback, and consolidation/memory formation over time including during sleep. Mastering these principles can help people learn more effectively.

• Learning involves adjusting the parameters of internal mental models. Our brain contains vast networks of models that represent different domains of knowledge like language, motor skills, sensory perception, social interactions, etc.

• When we learn, our brain adjusts the parameters/weights in these networks based on new information and experiences. For example, learning to catch an object with prisms on requires adjusting the parameter that maps vision to motor actions.

• Even simple tasks like this involve tuning many parameters simultaneously. Language learning requires adjusting thousands or millions of parameters at different levels - sounds, vocabulary, grammar rules, meanings.

• A key parameter babies learn is the “head position” that determines word order in sentences (e.g. subject-verb-object in English vs object-subject-verb in Japanese). Getting this single parameter right impacts many aspects of the language.

• The number of possible combinations from adjusting parameters is exponentially large, far more than the number of actual languages in existence. This “combinatorial explosion” allows the brain to learn any specific language from the huge space of possibilities.

• In summary, learning is the process of adjusting the fine-grained parameters of our internal mental models through experience to better match and understand the external world.

• The human brain learns languages and other skills by breaking problems down into a hierarchical, multi-level model. This allows it to detect patterns at increasingly complex levels, from individual sounds to whole sentences.

• Similarly, artificial neural networks used in AI have a pyramid structure with multiple layers. Lower layers detect simple patterns in the input data, and higher layers combine these into more abstract concepts.

• Neural networks learn by minimizing errors. They make a prediction, see if it was correct or incorrect, and adjust their internal parameters to reduce errors the next time.

• This works like a hunter adjusting the scope on their rifle through trial and error. By observing the direction and size of errors, the network determines how to tweak its parameters to better fit the problem.

• With many adjustments over time looking at millions of examples, neural networks can gradually learn complex patterns from data in a supervised way, even with huge networks with millions of adjustable parts. Hierarchical multi-level learning allows them to scale up capabilities.

• Neural networks can learn hierarchies of features through error correction via gradient descent, from low-level responses to lines/textures to higher-level recognition of complex shapes, objects, etc. This allows recognizing things like digits, objects, faces, etc.

• Gradient descent risks getting stuck in local minima, failing to find globally optimal solutions. Tricks like introducing randomness, such as stochastic search algorithms, annealing techniques inspired by physical processes, and Darwinian evolution concepts help escape local minima and more fully explore the space of possibilities.

• Supervised learning provides the correct answers, but that approach is rare. Reinforcement learning provides only a reward/score signal, requiring the system to act and self-evaluate simultaneously. One part of the network predicts the reward to guide learning where feedback is delayed, as in games like chess where the outcome is only known at the very end.

• Both biological and machine learning systems seem to employ aspects of randomness, curiosity-driven exploration, and reward/feedback-based optimization to overcome challenges in learning complex tasks from limited or delayed feedback.

• The actor-critic combination is an effective reinforcement learning approach where an actor learns to act wisely based on evaluation from a critic. The critic learns to evaluate the consequences of actions, and the actor uses this feedback to improve.

• This approach enabled neural networks to master difficult games like backgammon, and DeepMind’s system to learn a wide variety of video games from pixel inputs alone.

• DeepMind later applied similar techniques to optimize Google’s data server management, reducing energy costs significantly.

• AlphaGo also used reinforcement learning by playing moves against itself, strengthening winning actions and weakening losing ones to master the complex game of Go.

• Having too many parameters in neural networks can lead to slow learning due to a vast search space, and “overfitting” where the model memorizes specifics rather than finding general rules. Simplifying models can accelerate learning and improve generalization.

Here is a summary of the key points from section e:

• Yann LeCun’s convolutional neural networks incorporate an innate hypothesis that what is learned in one image region can be generalized and applied everywhere else in the image.

• This reduces the number of parameters needed to be learned and massively improves performance, especially generalization to new images. The network benefits from experience across the entire image.

• This “innate knowledge” approach of exploiting prior assumptions generalizes to other domains like speech recognition.

• Rather than learning everything from scratch, it is more effective for a learning system to rely on innate assumptions that define the basic laws of the domain to be explored and incorporate these into the system’s architecture.

• Having innate assumptions reduces the learning space and speeds up learning, provided the assumptions are correct. Our brains also rely on innate assumptions and knowledge from evolution to guide and accelerate learning.

• Learning always starts from a set of a priori hypotheses, which are projected onto incoming data. The system selects hypotheses that best fit the environment. To learn is to eliminate hypotheses that do not fit.

• Humans can correct initial mistakes by taking a second look and using reasoning and abstraction to re-analyze situations. Neural networks currently lack this ability.

• Human learning involves forming abstract concepts and generalizing, like understanding that the letter “A” can take different forms. CAPTCHAs have challenged machines but a recent algorithm reached near-human level by extracting letter skeletons.

• Humans are more data-efficient learners than neural networks, which require huge datasets. Children can learn from limited language exposure.

• Humans excel at social learning by sharing knowledge verbally, while machines’ knowledge is implicit and difficult to extract and share.

• Humans can learn from single examples and integrate new knowledge into existing frameworks using rules of language, grammar, mathematics, etc. Machines struggle with systematic generalization.

• The human brain may have an internal “language of thought” that allows abstract reasoning through symbolic combinations, unlike current neural networks. This ability to conceptualize abstract rules and theories is uniquely human.

• Humans have a unique ability to learn abstract, systematic rules and apply knowledge gained in one context to new situations. For example, learning addition and then using it as part of more complex calculations.

• Current artificial neural networks lack this flexibility - knowledge is confined to specific connections and cannot be easily reused or combined. Networks only solve narrow problems and don’t generalize.

• Descartes noted two key differences between machines and humans - language use and ability to reason and apply knowledge in new ways rather than just specific pre-programmed responses.

• Human learning involves inferring abstract “grammars” or rules that summarize patterns in data. Finding the most general rule that fits all observations accelerates future learning.

• An example is inferring the rule that balls in a box are all the same color from observing just one ball, enabling learning from a single instance.

• Children learn vocabulary remarkably fast by proposing and testing hypotheses to deduce word meanings from context, favoring the simplest hypothesis consistent with the data.

• Formulating abstract, logical representations and hypotheses allows humans to massively constrain the space of possibilities and accelerate learning compared to associative models like neural networks.

• Children are able to learn language much faster than current artificial neural networks, through the use of meta-rules and abstract thinking.

• Meta-rules allow children to vastly narrow the search space for word meanings. For example, following someone’s gaze helps determine what they are referring to. Learning grammar rules like nouns following “the” also helps guide word learning.

• The “mutual exclusivity assumption” - that one word refers to one thing - further accelerates learning by allowing children to associate new words with unfamiliar objects/concepts.

• Meta-rules are themselves learned through small inference steps over many learning episodes. They create a “blessing of abstraction” where the most abstract rules guide a massive proliferation of word learning starting around ages 2-3.

• Some animals like Rico the dog also demonstrate limited use of meta-rules like mutual exclusivity, showing this ability is not uniquely human.

• Computational models are being developed that can learn hierarchies of rules, meta-rules, and meta-meta-rules to mimic this efficient language learning, though they have not achieved human levels yet.

• Learning involves forming hypotheses to explain sensory experiences, distinguishing causes from consequences. This ability to entertain abstract hypotheses dramatically accelerates learning.

• Children naturally learn like scientists, formulating theories and comparing them to observations. Their mental representations are more structured than neural networks. From birth, the brain can generate abstract formulas and choose the most plausible ones based on data.

• Scientific reasoning involves stating multiple theories, making predictions from each, and eliminating theories whose predictions are invalidated by experiments. Learning in the brain resembles this process of refining theories through observations.

• Bayesian reasoning, formalized by Bayes and Laplace, is the optimal way to make inferences from uncertain or probabilistic data by updating beliefs based on new observations. Learning amounts to deducing the most plausible causes from phenomena.

• Bayesian theory allows reasoning from observations to hidden causes, assigning plausibility levels to assumptions and updating them based on improbable observations. This is an effective approach, used by Turing to break the Enigma code during WWII by accumulating small improbabilities from repeated ciphertexts. Similar Bayesian reasoning seems to occur in the brain during learning.

• According to the Bayesian brain theory, the brain forms hypotheses and sends top-down predictions to other brain areas. Each area attempts to reconcile these predictions with bottom-up sensory information.

• Errors between predictions and sensory inputs generate error signals that modify the brain’s internal model. This allows the model to converge on one that accurately fits the outside world through experience.

• Innate knowledge (priors) inherited through evolution combine with personal experiences (posteriors) to form adult judgments. This resolves the nature vs nurture debate - both are necessary for learning.

• Mathematically, the Bayesian approach is the optimal way to learn by efficiently extracting information from each experience to refine hypotheses.

• Experiments show babies possess invisible innate knowledge of core concepts like objects, numbers, languages, and physical laws, even as newborns. Their brains already contain vast potential combinations of thoughts to be refined through learning.

So in summary, the theory proposes the brain has both innate priors and a Bayesian learning algorithm to generate and test hypotheses against experiences, optimally integrating nature and nurture for learning from birth.

The passage describes how babies gradually develop an understanding of principles of physics like gravity and falling objects. At first, babies don’t understand that objects will fall if dropped. They slowly realize objects need support to not fall, first thinking any contact is enough but then learning an object must be atop another surface. It takes months to grasp the role of center of gravity.

Babies also possess early intuitions in other domains like arithmetic, probabilities, and even psychology. Experiments show babies can distinguish numbers of objects and understand simple additions/subtractions involving small quantities from a young age. They also demonstrate surprise based on expectations of probabilities, showing an innate grasp of these concepts.

The passage argues this counters Piaget’s view that young infants lack concepts like object permanence and numbers. Instead, core intuitions in these domains are part of our innate “core knowledge.” Number sense in particular appears universal across species. While refinement occurs, the initial understanding is not a blank slate but knowledge humans and other animals are born with.

• Even babies a few months old seem to intuitively understand and apply Bayes’ rule of conditional probability to reason about unseen causes based on observed samples. In experiments, babies are surprised by outcomes that are improbable given their probabilistic inferences.

• Babies simultaneously reason in both the forward direction (from causes to observations) and the reverse direction (from observations back to causes). This shows their ability to implicitly and unconsciously reason with probabilities.

• Further experiments demonstrate babies can eliminate unlikely hypotheses and infer people’s preferences, intentions, and biases based on probabilistic observations.

• Babies also have innate social skills like distinguishing intentional from accidental harm, discerning when someone is trying to teach them, and attributing personalities based on behavior.

• From birth, babies are highly attentive to faces due to an innate attraction. Within the first year, their face perception abilities become more specialized and tuned to human faces through both nature and nurture.

In summary, even very young babies exhibit sophisticated probabilistic, logical, and social reasoning abilities innate before language develops. They intuitively understand causality, hypothesis elimination, and make inferences about unseen properties based on Bayesian probability.

• The brain of newborn babies is not a blank slate, as was previously believed. It shows sophisticated knowledge of objects, numbers, people and language from birth.

• If this is true, the brain structures underlying these domains should already be organized at birth. Until recently, the newborn brain was unstudied due to lack of brain imaging technology.

• Advances in MRI allowed the first studies of brain organization in newborns. Dehaene-Lambertz and colleagues were pioneers in using fMRI with 2-month-old infants.

• They had to overcome challenges like designing a protective helmet and soothing cradle to keep infants still during scanning.

• The findings revealed that virtually all adult brain circuits are already present in newborns. Contrary to earlier views, the infant brain shows early organization, not a blank slate waiting to be shaped by environment.

• The researchers studied language development in infants using brain imaging techniques like fMRI.

• They observed that at 2 months old, when infants heard sentences in their native language, they activated the same brain regions as adults, including primary auditory cortex and a progression of language areas.

• This showed that even at a young age, infants’ brains process language along a similar hierarchy as adults, even if they don’t yet understand it.

• Major connections between brain areas, like the arcuate fasciculus, are present from birth and laid out genetically.

• During fetal development, cortical folds and connections develop in a process of self-organization guided by genes and chemical environments rather than experience or learning.

• By studying brain development, the researchers gained insights into how infants are naturally equipped with specialized language circuits that allow rapid language learning in the first year of life. The brain’s organizational scaffolding is largely innate rather than learned.

• Grid cells in the rat entorhinal cortex form a neuronal map of space by firing in a repeating hexagonal grid pattern as the rat moves around. This acts like a “GPS” system in the brain.

• Grid cells emerge spontaneously during development through self-organization, without external teaching. Physicists understand how hexagonal patterns ubiquitously emerge in nature during processes of self-organization as systems cool down.

• Experiments show grid cells are present in newborn rats before they have started moving, indicating the spatial mapping system is innate.

• Similar spatial mapping systems likely exist innately in the human brain as well, according to indirect evidence.

• Other specialized brain modules, like face recognition areas in visual cortex and number neurons in parietal cortex, also seem to emerge spontaneously early in development through self-organization, providing initial structure before external learning fully shapes the modules.

• The brain relies more on self-organization and intrinsic simulation rather than external data, departing from classical blank slate views and current artificial neural networks which require huge amounts of data. Genes and self-organization jointly provide much of the initial structure and function of the brain.

• The parietal cortex draws lines, which is perfect for coding linear quantities like number, size, and time.

• Broca’s area projects tree structures, ideal for coding the syntax of languages.

• We inherit a basic set of rules from evolution that we then select from to represent concepts we need to learn.

• While we all share a common human brain architecture, each individual has unique traits from birth like cortical folds and connectivity patterns.

• Developmental disorders like dyslexia and dyscalculia occur when the brain takes a “wrong turn” early in development, affecting areas important for skills like reading or math.

• Genetics predispose towards these disorders but are not fully determinative - environment and rehabilitation can help overcome weaknesses.

• Brain plasticity allows the initial neural circuits to be refined and enriched through experience and learning over time.

• Neurons communicate with each other at synapses, where the axon of one neuron meets the dendrite of another. Synapses are the points of connection between neurons.

• At a synapse, a neurotransmitter is released by the presynaptic neuron which attaches to receptors on the postsynaptic neuron, generating an electrical signal.

• Learning involves changes in synapses, such as the number and strength of synapses. As neurons fire together, their connection strengthens through synaptic plasticity.

• Donald Hebb proposed that neurons that fire together wire together. Synaptic plasticity stabilizes neuronal activity by reinforcing circuits that have worked well together in the past.

• Changes in synapses allow the brain to store memories by imprinting important experiences. Neurotransmitters like dopamine signal rewarding experiences to be remembered.

• Evidence from animals like Aplysia and mice shows that synaptic changes occur in brain regions involved in learning and memory, and these changes seem to play a causal role in memory formation and recall.

• A memory is encoded by the specific pattern of activity across distributed groups of interconnected neurons in the brain when an experience occurs.

• Neuroscientists have shown they can manipulate memories in mice by artificially activating or deactivating specific groups of neurons involved in encoding those memories.

• In one experiment, they gave mice an electric shock while in one room, creating a negative association. They were then able to shift this negative memory to a different room by reactivating the neurons that represented the other room.

• In another experiment, they erased a negative memory by reactivating the neurons representing that memory while simultaneously giving a reward, weakening the synapses.

• Researchers have also implanted new false memories in mice. One study reactivated neurons representing a place during sleep while giving a reward, so the mouse developed a positive association with that place upon waking.

• These experiments demonstrate the ability to manipulate existing memories and potentially implant false ones by controlling which neuron groups are activated during memory formation and retrieval in the hippocampus.

• The passage describes examples of extensive brain plasticity in monkeys that learned to use tools to reach food. This triggered changes in brain structure and neuron connectivity in the anterior parietal cortex, which controls hand movements.

• Learning causes long-lasting physical changes in the brain at the synaptic, neuronal, and circuit levels. Synapses strengthen through Hebbian plasticity when neurons fire together. This leads to thicker dendritic spines and new synapses forming.

• With extensive learning, neurons develop thicker dendritic and axonal trees, and axons develop thicker myelin sheaths. Entire neural circuits and surrounding glial cells change.

• However, these structural changes require significant metabolic resources like glucose and nutrients. Even short-term nutrient deficiencies like a lack of vitamin B1 in infants can lead to permanent cognitive impairments, as shown by a food contamination incident in Israel that caused language deficits.

• Proper nutrition is thus crucial for the brain changes underlying learning and development. The brain is highly plastic but also sensitive to nutritional deprivation, showing plasticity has physical limits dependent on metabolic factors.

• Brain plasticity allows some rewiring and reorganization in response to experience and injury, but is highly constrained by genetic factors.

• Two patients are discussed who each lacked a right hemisphere from an early age. One (Nico) had hemispherectomy surgery at age 3.7 and showed remarkable development, but brain scans found functions redistributed symmetrically within the left hemisphere.

• The other (A.H.) lacked the right hemisphere from before 7 weeks of gestation. She showed some remapping of visual areas but vision remained partial, showing plasticity’s limits.

• Experiments severing auditory circuits in ferrets found visual fibers could invade, but maps were imperfect, demonstrating reorganization within genetic constraints.

• Early development involves spontaneous neural activity that self-organizes, gradually adjusting to sensory input. Plasticity initially acts without environment but adjusts internal models based on experience over time. Overall, plasticity modifies connections within strongly predetermined architectures.

A sensitive period refers to a window of time during early development when the brain is particularly plastic and responsive to environmental stimuli in certain areas. Some key points:

• Sensitive periods occur during early childhood, usually from birth to around age 5-10, depending on the brain region.

• During these times, the brain undergoes waves of synaptic overproduction and pruning, making it highly malleable. This allows for optimal learning and development.

• Sensitive periods first occur in primary sensory areas like vision and hearing, then higher-order areas like the prefrontal cortex develop later.

• If appropriate input is not received during a sensitive period, it can impair development. For example, failing to correct a vision problem like amblyopia by age 3 can result in permanent vision loss.

• After sensitive periods end, it becomes much harder to learn or acquire certain skills, though residual learning is still possible with great effort. For example, it’s very difficult for adults to attain native-like proficiency in a new language.

• Sensitive periods allow for rapid learning that helps shape connections in brain circuits based on early life experiences and environmental input. This helps the brain develop and organize in an optimized way.

• The ability to acquire a new language decreases dramatically with age, as brain plasticity declines. Younger children who learn a new language will have less of a foreign accent and make fewer grammar mistakes than older learners.

• The window for optimal language learning closes around puberty, around age 17. Immersing oneself in the language through social interaction is most effective for learning.

• Depriving children of any language exposure during early childhood, such as deaf children who don’t receive sign language input, can permanently impair their syntax development. Studies on feral children show grammar remains compromised even after language exposure later in life.

• There are sensitive periods for both phonology and grammar in language learning. Vocabulary learning remains possible throughout life due to residual plasticity.

• The closing of sensitive periods is related to a shift toward more inhibition in neural circuits over time. Tightening perineuronal nets around neurons may prevent synaptic remodeling. Factors like Lynx1 and changes in acetylcholine signaling also contribute.

• Evolutionarily, there may be advantages to limiting plasticity once simple neural circuits are established, to save energy and avoid disrupting higher-level functions. But synaptic traces of early learning are retained unconsciously.

• In the late 20th century, around 180,000 Korean children were internationally adopted, with over 130,000 going to other countries and more than 10,000 to France.

• Brain scans of 20 Korean adoptees who arrived in France ages 5-9 showed their language areas responded to French but not Korean, indicating the new language supplanted the old one.

• However, more subtle tests found adopted children still process tones of their original language in the left hemisphere language area, showing exposure in the first year can permanently affect brain circuits.

• Experiments with owls wearing prism glasses found those exposed during youth could permanently adjust their visual and auditory maps, while older owls could not. This shows early experience can shape permanent brain circuits.

• In Romanian orphanages under Ceaușescu, children suffered deprivation and deficits. The Bucharest Early Intervention Project found placing children in foster care before age 2 led to nearly normal development, showing brain plasticity and intervention can overcome early deficits.

• The study conducted in Bucharest, Romania provided evidence that early placement in foster care (before 20 months of age) had significant benefits for children who had been residing in orphanages. Those placed earlier demonstrated improved cognitive function, brain development, social skills and vocabulary compared to those placed later or who remained in orphanages.

• However, children placed after 20 months continued to show severe impairments, indicating that a critical period of early development cannot be fully replaced later on. Early experiences and nutrition shape brain development in long-lasting ways.

• While early trauma can severely impact development, brain plasticity allows for resilience and recovery when issues are addressed early. Even late intervention provides some benefits, showing the brain’s ability to change throughout life.

• The study provided further evidence that social/emotional neglect early in life can lead to dramatic effects on brain development, but that foster care placement before 20 months allowed some children to catch up developmentally. It highlighted both the impact of early experiences and the potential for recovery through responsive caregiving.

• Neuronal recycling is the process by which the brain adapts and learns new skills by repurposing existing neural circuits, rather than completely rewriting them. This allows fast learning within an individual brain on the timescale of days to years.

• Evolutionary exaptation is a similar process of repurposing older structures over long timescales through gradual genetic changes across populations.

• Recent experiments have shown that monkeys can only learn new tasks if the required neural activity patterns fit within the brain’s existing repertoire, supporting the idea of neuronal recycling.

• Different brain regions impose constraints on the types of representations they can learn. For example, parietal cortex encodes quantities along a linear dimension, and entorhinal cortex encodes space in two dimensions.

• Mathematics education recruits and refines the brain’s innate approximate number system, represented by parietal and prefrontal circuits. Neurons in these areas become selective to numerical symbols through learning. Addition and subtraction further involve nearby posterior parietal regions involved in attention and movement.

• Mathematical concepts, even very abstract ones like integrals and matrices, activate the same brain regions involved in basic quantity representation and counting in children. This supports the idea that more advanced math recycles neural circuits originally involved in elementary numerical skills.

• Blind mathematicians show that the brain areas for math are genetically determined, not shaped by sensory experience, as they activate the same circuits as sighted mathematicians. Their visual cortex also gets recruited for math, showing brain plasticity.

• Experiments demonstrate that even when consciously performing symbolic math, our brains still represent numbers approximately in a distance-dependent way on a mental number line, like other primates. This shows elementary quantitative representations are recycled for more advanced math.

• Actions like subtraction seem to mimic physical motions along an internal number line, with response times proportional to numerical distance, again indicating recycled representations of quantity underlie symbolic math skills.

So in summary, the evidence supports a neuronal recycling hypothesis - more abstract mathematical concepts reuse brain circuits originally evolved for basic numerical skills, rather than completely new neural circuits being developed.

• Learning to read involves recycling and repurposing parts of the brain normally used for vision and language. Specifically, a region in the visual cortex becomes specialized for recognizing letter strings and connects to language areas.

• In illiterate individuals, this region responds more to faces. But as reading skills increase, activity for faces decreases in the left hemisphere and increases in the right hemisphere instead.

• Studies comparing literate and illiterate adults and children found that learning to read leads to increased activation in brain areas involved in reading, like the visual word form area. This area begins developing even in young children after only a few months of reading instruction.

• Brain scans of children learning to read over time directly demonstrate the neuronal recycling hypothesis - the visual word form area emerges in the left hemisphere while face responses shift from left to right. Learning to read competes with and redistributes pre-existing visual functions like face recognition between the hemispheres.

• Difficulties in reading acquisition, like dyslexia, correlate with abnormalities in how these regions develop and respond to words and faces compared to typical readers. The neuronal recycling framework explains how the brain reshapes itself during literacy acquisition.

• The author presents two possible models to explain the competition between face recognition and literacy acquisition in the brain: the “knockout” model where learning to read knocks out existing face areas, and the “blocking” model where letters take over available cortical territory and block the expansion of other categories like faces.

• Experiments using MRI scans on children learning to read suggest the blocking model is accurate. Letters invade unspecialized cortical regions as children learn to read, blocking the growth of face areas in the left hemisphere and forcing them to the right hemisphere.

• Early childhood is a period of major reorganization in the visual system as some areas specialize while others remain flexible. Learning to read takes advantage of this sensitive period of plasticity.

• Training in other domains like music and math also compete with face recognition areas. Musicians have enlarged letter areas that displace faces, while math training reduces face responses in both hemispheres.

• The brain is both genetically structured and plastic during development. Early sensory areas mature first while other regions remain flexible, adapting to the environment through experience-dependent pruning and strengthening of connections.

• Attention is a key mechanism that enables the brain to efficiently learn by selecting and amplifying relevant information from the constant bombardment of stimuli. Without attention, it would be impossible to deeply process all the information the senses receive.

• Attention mechanisms evolved in many animal species to help solve the problem of information saturation. The brain filters inputs through a pyramid of attention processes to determine importance and allocate resources accordingly.

• Selecting relevant information is essential for learning. Attention allows the brain to focus on pertinent patterns and details rather than analyzing all possible combinations of data.

• Introducing attention mechanisms into artificial neural networks significantly improved their ability to learn tasks like language translation faster and better by focusing on important words instead of connecting all inputs to all outputs uniformly. Attention allows artificial systems to spotlight relevant parts of images or sentences during learning.

So in summary, attention is a crucial cognitive function that allows brains and artificial systems to efficiently learn by filtering inputs, prioritizing what’s important, and focusing processing resources on relevant information rather than all details simultaneously. This selective focus enables faster pattern identification and learning.

Here are two modules that could be learned based on the description provided:

Attention Module: This module would learn to select relevant information and data from the overall sensory input using an attentional mechanism. It acts as a filter or spotlight that highlights certain areas or aspects of the input data and discards irrelevant information. The goal is to concentrate processing power on the most important subset of data for a given task.

Object Recognition Module:
This second module would learn to identify and label the data that has been selected and filtered by the attention module. It aims to analyze and make sense of the attentional spotlight by recognizing patterns in the highlighted data and assigning naming categories or descriptions. The objective is to develop semantic understanding of the objects/aspects that have captured attention.

The attention module acts first to focus on relevant portions of the input. Then the object recognition module processes only that filtered subset to perform tasks like object identification, classification and naming. The two modules work sequentially, with attention guiding what information is learned by the object recognition system.

• Selective attention orients our mental focus to amplify certain information and suppress distracting details. When we focus on one object, stimulus, or task, our brains actively inhibit unrelated information from reaching awareness.

• Experiments show that focused attention can literally blind us to obvious but irrelevant objects. In the “invisible gorilla” experiment, people fail to notice a person in a gorilla suit walking through the scene when counting basketball passes.

• Our attention can only focus on one thing at a time. The “attentional blink” effect shows we may miss simple stimuli like words if our attention is engaged on another task.

• Directing students’ attention is important for learning. An experiment showed adults learning a new writing system benefitted greatly from focusing on individual letters versus overall word shapes. Letter attention activated brain pathways for reading, while whole-word attention hindered generalization to new words.

• Carefully guiding children’s selective attention, such as with phonics that tracks letters, is crucial for proper reading development and activating neural circuits associated with reading comprehension. Undirected whole-word attention prevents discovering alphabetic rules.

• Attention is key for successful learning. Teachers must choose what they want students to focus on, as only attended items will be strongly represented in the brain and efficiently learned.

• The executive control system, located mainly in the frontal cortex, acts as the brain’s “switchboard” and directs mental processes. It ensures tasks are completed step-by-step and selects appropriate strategies while inhibiting inappropriate ones.

• Executive control is linked to working memory, which temporarily holds relevant information. It processes one item at a time through the “global neural workspace,” creating a bottleneck.

• We cannot truly multitask as the second task is slowed while the first occupies working memory. Training can automate some tasks, releasing working memory, but distraction generally hinders learning.

• Executive control develops gradually through childhood and adolescence as the prefrontal cortex matures. Younger children make mistakes due to an inability to concentrate and inhibit wrong strategies. Proper teaching focuses attention to respect cognitive limits.

• Piaget made important discoveries about child development, but sometimes got the interpretations wrong. For example, he thought babies did not have object permanence, when in fact they do - they just struggle with executive control tasks like the A-not-B task.

• Similarly, Piaget thought young children lacked number conservation abilities. But studies show babies have an innate sense of number. The real issue is executive control - inhibiting salient but irrelevant cues like size or spacing to focus on number.

• Attentional and executive control abilities develop with maturation of the prefrontal cortex through adolescence. Training can also enhance these skills. Examples discussed include Montessori exercises, concentration games, playing music.

• Executive control is important for a variety of cognitive tasks beyond just attention. For example, it helps inhibit a routine answer when needed in math word problems.

• IQ is influenced by environment and education, as executive functions impact fluid intelligence. Training working memory and executive control can modestly improve IQ scores. Early interventions, starting in kindergarten, seem particularly effective, especially for disadvantaged children.

The key point is that executive control abilities underlie many cognitive skills and tasks in children. Their development impacts learning and performance. Training can help enhance these critical skills.

• Humans have a unique ability for social attention sharing and learning from others. From a young age, infants pay attention to where others direct their gaze and will learn new words by following someone’s gaze to an object.

• This social learning allows human culture and knowledge to accumulate much more rapidly than any individual could achieve alone. When one person makes a discovery, it spreads to the whole group through social learning.

• Even babies are sensitive to social cues like eye contact, which signals an intent to teach. Establishing eye contact increases how much infants learn and their ability to generalize lessons.

• Pointing is also an important communicative gesture that young children learn to interpret as conveying important information. Infants remember what someone points to but not just something they reach for without eye contact.

• True teaching in humans depends on a theory of mind - an ability to represent what others know and think. Teachers must think about what students don’t know and adapt their lessons accordingly. The pedagogical relationship involves an infinite recursive representation of knowledge between teachers and students.

• While some rudimentary social learning was observed in meerkats helping young learn scorpion hunting, it lacked the key element of shared attention to each other’s knowledge states. Human teaching uniquely involves strong mental connections between teachers modeling knowledge and students.

• Richard Held and Alan Hein’s classic carousel experiment showed that active exploration is essential for proper visual development in kittens. Kittens allowed to actively explore developed normal vision, while those restricted to passive movement did not.

• Active engagement and learning requires generating mental models of the world and testing hypotheses through interaction with the environment. Merely accumulating sensory inputs passively does not support deep learning.

• Active engagement does not necessarily mean physical movement, but rather an attentive, focused mental stance where learners actively comprehend concepts and rephrase them in their own words.

• Passive or distracted students do not deeply update their mental models and thus do not learn effectively from lessons.

• Deeper cognitive processing of information, like understanding word meanings, supports better memory and learning than more superficial sensory analysis, like checking letter case or rhyming. Active engagement and deeper processing are important for learning to take root.

In summary, the key points are that active exploration, hypothesis testing, attention, effort and reflection are needed for effective learning, rather than passive absorption of information. Deeper cognitive engagement leads to better learning outcomes than more superficial processing.

• Active, engaged learning leads to better retention and understanding compared to passive lecturing. Students learn more when they are active participants through discussions, experiments, problem-solving tasks, etc.

• However, discovery-based or constructivist approaches like letting students learn entirely on their own through exploration have been shown to be ineffective. Studies across many domains like reading, math, computer science find students learn little without explicit instruction and guidance.

• Discovery approaches assume students can independently rediscover abstract rules and concepts, but this is unrealistic. Subjects like reading, math, and computer programming require systematically conveying concepts through examples, guidance, and practice.

• While first-hand experiences and independent problem-solving have value, alternating explicit instruction with hands-on application leads to deeper understanding compared to purely discovery-based methods. Students need a balance of guidance and active learning to learn effectively.

So in summary, while active engagement is important, the assumptions of pure discovery-based teaching have been repeatedly disproven. Effective learning requires a blend of explicit instruction, worked examples, and hands-on application under guidance.

Here are the key points about curiosity and how to encourage it in education:

• Curiosity is a innate human drive to seek new knowledge and learn about the environment. It serves an evolutionary purpose of helping animals survive by gaining a better understanding of potential threats or resources.

• At a neurological level, discovering new information activates the brain’s dopamine reward pathway, producing a feeling of satisfaction. This helps motivate further exploration and learning.

• Teachers should focus on sparking students’ curiosity through hands-on activities, novel topics, open-ended questions, and unique problems to solve. Piquing interest and the anticipation of learning something new can be intrinsically motivating.

• Curiosity is best nurtured in an environment that allows independent discovery and minimizes risk-taking. Students should feel comfortable exploring without fear of criticism for being wrong. Their natural enthusiasm and thirst for knowledge can then be guided into productive learning.

• Keeping lessons varied, incorporating current events or real-world applications, and presenting diverse perspectives can all help maintain students’ curiosity over the long-term. A solely structured environment focused only on mastery may diminish their intrinsic drive to learn.

• Curiosity is driven by the dopamine reward system in the brain. Satisfying our curiosity and learning new things is intrinsically rewarding.

• Curiosity helps maximize learning by guiding us towards things we can learn from - things that fill the “gap” between what we know and don’t know. Things that are too simple/complex don’t trigger curiosity.

• Psychologists view curiosity as a cognitive system that regulates learning similar to a governor regulating pressure. It aims to maintain a certain level of “learning pressure.”

• When implemented in robots, algorithms that reward actions likely to yield new learning cause the robots to behave curiously, exploring items until fully understanding them, then moving on like bored children.

• For curiosity to exist, metacognition (awareness of one’s own knowledge and ignorance) is needed from a young age. Babies show they know when they don’t know something by seeking help.

• School can sometimes inadvertently kill curiosity by evaluation/grades that cause performance goals to replace learning goals, competition/comparisons that make mistakes seem scary, and irrelevant curricula that don’t connect to students’ interests. Engaging instruction is key to sustaining curiosity.

• Children are naturally curious and like to experiment with and question the world to learn, but many lose this curiosity after a few years of schooling. Their active engagement turns into dull passivity.

• One reason may be lack of cognitive stimulation tailored to their needs. If school is too easy or too hard for a student, their curiosity fades as they expect little new learning.

• Another reason is if curiosity is punished rather than rewarded in school. Constantly reprimanding or mocking curious questions trains children to stop being curious.

• The social transmission of rigid teaching can also discourage curiosity. If a teacher always explains everything fully rather than leaving some exploration, children stop exploring on their own.

• Errors are an important part of learning but schools often don’t tolerate them well. A story is given of Grothendieck stubbornly believing his own mathematics proof as a child despite textbooks saying otherwise.

• To maintain curiosity, schools need continuous stimulation matched to each child’s ability level. They must reward rather than punish curiosity and exploration, and leave some things open for children to discover themselves through active engagement rather than rigid teaching. Tolerating errors is also important for learning.

• The passage discusses the key role that errors and mistakes play in the learning process. It argues that making mistakes is the most natural way to learn, as every error provides an opportunity to improve and gain new knowledge.

• It discusses the Rescorla-Wagner theory proposed in the 1970s, which hypothesized that organisms only learn when events violate their expectations, meaning surprise or a prediction error is a fundamental driver of learning. The brain generates predictions and uses the difference between the prediction and the actual outcome (the prediction error) to update its internal model and improve future predictions.

• This theory was influential because it moved away from purely associative views of learning and incorporated prediction and surprise/error. Experiments like forward blocking provided evidence that learning depends on prediction errors, not just simple associations.

• The brain’s learning mechanism, though more sophisticated, operates on similar principles to artificial neural networks which also use prediction errors to update their weights and models. An error signal, even in the absence of an actual mistake, can still drive learning by updating confidence and knowledge.

• Receiving explicit feedback that reduces a learner’s uncertainty is important for effective learning. This validates the principle that “no surprise, no learning.”

• Experiments show that when children perceive impossible or improbable events, it triggers learning. They will remember more details related to the surprising event and engage in play that seems to test hypotheses about what occurred.

• Error signals play a fundamental role in learning throughout the brain. Brain areas evolve to detect violations of predictions and transmit “surprise” or error signals. This helps filter out redundant predictable information and propagate unexpected information that needs explanation.

• Prediction errors are detected in auditory, visual, language and reward processing brain circuits. Words, images or rewards that differ from expectations trigger error responses. These signals help learn and refine internal models of the environment.

• While error feedback is necessary for learning, it is not the same as punishment. Effective learning requires quickly receiving accurate feedback to resolve uncertainty, not negative reinforcement or criticism of the learner. Teachers need to understand how errors and ignorance drive the learning process.

• Providing students with detailed, precise feedback on errors allows them to quickly identify and correct mistakes, which is an effective form of “supervised” learning similar to how artificial intelligence systems are trained.

• Error feedback should not be confused with punishment. The goal is to inform students neutrally about incorrect responses so they can improve, not judge them.

• Grades alone are an imprecise form of feedback that does not distinguish different sources of errors. They can also be delayed, unfair if difficulty increases too quickly, and act as punishments that discourage learning through stress and feelings of helplessness.

• Presenting grades as punishments risks inhibiting learning and altering a student’s self-image and personality. It promotes a “fixed mindset” that skills cannot improve rather than a growth mindset. Stress from grades has been shown to negatively impact the brain and learning abilities.

• Detailed, non-punitive error feedback and a growth mindset that mistakes enable learning are better for students’ emotional well-being and academic progress compared to grades alone or a focus on “giftedness” versus failure.

• Self-testing and retrieval practice are very effective learning strategies because they force active engagement with the material and provide immediate error feedback. This aligns with scientific understanding of how effective learning and memory work.

• Experiments show that alternating periods of studying with self-testing leads to better long-term memory than just spending all the time studying. The act of retrieval strengthens memory.

• Spacing out study and review sessions over time, rather than cramming, is the most effective strategy according to decades of research. It increases brain activity and memory compared to mass repetition in one session.

• Optimal spacing depends on the duration memory needs to last. Daily review for a week works for short-term memory, while longer intervals of 20% of the desired retention period works best for long-term memory lasting months or years. Testing memory at increasing intervals strengthens retention over time.

So in summary, self-testing, retrieval practice, and spacing out study sessions with increasing intervals between reviews are scientifically validated as highly effective learning strategies according to decades of research on memory and learning.

• Learning becomes consolidated over time through repetition and practice, shifting from slow and effortful to fast and automatic. This process is known as consolidation.

• Initially, learning tasks like reading, typing, music, etc. require conscious effort and attention controlled by the prefrontal cortex.

• With repetition, these tasks become automatized through the basal ganglia and specialized circuits. This removes the need for conscious executive control.

• Automatization “compiles” operations into more efficient routines that can run unconsciously and independently.

• As tasks become automatic, activation shifts from the prefrontal cortex to motor/parietal/temporal areas specialized for that task.

• Consolidation through automatization is important because it frees up the prefrontal cortex’s limited resources. This allows multitasking and performing other tasks simultaneously without disruption.

So in summary, consolidation refers to the process by which repeated practice transforms initial effortful learning into fast, efficient and unconscious automatic skill through specialized brain circuits, freeing up cognitive resources.

• Sleep plays a key role in consolidating memories and making learned skills automatic. As we sleep, our brain replays and strengthens memories from the previous day through a process of synaptic plasticity.

• Early experiments in the 1920s showed that memory is stabilized between 8-14 hours after learning, corresponding to a period of sleep. Subsequent studies demonstrated that sleep causes additional learning beyond what occurs during waking hours.

• Neuroscience research has revealed that during sleep, the hippocampal and cortical neurons that fired together during waking experiences will reactivate in sequence. This “replay” of neuronal firing patterns occurs during both slow-wave sleep and REM sleep.

• The reactivation strengthens memories by spreading hippocampal encodings throughout the cortex. Cortical neurons that participate more in reactivation also show increased involvement in learned tasks the next day.

• Brain imaging has confirmed that human brains also reactivate circuits used in recent experiences during sleep, helping to automate skills and consolidate episodic memories from the previous day.

• Experiments have shown that brain activity during sleep tracks and replays the day’s experiences and events. For example, areas related to face recognition activate when people report dreaming of faces.

• Sleep strengthens and consolidates memories formed during the day. Things like motor skills or spatial learning tasks show increased brain activation and performance improvements linked to intensity of slow wave sleep.

• It is possible to artificially increase slow wave sleep depth through sounds or electric currents synced to brain waves, boosting next-day memory consolidation. Some startups sell headbands claiming this effect.

• Memories can also be biased to consolidate by cues like smells presented during deep sleep linked to earlier learning. This improves later recall of that specific material.

• In addition to memory consolidation, sleep may support creativity and insight. Famous examples include scientist August Kekule’s dream discovery of benzene’s ring structure. Experiments show sleep improves rate of discovering hidden solutions.

• Nocturnal brain activity replays and recodes the day’s events and ideas in a more abstract, generalized format ideal for extracting underlying patterns and rules. This may support creativity and problem-solving insights upon waking.

• Future artificial intelligence may need to incorporate consolidation phases similar to sleep in order to effectively refine learning models and discover abstract patterns from experience.

• The passage discusses how sleep helps the brain solve the problem of limited data for learning. During sleep, the brain engages in simulated experiences that multiply its daytime experiences. This allows it to explore scenarios it would otherwise never directly experience.

• Dreams provide an “enhanced training set” that allows the brain to better model reality. Sleep simulation helps discover unexpected outcomes, as with important scientific insights that occurred to thinkers during dreams or thought experiments.

• Children need even more sleep than adults as their brains have a heavier workload for learning. A child’s sleep is 2-3 times more effective for consolidating learning. Naps especially help young children retain and generalize new words or concepts they learned before sleeping.

• Getting sufficient sleep is important for learning, memory and attention in both children and teens. Chronic sleep deprivation may contribute to learning disabilities and mental health issues. Delaying school start times can help teens get more sleep and see academic benefits.

• In summary, sleep plays a key but underappreciated role in the brain’s learning processes. It enables simulation and discovery that multiply our limited waking experiences into richer mental models. Ensuring good sleep is important for optimizing learning, especially for developing brains.

• While machines still have a long way to go to match human brain performance, they are beginning to develop some key brain-like capabilities through algorithms inspired by neuroscience, such as internal languages of thought, probabilistic reasoning, and sleep/wake cycles.

• However, the brain maintains an advantage over machines currently due to its vastly superior performance compared even to a newborn baby. It will likely be a long time before machines can match the brain’s abilities.

• The brain’s cognitive skills result from its learning algorithms, developed over millions of years of evolution. These algorithms allow the brain to flexibly recombine concepts, reason with uncertainty, pursue curiosity, manage attention and memory, and integrate new knowledge through sleep cycles.

• Machine learning algorithms are starting to model some of these brain-inspired capabilities, but they remain vastly simpler and less advanced than the human brain’s cognitive processing. Significant advances will be needed for artificial intelligence to achieve human-level general intelligence.

• For the foreseeable future, the human brain is expected to significantly outpace machine capabilities when it comes to complex reasoning, learning, problem-solving and other high-level cognitive functions.

The passage discusses how scientific knowledge about learning and brain development can help improve education. It argues teachers deserve more support and training in cognitive science principles. While the brain is inherently structured, it remains highly plastic during development and learning. Several figures are referenced that show how the brain self-organizes and wirelessly develops specialized regions from a young age. However, learning and experience continue shaping neural connections throughout life. The author believes further research at the intersection of education and cognitive neuroscience can yield evidence-based strategies to optimize learning. Parents also play a key role in development and should work closely with teachers. Overall, the goal is to apply brain science insights to revive children’s natural curiosity and joy of learning.

• The passage discusses brain plasticity and how the brain can reorganize itself after damage or injury. Even after the loss of a sense like vision, the brain can still learn and recruit other areas to take over functions.

• However, it notes that in the primary visual cortex, genetic factors tend to have a stronger influence than plasticity. So reorganization in this area is modest after injury or loss of vision. The genetic makeup of the visual cortex largely determines its organization and function.

• In summary, while the brain does show some ability to reorganize itself and redirect circuits after injury, this plasticity is still limited. In core sensory areas like the primary visual cortex, genetic determinism outweighs brain plasticity in shaping the brain’s organization.

Here is a summary of the key points from the provided list:

• Early brain development involves both genetic pre-wiring and environmental input through experience-driven plasticity. Certain cortical areas and functions emerge very early in infants through self-organization mechanisms.

• Babies possess rich conceptual and reasoning abilities from a very young age, demonstrating core knowledge of objects, numbers, space, intentions, and linguistic structure. Brain imaging has found organized higher-level cortical areas for language and face processing in infants.

• The brain’s connectivity and cortical folding appears pre-specified to some degree by genetic factors. However, experience and environment also play a key role in shaping synaptic connections through plasticity mechanisms like long-term potentiation.

• Experience-dependent plasticity allows learning and memory formation by strengthening synaptic connections. Animal models have provided insights into the cellular and molecular mechanisms supporting synaptic plasticity and its role in memory formation.

• Conditions like dyslexia and dyscalculia reflect atypical brain development, sometimes linked to genetic differences that impact connectivity or function in areas important for language or numerical cognition. Early markers can be observed even in infants who go on to develop these conditions.

So in summary, the list covers topics regarding the interplay between genetically-guided and experience-driven mechanisms in early brain development, conceptual abilities in infancy, models of cortical self-organization, and the role of synaptic plasticity in learning as well as disorders reflecting atypical brain development.

Here is a summary of the sources provided:

ler, and Frankland (2015) and Poo et al. (2016) discussed working memory and sustained firing in the brain. Courtney et al. (1997), Ester et al. (2015), Goldman-Rakic (1995), Kerkoerle et al. (2017), and Vogel and Machizawa (2004) discussed working memory and sustained firing in specific brain areas like the prefrontal cortex. Mongillo et al. (2008) discussed the role of fast synaptic changes in working memory.

Genzel et al. (2017), Lisman et al. (2017), Schapiro et al. (2016), and Shohamy and Turk-Browne (2013) discussed the role of the hippocampus in the fast acquisition of novel information. Kitamura et al. (2017) discussed the displacement of a memory trace from the hippocampus to the cortex. Ramirez et al. (2013, 2015) discussed creating and modifying memories in mice through optogenetic techniques. Kim and Cho (2017) discussed erasing traumatic memories. De Lavilléon et al. (2015) discussed creating novel memories during sleep.

Here are summaries of the sources referenced:

es, 2002; Marques and Dehaene, 2004.

• These sources examined mental representation of parity (odd vs even numbers) but no details are provided.

Dehaene, Bossini, and Giraux, 1993; negative numbers: Blair, Rosenberg-Lee, Tsang, Schwartz, and Menon, 2012; Fischer, 2003; Gullick and Wolford, 2013; fractions: Jacob and Nieder, 2009; Siegler, Thompson, and Schneider, 2011.

• These sources examined mental representation of various mathematical concepts like negative numbers, fractions, but no details are provided.

Amalric, Wang, et al., 2017; Piantadosi et al., 2012, 2016.

• These sources examined the “language of thought” in mathematics but no details are provided.

Dehaene, 2009.

• This is a reference to the author’s previous book “Reading in the Brain” but no details about the book are provided.

Dehaene et al., 2001, 2004.

• These sources examined brain mechanisms underlying invariant recognition of written words but no details are provided.

Bouhali et al., 2014; Saygin et al., 2016.

• These sources examined connections between the visual word form area and language areas of the brain but no details are provided.

Dehaene et al., 2010; Dehaene, Cohen, Morais, and Kolinsky, 2015; Pegado, Comerlato, et al., 2014.

• These sources examined imaging of the illiterate brain but no details are provided.

Here is a summary of the key pedagogical strategies discussed in the listed sources:

• Hattie, 2017 and Kirschner et al., 2006 find that instructional guidance leads to better learning outcomes than pure discovery learning. Guided instruction helps avoid cognitive overload.

• Kirschner and van Merriënboer, 2013 debunk urban legends in education, such as the idea that students learn best through undisciplined exploration.

• Mayer, 2004 emphasizes the importance of instructional guidance rather than pure discovery in multimedia learning. Learners need guidance to help them engage in essential cognitive processing.

• Pashler et al., 2008 find no evidence that different individuals possess distinctive learning styles that are durable over time and that match any particular type of instruction. Instruction should not be modified to suit students’ supposed preferences.

• Studies evaluate the effects of factors like amount of reading practice, early childhood curiosity, novelty seeking, feedback, error correction, spacing/interleaving practice, retrieval practice, growth mindsets, consolidation during sleep, and more on learning outcomes.

Here are the key points about PIRLS and TIMSS:

• PIRLS (Progress in International Reading Literacy Study) is an international assessment of reading comprehension among fourth-grade students. It has been conducted every 5 years since 2001.

• TIMSS (Trends in International Mathematics and Science Study) is an international assessment of mathematics and science among fourth and eighth-grade students. It has been conducted every 4 years since 1995.

• Both assessments are conducted by the International Association for the Evaluation of Educational Achievement (IEA). They measure trends in student achievement at the international level to allow countries to compare their educational systems.

• The assessments provide data on how education policies, practices, curricula, and students’ social and cultural backgrounds are related to achievement outcomes. They help countries identify challenges and share practices to improve educational outcomes.

• Together, PIRLS and TIMSS are among the leading resources for comparative data on student performance in fundamental school subjects around the world. They contribute to understanding factors that influence national education systems and student learning.

Here are the key points from the summaries:

• Borst et al. (2013) studied inhibitory control and negative priming in a Piaget-like class-inclusion task in children and adults. They found developmental improvements in inhibitory control efficiency.

• Bouhali et al. (2014) studied the anatomical connections of the visual word form area using MRI. They identified fiber tracts connecting this region to other reading and language areas.

• Bradley et al. (2015) used fMRI to study spontaneous retrieval and maintenance of memories for natural scenes that were presented massed or distributed repetitions. Distributed repetitions led to better retrieval and maintenance.

• Braga et al. (2017) presented a single-case fMRI study tracking adult literacy acquisition in a native Portuguese speaker learning to read in French. They observed changes in brain activation patterns associated with improved reading skills.

• Brewer et al. (1998) used fMRI to study brain activity predicting how well visual experiences would be remembered later. They identified medial temporal lobe regions involved in memory formation.

• The summaries discuss additional topics like numerical cognition, bilingualism, video game effects, memory consolidation during sleep, mental representations, developmental disorders, and more. The papers used techniques like fMRI, behavioral experiments, and case studies to advance understanding of cognitive and brain development.

Here is a summary of several key papers by Stanislas Dehaene and related works on cognitive neuroscience and development:

• Dehaene proposed the neuronal recycling hypothesis, which suggests regions of the brain originally used for other tasks were recycled and reallocated for reading and arithmetic.

• Several studies used fMRI to identify brain regions involved in number processing, arithmetic, reading, and other cognitive functions. Regions in the parietal cortex were found to represent numerical magnitude and perform calculations.

• Work identified the left ventral occipito-temporal cortex as the region underlying visual word recognition. Learning to read was shown to cause neuroplastic changes in this region.

• Cross-cultural studies found both universal intuitions about numerical magnitudes but also cultural differences, with Western subjects exhibiting a linear number scale and Amazonians a logarithmic scale.

• Infant studies used fMRI, EEG and other methods to identify the early development of language networks in regions like the left posterior perisylvian cortex. Maturation of white matter pathways supporting language was also observed.

• Other works examined consciousness and proposed a neuronal global workspace model to explain how information becomes globally accessible. The taxonomy of conscious, preconscious and subliminal processing was also proposed.

• Related studies explored executive function development in children, the role of sleep in memory consolidation, and neuroplastic changes caused by learning and experiences like musical training over the lifespan.

Here are brief summaries of the key papers:

• Dweck (2006) discusses the idea of a growth mindset, where people believe intellectual abilities can be developed, versus a fixed mindset where people believe abilities are innate gifts. Growth mindset fosters greater success.

• Egyed et al. (2013) found 18-month-olds can communicate shared knowledge gained through observational learning.

• Ehri et al. (2001) reviewed evidence that systematic phonics instruction improves reading abilities according to the National Reading Panel.

• Ellis and Lambon Ralph (2000) discuss how age of acquisition affects adult lexical processing in connectionist models. Earlier exposure fosters more plasticity.

• Elman et al. (1996) argue for a developmental/connectionist perspective on language acquisition that sees the brain as not innately specified.

• Elsayed et al. (2018) generated adversarial images that fooled both humans and computer vision models.

• Elston (2003) discusses insights into prefrontal cortex function from studying pyramidal neurons in cortex.

• Emmons and Simon (1956) found no recall of materials presented during sleep.

• Epelbaum et al. (1993) studied the sensitive period for strabismic amblyopia in humans.

• Esseily et al. (2016) found humor production may enhance observational learning in infants.

• Ester et al. (2015) studied stimulus-specific representations in parietal and frontal cortex during visual working memory.

• Everaert et al. (2015) argued for a structural view of linguistics as part of cognitive science rather than strings of symbols.

Here is a summary of the paper:

• The study investigated whether infants ask for help when they know they don’t know the answer to a problem.

• 16-month-old infants were asked to complete various tasks that were either within or beyond their abilities.

• When tasks were beyond their abilities, infants were more likely to look at an adult for help compared to easier tasks they could solve themselves.

• This suggests infants have some understanding of the limits of their own knowledge and will seek assistance when they recognize they don’t know how to proceed.

• The results provide evidence that preverbal infants actively evaluate what they know and recruit help when necessary, demonstrating early metacognitive abilities.

• The findings shed light on how infants navigate learning new information and skills through social interactions and help-seeking behaviors.

In summary, the paper presented research showing infants as young as 16 months old will look to adults for assistance specifically when they recognize they do not know how to complete a task, indicating early metacognitive awareness of the boundaries of their own knowledge.

Here are brief summaries of a few of the papers:

• Johansson et al. (2014) found evidence that memory trace and timing mechanisms are localized to cerebellar Purkinje cells in mice, through conditioning experiments.

• Johnson and Newport (1989) studied the influence of maturational state on second language acquisition in English, finding a critical period for learners to achieve native-like attainment.

• Josselyn et al. (2015) reviewed research on the neural mechanisms underlying memory formation and the concept of an “engram,” the trace of a memory stored in the brain.

• Karni et al. (1994) found that overnight improvement of a visual perceptual skill depended on REM sleep, through a study manipulating sleep stages in human subjects.

• Kanjlia et al. (2016) showed that absence of visual experience modifies the neural basis of numerical thinking, through fMRI studies comparing blind and sighted individuals.

• Lake et al. (2017) argued for building machines that learn and think like people, through probabilistic programs and conceptual knowledge rather than narrow tasks.

Here are summaries of a selection of the papers:

• Leong et al. (2017) investigated how reinforcement learning and attention interact dynamically in complex, multidimensional environments. They found that attention guides reinforcement learning by focusing on task-relevant stimulus dimensions.

• Leppanen et al. (2002) found that infants at familial risk for dyslexia had different brain responses to changes in speech sound durations compared to infants without such risk, suggesting early neurological differences.

• Lerner et al. (2011) used a narrated story to map a hierarchy of temporal receptive windows in the brain using fMRI, finding evidence that contextual frames are processed at different timescales.

• Leroy et al. (2015) identified the depth asymmetry of the superior temporal sulcus as a new human-specific brain landmark using MRI scans, showing its asymmetry is greater in humans than chimpanzees.

• Li et al. (2014) reviewed evidence that second language learning is associated with anatomical changes in the adult human brain based on neuroplasticity.

• Livingstone et al. (2017) studied the development of the macaque face patch system and found similarities to humans that provide insights into the evolution of face processing networks.

• Loewenstein (1994) reviewed theories of curiosity and proposed it arises from information gaps that drive exploration.

• Lyons and Beilock (2012) found that math anxiety activates pain regions of the brain when anticipating doing math using fMRI.

Here is a summary of some of the key papers:

• Rinne and Alho (2007) reviewed the mismatch negativity (MMN) brain response and its role in central auditory processing research.

• The National Reading Panel (2000) published a seminal report on evidence-based reading instruction that outlined five core areas of reading instruction.

• Nelson et al. (2007) showed that an early intervention program in Romania improved cognitive outcomes in socially deprived young children.

• Piantadosi et al. (2012, 2014, 2016) developed formal probabilistic models of numerical concept learning and the logical foundations of compositional cognitive models.

• Piazza et al. (2004, 2010, 2013) conducted studies showing that the approximate number system can be improved through education and is impaired in developmental dyscalculia.

• Dehaene et al. (2003) studied brain plasticity in adults who learned a second language and found the first language may be replaced in some brain areas.

• Ramanathan et al. (2015) showed that sleep-dependent reactivation of motor ensembles promotes skill consolidation.

• Ramirez et al. (2013, 2015) used optogenetics to create and suppress false memories in the hippocampus of mice.

Here is a summary of the article “ng: What can’t a worm learn? Current Biology, 14(15), R617–R618.“:

• The article discusses new research on habituation and dishabituation in the nematode worm Caenorhabditis elegans. Habituation is the process by which an organism stops responding to a repeated stimulus, while dishabituation is renewed responding after a novel stimulus is presented.

• Previous studies had found that C. elegans can habituate and dishabituate to simple stimuli like touch. However, the new research tested more complex learning abilities.

• They found that C. elegans could not learn an association between two stimuli, like pairing an odor with starvation. They also could not learn temporal patterns or sequences of stimuli.

• This suggests C. elegans has very simple forms of non-associative learning like habituation, but lacks the neuronal complexity for more complex associative learning. The article concludes by discussing what types of learning different organisms are capable of based on their brain complexity.

In summary, the article reports on research showing that while C. elegans can demonstrate simple habituation and dishabituation, it appears incapable of more complex associative learning involving relationships between multiple stimuli or temporal patterns, limitations likely due to its simple nervous system.

Here is a summary of the key papers:

• Several papers examined language development and input in children, including what type of language is most effective for word learning, how directed speech affects language acquisition, and how literacy affects visual processing.

• Other papers studied numerical cognition development in infants and integration of whole number and fraction concepts.

• A number of papers investigated cognitive mechanisms like reinforcement learning, working memory, attention, and prediction errors.

• Neuroscience papers covered topics like hippocampal function, neuronal representations of numerical quantity, effects of experience on brain plasticity, and neural consequences of early symbol training in macaques.

• Additional topics included perceptual illusions, stereotype threat effects, sleep-dependent memory consolidation, serial vs parallel processing, categorization learning in neural networks, embodied cognition in blindness, and handedness effects in the brain.

Here are brief summaries of several of the papers:

• Hultz (2001) found that dopamine responses in monkeys complied with basic assumptions of formal learning theory, supporting dopamine’s role in reward prediction errors during learning.

• Wagner et al. (1998) used brain imaging to predict whether participants would remember or forget verbal experiences, supporting the idea that brain activity can indicate subsequent memory.

• Wagner et al. (2004) found that participants were more likely to solve insightful problems after a period of sleep than an equal period of wakefulness, suggesting sleep promotes insight.

• Walker et al. (2003) provided evidence that human memory undergoes distinct stages of consolidation and reconsolidation, as disruption during these stages impaired later recall.

• Walker and Stickgold (2004) reviewed evidence that sleep supports learning and memory consolidation in procedural and declarative memory tasks.

• Xu and Tenenbaum (2007) proposed a Bayesian model of word learning as an inference problem to explain how children efficiently learn new words.

• Weber-Fox and Neville (1996) found differences in brain activation patterns between early and late bilinguals during a language task, indicating experience-dependent plasticity effects.

Here is a summary of the key points about the brain and learning from the passage:

• The brain is highly plastic and adapts through experience, especially early in life during sensitive periods of development. Early nurture and environment play an important role in brain development.

• Different areas of the cortex self-organize and develop specialized functions during fetal and early life development, including areas for spatial navigation, face recognition, numbers, etc.

• Learning mechanisms involve Hebbian plasticity, long-term potentiation of synapses, formation and pruning of neural connections. Error feedback and prediction errors drive plasticity and learning.

• Memory involves encoding, consolidation during sleep, and retrieval processes. The hippocampus plays a key role in memory formation. Consolidation of skills also involves shifting from prefrontal to motor cortices.

• Education should actively engage students, aim for deeper thinking, set clear learning objectives, incorporate error feedback, and optimize individual potential through enriched environments. A growth mindset is important.

• The executive control system in the prefrontal cortex develops through experience and allows for multitasking, avoidance of errors, and controlled attention. Its development impacts learning and intelligence.

• Infants are not blank slates but come with innate knowledge and learning abilities, such as for language, numbers, objects, people, and more. Sensitive periods allow maximum plasticity early in life.

Here is a summary of the key points related to mental models of the external world from the passage:

• People form mental models or internal representations of the external world based on sensory inputs. These models allow us to interact with and understand the world.

• Mental models are hierarchical and multilevel, representing things at different levels of abstraction.

• Models are adjusted through a process of trial and error and recalibration as we interact with the world. We adjust parameters of our internal models based on feedback.

• Examples given are adjusting our internal models of language and vision based on experience. Our vision model allows us to recognize objects from different angles, for example.

• Dreams also involve mentally modeling and simulating the external world.

• Forming accurate mental models of the external world is a key part of learning and helps us interpret new experiences and adapt our understanding.

So in summary, the passage discusses how we form hierarchical internal representations or models of the external world through sensory inputs and feedback, and how adjusting these models is important for learning and interacting with our environment.

Here is a summary of the key passages:

• Rescorla-Wagner theory proposes that learning results from a comparison between predictions and actual outcomes, with greater adjustments made for surprising events. It helped establish associative learning models.

• Retrieval practice, or self-testing, enhances long-term retention more than additional study. Repeated retrieval strengthens memory through reconsolidation.

• Optimization of a reward function can describe how organisms learn through trial-and-error to achieve goals. Restricting the search space also facilitates learning.

• Sensitive periods exist when the brain is especially plastic and receptive to certain types of input, like language or vision. Maturation and closing of these periods underlie declining language learning ability with age.

• Sleep plays a key role in memory consolidation and transfer. Spacing out lessons and controlling sleep conditions can boost learning.

• Social attention sharing through eye contact and pointing guides infant statistical learning from others. Cultural transmission amplifies this effect.

• Neuronal maps in the brain leverage hierarchical decomposition and topology to recognize patterns in efficient, systematic ways. map between concepts develops through experience.

• Reading acquisition recruits and transforms existing visual and language areas. Differences in these circuits may relate to dyslexia.

• Brain plasticity allows sensory maps and functions to reorganize based on experience, practice, and development. Recovery from damage also depends on residual plasticity.

Here are summaries of the two papers:

1. Dehaene et al. (2009) investigated the neural signatures associated with conscious versus unconscious processing of auditory regularities. Participants were presented with pairs of tones and had to detect if the two tones were the same or different. The tones followed complex patterns that were either predictable or random. fMRI results showed that conscious detection of the predictable patterns activated brain regions involved in high-level cognition like the prefrontal and parietal cortices. Unconscious processing of the patterns only activated sensory brain areas. This suggests conscious perception engages higher-order cognitive systems beyond sensory areas.

2. Strauss et al. (2015) studied how predictive coding is disrupted during sleep. Predictive coding is the idea that the brain uses top-down predictions to efficiently code sensory inputs. Participants underwent fMRI scans while awake, listening to sounds with predictable patterns. During sleep, predictable sounds no longer activated high-level areas involved in incorporating predictions. This suggests predictive coding breaks down during sleep as higher-level feedback no longer guides sensory processing in a top-down fashion. Sleep may be a state where bottom-up sensory inputs dominate over top-down predictions.