INTRODUCTION
1. See the movies The Miracle Worker (1962) and Marie’s Story (2014), as well as read the following books: Arnould, 1900; Keller, 1903.
2. Learning in the nematode C. elegans: Bessa, Maciel, and Rodrigues, 2013; Kano et al., 2008; Rankin, 2004.
3. Website of the Education Endowment Foundation (EEF): educationendowmentfoundation.org.uk.
4. The brain constantly keeps track of uncertainty: Meyniel and Dehaene, 2017; Heilbron and Meyniel, 2019.
CHAPTER 1: SEVEN DEFINITIONS OF LEARNING
1. You can try this experiment for yourself at the C3RV34U exhibition I organized at the Cité des sciences, Paris’s main science museum.
2. LeNet artificial neural network: LeCun, Bottou, Bengio, and Haffner, 1998.
3. Visualizing the hierarchy of hidden units in the GoogLeNet artificial neural network: Olah, Mordvintsev, and Schubert, 2017.
4. Progressive separation of the ten digits by a deep neural network: Guerguiev, Lillicrap, and Richards, 2017.
5. Reinforcement learning: Mnih et al., 2015; Sutton and Barto, 1998.
6. Artificial neural network that learns to play Atari video games: Mnih et al., 2015.
7. Artificial neural network that learns to play Go: Banino et al., 2018; Silver et al., 2016.
8. Adversarial learning: Goodfellow et al., 2014.
9. Convolutional neural networks: LeCun, Bengio, and Hinton, 2015; LeCun et al., 1998.
10. Darwin’s natural selection algorithm: Dennett, 1996.
CHAPTER 2: WHY OUR BRAIN LEARNS BETTER THAN CURRENT MACHINES
1. Artificial neural networks primarily implement the unconscious operations of the brain: Dehaene, Lau, and Kouider, 2017.
2. Artificial neural networks tend to learn superficial regularities: Jo and Bengio, 2017.
3. Generation of images that confuse humans as well as artificial neural networks: Elsayed et al., 2018.
4. Artificial neural network that learns to recognize CAPTCHAs: George et al., 2017.
5. Critique of the learning speed in artificial neural networks: Lake, Ullman, Tenenbaum, and Gershman, 2017.
6. Lack of systematicity in artificial neural networks: Fodor and Pylyshyn, 1988; Fodor and McLaughlin, 1990.
7. Language of thought hypothesis: Amalric, Wang, et al., 2017; Fodor, 1975.
8. Learning to count as program inference: Piantadosi, Tenenbaum, and Goodman, 2012; see also Piantadosi, Tenenbaum, and Goodman, 2016.
9. Recursive representations as a singularity of the human species: Dehaene, Meyniel, Wacongne, Wang, and Pallier, 2015; Everaert, Huybregts, Chomsky, Berwick, and Bolhuis, 2015; Hauser, Chomsky, and Fitch, 2002; Hauser and Watumull, 2017.
10. Human singularity in coding an elementary sequence of sounds: Wang, Uhrig, Jarraya, and Dehaene, 2015.
11. Acquisition of geometrical rules—slow in monkeys, ultrafast in children: Jiang et al., 2018.
12. The conscious human brain resembles a serial Turing machine: Sackur and Dehaene, 2009; Zylberberg, Dehaene, Roelfsema, and Sigman, 2011.
13. Fast learning of word meaning: Tenenbaum, Kemp, Griffiths, and Goodman, 2011; Xu and Tenenbaum, 2007.
14. Word learning based on shared attention: Baldwin et al., 1996.
15. Knowledge of determiners and other function words at twelve months: Cyr and Shi, 2013; Shi and Lepage, 2008.
16. Mutual exclusivity principle in word learning: Carey and Bartlett, 1978; Clark, 1988; Markman and Wachtel, 1988; Markman, Wasow, and Hansen, 2003.
17. Reduced reliance on mutual exclusivity in bilinguals: Byers-Heinlein and Werker, 2009.
18. Rico, a dog who learned hundreds of words: Kaminski, Call, and Fischer, 2004.
19. Modelling of an “artificial scientist”: Kemp and Tenenbaum, 2008.
20. Discovering the causality principle: Goodman, Ullman, and Tenenbaum, 2011; Tenenbaum et al., 2011.
21. The brain as a generative model: Lake, Salakhutdinov, and Tenenbaum, 2015; Lake et al., 2017.
22. Probability theory is the logic of science: Jaynes, 2003.
23. Bayesian model of information processing in the cortex: Friston, 2005. For empirical data on hierarchical passing of probabilistic error messages in the cortex, see, for instance, Chao, Takaura, Wang, Fujii, and Dehaene, 2018; Wacongne et al., 2011.
CHAPTER 3: BABIES’ INVISIBLE KNOWLEDGE
1. Object concept in infants: Baillargeon and DeVos, 1991; Kellman and Spelke, 1983.
2. Fast acquisition of how objects fall, and what suffices to keep them supported: Baillargeon, Needham, and DeVos, 1992; Hespos and Baillargeon, 2008.
3. Number concept in infants: Izard, Dehaene-Lambertz, and Dehaene, 2008; Izard, Sann, Spelke, and Streri, 2009; Starkey and Cooper, 1980; Starkey, Spelke, and Gelman, 1990. A detailed review of these findings can be found in the second edition of my book The Number Sense (Dehaene, 2011).
4. Multimodal knowledge of numbers in neonates: Izard et al., 2009.
5. Small-number addition and subtraction in infants: Koechlin, Dehaene, and Mehler, 1997; Wynn, 1992.
6. Large-number addition and subtraction in infants: McCrink and Wynn, 2004.
7. The accuracy of number sense gets refined with age and education: Halberda and Feigenson, 2008; Piazza et al., 2010; Piazza, Pica, Izard, Spelke, and Dehaene, 2013.
8. Number sense in chicks: Rugani, Fontanari, Simoni, Regolin, and Vallortigara, 2009; Rugani, Vallortigara, Priftis, and Regolin, 2015.
9. Number neurons in untrained animals: Ditz and Nieder, 2015; Viswanathan and Nieder, 2013.
10. Brain-imaging and single-cell evidence for number neurons in humans: Piazza, Izard, Pinel, Le Bihan, and Dehaene, 2004; Kutter, Bostroem, Elger, Mormann, and Nieder, 2018.
11. Core knowledge in infants: Spelke, 2003.
12. Bayesian reasoning in infants: Xu and Garcia, 2008.
13. The child as a “scientist in the crib”: Gopnik, Meltzoff, and Kuhl, 1999; Gopnik et al., 2004.
14. Infants’ understanding of probabilities, containers, and randomness: Denison and Xu, 2010; Gweon, Tenenbaum, and Schulz, 2010; Kushnir, Xu, and Wellman, 2010.
15. Babies distinguish whether a machine or a human draws from a container: Ma and Xu, 2013.
16. Logical reasoning in twelve-month-old babies: Cesana-Arlotti et al., 2018.
17. Infants’ understanding of intentions: Gergely, Bekkering, and Király, 2002; Gergely and Csibra, 2003; see also Warneken and Tomasello, 2006.
18. Ten-month-old infants infer other people’s preferences: Liu, Ullman, Tenenbaum, and Spelke, 2017.
19. Babies evaluate other people’s actions: Buon et al., 2014.
20. Babies distinguish intentional and accidental actions: Behne, Carpenter, Call, and Tomasello, 2005.
21. Face processing by fetuses in utero: Reid et al., 2017.
22. Face recognition in infancy and development of cortical responses to faces: Adibpour, Dubois, and Dehaene-Lambertz, 2018; Deen et al., 2017; Livingstone et al., 2017.
23. Face recognition in the first year of life: Morton and Johnson, 1991.
24. Babies prefer to listen to their maternal language: Mehler et al., 1988.
25. “The baby in my womb leaped for joy”: Luke 1:44.
26. See my book Consciousness and the Brain (2014).
27. Lateralization of language and voice processing in premature babies: Mahmoudzadeh et al., 2013.
28. Word segmentation in infants: Hay, Pelucchi, Graf Estes, and Saffran, 2011; Saffran, Aslin, and Newport, 1996.
29. Young children detect grammatical violations: Bernal, Dehaene-Lambertz, Millotte, and Christophe, 2010.
30. Limits of language-learning experiments in animals: see, for instance, Penn, Holyoak, and Povinelli, 2008; Terrace, Petitto, Sanders, and Bever, 1979; Yang, 2013.
31. Fast emergence of language in deaf communities: Senghas, Kita, and Özyürek, 2004.
CHAPTER 4: THE BIRTH OF A BRAIN
1. Brain imaging of language in infants: Dehaene-Lambertz et al., 2006; Dehaene-Lambertz, Dehaene, and Hertz-Pannier, 2002.
2. Empiricist view of the infant’s brain: see, for instance, Elman et al., 1996; Quartz and Sejnowski, 1997.
3. Evolution of cortical areas (figure 7 in the color insert): Krubitzer, 2007.
4. Hierarchy of cortical responses to language in humans: Lerner, Honey, Silbert, and Hasson, 2011; Pallier, Devauchelle, and Dehaene, 2011.
5. Organization of major long-range cortical fiber tracts at birth: Dehaene-Lambertz and Spelke, 2015; Dubois et al., 2015.
6. Hypothesis of a disorganized brain that receives the imprint of the environment: Quartz and Sejnowski, 1997.
7. The peripheral nervous system is already remarkably organized by two months of gestation: Belle et al., 2017.
8. Subdivision of the cortex into Brodmann areas: Amunts et al., 2010; Amunts and Zilles, 2015; Brodmann, 1909.
9. Early gene expression in delimited cortical areas: Kwan et al., 2012; Sun et al., 2005.
10. Early origins of brain asymmetries: Dubois et al., 2009; Leroy et al., 2015.
11. Brain asymmetries in left- and right-handers: Sun et al., 2012.
12. Self-organizing model of cortical folds: Lefevre and Mangin, 2010.
13. Grid cells in rats: Banino et al., 2018; Brun et al., 2008; Fyhn, Molden, Witter, Moser, and Moser, 2004; Hafting, Fyhn, Molden, Moser, and Moser, 2005.
14. Self-organizing models of grid cells: Kropff and Treves, 2008; Shipston-Sharman, Solanka, and Nolan, 2016; Widloski and Fiete, 2014; Yoon et al., 2013.
15. Fast emergence of grid cells, place cells, and head direction cells during development: Langston et al., 2010; Wills, Cacucci, Burgess, and O’Keefe, 2010.
16. Grid cells in humans: Doeller, Barry, and Burgess, 2010; Nau, Navarro Schröder, Bellmund, and Doeller, 2018.
17. Spatial navigation in a blind child: Landau, Gleitman, and Spelke, 1981.
18. Fast emergence of cortical areas for faces versus places: Deen et al., 2017; Livingstone et al., 2017.
19. Tuning to numbers in parietal cortex: Nieder and Dehaene, 2009.
20. Self-organizing model of number neurons: Hannagan, Nieder, Viswanathan, and Dehaene, 2017.
21. Self-organization based on an internal “game engine in the head”: Lake et al., 2017.
22. Genes and cell migration in dyslexia: Galaburda, LoTurco, Ramus, Fitch, and Rosen, 2006.
23. Connectivity anomalies in dyslexia: Darki, Peyrard-Janvid, Matsson, Kere, and Klingberg, 2012; Hoeft et al., 2011; Niogi and McCandliss, 2006.
24. Phonological predictors of dyslexia in six-month-old children: Leppanen et al., 2002; Lyytinen et al., 2004.
25. Attentional dyslexia: Friedmann, Kerbel, and Shvimer, 2010.
26. Visual dyslexia with mirror errors: McCloskey and Rapp, 2000.
27. Bell curve for dyslexia: Shaywitz, Escobar, Shaywitz, Fletcher, and Makuch, 1992.
28. Cognitive and neurological impairments in dyscalculia: Butterworth, 2010; Iuculano, 2016.
29. Parietal gray-matter loss in premature children with dyscalculia: Isaacs, Edmonds, Lucas, and Gadian, 2001.
CHAPTER 5: NURTURE’S SHARE
1. Synaptic hypothesis of brain plasticity: Holtmaat and Caroni, 2016; Takeuchi, Duszkiewicz, and Morris, 2014.
2. Music activates reward circuits: Salimpoor et al., 2013.
3. Long-term potentiation of synapses: Bliss and Lømo, 1973; Lømo, 2018.
4. Aplysia, hippocampus, and synaptic plasticity: Pittenger and Kandel, 2003.
5. Hippocampus and memory for places: Whitlock, Heynen, Shuler, and Bear, 2006.
6. Memory for fearful sounds in mice: Kim and Cho, 2017.
7. Causal role of synaptic changes: Takeuchi et al., 2014.
8. Nature of the engram, the neuronal basis of a memory: Josselyn, Köhler, and Frankland, 2015; Poo et al., 2016.
9. Working memory and sustained firing: Courtney, Ungerleider, Keil, and Haxby, 1997; Ester, Sprague, and Serences, 2015; Goldman-Rakic, 1995; Kerkoerle, Self, and Roelfsema, 2017; Vogel and Machizawa, 2004.
10. Working memory and fast synaptic changes: Mongillo, Barak, and Tsodyks, 2008.
11. Role of the hippocampus in the fast acquisition of novel information: Genzel et al., 2017; Lisman et al., 2017; Schapiro, Turk-Browne, Norman, and Botvinick, 2016; Shohamy and Turk-Browne, 2013.
12. Displacement of a memory engram from hippocampus to cortex: Kitamura et al., 2017.
13. Creation of a false memory in mice: Ramirez et al., 2013.
14. Turning a bad memory into a good one: Ramirez et al., 2015.
15. Erasing a traumatic memory: Kim and Cho, 2017.
16. Creating a novel memory during sleep: de Lavilléon et al., 2015.
17. Tool and symbol learning in macaque monkeys: Iriki, 2005; Obayashi et al., 2001; Srihasam, Mandeville, Morocz, Sullivan, and Livingstone, 2012.
18. Distant synaptic changes: Fitzsimonds, Song, and Poo, 1997.
19. Anatomical changes due to music training: Gaser and Schlaug, 2003; Oechslin, Gschwind, and James, 2018; Schlaug, Jancke, Huang, Staiger, and Steinmetz, 1995.
20. Anatomical changes due to literacy: Carreiras et al., 2009; Thiebaut de Schotten, Cohen, Amemiya, Braga, and Dehaene, 2014.
21. Anatomical changes after learning to juggle: Draganski et al., 2004; Gerber et al., 2014.
22. Brain changes in London taxi drivers: Maguire et al., 2000, 2003.
23. Non-synaptic mechanism of memory in the cerebellum: Johansson, Jirenhed, Rasmussen, Zucca, and Hesslow, 2014; Rasmussen, Jirenhed, and Hesslow, 2008.
24. Effects of physical exercise and nutrition on the brain: Prado and Dewey, 2014; Voss, Vivar, Kramer, and van Praag, 2013.
25. Cognitive deficits in children with vitamin B1 (thiamine) deficiency: Fattal, Friedmann, and Fattal-Valevski, 2011.
26. Brain plasticity in a child born without a right hemisphere: Muckli, Naumer, and Singer, 2009.
27. Turning auditory cortex into visual cortex: Sur, Garraghty, and Roe, 1988; Sur and Rubenstein, 2005.
28. Hypothesis of a disorganized brain that receives the imprint of the environment: Quartz and Sejnowski, 1997.
29. Self-organization of visual maps by retinal waves: Goodman and Shatz, 1993; Shatz, 1996.
30. Progressive adjustment of cortical spontaneous activity: Berkes, Orbán, Lengyel, and Fiser, 2011; Orbán, Berkes, Fiser, and Lengyel, 2016.
31. Review of the concept of sensitive periods: Werker and Hensch, 2014.
32. Growth of human cortical neurons: Conel, 1939; Courchesne et al., 2007.
33. Synaptic overproduction and elimination in the course of development: Rakic, Bourgeois, Eckenhoff, Zecevic, and Goldman-Rakic, 1986.
34. Distinct phases of synaptic elimination in humans: Huttenlocher and Dabholkar, 1997.
35. Progressive myelination of cortical bundles: Dubois et al., 2007, 2015; Flechsig, 1876.
36. Acceleration of visual responses in babies: Adibpour et al., 2018; Dehaene-Lambertz and Spelke, 2015.
37. Slowness of conscious processing in babies: Kouider et al., 2013.
38. Sensitive period for binocular vision: Epelbaum, Milleret, Buisseret, and Duffer, 1993; Fawcett, Wang, and Birch, 2005; Hensch, 2005.
39. Loss of the capacity to discriminate non-native phonemes: Dehaene-Lambertz and Spelke, 2015; Maye, Werker, and Gerken, 2002; Pena, Werker, and Dehaene-Lambertz, 2012; Werker and Tees, 1984.
40. Partial recovery of the discrimination of /R/ and /L/ in Japanese speakers: McCandliss, Fiez, Protopapas, Conway, and McClelland, 2002.
41. Auditory cortex anatomy predicts the capacity to learn foreign contrasts: Golestani, Molko, Dehaene, Le Bihan, and Pallier, 2007.
42. Sensitive period for second-language learning: Flege, Munro, and MacKay, 1995; Hartshorne, Tenenbaum, and Pinker, 2018; Johnson and Newport, 1989; Weber-Fox and Neville, 1996.
43. Sharp decline in the speed of second-language grammar learning around seventeen years of age (analysis of data from several million people): Hartshorne et al., 2018.
44. Sensitive period for language learning in deaf people with a cochlear implant: Friedmann and Rusou, 2015.
45. Biological mechanisms for the opening and closing of sensitive periods: Caroni, Donato, and Muller, 2012; Friedmann and Rusou, 2015; Werker and Hensch, 2014.
46. Restoring brain plasticity: Krause et al., 2017.
47. Reorganization of language areas in adopted children: Pallier et al., 2003. Similar results have been observed in the domain of face recognition: when adopted in a Western country before the age of nine, Korean children lose the advantage that is usually observed for recognizing members of one’s own race (Sangrigoli, Pallier, Argenti, Ventureyra, and de Schonen, 2005).
48. Dormant trace of the first language in adopted children: Pierce, Klein, Chen, Delcenserie, and Genesee, 2014.
49. Dormant connections in owls: Knudsen and Knudsen, 1990; Knudsen, Zheng, and DeBello, 2000.
50. Age-of-acquisition effect in word processing: Ellis and Lambon Ralph, 2000; Gerhand and Barry, 1999; Morrison and Ellis, 1995.
51. Bucharest Early Intervention Project: Almas et al., 2012; Berens and Nelson, 2015; Nelson et al., 2007; Sheridan, Fox, Zeanah, McLaughlin, and Nelson, 2012; Windsor, Moraru, Nelson, Fox, and Zeanah, 2013.
52. Ethics of the Bucharest project: Millum and Emanuel, 2007.
CHAPTER 6: RECYCLE YOUR BRAIN
1. Nabokov, 1962.
2. Difficulties of illiterates in picture recognition: Kolinsky et al., 2011; Kolinsky, Morais, Content, and Cary, 1987; Szwed, Ventura, Querido, Cohen, and Dehaene, 2012.
3. Difficulties of illiterates in processing mirror images: Kolinsky et al., 2011, 1987; Pegado, Nakamura, et al., 2014.
4. Difficulties of illiterates in attending to part of a face: Ventura et al., 2013.
5. Difficulties of illiterates in recognizing and remembering spoken words: Castro-Caldas, Petersson, Reis, Stone-Elander, and Ingvar, 1998; Morais, 2017; Morais, Bertelson, Cary, and Alegria, 1986; Morais and Kolinsky, 2005.
6. Impact of arithmetic education: Dehaene, Izard, Pica, and Spelke, 2006; Dehaene, Izard, Spelke, and Pica, 2008; Piazza et al., 2013; Pica, Lemer, Izard, and Dehaene, 2004.
7. Counting and arithmetic in Amazon Indians: Pirahã: Frank, Everett, Fedorenko, and Gibson, 2008; Munduruku: Pica et al., 2004; Tsimane: Piantadosi, Jara-Ettinger, and Gibson, 2014.
8. Acquisition of the number line concept: Dehaene, 2003; Dehaene et al., 2008; Siegler and Opfer, 2003.
9. Neuronal recycling hypothesis: Dehaene, 2005, 2014; Dehaene and Cohen, 2007.
10. Evolution by duplication of brain circuits: Chakraborty and Jarvis, 2015; Fukuchi-Shimogori and Grove, 2001.
11. Learning confined to a neuronal subspace: Galgali and Mante, 2018; Golub et al., 2018; Sadtler et al., 2014.
12. One-dimensional coding in parietal cortex: Chafee, 2013; Fitzgerald et al., 2013.
13. Role of parietal cortex in the comparison of social status: Chiao, 2010.
14. Two-dimensional coding in entorhinal cortex: Yoon et al., 2013.
15. Coding of an arbitrary two-dimensional space by grid cells: Constantinescu, O’Reilly, and Behrens, 2016.
16. Coding of syntactic trees in Broca’s area: Musso et al., 2003; Nelson et al., 2017; Pallier et al., 2011.
17. The number sense: Dehaene, 2011.
18. Number neurons in untrained animals: Ditz and Nieder, 2015; Viswanathan and Nieder, 2013.
19. Effect of training on number neurons: Viswanathan and Nieder, 2015.
20. Acquisition of Arabic numerals in monkeys: Diester and Nieder, 2007.
21. Relation between addition, subtraction, and movements of spatial attention: Knops, Thirion, Hubbard, Michel, and Dehaene, 2009; Knops, Viarouge, and Dehaene, 2009.
22. Functional MRI of professional mathematicians: Amalric and Dehaene, 2016, 2017.
23. Brain imaging of number processing in babies: Izard et al., 2008.
24. Functional MRI of early math in preschoolers: Cantlon, Brannon, Carter, and Pelphrey, 2006. Cantlon and Li, 2013, show that cortical areas for language and number are already active when a four-year-old watches the corresponding sections of Sesame Street movies, and that their activity predicts the child’s language and math skills.
25. Blind mathematicians: Amalric, Denghien, and Dehaene, 2017.
26. Recycling of occipital cortex for math in the blind: Amalric, Denghien, et al., 2017; Kanjlia, Lane, Feigenson, and Bedny, 2016.
27. Language processing in the occipital cortex of the blind: Amedi, Raz, Pianka, Malach, and Zohary, 2003; Bedny, Pascual-Leone, Dodell-Feder, Fedorenko, and Saxe, 2011; Lane, Kanjlia, Omaki, and Bedny, 2015; Sabbah et al., 2016.
28. Debate on cortical plasticity in the blind: Bedny, 2017; Hannagan, Amedi, Cohen, Dehaene-Lambertz, and Dehaene, 2015.
29. Retinotopic maps in the blind: Bock et al., 2015.
30. Recycling of visual cortex in the blind: Abboud, Maidenbaum, Dehaene, and Amedi, 2015; Amedi et al., 2003; Bedny et al., 2011; Mahon, Anzellotti, Schwarzbach, Zampini, and Caramazza, 2009; Reich, Szwed, Cohen, and Amedi, 2011; Striem-Amit and Amedi, 2014; Strnad, Peelen, Bedny, and Caramazza, 2013.
31. Connectivity predicts function in visual cortex: Bouhali et al., 2014; Hannagan et al., 2015; Saygin et al., 2012, 2013, 2016.
32. Distance effect in number comparison: Dehaene, 2007; Dehaene, Dupoux, and Mehler, 1990; Moyer and Landauer, 1967.
33. Distance effect when deciding that two numbers are different: Dehaene and Akhavein, 1995; Diester and Nieder, 2010.
34. Distance effect when verifying addition and subtraction problems: Groen and Parkman, 1972; Pinheiro-Chagas, Dotan, Piazza, and Dehaene, 2017.
35. Mental representation of prices: Dehaene and Marques, 2002; Marques and Dehaene, 2004.
36. Mental representation of parity: 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.
37. Language of thought in mathematics: Amalric, Wang, et al., 2017; Piantadosi et al., 2012, 2016.
38. See my previous book Reading in the Brain: Dehaene, 2009.
39. Brain mechanisms of the invariant recognition of written words: Dehaene et al., 2001, 2004.
40. Connections between the visual word form area and language areas: Bouhali et al., 2014; Saygin et al., 2016.
41. Imaging of the illiterate brain: Dehaene et al., 2010; Dehaene, Cohen, Morais, and Kolinsky, 2015; Pegado, Comerlato, et al., 2014.
42. Specialization of early visual cortex for reading: Chang et al., 2015; Dehaene et al., 2010; Szwed, Qiao, Jobert, Dehaene, and Cohen, 2014.
43. Literacy competes with face processing the left hemisphere: Dehaene et al., 2010; Pegado, Comerlato, et al., 2014.
44. Development of reading and face recognition: Dehaene-Lambertz, Monzalvo, and Dehaene, 2018; Dundas, Plaut, and Behrmann, 2013; Li et al., 2013; Monzalvo, Fluss, Billard, Dehaene, and Dehaene-Lambertz, 2012.
45. Insufficient activity evoked by words and faces in dyslexic children: Monzalvo et al., 2012.
46. Universal marker of reading difficulties: Rueckl et al., 2015.
47. Competition between words and faces—knockout or blocking?: Dehaene-Lambertz et al., 2018.
48. Learning to read in adulthood: Braga et al., 2017; Cohen, Dehaene, McCormick, Durant, and Zanker, 2016.
49. Displacement of the visual word form area in musicians: Mongelli et al., 2017.
50. Reduced response to faces in mathematicians: Amalric and Dehaene, 2016.
51. Numerous long-term effects of early education: see the Abecedarian program (Campbell et al., 2012, 2014; Martin, Ramey, and Ramey, 1990), the Perry preschool program (Heckman, Moon, Pinto, Savelyev, and Yavitz, 2010; Schweinhart, 1993), and the Jamaican Study (Gertler et al., 2014; Grantham-McGregor, Powell, Walker, and Himes, 1991; Walker, Chang, Powell, and Grantham-McGregor, 2005).
52. Child-directed speech and vocabulary growth: Shneidman, Arroyo, Levine, and Goldin-Meadow, 2013; Shneidman and Goldin-Meadow, 2012.
53. Increased response to speech following parent-child story reading: Hutton et al., 2015, 2017; see also Romeo et al., 2018.
54. Advantages of early bilingualism: Bialystok, Craik, Green, and Gollan, 2009; Costa and Sebastián-Gallés, 2014; Li, Legault, and Litcofsky, 2014.
55. Benefits of an enriched environment: Donato, Rompani, and Caroni, 2013; Knudsen et al., 2000; van Praag, Kempermann, and Gage, 2000; Voss et al., 2013; Zhu et al., 2014.
CHAPTER 7: ATTENTION
1. Attention in mice: Wang and Krauzlis, 2018.
2. Attention in artificial neural networks: Bahdanau, Cho, and Bengio, 2014; Cho, Courville, and Bengio, 2015.
3. Attention in an artificial neural network learning to caption pictures (figure on this page): Xu et al., 2015.
4. Inattention strongly reduces learning: Ahissar and Hochstein, 1993.
5. Reduced learning in the absence of attention and consciousness: Seitz, Lefebvre, Watanabe, and Jolicoeur, 2005; Watanabe, Nanez, and Sasaki, 2001.
6. Prefrontal ignition and access to consciousness: Dehaene and Changeux, 2011; van Vugt et al., 2018.
7. Acetylcholine, dopamine, brain plasticity, and alteration of cortical maps: Bao, Chan, and Merzenich, 2001; Froemke, Merzenich, and Schreiner, 2007; Kilgard and Merzenich, 1998.
8. Balance between inhibition and excitation, and reopening of brain plasticity: Werker and Hensch, 2014.
9. Activation of reward and alerting circuits by video games: Koepp et al., 1998.
10. Positive effects of video game training: Bavelier et al., 2011; Cardoso-Leite and Bavelier, 2014; Green and Bavelier, 2003.
11. Cognitive training using video games: see our math software at www.thenumberrace.com and www.thenumbercatcher.com; for reading acquisition, visit grapholearn.fr.
12. Spatial attention orienting: Posner, 1994.
13. Amplification by attention: Çukur, Nishimoto, Huth, and Gallant, 2013; Desimone and Duncan, 1995; Kastner and Ungerleider, 2000.
14. Inattentional blindness: Mack and Rock, 1998; Simons and Chabris, 1999.
15. Attentional blink: Marois and Ivanoff, 2005; Sergent, Baillet, and Dehaene, 2005.
16. Unattended items induce little or no learning: Leong, Radulescu, Daniel, DeWoskin, and Niv, 2017.
17. Adult experiment on attention to letters versus whole words: Yoncheva, Blau, Maurer, and McCandliss, 2010.
18. Educational studies of phonics versus whole-word reading: Castles, Rastle, and Nation, 2018; Ehri, Nunes, Stahl, and Willows, 2001; National Institute of Child Health and Human Development, 2000; see also Dehaene, 2009.
19. Organization of executive control in prefrontal cortex: D’Esposito and Grossman, 1996; Koechlin, Ody, and Kouneiher, 2003; Rouault and Koechlin, 2018.
20. Prefrontal expansion in the human species: Elston, 2003; Sakai et al., 2011; Schoenemann, Sheehan, and Glotzer, 2005; Smaers, Gómez-Robles, Parks, and Sherwood, 2017.
21. Prefrontal hierarchy and metacognitive control: Fleming, Weil, Nagy, Dolan, and Rees, 2010; Koechlin et al., 2003; Rouault and Koechlin, 2018.
22. Global neuronal workspace: Dehaene and Changeux, 2011; Dehaene, Changeux, Naccache, Sackur, and Sergent, 2006; Dehaene, Kerszberg, and Changeux, 1998; Dehaene and Naccache, 2001.
23. Central bottleneck: Chun and Marois, 2002; Marti, King, and Dehaene, 2015; Marti, Sigman, and Dehaene, 2012; Sigman and Dehaene, 2008.
24. Unawareness of the dual-task delay: Corallo, Sackur, Dehaene, and Sigman, 2008; Marti et al., 2012.
25. Debate on the ability to split attention and execute two tasks in parallel: Tombu and Jolicoeur, 2004.
26. An exceedingly decorated classroom distracts pupils: Fisher, Godwin, and Seltman, 2014.
27. Use of electronic devices in class reduces exam performance: Glass and Kang, 2018.
28. A-not-B error and development of prefrontal cortex: Diamond and Doar, 1989; Diamond and Goldman-Rakic, 1989.
29. Development of executive control and number perception: Borst, Poirel, Pineau, Cassotti, and Houdé, 2013; Piazza, De Feo, Panzeri, and Dehaene, 2018; Poirel et al., 2012.
30. Effect of number training on prefrontal cortex: Viswanathan and Nieder, 2015.
31. Role of executive control in cognitive and emotional development: Houdé et al., 2000; Isingrini, Perrotin, and Souchay, 2008; Posner and Rothbart, 1998; Sheese, Rothbart, Posner, White, and Fraundorf, 2008; Siegler, 1989.
32. Effects of training on executive control and working memory: Diamond and Lee, 2011; Habibi, Damasio, Ilari, Elliott Sachs, and Damasio, 2018; Jaeggi, Buschkuehl, Jonides, and Shah, 2011; Klingberg, 2010; Moreno et al., 2011; Olesen, Westerberg, and Klingberg, 2004; Rueda, Rothbart, McCandliss, Saccomanno, and Posner, 2005.
33. Randomized studies of Montessori pedagogy: Lillard and Else-Quest, 2006; Marshall, 2017.
34. Effects of musical training on the brain: Bermudez, Lerch, Evans, and Zatorre, 2009; James et al., 2014; Moreno et al., 2011.
35. Relation between executive control, prefrontal cortex, and intelligence: Duncan, 2003, 2010, 2013.
36. Training effects on fluid intelligence: Au et al., 2015.
37. Impact of adoption on IQ: Duyme, Dumaret, and Tomkiewicz, 1999.
38. Impact of education on IQ: Ritchie and Tucker-Drob, 2018.
39. Effects of cognitive training on concentration, reading, and arithmetic: Bergman-Nutley and Klingberg, 2014; Blair and Raver, 2014; Klingberg, 2010; Spencer-Smith and Klingberg, 2015.
40. Correlation between working memory and subsequent math scores: Dumontheil and Klingberg, 2011; Gathercole, Pickering, Knight, and Stegmann, 2004; Geary, 2011.
41. Joint training of working memory and the number line: Nemmi et al., 2016.
42. Learning Chinese with a nanny, but not with a video: Kuhl, Tsao, and Liu, 2003.
43. Shared attention and the pedagogical stance: Csibra and Gergely, 2009; Egyed, Király, and Gergely, 2013.
44. Object pointing and memory of object’s identity: Yoon, Johnson, and Csibra, 2008.
45. Pseudo-teaching in meerkats: Thornton and McAuliffe, 2006.
46. Intelligent versus slavish copying of actions by fourteen-month-olds: Gergely et al., 2002.
47. Social conformism in perception: see, for instance, Bond and Smith, 1996.
CHAPTER 8: ACTIVE ENGAGEMENT
1. Classic experiment comparing active and passive kittens: Held and Hein, 1963.
2. Statistical learning of syllables and words: Hay et al., 2011; Saffran et al., 1996; see also ongoing research in G. Dehaene-Lambertz’s lab on learning in sleeping neonates.
3. Effect of word processing depth on explicit memory: Craik and Tulving, 1975; Jacoby and Dallas, 1981.
4. Memory for sentences: Auble and Franks, 1978; Auble, Franks, and Soraci, 1979.
5. “Making learning conditions more difficult . . .”: Zaromb, Karpicke, and Roediger, 2010.
6. Brain imaging of the effect of word processing depth on memory: Kapur et al., 1994.
7. The activation of prefrontal-hippocampal loops during incidental learning predicts subsequent memory: Brewer, Zhao, Desmond, Glover, and Gabrieli, 1998; Paller, McCarthy, and Wood, 1988; Sederberg et al., 2006; Sederberg, Kahana, Howard, Donner, and Madsen, 2003; Wagner et al., 1998.
8. Memory for conscious and unconscious words: Dehaene et al., 2001.
9. Active learning of physics concepts: Kontra, Goldin-Meadow, and Beilock, 2012; Kontra, Lyons, Fischer, and Beilock, 2015.
10. Comparison of traditional lecturing versus active learning: Freeman et al., 2014.
11. Failure of discovery learning and related pedagogical strategies: Hattie, 2017; Kirschner, Sweller, and Clark, 2006; Kirschner and van Merriënboer, 2013; Mayer, 2004.
12. To add all numbers from 1 to 100, pair 1 with 100, 2 with 99, 3 with 98, and so forth. Each of these pairs adds up to 101, and there are fifty of them, hence the total is 5050.
13. Instructional guidance rather than pure discovery: Mayer, 2004.
14. Urban legends in education: Kirschner and van Merriënboer, 2013.
15. The myth of learning styles: Pashler, McDaniel, Rohrer, and Bjork, 2008.
16. Variations in amount of reading in first grade: Anderson, Wilson, and Fielding, 1988.
17. Early childhood curiosity and academic achievement: Shah, Weeks, Richards, and Kaciroti, 2018.
18. Dopaminergic neurons sensitive to new information: Bromberg-Martin and Hikosaka, 2009.
19. Novelty seeking in rats: Bevins, 2001.
20. Brain imaging of curiosity: Gruber, Gelman, and Ranganath, 2014; see also Kang et al., 2009.
21. Laughter as an epistemic emotion unique to humans: Hurley, Dennett, and Adams, 2011.
22. Laughter and learning: Esseily, Rat-Fischer, Somogyi, O’Regan, and Fagard, 2016.
23. Review of psychological theories of curiosity: Loewenstein, 1994.
24. Inverted-U curve of curiosity: Kang et al., 2009; Kidd, Piantadosi, and Aslin, 2012, 2014; Loewenstein, 1994.
25. Curiosity in a robot: Gottlieb, Oudeyer, Lopes, and Baranes, 2013; Kaplan and Oudeyer, 2007.
26. Goldilocks effect in eight-month-olds: Kidd et al., 2012, 2014.
27. Metacognition in young children: Dehaene et al., 2017; Goupil, Romand-Monnier, and Kouider, 2016; Lyons and Ghetti, 2011.
28. Gender and race stereotypes in mathematics: Spencer, Steele, and Quinn, 1999; Steele and Aronson, 1995.
29. Stress, anxiety, learned helplessness, and the inability to learn: Caroni et al., 2012; Donato et al., 2013; Kim and Diamond, 2002; Noble, Norman, and Farah, 2005.
30. Explicit teaching may kill curiosity: Bonawitz et al., 2011.
CHAPTER 9: ERROR FEEDBACK
1. Grothendieck, 1986.
2. John Hattie’s meta-analysis grants feedback an effect size of 0.73 standard deviations, which makes it one of the most powerful modulators of learning (Hattie, 2008).
3. Rescorla-Wagner learning rule: Rescorla and Wagner, 1972.
4. For a detailed criticism of associative learning, see Balsam and Gallistel, 2009; Gallistel, 1990.
5. Blocking of animal conditioning: Beckers, Miller, De Houwer, and Urushihara, 2006; Fanselow, 1998; Waelti, Dickinson, and Schultz, 2001.
6. Surprise enhances infants’ learning and exploration: Stahl and Feigenson, 2015.
7. Error signals in the brain: Friston, 2005; Naatanen, Paavilainen, Rinne, and Alho, 2007; Schultz, Dayan, and Montague, 1997.
8. Surprise reflects the violation of a prediction: Strauss et al., 2015; Todorovic and de Lange, 2012.
9. Hierarchy of local and global error signals: Bekinschtein et al., 2009; Strauss et al., 2015; Uhrig, Dehaene, and Jarraya, 2014; Wang et al., 2015.
10. Surprise due to an unexpected picture: Meyer and Olson, 2011.
11. Surprise due to a semantic violation: Curran, Tucker, Kutas, and Posner, 1993; Kutas and Federmeier, 2011; Kutas and Hillyard, 1980.
12. Surprise due to a grammatical violation: Friederici, 2002; Hahne and Friederici, 1999; but see also Steinhauer and Drury, 2012, for a critical discussion.
13. Prediction error in the dopamine network: Pessiglione, Seymour, Flandin, Dolan, and Frith, 2006; Schultz et al., 1997; Waelti et al., 2001.
14. Importance of high-quality feedback at school: Hattie, 2008.
15. Learning by trial and error in adults versus adolescents: Palminteri, Kilford, Coricelli, and Blakemore, 2016.
16. Pennac, D. (2017, February 11). Daniel Pennac: “J’ai été d’abord et avant tout professeur.” Le Monde. Retrieved from lemonde.fr.
17. Math anxiety syndrome: Ashcraft, 2002; Lyons and Beilock, 2012; Maloney and Beilock, 2012; Young, Wu, and Menon, 2012.
18. Effect of fear conditioning on synaptic plasticity: Caroni et al., 2012; Donato et al., 2013.
19. Fixed versus growth mindset: Claro, Paunesku, and Dweck, 2016; Dweck, 2006; Rattan, Savani, Chugh, and Dweck, 2015. Note, however, that the size of these effects, and therefore their practical relevance at school, has been recently questioned: Sisk, Burgoyne, Sun, Butler, and Macnamara, 2018.
20. Massive effect of retrieval practice on learning: Carrier and Pashler, 1992; Karpicke and Roediger, 2008; Roediger and Karpicke, 2006; Szpunar, Khan, and Schacter, 2013; Zaromb and Roediger, 2010. For an excellent review of the relative efficacy of various learning techniques, see Dunlosky, Rawson, Marsh, Nathan, and Willingham, 2013.
21. Making retrospective memory judgments facilitates learning: Robey, Dougherty, and Buttaccio, 2017.
22. Retrieval practice facilitates the acquisition of foreign vocabulary: Carrier and Pashler, 1992; Lindsey, Shroyer, Pashler, and Mozer, 2014.
23. Spacing the learning improves memory retention: Cepeda et al., 2009; Cepeda, Pashler, Vul, Wixted, and Rohrer, 2006; Rohrer and Taylor, 2006; Schmidt and Bjork, 1992.
24. Brain imaging of the spacing effect: Bradley et al., 2015; Callan and Schweighofer, 2010.
25. Effect of progressively increasing the time between lessons: Kang, Lindsey, Mozer, and Pashler, 2014.
26. The shuffling of mathematics problems improves learning: Rohrer and Taylor, 2006, 2007.
27. Feedback improves memory even on correct trials: Butler, Karpicke, and Roediger, 2008.
CHAPTER 10: CONSOLIDATION
1. Moving from serial to parallel reading in the course of learning to read: Zoccolotti et al., 2005.
2. Longitudinal brain imaging of the acquisition of reading: Dehaene-Lambertz et al., 2018.
3. Contribution of parietal cortex to expert reading, only for degraded words: Cohen, Dehaene, Vinckier, Jobert, and Montavont, 2008; Vinckier et al., 2006.
4. Visual recognition of frequent combinations of letters: Binder, Medler, Westbury, Liebenthal, and Buchanan, 2006; Dehaene, Cohen, Sigman, and Vinckier, 2005; Grainger and Whitney, 2004; Vinckier et al., 2007.
5. Tuning of early visual cortex to letter perception: Chang et al., 2015; Dehaene et al., 2010; Sigman et al., 2005; Szwed et al., 2011, 2014.
6. Unconscious reading: Dehaene et al., 2001, 2004.
7. Automatization of arithmetic: Ansari and Dhital, 2006; Rivera, Reiss, Eckert, and Menon, 2005. The hippocampus also seems to strongly contribute to the memory for arithmetic facts: Qin et al., 2014.
8. Sleep interrupts the forgetting curve: Jenkins and Dallenbach, 1924.
9. REM sleep improves learning: Karni, Tanne, Rubenstein, Askenasy, and Sagi, 1994.
10. Sleep and the consolidation of recent learning: Huber, Ghilardi, Massimini, and Tononi, 2004; Stickgold, 2005; Walker, Brakefield, Hobson, and Stickgold, 2003; Walker and Stickgold, 2004.
11. Overexpression of the zif-268 gene during sleep: Ribeiro, Goyal, Mello, and Pavlides, 1999.
12. Neuronal replay during the night: Ji and Wilson, 2007; Louie and Wilson, 2001; Skaggs and McNaughton, 1996; Wilson and McNaughton, 1994.
13. Decoding brain activity during sleep: Chen and Wilson, 2017; Horikawa, Tamaki, Miyawaki, and Kamitani, 2013.
14. Theories of the memory function of sleep: Diekelmann and Born, 2010.
15. Replay during sleep facilitates memory consolidation: Ramanathan, Gulati, and Ganguly, 2015; see also Norimoto et al., 2018, for the direct effect of sleep on synaptic plasticity.
16. Cortical and hippocampal reactivation during sleep in humans: Horikawa et al., 2013; Jiang et al., 2017; Peigneux et al., 2004.
17. Increased slow wave sleep and post-sleep performance improvement: Huber et al., 2004.
18. Brain imaging of the effects of sleep on motor learning: Walker, Stickgold, Alsop, Gaab, and Schlaug, 2005.
19. Boosting slow oscillations during sleep improves memory: Marshall, Helgadóttir, Mölle, and Born, 2006; Ngo, Martinetz, Born, and Mölle, 2013.
20. Odors can bias memory consolidation during sleep: Rasch, Büchel, Gais, and Born, 2007.
21. Sounds can bias replay during sleep and improve subsequent memory: Antony, Gobel, O’Hare, Reber, and Paller, 2012; Bendor and Wilson, 2012; Rudoy, Voss, Westerberg, and Paller, 2009.
22. No learning of novel facts during sleep: Bruce et al., 1970; Emmons and Simon, 1956. Nevertheless, a very recent study suggests that during sleep, we may be able to learn the association between a tone and a smell (Arzi et al., 2012).
23. Gazsi, M. (2018, June 8). Philippe Starck: “I couldn’t care less about my life.” The Guardian, theguardian.com.
24. Mathematical insight during sleep: Wagner, Gais, Haider, Verleger, and Born, 2004.
25. Sleep-wake learning algorithms: Hinton, Dayan, Frey, and Neal, 1995; Hinton, Osindero, and Teh, 2006.
26. Hypothesis that the memory function of sleep may be more efficient in humans: Samson and Nunn, 2015.
27. Greater efficiency of sleep in children than in adults: Wilhelm et al., 2013.
28. Babies generalize word meanings after sleeping: Friedrich, Wilhelm, Born, and Friederici, 2015; Seehagen, Konrad, Herbert, and Schneider, 2015.
29. Positive effect of naps in preschoolers: Kurdziel, Duclos, and Spencer, 2013.
30. Sleep deficits and attention disorders: Avior et al., 2004; Cortese et al., 2013; Hiscock et al., 2015; Prehn-Kristensen et al., 2014.
31. Beneficial effects of delaying school start times for adolescents: American Academy of Pediatrics, 2014; Dunster et al., 2018.
CONCLUSION: RECONCILING EDUCATION WITH NEUROSCIENCE
1. Artificial intelligence inspired by neuroscience and cognitive science: Hassabis, Kumaran, Summerfield, and Botvinick, 2017; Lake et al., 2017.
2. See PISA (Program for International Student Assessment, oecd.org/pisa-fr), TIMSS (Trends in International Mathematics and Science Study), and PIRLS (Progress in International Reading Literacy Study, timssandpirls.bc.edu).