J. Grainger, S. Dufau, and J. C. Ziegler, A vision of reading, Trends Cogn. Sci, vol.20, pp.171-179, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01432252

S. Dehaene, L. Cohen, M. Sigman, and F. Vinckier, The neural code for written words: a proposal, Trends Cogn. Sci, vol.9, pp.335-341, 2005.

G. E. Legge and C. A. Bigelow, Does print size matter for reading? A review of findings from vision science and typography, J. Vis, vol.11, 2011.

L. Cohen, The visual word form area: spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients, Brain, vol.123, pp.291-307, 2000.

S. Dehaene, Reading in the Brain, 2009.

S. Dehaene and L. Cohen, Cultural recycling of cortical maps, Neuron, vol.56, pp.384-398, 2007.

G. Dehaene-lambertz, K. Monzalvo, and S. Dehaene, The emergence of the visual word form: longitudinal evolution of category-specific ventral visual areas during reading acquisition, PLoS Biol, vol.16, p.2004103, 2018.
URL : https://hal.archives-ouvertes.fr/inserm-02136502

S. Dehaene, How learning to read changes the cortical networks for vision and language, Science, vol.330, pp.1359-1364, 2010.
URL : https://hal.archives-ouvertes.fr/cea-00819208

S. Dehaene, L. Cohen, J. Morais, and R. Kolinsky, Illiterate to literate: behavioural and cerebral changes induced by reading acquisition, Nat. Rev. Neurosci, vol.16, pp.234-244, 2015.

D. J. Felleman and D. C. Van-essen, Distributed hierarchical processing in the primate cerebral cortex, Cereb. Cortex, vol.1, pp.1-47, 1991.

R. Passingham, How good is the macaque monkey model of the human brain?, Curr. Opin. Neurobiol, vol.19, pp.6-11, 2009.

N. Kriegeskorte, Matching categorical object representations in inferior temporal cortex of man and monkey, Neuron, vol.60, pp.1126-1141, 2008.

D. Mantini, Interspecies activity correlations reveal functional correspondence between monkey and human brain areas, Nat. Methods, vol.9, pp.277-282, 2012.

G. A. Orban, D. Van-essen, and W. Vanduffel, Comparative mapping of higher visual areas in monkeys and humans, Trends Cogn. Sci, vol.8, pp.315-324, 2004.

R. Rajalingham, K. Schmidt, and J. J. Dicarlo, Comparison of object recognition behavior in human and monkey, J. Neurosci, vol.35, pp.12127-12136, 2015.

R. Rajalingham, Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks, J. Neurosci, vol.38, pp.7255-7269, 2018.

J. Grainger, S. Dufau, M. Montant, J. C. Ziegler, and J. Fagot, Orthographic processing in baboons (Papio papio), Science, vol.336, pp.245-248, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01152186

K. Srihasam, J. L. Vincent, and M. S. Livingstone, Novel domain formation reveals proto-architecture in inferotemporal cortex, Nat. Neurosci, vol.17, pp.1776-1783, 2014.

K. Tanaka, Inferotemporal cortex and object vision, Annu. Rev. Neurosci, vol.19, pp.109-139, 1996.

N. K. Logothetis and D. L. Sheinberg, Visual object recognition, Annu. Rev. Neurosci, vol.19, pp.577-621, 1996.

J. J. Dicarlo, D. Zoccolan, and N. C. Rust, How does the brain solve visual object recognition?, Neuron, vol.73, pp.415-434, 2012.

N. K. Logothetis, J. Pauls, and T. Poggio, Shape representation in the inferior temporal cortex of monkeys, Curr. Biol, vol.5, pp.552-563, 1995.

N. C. Rust and J. J. Dicarlo, Selectivity and tolerance ("invariance") both increase as visual information propagates from cortical area V4 to IT, J. Neurosci, vol.30, pp.12978-12995, 2010.

M. A. Changizi, Q. Zhang, H. Ye, and S. Shimojo, The structures of letters and symbols throughout human history are selected to match those found in objects in natural scenes, Am. Nat, vol.167, pp.117-139, 2006.

N. J. Majaj, H. Hong, E. A. Solomon, and J. J. Dicarlo, Simple learned weighted sums of inferior temporal neuronal firing rates accurately predict human core object recognition performance, J. Neurosci, vol.35, pp.13402-13418, 2015.

R. Rajalingham and J. J. Dicarlo, Reversible inactivation of different millimeterscale regions of primate IT results in different patterns of core object recognition deficits, Neuron, vol.102, pp.493-505, 2019.

A. Afraz, E. S. Boyden, and J. J. Dicarlo, Optogenetic and pharmacological suppression of spatial clusters of face neurons reveal their causal role in face gender discrimination, Proc. Natl Acad. Sci. USA, vol.112, pp.6730-6735, 2015.

S. Dehaene, Cerebral mechanisms of word masking and unconscious repetition priming, Nat. Neurosci, vol.4, pp.752-758, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00349842

J. Kubilius, CORnet: modeling the neural mechanisms of core object recognition, 2018.

M. Schrimpf, Brain-Score: which artificial neural network for object recognition is most brain-like?, 2018.

K. Kar, J. Kubilius, K. Schmidt, E. B. Issa, and J. J. Dicarlo, Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior, Nat. Neurosci, vol.22, p.974, 2019.

G. E. Legge and C. A. Bigelow, Does print size matter for reading? A review of findings from vision science and typography, J. Vis, vol.11, pp.8-8, 2011.

C. Whitney, How the brain encodes the order of letters in a printed word: the SERIOL model and selective literature review, Psychon. Bull. Rev, vol.8, pp.221-243, 2001.

C. J. Davis, The spatial coding model of visual word identification, Psychol. Rev, vol.117, pp.713-758, 2010.

J. Grainger and W. Van-heuven, The mental lexicon, p.24, 2003.

J. E. Rollenhagen and C. R. Olson, Mirror-image confusion in single neurons of the macaque inferotemporal cortex, Science, vol.287, pp.1506-1508, 2000.

W. A. Freiwald and D. Y. Tsao, Functional compartmentalization and viewpoint generalization within the macaque face-processing system, Science, vol.330, pp.845-851, 2010.

G. C. Baylis and J. Driver, Shape-coding in IT cells generalizes over contrast and mirror reversal, but not figure-ground reversal, Nat. Neurosci, vol.4, p.937, 2001.

O. Miranda-dominguez, Bridging the gap between the human and macaque connectome: a quantitative comparison of global interspecies structure-function relationships and network topology, J. Neurosci, vol.34, pp.5552-5563, 2014.

R. B. Tootell, D. Tsao, and W. Vanduffel, Neuroimaging weighs in: humans meet macaques in "primate" visual cortex, J. Neurosci, vol.23, pp.3981-3989, 2003.

W. Bains, Orthographic processing in baboons, Science, vol.337, pp.1173-1173, 2012.

T. Hannagan, J. C. Ziegler, S. Dufau, J. Fagot, and J. Grainger, Deep learning of orthographic representations in baboons, PLoS ONE, vol.9, p.84843, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01152174

S. Dehaene, Why do children make mirror errors in reading? Neural correlates of mirror invariance in the visual word form area, NeuroImage, vol.49, pp.1837-1848, 2010.

A. J. Kersey and J. F. Cantlon, Neural tuning to numerosity relates to perceptual tuning in 3-6-year-old children, J. Neurosci, vol.37, pp.512-522, 2017.

P. Viswanathan and A. Nieder, Neuronal correlates of a visual "sense of number" in primate parietal and prefrontal cortices, Proc. Natl Acad. Sci. USA, vol.110, pp.11187-11192, 2013.

E. F. Kutter, J. Bostroem, C. E. Elger, F. Mormann, and A. Nieder, Single neurons in the human brain encode numbers, Neuron, vol.100, pp.753-761, 2018.

K. Nasr, P. Viswanathan, and A. Nieder, Number detectors spontaneously emerge in a deep neural network designed for visual object recognition, Sci. Adv, vol.5, p.7903, 2019.

A. W. Roe, S. L. Pallas, J. Hahm, and M. Sur, A map of visual space induced in primary auditory cortex, Science, vol.250, pp.818-820, 1990.

D. L. Yamins, Performance-optimized hierarchical models predict neural responses in higher visual cortex, Proc. Natl Acad. Sci. USA, vol.111, pp.8619-8624, 2014.

S. Khaligh-razavi and N. Kriegeskorte, Deep supervised, but not unsupervised, models may explain IT cortical representation, PLoS computational Biol, vol.10, p.1003915, 2014.

K. Srihasam, J. B. Mandeville, I. A. Morocz, K. J. Sullivan, and M. S. Livingstone, Behavioral and anatomical consequences of early versus late symbol training in Macaques, Neuron, vol.73, pp.608-619, 2012.

N. A. Macmillan, Signal detection theory as data analysis method and psychological decision model, A handbook for data analysis in the behavioral sciences: Methodological issues, pp.21-57, 1993.

K. O. Johnson, S. S. Hsiao, and T. Yoshioka, Neural coding and the basic law of psychophysics, Neuroscientist, vol.8, pp.111-121, 2002.

J. J. Dicarlo and K. O. Johnson, Velocity invariance of receptive field structure in somatosensory cortical area 3b of the alert monkey, J. Neurosci, vol.19, pp.401-419, 1999.

P. A. Moran, Notes on continuous stochastic phenomena, Biometrika, vol.37, pp.17-23, 1950.

W. E. Vinje and J. L. Gallant, Sparse coding and decorrelation in primary visual cortex during natural vision, Science, vol.287, pp.1273-1276, 2000.