Emergence of a compositional neural code for written words: Recycling of a convolutional neural network for reading.

Autor:	Hannagan T; Cognitive Neuroimaging Unit, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, INSERM, Université Paris-Saclay, NeuroSpin, Gif-Sur-Yvette 91191, France.; Collège de France, Université Paris Sciences Lettres 75005 Paris, France., Agrawal A; Cognitive Neuroimaging Unit, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, INSERM, Université Paris-Saclay, NeuroSpin, Gif-Sur-Yvette 91191, France.; Collège de France, Université Paris Sciences Lettres 75005 Paris, France., Cohen L; Sorbonne Université, INSERM U1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinièr, Hôpital de la Pitié-Salpêtrière, Paris 75013, France.; Assistance Publique-Hôpitaux de Paris, Hôpital de la Pitié Salpêtrière, Fédération de Neurologie, Paris F-75013, France., Dehaene S; Cognitive Neuroimaging Unit, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, INSERM, Université Paris-Saclay, NeuroSpin, Gif-Sur-Yvette 91191, France; stanislas.dehaene@cea.fr.; Collège de France, Université Paris Sciences Lettres 75005 Paris, France.
Jazyk:	angličtina
Zdroj:	Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2021 Nov 16; Vol. 118 (46).
DOI:	10.1073/pnas.2104779118
Abstrakt:	The visual word form area (VWFA) is a region of human inferotemporal cortex that emerges at a fixed location in the occipitotemporal cortex during reading acquisition and systematically responds to written words in literate individuals. According to the neuronal recycling hypothesis, this region arises through the repurposing, for letter recognition, of a subpart of the ventral visual pathway initially involved in face and object recognition. Furthermore, according to the biased connectivity hypothesis, its reproducible localization is due to preexisting connections from this subregion to areas involved in spoken-language processing. Here, we evaluate those hypotheses in an explicit computational model. We trained a deep convolutional neural network of the ventral visual pathway, first to categorize pictures and then to recognize written words invariantly for case, font, and size. We show that the model can account for many properties of the VWFA, particularly when a subset of units possesses a biased connectivity to word output units. The network develops a sparse, invariant representation of written words, based on a restricted set of reading-selective units. Their activation mimics several properties of the VWFA, and their lesioning causes a reading-specific deficit. The model predicts that, in literate brains, written words are encoded by a compositional neural code with neurons tuned either to individual letters and their ordinal position relative to word start or word ending or to pairs of letters (bigrams). Competing Interests: The authors declare no competing interest.
Databáze:	MEDLINE
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