Cognitive Science 36 (4):575-606 (2012)

Abstract
It has been recently argued that some machine learning techniques known as Kernel methods could be relevant for capturing cognitive and neural mechanisms (Jäkel, Schölkopf, & Wichmann, 2009). We point out that ‘‘String kernels,’’ initially designed for protein function prediction and spam detection, are virtually identical to one contending proposal for how the brain encodes orthographic information during reading. We suggest some reasons for this connection and we derive new ideas for visual word recognition that are successfully put to the test. We argue that the versatility and performance of String kernels makes a compelling case for their implementation in the brain
Keywords Orthographic coding  Visual Word Form Area  String kernels  Open‐bigrams  Visual word recognition
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DOI 10.1111/j.1551-6709.2012.01236.x
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References found in this work BETA

The Neural Code for Written Words: A Proposal.S. Dehaene, L. Cohen, M. SigMan & F. Vinckier - 2005 - Trends in Cognitive Sciences 9 (7):335-341.

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A Vision of Reading.Jonathan Grainger, Stéphane Dufau & Johannes C. Ziegler - 2016 - Trends in Cognitive Sciences 20 (3):171-179.

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