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Protein Analysis Meets Visual Word Recognition: A Case for String Kernels in the Brain
Author(s) -
Hannagan Thomas,
Grainger Jonathan
Publication year - 2012
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/j.1551-6709.2012.01236.x
Subject(s) - string (physics) , computer science , kernel (algebra) , word (group theory) , artificial intelligence , natural language processing , point (geometry) , speech recognition , reading (process) , string kernel , pattern recognition (psychology) , kernel method , linguistics , support vector machine , mathematics , radial basis function kernel , combinatorics , geometry , philosophy , mathematical physics
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.