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Computing Machinery and Understanding
Author(s) -
Ramscar Michael
Publication year - 2010
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.1551-6709.2010.01120.x
Subject(s) - metaphor , analogy , symbol (formal) , computer science , cognitive science , turing , turing machine , artificial intelligence , natural (archaeology) , cognition , theoretical computer science , natural computing , computation , turing test , epistemology , algorithm , psychology , programming language , philosophy , linguistics , archaeology , neuroscience , history
How are natural symbol systems best understood? Traditional “symbolic” approaches seek to understand cognition by analogy to highly structured, prescriptive computer programs. Here, we describe some problems the traditional computational metaphor inevitably leads to, and a very different approach to computation (Ramscar, Yarlett, Dye, Denny, & Thorpe, 2010; Turing, 1950) that allows these problems to be avoided. The way we conceive of natural symbol systems depends to a large degree on the computational metaphors we use to understand them, and machine learning suggests an understanding of symbolic thought that is very different to traditional views (Hummel, 2010). The empirical question then is: Which metaphor is best?