Computing Machinery and Understanding
- 3 August 2010
- journal article
- letter
- Published by Wiley in Cognitive Science
- Vol. 34 (6), 966-971
- https://doi.org/10.1111/j.1551-6709.2010.01120.x
Abstract
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?.Keywords
This publication has 14 references indexed in Scilit:
- Reading TE leaves: New approaches to the identification of transposable element insertionsGenome Research, 2011
- Redundancy and reduction: Speakers manage syntactic information densityCognitive Psychology, 2010
- Expectation-based syntactic comprehensionCognition, 2008
- A Rational Analysis of Rule‐Based Concept LearningCognitive Science, 2008
- A theory of the discovery and predication of relational concepts.Psychological Review, 2008
- The problem with using associations to carry binding informationBehavioral and Brain Sciences, 2006
- A symbolic-connectionist theory of relational inference and generalization.Psychological Review, 2003
- Distributed representations of structure: A theory of analogical access and mapping.Psychological Review, 1997
- A Brief History of TimePhysics Today, 1988
- I.—COMPUTING MACHINERY AND INTELLIGENCEMind, 1950