Computing Machinery and Understanding

Cognitive Science 34 (6):966-971 (2010)
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 Symbolic models  Natural language  Computational metaphors  Prediction
Categories (categorize this paper)
DOI 10.1111/j.1551-6709.2010.01120.x
Options
 Save to my reading list
Follow the author(s)
My bibliography
Export citation
Find it on Scholar
Edit this record
Mark as duplicate
Revision history
Request removal from index
Download options
Our Archive


Upload a copy of this paper     Check publisher's policy     Papers currently archived: 28,208
Through your library
References found in this work BETA
Expectation-Based Syntactic Comprehension.Roger Levy - 2008 - Cognition 106 (3):1126-1177.
Computing Machinery and Intelligence.Alan M. Turing - 1950 - Mind 59 (October):433-60.

View all 10 references / Add more references

Citations of this work BETA

No citations found.

Add more citations

Similar books and articles

Monthly downloads

Added to index

2010-08-16

Total downloads

27 ( #190,306 of 2,172,871 )

Recent downloads (6 months)

1 ( #324,901 of 2,172,871 )

How can I increase my downloads?

My notes
Sign in to use this feature


Discussion
Order:
There  are no threads in this forum
Nothing in this forum yet.

Other forums