association and semantic comparison are more productively viewed as task-driven operations on the same vector-based cognitive representation (cf.[1]). That the same underlying structure can be accessed differently according to different task demands is intuitive and matches observed behavior. Colunga and Smith[2] note that when children are asked to group a carrot, tomato, and rabbit, children will group rabbits and carrots together. However if children are told the carrot is a dax and are asked to find another dax, children will get the tomato. In the same way, comparison tasks evoke category structure and holistic judgments, whereas raw association as evoked by grouping or production tasks may be based on a single strong point of association, e.g., rabbit–carrot.
8. Conclusions
In summary, the internal cognitive-linguistic processes engaged in constructing Wikipedia has created
a cognitive-linguistic environment that can be exploited by computational cognitive models. The
crowd-sourcing process of creating, merging, and deleting article pages establishes a common view
of shared concepts and topics for discussion. The words used within each page are a collaborative
minimal summary of that concept, and the links between pages represent relevant associations between
concepts. Wikipedia is perhaps unique in that it provides moderately clean structural relationships in
natural language. As a product of the human mind, Wikipedia reflects aspects of human semantic
memory in its structure. Our W3C3 model capitalizes on the cognitive-linguistic structure at different
resolutions just as theories of memory purport access at different resolutions: COALS represents words
in word contexts, ESA represents words in concepts, and WLM represents the links between concepts.
The work we have presented in these studies suggests that these three resolutions may contribute to a
more complete model of semantic association. Thus in creating Wikipedia to describe the world, we
have created a resource that may reveal the subtleties of the human mind.
Acknowledgements
The authors would like to thank Doug Rhode for his remarks on deriving the Pearson correlation of
binary variables and the anonymous reviewers for their constructive feedback. The research reported
here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant
R305A080594 and by the National Science Foundation, through Grant BCS0826825, to the University
of Memphis. The opinions expressed are those of the authors and do not represent views of the Institute
or the U.S. Department of Education or the National Science Foundation.
© 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article
distributed under the terms and conditions of the Creative Commons Attribution license
(http://creativecommons.org/licenses/by/3.0/).
References
- ↑ Maki, W.; Buchanan, E. Latent structure in measures of associative, semantic, and thematic knowledge. Psychon. Bull. Rev. 2008, 15, 598–603.
- ↑ Colunga, E.; Smith, L.B. The emergence of abstract ideas: Evidence from networks and babies. Philos. Trans. R. Soc. Lond. Series B Biol. Sci. 2003, 358, 1205–1214.
