David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Simulations of cognitive functions, like learning to speak, or discovering syntactical structures in language, are the basis for many claims about human capacities in language acquisition. This raises the question whether results obtained from experiments that are essentially performed on data structures are equivalent to results from "real" experiments. This paper examines some design methodologies for models of cognitive functions using artificial neural nets. The process of conducting the cognitive simulations is largely a projection of theories, or even unsubstantiated conjectures, onto simulated neural structures and an interpretation of the experimental results in terms of the human brain. The problem with this process is that results from virtual experiments are taken to refer unambiguously to the human brain; and the more the language of human cognitive function is deployed in both theory construction and (virtual) experimental interpretation, the more convincing the impression. Additionally, the complexity of the methodologies, principles, and visualization techniques, in the implementation of the computational model, masks the lack of actual similarities between model and real world phenomena. Some computational models involving artificial neural nets have had some success, even commercially, but there are indications that the results from virtual experiments have little value in explaining cognitive functions. The problem seems to be in relating computational, or mathematical, entities to real world objects, like neurons and brains. I argue that the role of Artificial Intelligence as a contributor to the knowledge base of Cognitive Science is diminished as a consequence.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Morton Wagman (1991). Cognitive Science and Concepts of Mind Toward a General Theory of Human and Artificial Intelligence. New York: Praeger.
Marco Mirolli & Domenico Parisi (2009). Language as a Cognitive Tool. Minds and Machines 19 (4):517-528.
David J. Buller (1993). Confirmation and the Computational Paradigm, or, Why Do You Think They Call It Artificial Intelligence? [REVIEW] Minds and Machines 3 (2):155-81.
Paul Thagard (1986). Computational Models in the Philosophy of Science. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986:329 - 335.
Marco Ernandes (2005). Artificial Intelligence & Games: Should Computational Psychology Be Revalued? Topoi 24 (2):229-242.
Peter Krebs (2007). Virtual Models and Simulations. Techne 11 (1):42-54.
Mary S. Morgan (2005). Experiments Versus Models: New Phenomena, Inference and Surprise. Journal of Economic Methodology 12 (2):317-329.
Gordana Dodig-Crnkovic (2013). Cognitive Revolution, Virtuality and Good Life. AI and Society 28 (3):319-327.
Added to index2009-01-28
Total downloads10 ( #235,035 of 1,726,249 )
Recent downloads (6 months)6 ( #118,705 of 1,726,249 )
How can I increase my downloads?