Perspectives on Science 16 (2):pp. 121-136 (2008)
Ever since 1956 when details of the Logic Theorist were published by Newell and Simon, a large literature has accumulated on computational models and theories of the creative process, especially in science, invention and design. But what exactly do these computational models/theories tell us about the way that humans have actually conducted acts of creation in the past? What light has computation shed on our understanding of the creative process? Addressing these questions, we put forth three propositions: (I) Computational models of the creative process are fundamentally flawed as theories of human creativity. Rather, the universal power of computational models lies elsewhere: (II) Computational models of particular acts of creation can serve as effective experiments to test universal hypotheses about creative processes and mechanisms; and (III) Computation-based architectures of the creative mind provide metaphorical frameworks that, like all good metaphors, can serve as rich sources of insight into aspects of the creative process. In this paper, we provide evidence for these three propositions by drawing upon some particular episodes in the cognitive history of science, technology, and art.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
References found in this work BETA
The Processes of Scientific Discovery: The Strategy of Experimentation.Deepak Kulkarni & Herbert A. Simon - 1988 - Cognitive Science 12 (2):139-175.
Laboratory Replication of Scientific Discovery Processes.Yulin Qin & Herbert A. Simon - 1990 - Cognitive Science 14 (2):281-312.
Multidisciplinary Creativity: The Case of Herbert A. Simon.Subrata Dasgupta - 2003 - Cognitive Science 27 (5):683-707.
The Logic Theory Machine. A Complex Information Processing System.Allen Newell & Herbert A. Simon - 1957 - Journal of Symbolic Logic 22 (3):331-332.
Citations of this work BETA
Beyond Simon's Means-Ends Analysis: Natural Creativity and the Unanswered 'Why' in the Design of Intelligent Systems for Problem-Solving. [REVIEW]Dongming Xu - 2010 - Minds and Machines 20 (3):327-347.
Similar books and articles
Story Planning: Creativity Through Exploration, Retrieval, and Analogical Transformation. [REVIEW]Mark Riedl - 2010 - Minds and Machines 20 (4):589-614.
Symposium on “Cognition and Rationality: Part I” The Rationality of Scientific Discovery: Abductive Reasoning and Epistemic Mediators. [REVIEW]Lorenzo Magnani - 2006 - Mind and Society 5 (2):213-228.
Confirmation and the Computational Paradigm, or, Why Do You Think They Call It Artificial Intelligence? [REVIEW]David J. Buller - 1993 - Minds and Machines 3 (2):155-81.
Criteria for the Design and Evaluation of Cognitive Architectures.Sashank Varma - 2011 - Cognitive Science 35 (7):1329-1351.
The Cambridge Handbook of Computational Psychology.Ron Sun (ed.) - 2008 - Cambridge University Press.
Explanation and Description in Computational Neuroscience.David Michael Kaplan - 2011 - Synthese 183 (3):339-373.
Computational Models in the Philosophy of Science.Paul Thagard - 1986 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986:329 - 335.
Added to index2009-01-28
Total downloads48 ( #108,492 of 2,168,148 )
Recent downloads (6 months)4 ( #82,720 of 2,168,148 )
How can I increase my downloads?