High-level perception, representation, and analogy:A critique of artificial intelligence methodology
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Journal of Experimental and Theoretical Artificial Intellige 4 (3):185 - 211 (1992)
High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models--”notably BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought--”and argue that these are flawed pre- cisely because they downplay the role of high-level perception. Further, we argue that perceptu- al processes cannot be separated from other cognitive processes even in principle, and therefore that traditional artificial-intelligence models cannot be defended by supposing the existence of a --œrepresentation module--� that supplies representations ready-made. Finally, we describe a model of high-level perception and analogical thought in which perceptual processing is integrated with analogical mapping, leading to the flexible build-up of representations appropriate to a given context.
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Peggy DesAutels (2012). Moral Perception and Responsiveness. Journal of Social Philosophy 43 (3):334-346.
Robert M. French (2002). The Computational Modeling of Analogy-Making. Trends in Cognitive Sciences 6 (5):200-205.
Daniel Corral & Matt Jones (2014). The Effects of Relational Structure on Analogical Learning. Cognition 132 (3):280-300.
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