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Models of categorization

In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press. pp. 267--301 (2008)

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  1. Models and Mechanisms in Psychological Explanation.Daniel A. Weiskopf - 2011 - Synthese 183 (3):313-338.
    Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of techniques for abstracting (...)
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  • Evolving Perceptual Categories.Cailin O’Connor - 2014 - Philosophy of Science 81 (5):110-121.
    This article uses sim-max games to model perceptual categorization with the goal of answering the following question: To what degree should we expect the perceptual categories of biological actors to track properties of the world around them? I argue that an analysis of these games suggests that the relationship between real-world structure and evolved perceptual categories is mediated by successful action in the sense that organisms evolve to categorize together states of nature for which similar actions lead to similar results. (...)
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  • Bargaining Over a Common Categorisation.Marco LiCalzi & Nadia Maagli - 2016 - Synthese 193 (3):705-723.
    Two agents endowed with different categorisations engage in bargaining to reach an understanding and agree on a common categorisation. We model the process as a simple non-cooperative game and demonstrate three results. When the initial disagreement is focused, the bargaining process has a zero-sum structure. When the disagreement is widespread, the zero-sum structure disappears and the unique equilibrium requires a retraction of consensus: two agents who individually associate a region with the same category end up rebranding it under a different (...)
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  • Naïve and Robust: Class‐Conditional Independence in Human Classification Learning.Jana B. Jarecki, Björn Meder & Jonathan D. Nelson - 2018 - Cognitive Science 42 (1):4-42.
    Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence holds, (...)
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  • Optimization and Quantization in Gradient Symbol Systems: A Framework for Integrating the Continuous and the Discrete in Cognition.Paul Smolensky, Matthew Goldrick & Donald Mathis - 2014 - Cognitive Science 38 (6):1102-1138.
    Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, (...)
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  • Inference in the Wild: A Framework for Human Situation Assessment and a Case Study of Air Combat.Ken McAnally, Catherine Davey, Daniel White, Murray Stimson, Steven Mascaro & Kevin Korb - 2018 - Cognitive Science 42 (7):2181-2204.
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