David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
Learn more about PhilPapers
Behavioral and Brain Sciences 24 (4):629-640 (2001)
Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models. Key Words: additive clustering; Bayesian inference; categorization; concept learning; contrast model; features; generalization; psychological space; similarity.
|Keywords||additive clustering Bayesian inference categorization concept learning contrast model features generalization psychological space similarity|
|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
Thomas L. Griffiths, Nick Chater, Charles Kemp, Amy Perfors & Joshua B. Tenenbaum (2010). Probabilistic Models of Cognition: Exploring Representations and Inductive Biases. Trends in Cognitive Sciences 14 (8):357-364.
Matt Jones & Bradley C. Love (2011). Bayesian Fundamentalism or Enlightenment? On the Explanatory Status and Theoretical Contributions of Bayesian Models of Cognition. Behavioral and Brain Sciences 34 (4):169-188.
Joshua B. Tenenbaum, Thomas L. Griffiths & Charles Kemp (2006). Theory-Based Bayesian Models of Inductive Learning and Reasoning. Trends in Cognitive Sciences 10 (7):309-318.
Amy Perfors, Joshua B. Tenenbaum & Terry Regier (2011). The Learnability of Abstract Syntactic Principles. Cognition 118 (3):306-338.
Amy Perfors, Joshua B. Tenenbaum, Thomas L. Griffiths & Fei Xu (2011). A Tutorial Introduction to Bayesian Models of Cognitive Development. Cognition 120 (3):302-321.
Similar books and articles
Shaughan Lavine (1992). A Spector-Gandy Theorem for cPCd(A) Classes. Journal of Symbolic Logic 57 (2):478 - 500.
Shimon Edelman (1998). Representation is Representation of Similarities. Behavioral and Brain Sciences 21 (4):449-467.
Michael D. Lee (2001). Extending Bayesian Concept Learning to Deal with Representational Complexity and Adaptation. Behavioral and Brain Sciences 24 (4):685-686.
Roger N. Shepard (2001). Perceptual-Cognitive Universals as Reflections of the World. Behavioral and Brain Sciences 24 (4):581-601.
Nick Chater, Paul M. B. Vitányi & Neil Stewart (2001). Universal Generalization and Universal Inter-Item Confusability. Behavioral and Brain Sciences 24 (4):659-660.
David Dowe & Graham Oppy (2001). Universal Bayesian Inference? Behavioral and Brain Sciences 24 (4):662-663.
Miklós Rédei (1992). When Can Non‐Commutative Statistical Inference Be Bayesian? International Studies in the Philosophy of Science 6 (2):129-132.
Miklós Rédei (1992). When Can Non-Commutative Statistical Inference Be Bayesian? International Studies in the Philosophy of Science 6 (2):129 – 132.
Emmanuel M. Pothos (2001). Context Effects Equally Applicable in Generalization and Similarity. Behavioral and Brain Sciences 24 (4):699-700.
Dedre Gentner (2001). Exhuming Similarity. Behavioral and Brain Sciences 24 (4):669-669.
Added to index2009-01-28
Total downloads25 ( #168,090 of 1,938,823 )
Recent downloads (6 months)10 ( #55,633 of 1,938,823 )
How can I increase my downloads?