* [Generalization, similarity, and Bayesian inference](https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/generalization-similarity-and-bayesian-inference/595CAA321C9C56270C624057021DE77A) - ***Behavioral and Brain Sciences***, 2001. [[All Versions](https://scholar.google.com/scholar?cluster=14074987155133342565)]. [[Preprint](http://web.mit.edu/cocosci/archive/Papers/tenenbaum_griffiths01.pdf)]. 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. The authors 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. This 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.
0 commit comments