Open AccessPosted Content
The Generalized Universal Law of Generalization
Nick Chater,Paul M. B. Vitányi +1 more
TLDR
In this paper, the authors show that the universal law of generalization holds with probability going to one-provided the confusion probabilities are computable, and they also give a mathematically more appealing form.Abstract:
It has been argued by Shepard that there is a robust psychological law that relates the distance between a pair of items in psychological space and the probability that they will be confused with each other. Specifically, the probability of confusion is a negative exponential function of the distance between the pair of items. In experimental contexts, distance is typically defined in terms of a multidimensional Euclidean space-but this assumption seems unlikely to hold for complex stimuli. We show that, nonetheless, the Universal Law of Generalization can be derived in the more complex setting of arbitrary stimuli, using a much more universal measure of distance. This universal distance is defined as the length of the shortest program that transforms the representations of the two items of interest into one another: the algorithmic information distance. It is universal in the sense that it minorizes every computable distance: it is the smallest computable distance. We show that the universal law of generalization holds with probability going to one-provided the confusion probabilities are computable. We also give a mathematically more appealing formread more
Citations
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Searching for image information content, its discovery, extraction, and representation
TL;DR: A technique for creating image information content descriptors as a set of descriptions of image data structures is developed and its algorithm is presented and elucidated with some examples, which demonstrate the effectiveness of the proposed approach.
Emerging Ideas About Categories
TL;DR: In this view, categories are enduringly real, object-like, truly out there in the world and also in our heads as mentioned in this paper, which is the traditional metaphor that views categories as discrete, bounded things that are stable over time and context.
Journal ArticleDOI
The Thrill Is Gone, but You Might Not Know: Habituation and Generalization of Biophysiological and Self-reported Arousal Responses to Video Games
Matthew Grizzard,Ron Tamborini,John L. Sherry,René Weber,Sujay Prabhu,Lindsay Hahn,Patrick Idzik +6 more
TL;DR: The authors found that repeated play leads to habituation in both biophysiological and self-report responses, and evidence of generalization was more apparent in the Biophysiological data than self-reported responses.
Posted Content
Set-based Complexity and Biological Information
TL;DR: A class of measures to quantify the contextual nature of the information in sets of objects, based on Kolmogorov's intrinsic complexity are proposed, which discount both random and redundant information and are inherent in that they do not require a defined state space to quantify the information.
Proceedings Article
Similarity as tractable transformation
TL;DR: Several different ways in which the informal theory of transformational similarity can be understood are investigated, providing a formalization for each possible reading, and the computational (in)tractability of each formalization is studied for a variety of parameter settings.
References
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Book
Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Book
The Mathematical Theory of Communication
TL;DR: The Mathematical Theory of Communication (MTOC) as discussed by the authors was originally published as a paper on communication theory more than fifty years ago and has since gone through four hardcover and sixteen paperback printings.
Journal ArticleDOI
On Computable Numbers, with an Application to the Entscheidungsproblem
TL;DR: This chapter discusses the application of the diagonal process of the universal computing machine, which automates the calculation of circle and circle-free numbers.