Applications to information theory: the algorithmic approach to the concept of quantity of information

1993 ◽  
pp. 179-183
Author(s):  
Vladimir Uspensky ◽  
Alexei Semenov
Keyword(s):  
Information Theory ◽  
Algorithmic Approach

Network Information Theory

2011 ◽  
Author(s):  
Abbas El Gamal ◽  
Young-Han Kim
Keyword(s):  
Information Theory ◽  
Network Information Theory ◽  
Network Information

Information Theory and Coding by Example

2009 ◽  
Author(s):  
Mark Kelbert ◽  
Yuri Suhov
Keyword(s):  
Information Theory

Constructing and Deconstructing Concepts

2016 ◽  
Vol 63 (5) ◽  
pp. 249-262 ◽  
Author(s):  
Charles A. Doan ◽  
Ronaldo Vigo
Keyword(s):  
Information Theory ◽  
Model Performance ◽  
Unsupervised Classification ◽  
Family Resemblance ◽  
Category Membership ◽  
Formal Models ◽  
Human Beings ◽  
One Dimensional ◽  
Relational Patterns ◽  
Optimal Classification

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.

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