A method for determining the weight of objective indoor environment and subjective response based on information theory

2021 ◽  
pp. 108426
Author(s):  
Zhongchen Zhang ◽  
Yang Geng ◽  
Xiaoying Wu ◽  
Hao Zhou ◽  
Borong Lin
Keyword(s):  
Information Theory ◽  
Indoor Environment ◽  
Subjective Response

scholarly journals Optimization of Indoor Environment Quality for Hypermarket Workers: From Subjective Response to Objective Design Criteria

2016 ◽  
Vol 101 ◽  
pp. 272-279 ◽  
Author(s):  
Francesco Martellotta ◽  
Alessandro Cannavale ◽  
Michele D’Alba ◽  
Sabrina Della Crociata ◽  
Antonio Simone
Keyword(s):  
Indoor Environment ◽  
Design Criteria ◽  
Subjective Response ◽  
Environment Quality ◽  
Indoor Environment Quality

scholarly journals The Relationship Between Language and the Environment

2009 ◽  
Vol 20 (9) ◽  
pp. 1100-1107 ◽  
Author(s):  
Roland Baddeley ◽  
David Attewell
Keyword(s):  
Information Theory ◽  
Indoor Environment ◽  
Natural World ◽  
Optimal Number ◽  
Rural Environment ◽  
Surface Reflectance ◽  
Fresh Snow ◽  
The Relationship ◽  
Over Time

The surface reflectance of objects is highly variable, ranging between 4% for, say, charcoal and 90% for fresh snow. When stimuli are presented simultaneously, people can discriminate hundreds of levels of visual intensity. Despite this, human languages possess a maximum of just three basic terms for describing lightness. In English, these are white (or light), black (or dark), and gray. Why should this be? Using information theory, combined with estimates of the distribution of reflectances in the natural world and the reliability of lightness recall over time, we show that three lightness terms is the optimal number for describing surface reflectance properties in a modern urban or indoor environment. We also show that only two lightness terms would be required in a forest or rural environment.

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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