Adding versus averaging as a stimulus-combination rule in forming impressions of groups.

1968 ◽  
Vol 10 (4) ◽  
pp. 363-369 ◽  
Author(s):  
Ralph L. Rosnow ◽  
Robert L. Arms
Keyword(s):  
Combination Rule ◽  
Stimulus Combination

Adding and Averaging Effects in Impression Formation as a Function of the Situational Context

1970 ◽  
Vol 31 (1) ◽  
pp. 127-135 ◽  
Author(s):  
Ralph L. Rosnow
Keyword(s):  
Impression Formation ◽  
Economic Status ◽  
Arithmetic Mean ◽  
Direct Relation ◽  
Combination Rule ◽  
Arithmetic Means ◽  
Mean Values ◽  
Stimulus Combination ◽  
The Mean ◽  
Simple Summation

Male and female Ss made comparative judgments of paired sets of simultaneously presented income stimuli. The pairs were constructed so that the sum of the values was higher in one set than in the other, while the mean income was simultaneously higher in the latter set than in the former. When the incomes within a set were represented as all belonging to the same person or when the incomes were attributed to different members of a family, both men and women tended to rate higher in economic status whichever sets of stimuli had the higher sums in direct relation to the manipulated discrepancy between sums. When the same stimuli were attributed to different members of a group, both sexes rated higher in economic status whichever sets had the higher arithmetic mean values in direct relation to the manipulated discrepancy between arithmetic means. The significance of this finding is in demonstrating that the stimulus-combination rule in impression formation is at least partially predicated upon situational determinants and that neither simple summation nor simple averaging is an exclusively valid or invalid combinatory principle.

A Non-Monotone Line Search Combination Technique for Unconstrained Optimization

2014 ◽  
Vol 8 (1) ◽  
pp. 218-221 ◽  
Author(s):  
Ping Hu ◽  
Zong-yao Wang
Keyword(s):  
Global Convergence ◽  
Unconstrained Optimization ◽  
Numerical Experiments ◽  
Line Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
New Combination ◽  
Combination Rule ◽  
Combination Technique ◽  
Unconstrained Optimization Problems

We propose a non-monotone line search combination rule for unconstrained optimization problems, the corresponding non-monotone search algorithm is established and its global convergence can be proved. Finally, we use some numerical experiments to illustrate the new combination of non-monotone search algorithm’s effectiveness.

scholarly journals Analysis of “mixing” combination rules and Smet’s combination rule

2019 ◽  
pp. 4-8
Author(s):  
Sofiia Alpert
Keyword(s):  
Satellite Images ◽  
Probability Distributions ◽  
Evidence Theory ◽  
Combination Rule ◽  
Multiple Sources ◽  
Combination Rules ◽  
Spectral Bands ◽  
Basic Probability ◽  
Shafer Theory ◽  
Theory Of Evidence

The process of solution of different practical and ecological problems, using hyperspectral satellite images usually includes a procedure of classification. Classification is one of the most difficult and important procedures. Some image classification methods were considered and analyzed in this work. These methods are based on the theory of evidence. Evidence theory can simulate uncertainty and process imprecise and incomplete information. It were considered such combination rules in this paper: “mixing” combination rule (or averaging), convolutive x-averaging (or c-averaging) and Smet’s combination rule. It was shown, that these methods can process the data from multiple sources or spectral bands, that provide different assessments for the same hypotheses. It was noted, that the purpose of aggregation of information is to simplify data, whether the data is coming from multiple sources or different spectral bands. It was shown, that Smet’s rule is unnormalized version of Dempster rule, that applied in Smet’s Transferable Belief Model. It also processes imprecise and incomplete data. Smet’s combination rule entails a slightly different formulation of Dempster-Shafer theory. Mixing (or averaging) rule was considered in this paper too. It is the averaging operation that is used for probability distributions. This rule uses basic probability assignments from different sources (spectral bands) and weighs assigned according to the reliability of the sources. Convolutive x-averaging (or c-averaging) rule was considered in this paper too. This combination rule is a generalization of the average for scalar numbers. This rule is commutative and not associative. It also was noted, that convolutive x-averaging (c-averaging) rule can include any number of basic probability assignments. It were also considered examples, where these proposed combination rules were used. Mixing, convolutive x-averaging (c-averaging) rule and Smet’s combination rule can be applied for analysis of hyperspectral satellite images, in remote searching for minerals and oil, solving different environmental and thematic problems.

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