Amount of information and intralist similarity in paired-associates learning.

To assess the effect of motivation and list characteristics on verbal learning performance, 60 Ss in a 3 × 2 factorial design learned paired associates consisting of CVC as stimuli and digits as responses, in lists of high or low formal intralist similarity and under high, low, or control Ego-involvement (E-I) conditions. No significant differences in errors were found as a function of ego involvement. The increase of errors with high formal intralist similarity was specific to the effect of stimulus generalization and did not represent an over-all increase in list difficulty: no significant differences were found between lists for non-generalization errors but significant list differences were found for stimulus-generalization intrusions ( p < .01).

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Statistical analysis of classification

Symposium - International Astronomical Union ◽

10.1017/s0074180900053420 ◽

1966 ◽

Vol 24 ◽

pp. 188-189

Author(s):

T. J. Deeming

Keyword(s):

Multivariate Analysis ◽

Statistical Analysis ◽

Classification Scheme ◽

Analytical Procedure ◽

Narrow Band ◽

Scientific Research ◽

Maximum Amount ◽

Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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Stain contamination and embedding in electron microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100141986 ◽

1986 ◽

Vol 44 ◽

pp. 50-53

Author(s):

Hilton H. Mollenhauer

Keyword(s):

Electron Microscopy ◽

Information Retrieval ◽

Epoxy Resins ◽

Image Contrast ◽

Sample Preservation ◽

Factors Associated ◽

Amount Of Information ◽

Electron Microscopical ◽

Contrast Information

Many factors (e.g., resolution of microscope, type of tissue, and preparation of sample) affect electron microscopical images and alter the amount of information that can be retrieved from a specimen. Of interest in this report are those factors associated with the evaluation of epoxy embedded tissues. In this context, informational retrieval is dependant, in part, on the ability to “see” sample detail (e.g., contrast) and, in part, on tue quality of sample preservation. Two aspects of this problem will be discussed: 1) epoxy resins and their effect on image contrast, information retrieval, and sample preservation; and 2) the interaction between some stains commonly used for enhancing contrast and information retrieval.

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