An approximation method for obtaining a maximized multiple criterion

A new approximation method for obtaining a maximized multiple criterion, based on the formula of Edgerton and Kolbe (2), is presented. By applications to examples from the literature (1, 2), the new method is evaluated in comparison with the Horst approximation (1), a suggested revision of the Horst procedure, and the more exact but more laborious iterative method for the principal axis solution of Hotelling.     

An efficient method for dichotic tape preparation

The paper describes a simple but efficient method for dichotic tape preparation. The system of instrumentation together with checks for synchrony of stimuli and interstimulus intervals is explained. Methodological issues of onset synchrony and lag effect are discussed.     

An efficient method for estimating semantic similarity based on feature overlap: Reliability and validity of semantic feature ratings

Two studies were conducted in which human participants rated pairs of words according to the perceived degree to which the words’ referents shared semantic features. The participants found the task intuitive, simple, and quick to complete. The ratings were reliable and valid. Interrater and interstudy correlations were high, and ratings were good predictors of known feature overlap values obtained from existing semantic feature norms.     

A method for obtaining an ordered metric scale

A method is presented for collecting data which will yield a scale on which the entities are ranked in preference (ordinality), the distances between the entities on the scale are ranked (ordered metric), and all combinations of the distances are ranked (higher-ordered metric). The sources drawn upon are von Neumann and Morgenstern (9), and lattice theory. An empirical example is given in which a higher-ordered metric scale is derived.I am grateful to Professor William L. Lepley (Department of Psychology) and Professor Jack R. Tessman (Department of Physics) for their critical reading of this paper. Paul Hurst and Robert Radlow participated in many discussions on the form of measurement discussed in this paper, and assisted in collecting data. I am also grateful to Professor T. C. Benton (Department of Mathematics) for certain source materials.     

An accurate and efficient multi-category edge detection method

Edge detection plays an important role in image processing. With the development of deep learning, the accuracy of edge detection has been greatly improved, and people have more requirements for edge detection tasks. Most edge detection algorithms are binary edge detection methods, but there are usually multiple categories of edges in an image. In this paper, we present an accurate multi-category edge detection network, the richer category-aware semantic edge detection network (R-CASENet). In order to make full use of convolutional neural network’s powerful feature expression capabilities, we attempt to use more information from feature maps for edge feature extraction and classification. Using the ResNet101 network as the backbone, firstly we merge the building blocks in different composite blocks and down-sample to obtain the feature maps. Then we fuse the feature maps in different composite blocks to obtain the final fused classifier. Experimental results show that R-CASENet can achieve state-of-the-art performance on the large SBD dataset. Furthermore, to get precise one-pixel width edges, we also propose an edge refinement network (ERN) structure. The proposed scheme is an end-to-end method and the proposed ERN can reduce redundant points and improve computational efficiency, especially for further image processing.     

A method for obtaining psychophysical estimates of movement costs

Although researchers generally accept the proposition that movement costs are taken into account in the planning of voluntary movements, there is no established psychophysical method for estimating such costs. The authors tested and introduce a possible method. Participants were given every possible pair of tasks from a set of tasks that varied along one or more dimensions. In each trial, they indicated which task was easier to perform. The authors used participants' probability of preferring any given task over others to estimate the judged relative cost (JRC) of that task, and they used the JRCs of all the tasks to quantify the relation between task preference and task properties. The method was applied in 3 experiments in which university students (Experiment 1, N = 20; Experiment 2, N = 40; Experiment 3, N = 20) chose which spatial target to move to in carrying an object from one position to another. The data were consistent with the hypothesis that the JRC of moving an object increases with the degree to which the object must be translated and rotated. The simplicity of the method encourages its application to a wide range of questions about motor behavior.     

An efficient algorithm for TUCKALS3 on data with large numbers of observation units

A modification of the TUCKALS3 algorithm is proposed that handles three-way arrays of order I × J × K for any I. When I is much larger than JK, the modified algorithm needs less work space to store the data during the iterative part of the algorithm than does the original algorithm. Because of this and the additional feature that execution speed is higher, the modified algorithm is highly suitable for use on personal computers. This research has been made possible by a fellowship from the Royal Netherlands Academy of Arts and Sciences to the first author.     

An efficient algorithm for PARAFAC of three-way data with large numbers of observation units

The CANDECOMP algorithm for the PARAFAC analysis ofn×m×p three-way arrays is adapted to handle arrays in whichn>mp more efficiently. For such arrays, the adapted algorithm needs less memory space to store the data during the iterations, and uses less computation time than the original CANDECOMP algorithm. The size of the arrays that can be handled by the new algorithm is in no way limited by the number of observation units (n) in the data.The authors are obliged to Jos ten Berge for his comments on an earlier version of this paper. The research of Henk A. L. Kiers has been made possible by a fellowship of the Royal Netherlands Academy of Arts and Sciences.     

A method for the study of interstimulus similarity

A generalized method for collecting data on interstimulus similarity is presented and its special cases evaluated by use of information theory. A method of analyzing the data by the Unfolding Technique is presented which permits the study of the latent attribute structure underlying the similarity of stimuli for a single individual. This study was carried out under Office of Naval Research Contract Nonr 374 (00) NR 041-011.     

Comparative scaling of unidimensional discrimination and similarity data

Scales constructed from paired-comparison designs under Thurstone's Case V model are discussed in relation to those derived from similarity data by means of the Unilateral Law of Comparative Judgment [3]. Factors inherent in the triadic similarity task are then considered with respect to scale invariance across experimental designs. Illustrative data, although revealing the influence of these factors upon similarity response consistency, indicate the similarity scale to be somewhat robust to their biasing effects.This research was supported in part by National Institute of Mental Health Grant MH-04439-05.     

An efficient algorithm for joint correspondence analysis

Joint correspondence analysis is a technique for constructing reduced-dimensional representations of pairwise relationships among categorical variables. The technique was proposed by Greenacre as an alternative to multiple correspondence analysis. Joint correspondence analysis differs from multiple correspondence analysis in that it focuses solely on between-variable relationships. Greenacre described one alternating least-squares algorithm for conducting joint correspondence analysis. Another alternating least-squares algorithm is described in this article. The algorithm is guaranteed to converge, and does so in fewer iterations than does the algorithm proposed by Greenacre. A modification of the algorithm for handling Heywood cases is described. The algorithm is illustrated on two data sets.     

A tabular method of obtaining tetrachoricr with median-cut variables

A method is presented that enables the immediate determination of tetrachoricr from a table if the proportion in the plus-plus cell for median-cut variables is known.     

Methods for obtaining menstrual-cycle data in menstrual-synchrony studies: commentary on Schank (2001)

J. C. Schank (2001) claimed to identify 2 categories of error-measurement error and recall biases-in the methodology used in studies of menstrual synchrony. This commentary focuses on the 2nd of these, recall biases, and other errors that he argued may result from "allowing participants to fill out menstrual-cycle-onset calendars" (p. 3). J. C. Schank's detailed discussion of various types of recall biases is inappropriate and misleading given that the majority of menstrual-synchrony studies have used prospective menstrual calendars. The use of menstrual calendars is a well-accepted method in a number of research areas and demands little of participants. J. C. Schank questioned the validity of menstrual calendar data on the basis of assumptions about recall bias for which he had no evidence.