Eye-movement data: Cumulative fixation time and cluster analysis

This paper describes procedures for the visual presentation and cluster analysis of eye-movement data. Methods for two- and three-dimensional representation of cumulative fixation time (CFT) and ways of enhancing the peaks in CFT distributions are outlined and illustrated by reference to examples from eye-movement studies of cognitive processes. CFT distributions may also be partitioned using thek means clustering technique (MacQueen, 1967), and applications of variants of this technique to eye-movement data are discussed. Cluster analyses such ask means require the user to make initial estimates of the number and value of the means. One classification procedure (Wallace & Boulton, 1968a, 1968b), based on information theory, avoids these initial assumptions. This procedure is applied to a CFT distribution and has its solution compared with that ofk means for the same distribution. Finally, programs that implement these procedures on Macintosh computers are listed and offered on floppy disk.     

Analysis of meteorological conditions in Spain by means of clustering techniques

A comprehensive analysis of clustering techniques is presented in this paper through their application to data on meteorological conditions. Six partitional and hierarchical clustering techniques (k-means, k-medoids, SOM k-means, Agglomerative Hierarchical Clustering, and Clustering based on Gaussian Mixture Models) with different distance criteria, together with some clustering evaluation measures (Calinski–Harabasz, Davies–Bouldin, Gap and Silhouette criterion clustering evaluation object), present various analyses of the main climatic zones in Spain. Real-life data sets, recorded by AEMET (Spanish Meteorological Agency) at four of its weather stations, are analyzed in order to characterize the actual weather conditions at each location. The clustering techniques process the data on some of the main daily meteorological variables collected at these stations over six years between 2004 and 2010.     

Religion, Psychotherapy and the Body-Soul Relationship

The split between body and soul in modern psychology and psychotherapy has Greek origins. The opposition of psyche and soma is Greek, as is the central myth of Oedipus. The present paper offers a Biblical integration of body and soul, focusing on the Hebrew concept of nefesh and the Biblical narrative of the Akedah (i.e., the “binding” of Isaac).     

Response competition effects insame-different judgments

An account ofsame-different discriminations that is based upon a continuous-flow model of visual information processing (C. W. Eriksen & Schultz, 1979) and response competition and inhibition between the responses by which the subject signifies his judgment is presented. We show that a response signifyingsame will on the average be executed faster due to less priming or incipient activation of the competing response,different. In the experiment, the subjects matched letters on the basis of physical identity. The degree of priming ofdifferent responses on same trials and ofsame responses ondifferent trials was manipulated by an extraneous noise letter placed in the display. Latency for judgments onsame trials increased as the feature overlap of noise and target letters decreased. Latencies were shorter ondifferent trials when the noise letter was dissimilar to either target letter than when the noise letter was the same as one of the targets. These results were consistent with the response-competition interpretation.     

Decidability ofstit theory with a single agent andRefref Equivalence

The purpose of this paper is to prove the decidability ofstit theory (a logic of “seeing to it that”) with a single agent andRefref Equivalence. This result is obtained through an axiomatization of the theory and a proof that it has thefinite model property. A notion ofcompanions to stit formulas is introduced and extensively used in the proof.     

Post-Established Harmony: Kant and Analogy Reconsidered

This essay is a response to John Milbank’s comparison of Kant and Aquinas’ theories of analogy in ‘A Critique of the Theology of Right’. A critique of Milbank’s essay forms the point of departure for my reconstruction of Kant’s actual theory of analogy. I show that the usual focus on the Prolegomena for this end is insufficient; in fact, the full extent of Kant’s theory of analogy only becomes clear in the Critique of Judgment. I also consider the significance of the Analogies of Experience in the Critique of Pure Reason. In conclusion, I draw on the work of Michel Guérin to designate Kantian analogy, ‘post-established harmony’.     

The role of team and sport social contexts: Are three-level models needed in studies of sport conduct?

In this work, I examined how variation in sport conduct is allocated across the three different levels (i.e., athlete, team, and sport) in order to consider how the team and sport environment might contribute to athletes' sport conduct. Specifically, three-level models were utilized for sportspersonship, technical and mental gamesmanship, and instrumental aggression to examine whether there is unique variance due to sport and team clustering with athletes (N = 1412) nested within teams (J = 92) that are in turn nested within sports (K = 17). Results showed that clustering due to sport accounted for unique variance above and beyond team clustering. However, sex and invasion team sport explained most of the sport-level variance. The implication is that it is important to account for the sport clustering. However, when accounting for the sport-level clustering is not feasible, controlling for sex is necessary at the very least.     

Data analysis implications of some concepts related to the cuts of a graph

Connections are pointed out between the concept of a cut in a graph and the data analysis problems of hierarchical clustering and seriation encountered in the social and behavioral sciences. An emphasis is placed on hierarchical clustering by the criterion of k-edge connectivity and on the relationship between several criteria for object seriation proposed in the literature and the appropriate graph-theoretical structures.     

Lifetime Acts of Violence Assessment (LAVA) predictors of laboratory aggression

While self-report data warrants interpretive caution in applied settings, these indices serve an important role in exploratory research. The Lifetime Assessment of Violent Acts (LAVA) inventory is a brief, reliable, face-valid questionnaire for estimating the frequency, triggers, and consequences (including injuries to others) of prior acts of aggression. The LAVA also identifies the situational contexts in which prior violence was triggered and provides a basis for risk classifications based on past reactive, intimate partner, alcohol-related, and/or weapon-related violence. Scores on the LAVA indices have been linked to a range of developmental and maladjustment indicators. Associations were found between lab-provoked (Taylor Aggression Paradigm) responding and both dimensional and risk classification scores in this sample (N = 92) of college men. Participants “competed” with a fictional opponent using electric shock as a retaliatory measure for perceived provocation. The total LAVA dimensional score predicted mean shock intensity ( d = .87), baseline responding ( d = 0.90), and past sexual aggression ( d = 1.01). Shock intensities in response to high provocation were predicted ( M d = 0.57) by all but one LAVA index. Participants who reported inflicting one or more injuries on another showed more intense escalations of aggression ( d = 0.46) in response to provocation than normative counterparts. Prior injuries to another ( RR = 2.71), reactive acts of aggression ( RR = 3.73), or intimate-partner violence ( RR = 4.19) elevated the risk of one or more prior acts of self-reported sexual aggression. The limitations and potential value of self-report data were discussed in regard to aggression research.     

Brain activation detection by neighborhood one-class SVM

Brain activation detection is an important problem in fMRI data analysis. In this paper, we propose a data-driven activation detection method called neighborhood one-class SVM (NOC-SVM). Based on the probability distribution assumption of the one-class SVM algorithm and the neighborhood consistency hypothesis, NOC-SVM identifies a voxel as either an activated or non-activated voxel by a weighted distance between its near neighbors and a hyperplane in a high-dimensional kernel space. The proposed NOC-SVM are evaluated by using both synthetic and real datasets. On two synthetic datasets with different SNRs, NOC-SVM performs better than K-means and fuzzy K-means clustering and is comparable to POM. On a real fMRI dataset, NOC-SVM can discover activated regions similar to K-means and fuzzy K-means. These results show that the proposed algorithm is an effective activation detection method for fMRI data analysis. Furthermore, it is stabler than K-means and fuzzy K-means clustering.     

Subspace K-means clustering

To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic, stochastic, and unsupervised learning approaches. To evaluate subspace K-means, we performed a comparative simulation study, in which we manipulated the overlap of subspaces, the between-cluster variance, and the error variance. The study shows that the subspace K-means algorithm is sensitive to local minima but that the problem can be reasonably dealt with by using partitions of various cluster procedures as a starting point for the algorithm. Subspace K-means performs very well in recovering the true clustering across all conditions considered and appears to be superior to its competitor methods: K-means, reduced K-means, factorial K-means, mixtures of factor analyzers (MFA), and MCLUST. The best competitor method, MFA, showed a performance similar to that of subspace K-means in easy conditions but deteriorated in more difficult ones. Using data from a study on parental behavior, we show that subspace K-means analysis provides a rich insight into the cluster characteristics, in terms of both the relative positions of the clusters (via the centroids) and the shape of the clusters (via the within-cluster residuals).     

Inertial eigenvalues, rod density, and rod diameter in length perception by dynamic touch

Four experiments addressed the relevance of the eigenvaluesI _k of the inertia tensor for perceiving length by dynamic touch. Experiments 1–2 focused on the consequences of limiting variation in the minimum eigenvalueI ₃. Both revealed that perceived length is a function ofI _k . Whether the contribution ofI ₃ is detected, however, depends on the range of values that characterize a particular object set. Experiments 3–4 considered the relationship between an independent index of a rod’s diameter, which does not affectI _k , and actual manipulation of a rod’s diameter, which does affectI _k . Whereas the former appeared as satisfaction of implicit instructions to alter reports of perceived length, the latter entailed actual differences in perceived length in accordance withI _k . Results are discussed with respect to the links among actual length, perceived length, andI _k , as well as, in particular, how these links guarantee that perceived length is in the range of actual lengths.     

An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling

A method of hierarchical clustering for relational data is presented, which begins by forming a new square matrix of product-moment correlations between the columns (or rows) of the original data (represented as an n × m matrix). Iterative application of this simple procedure will in general converge to a matrix that may be permuted into the blocked form [_?1¹₁^?1]. This convergence property may be used as the basis of an algorithm (CONCOR) for hierarchical clustering. The CONCOR procedure is applied to several illustrative sets of social network data and is found to give results that are highly compatible with analyses and interpretations of the same data using the blockmodel approach of White (White, Boorman & Breiger, 1976). The results using CONCOR are then compared with results obtained using alternative methods of clustering and scaling (MDSCAL, INDSCAL, HICLUS, ADCLUS) on the same data sets.     

Measuring nominal scale agreement between a judge and a known standard

Two designs for comparing a judge's ratings with a known standard are presented and compared. Design A pertains to the situation where the judge is asked to categorize each ofN subjects into one ofr (known) classes with no knowledge of the actual number in each class. Design B is employed when the judge is given the actual number in each class and is asked to categorize the individuals subject to these constraints. The probability distribution of the total number of correct choices is developed in each case. A power comparison of the two procedures is undertaken.     

The representation of Takeuti's\begin{array}{*{20}c} \parallel \\ \_ \\ \end{array} -operator

Gaisi Takeuti has recently proposed a new operation on orthomodular latticesL, \(\begin{array}{*{20}c} \parallel \\ \_ \\ \end{array} \) :P(L)»L. The properties of \(\begin{array}{*{20}c} \parallel \\ \_ \\ \end{array} \) suggest that the value of \(\begin{array}{*{20}c} \parallel \\ \_ \\ \end{array} \) (A) (A) \( \subseteq \) L) corresponds to the degree in which the elements ofA behave classically. To make this idea precise, we investigate the connection between structural properties of orthomodular latticesL and the existence of two-valued homomorphisms onL.     

Nonmetric grouping: Clusters and cliques

A class of related nonmetric (“monotone invariant”) hierarchical grouping methods is presented. The methods are defined in terms of generalized cliques, based on a systematically varying specification of the degree of indirectness of permitted relationships (i.e., degree of “chaining”). This approach to grouping is shown to provide a useful framework for grouping methods based on ana priori specification of the properties of the desired subsets, and includes a natural generalization for “complete linkage” and “single linkage” clustering, such as the methods of Johnson [1967]. The central feature of the class of methods is a simple iterative matrix operation on the original disparities (“inverse-proximities” or “dissimilarities”) matrix, and one of the methods also constitutes a very efficient single linkage clustering procedure.     

BINOMIX: A BASIC program for maximum likelihood analyses of finite and beta-binomial mixture distributions

If a discrete random variableY is binomially distributed for each observation unit in a sample, and if the binomial parameterp varies between different units, then the distribution ofY underlying a sample of observations based on different units is called a binomial mixture distribution. BINOMIX is a BASIC program to estimate parameters and to evaluate the goodness of fit for the two most important types of binomial mixtures, finite and beta-binomial.     

Not guppies, nor goldfish, but tumble dryers, Noriega, Jesse Jackson, panties, car crashes, bird books, and Stevie Wonder

This paper focuses on theguppy effect (Osherson & Smith, 1981), that is, on the existence of examples of conjunctive concepts that are more typical of the conjunction than of both constituents. The most frequently given examples of this effect,guppy andgoldfish, are shown not to be more typical of the conjunctionpet fish than offish in two between-subjects and one within-subjects experiment. The frequency of the effect in a large empirical study is investigated, and better examples of the effect are suggested.     

A variable neighborhood search method for generalized blockmodeling of two-mode binary matrices

The clustering of two-mode proximity matrices is a challenging combinatorial optimization problem that has important applications in the quantitative social sciences. We focus on one particular type of problem related to the clustering of a two-mode binary matrix, which is relevant to the establishment of generalized blockmodels for social networks. In this context, clusters for the rows of the two-mode matrix intersect with clusters of the columns to form blocks, which should ideally be either complete (all 1s) or null (all 0s). A new procedure based on variable neighborhood search is presented and compared to an existing two-mode K-means clustering algorithm. The new procedure generally provided slightly greater explained variation; however, both methods yielded exceptional recovery of cluster structure.