Simultaneous classification and multidimensional scaling with external information

For the exploratory analysis of a matrix of proximities or (dis)similarities between objects, one often uses cluster analysis (CA) or multidimensional scaling (MDS). Solutions resulting from such analyses are sometimes interpreted using external information on the objects. Usually the procedures of CA, MDS and using external information are carried out independently and sequentially, although combinations of two of the three procedures (CA and MDS, or multidimensional scaling and using external information) have been proposed in the literature. The present paper offers a procedure that combines all three procedures in one analysis, using a model that describes a partition of objects with cluster centroids represented in a low-dimensional space, which in turn is related to the information in the external variables. A simulation study is carried out to demonstrate that the method works satisfactorily for data with a known underlying structure. Also, to illustrate the method, it is applied to two empirical data sets.     

On the inference of personal authorship: enhancing experienced agency by priming effect information

Three experiments examined whether the mere priming of potential action effects enhances people's feeling of causing these effects when they occur. In a computer task, participants and the computer independently moved a rapidly moving square on a display. Participants had to press a key, thereby stopping the movement. However, the participant or the computer could have caused the square to stop on the observed position, and accordingly, the stopped position of the square could be conceived of as the potential effect resulting from participants' action of pressing the stop key. The location of this position was primed or not just before participants had to stop the movement. Results showed that (subliminal as well as supraliminal) priming of the position enhanced experienced authorship of stopping the square. Additional experimentation demonstrated that this priming of agency was not mediated by the goal or intention to produce the effect.     

The role of figure-ground segregation in change blindness

Partial report methods have shown that a large-capacity representation exists for a few hundred milliseconds after a picture has disappeared. However,change blindness studies indicate that very limited information remains available when a changed version of the image is presented subsequently. What happens to the large-capacity representation? New input after the first image may interfere, but this is likely to depend on the characteristics of the new input. In our first experiment, we show that a display containing homogeneous image elements between changing images does not render the largecapacity representation unavailable. Interference occurs when these new elements define objects. On that basis we introduce a new method to produce change blindness: The second experiment shows that change blindness can be induced by redefining figure and background, without an interval between the displays. The local features (line segments) that defined figures and background were swapped, while the contours of the figures remained where they were. Normally, changes are easily detected when there is no interval. However, our paradigm results in massive change blindness. We propose that in a change blindness experiment, there is a large-capacity representation of the original image when it is followed by a homogeneous interval display, but that change blindness occurs whenever the changed image forces resegregation of figures from the background.     

Changes in personality pathology after pharmacotherapy and combined therapy for depressed patients

The central question addressed by this article is whether courses of treatment consisting of pharmacotherapy or pharmacotherapy combined with psychotherapy (combined therapy) produce different changes in personality pathology at follow-up after 40 weeks. We also examined whether recovery from depression has an influence on outcome. The study population consisted of 128 outpatients in whom personality pathology and severity of depression were determined at the start of the study. For 72 patients, personality pathology and severity of depression were determined again after 40 weeks. Of the group of 72 patients, 25 patients received only pharmacotherapy for 6 months, and 47 patients received combined treatment (pharmacotherapy and psychodynamic supportive psychotherapy). The antidepressant protocol provides for three successive steps in case of intolerance or inefficacy: fluoxetine, amitriptyline, and moclobemide. The combined therapy condition consisted of 16 sessions of Short Psychodynamic Supportive Psychotherapy in addition to pharmacotherapy. In the combined therapy condition there was a significant reduction in personality pathology in patients who recovered from depression but also in patients who had not. In the pharmacotherapy condition the significant decrease was restricted to patients who recovered from depression. The results were most striking for Cluster C psychopatology. Patients with cluster B pathology changed the least. Depressed patients with comorbid personality pathology appear to benefit most from a combination of pharmacotherapy and a form of short, psychodynamic, supportive psychotherapy.     

Retrieving the correlation matrix from a truncated PCA solution: The inverse principal component problem

Whenr Principal Components are available fork variables, the correlation matrix is approximated in the least squares sense by the loading matrix times its transpose. The approximation is generally not perfect unlessr =k. In the present paper it is shown that, whenr is at or above the Ledermann bound,r principal components are enough to perfectly reconstruct the correlation matrix, albeit in a way more involved than taking the loading matrix times its transpose. In certain cases just below the Ledermann bound, recovery of the correlation matrix is still possible when the set of all eigenvalues of the correlation matrix is available as additional information.     

Predicting Behavior From Actions in the Past: Repeated Decision Making or a Matter of Habit?

This paper summarizes research on determinants of repeated behaviors, and the decision processes underlying them. The present research focuses on travel mode choices as an example of such behaviors. It is proposed that when behavior is performed repeatedly and becomes habitual, it is guided by automated cognitive processes, rather than being preceded by elaborate decision processes (i.e., a decision based on attitudes and intentions). First, current attitude-behavior models are discussed, and the role of habit in these models is examined. Second, research is presented on the decision processes preceding travel mode choices. Based on the present theoretical and empirical overview, it is concluded that frequently performed behavior is often a matter of habit, thereby establishing a boundary condition for the applicability of attitude-behavior models. However, more systematic research is required to disentangle the role of habit in attitude-behavior models and to learn more about the cognitive processes underlying habitual behavior.     

A case of extreme simplicity of the core matrix in three-mode principal components analysis

In three-mode Principal Components Analysis, theP ×Q ×R core matrixG can be transformed to simple structure before it is interpreted. It is well-known that, whenP=QR,G can be transformed to the identity matrix, which implies that all elements become equal to values specified a priori. In the present paper it is shown that, whenP=QR − 1,G can be transformed to have nearly all elements equal to values spectified a priori. A cllsed-form solution for this transformation is offered. Theoretical and practical implications of this simple structure transformation ofG are discussed. Constructive comments from anonymous reviewers are gratefully acknowledged.     

Simplimax: Oblique rotation to an optimal target with simple structure

Factor analysis and principal component analysis are usually followed by simple structure rotations of the loadings. These rotations optimize a certain criterion (e.g., varimax, oblimin), designed to measure the degree of simple structure of the pattern matrix. Simple structure can be considered optimal if a (usually large) number of pattern elements is exactly zero. In the present paper, a class of oblique rotation procedures is proposed to rotate a pattern matrix such that it optimally resembles a matrix which has an exact simple pattern. It is demonstrated that this method can recover relatively complex simple structures where other well-known simple structure rotation techniques fail.This research has been made possible by a fellowship from the Royal Netherlands Academy of Arts and Sciences. The author is obliged to Jos ten Berge for helpful comments on an earlier version.     

The role of analogy in the interpretation of novel compounds

It was investigated whether the on-line interpretative processing of novel nominal compounds is affected by analogous lexicalized compounds. In Experiment 1, novel compounds were presented as nonwords in a lexical decision task. Constituent nouns of novel compounds were of high or low productivity as measured by the number of lexicalized compounds of which they were part. Effects of productivity and of compound interpretability on decision latencies were found to be additive, suggesting that the activated lexicalized compounds do not contribute to the interpretation of novel compounds. In Experiment 2, a semantic priming paradigm was used in a lexical decision task. Lexicalized compounds were presented as targets and novel compounds as primes. Second nouns of lexicalized and novel compounds were identical and first nouns of the two types of compounds were semantically related. Target compounds differed in frequency. Equal priming effects were obtained for high- and low-frequency target compounds, indicating that novel compounds do not activate more strongly the most similar lexicalized compound. The role of analogous interpretative processing is considered in the context of alternative models for the interpretation of novel compounds.     

A numerical approach to the approximate and the exact minimum rank of a covariance matrix

A concept of approximate minimum rank for a covariance matrix is defined, which contains the (exact) minimum rank as a special case. A computational procedure to evaluate the approximate minimum rank is offered. The procedure yields those proper communalities for which the unexplained common variance, ignored in low-rank factor analysis, is minimized. The procedure also permits a numerical determination of the exact minimum rank of a covariance matrix, within limits of computational accuracy. A set of 180 covariance matrices with known or bounded minimum rank was analyzed. The procedure was successful throughout in recovering the desired rank.The authors are obliged to Paul Bekker for stimulating and helpful comments.