Monotone spline transformations for dimension reduction

Consider a set of data consisting of measurements ofn objects with respect top variables displayed in ann ×p matrix. A monotone transformation of the values in each column, represented as a linear combination of integrated basis splines, is assumed determined by a linear combination of a new set of values characterizing each row object. Two different models are used: one, an Eckart-Young decomposition model, and the other, a multivariate normal model. Examples for artificial and real data are presented. The results indicate that both methods are helpful in choosing dimensionality and that the Eckart-Young model is also helpful in displaying the relationships among the objects and the variables. Also, results suggest that the resulting transformations are themselves illuminating.     

Dividing attention between color and shape: Evidence of coactivation

When attention is divided between spatially distinct objects, the time-to detect a target decreases when two or more targets are present. This redundancy gain can be accounted for by an interactive race model (Mordkoff & Yantis, 1991) in which separate decisions are made about each object, but environmental contingencies among the objects can influence decision times. In the present study, we examined whether the model also accounts for performance when attention must be divided between stimulus attributes other than spatial location. Subjects made target-present responses when displays included a prespecified color, a prespecified letter, or both target features. The data violated the predictions of all separate-activations models, including the interactive race model. Two control experiments ruled out an alternative account based on task complexity. We conclude that coactivation occurs when target attributes from two separable dimensions are simultaneously present, but not when target attributes come from the same dimension. A modular hybrid of race and coactivation models is tentatively proposed.     

Uniform Test Assembly

In educational practice, a test assembly problem is formulated as a system of inequalities induced by test specifications. Each solution to the system is a test, represented by a 0–1 vector, where each element corresponds to an item included (1) or not included (0) into the test. Therefore, the size of a 0–1 vector equals the number of items n in a given item pool. All solutions form a feasible set—a subset of 2 ⁿ vertices of the unit cube in an n-dimensional vector space. Test assembly is uniform if each test from the feasible set has an equal probability of being assembled. This paper demonstrates several important applications of uniform test assembly for educational practice. Based on Slepian’s inequality, a binary program was analytically studied as a candidate for uniform test assembly. The results of this study establish a connection between combinatorial optimization and probability inequalities. They identify combinatorial properties of the feasible set that control the uniformity of the binary programming test assembly. Computer experiments illustrating the concepts of this paper are presented.     

Clusteringn objects intok groups under optimal scaling of variables

We propose a method to reduce many categorical variables to one variable withk categories, or stated otherwise, to classifyn objects intok groups. Objects are measured on a set of nominal, ordinal or numerical variables or any mix of these, and they are represented asn points inp-dimensional Euclidean space. Starting from homogeneity analysis, also called multiple correspondence analysis, the essential feature of our approach is that these object points are restricted to lie at only one ofk locations. It follows that thesek locations must be equal to the centroids of all objects belonging to the same group, which corresponds to a sum of squared distances clustering criterion. The problem is not only to estimate the group allocation, but also to obtain an optimal transformation of the data matrix. An alternating least squares algorithm and an example are given.The authors thank Eveline Kroezen and Teije Euverman for their comments on a previous draft of this paper.     

The relation between hierarchical and euclidean models for psychological distances

In one well-known model for psychological distances, objects such as stimuli are placed in a hierarchy of clusters like a phylogenetic tree; in another common model, objects are represented as points in a multidimensional Euclidean space. These models are shown theoretically to be mutually exclusive and exhaustive in the following sense. The distances among a set ofn objects will be strictly monotonically related either to the distances in a hierarchical clustering system, or else to the distances in a Euclidean space of less thann — 1 dimensions, but not to both. Consequently, a lower bound on the number of Euclidean dimensions necessary to represent a set of objects is one less than the size of the largest subset of objects whose distances satisfy the ultrametric inequality, which characterizes the hierarchical model.This work was supported in part by Grant GB-13588X from the National Science Foundation. I would like to thank L. M. Kelly and A. A. J. Marley for their helpful comments and suggestions.     

Fitting the off-diagonal dedicom model in the least-squares sense by a generalization of the harman and jones minres procedure of factor analysis

Harshman's DEDICOM model providesa framework for analyzing square but asymmetric materices of directional relationships amongn objects or persons in terms of a small number of components. One version of DEDICOM ignores the diagonal entries of the matrices. A straight-forward computational solution for this model is offered in the present paper. The solution can be interpreted as a generalized Minres procedure suitable for handing asymmetric matrices.     

Spatial updating relies on an egocentric representation of space: Effects of the number of objects

Models of spatial updating attempt to explain how representations of spatial relationships between the actor and objects in the environment change as the actor moves. In allocentric models, object locations are encoded in an external reference frame, and only the actor’s position and orientation in that reference frame need to be updated. Thus, spatial updating should be independent of the number of objects in the environment (set size). In egocentric updating models, object locations are encoded relative to the actor, so the location of each object relative to the actor must be updated as the actor moves. Thus, spatial updating efficiency should depend on set size. We examined which model better accounts for human spatial updating by having people reconstruct the locations of varying numbers of virtual objects either from the original study position or from a changed viewing position. In consistency with the egocentric updating model, object localization following a viewpoint change was affected by the number of objects in the environment.     

Models for ordinal hierarchical classes analysis

This paper proposes an ordinal generalization of the hierarchical classes model originally proposed by De Boeck and Rosenberg (1998). Any hierarchical classes model implies a decomposition of a two-way two-mode binary arrayM into two component matrices, called bundle matrices, which represent the association relation and the set-theoretical relations among the elements of both modes inM. Whereas the original model restricts the bundle matrices to be binary, the ordinal hierarchical classes model assumes that the bundles are ordinal variables with a prespecified number of values. This generalization results in a classification model with classes ordered along ordinal dimensions. The ordinal hierarchical classes model is shown to subsume Coombs and Kao's (1955) model for nonmetric factor analysis. An algorithm is described to fit the model to a given data set and is subsequently evaluated in an extensive simulation study. An application of the model to student housing data is discussed.     

Identification of objects in 9‐month‐old infants: integrating ‘what’ and ‘where’ information

Following Leslie, Xu, Tremoulet and Scholl (1998) , we distinguish between individuation (the establishment of an object representation) and identification (the use of information stored in the object representation to decide which previously individuated object is being encountered). Although there has been much work on how infants individuate objects, there is relatively little on the question of when and how property information is used to identify objects. Experiment 1 shows that 9‐month‐old infants use shape, but apparently not color, information in identifying objects that are each moved behind spatially separated screens. Infants could not simply have associated a shape with a location or a screen without regard to objecthood, because on alternate trials the objects switched locations/screens. Infants therefore had to bind shape information to the object representation while tracking the objects’ changing location. In Experiment 2, we tested if infants represented both objects rather than ‘sampled’ only one of them. Using the same alternation procedure, infants again succeeded in using shape (but not color) information when only one of the screens was removed – the screen that occluded the first‐hidden object (requiring the longer time in memory). Finally, we relate our behavioral findings both to a cognitive model and to recent neuroscientific studies, concluding that ventral ‘what’ and dorsal ‘where’ pathways may be functionally integrated by 9 months.     

A stochastic multidimensional scaling vector threshold model for the spatial representation of “pick any/n” data

This paper presents a new stochastic multidimensional scaling vector threshold model designed to analyze pick any/n choice data (e.g., consumers rendering buy/no buy decisions concerning a number of actual products). A maximum likelihood procedure is formulated to estimate a joint space of both individuals (represented as vectors) and stimuli (represented as points). The relevant psychometric literature concerning the spatial treatment of such binary choice data is reviewed. The nonlinear probit type model is described, as well as the conjugate gradient procedure used to estimate parameters. Results of Monte Carlo analyses investigating the performance of this methodology with synthetic choice data sets are presented. An application concerning consumer choices for eleven competitive brands of soft drinks is discussed. Finally, directions for future research are presented in terms of further applications and generalizing the model to accommodate three-way choice data.     

Predicting the unpredictable

A major difficulty for currently existing theories of inductive inference involves the question of what to do when novel, unknown, or previously unsuspected phenomena occur. In this paper one particular instance of this difficulty is considered, the so-called sampling of species problem.The classical probabilistic theories of inductive inference due to Laplace, Johnson, de Finetti, and Carnap adopt a model of simple enumerative induction in which there are a prespecified number of types or species which may be observed. But, realistically, this is often not the case. In 1838 the English mathematician Augustus De Morgan proposed a modification of the Laplacian model to accommodate situations where the possible types or species to be observed are not assumed to be known in advance; but he did not advance a justification for his solution.In this paper a general philosophical approach to such problems is suggested, drawing on work of the English mathematician J. F. C. Kingman. It then emerges that the solution advanced by De Morgan has a very deep, if not totally unexpected, justification. The key idea is that although exchangeable random sequences are the right objects to consider when all possible outcome-types are known in advance, exchangeable random partitions are the right objects to consider when they are not. The result turns out to be very satisfying. The classical theory has several basic elements: a representation theorem for the general exchangeable sequence (the de Finetti representation theorem), a distinguished class of sequences (those employing Dirichlet priors), and a corresponding rule of succession (the continuum of inductive methods). The new theory has parallel basic elements: a representation theorem for the general exchangeable random partition (the Kingman representation theorem), a distinguished class of random partitions (the Poisson-Dirichlet process), and a rule of succession which corresponds to De Morgan's rule.     

Identifying a Reordering of Rows and Columns for Multiple Proximity Matrices Using Multiobjective Programming

This paper is concerned with a problem where K (n×n) proximity matrices are available for a set of n objects. The goal is to identify a single permutation of the n objects that provides an adequate structural fit, as measured by an appropriate index, for each of the K matrices. A multiobjective programming approach for this problem, which seeks to optimize a weighted function of the K indices, is proposed, and illustrative examples are provided using a set of proximity matrices from the psychological literature. These examples show that, by solving the multiobjective programming model under different weighting schemes, the quantitative analyst can uncover information about the relationships among the matrices and often identify one or more permutations that provide good to excellent index values for all matrices.     

Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions

Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among n objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors. We present a new permutation method (called “double semi-partialing”, or DSP) that complements the family of extant approaches to MRQAP tests. We assess the statistical bias (type I error rate) and statistical power of the set of five methods, including DSP, across a variety of conditions of network autocorrelation, of spuriousness (size of confounder effect), and of skewness in the data. These conditions are explored across three assumed data distributions: normal, gamma, and negative binomial. We find that the Freedman–Lane method and the DSP method are the most robust against a wide array of these conditions. We also find that all five methods perform better if the test statistic is pivotal. Finally, we find limitations of usefulness for MRQAP tests: All tests degrade under simultaneous conditions of extreme skewness and high spuriousness for gamma and negative binomial distributions. Special thanks go to Cajo Ter Braak, Philip Hans Franses, Patrick Houweling, Pierre Legendre, three anonymous reviewers, the associate editor, and the editor for comments.     

Choice model analysis of the “pick any/n” type of binary data

In the “pick any/n” method, subjects are asked to choose any number of items from a list of n items according to some criterion. This kind of data can be analyzed as a special case of either multiple-choice data or successive categories data where the number of response categories is limited to two. An item response model was proposed for the latter case, which is a combination of an unfolding model and a choice model. The marginal maximum-likelihood estimation method was developed for parameter estimation to avoid incidental parameters, and an expectation-maximization algorithm used for numerical optimization. Two examples of analysis are given to illustrate the proposed method, which we call MAXSC.     

Learning From Gesture and Action: An Investigation of Memory for Where Objects Went and How They Got There

Speakers often use gesture to demonstrate how to perform actions—for example, they might show how to open the top of a jar by making a twisting motion above the jar. Yet it is unclear whether listeners learn as much from seeing such gestures as they learn from seeing actions that physically change the position of objects (i.e., actually opening the jar). Here, we examined participants' implicit and explicit understanding about a series of movements that demonstrated how to move a set of objects. The movements were either shown with actions that physically relocated each object or with gestures that represented the relocation without touching the objects. Further, the end location that was indicated for each object covaried with whether the object was grasped with one or two hands. We found that memory for the end location of each object was better after seeing the physical relocation of the objects, that is, after seeing action, than after seeing gesture, regardless of whether speech was absent (Experiment 1) or present (Experiment 2). However, gesture and action built similar implicit understanding of how a particular handgrasp corresponded with a particular end location. Although gestures miss the benefit of showing the end state of objects that have been acted upon, the data show that gestures are as good as action in building knowledge of how to perform an action.     

Parallels Between Action‐Object Mapping and Word‐Object Mapping in Young Children

Across a series of four experiments with 3‐ to 4‐year‐olds we demonstrate how cognitive mechanisms supporting noun learning extend to the mapping of actions to objects. In Experiment 1 (n = 61) the demonstration of a novel action led children to select a novel, rather than a familiar object. In Experiment 2 (n = 78) children exhibited long‐term retention of novel action‐object mappings and extended these actions to other category members. In Experiment 3 (n = 60) we showed that children formed an accurate sensorimotor record of the novel action. In Experiment 4 (n = 54) we demonstrate limits on the types of actions mapped to novel objects. Overall these data suggest that certain aspects of noun mapping share common processing with action mapping and support a domain‐general account of word learning.     

Estimating multiple classification latent class models

This paper presents a new class of models for persons-by-items data. The essential new feature of this class is the representation of the persons: every person is represented by its membership tomultiple latent classes, each of which belongs to onelatent classification. The models can be considered as a formalization of the hypothesis that the responses come about in a process that involves the application of a number ofmental operations. Two algorithms for maximum likelihood (ML) and maximum a posteriori (MAP) estimation are described. They both make use of the tractability of the complete data likelihood to maximize the observed data likelihood. Properties of the MAP estimators (i.e., uniqueness and goodness-of-recovery) and the existence of asymptotic standard errors were examined in a simulation study. Then, one of these models is applied to the responses to a set of fraction addition problems. Finally, the models are compared to some related models in the literature.Thanks are to Paul De Boeck for creating the intellectually stimulating atmosphere in which this class of models came about, Iven van Mechelen for theone-sided idea, Kikumi Tatsuoka for the use of her data, and Theodoor Bouw for running part of the simulation study.     

The CHIC Model: A Global Model for Coupled Binary Data

Often problems result in the collection of coupled data, which consist of different N-way N-mode data blocks that have one or more modes in common. To reveal the structure underlying such data, an integrated modeling strategy, with a single set of parameters for the common mode(s), that is estimated based on the information in all data blocks, may be most appropriate. Such a strategy implies a global model, consisting of different N-way N-mode submodels, and a global loss function that is a (weighted) sum of the partial loss functions associated with the different submodels. In this paper, such a global model for an integrated analysis of a three-way three-mode binary data array and a two-way two-mode binary data matrix that have one mode in common is presented. A simulated annealing algorithm to estimate the model parameters is described and evaluated in a simulation study. An application of the model to real psychological data is discussed. T. Wilderjans is a Research Assistant of the Fund for Scientific Research—Flanders (Belgium). The research reported in this paper was partially supported by the Research Council of K.U. Leuven (GOA/2005/04). We are grateful to Kristof Vansteelandt for providing us with an interesting data set. We also thank three anonymous reviewers for their useful comments.     

An IRT Model with a Parameter-Driven Process for Change

An IRT model with a parameter-driven process for change is proposed. Quantitative differences between persons are taken into account by a continuous latent variable, as in common IRT models. In addition, qualitative interindividual differences and autodependencies are accounted for by assuming within-subject variability with respect to the parameters of the IRT model. In particular, the parameters of the IRT model are governed by an unobserved or “hidden'” homogeneous Markov process. The model includes the mixture linear logistic test model (Mislevy & Verhelst, 1990), the mixture Rasch model (Rost, 1990), and the Saltus model (Wilson, 1989) as specific instances. The model is applied to a longitudinal experiment on discontinuity in conservation acquisition (van der Maas, 1993). Frank Rijmen was supported by the Fund for Scientific Research Flanders (FWO), the GOA/2000/02 granted by the Katholieke Universiteit Leuven to Paul De Boeck and Iven Van Mechelen, and the PDM/02/067 granted by the Katholieke Universiteit Leuven to Paul De Boeck.