Probabilistic,multidimensional unfolding analysis

A probabilistic, multidimensional version of Coombs' unfolding model is obtained by assuming that the projections of each stimulus and each individual on each axis are normally distributed. Exact equations are developed for the single dimensional case and an approximate one for the multidimensional case. Both types of equations are expressed solely in terms of univariate normal distribution functions and are therefore easy to evaluate. A Monte Carlo experiment, involving 9 stimuli and 3 subjects in a 2 dimensional space, was run to determine the degree of accuracy of the multidimensional equation and the feasibility of using iterative methods to obtain maximum likelihood estimates of the stimulus and subject coordinates. The results reported here are gratifying in both respects.     

A method of optimal scaling for multivariate ordinal data and its extensions

This paper develops a method of optimal scaling for multivariate ordinal data, in the framework of a generalized principal component analysis. This method yields a multidimensional configuration of items, a unidimensional scale of category weights for each item and, optionally, a multidimensional configuration of subjects. The computation is performed by alternately solving an eigenvalue problem and executing a quasi-Newton projection method. The algorithm is extended for analysis of data with mixed measurement levels or for analysis with a combined weighting of items. Numerical examples and simulations are provided. The algorithm is discussed and compared with some related methods.Earlier results of this research appeared in Saito and Otsu (1983). The authors would like to acknowledge the helpful comments and encouragement of the editor.     

The Development and Validation of the Psychiatric O.T. Evaluation of Needs and Treatment Instrument

This study evaluates a behavioral rating tool used in a psychiatric setting for diverse populations including acute and chronically ill, and young adults to geriatrics. The need for, and development of the 25-item tool is described. Validity and reliability statistics support the use of the tool, and multi dimensional scaling differentiated four global dimensions that may be useful in tailoring group programs to the residents' individual needs. A case example is included.     

A formal model for structuring local perceptions of environmental space

The problem of constructing computational models of space that mimic those found in human spatial reasoning is addressed. This paper extends a formal model that addressed point objects in a 2D world to include the presence of line objects and barriers. The problem consists of determining an appropriate global model for the spatial configuration given a sequence of local views or observations. A formal model is proposed based on constraint network theory. The model leads to the identification of stable regions within which perceptual information about the environment changes slowly, and zones of transition within which the perceptual information changes more quickly. A special case of transition zones is a gateway, which may be viewed as a kind of discontinuity or singularity in the model. It is expected that the model obtained will be used to mimic human mental representations of navigable outdoor environments.     

A multiple-objective dispersion problem

In this paper we study a multiple-objective dispersion problem from an efficient solution and value function perspective. A general fundamental theorem on non-dominance is given and a polar co-ordinate elimination heuristic is given for the case of R². Some general results are given for the existence of a linearly weighted objective function whose maximal value will give a most preferred solution, for convex or concave value functions, and a heuristic is developed for the case where these conditions do not hold.     

Stochastic PERT networks as models of cognition: Derivation of the mean,variance, and distribution of reaction time using Order-of-Processing (OP) diagrams

It is frequently assumed that the mental activity which leads to a given response is made up of separable components or processes. One or more of the processes are assumed to contribute to the time required to respond. Computation of the mean, variance, and distribution of the reaction time is relatively straightforward when all processes are arranged in series or parallel. However, such is not the case when the processes have complex arrangements. A solution to a useful special case of the above problem is proposed. Specifically, it is shown that simple computations yield closed form expressions for the mean, variance, and distribution of reaction time when the processes can be arranged in a stochastic PERT network and when the durations of individual processes are sums of mutually independent, exponentially distributed random variables. The method of solution relies on the construction of an Order-of-Processing (OP) diagram from the original PERT network representation of behavior.     

A framework for integrating dimensional and categorical classifications of personality disorder

Although empirical evidence strongly supports a dimensional representation of personality disorder, there is strong resistance to dimensional classification due in part to concerns about clinical utility. Acceptance of an evidence-based dimensional classification would be facilitated by information on how such a system would map onto existing diagnoses. With this objective in mind, an integrated framework is proposed that combines categorical and dimensional diagnoses. A two-component classification is adopted that distinguishes between the diagnosis of general personality disorder and the assessment of individual differences in the form the disorder takes. Then, the DSM definition of personality disorders is extended by defining individual disorders as categories of trait dimensions. This makes it possible to develop an integrated classification organized around a set of empirically derived primary traits. Assessments of these traits may then be combined to generate categorical and dimensional diagnoses. It is argued that this approach would introduce an etiological perspective into the classification of personality disorder and improve categorical classification by providing an explicit definition of each diagnosis. The clinical utility of incorporating a dimensional classification is discussed in terms of convenience and acceptability, value in predicting outcomes and treatment planning, and usefulness in organizing and selecting interventions.     

Matrix approximations and subspace fitting

The problem of fitting a least-squares linear subspace to a higher dimensional set of points is solved by the use of matrix approximation results, both when the subspace is required to pass through a given point, and when it is not so restricted. In the latter case the subspace is shown to pass through the centroid of the point set. It is noted that the principal axes of the set of points enter into the problem in a natural manner.The author is indebted to Prof. Carl Eckart of the Department of Physics, University of Chicago, for much helpful comment and suggestion. To him are due the example of the last section and various other items throughout.     

基于空间方位信息的构型对视觉工作记忆绩效的影响

通过考察构成视觉客体构型的两种因素在视觉工作记忆任务中的影响, 探讨了构型对视觉工作记忆绩效的作用机制。采用变化觉察实验范式, 在构型朝向不变和旋转两种条件下, 系统地控制构型中的客体相对方位和整体几何形态两种因素的变化, 考察其对视觉工作记忆绩效的影响。实验结果显示, 无论构型朝向不变还是旋转, 只要客体相对方位保持一致, 工作记忆绩效就可以维持较高水平, 而几何形态没有表现出显著作用。上述发现提示构型客体相对方位是构型影响视觉工作记忆绩效的主要因素。     

Three applications of functional analysis with group dynamic cognitive behavioral group therapy

Case illustrations from group dynamic cognitive behavioral group therapy are presented to demonstrate three applications of functional analysis and the resulting cognitive behavioral interventions. The principles of group dynamic cognitive behavioral group therapy are explained. A functional analysis is applied first to the problem behavior of an individual group member. A clinical case illustrates how the group members help to change this individual group member's behavior from a learning theory perspective. Next, the circular interactional problem behavior between two group members is reduced to the individual functional analysis of each of the two member's problem behaviors. It is then illustrated how the two group member's problem behaviors, as well as feedback from others, contribute toward helping to change each others behavior. The paper concludes that functional analysis and ensuing behavioral interventions can be also applied to group as a whole behavior.     

Measuring the importance of information in judgment: Individual differences in weighting ability and effort

Measuring the importance of cues in social judgment has been a longstanding problem. Past research has shown individual differences in the impact of ability and study effort on judgments of student performance. This research examined the hypothesis that the individual differences are due to the weight given to the information as opposed to the perceived extremity of the values of the information. Subjects judged the performance of hypothetical students described by ability, study effort, and effort during an exam. Individual differences in the impact of the three types of information were obtained and were predictable from self-reports of the relative importance of information. Different values of weights were necessary to account for the differences in the impact of information, although there were also differences in the perceived extremity of the information. The theoretical and applied significance of distinguishing the weight given to information in judgment from its subjective value are discussed. On the basis of a new review of the literature, as well as the present results, it is concluded that self-reports of the relative importance of information are predictive of individual differences in information use in judgment, but that they may reflect the total impact of information rather than only the weight.     

Temperament, personality, and developmental psychopathology as childhood antecedents of personality disorders

To contribute to the case for a dimensional conceptualization of psychopathology in general and maladaptive personality or personality disorders in particular, the present paper reviews the evidence for a dimensional representation of childhood temperament and personality. The review of temperament and variable-centered as well as person-centered approaches to childhood personality leads us to propose five broadband dimensions that capture individual differences in children and adolescents: extraversion, emotional stability, agreeableness, conscientiousness, and openness/intellect. Our analysis of the CBCL (Achenbach, 1991) and the DIPSI (currently under development at Ghent University), two dimensional models for childhood psychopathology, suggests two common broadband factors, internalizing and externalizing. The relations between the dimensional representation of childhood temperament/personality and psychopathology are documented with data from general population and clinical samples of children and adolescents. The article concludes with a proposal on how the higher-order dimensions emerging from studies of adaptive and maladaptive individual differences in childhood could be integrated in a common dimensional model.     

Learning How to Generalize

Generalization is a fundamental problem solved by every cognitive system in essentially every domain. Although it is known that how people generalize varies in complex ways depending on the context or domain, it is an open question how people learn the appropriate way to generalize for a new context. To understand this capability, we cast the problem of learning how to generalize as a problem of learning the appropriate hypothesis space for generalization. We propose a normative mathematical framework for learning how to generalize by learning inductive biases for which properties are relevant for generalization in a domain from the statistical structure of features and concepts observed in that domain. More formally, the framework predicts that an ideal learner should learn to generalize by either taking the weighted average of the results of generalizing according to each hypothesis space, with weights given by how well each hypothesis space fits the previously observed concepts, or by using the most likely hypothesis space. We compare the predictions of this framework to human generalization behavior with three experiments in one perceptual (rectangles) and two conceptual (animals and numbers) domains. Across all three studies we find support for the framework's predictions, including individual‐level support for averaging in the third study.     

Sequential decision making: An aggregate scheduling methodology

Recent studies of sequential decision making performance have utilized optimal dynamic programming solutions as the criteria for evaluating the quality of human decisions. The difficulties incumbent in increasing the dimensionality of dynamic programming formulations effectively prohibit their extension beyond the case of a single decision variable. In the following article an alternative optimization formulation for a two-decision-variable case is presented, followed by the development of a corresponding one-decision-variable problem. In addition, related methodological problems limiting the comparability of results among previous studies are presented. It is suggested that the proposed problem formulation and its accompanying interpretation for the decision maker largely eliminate these previous methodological problems. Furthermore, the problem formulation permits quantification of the relative weights that should optimally be placed on future decision periods in the multiple-stage decision problem.     

Optimists or optimistic: Replication of a taxometric study on optimism

Hope and optimism are variants of individual differences regarding positive expectancies for the future. Previous literature describes the relationship of hope and optimism to various outcomes such as psychological adjustment, achievement, problem solving, and health-related concerns; however, few studies investigate its latent structure. Eichner, Kwon, and Marcus (2014) explore optimism’s latent structure through taxometric procedures and provide support for a dimensional construct. In light of recent debate over the replicability of scientific findings, the present study aimed to replicate these findings in an independent sample. Taxometric analyses are consistent with the original study, providing further support for the dimensional latent structure of optimism.     

On the essence of empty properties

This paper deals with generalisations of modally based criteria for determining whether a given property is essential to an individual to the case of generic essences. These criteria usually presuppose extensionally individuated properties. The limitations of their generalisations are demonstrated using the case of the necessarily empty individual property and the necessarily empty individual office. I do not present a novel stance on the discussion of individual essences. The novelty of this paper lies in its claim that none of these criteria can be generalised to generic essences.

     

The use of a discriminant function in the treatment of psychological group differences

A technique for determining a set of weights for the linear combination of a number of measures is applied to a concrete problem. The set of weights meets the criterion of maximum separation of the total scores of two different occupational groups.     

Classification by multivariate analysis

The problem considered is the use of a set of measurements on an individual to decide from which of several populations he has been drawn. It is assumed that in each population there is a probability distribution of the measurements. Principles for choosing the rule of classification are based on costs of misclassification. Optimum procedures are derived in general terms. If the measurements are normally distributed, the procedures use one discriminant function in the case of two populations and several discriminant functions in the cases of more populations. The numerical example given involves three normal populations.Sponsored in part by the Office of Naval Research.     

The Status of our Ordinary Three Dimensions in a Quantum Universe 1

There are now several, realist versions of quantum mechanics on offer. On their most straightforward, ontological interpretation, these theories require the existence of an object, the wavefunction, which inhabits an extremely high‐dimensional space known as configuration space. This raises the question of how the ordinary three‐dimensional space of our acquaintance fits into the ontology of quantum mechanics. Recently, two strategies to address this question have emerged. First, Tim Maudlin, Valia Allori, and her collaborators argue that what I have just called the ‘most straightforward’ interpretation of quantum mechanics is not the correct one. Rather, the correct interpretation of realist quantum mechanics has it describing the world as containing objects that inhabit the ordinary three‐dimensional space of our manifest image. By contrast, David Albert and Barry Loewer maintain the straightforward, wavefunction ontology of quantum mechanics, but attempt to show how ordinary, three‐dimensional space may in a sense be contained within the high‐dimensional configuration space the wavefunction inhabits. This paper critically examines these attempts to locate the ordinary, three‐dimensional space of our manifest image “within” the ontology of quantum mechanics. I argue that we can recover most of our manifest image, even if we cannot recover our familiar three‐dimensional space.