Indistinguishability tests in the actor–partner interdependence model

When considering dyadic data, one of the questions is whether the roles of the two dyad members can be considered equal. This question may be answered empirically using indistinguishability tests in the actor–partner interdependence model. In this paper several issues related to such indistinguishability tests are discussed: the difference between maximum likelihood and restricted maximum likelihood based tests for equality in variance parameters; the choice between the structural equation modelling and multilevel modelling framework; and the use of sequential testing rather than one global test for a set of indistinguishability tests. Based on simulation studies, we provide guidelines for best practice. All different types of tests are illustrated with cross-sectional and longitudinal data, and corroborated with corresponding R code.     

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.     

RSVP任务下双刺激重复的优势与劣势

本研究探讨重复知盲发生在知觉阶段还是在记忆阶段。采用快速视觉系列呈现(rapid serial visual representation, 简称RSVP)任务, 让被试即时回忆80个词表中呈现的词。操作了词表中目标词的重复数, 分为无重复、单刺激重复(重复一对)和双刺激重复(重复两对)三种, 和目标词的情绪效价, 分为中性词和情绪词两种。结果发现, 对目标词回忆的正确率是：当目标词和非目标词都是中性词汇, 即二者的情绪效价强度相当时(实验1), 在无重复条件与双刺激重复条件下无差异, 二者均大于单刺激重复条件; 当目标词是消极词汇, 非目标词是中性词汇, 即目标刺激的情绪效价强度大于非目标刺激时(实验2), 无重复条件与单刺激重复条件无差异, 二者均大于双刺激重复条件。结果表明：(1)RSVP 任务下词表中有双刺激重复时, 如果刺激的效价强度相当, 出现重复优势; 如果目标刺激的效价强度高于非目标刺激, 出现重复劣势; (2)人们会主动分配更多的注意资源给效价高的刺激, 重复知盲产生在记忆阶段, 支持注意资源分配最优化假设。     

Extending Energy Optimization in Goal-Directed Aiming from Movement Kinematics to Joint Angles

Energy optimization in goal-directed aiming has been demonstrated as an undershoot bias in primary movement endpoint locations, especially in conditions where corrections to target overshoots must be made against gravity. Two-component models of upper limb movement have not yet considered how joint angles are organized to deal with the energy constraints associated with moving the upper limb in goal-directed aiming tasks. To address this limitation, participants performed aiming movements to targets in the up and down directions with the index finger and two types of rod extensions attached to the index finger. The rod extensions were expected to invoke different energy optimizing strategies in the up and down directions by allowing the distal joints the opportunity to contribute to end effector displacement. Primary movements undershot the farthest target to a greater extent in the downward direction compared to the upward direction, showing that movement kinematics optimize energy expenditure in consideration of the effects of gravity. As rod length increased, shoulder elevation was optimized in movements to the far-up target and elbow flexion was optimally minimized in movements to the far-down target. The results suggest energy optimization in the control of joint angles independent of the force of gravity.     

Output‐sensitive complexity of multiobjective combinatorial optimization

We study output‐sensitive algorithms and complexity for multiobjective combinatorial optimization problems. In this computational complexity framework, an algorithm for a general enumeration problem is regarded efficient if it is output‐sensitive, that is, its running time is bounded by a polynomial in the input and the output size. We provide both practical examples of multiobjective combinatorial optimization problems for which such an efficient algorithm exists as well as problems for which no efficient algorithm exists under mild complexity theoretic assumptions.     

Reducing stimulus overselectivity through an increased observing-response requirement

An adult with autism and a mild intellectual disability participated in a 0-s delayed matching-to-sample task. In each trial, two sample stimuli were presented together until the participant completed an observing-response requirement consisting of 1 or 10 mouse clicks in the baseline and experimental phases, respectively. One of the two sample stimuli then appeared randomly as a comparison stimulus (S+), along with two other comparison stimuli (S-). Higher levels of correct responding occurred under the larger observing-response requirement, and the proportion of errors related to one of the two sample stimuli decreased. Thus, stimulus overselectivity was reduced without requiring differential observing responses.     

Sample stimulus control shaping and restricted stimulus control in capuchin monkeys: a methodological note

This paper reports use of sample stimulus control shaping procedures to teach arbitrary matching-to-sample to 2 capuchin monkeys (Cebus apella). The procedures started with identity matching-to-sample. During shaping, stimulus features of the sample were altered gradually, rendering samples and comparisons increasingly physically dissimilar. The objective was to transform identity matching into arbitrary matching (i.e., matching not based on common physical features of the sample and comparison stimuli). Experiment 1 used a two-comparison procedure. The shaping procedure was ultimately effective, but occasional high error rates at certain program steps inspired a follow-up study. Experiment 2 used the same basic approach, but with a three-comparison matching task. During shaping, the monkey performed accurately until the final steps of the program. Subsequent experimentation tested the hypothesis that the decrease in accuracy was due to restricted stimulus control by sample stimulus features that had not yet been changed in the shaping program. Results were consistent with this hypothesis, thus suggesting a new approach that may transform the sample stimulus control shaping procedure from a sometimes useful laboratory tool to a more general approach to teaching the first instance of arbitrary matching performances to participants who show protracted difficulties in learning such performances.     

耐多药肺结核的最优化治疗模式的几点思考

耐多药结核病是结核病治疗中的一个难点,由于耐药成因和耐药种类的多样性决定了治疗应该个体化,对耐多药肺结核的最优化治疗模式的应用和价值进行了思考和总结,这对结核病的治疗有极为重要的意义。     

A branch-and-bound algorithm for fitting anti-robinson structures to symmetric dissimilarity matrices

The seriation of proximity matrices is an important problem in combinatorial data analysis and can be conducted using a variety of objective criteria. Some of the most popular criteria for evaluating an ordering of objects are based on (anti-) Robinson forms, which reflect the pattern of elements within each row and/or column of the reordered matrix when moving away from the main diagonal. This paper presents a branch-and-bound algorithm that can be used to seriate a symmetric dissimilarity matrix by identifying a reordering of rows and columns of the matrix optimizing an anti-Robinson criterion. Computational results are provided for several proximity matrices from the literature using four different anti-Robinson criteria. The results suggest that with respect to computational efficiency, the branch-and-bound algorithm is generally competitive with dynamic programming. Further, because it requires much less storage than dynamic programming, the branch-and-bound algorithm can provide guaranteed optimal solutions for matrices that are too large for dynamic programming implementations.     

Riemannian Newton and trust-region algorithms for analytic rotation in exploratory factor analysis

In exploratory factor analysis, latent factors and factor loadings are seldom interpretable until analytic rotation is performed. Typically, the rotation problem is solved by numerically searching for an element in the manifold of orthogonal or oblique rotation matrices such that the rotated factor loadings minimize a pre-specified complexity function. The widely used gradient projection (GP) algorithm, although simple to program and able to deal with both orthogonal and oblique rotation, is found to suffer from slow convergence when the number of manifest variables and/or the number of latent factors is large. The present work examines the effectiveness of two Riemannian second-order algorithms, which respectively generalize the well-established truncated Newton and trust-region strategies for unconstrained optimization in Euclidean spaces, in solving the rotation problem. When approaching a local minimum, the second-order algorithms usually converge superlinearly or even quadratically, better than first-order algorithms that only converge linearly. It is further observed in Monte Carlo studies that, compared to the GP algorithm, the Riemannian truncated Newton and trust-region algorithms require not only much fewer iterations but also much less processing time to meet the same convergence criterion, especially in the case of oblique rotation.