Co-learning binary classifiers for LP-based multi-label classification

A simple yet practical multi-label learning method, called label powerset (LP), considers each different combination of labels that appear in the training set as a different class value of a single-label classification task. However, because those classes source from multiple labels, there may be some inherent relationships among them. To tackle this challenge, we propose a novel model which aims to co-learn binary classifiers, by combining the training of binary classifiers and the characterizing the relationship among them into a unified objective function. In addition, we develop an alternating optimization algorithm to solve the proposed problem. Extensive experimental results on various kinds of datasets well demonstrate the effectiveness of the proposed model.     

Stopping rules for multi-category computerized classification testing

Computerized classification testing (CCT) aims to classify persons into one of two or more possible categories to make decisions such as mastery/non-mastery or meet most/meet all/exceed. A defining feature of CCT is its stopping criterion: the test terminates when there is enough confidence to make a decision. There is abundant research on CCT with a single cut-off, and two common stopping criteria are the sequential probability ratio test (SPRT) statistic and the generalized likelihood ratio statistic (GLR). However, there is a relative scarcity of research extending the SPRT to the multi-hypothesis case for when there is more than one cut-off. In this paper, we propose a new multi-category GLR (mGLR) statistic as well as a stochastically curtailed version of the CCT with three or more categories. A simulation study was conducted to show that the mGLR statistic outperformed the existing stopping rules by generating shorter average test length without sacrificing classification accuracy. Results also revealed that the stochastically curtailed mGLR successfully increased test efficiency in certain testing conditions.     

Is monkey maternal abuse of offspring aggressive behavior?

This study was intended to probe the aggressive nature of monkey infant abuse by comparing the morphology of abusive behavior patterns with that of patterns allocated to both aggression and other categories of behavioral repertoires of macaques. This morphological analysis indicated that most abusive patterns should not be classified in the aggression category as they, at the motor end, differ greatly from macaque aggressive acts. In contrast, they perfectly resemble behaviors that monkeys usually display while manipulating inanimate objects. The implications of this finding for the pathogenic study of monkey infant abuse are discussed.     

Do incompatible alternatives in a testimony affect witness credibility?

The qualitative regulation of grain size allows witnesses to increase the accuracy of their reports by adding alternatives (e.g., “the robber concealed his face with a mask, with a stocking, or with a balaclava”). However, such answers may include incompatible alternatives which may make police officers and juries distrust witnesses. In four preregistered experiments, we tested the effect of information with incompatible alternatives on witness credibility. In Experiments 1a, 1b, and 1c, we presented two short testimonies, one with incompatible alternatives and another without and credibility was lower with incompatible alternatives. In Experiment 2, we told participants that witnesses could report several alternatives and the effect was reduced. We explain the effect of incompatible alternatives on credibility from participants' inferences that a witness producing an answer with incompatible alternatives is not confident in their memory and a failure to fully appreciate the advantages of adding alternatives.     

Empathic accuracy and cognitions during conflict: An in‐depth analysis of understanding scores

Empathic accuracy research indicates that partners achieve only moderate success at reading each other's thoughts. The current study identifies specific patterns of online thought that contribute to empathic inaccuracy during conflict interactions. Married/cohabiting partners completed a conflict interaction and reported their own thoughts during video‐assisted recall of the interaction, also inferring the thoughts of the other partner. Content analysis of these online thoughts demonstrated a high degree of mindfulness about the process of communication, along with a perspective bias, in which partners tended to construe their own communication as constructive and the other partner's communication as avoidant and confrontational. Specific mind‐reading errors linked to both the thematic content and affective tone of online thought predicted lower overall empathic accuracy.     

Consciousness as a logically consistent and prognostic model of reality

The work demonstrates that brain might reflect the external world causal relationships in the form of a logically consistent and prognostic model of reality, which shows up as consciousness. The paper analyses and solves the problem of statistical ambiguity and provides a formal model of causal relationships as probabilistic maximally specific rules. We suppose that brain makes all possible inferences from causal relationships. We prove that the suggested formal model has a property of an unambiguous inference: from consistent premises we infer a consistent conclusion. It enables a set of all inferences to form a consistent model of the perceived world. Causal relationships may create fixed points of cyclic inter-predictable properties. We consider the “natural” classification introduced by John St. Mill and demonstrate that a variety of fixed points of the objects’ attributes forms a “natural” classification of the external world. Then we consider notions of “natural” categories and causal models of categories, introduced by Eleanor Rosch and Bob Rehder and demonstrate that fixed points of causal relationships between objects attributes, which we perceive, formalize these notions. If the “natural” classification describes the objects of the external world, and “natural” concepts the perception of these objects, then the theory of integrated information, introduced by G. Tononi, describes the information processes of the brain for “natural” concepts formation that reflects the “natural” classification. We argue that integrated information provides high accuracy of the objects identification. A computer-based experiment is provided that illustrates fixed points formation for coded digits.     

FOK判断等级及其准确性的实验研究

通过实验集中探讨了FOK判断等级及其准确性的影响因素。实验以中文字串为学习材料,使用组内设计的实验方法研究了被激活信息的量和强度对FOK等级及准确性的影响。实验结果表明：FOK判断的等级是由被激活信息的总量决定的,和被激活信息的正确与否无关;FOK判断的准确性是由被激活信息的强度决定的;且二者之间是有内在联系的。     

青少年自我描述归类与人格维度特点

对青少年自我描述的内容进行归类时发现,依据不同的归类标准会得到不同的人格结构。500多名青少年学生提供了620个不同的对自我的描述,除9个项目属于对身体和家庭状况的描述外,所有的人格特征既可以归属到中国青少年人格结构的19个二级因素,又可以归属到西方大五人格结构的30个层面。这一结果表明,外显的人格特征分类无法准确表达人格结构的实际构成,人格结构的确定还需要实证评定及对内隐结构的探索。文中还讨论了心理学研究中国化（本土化）的思路与策略。     

变长CD-CAT中的曝光控制与终止规则

本研究借鉴传统计算机化自适应测验的思想, 并结合认知诊断的特点, 在认知诊断框架下提出了4种变长CD-CAT的终止规则, 分别是属性标准误法(SEA)、邻近后验概率之差法(DAPP)、二等分法(HA)以及混合法(HM)。在未控制曝光和采用不同曝光控制条件下, 与HSU法及KL法进行了比较。研究结果表明：(1) 终止条件越严格, 平均测验长度越长, 按测验长度最大值终止的测验百分比越大, 模式判准率越高。(2) 当未加入曝光控制时, 4种新的终止规则均有较好表现, 与HSU法十分接近。随着最大后验概率预设值的增加或e的减小, 模式判准率呈上升趋势, 平均测验长度逐渐增加, 但在题库使用率方面均较差。(3) 当加入项目曝光控制时, 6种变长终止规则下的题库使用率有了极大的提升, 仍能保持较高的模式判准率, 并且不同的曝光控制方法对终止规则的影响是不同的。其中, 相对标准终止规则极易受到曝光控制方法的影响。(4) 综合来看, SEA、HM以及HA法在各项指标上的表现与HSU法基本一致, 其次为KL法和DAPP法。     

词频和年级对FOK判断的影响

本实验以识记材料的词频高低和被试年级为自变量对被试元记忆监测的FOK(feeling of knowing)判断进行研究。实验采用Hart提出的RJR(回忆—FOK判断—标准测验 )的经典范式。结果表明 :被试的年级影响FOK判断等级和准确性 ,大学生的FOK判断等级和准确性均高于高一年级学生 ;识记材料词频影响FOK判断等级的高低 ,不影响FOK判断准确性 ,被试对高频词对的FOK判断等级高于对低频词对的FOK判断等级 ,但FOK判断准确性无明显差异。