On the Bock-Aitkin procedure—From an EM algorithm perspective

The relationship between the EM algorithm and the Bock-Aitkin procedure is described with a continuous distribution of ability (latent trait) from an EM-algorithm perspective. Previous work has been restricted to the discrete case from a probit-analysis perspective. The author is grateful to Bradley A. Hanson for valuable discussion and comments. Thanks also go to Terry A. Ackerman, Meichu Fan, Subrata Kundu, and Robert K. Tsutakawa for their help and encouragement in this study.     

Generalized latent trait models

In this paper we discuss a general model framework within which manifest variables with different distributions in the exponential family can be analyzed with a latent trait model. A unified maximum likelihood method for estimating the parameters of the generalized latent trait model will be presented. We discuss in addition the scoring of individuals on the latent dimensions. The general framework presented allows, not only the analysis of manifest variables all of one type but also the simultaneous analysis of a collection of variables with different distributions. The approach used analyzes the data as they are by making assumptions about the distribution of the manifest variables directly.     

On Learnability of Restricted Classes of Categorial Grammars

In this paper we present learning algorithms for classes of categorial grammars restricted by negative constraints. We modify learning functions of Kanazawa [10] and apply them to these classes of grammars. We also prove the learnability of intersection of the class of minimal grammars with the class of k-valued grammars. Presented by Wojciech Buszkowski     

汉字通透性算法以及对结构方式效应的解释

在先前提出的汉字通透性算法中 ,包括了水平通透性和垂直通透性 ,而汉字作为整体 ,其通透性也应该是一个整体 ,所以 ,当前研究对这一算法进行了更正 ,然后运用新的算法对先前实验材料和基本汉字集 (6 72 4 )中的配对上下字和左右字进行通透性分析 ,证明了它的可行性。在实验研究中 ,采用新算法选择实验用字 ,以达到使上下字的通透性大于左右字的通透性或者使它们相等的目的 ,以此为前提观察到了逆转的结构方式效应以及结构方式效应消失。这一实验结果表明 ,结构方式本身并不影响汉字识别 ,影响汉字识别的直接因素是隐藏在结构方式之下的汉字通透性     

Model specification search using a genetic algorithm with factor reordering for a simple structure factor analysis model

Abstract:  Many techniques for automated model specification search based on numerical indices have been proposed, but no single decisive method has yet been determined. In the present article, the performance and features of the model specification search method using a genetic algorithm (GA) were verified. A GA is a robust and simple metaheuristic algorithm with great searching power. While there has already been some research applying metaheuristics to the model fitting task, we focus here on the search for a simple structure factor analysis model and propose a customized algorithm for dealing with certain problems specific to that situation. First, implementation of model specification search using a GA with factor reordering for a simple structure factor analysis is proposed. Then, through a simulation study using generated data with a known true structure and through example analysis using real data, the effectiveness and applicability of the proposed method were demonstrated.     

Latent variable models for analyzing children's processing ability

In this paper, we develop a latent processing ability model to analyze the speed of processing ability data. Our approach can not only effectively evaluate the effects of covariates on the latent processing ability, but also estimate the latent trait of each child by calculating its posterior mean. In addition, we derive the correlations structure of latent traits among different age groups. Simulations are conducted to evaluate the performance of our proposed model. The results indicated that the estimation of model parameters is satisfactory overall. The method is evaluated using real data from children aged 4–7 years in Changchun, China.     

医疗情感计算应用的多维困境及其法治实现

情感计算技术依托算法的数据处理能力和自主学习能力已经在医疗健康领域有所实践。但由于医疗领域的特殊性,情感计算技术医疗应用过程中存在着道德合理性风险、规制盲区以及技术羁绊等多重隐患,亟须法律规制。首先,应当坚守“拟主体”身份的道德底线,追求伦理与法治的最佳耦合;其次,应构建权责明晰的算法责任嵌入机制,建立多元协同的算法权力治理机制;最后,应建立健全医疗情感计算技术审查机制,完善情感计算算法风险等级治理体系。

     

Association rule learning in neuropsychological data analysis for Alzheimer’s disease

Efficient methods of analysis readily available for clinicians continue to be limited within neuropsychological assessment at the raw data level. Here, a novel approach for generating predictive response patterns and analysing neuropsychological raw data is offered. In order to assess the usefulness of association rule learning as an analysis tool for neuropsychological raw data, Frequent Pattern Growth (FP-Growth) was used to mine patterns from the Consortium to Establish a Registry for Alzheimer’s Disease Neuropsychological Battery (CERAD-NB) database. Complete assessment data for 84 post-mortem confirmed Alzheimer’s disease (AD) cases and 294 age, race, and education matched controls were analysed across baseline and one-year follow-up using FP-Growth, for the purpose of assessing the clinical utility of a finer analysis at the raw data level and the feasibility of predicting response patterns for clinical/control groups. Output from FP-Growth, in terms of the number of frequent itemsets retained across both evaluation timepoints, was discernable between controls, mild, and moderate to severe Alzheimer’s disease cases (p < .001, and η² = .488). Patterns within raw data scores, both in terms of frequent itemsets and predictive association rules, appear to be differentiable across groups within neuropsychological analysis, which indicates that FP-Growth is applicable as a supplementary analysis tool for neuropsychological assessment by means of offering an additional level of data analysis, predictive item response capabilities, and aiding in clinical decision making.     

A systematic review of algorithm aversion in augmented decision making

Despite abundant literature theorizing societal implications of algorithmic decision making, relatively little is known about the conditions that lead to the acceptance or rejection of algorithmically generated insights by individual users of decision aids. More specifically, recent findings of algorithm aversion—the reluctance of human forecasters to use superior but imperfect algorithms—raise questions about whether joint human-algorithm decision making is feasible in practice. In this paper, we systematically review the topic of algorithm aversion as it appears in 61 peer-reviewed articles between 1950 and 2018 and follow its conceptual trail across disciplines. We categorize and report on the proposed causes and solutions of algorithm aversion in five themes: expectations and expertise, decision autonomy, incentivization, cognitive compatibility, and divergent rationalities. Although each of the presented themes addresses distinct features of an algorithmic decision aid, human users of the decision aid, and/or the decision making environment, apparent interdependencies are highlighted. We conclude that resolving algorithm aversion requires an updated research program with an emphasis on theory integration. We provide a number of empirical questions that can be immediately carried forth by the behavioral decision making community.     

道德困境研究的范式沿革及其理论价值

通过评述道德困境研究范式的发展过程, 系统阐释了经典两难法、加工分离法、CNI模型法和CAN算法的优缺点和理论价值。后来的研究范式均在一定程度上克服了之前研究范式的局限。加工分离法克服了经典两难法的加工纯粹性假设等局限, CNI模型法在加工分离法基础上进一步分离了道德困境决策的多种心理过程, CAN算法则修正了CNI模型法的序列加工的不恰当预设。研究范式的沿革启示研究者综合应用新方法来解决研究争议和重新审视以往道德理论, 合理应用新方法来探索其他具有潜在冲突性的研究议题。总之, 本文为道德困境及相关研究提供了方法学参考。