Preference Structures and Co-comparability Graphs

Order structures such as linear orders, weak orders, semiorders and interval orders are often considered as models of a decision maker's preferences. In this paper we introduce and study new order structures characterized by their symmetric part belonging to certain classes of co-comparability graphs. We outline possible interpretations and suggest special representations of these structures and we point out their potential use for approximating relations obtained through a multicriteria aggregation procedure. We provide various characterizations of the new structures (as well as of older ones) in terms of minimal forbidden configurations and by algebraic conditions.     

Stronger effects of self-generated than cue-induced expectations when verifying predictions in data graphs

Expectations can come in different forms when analyzing and presenting data. Prior studies have documented stronger effects (behavioral and electrophysiological) of self-generated predictions as compared to cues. While participants presumably cannot help but use their own predictions, they might sometimes ignore cues (of low validity). In two experiments we compared the impact of cues (verbal and visual) and self-generated predictions on the performance of participants checking their current prediction against a presented data graph (linear upward or downward trend). Different from prior studies, the setup allowed for within-experiment comparison of different cue formats and ensured that cues could not be ignored. Nevertheless we found that self-generated predictions had a stronger impact than cues. Verbal cues had a stronger effect than visual cues without verbalization. Responses to graphs with a linear upward trend were faster and were influenced more strongly by predictions, than the response to graphs with a downward trend.     

Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp

Spanning nearly sixty years of research, statistical network analysis has passed through (at least) two generations of researchers and models. Beginning in the late 1930's, the first generation of research dealt with the distribution of various network statistics, under a variety of null models. The second generation, beginning in the 1970's and continuing into the 1980's, concerned models, usually for probabilities of relational ties among very small subsets of actors, in which various simple substantive tendencies were parameterized. Much of this research, most of which utilized log linear models, first appeared in applied statistics publications.But recent developments in social network analysis promise to bring us into a third generation. The Markov random graphs of Frank and Strauss (1986) and especially the estimation strategy for these models developed by Strauss and Ikeda (1990; described in brief in Strauss, 1992), are very recent and promising contributions to this field. Here we describe a large class of models that can be used to investigate structure in social networks. These models include several generalizations of stochastic blockmodels, as well as models parameterizing global tendencies towards clustering and centralization, and individual differences in such tendencies. Approximate model fits are obtained using Strauss and Ikeda's (1990) estimation strategy.In this paper we describe and extend these models and demonstrate how they can be used to address a variety of substantive questions about structure in social networks.This research was supported by grants from the Australian Research Council and the National Science Foundation (#SBR93-10184). This paper was presented at the 1994 Annual Meeting of the Psychometric Society, Champaign, Illinois, June 1994. Special thanks go to Sarah Ardu for programming assistance, Laura Koehly and Garry Robins for help with this research, and to Shizuhiko Nishisato and three reviewers for their comments. INTERNETemail addresses:pattison@psych.unimelb.edu.au (PP);stanwass@uiuc.edu (SW). Affiliations: Department of Psychology, University of Melbourne (PP); Department of Psychology, Department of Statistics, and The Beckman Institute for Advanced Science and Technology, University of Illinois (SW).     

Efficient spatio-temporal data mining with GenSpace graphs

We describe a method for spatio-temporal data mining based on GenSpace graphs. Using familiar calendar and geographical concepts, such as workdays, weeks, climatic regions, and countries, spatio-temporal data can be aggregated into summaries in many ways. We automatically search for a summary with a distribution that is anomalous, i.e., far from user expectations. We repeatedly ranking possible summaries according to current expectations, and then allow the user to adjust these expectations. We also choose a propagation path in the GenSpace subgraph that reduces the storage and time costs of the mining process.     

Social networks with two sets of actors

Traditional network research analyzes relational ties within a single group of actors: the models presented in this paper involve relational ties exist beteen two distinct sets of actors. Statistical models for traditional networks in which relations are measured within a group simplify when modeling unidirectional relations measured between groups. The traditional paradigm results in a one-mode socionatrix; the network paradigm considered in this paper results in a two-mode socionatrix; A statistical model is presented, illustrated on a sample data set, and compared to its traditional counterpart. Extensions are discussed, including those that model multivariate relations simultaneously, and those that allow for the inclustion of attributes of the individuals in the group.We thank the Editor and two anonymous reviewers for their helpful comments. We are also grateful to George Barnett for allowing us to analyze his data.     

A Field-Tested Task Analysis for Creating Single-Subject Graphs Using Microsoft<Superscript>®</Superscript> Office Excel

Creating single-subject (SS) graphs is challenging for many researchers and practitioners because it is a complex task with many steps. Although several authors have introduced guidelines for creating SS graphs, many users continue to experience frustration. The purpose of this article is to minimize these frustrations by providing a field-tested task analysis for creating SS graphs using Microsoft^® Office Excel. Results from the field test are presented and the task analysis, which includes steps for creating a variety of SS graphs, is provided. The article includes various illustrations, a list of prerequisite skills, tips, and troubleshooting items.     

阅读障碍儿童识字特点研究

通过作业分析方法,定量分析了小学二至五年级阅读障碍儿童的错别字类型,定性分析了其易错或易忘字的类型特点.初步探讨了阅读障碍儿童识记汉字的方法,从而推导障碍原因.研究结果表明:(1)汉语阅读困难儿童的错别字分五类,以形似错误为主.(2)随年级增高,形似错误的比例增加.依此结果可以推论:汉语阅读障碍儿童的识字方式是以图形式记忆为主的,未建立有效且强的形-义联结,其心理词典的词条数目少且精确性较差.     

Comparing instructor-led,video-model,and no-instruction control tutorials for creating single-subject graphs in Microsoft Excel: A systematic replication and extension

Visual inspection of single-subject data is the primary method for behavior analysts to interpret the effect of an independent variable on a dependent variable; however, there is no consensus on the most suitable method for teaching graph construction for single-subject designs. We systematically replicated and extended Tyner and Fienup (2015) using a repeated-measures between-subjects design to compare the effects of instructor-led, video-model, and no-instruction control tutorials on the graphing performance of 81 master's students with some reported Microsoft Excel experience. Our mixed-design analysis revealed a statistically significant main effect of pretest, tutorial, and posttest submissions for each tutorial group and a nonsignificant main effect of tutorial group. Tutorial group significantly interacted with submissions, suggesting that both instructor-led and video-model tutorials may be superior to providing graduate students with a written list of graphing conventions (i.e., control condition). Finally, training influenced performance on an untrained graph type (multielement) for all tutorial groups.     

Compositionality,Relevance, and Peirce’s Logic of Existential Graphs

Charles S. Peirce’s pragmatist theory of logic teaches us to take the context of utterances as an indispensable logical notion without which there is no meaning. This is not a spat against compositionality per se , since it is possible to posit extra arguments to the meaning function that composes complex meaning. However, that method would be inappropriate for a realistic notion of the meaning of assertions. To accomplish a realistic notion of meaning (as opposed e.g. to algebraic meaning), Sperber and Wilson’s Relevance Theory (RT) may be applied in the spirit of Peirce’s Pragmatic Maxim (PM): the weighing of information depends on (i) the practical consequences of accommodating the chosen piece of information introduced in communication, and (ii) what will ensue in actually using that piece in further cycles of discourse. Peirce’s unpublished papers suggest a relevance-like approach to meaning. Contextual features influenced his logic of Existential Graphs (EG). Arguments are presented pro and con the view in which EGs endorse non-compositionality of meaning.     

Beyond inference by eye: Statistical and graphing practices in JEAB, 1992‐2017

Debates about the utility of p values and correct ways to analyze data have inspired new guidelines on statistical inference by the American Psychological Association (APA) and changes in the way results are reported in other scientific journals, but their impact on the Journal of the Experimental Analysis of Behavior (JEAB) has not previously been evaluated. A content analysis of empirical articles published in JEAB between 1992 and 2017 investigated whether statistical and graphing practices changed during that time period. The likelihood that a JEAB article reported a null hypothesis significance test, included a confidence interval, or depicted at least one figure with error bars has increased over time. Features of graphs in JEAB, including the proportion depicting single‐subject data, have not changed systematically during the same period. Statistics and graphing trends in JEAB largely paralleled those in mainstream psychology journals, but there was no evidence that changes to APA style had any direct impact on JEAB. In the future, the onus will continue to be on authors, reviewers and editors to ensure that statistical and graphing practices in JEAB continue to evolve without interfering with characteristics that set the journal apart from other scientific journals.     

Matching the graphical display of data to avoidance versus approach motivation increases outcome expectancies

This study is the first demonstration of the effect of motivational focus (approach vs. avoidance) on the interpretation of graphical view of personal data, specifically, weight loss progress. In two experiments, participants were randomly assigned to see the bogus weight loss data charted against either a goal or a baseline reference line. In the first experiment, we accessed participants' chronic motivational focus. In the second study, we primed motivation by exposing the participants to either a gain- or loss-focused health message. The results demonstrate that participants with either chronic or manipulated approach motivation predicted higher future weight loss in the goal reference line condition than in the baseline reference line condition. The opposite pattern was observed for participants with either chronic or manipulated avoidance motivation. The potential of matching graphical data display to personality characteristics to promote effective health management is discussed.     

Salience Versus Proportional Reasoning: Rethinking the Mechanism Behind Graphical Display Effects

Two experiments examined predictions from two separate explanations for previously observed display effects for communicating low‐probability risks: foreground:background salience and proportional reasoning. According to foreground:background salience, people's risk perceptions are based on the relative salience of the foreground (number of people harmed) versus the background (number of people at risk), such that calling attention to the background makes the risk seem smaller. Conversely, the proportional reasoning explanation states that what matters is whether the respondent attends to the proportion, which conveys how small the risk is. In Experiment 1, we made the background more salient via color and bolding; in contrast to the foreground:background salience prediction, this manipulation did not influence participants' risk aversion. In Experiment 2, we separately manipulated whether the foreground and the background were displayed graphically or numerically. In keeping with the proportional reasoning hypothesis, there was an interaction whereby participants given formats that displayed the foreground and background in the same modality (graphs or numbers, thereby making the proportion easier to form) saw the probability as smaller and were less risk averse than participants given the information in different modalities. There was also a main effect of displaying the background graphically, providing some support for foreground:background salience. In total, this work suggests that the proportional reasoning account provides a good explanation of many display effects related to communicating low‐probability risks, although there is some role for foreground:background salience as well. Copyright © 2018 John Wiley & Sons, Ltd.     

两维特征图形的客体和空间工作记忆存储研究

研究采用单探测变化检测范式,考察了两维特征图形在视觉客体和视觉空间工作记忆中的存储机制,并对其容量进行测定。40名被试(平均年龄20.56±1.73岁)随机分为两个等组,分别完成实验一和实验二。实验一的刺激图形由颜色和形状两基本特征组成,实验二的刺激为由不同颜色和开口朝向组成的兰道环。两个实验结果均显示:(1)特征交换变化条件下的记忆成绩与单特征变化条件下最差的记忆成绩差异不显著;(2)空间工作记忆任务的成绩显著优于客体工作记忆任务;(3)被试在视觉工作记忆中能存储2~3个客体和3~4个空间位置。这表明,由两种不同维度特征组成的图形在视觉客体和视觉空间工作记忆中均以整合方式进行存储,空间工作记忆的容量大于客体工作记忆。     

“Minimax length links” of a dissimilarity matrix and minimum spanning trees

A theorem is proved stating that the set of all minimax links, defined as links minimizing, over paths, the maximum length of links in any path connecting a pair of objects comprising nodes in an undirected weighted graph, comprise the union of all minimum spanning trees of that graph. This theorem is related to methods of fitting network models to dissimilarity data, particularly a method called Pathfinder due to Schvaneveldt and his colleagues, as well as to single linkage clustering, and results concerning the relationship between minimum spanning trees and single linkage hierarchical trees.Acknowledgments: The author thanks Phipps Arabie, Lawrence J. Hubert, and K. Christoph Klauer for a number of helpful suggestions and comments on various aspects of this paper.     

立体视觉研究中心理物理学和生理学实验的系统及其方法

双眼立体视觉涉及复杂的神经机制,因此在研究这个问题时,一般只能从某一侧面或某种方法入手,本文重点研究采用计算机控制的多功能刺激图形的产生及其在心理物理学研究中的效果,同时介绍了一种可以广泛用于立体视觉研究中的多功能刺激图形发生系统的工作原理和结构设计。     

大学生问题发现过程的问题行为图研究

在潜藏式与矛盾式情境两类问题发现任务中,采用出声思维的方法研究问题发现能力高与低两类各10名大学生问题发现过程的内部认知活动。利用问题行为图技术进行个案分析,着重考察大学生在知识状态、思维操作及问题行为图等方面的表现。基于加工性质把知识编码为陈述性、程序性和策略性知识;基于信息来源编码为情境、内部和推断信息;基于思维关联与转换,把思维操作编码为独立、横向、纵向与元认知思维。研究表明:(1)问题发现中,在内部信息、纵向思维以及纵横交错型行为图的使用频次上,潜藏情境高于矛盾情境;在情境信息、横向思维以及横向平行和横向归纳型行为图的使用频次上,矛盾情境高于潜藏情境。(2)高低能力组被试在知识状态、思维操作与问题行为图的使用上差异均不显著,可能是由于上述方面涉及问题发现的基本认知过程和思维规律,具有普遍性特征。     

Comparing computer‐based training and lecture formats to teach visual analysis of baseline‐treatment graphs

Accurate analysis of data is vital to the validation of interventions. As such, there has been a recent increase in studies evaluating visual analysis training procedures. However, past investigations have not evaluated direct and indirect visual analysis training methods with matched instructional content that was systematically designed. Furthermore, training has rarely included assessment of generalization and maintenance of visual analysis skills. The purpose of the current dissertation study was to compare the effectiveness and efficiency of (a) computer‐based training, (b) lecture formats with and (c) without the opportunity to pause, and (d) a no‐training group to teach visual analysis of AB graphs to university students. To make these formats directly comparable, the instructional content was equated by ensuring information and examples were identical across the three training procedures. Eighty‐three students were randomly assigned to one of the four groups. Results showed that all three training formats produced increases in accurate responding compared to the no‐training group. Visual analysis skills generalized to novel graphs and maintained approximately 2 weeks following all trainings. These results suggest that structured approaches that are carefully designed to train visual analysis are effective and lead to gains that generalize and maintain in the absence of training.     

三维图形的视空间工作记忆容量研究

采用三维几何图形为材料,通过单探测变化觉察范式来测定视觉工作记忆的存储容量,并比较客体工作记忆和空间工作记忆容量的差异。实验1、2的材料分别为由颜色和形状两个基本特征组成的三维图形和由一个基本特征和一个细节特征组成的三维图形。两个实验结果显示,被试能在视觉工作记忆中存储约2-3个客体和4个空间位置,空间工作记忆容量显著大于客体工作记忆容量。进一步的比较发现,被试对由两个基本特征组成的三维图形的存储容量大于由一个基本特征和一个细节特征组成的三维图形。这表明,组成三维图形的特征类型对视空间工作记忆的存储容量有显著影响。     

三维图形的客体和空间工作记忆存储时程研究

研究采用单探测变化检测范式,探讨了三维图形在视觉客体和空间工作记忆中的存储时程。实验一的三维图形由不同颜色、形状组成,实验二的图形由不同颜色、图案组成。两个实验的结果均发现:被试的正确率随刺激间隔时间的延长而显著下降; 空间工作记忆任务的正确率显著高于客体工作记忆任务。研究结果表明,视觉刺激消失后,不论是客体信息还是空间信息,均随间隔时间的延长而逐渐衰退,且客体信息衰退的速度比空间信息快。总体来看,三维图形的客体和空间信息在视觉工作记忆满负荷条件下能保持大约3～5秒的时间。     

Methodological Challenges in Causal Research on Racial and Ethnic Patterns of Cognitive Trajectories: Measurement, Selection, and Bias

Research focused on understanding how and why cognitive trajectories differ across racial and ethnic groups can be compromised by several possible methodological challenges. These difficulties are especially relevant in research on racial and ethnic disparities and neuropsychological outcomes because of the particular influence of selection and measurement in these contexts. In this article, we review the counterfactual framework for thinking about causal effects versus statistical associations. We emphasize that causal inferences are key to predicting the likely consequences of possible interventions, for example in clinical settings. We summarize a number of common biases that can obscure causal relationships, including confounding, measurement ceilings/floors, baseline adjustment bias, practice or retest effects, differential measurement error, conditioning on common effects in direct and indirect effects decompositions, and differential survival. For each, we describe how to recognize when such biases may be relevant and some possible analytic or design approaches to remediating these biases.