The quantitative methods for psychology最新文献_第10页

Interactive Parallel Models: No Virginia, Violation of Miller's Race Inequality does not Imply Coactivation and Yes Virginia, Context Invariance is Testable 交互式并行模型：没有弗吉尼亚，违反Miller种族不等式不意味着共激活，是的弗吉尼亚，上下文不变性是可测试的

The quantitative methods for psychology Pub Date : 2020-04-01 DOI: 10.20982/tqmp.16.2.p192

J. Townsend, Yanjun Liu, Ru Zhang, M. Wenger

{"title":"Interactive Parallel Models: No Virginia, Violation of Miller's Race Inequality does not Imply Coactivation and Yes Virginia, Context Invariance is Testable","authors":"J. Townsend, Yanjun Liu, Ru Zhang, M. Wenger","doi":"10.20982/tqmp.16.2.p192","DOIUrl":"https://doi.org/10.20982/tqmp.16.2.p192","url":null,"abstract":"One vein of our research on psychological systems has focused on parallel processing models in disjunctive (OR) and conjunctive (AND) stopping-rule designs. One branch of that research has emphasized that a common strategy of inference in the OR situations is logically ﬂawed. That strategy equates a violation of the popular Miller race bound with a coactive parallel system. Pointedly, Townsend & Nozawa (1997) revealed that even processing systems associated with extreme limited capacity are capable of violating that bound. With regard to the present investigation, previous theoretical work has proven that interactive parallel models with separate decision criteria on each channel can readily evoke capacity suﬃciently super to violate that bound (e. g., Colonius & Townsend, 1997; Townsend & Nozawa, 1995; Townsend & Wenger, 2004). In addition, we have supplemented the usual OR task with an AND task to seek greater testability of architectural, decisional, and capacity mechanisms (e. g., Eidels et al., 2011; Eidels et al., 2015). The present study presents a broad meta-theoretical structure within which the past and new theoretical results are embedded. We further exploit the broad class of stochastic linear systems and discover that inter-esting classical results from Colonius (1990) can be given an elegant process interpretation within that class. In addition, we learn that conjoining OR with AND data affords an experimental test of the crucial assumption of context invariance, long thought to be untestable.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46975147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Evidence accumulation models with R: A practical guide to hierarchical Bayesian methods 证据积累模型与R:层次贝叶斯方法的实用指南

The quantitative methods for psychology Pub Date : 2020-04-01 DOI: 10.20982/TQMP.16.2.P133

Yi Lin, L. Strickland

{"title":"Evidence accumulation models with R: A practical guide to hierarchical Bayesian methods","authors":"Yi Lin, L. Strickland","doi":"10.20982/TQMP.16.2.P133","DOIUrl":"https://doi.org/10.20982/TQMP.16.2.P133","url":null,"abstract":"Evidence accumulation models are a useful tool to allow researchers to investigate the latent cognitive variables that underlie response time and response accuracy. However, applying evidence accumulation models can be difficult because they lack easily computable forms. Numerical methods are required to determine the parameters of evidence accumulation that best correspond to the fitted data. When applied to complex cognitive models, such numerical methods can require substantial computational power which can lead to infeasibly long compute times. In this paper, we provide efficient, practical software and a step-by-step guide to fit evidence accumulation models with Bayesian methods. The software, written in C++, is provided in an R package: 'ggdmc'. The software incorporates three important ingredients of Bayesian computation, (1) the likelihood functions of two common response time models, (2) the Markov chain Monte Carlo (MCMC) algorithm (3) a population-based MCMC sampling method. The software has gone through stringent checks to be hosted on the Comprehensive R Archive Network (CRAN) and is free to download. We illustrate its basic use and an example of fitting complex hierarchical Wiener diffusion models to four shooting-decision data sets.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47989676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Visualizing Items and Measures: An Overview and Demonstration of the Kernel Smoothing Item Response Theory Technique 可视化项目与措施:核平滑项目反应理论技术综述与论证

The quantitative methods for psychology Pub Date : 2020-03-06 DOI: 10.31234/osf.io/j3btw

Gordana Rajlic

引用次数: 1

A Tutorial on Testing the Equality of Standardized Regression Coefficients in Structural Equation Models using Wald Tests with lavaan 使用Lavan的Wald检验检验结构方程模型中标准化回归系数相等性的教程

The quantitative methods for psychology Pub Date : 2019-12-16 DOI: 10.31234/osf.io/c6mjn

E. Klopp

引用次数: 17

How to implement real-time interaction between participants in online surveys: A practical guide to SMARTRIQS 如何实现在线调查参与者之间的实时互动：SMARTRIQS实用指南

The quantitative methods for psychology Pub Date : 2019-11-09 DOI: 10.31234/osf.io/6wmny

A. Molnár

{"title":"How to implement real-time interaction between participants in online surveys: A practical guide to SMARTRIQS","authors":"A. Molnár","doi":"10.31234/osf.io/6wmny","DOIUrl":"https://doi.org/10.31234/osf.io/6wmny","url":null,"abstract":"While online experimentation is becoming increasingly popular in psychology, implementing real-time interaction between participants in online studies remains uniquely challenging, as it requires researchers to have advanced programming skills, or to purchase expensive third-party services. These challenges prevent many psychologists and other social scientists from utilizing online methods when they wish to study a vast array of social behaviors and interpersonal decision-making. SMARTRIQS is a free and open-source application that fills in this crucial gap in contemporary experimental research methods. SMARTRIQS offers researchers the ability to design surveys that feature real-time interaction between participants—including live text chat—without requiring re-searchers to learn any programming language, install any software, or pay for any third-party services. This paper provides researchers a practical guide to SMARTRIQS and shows them howto turn regular (non-interactive) online surveys into fully interactive experiments. The paper not only provides a comprehensive guide to designing interactive studies in SMARTRIQS in general but also walks readers through the step-by-step instructions for setting up a particular study (Dictator Game with chat). The tutorial starts from the very basics, assuming no prior expertise in online experimentation, and is accessible to everyone, even those who are less—or not at all—familiar with Qualtrics.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41334538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

An LBA account of decisions in the multiple object tracking task 多目标跟踪任务中决策的LBA说明

The quantitative methods for psychology Pub Date : 2019-11-06 DOI: 10.31234/osf.io/hkj2g

R. Innes, Caroline L. Kuhne

{"title":"An LBA account of decisions in the multiple object tracking task","authors":"R. Innes, Caroline L. Kuhne","doi":"10.31234/osf.io/hkj2g","DOIUrl":"https://doi.org/10.31234/osf.io/hkj2g","url":null,"abstract":"Decision making is a vital aspect of our everyday functioning, from simple perceptual demands to more complex and meaningful decisions. The strategy adopted to make such decisions is often viewed as balancing elements of speed and caution, i.e. making fast or careful decisions. Using sequential sampling models to analyse decision making data can allow us to tease apart strategic differences, such as being more or less cautious, from processing differences, which would otherwise be indistinguishable in behavioural data. Our study used a multiple object tracking task where student participants and a highly skilled military group were compared on their ability to track several items at once. Using a mathematical model of decision making (the linear ballistic accumulator), we show the underpinnings of how two groups differ in performance. Results showed a large difference between the groups on accuracy, with the RAAF group outperforming students. An interaction effect was observed between groups and level of difficulty in response times, where RAAF response times slowed at a greater rate than the student group as difficulty increased. Model results indicated that the RAAF personnel were more cautious in their decisions than students, and had faster processing in some conditions. Our study shows the strength of sequential sampling models, as well as providing a first attempt at fitting a sequential sampling model to data from a multiple object tracking task.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42677225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Longitudinal item response modeling and posterior predictive checking in {R} and {Stan} {R}和{Stan}的纵向项目反应建模及后验预测检验

The quantitative methods for psychology Pub Date : 2019-09-01 DOI: 10.20982/tqmp.15.2.p075

A. Scharl, Timo Gnambs

引用次数: 0

The Area of Resilience to Stress Event ({ARSE}): A New Method for Quantifying the Process of Resilience 应力事件弹性区域（{ARSE}）：一种量化弹性过程的新方法

The quantitative methods for psychology Pub Date : 2019-09-01 DOI: 10.20982/tqmp.15.2.p148

Nathaniel J. Ratcliff, Devika T. Mahoney-Nair, Joshua Goldstein

引用次数: 3

RSE-box: An analysis and modelling package to study response times to multiple signals RSE-box：用于研究对多个信号的响应时间的分析和建模包

The quantitative methods for psychology Pub Date : 2019-09-01 DOI: 10.20982/tqmp.15.2.p112

Thomas U. Otto

引用次数: 12

A tutorial on how to compute traditional IAT effects with {R} 关于如何使用{R}计算传统IAT效果的教程

The quantitative methods for psychology Pub Date : 2019-09-01 DOI: 10.20982/tqmp.15.2.p134

Jessica R IeC ohner, Philipp Thoss

引用次数: 6