Behavior Research Methods最新文献_第4页

Publisher Correction: Dimensionality and optimal combination of autonomic fear-conditioning measures in humans. 出版商更正：人类自律神经恐惧调节测量的维度和最佳组合。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2024-10-01 DOI: 10.3758/s13428-024-02391-7

Federico Mancinelli, Juliana K Sporrer, Vladislav Myrov, Filip Melinscak, Josua Zimmermann, Huaiyu Liu, Dominik R Bach

引用次数: 0

Factor retention in ordered categorical variables: Benefits and costs of polychoric correlations in eigenvalue-based testing. 有序分类变量中的因子保留：基于特征值的测试中多变量相关性的好处和代价。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2024-10-01 Epub Date: 2024-05-06 DOI: 10.3758/s13428-024-02417-0

Nils Brandenburg

{"title":"Factor retention in ordered categorical variables: Benefits and costs of polychoric correlations in eigenvalue-based testing.","authors":"Nils Brandenburg","doi":"10.3758/s13428-024-02417-0","DOIUrl":"10.3758/s13428-024-02417-0","url":null,"abstract":"An essential step in exploratory factor analysis is to determine the optimal number of factors. The Next Eigenvalue Sufficiency Test (NEST; Achim, 2017) is a recent proposal to determine the number of factors based on significance tests of the statistical contributions of candidate factors indicated by eigenvalues of sample correlation matrices. Previous simulation studies have shown NEST to recover the optimal number of factors in simulated datasets with high accuracy. However, these studies have focused on continuous variables. The present work addresses the performance of NEST for ordinal data. It has been debated whether factor models - and thus also the optimal number of factors - for ordinal variables should be computed for Pearson correlation matrices, which are known to underestimate correlations for ordinal datasets, or for polychoric correlation matrices, which are known to be instable. The central research question is to what extent the problems associated with Pearson correlations and polychoric correlations deteriorate NEST for ordinal datasets. Implementations of NEST tailored to ordinal datasets by utilizing polychoric correlations are proposed. In a simulation, the proposed implementations were compared to the original implementation of NEST which computes Pearson correlations even for ordinal datasets. The simulation shows that substituting polychoric correlations for Pearson correlations improves the accuracy of NEST for binary variables and large sample sizes (N = 500). However, the simulation also shows that the original implementation using Pearson correlations was the most accurate implementation for Likert-type variables with four response categories when item difficulties were homogeneous.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11362475/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140852527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Connecting process models to response times through Bayesian hierarchical regression analysis. 通过贝叶斯分层回归分析将流程模型与响应时间联系起来。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2024-10-01 Epub Date: 2024-05-15 DOI: 10.3758/s13428-024-02400-9

Thea Behrens, Adrian Kühn, Frank Jäkel

{"title":"Connecting process models to response times through Bayesian hierarchical regression analysis.","authors":"Thea Behrens, Adrian Kühn, Frank Jäkel","doi":"10.3758/s13428-024-02400-9","DOIUrl":"10.3758/s13428-024-02400-9","url":null,"abstract":"Process models specify a series of mental operations necessary to complete a task. We demonstrate how to use process models to analyze response-time data and obtain parameter estimates that have a clear psychological interpretation. A prerequisite for our analysis is a process model that generates a count of elementary information processing steps (EIP steps) for each trial of an experiment. We can estimate the duration of an EIP step by assuming that every EIP step is of random duration, modeled as draws from a gamma distribution. A natural effect of summing several random EIP steps is that the expected spread of the overall response time increases with a higher EIP step count. With modern probabilistic programming tools, it becomes relatively easy to fit Bayesian hierarchical models to data and thus estimate the duration of a step for each individual participant. We present two examples in this paper: The first example is children's performance on simple addition tasks, where the response time is often well predicted by the smaller of the two addends. The second example is response times in a Sudoku task. Here, the process model contains some random decisions and the EIP step count thus becomes latent. We show how our EIP regression model can be extended to such a case. We believe this approach can be used to bridge the gap between classical cognitive modeling and statistical inference and will be easily applicable to many use cases.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11362371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140943398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Speech production and perception data collection in R: A tutorial for web-based methods using speechcollectr. 用 R 语言收集语音生成和感知数据：使用 speechcollectr 的网络方法教程。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2024-10-01 Epub Date: 2024-06-03 DOI: 10.3758/s13428-024-02399-z

Abbey L Thomas, Peter F Assmann

{"title":"Speech production and perception data collection in R: A tutorial for web-based methods using speechcollectr.","authors":"Abbey L Thomas, Peter F Assmann","doi":"10.3758/s13428-024-02399-z","DOIUrl":"10.3758/s13428-024-02399-z","url":null,"abstract":"This tutorial is designed for speech scientists familiar with the R programming language who wish to construct experiment interfaces in R. We begin by discussing some of the benefits of building experiment interfaces in R-including R's existing tools for speech data analysis, platform independence, suitability for web-based testing, and the fact that R is open source. We explain basic concepts of reactive programming in R, and we apply these principles by detailing the development of two sample experiments. The first of these experiments comprises a speech production task in which participants are asked to read words with different emotions. The second sample experiment involves a speech perception task, in which participants listen to recorded speech and identify the emotion the talker expressed with forced-choice questions and confidence ratings. Throughout this tutorial, we introduce the new R package speechcollectr, which provides functions uniquely suited to web-based speech data collection. The package streamlines the code required for speech experiments by providing functions for common tasks like documenting participant consent, collecting participant demographic information, recording audio, checking the adequacy of a participant's microphone or headphones, and presenting audio stimuli. Finally, we describe some of the difficulties of remote speech data collection, along with the solutions we have incorporated into speechcollectr to meet these challenges.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141198617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Graded Incomplete Letters Test (GILT): a rapid test to detect cortical visual loss, with UK Biobank implementation. 分级不完整字母测试 (GILT)：检测大脑皮层视力损失的快速测试，在英国生物银行实施。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2024-10-01 Epub Date: 2024-06-18 DOI: 10.3758/s13428-024-02448-7

Kxx Yong, A Petzold, P Foster, A Young, S Bell, Y Bai, A P Leff, S Crutch, J A Greenwood

{"title":"The Graded Incomplete Letters Test (GILT): a rapid test to detect cortical visual loss, with UK Biobank implementation.","authors":"Kxx Yong, A Petzold, P Foster, A Young, S Bell, Y Bai, A P Leff, S Crutch, J A Greenwood","doi":"10.3758/s13428-024-02448-7","DOIUrl":"10.3758/s13428-024-02448-7","url":null,"abstract":"Impairments of object recognition are core features of neurodegenerative syndromes, in particular posterior cortical atrophy (PCA; the 'visual-variant Alzheimer's disease'). These impairments arise from damage to higher-level cortical visual regions and are often missed or misattributed to common ophthalmological conditions. Consequently, diagnosis can be delayed for years with considerable implications for patients. We report a new test for the rapid measurement of cortical visual loss - the Graded Incomplete Letters Test (GILT). The GILT is an optimised psychophysical variation of a test used to diagnose cortical visual impairment, which measures thresholds for recognising letters under levels of increasing visual degradation (decreasing \"completeness\") in a similar fashion to ophthalmic tests. The GILT was administered to UK Biobank participants (total n=2,359) and participants with neurodegenerative conditions characterised by initial cortical visual (PCA, n=18) or memory loss (typical Alzheimer's disease, n=9). UK Biobank participants, including both typical adults and those with ophthalmological conditions, were able to recognise letters under low levels of completeness. In contrast, participants with PCA consistently made errors with only modest decreases in completeness. GILT sensitivity to PCA was 83.3% for participants reaching the 80% accuracy cut-off, increasing to 88.9% using alternative cut-offs (60% or 100% accuracy). Specificity values were consistently over 94% when compared to UK Biobank participants without or with documented visual conditions, regardless of accuracy cut-off. These first-release UK Biobank and clinical verification data suggest the GILT has utility in both rapidly detecting visual perceptual losses following posterior cortical damage and differentiating perceptual losses from common eye-related conditions.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11362218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141417597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comparative evaluation of measures to assess randomness in human-generated sequences. 人类生成序列随机性评估措施的比较评估。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2024-10-01 Epub Date: 2024-07-01 DOI: 10.3758/s13428-024-02456-7

Tim Angelike, Jochen Musch

{"title":"A comparative evaluation of measures to assess randomness in human-generated sequences.","authors":"Tim Angelike, Jochen Musch","doi":"10.3758/s13428-024-02456-7","DOIUrl":"10.3758/s13428-024-02456-7","url":null,"abstract":"Whether and how well people can behave randomly is of interest in many areas of psychological research. The ability to generate randomness is often investigated using random number generation (RNG) tasks, in which participants are asked to generate a sequence of numbers that is as random as possible. However, there is no consensus on how best to quantify the randomness of responses in human-generated sequences. Traditionally, psychologists have used measures of randomness that directly assess specific features of human behavior in RNG tasks, such as the tendency to avoid repetition or to systematically generate numbers that have not been generated in the recent choice history, a behavior known as cycling. Other disciplines have proposed measures of randomness that are based on a more rigorous mathematical foundation and are less restricted to specific features of randomness, such as algorithmic complexity. More recently, variants of these measures have been proposed to assess systematic patterns in short sequences. We report the first large-scale integrative study to compare measures of specific aspects of randomness with entropy-derived measures based on information theory and measures based on algorithmic complexity. We compare the ability of the different measures to discriminate between human-generated sequences and truly random sequences based on atmospheric noise, and provide a systematic analysis of how the usefulness of randomness measures is affected by sequence length. We conclude with recommendations that can guide the selection of appropriate measures of randomness in psychological research.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11362514/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141490709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An abbreviated Chinese dyslexia screening behavior checklist for primary school students using a machine learning approach. 利用机器学习方法编制简略的中国小学生阅读障碍筛查行为检查表。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2024-10-01 Epub Date: 2024-07-29 DOI: 10.3758/s13428-024-02461-w

Yimin Fan, Yixun Li, Mingyue Luo, Jirong Bai, Mengwen Jiang, Yi Xu, Hong Li

{"title":"An abbreviated Chinese dyslexia screening behavior checklist for primary school students using a machine learning approach.","authors":"Yimin Fan, Yixun Li, Mingyue Luo, Jirong Bai, Mengwen Jiang, Yi Xu, Hong Li","doi":"10.3758/s13428-024-02461-w","DOIUrl":"10.3758/s13428-024-02461-w","url":null,"abstract":"To increase early identification and intervention of dyslexia, a prescreening instrument is critical to identifying children at risk. The present work sought to shorten and validate the 30-item Mandarin Dyslexia Screening Behavior Checklist for Primary School Students (the full checklist; Fan et al., , 19, 521-527, 2021). Our participants were 15,522 Mandarin-Chinese-speaking students and their parents, sampled from classrooms in grades 2-6 across regions in mainland China. A machine learning approach (lasso regression) was applied to shorten the full checklist (Fan et al., , 19, 521-527, 2021), constructing grade-specific brief checklists first, followed by a compilation of the common brief checklist based on the similarity across grade-specific checklists. All checklists (the full, grade-specific brief, and common brief versions) were validated and compared with data in our sample and an external sample (N = 114; Fan et al., , 19, 521-527, 2021). The results indicated that the six-item common brief checklist showed consistently high reliability (αs > .82) and reasonable classification performance (about 60% prediction accuracy and 70% sensitivity), comparable to that of the full checklist and all grade-specific brief checklists across our current sample and the external sample from Fan et al., , 19, 521-527, (2021). Our analysis showed that 2.42 (out of 5) was the cutoff score that helped classify children's reading status (children who scored higher than 2.42 might be considered at risk for dyslexia). Our final product is a valid, accessible, common brief checklist for prescreening primary school children at risk for Chinese dyslexia, which can be used across grades and regions in mainland China.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141791730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees. 使用广义线性混合模型 (GLMM) 树检测线性增长曲线模型中的亚组。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2024-10-01 Epub Date: 2024-05-29 DOI: 10.3758/s13428-024-02389-1

Marjolein Fokkema, Achim Zeileis

{"title":"Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees.","authors":"Marjolein Fokkema, Achim Zeileis","doi":"10.3758/s13428-024-02389-1","DOIUrl":"10.3758/s13428-024-02389-1","url":null,"abstract":"Growth curve models are popular tools for studying the development of a response variable within subjects over time. Heterogeneity between subjects is common in such models, and researchers are typically interested in explaining or predicting this heterogeneity. We show how generalized linear mixed-effects model (GLMM) trees can be used to identify subgroups with different trajectories in linear growth curve models. Originally developed for clustered cross-sectional data, GLMM trees are extended here to longitudinal data. The resulting extended GLMM trees are directly applicable to growth curve models as an important special case. In simulated and real-world data, we assess performance of the extensions and compare against other partitioning methods for growth curve models. Extended GLMM trees perform more accurately than the original algorithm and LongCART, and similarly accurate compared to structural equation model (SEM) trees. In addition, GLMM trees allow for modeling both discrete and continuous time series, are less sensitive to (mis-)specification of the random-effects structure and are much faster to compute.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11543751/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141173769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Statistical indices of masculinity-femininity: A theoretical and practical framework. 男性-女性的统计指数：理论与实践框架。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2024-10-01 Epub Date: 2024-03-04 DOI: 10.3758/s13428-024-02369-5

Marco Del Giudice

{"title":"Statistical indices of masculinity-femininity: A theoretical and practical framework.","authors":"Marco Del Giudice","doi":"10.3758/s13428-024-02369-5","DOIUrl":"10.3758/s13428-024-02369-5","url":null,"abstract":"Statistical indices of masculinity-femininity (M-F) summarize multivariate profiles of sex-related traits as positions on a single continuum of individual differences, from masculine to feminine. This approach goes back to the early days of sex differences research; however, a systematic discussion of alternative M-F indices (including their meaning, their mutual relations, and their psychometric properties) has been lacking. In this paper I present an integrative theoretical framework for the statistical assessment of masculinity-femininity, and provide practical guidance to researchers who wish to apply these methods to their data. I describe four basic types of M-F indices: sex-directionality, sex-typicality, sex-probability, and sex-centrality. I examine their similarities and differences in detail, and consider alternative ways of computing them. Next, I discuss the impact of measurement error on the validity of these indices, and outline some potential remedies. Finally, I illustrate the concepts presented in the paper with a selection of real-world datasets on body morphology, brain morphology, and personality. An R function is available to easily calculate multiple M-F indices from empirical data (with or without correction for measurement error) and draw summary plots of their individual and joint distributions.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11362193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140027309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enabling analytical power calculations for multilevel models with autocorrelated errors through deriving and approximating the precision matrix. 通过推导和近似精度矩阵，对具有自相关误差的多级模型进行分析功率计算。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2024-10-01 Epub Date: 2024-07-15 DOI: 10.3758/s13428-024-02435-y

Ginette Lafit, Richard Artner, Eva Ceulemans

{"title":"Enabling analytical power calculations for multilevel models with autocorrelated errors through deriving and approximating the precision matrix.","authors":"Ginette Lafit, Richard Artner, Eva Ceulemans","doi":"10.3758/s13428-024-02435-y","DOIUrl":"10.3758/s13428-024-02435-y","url":null,"abstract":"To unravel how within-person psychological processes fluctuate in daily life, and how these processes differ between persons, intensive longitudinal (IL) designs in which participants are repeatedly measured, have become popular. Commonly used statistical models for those designs are multilevel models with autocorrelated errors. Substantive hypotheses of interest are then typically investigated via statistical hypotheses tests for model parameters of interest. An important question in the design of such IL studies concerns the determination of the number of participants and the number of measurements per person needed to achieve sufficient statistical power for those statistical tests. Recent advances in computational methods and software have enabled the computation of statistical power using Monte Carlo simulations. However, this approach is computationally intensive and therefore quite restrictive. To ease power computations, we derive simple-to-use analytical formulas for multilevel models with AR(1) within-person errors. Analytic expressions for a model family are obtained via asymptotic approximations of all sample statistics in the precision matrix of the fixed effects. To validate this analytical approach to power computation, we compare it to the simulation-based approach via a series of Monte Carlo simulations. We find comparable performances making the analytic approach a useful tool for researchers that can drastically save them time and resources.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141619152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0