Behavior Research Methods最新文献_第4页

Evaluating mobile-based data collection for crowdsourcing behavioral research. 为众包行为研究评估基于移动设备的数据收集。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2025-02-28 DOI: 10.3758/s13428-025-02618-1

Dennis T Esch, Nikolaos Mylonopoulos, Vasilis Theoharakis

{"title":"Evaluating mobile-based data collection for crowdsourcing behavioral research.","authors":"Dennis T Esch, Nikolaos Mylonopoulos, Vasilis Theoharakis","doi":"10.3758/s13428-025-02618-1","DOIUrl":"10.3758/s13428-025-02618-1","url":null,"abstract":"Online crowdsourcing platforms such as MTurk and Prolific have revolutionized how researchers recruit human participants. However, since these platforms primarily recruit computer-based respondents, they risk not reaching respondents who may have exclusive access or spend more time on mobile devices that are more widely available. Additionally, there have been concerns that respondents who heavily utilize such platforms with the incentive to earn an income provide lower-quality responses. Therefore, we conducted two studies by collecting data from the popular MTurk and Prolific platforms, Pollfish, a self-proclaimed mobile-first crowdsourcing platform, and the Qualtrics audience panel. By distributing the same study across these platforms, we examine data quality and factors that may affect it. In contrast to MTurk and Prolific, most Pollfish and Qualtrics respondents were mobile-based. Using an attentiveness composite score we constructed, we find mobile-based responses comparable with computer-based responses, demonstrating that mobile devices are suitable for crowdsourcing behavioral research. However, platforms differ significantly in attentiveness, which is also affected by factors such as the respondents' incentive for completing the survey, their activity before engaging, environmental distractions, and having recently completed a similar study. Further, we find that a stronger system 1 thinking is associated with lower levels of attentiveness and acts as a mediator between some of the factors explored, including the device used and attentiveness. In addition, we raise a concern that most MTurk users can pass frequently used attention checks but fail less utilized measures, such as the infrequency scale.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 4","pages":"106"},"PeriodicalIF":4.6,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11870873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143531008","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

The Animal-AI Environment: A virtual laboratory for comparative cognition and artificial intelligence research. 动物人工智能环境：比较认知和人工智能研究的虚拟实验室。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2025-02-28 DOI: 10.3758/s13428-025-02616-3

Konstantinos Voudouris, Ben Slater, Lucy G Cheke, Wout Schellaert, José Hernández-Orallo, Marta Halina, Matishalin Patel, Ibrahim Alhas, Matteo G Mecattaf, John Burden, Joel Holmes, Niharika Chaubey, Niall Donnelly, Matthew Crosby

{"title":"The Animal-AI Environment: A virtual laboratory for comparative cognition and artificial intelligence research.","authors":"Konstantinos Voudouris, Ben Slater, Lucy G Cheke, Wout Schellaert, José Hernández-Orallo, Marta Halina, Matishalin Patel, Ibrahim Alhas, Matteo G Mecattaf, John Burden, Joel Holmes, Niharika Chaubey, Niall Donnelly, Matthew Crosby","doi":"10.3758/s13428-025-02616-3","DOIUrl":"10.3758/s13428-025-02616-3","url":null,"abstract":"The Animal-AI Environment is a unique game-based research platform designed to facilitate collaboration between the artificial intelligence and comparative cognition research communities. In this paper, we present the latest version of the Animal-AI Environment, outlining several major features that make the game more engaging for humans and more complex for AI systems. These features include interactive buttons, reward dispensers, and player notifications, as well as an overhaul of the environment's graphics and processing for significant improvements in agent training time and quality of the human player experience. We provide detailed guidance on how to build computational and behavioural experiments with the Animal-AI Environment. We present results from a series of agents, including the state-of-the-art deep reinforcement learning agent Dreamer-v3, on newly designed tests and the Animal-AI testbed of 900 tasks inspired by research in the field of comparative cognition. The Animal-AI Environment offers a new approach for modelling cognition in humans and non-human animals, and for building biologically inspired artificial intelligence.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 4","pages":"107"},"PeriodicalIF":4.6,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11870899/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143531029","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

Correction: Assessing the distortions introduced when calculating d': A simulation approach. 修正：评估在计算d': A模拟方法时引入的扭曲。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2025-02-27 DOI: 10.3758/s13428-025-02617-2

Yiyang Chen, Heather R Daly, Mark A Pitt, Trisha Van Zandt

引用次数: 0

Approaches to inducing mental fatigue: A systematic review and meta-analysis of (neuro)physiologic indices. 诱导精神疲劳的方法：神经生理指标的系统回顾和荟萃分析。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2025-02-26 DOI: 10.3758/s13428-025-02620-7

Stephen P J Goodman, Blake Collins, Kathleen Shorter, Ashleigh T Moreland, Christopher Papic, Adam S Hamlin, Brendon Kassman, Frank E Marino

{"title":"Approaches to inducing mental fatigue: A systematic review and meta-analysis of (neuro)physiologic indices.","authors":"Stephen P J Goodman, Blake Collins, Kathleen Shorter, Ashleigh T Moreland, Christopher Papic, Adam S Hamlin, Brendon Kassman, Frank E Marino","doi":"10.3758/s13428-025-02620-7","DOIUrl":"10.3758/s13428-025-02620-7","url":null,"abstract":"Mental fatigue is a transient psychophysiological state characterized by impaired cognition and behavior across a range of dynamic contexts. Despite increasing interest in this phenomenon, its (neuro)physiologic representations remain unclear. This systematic review aimed to quantify the range of (neuro)physiologic outcomes and methodologies used to investigate mental fatigue in laboratory-based settings. Across the 72 studies meeting our inclusion criteria, we identified 30 unique physiologic, four visual outcomes, and the application of several neuroimaging techniques investigating neuronal function. Mental fatigue increased heart rate, systolic and diastolic blood pressure, mean arterial pressure, low frequency, and root mean square of successive differences (RMSSD), and reduced standard deviation of normal-to-normal intervals (SDNN) (all P ≤ 0.04) when compared with controls. Applying electroencephalography to investigate delta, theta, and alpha bandwidths may provide useful insights into this phenomenon, and functional near-infra-red spectroscopy to right-lateralized frontoparietal regions would be helpful to investigate cortical activity change in response to mental fatigue. More data are needed across a range of methodological contexts in order to further determine the (neuro)physiological manifestations of mental fatigue. However, this review provides direction to researchers and will assist them in navigating and considering the range of options available.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 4","pages":"102"},"PeriodicalIF":4.6,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11865143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143514426","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

Structural after measurement (SAM) approaches for accommodating latent quadratic and interaction effects. 结构测量后（SAM）方法适应潜在的二次效应和相互作用。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2025-02-26 DOI: 10.3758/s13428-024-02532-y

Yves Rosseel, Elissa Burghgraeve, Wen Wei Loh, Karin Schermelleh-Engel

{"title":"Structural after measurement (SAM) approaches for accommodating latent quadratic and interaction effects.","authors":"Yves Rosseel, Elissa Burghgraeve, Wen Wei Loh, Karin Schermelleh-Engel","doi":"10.3758/s13428-024-02532-y","DOIUrl":"10.3758/s13428-024-02532-y","url":null,"abstract":"Established strategies commonly used to address latent quadratic and interaction effects within structural equation models, such as the unconstrained product indicator (UPI) approach or the latent moderated structural equations (LMS) approach, tend to perform effectively in models featuring only a limited number of nonlinear effects. However, as the complexity of the model increases with a higher number of nonlinear terms, the feasibility of joint or one-step methods such as UPI and LMS progressively diminishes. In response to this challenge, this paper advocates the adoption of structural after measurement (SAM) approaches to overcome this limitation. In a SAM approach, estimation proceeds in two stages. In a first stage, we estimate the parameters related to the measurement part of the model, while in a second stage, we estimate the parameters related to the structural part of the model. In this paper, we discuss three SAM approaches already documented in the literature and introduce a novel method based on the local SAM approach. To illustrate the utility of these SAM approaches, we conduct a modest simulation study, demonstrating that SAM approaches for latent quadratic and interaction effects offer a practical and viable alternative to the well-established one-step approaches.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 4","pages":"101"},"PeriodicalIF":4.6,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498124","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

SUBTLEX-AR: Arabic word distributional characteristics based on movie subtitles. 基于电影字幕的阿拉伯语单词分布特征。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2025-02-26 DOI: 10.3758/s13428-024-02560-8

Sami Boudelaa, Manuel Carreiras, Nazrin Jariya, Manuel Perea

引用次数: 0

Impact of prior specifications on performance of Bayesian factor mixture modeling. 先验规范对贝叶斯因子混合建模性能的影响。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2025-02-26 DOI: 10.3758/s13428-025-02619-0

Yan Wang, Eunsook Kim, Hsien-Yuan Hsu

引用次数: 0

Testing for group differences in multilevel vector autoregressive models. 检验多水平向量自回归模型的组差异。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2025-02-20 DOI: 10.3758/s13428-024-02541-x

Jonas M B Haslbeck, Sacha Epskamp, Lourens J Waldorp

引用次数: 0

Jiwar: A database and calculator for word neighborhood measures in 40 languages. Jiwar: 40种语言的单词邻域测量的数据库和计算器。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2025-02-19 DOI: 10.3758/s13428-025-02612-7

Alaa Alzahrani

{"title":"Jiwar: A database and calculator for word neighborhood measures in 40 languages.","authors":"Alaa Alzahrani","doi":"10.3758/s13428-025-02612-7","DOIUrl":"10.3758/s13428-025-02612-7","url":null,"abstract":"The majority of neighborhood calculators are restricted to one language. The limited availability of multilingual neighborhood calculators could pose challenges for conducting psycholinguistic research on low-resource languages. Therefore, this study introduced Jiwar, a database and calculator for neighborhood information across three levels (orthographic, phonological, and phonographic) across 40 languages. The database contains information for 24 linguistic and neighborhood measures, while the Python-based calculator allows users to compute more than 46 neighborhood measures for words and nonwords. This study further examined the Jiwar calculator's instrument reliability and validity. Correlations with previous datasets across several languages suggested the strong reliability of two key Jiwar measures. Multiple-linear regression models revealed that a subset of Jiwar measures significantly predicted behavioral results in lexical decision and visual naming tasks, indicating the validity of the Jiwar calculator. Jiwar is an open-source, Python-based tool that is designed to expand to more languages and functions.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 3","pages":"98"},"PeriodicalIF":4.6,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143456748","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

Distribution-free Bayesian analyses with the DFBA statistical package. 使用DFBA统计包的无分布贝叶斯分析。

IF 4.6 2区心理学

Behavior Research Methods Pub Date : 2025-02-19 DOI: 10.3758/s13428-025-02605-6

Richard A Chechile, Daniel H Barch

{"title":"Distribution-free Bayesian analyses with the DFBA statistical package.","authors":"Richard A Chechile, Daniel H Barch","doi":"10.3758/s13428-025-02605-6","DOIUrl":"10.3758/s13428-025-02605-6","url":null,"abstract":"Nonparametric (or distribution-free) statistics have been widely used in psychological research because behavioral data can be messy and inconsistent with the Gaussian model for measurement error. Distribution-free procedures only use categorical or rank information, so they avoid the problems of outliers and violations of distributional assumptions. Yet frequentist nonparametric procedures are still subject to the limitation of relative frequency theory, which stems from the founding assumption that population parameters cannot be represented by probability distributions. Bayesian statistical methods, by contrast, allow for prior and posterior probability distributions for population parameters, so they rigorously provide experimental scientists with a probability representation of the population parameters of interest. The Bayesian counterpart for a set of distribution-free statistical methods is a relatively recent development. This paper is a detailed discussion of the DFBA package of R functions, which is designed for doing distribution-free Bayesian analyses for the common nonparametric procedures. Included in the package are functions that enable the user to explore the relative power for computer-based data that can be sampled from nine different probability models. The distribution-free procedures have almost the same power as the t test when the data are normally distributed, but for eight other alternative probability models, the distribution-free Bayesian procedures have greater power than the frequentist t.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 3","pages":"99"},"PeriodicalIF":4.6,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839880/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143456745","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