Behavior Research Methods最新文献_第2页

duet: An R package for dyadic analysis of motion data generated by OpenPose. duet：一个R包，用于对OpenPose生成的运动数据进行二进分析。

IF 3.9 2区心理学

Behavior Research Methods Pub Date : 2025-09-24 DOI: 10.3758/s13428-025-02817-w

Themis N Efthimiou, Catherine J Crompton

{"title":"duet: An R package for dyadic analysis of motion data generated by OpenPose.","authors":"Themis N Efthimiou, Catherine J Crompton","doi":"10.3758/s13428-025-02817-w","DOIUrl":"10.3758/s13428-025-02817-w","url":null,"abstract":"Research into dyadic social interactions has expanded significantly, enabling a deeper understanding of the dynamic processes underlying interpersonal communication. As the use of larger datasets becomes increasingly common in this field, the need for scalable and efficient analytical tools has grown. Automated coding methods, such as those provided by OpenPose, an open-source software for detecting and tracking human motion, offer significant advantages for analysing the movement of two individuals during an interaction. However, the processing and analysis of large quantities of JSON output files generated by OpenPose remain a considerable challenge. To address this, we introduce duet, an R package designed to streamline the processing and analysis of OpenPose output data, particularly in the context of dyadic interactions. The package provides a suite of functions for data cleaning, interpolation, kinematic analysis, and visualisation, offering researchers a comprehensive and user-friendly workflow. By simplifying the handling of OpenPose data, duet aims to facilitate large-scale, automated analysis of dyadic social interactions, with minimal coding experience, thereby advancing methodological capabilities in social and behavioural sciences.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 11","pages":"295"},"PeriodicalIF":3.9,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12460591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136195","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

Experimenter evidence unmasking as a confound in optional stopping. 在选择性停止中混淆实验证据的揭露。

IF 3.9 2区心理学

Behavior Research Methods Pub Date : 2025-09-24 DOI: 10.3758/s13428-025-02813-0

Renata Sadibolova, Devin B Terhune

引用次数: 0

Exploring the synthesis of mouse cursor tracking and drift diffusion modeling in a perceptual decision-making task. 探索在感知决策任务中鼠标光标跟踪和漂移扩散建模的综合。

IF 3.9 2区心理学

Behavior Research Methods Pub Date : 2025-09-24 DOI: 10.3758/s13428-025-02805-0

Oliver Grenke, Stefan Scherbaum, Martin Schoemann

{"title":"Exploring the synthesis of mouse cursor tracking and drift diffusion modeling in a perceptual decision-making task.","authors":"Oliver Grenke, Stefan Scherbaum, Martin Schoemann","doi":"10.3758/s13428-025-02805-0","DOIUrl":"10.3758/s13428-025-02805-0","url":null,"abstract":"Process tracing and process modeling are the two primary behavioral approaches for uncovering human decision-making processes. However, both approaches face significant limitations: process tracing offers a large and oftentimes confusing number of measures, while process modeling relies on a minimal number of comparable trials for reliable model fitting. In our study, we explore how we can combine mouse cursor tracking and the drift diffusion model (DDM) in order to both reduce the number of cursor measures and circumvent the minimal trial amount requirements of DDM fitting. One hundred three participants completed 90 trials in a random dot kinematogram (RDK). A total of 18 cursor measures were taken from the mouse cursor tracking literature and used to predict drift rate, threshold separation, and non-decision time of the DDM via partial least squares regression. Four cursor measures contributed significantly to the prediction of the DDM parameters. When reducing the available trials, these cursor measures, in combination with response time and accuracy, performed better and remained more stable in the prediction of DDM parameters than model fitting. Our results lower the barrier for applying mouse cursor tracking for novice researchers by highlighting important cursor measures and their mapping to psychological constructs of decision-making, while also offering an approach for behavioral scientists to investigate DDM components in experimental setups with a restricted number of trials.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 11","pages":"297"},"PeriodicalIF":3.9,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12460430/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136198","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: Application of facial neuromuscular electrical stimulation (fNMES) in psychophysiological research: Practical recommendations based on a systematic review of the literature. 修正：面部神经肌肉电刺激（fNMES）在心理生理学研究中的应用：基于文献系统回顾的实用建议。

IF 3.9 2区心理学

Behavior Research Methods Pub Date : 2025-09-23 DOI: 10.3758/s13428-025-02836-7

Themis Nikolas Efthimiou, Monica Perusquía-Hernández, Arthur Elsenaar, Marc Mehu, Sebastian Korb

引用次数: 0

Simulating lexical decision times with large language models to supplement megastudies and crowdsourcing. 用大型语言模型模拟词汇决策时间，以补充大型研究和众包。

IF 3.9 2区心理学

Behavior Research Methods Pub Date : 2025-09-23 DOI: 10.3758/s13428-025-02829-6

Gonzalo Martínez, Javier Conde, Pedro Reviriego, Marc Brysbaert

{"title":"Simulating lexical decision times with large language models to supplement megastudies and crowdsourcing.","authors":"Gonzalo Martínez, Javier Conde, Pedro Reviriego, Marc Brysbaert","doi":"10.3758/s13428-025-02829-6","DOIUrl":"10.3758/s13428-025-02829-6","url":null,"abstract":"Megastudies and crowdsourcing studies are a rich source of information for word recognition research because they provide processing times for thousands of words. However, the high cost makes it impossible to include all words of interest and all relevant participant groups. This study explores the potential of fine-tuned large language models (LLMs) to generate lexical decision times (RTs) similar to those of humans. Building on recent findings that LLMs can accurately estimate word features, we fine-tuned GPT-4o mini with 3000 words from a megastudy. We then gave the model the task of generating RT estimates for the remaining words in the dataset. Our findings showed a high correlation between AI-generated and observed RTs. We discuss three applications: (1) estimating missing RT data, where AI can fill in gaps for words missing in some megastudies, (2) verifying results of virtual experiments, where AI-generated data can provide an additional layer of validation for results of virtual experiments, and (3) optimizing human data collection, as researchers can run simulations before conducting studies with humans. While AI-generated RTs are not a replacement for human data, they have the potential to increase the flexibility and efficiency of megastudy research.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 10","pages":"294"},"PeriodicalIF":3.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129979","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

Chinese Face and Body Dataset (CFBD): Lab and personal photos of the same individuals. 中国人面部和身体数据集（CFBD）：同一个人的实验室和个人照片。

IF 3.9 2区心理学

Behavior Research Methods Pub Date : 2025-09-19 DOI: 10.3758/s13428-025-02815-y

Ying Hu, Ruyu Pan, Yaqi Xiao, Zihan Zhu, Geraldine Jeckeln, Xiaolan Fu

{"title":"Chinese Face and Body Dataset (CFBD): Lab and personal photos of the same individuals.","authors":"Ying Hu, Ruyu Pan, Yaqi Xiao, Zihan Zhu, Geraldine Jeckeln, Xiaolan Fu","doi":"10.3758/s13428-025-02815-y","DOIUrl":"10.3758/s13428-025-02815-y","url":null,"abstract":"Face stimuli used in face perception research often focus on between-model variability, underrepresenting within-model variability across conditions and time. However, exposure to within-model variability is crucial for developing stable representations of faces. Here, we introduce the Chinese Face and Body Dataset (CFBD), a publicly accessible resource that captures within-model variability to represent a broad spectrum of appearance and image variations in laboratory and natural settings. The CFBD comprises 2,195 images from 117 models, including both laboratory photos taken by researchers and personal photos donated by models. Each model is depicted in 10 to 31 photos, coded for attributes such as the time photos were taken, facial expressions, viewing angles, and environmental contexts. Independent participants also rated these photos based on facial attractiveness, trustworthiness, and distinctiveness. The results revealed that the CFBD captures a wide range of variations across appearances and image attributes, and the within-model variances in trait ratings are comparable to, if not greater than, the between-model variances. Moreover, within-model variances in the trait ratings differ by image type, with personal photos being rated as more attractive, distinctive, and trustworthy than their laboratory counterparts. By capturing a diverse range of appearances and images of Chinese individuals, the CFBD provides valuable resources that expand face datasets, potentially advancing our understanding of robust face representation.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 10","pages":"292"},"PeriodicalIF":3.9,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145091163","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

Multiverse simulation to explore the impact of analytical choices on type I and type II errors in a reaction time study. 多元宇宙模拟，探讨在反应时间研究中，分析选择对I型和II型错误的影响。

IF 3.9 2区心理学

Behavior Research Methods Pub Date : 2025-09-18 DOI: 10.3758/s13428-025-02807-y

Miklos Bognar, Marton A Varga, Don van Ravenzwaaij, Zoltan Kekecs, James A Grange, Mate Gyurkovics, Balazs Aczel

{"title":"Multiverse simulation to explore the impact of analytical choices on type I and type II errors in a reaction time study.","authors":"Miklos Bognar, Marton A Varga, Don van Ravenzwaaij, Zoltan Kekecs, James A Grange, Mate Gyurkovics, Balazs Aczel","doi":"10.3758/s13428-025-02807-y","DOIUrl":"10.3758/s13428-025-02807-y","url":null,"abstract":"Researcher degrees of freedom in data analysis present significant challenges in social sciences, where different analytical decisions can lead to varying conclusions. In this work, we propose an example of an exploratory multiverse simulation to empirically compare various decision pathways to identify an effect's sensitivity to different analytical choices. The approach is demonstrated on the congruency sequence effect (CSE), a well-studied phenomenon in cognitive control research. We reviewed existing literature to identify common non-theory-specific analytical decisions, such as outlier exclusion criteria and hypothesis testing methods, and incorporated these into our simulation framework. Using 20,000 simulated datasets, we compared the true positive rates (TPR) and false positive rates (FPR) across 50 different decision pathways, resulting in a total of 1 million analyses. Our results indicate substantial differences in power and type I error rates across the analytical pathways, with some posing a significant risk of producing high false positives. The findings underscore the importance of running extensive simulations to investigate different data handling and hypothesis testing approaches in certain research fields. This case study serves as an example for conducting similar simulation procedures in research fields characterized by high variability in analytical decisions when investigating an otherwise widely accepted effect.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 10","pages":"291"},"PeriodicalIF":3.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145084906","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

Optimeet: A computational tool to enhance participant attendance in group research. Optimeet：一种提高小组研究参与者出勤率的计算工具。

IF 3.9 2区心理学

Behavior Research Methods Pub Date : 2025-09-17 DOI: 10.3758/s13428-025-02745-9

Lukas W Mayer, Desislava Bocheva, Joanne Hinds, Olivia Brown, Lukasz Piwek, David A Ellis

{"title":"Optimeet: A computational tool to enhance participant attendance in group research.","authors":"Lukas W Mayer, Desislava Bocheva, Joanne Hinds, Olivia Brown, Lukasz Piwek, David A Ellis","doi":"10.3758/s13428-025-02745-9","DOIUrl":"10.3758/s13428-025-02745-9","url":null,"abstract":"Across disciplines, research often relies on groups of people to participate in experiments or attend events at the same time. Typically, researchers try to maximize attendance by manually identifying a set of times that suit the diaries of many individuals. However, this is inefficient, is prone to error, and can lead to a final sample that is not large enough to provide meaningful inferences. While current scheduling tools are useful for individual-based research, enabling participants to select times convenient to them within a researcher's preset parameters, they are less useful in research that requires specific or flexible group sizes. In response, we present Optimeet, a web application that allows researchers to upload participants' availability data and generate an optimal allocation schedule for multiple groups. We describe the function of the underlying applet, which identifies a schedule to maximize attendance by treating it as a computational problem involving combinatorial optimization (Experiment 1). Our solution relies on an empirical comparison of parameter-free heuristics to make allocation decisions that make the best use of participants' availabilities and the derivation of appropriate performance metrics. Of the algorithms evaluated, one consistently outperformed comparable versions of existing tools, which we verified in a further exercise (Experiment 2) involving a large human sample (N = 5,289). We consider the methodological utility and practical value of these developments, and include detailed documentation, code, and a video tutorial so that researchers can rapidly employ Optimeet to support group research.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 10","pages":"289"},"PeriodicalIF":3.9,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443933/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145079585","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: The Polish Vocabulary Size Test: A novel adaptive test for receptive vocabulary assessment. 更正：波兰语词汇量测试：一种新的接受性词汇评估的适应性测试。

IF 3.9 2区心理学

Behavior Research Methods Pub Date : 2025-09-17 DOI: 10.3758/s13428-025-02825-w

Danil Fokin, Monika Płużyczka, Grigory Golovin

引用次数: 0

Algorithmic gaze annotation for mobile eye-tracking. 移动眼动追踪的注视标注算法。

IF 3.9 2区心理学

Behavior Research Methods Pub Date : 2025-09-17 DOI: 10.3758/s13428-025-02803-2

Daniel Mueller, David Mann

{"title":"Algorithmic gaze annotation for mobile eye-tracking.","authors":"Daniel Mueller, David Mann","doi":"10.3758/s13428-025-02803-2","DOIUrl":"10.3758/s13428-025-02803-2","url":null,"abstract":"Mobile eye-tracking is increasingly used to study human behavior in situ; however, the analysis of the footage is typically performed manually and therefore is slow and laborious. The aim of this study was to examine the extent to which the footage obtained using mobile eye-tracking could be annotated automatically using computer vision algorithms. We developed an open-source Python package that combined two computer vision algorithms to automatically annotate human-body-related areas of interest when two participants interacted with each other. To validate the algorithm, three experienced human raters coded the gaze direction with respect to one of seven a priori defined areas of interest during the task. To test the reliability of the algorithm, the agreement between the human raters was compared with the results obtained from the algorithm. A total of 1,188 frames from 13 trials were compared, with the results revealing substantial agreement between the algorithm and human raters (Krippendorff's alpha = 0.61). The algorithm strictly annotated whether gaze was within or outside of the specified areas of interest, whereas human raters seemed to apply a tolerance when gaze was lying slightly outside the areas of interest. In sum, the computer algorithmic approach appears to provide a valid means of automatically annotating mobile eye-tracking footage in highly dynamic contexts. The possibility of automatically annotating eye-tracking footage of human interactions allows for automatic assessment of visual attention, gaze, and intentions across sectors such as educational settings, pedestrian navigation, and sport.","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 10","pages":"290"},"PeriodicalIF":3.9,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145079587","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