Behavior Research Methods最新文献

{"title":"Segmented profile analysis (SEPA): Plane-wise decomposition of within-person variation via ipsatized singular value decomposition.","authors":"Se-Kang Kim, Joe Grochowalski","doi":"10.3758/s13428-026-03102-0","DOIUrl":"10.3758/s13428-026-03102-0","url":null,"abstract":"<p><p>Traditional profile analyses summarize multivariate person data with overall mean levels and relative patterns, but existing methods often blur these sources of variation or reduce each individual to a single best-fitting profile. Segmented Profile Analysis (SEPA) offers a unified, ipsatized singular-value decomposition (SVD) framework that decomposes individual profiles into orthogonal level (LE) and pattern (PE) effects and, crucially, introduces plane-wise segment profiles (summaries of each person's response pattern within each variable-contrast dimension) as primary person-oriented objects. Within each low-dimensional plane, SEPA defines a projected response pattern (segment profile), domain-person cosines that index variable-by-variable alignment, and plane-fit correlations that summarize how closely an individual's pattern follows the plane's domain structure. Across planes, SEPA aggregates information via singular-value weighting to yield an overall segment profile while preserving contrast-specific signal. A practical workflow combines ipsatized SVD, parallel analysis for PE dimensionality, marker-domain rules, bootstrap confidence intervals, and subspace-stability diagnostics. Using multivariate cognitive data from the Woodcock-Johnson IV, SEPA identifies interpretable marker domains, reveals distinct pattern facets across planes, and provides person-oriented indices that can be carried into standard regression models. Simulation studies examine the stability of segment profiles and cosines under varying sample sizes and variance structures. SEPA thus supplies a reproducible, geometry-based foundation for person-centered measurement that connects Q-type factor analysis, biplot methods, and contemporary within-person profiling in multidomain assessments.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"58 9","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148560714","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}

{"title":"Measurement error is lower for visual analogue scales than for slider scales.","authors":"Tim Angelike, Ulf-Dietrich Reips","doi":"10.3758/s13428-026-03108-8","DOIUrl":"10.3758/s13428-026-03108-8","url":null,"abstract":"<p><p>Errors in measurement can arise in study and survey responses when there is a discrepancy between the intended and selected response. A significant portion of the scientific discourse has centered on the comparison of discrete and continuous response scales. In this study, we examined various continuous rating scales to ascertain which scale would yield the lowest measurement error. To this end, we compared visual analogue scales (VAS) with different slider scales in an online study (N = 222) that built upon the original work by Reips and Funke (2008). In this study, participants were asked to estimate where a percentage value would lie on a line ranging from 0 to 100%. The slider scale conditions differed in the initialization of the slider thumb (left, middle, right), with an additional slider condition that employed the default HTML slider with a large thumb without any modifications. The findings of this study suggest that the measurement error in estimating the true value was lower in VAS than in slider scales. Additionally, our findings suggest that VAS lead to reduced variability in errors within and between individuals. Contrary to our initial hypothesis, we did not find a higher measurement error in the default slider than in the other slider conditions. The findings of this study suggest that VAS may offer a more precise means to the assessment of ratings. Practitioners who intend to employ continuous rating scales should consider employing VAS or at least carefully consider the pitfalls of slider scales.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"58 9","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391775/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148560643","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}

{"title":"AI-generated familiarity estimates are a useful new source of information about word knowledge in Simplified Chinese.","authors":"Ziyi Ding, Kangning Qin, Marc Brysbaert, Qing Cai","doi":"10.3758/s13428-026-03120-y","DOIUrl":"10.3758/s13428-026-03120-y","url":null,"abstract":"<p><p>This study evaluated the usefulness of AI-generated estimates of word familiarity for predicting word difficulty in Simplified Chinese, building on previous research in alphabetic languages. We found that familiarity estimates produced using large language models (LLMs) showed moderate-to-strong correlations with human familiarity ratings. These LLM estimates were the most effective predictors of both word naming and lexical decision times, surpassing traditional metrics such as word frequency and human familiarity ratings, while the latter still provided modest, non-overlapping variance. GPT-4o with English instructions produced superior results compared to the Chinese-centered models currently available. The results imply that LLM familiarity estimates are a valuable resource for Chinese psycholinguistics, supporting work across experimental design, modeling, and norming. We release familiarity estimates for 27,624 words for unrestricted research and educational use.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"58 9","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148560651","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}

{"title":"Measuring complex constructs in large-scale text with computational social mixed methods.","authors":"Alina Herderich, Jana Lasser, Mirta Galesic, Segun Aroyehun, David Garcia, Joshua Garland","doi":"10.3758/s13428-026-03046-5","DOIUrl":"10.3758/s13428-026-03046-5","url":null,"abstract":"<p><p>A growing convergence between social science and machine learning enables, in principle, large-scale analyses of complex social phenomena through text. Yet, approaches leveraging supervised text classification based on human-annotated data for statistical analysis often treat conceptual validity and technical performance as separate challenges, impairing measurement quality. We provide guidelines to bridge this gap in what we call computational social mixed methods pipelines across three stages: data annotation, model training, and statistical analysis. Building on best practices and our own methodological innovations, such as \"Iterative Annotation\" and \"Training on Confident Examples\", we address recurring pitfalls like unbalanced training data or stagnant model performance. We also discuss when large language models constitute a viable alternative to transformer-based classifiers. Using a case study on countering online hate, we illustrate how consequently integrating social science and machine learning expertise improves the validity and comparability of computational social science.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"58 9","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13388774/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148547736","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}

{"title":"Modeling intraindividual variability in affect (MIVA): Formalized theoretical approach, computational model, and parameter recovery study.","authors":"Maria Wirth, Andreas Voss, Stefan T Radev, Klaus Rothermund","doi":"10.3758/s13428-026-03122-w","DOIUrl":"10.3758/s13428-026-03122-w","url":null,"abstract":"<p><p>No matter how angry, sad, or happy we are, eventually, we will feel different. Studying this ebb and flow of affective experience in daily life provides important insights into psychological functioning and well-being. We have developed a parsimonious formalized model of intraindividual variability in affect (MIVA), resting on the assumption that such affective changes reflect transactions between an individual and their proximal environment. We provide an outline of its theoretical background, scope, and mathematical formulation. We situate MIVA within the research field of affect dynamics and illustrate the models' behavior under realistic conditions using a simulation study. We use simulation-based inference to train a custom neural network on MIVA simulations, which we employ to rapidly estimate the model's parameters on a multitude of synthetic experiments with different configurations. Our simulation study demonstrates that the synthesis between a computational model and probabilistic neural networks results in an efficient and flexible tool for model-based inference of affect dynamics. Our simulation study also offers insights into the data requirements for a precise recovery of the model's parameters and recommendations for future data collection. The potential of MIVA for providing insights into affect dynamics is discussed.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"58 9","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13384981/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148534953","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}

{"title":"Multidimensional Bayesian adaptive testing.","authors":"Aron Fink, Christoph König, Andreas Frey","doi":"10.3758/s13428-026-03123-9","DOIUrl":"10.3758/s13428-026-03123-9","url":null,"abstract":"<p><p>This paper introduces a fully Bayesian approach to multidimensional adaptive testing (MBAT). By incorporating uncertainty in both item and person parameter estimates, MBAT addresses limitations in conventional multidimensional adaptive testing (MAT), which relies on point item and person parameter estimates. A Monte Carlo simulation was conducted to evaluate the performance of MBAT compared to conventional MAT. The study was based on a four-factorial design, with the factors calibration sample size (N = 250, N = 500, N = 1,000), test length (t = 30, t = 60), true trait level (- 2.0, - 1.5, …, 2.0), and MAT algorithm (MAT, MBAT), across a three-dimensional trait structure with within- and between-item multidimensionality. The results showed that MBAT consistently outperformed conventional MAT in terms of the bias and mean squared error (MSE) of the final person parameter estimates, especially at the extremes of the trait distributions. Implemented in general-purpose software (Stan, R), the approach is computationally feasible and adaptable, providing a practical foundation for future research and applications in MAT.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"58 8","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13379477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148468578","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}

{"title":"Standardized synchronization and validation pipeline for physiological biomarkers across multiple devices.","authors":"Selin Acan, Clàudia Valenzuela-Pascual, Filippo Corponi, Bryan M Li, Diego Hidalgo-Mazzei, Dick Thijssen, Gideon Vos, Stefan Bogaerts, Erno Hermans, Mitra Baratchi, Martin Dresler, Peter De Looff","doi":"10.3758/s13428-026-03097-8","DOIUrl":"10.3758/s13428-026-03097-8","url":null,"abstract":"<p><p>Wearable devices enable continuous monitoring of physiological signals in real-world settings, yet a standardized approach for synchronizing signals across devices remains lacking. We present a generalizable, user-friendly pipeline that enables synchronization of any two devices capturing the same physiological signals, without requiring coding expertise. The pipeline performs resampling, dynamic time warping alignment, amplitude correction via wavelet transforms, and signal standardization, followed by agreement analyses at both waveform and feature levels. To demonstrate its validity, we applied the pipeline to a case study comparing two research-grade devices, the Empatica E4 and the EmbracePlus, using up to 48 h of concurrent recordings from 31 participants. We compared signal-level agreement across waveform similarity, amplitude distribution, spectral content, and extracted features between the two research-grade devices to determine their interchangeability for longitudinal and multi-site studies. Specifically, we aimed to determine how well these devices agree at the signal level and to identify which physiological signals are most robust to device-specific variability. Four signals were examined (blood volume pulse, electrodermal activity, accelerometry, and temperature) using NeuroKit2 and FLIRT. We computed Pearson and concordance correlation coefficients, Bland-Altman bias and limits of agreement, root mean squared error (RMSE), KL divergence, spectral coherence, mutual information, and feature-level correlations using NeuroKit2 and FLIRT. Results showed near-perfect agreement for BVP (concordance correlation coefficient (CCC) ≈ 1.0; coherence ≤ 0.98) and phasic EDA features (CCC 0.85-0.99), whereas tonic EDA, temperature, and accelerometry exhibited systematic amplitude biases (EmbracePlus lower) and axis-dependent variability (Z-axis CCC = 0.85; Y-axis = 0.19). Relative signal dynamics were preserved across devices despite differences in absolute levels. These findings support integration of BVP, EDA, and TEMP data across E4 and EmbracePlus with proper preprocessing, while highlighting calibration needs for movement signals.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"58 8","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13375695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148468631","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}

{"title":"The Arabic Lexicon Project: Lexical decision data for 10,000 Modern Standard Arabic words.","authors":"Alaa Alzahrani, Hassan Alshumrani, Wafa Aljuaythin, Iman Mahfouz, Laila Alsuwaiyan, Noura Aljadaan, Sharif Alghazo, Imane Tiaiba, Zaha Alanzi","doi":"10.3758/s13428-026-03115-9","DOIUrl":"10.3758/s13428-026-03115-9","url":null,"abstract":"<p><p>The collection of large-scale lexical decision data has introduced a methodological shift in the psycholinguistic field, enabling researchers to perform virtual experiments on many lexical items without the inherent restrictions imposed by standard factorial design. However, Modern Standard Arabic (MSA) lacks such a resource. Therefore, the current study developed, validated, and illustrated the advantage of a lexical decision database for MSA in three (virtual) experiments. In the Main Experiment, we used a visual lexical decision task to collect reaction time and accuracy data on 10,000 MSA words and 10,000 nonwords from 604 first-language (L1) speakers, and on a subset of these stimuli (N words = 1,000; N nonwords = 1,000) from 43 s-language (L2) MSA speakers. The collected data showed good reliability. In Virtual Experiment 1, we showed that our database largely replicates well-documented effects in visual word recognition (lexicality, word length, word frequency, word neighborhood size), establishing its validity. In Virtual Experiment 2, we revealed the significant role of three less-explored MSA-specific predictors (perceptual complexity, clitics, diglossic distance) on L1 and L2 MSA visual word recognition, demonstrating one potential application of the database. We make our database freely available to support research on MSA visual word recognition.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"58 8","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148468595","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}

{"title":"Introducing openESM: A database of openly available experience sampling datasets.","authors":"Björn S Siepe, Jonas M B Haslbeck, Matthias Kloft, Anabel Büchner, Yong Zhang, Eiko I Fried, Daniel W Heck","doi":"10.3758/s13428-026-03112-y","DOIUrl":"10.3758/s13428-026-03112-y","url":null,"abstract":"<p><p>Experience sampling via mobile devices enables unprecedented insights into daily life. However, individual studies often cannot answer research questions conclusively, and open data are scattered across repositories in different formats. This impedes research into robustness, generalizability, and heterogeneity. We address this issue by introducing openESM, an open-source database of openly available experience sampling datasets in a harmonized format. The growing database currently comprises 60 datasets with more than 16,000 participants and more than 740,000 observations. Metadata can be searched via our website ( openesmdata.org ) to select and download datasets via packages in R and Python. We demonstrate the potential of openESM through an analysis of within-person correlations of positive and negative affect in 39 datasets, providing evidence for a large negative momentary correlation ( <math><mrow><mo>-</mo> <mn>0.49</mn></mrow> </math> , 95% CI: [ <math><mrow><mo>-</mo> <mn>0.54</mn></mrow> </math> , <math><mrow><mo>-</mo> <mn>0.42</mn></mrow> </math> ]). We end by discussing the design principles that will allow openESM to become a continuously evolving community resource for cumulative experience sampling research.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"58 8","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13375734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148468580","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}

{"title":"Collecting data from YouTube using the official API: A tutorial and a ready-to-use tool.","authors":"Kévin Campos-Moinier","doi":"10.3758/s13428-026-03116-8","DOIUrl":"10.3758/s13428-026-03116-8","url":null,"abstract":"<p><p>Online data from social media platforms provide an observational window into a wide range of psychological phenomena and real-world social interactions. Accessing these data typically requires the use of application programming interfaces (APIs), which are designed to ensure compliance with the legal and ethical frameworks governing the access, processing, and storage of potentially identifiable information. However, implementing API-based data collection within a dedicated program remains a major barrier for many researchers. This article addresses this challenge by introducing a ready-to-use data collection tool based on the official YouTube Data API (v3) and providing step-by-step guidance for its use. Geared toward researchers and practitioners with little or no prior programming experience, the tutorial enables users to implement transparent and reproducible API-based data collection procedures in their research. The tool and a fully documented interactive Python notebook are freely available via a public GitHub repository.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"58 8","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148454498","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}